Carbon tax vs. carbon trading in China: which is better for promoting sustainable development of remanufacturing companies?

To accelerate achieving carbon neutrality, the promotion of low-carbon development in the manufacturing industry has been facilitated by the government's implementation of policies such as carbon taxation and carbon emissions trading. These measures have been put in place to reduce carbon emissions and enhance sustainability within the manufacturing sector. Remanufacturing is an important direction for the low-carbon transformation of enterprises, and improving remanufactured product quality is crucial to the sustainability of remanufacturing enterprises. To elucidate the influence of policies aimed at reducing carbon emissions on the quality of remanufactured products, we developed a game model involving three key players: the original equipment manufacturer (OEM), the remanufacturer (IR), and retailers. This model was constructed based on the heterogeneous consumer demand for both new and remanufactured products. The study delved into the effects of various governmental policies aimed at reducing carbon emissions on the quality-related decisions made by remanufacturing enterprises. Our primary focus was on the implementation of two specific policies: a high-level carbon taxation policy and a carbon trading policy characterized by elevated carbon pricing. These policies create a favorable environment for remanufacturers (IR) to enhance the quality of their products. The sales of remanufactured products are influenced by the purchasing preferences of consumers, and carbon reduction policies can be effective in reducing the total environmental impact of manufacturing. Carbon trading policy is most conducive to environmental protection and achieves a win-win situation for economic and environmental benefits for OEMs and IRs when the carbon tax per unit is compared with the carbon trading price. Hence, this situation is favorable for the sustainable growth of existing remanufacturing businesses. Consequently, the government's requirement for subsidies to enhance the quality of remanufactured products and boost the competitiveness of IRs in the market becomes less pronounced.

Low-carbon technology service mode with revenue-sharing contract considering advance funding risk.

This study aims to effectively reduce carbon emissions by selecting the low-carbon technology service emission reduction modes. This paper constructs a revenue-sharing contract model based on game theory. The purpose of this paper is to analyze the choice of carbon emission reduction strategies for carbon emission-dependent manufacturers and low-carbon technology service providers with advance funding risk aversion. This study analyzes the effects on the optimal decision-making and profits of both parties of the degree of risk aversion and the investment cost coefficient of carbon emissions reduction for service providers, and initial carbon emissions of manufacturers. The optimal carbon emissions reduction strategy of manufacturers and service providers with numerical analysis is obtained, and the revenue-sharing contract coordination is realized. Finally, the validity of the contract is verified by simulation analysis. The results show that manufacturers and service providers can only achieve optimal cooperation for emission reduction within a certain range of parameters. With different investment cost coefficients of carbon emissions reduction and with different initial carbon emissions, manufacturers will adjust the fixed emissions reduction fee and the revenue-sharing coefficient to encourage service providers to offer the optimal carbon emissions reduction rate. This study enriches theoretical research on low-carbon service chains. It also provides important practical evidence to help manufacturers and service providers choose optimal strategies for coordinating revenue-sharing contracts.

Fol-milR1, a pathogenicity factor of Fusarium oxysporum, confers tomato wilt disease resistance by impairing host immune responses.

