Search Results (2,918)

Understanding the carbon emission efficiency of apple production (APCEE) is critical for promoting green and low-carbon agricultural development. However, the spatiotemporal dynamics and driving factors of APCEE in China remain inadequately explored. This study employs life cycle assessment, super-efficiency slacks-based measures, and a panel Tobit model to evaluate the carbon footprint, APCEE, and its determinants in China’s two major production regions from 2003 to 2022. The results reveal that: (1) Producing one ton of apples in China results in 0.842 t CO₂e emissions. Land carbon intensity and total carbon emissions peaked in 2010 (28.69 t CO₂e/ha) and 2014 (6.52 × 10⁷ t CO₂e), respectively, exhibiting inverted U-shaped trends. Carbon emissions from various production areas show significant differences, with higher pressure on carbon emission reduction in the Loess Plateau region, especially in Gansu Province. (2) The APCEE in China exhibits a W-shaped trend (mean: 0.645), with overall low efficiency loss. The Bohai Bay region outperforms the Loess Plateau and national averages. (3) The structure of the apple industry, degree of agricultural mechanization, and green innovation positively influence APCEE, while the structure of apple cultivation, education level, and agricultural subsidies negatively impact it. Notably, green innovation and agricultural subsidies display lagged effects. Moreover, the drivers of APCEE differ significantly between the two major production regions. These findings provide actionable pathways for the green and low-carbon transformation of China’s apple industry, emphasizing the importance of spatially tailored green policies and technology-driven decarbonization strategies. Full article

Microbial carbon use efficiency (CUE) is an important indicator of soil organic carbon accumulation and loss and a key parameter in biogeochemical cycling models. Its regulatory mechanism is highly dependent on microbial communities and their dynamic mediation of abiotic factors. Land-use change (e.g., agricultural expansion, deforestation, urbanization) profoundly alter carbon input patterns and soil physicochemical properties, further exacerbating the complexity and uncertainty of CUE. Existing carbon cycle models often neglect microbial ecological processes, resulting in an incomplete understanding of how microbial traits interact with environmental factors to regulate CUE. This paper provides a comprehensive review of the microbial regulation mechanisms of CUE under land-use change and systematically explores how microorganisms drive organic carbon allocation through community compositions, interspecies interactions, and environmental adaptability, with particular emphasis on the synergistic response between microbial communities and abiotic factors. We found that the buffering effect of microbial communities on abiotic factors during land-use change is a key factor determining CUE change patterns. This review not only provides a theoretical framework for clarifying the microbial-dominated carbon turnover mechanism but also lays a scientific foundation for the precise implementation of sustainable land management and carbon neutrality goals. Full article

The ongoing degradation of global forests has severely weakened ecosystem service functions, and traditional judicial remedies have struggled to quantify intangible ecological losses. China has become an important testing ground for restorative justice through the establishment of specialized environmental courts and the practice of environmental public interest litigation. Since 2015, China has actively explored and institutionalized the application of the concept of restorative justice in its environmental justice reform. This concept emphasizes compensating environmental damages through actual ecological restoration acts rather than relying solely on financial compensation. This shift reflects a deep understanding of the limitations of traditional environmental justice and an institutional response to China’s ecological civilization construction, providing critical support for forest ecosystem restoration and enabling ecological restoration activities, such as replanting and re-greening, habitat reconstruction, etc., to be enforced through judicial decisions. This study conducts a qualitative analysis of judicial rulings in forest restoration cases to systematically evaluate the effectiveness of restorative justice in compensating for losses in forest ecosystem service functions. The findings reveal the following: (1) restoration measures in judicial practice are disconnected from the types of ecosystem services available; (2) non-market values and long-term cumulative damages are systematically underestimated, with monitoring mechanisms exhibiting fragmented implementation and insufficient effectiveness; (3) management cycles are set in violation of ecological restoration principles, and acceptance standards lack function-oriented indicators; (4) participation of key stakeholders is severely lacking, and local knowledge and professional expertise have not been integrated. In response, this study proposes a restorative judicial framework oriented toward forest ecosystem services, utilizing four mechanisms: independent recognition of legal interests, function-matched restoration, application of scientific assessment tools, and multi-stakeholder collaboration. This framework aims to drive a paradigm shift from formal restoration to substantive functional recovery, providing theoretical support and practical pathways for environmental judicial reform and global forest governance. Full article