Although it is well known that miRNAs play crucial roles in multiple biological processes, there is currently no evidence indicating that milRNAs from Fusarium oxysporum f. sp. lycopersici (Fol) interfere with tomato resistance during infection. Here, using sRNA-seq, we demonstrate that Fol-milR1, a trans-kingdom small RNA, is exported into tomato cells after infection. The knockout strain ∆Fol-milR1 displays attenuated pathogenicity to the susceptible tomato cultivar 'Moneymaker'. On the other hand, Fol-milR1 overexpression strains exhibit enhanced virulence against the resistant cultivar 'Motelle'. Several tomato mRNAs are predicted targets of Fol-milR1. Among these genes, Solyc06g007430 (encoding the CBL-interacting protein kinase, SlyFRG4) is regulated at the posttranscriptional level by Fol-milR1. Furthermore, SlyFRG4 loss-of-function alleles created using CRISPR/Cas9 in tomato ('Motelle') exhibit enhanced disease susceptibility to Fol, further supporting the idea that SlyFRG4 is essential for tomato wilt disease resistance. Notably, our results using immunoprecipitation with specific antiserum suggest that Fol-milR1 interferes with the host immunity machinery by binding to tomato ARGONAUTE 4a (SlyAGO4a). Furthermore, virus-induced gene silenced (VIGS) knock-down SlyAGO4a plants exhibit reduced susceptibility to Fol. Together, our findings support a model in which Fol-milR1 is an sRNA fungal effector that suppresses host immunity by silencing a disease resistance gene, thus providing a novel virulence strategy to achieve infection.

Decision-adjusted driver risk predictive models using kinematics information.

Accurate prediction of driving risk is challenging due to the rarity of crashes and individual driver heterogeneity. One promising direction of tackling this challenge is to take advantage of telematics data, increasingly available from connected vehicle technology, to obtain dense risk predictors. In this work, we propose a decision-adjusted framework to develop optimal driver risk prediction models using telematics-based driving behavior information. We apply the proposed framework to identify the optimal threshold values for elevated longitudinal acceleration (ACC), deceleration (DEC), lateral acceleration (LAT), and other model parameters for predicting driver risk. The Second Strategic Highway Research Program (SHRP 2) naturalistic driving data were used with the decision rule of identifying the top 1% to 20% of the riskiest drivers. The results show that the decision-adjusted model improves prediction precision by 6.3% to 26.1% compared to a baseline model using non-telematics predictors. The proposed model is superior to models based on a receiver operating characteristic curve criterion, with 5.3% and 31.8% improvement in prediction precision. The results confirm that the optimal thresholds for ACC, DEC and LAT are sensitive to the decision rules, especially when predicting a small percentage of high-risk drivers. This study demonstrates the value of kinematic driving behavior in crash risk prediction and the necessity for a systematic approach for extracting prediction features. The proposed method can benefit broad applications, including fleet safety management, use-based insurance, driver behavior intervention, as well as connected-vehicle safety technology development.

Driving safety assessment for ride-hailing drivers.

Ride-hailing services, which have become increasingly prevalent in the last decade, provide an efficient travel mode by matching drivers and travelers via smartphone apps. Ride-hailing services enable millions of non-traditional taxi drivers to provide travel services, but may also raise safety concerns due to heterogeneity in the driver population. This study evaluated crash risk factors for ride-hailing drivers, including driving history and ride-hailing operational characteristics, using a sample of 189,815 drivers. We utilized the Poisson generalized additive model to accommodate for the potential nonlinear relationship between crash rate and risk factors. Results showed that crash history, the percentage of long-shift bookings, driving distance, operations during peak hours, years of being a ride-hailing driver, and passenger rating were significantly associated with crash risk. Several factors showed nonlinear relationships with crash risk. We adopted the SHapley Additive exPlanation (SHAP) method to assess and visualize the impact of each risk factor. The results indicated that passenger average rating, total driving distance, and crash history were the leading contributing factors. The findings of this study provide critical information for the development of safety countermeasures, driver education programs, as well as safety regulations for the ride-hailing industry.

SHOX CNE9/10 Knockout in U2OS Osteosarcoma Cells and Its Effects on Cell Growth and Apoptosis.

BACKGROUND Osteosarcoma is a common malignant tumor of musculoskeletal stromal cells. Osteosarcoma clinical behavior depends mostly on the histologic grade, the site of primary tumor, the response to chemotherapy, and the presence of pulmonary metastases. The aim of this study was to knockout SHOX CNE9/10 in U2OS osteosarcoma cells and to analyze the effects on cell growth and apoptosis. MATERIAL AND METHODS U2OS cells with CNE9 knockout and U2OS cells with CNE10 knockout were established via the CRISPR/Cas9 system. Sanger sequencing was used to detect the success of the knockdown experiment. Western blotting and quantitative polymerase chain reaction were used to detect the expression levels of short stature homeobox-containing gene (SHOX) protein and messenger RNA (mRNA) after knockdown of CNE9 and CNE10. The cell viability and apoptotic rate were detected by the Cell Counting Kit-8 method and by flow cytometry. RESULTS The Sanger sequencing results showed that the knockdown experiment was successful. The levels of SHOX mRNA and protein were significantly reduced after knocking down CNE9 and CNE10. Knockdown of CNE9 and CNE10 significantly increased the growth and inhibited the apoptosis of U2OS osteosarcoma cells. CNE9/CNE10 knockdown U2OS cells were successfully constructed. CONCLUSIONS Knockdown of CNE9 and CNE10 promoted U2OS cell growth and inhibited apoptosis by decreasing SHOX expression. This CNE9/CNE10 knockout U2OS cell model could provide a bridge for the research on SHOX and CNEs in osteosarcoma.

FolVps9, a Guanine Nucleotide Exchange Factor for FolVps21, Is Essential for Fungal Development and Pathogenicity in Fusarium oxysporum f. sp. lycopersici.

The soil-borne, asexual fungus Fusarium oxysporum f.sp. lycopersici (Fol) is the causal agent of tomato wilt disease. Autophagy plays a crucial role in the development and virulence of Fol. The Fol endosomal system is highly dynamic and has been shown to be associated with conidiogenesis and pathogenicity. Rab GTPases and the regulators are highly conserved in regulating autophagy and endocytosis in most eukaryotes. Identification and characterization of additional Rab regulators in fungal pathogens should facilitate the understanding of the autophagy and endocytosis in different filamentous fungi. Here, we have identified and characterized the yeast VPS9 homolog FolVPS9 in Fol. Targeted gene deletion showed that FolVPS9 is important for growth, conidiation and virulence in Fol. Cytological examination revealed that FolVps9 co-localized with FolVps21 (a marker of early endosome) and played a critical role in endocytosis and autophagosome degradation. Pull-down assays showed that FolVps9 interacted with FolVps21, which was also important for development and plant infection in Fol. Yeast two-hybrid, bimolecular fluorescence complementation and co-immunoprecipitation assays revealed that FolVps9 specifically interacts with the GDP-bound form of FolVps21. Furthermore, a constitutively active form of FolVps21 (Q72L) was able to rescue defects of ΔFolvps9 and ΔFolvps21 mutants. In summary, our study provides solid evidence that FolVps9 acts as a FolVps21 guanine nucleotide exchange factor (GEFs) to modulate endocytosis and autophagy, thereby controlling vegetative growth, asexual development and pathogenicity in Fol.

Adjusting finite sample bias in traffic safety modeling.

Poisson and negative binomial regression models are fundamental statistical analysis tools for traffic safety evaluation. The regression parameter estimation could suffer from the finite sample bias when event frequency is low, which is commonly observed in safety research as crashes are rare events. In this study, we apply a bias-correction procedure to the parameter estimation of Poisson and NB regression models. We provide a general bias-correction formulation and illustrate the finite sample bias through a special scenario with a single binary explanatory variable. Several factors affecting the magnitude of bias are identified, including the number of crashes and the balance of the crash counts within strata of a categorical explanatory variable. Simulations are conducted to examine the properties of the bias-corrected coefficient estimators. The results show that the bias-corrected estimators generally provide less bias and smaller variance. The effect is especially pronounced when the crash count in one stratum is between 5 and 50. We apply the proposed method to a case study of infrastructure safety evaluation. Three scenarios were evaluated, all crashes collected in three years, and two hypothetical situations, where crash information was collected for "half-year" and "quarter-year" periods. The case-study results confirm that the magnitude of bias correction is larger for smaller crash counts. This paper demonstrates the finite sample bias associated with the small number of crashes and suggests bias adjustment can provide more accurate estimation when evaluating the impacts of crash risk factors.