Inspired by the Toyota Mirai, this study presents a high-fidelity data-driven approach for modelling and simulation of a fuel cell hybrid electric powertrain. This study utilises technical assessment data sourced from Argonne National Laboratory’s publicly available report, faithfully modelling most of the vehicle subsystems as data-driven entities. The simulation framework is developed in the MATLAB/Simulink environment and is based on a power dynamics approach, capturing nonlinear interactions and performance intricacies between different powertrain elements. This study investigates subsystem synergies and performance boundaries under a combined driving cycle composed of the NEDC, WLTP Class 3 and US06 profiles, representing urban, extra-urban and aggressive highway conditions. To emulate the real-world load-following strategy, a state transition power management and allocation method is synthesised. The proposed method dynamically governs the power flow between the fuel cell stack and the traction battery across three operational states, allowing the battery to stay within its allocated bounds. This simulation framework offers a near-accurate and computationally efficient digital counterpart to a commercial hybrid powertrain, serving as a valuable tool for educational and research purposes. Full article

This paper presents a novel data-driven framework for real-time State of Charge (SOC) estimation in lithium-ion battery systems using a data-driven Prognostics and Health Management (PHM) approach. The method leverages an optimized bidirectional Long Short-Term Memory (Bi-LSTM) network, trained with enhanced datasets filtered via exponentially weighted moving averages (EWMAs) and refined through SHAP-based feature attribution. Compared against a Extended Kalman Filter (EKF) and Unscented Kalman Filter (UKF) across ten diverse drive cycles, the proposed model consistently achieved superior performance, with mean absolute errors (MAEs) as low as 0.40%, outperforming EKF (0.66%) and UKF (1.36%). The Bi-LSTM model also demonstrated higher R² values (up to 0.9999) and narrower 95% confidence intervals, confirming its precision and robustness. Real-time implementation on embedded platforms yielded inference times of 1.3–2.2 s, validating its deployability for edge applications. The framework’s model-free nature makes it adaptable to other nonlinear, time-dependent systems beyond battery SOC estimation. Full article

Dysbiosis is a strategy to control insect pests through disrupting symbiotic bacteria essential for their life cycle. The olive fly, Bactrocera oleae, has been considered a suitable system for dysbiosis, as the insect is strictly dependent on its unique symbiont Candidatus Erwinia dacicola. Here, we review older and recent results from studies of the interaction of the symbiont and its host fly. We then discuss possible methods for disrupting the symbiosis as a means to control the fly. Specifically, we summarize studies using microscopy methods that have investigated in great detail the organs where the bacterium resides and it is always extracellular. Furthermore, we discuss how genome sequences of both host and bacterium can provide valuable resources for understanding the interaction and transcriptomic analyses that have revealed important insights that can be exploited for dysbiosis strategies. We also assess experiments where compounds have been tested against the symbiont. The hitherto limited efficacy in decreasing bacterial abundance suggests that novel molecules and/or new ways for the delivery of agents will be important for successful dysbiosis strategies. Finally, we discuss how gene drive methods could be implemented in olive fly control, though a number of hurdles would need to be overcome. Full article

Background: During infection with the cytomegalovirus (CMV), the membrane system of the infected cell is remodelled into a megastructure called the assembly compartment (AC). These extensive changes may involve the manipulation of the host cell proteome by targeting a pleiotropic function of the cell such as ubiquitination (Ub). In this study, we investigate whether the Ub system is required for the establishment and maintenance of the AC in murine CMV (MCMV)-infected cells Methods: NIH3T3 cells were infected with wild-type and recombinant MCMVs and the Ub system was inhibited with PYR-41. The expression of viral and host cell proteins was analyzed by Western blot. AC formation was monitored by immunofluorescence with confocal imaging and long-term live imaging as the dislocation of the Golgi and expansion of Rab10-positive tubular membranes (Rab10 TMs). A cell line with inducible expression of hemagglutinin (HA)-Ub was constructed to monitor ubiquitination. siRNA was used to deplete host cell factors. Infectious virion production was monitored using the plaque assay. Results: The Ub system is required for the establishment of the infection, progression of the replication cycle, viral gene expression and production of infectious virions. The Ub system also regulates the establishment and maintenance of the AC, including the expansion of Rab10 TMs. Increased ubiquitination of WASHC1, which is recruited to the machinery that drives the growth of Rab10 TMs, is consistent with Ub-dependent rheostatic control of membrane tubulation and the continued expansion of Rab10 TMs. Conclusions: The Ub system is intensively utilized at all stages of the MCMV replication cycle, including the reorganization of the membrane system into the AC. Disruption of rheostatic control of the membrane tubulation by ubiquitination and expansion of Rab10 TREs within the AC may contribute to the development of a sufficient amount of tubular membranes for virion envelopment. Full article

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

The rising demand for sustainable protein is driving interest in insects as a raw material for advanced food ingredients. This review collates and critically analyses over 300 studies on the conversion of crickets, mealworms, black soldier flies, and other farmed species into powders, protein isolates, oils, and chitosan-rich fibers with targeted techno-functional roles. This survey maps how thermal pre-treatments, blanch–dry–mill routes, enzymatic hydrolysis, and isoelectric solubilization–precipitation preserve or enhance the water- and oil-holding capacity, emulsification, foaming, and gelation, while also mitigating off-flavors, allergenicity, and microbial risks. A meta-analysis shows insect flours can absorb up to 3.2 g of water g⁻¹, stabilize oil-in-water emulsions for 14 days at 4 °C, and form gels with 180 kPa strength, outperforming or matching eggs, soy, or whey in specific applications. Case studies demonstrate a successful incorporation at 5–15% into bakery, meat analogs and dairy alternatives without sensory penalties, and chitin-derived chitosan films extend the bread shelf life by three days. Comparative life-cycle data indicate 45–80% lower greenhouse gas emissions and land use than equivalent animal-derived ingredients. Collectively, the evidence positions insect-based ingredients as versatile, safe, and climate-smart tools to enhance food quality and sustainability, while outlining research gaps in allergen mitigation, consumer acceptance, and regulatory harmonization. Full article

Transforming growth factor-β (TGF-β) is a key protein family member that includes activins, inhibins, and bone morphogenetic proteins (BMPs). It is essential in numerous biological processes, such as chemotaxis, apoptosis, differentiation, growth, and cell migration. TGF-β receptors initiate signaling through two primary pathways: the canonical pathway involving Smad proteins and non-canonical pathways that utilize alternative signaling mechanisms. When TGF-β signaling is disrupted, it has been shown to contribute to the development of various diseases, including cancer. Initially, TGF-β effectively inhibits the cell cycle and promotes apoptosis. However, its role can transition to facilitating tumor growth and metastasis as the disease progresses. Moreover, TGF-β drives cancer progression through epithelial–mesenchymal transition (EMT), modulation of factor expression, and evasion of immune responses. This complexity establishes the need for further research, particularly into pharmacological agents targeting TGF-β, which are emerging as promising therapeutic options. Current clinical and preclinical studies are making significant strides toward mitigating the adverse effects of TGF-β. This underscores the critical importance of understanding its underlying mechanisms to enhance treatment effectiveness and improve survival rates for cancer patients. Full article

To investigate the characteristics of rhizosphere soil microbial communities associated with Schisandra sphenanthera across different altitudinal gradients and to reveal the driving factors of microbial community dynamics, this study collected rhizosphere soil samples at four elevations: 900 m (HB1), 1100 m (HB2), 1300 m (HB3), and 1500 m (HB4). High-throughput sequencing and molecular ecological network analysis were employed to analyze the microbial community composition and species interactions. A null model was applied to elucidate community assembly mechanisms. The results demonstrated that bacterial communities were dominated by Proteobacteria, Acidobacteriota, Actinobacteriota, and Chloroflexi. The relative abundance of Proteobacteria increased with elevation, while that of Acidobacteriota and Actinobacteriota declined. Fungal communities were primarily composed of Ascomycota and Basidiomycota, with both showing elevated relative abundances at higher altitudes. Diversity indices revealed that HB2 exhibited the highest bacterial Chao, Ace, and Shannon indices but the lowest Simpson index. For fungi, HB3 displayed the highest Chao and Ace indices, whereas HB4 showed the highest Shannon index and the lowest Simpson index. Ecological network analysis indicated stronger bacterial competition at lower elevations and enhanced cooperation at higher elevations, contrasting with fungal communities that exhibited increased competition at higher altitudes. Altitude and soil nutrients were negatively correlated with soil carbon content, while plant nutrients and fungal diversity positively correlated with soil carbon. Null model analysis suggested that deterministic processes dominated bacterial community assembly, whereas stochastic processes governed fungal assembly. These findings highlight significant altitudinal shifts in the microbial community structure and assembly mechanisms in S. sphenanthera rhizosphere soils, driven by the synergistic effects of soil nutrients, plant growth, and fungal diversity. This study provides critical insights into microbial ecology and carbon cycling in alpine ecosystems, offering a scientific basis for ecosystem management and conservation. Full article

This study presents a hybrid method for state-of-charge (SOC) estimation of lithium-ion batteries using LSTM neural networks optimized with genetic algorithms (GA), combined with Coulomb Counting (CC) as an initial estimator. Experimental tests were conducted using medium-voltage (48–72 V) lithium-ion battery packs under standardized driving cycles (NEDC and WLTP). The proposed method enhances prediction accuracy under dynamic conditions by recalibrating the LSTM output with CC estimates through a dynamic fusion parameter $\alpha$. The novelty of this approach lies in the integration of machine learning and physical modeling, optimized via evolutionary algorithms, to address limitations of standalone methods in real-time applications. The hybrid model achieved a mean absolute error (MAE) of 0.181%, outperforming conventional estimation strategies. These findings contribute to more reliable battery management systems (BMS) for electric vehicles and second-life applications. Full article