The SNARE protein FolVam7 mediates intracellular trafficking to regulate conidiogenesis and pathogenicity in Fusarium oxysporum f. sp. lycopersici.

Soluble N-ethylmaleimide-sensitive factor attachment protein receptors (SNAREs) are conserved in fungi, plants and animals. The Vam7 gene encodes a v-SNARE protein that involved in vesicle trafficking in fungi. Here, we identified and characterized the function of FolVam7, a homologue of the yeast SNARE protein Vam7p in Fusarium oxysporum f. sp. lycopersici (Fol), a fungal pathogen of tomato. FolVam7 contains SNARE and PX (Phox homology) domains that are indispensable for normal localization and function of FolVam7. Targeted gene deletion showed that FolVam7-mediated vesicle trafficking is important for vegetative growth, asexual development, conidial morphology and plant infection. Further cytological examinations revealed that FolVam7 is localized to vesicles and vacuole membranes in the hyphae stage. Moreover, the ΔFolvam7 mutant is insensitive to salt and osmotic stresses and hypersensitive to cell wall stressors. Taken together, our results suggested that FolVam7-mediated vesicle trafficking promotes vegetative growth, conidiogenesis and pathogenicity of Fol.

An integrated analysis of mRNA and sRNA transcriptional profiles in tomato root: Insights on tomato wilt disease.

Tomato wilt disease caused by Fusarium oxysporum f. sp. lycopersici (FOL) is a worldwide destructive disease of tomato. As exploring gene expression and function approaches constitute an initial point for investigating pathogen-host interaction, we performed RNA-seq and sRNA-seq analysis to investigate the transcriptome of tomato root under FOL infection. Differentially expressed (DE) protein-coding gene and miRNA gene profiles upon inoculation with FOL were presented at twenty-four hours post-inoculation in four treatments. A total of more than 182.6 million and 132.2 million high quality clean reads were obtained by RNA-seq and sRNA-seq, respectively. A large overlap was found in DE mRNAs between susceptible cultivar Moneymaker and resistant cultivar Motelle. Gene Ontology terms were mainly classified into catalytic activity, metabolic process and binding. Combining with qRT-PCR and Northern blot, we validated the transcriptional profile of five genes and five miRNAs conferred to FOL infection. Our work allowed comprehensive understanding of different transcriptional reaction of genes/miRNAs between the susceptible and resistant cultivars tomato to the FOL challenge, which could offer us with a future direction to generate models of mediated resistance responses.

FRG3, a Target of slmiR482e-3p, Provides Resistance against the Fungal Pathogen Fusarium oxysporum in Tomato.

The vast majority of plant disease resistance (R) genes encode nucleotide binding site-leucine-rich repeat (NBS-LRR) proteins, which specifically determine the plant immune response and have been demonstrated to be targets of several microRNA (miRNA) families. The fungus Fusarium oxysporum f. sp. lycopersici (FOL) causes vascular wilt disease in tomato worldwide. Here, we explored a possible role for FGR3 in tomato defense against FOL. FRG3 is a predicted NBS-LRR like gene that is targeted by slmiR482e-3p, a member of slmiR482 miRNA family. Northern blot data demonstrated that all seven members of the slmiR482 family were regulated in diverse ways after infection by FOL. The ability of FRG3 to be regulated by slmiR482e-3p was confirmed at the transcript level by co-expression studies in Nicotiana benthamiana. A virus-induced gene silencing (VIGS) approach revealed that FRG3 confers resistance to the Motelle tomato cultivar. Taken together, our study has identified a novel R gene, FRG3, which is targeted by slmiR482e-3p at the transcript level, and is necessary for resistance to tomato wilt disease in planta.