Electric buses are key in the strategy towards a greenhouse-gas-neutral fleet. However, their restrictions in terms of range and refueling as well as their increased price point present new challenges for public transport companies. This study aims to address, based on real-world operational data, how energy consumption and charging behavior affect battery aging and how operational strategies can be optimized to extend battery life under realistic conditions. This article presents an energy consumption analysis with respect to ambient temperatures and average vehicle speed based exclusively on real-world data of an urban bus fleet, providing a data foundation for range forecasting and infrastructure planning optimized for public transport needs. Additionally, the State of Charge (SOC) window during operation and vehicle idle time as well as the charging power were analyzed in this case study to formulate recommendations towards a more battery-friendly treatment. The central research question is whether battery-friendly operational strategies—such as reduced charging power and lower SOC windows—can realistically be implemented in daily public transport operations. The impact of the recommendations on battery lifetime is estimated using a battery aging model on drive cycles. Finally, the reduction in CO₂ emissions compared to diesel buses is estimated. Full article

Soil bacteria drive biogeochemical cycles and influence disease suppression, playing pivotal roles in sustainable agriculture. Using Illumina MiSeq sequencing, we assessed how six ridge-furrow film mulching patterns affect soil bacterial diversity in a continuous potato system. The Shannon index showed significantly higher diversity in fully mulched treatments (T2–T3) versus controls (CK), suggesting mulching enhances microbial community richness. This result suggests that complete mulching combined with ridge planting (T2) may significantly enhance bacterial proliferation in soil. The bacterial communities were predominantly composed of Acidobacteria, Pseudomonadota, Bacteroidota, Chloroflexota, and Planctomycetota. Among these, Acidobacteria showed the highest abundance, with ridge planting patterns favoring greater Acidobacteria richness compared to furrow planting. In contrast, Pseudomonadota exhibited higher abundance under half-mulching conditions than under complete mulching. At class level, Acidobacteria and Proteobacteria emerged as the most abundant groups, with Proteobacteria constituting 22.6–35.7% of total microbial populations. Notably, Proteobacteria demonstrated particular dominance under the complete mulching with ridge planting pattern (T2). At the genus level, Subgroup_6_norank represented the most dominant taxon among the 439 identified bacterial genera, accounting for 14.0–20.2% of communities across all treatments, with half-mulching ridge planting (T4) showing the highest relative abundance. Our findings demonstrate that different ridge-furrow film mulching patterns significantly influence soil microbial diversity. While traditional non-mulched (CK) and mulched flat plots (T1) exhibited similar impacts on bacterial community structure, other treatments displayed distinct taxonomic profiles. Complete mulching patterns, particularly ridge planting (T2), appear most conducive to microbial development, suggesting their potential to enhance soil biogeochemical cycling in continuous cropping systems. These results provide valuable insights for optimizing mulching practices to improve soil health in agricultural ecosystems. Full article

Climate concerns are driving the automotive industry to adopt advanced manufacturing technologies that aim to improve energy efficiency and reduce vehicle weight. In this context, lightweight structural materials such as aluminium alloys have gained significant attention due to their favorable strength-to-weight ratio. Laser welding plays a crucial role in assembling such materials, offering high flexibility and fast joining capabilities for thin aluminium sheets. However, welding these materials presents specific challenges, particularly in controlling heat input to minimize distortions and ensure consistent weld quality. As a result, numerical simulations based on the Finite Element Method (FEM) are essential for predicting weld-induced phenomena and optimizing process performance. This study investigates welding-induced distortions in laser butt welding of 1.5 mm-thick Al 6061 samples through FEM simulations performed in the SYSWELD 2024.0 environment. The methodology provided by the software is based on the Moving Heat Source (MHS) model, which simulates the physical movement of the heat source and typically requires extensive calibration through destructive metallographic testing. This transient approach enables the detailed prediction of thermal, metallurgical, and mechanical behavior, but it is computationally demanding. To improve efficiency, the Imposed Thermal Cycle (ITC) model is often used. In this technique, a thermal cycle, extracted from an MHS simulation or experimental data, is imposed on predefined subregions of the model, allowing only mechanical behavior to be simulated while reducing computation time. To avoid MHS-based calibration, this work proposes using thermal cycles acquired in-line during welding via infrared thermography as direct input for the ITC model. The method was validated experimentally and numerically, showing good agreement in the prediction of distortions and a significant reduction in workflow time. The distortion values from simulations differ from the real experiment by less than 0.3%. Our method exhibits a slight decrease in performance, resulting in an increase in estimation error of 0.03% compared to classic approaches, but more than 85% saving in computation time. The integration of real process data into the simulation enables a virtual representation of the process, supporting future developments toward Digital Twin applications. Full article