Search

Despite the burgeoning research interest in collaborative L2 writing, it is underrepresented in the literature regarding how teachers use such activities within real-world classroom settings. This case study seeks to offer a comprehensive understanding of the practices adopted by two EFL teachers in implementing collaborative writing within Chinese university contexts. Drawing upon Activity Theory, the study delineates the teachers' practices into two distinct activity systems, elucidating the production, distribution and exchange aspects of their activities taken in implementing a collaborative writing project. The findings reveal that while both teachers utilised a variety of instructional tools and pursued similar outcomes, they exhibited different features in the exchange and distribution aspects of their activity systems. The differences were manifested in their adherence to the norms, regulations and principles governing collaborative writing in their teaching environments, wherein the teacher, students and university each assumed distinct roles. Implications are provided for the effective integration of collaborative writing practices in language education.

Abstract Development of large-scale alkaline seawater electrolysis requires robust and corrosion-resistant anodes. Here we propose engineering NiFe layered double hydroxide (LDH)-based anodes by incorporating a series of anions into the LDH interlayers. The most optimal NiFe LDH anode with intercalated phosphates demonstrates stable operation at a high current density of 1.0 A cm−2 for over 1000 hours in a 2 W-scale alkaline seawater electrolyzer (ASWE). Fundamental studies indicate that the basicity, indicated by pK a values, of the intercalated anions in NiFe LDH governs its oxygen evolution reaction activity and corrosion resistance. Highly basic anions (i.e., phosphates) securely anchor Fe sites and facilitate proton transfer to boost both durability and activity. Notably, we demonstrate the proof-of-concept for the NiFe anode in an industrial 1 kW-scale ASWE stack (1,081.2 cm2 anode area in total). This unit achieves a stable operating current density of 0.5 A cm−2 at about 2.0 V, twice that of the commercial alkaline pure water electrolyzer, contributing to an economically competitive hydrogen production cost of US$ 1.96 kgH2 −1.

Abstract Lung carcinoma, predominantly manifested as non-small cell lung cancer (NSCLC), significantly contributes to oncological mortality, underscoring an imperative for novel therapeutic paradigms. Amidst this context, the present investigation delineates the synergistic potentiation of doxorubicin (DOX)—a canonical chemotherapeutic—by Ursodeoxycholic acid (UDCA), a compound with a historical pedigree in hepatobiliary medicine, now repositioned within oncological pharmacotherapy due to its dichotomous cellular modulation—affording cytoprotection to non-malignant epithelia whilst eliciting apoptotic cascades in neoplastic counterparts. This study, through a rigorous methodological framework, elucidates UDCA’s capacity to inhibit NSCLC cellular proliferation and induce apoptosis, thereby significantly amplifying DOX’s chemotherapeutic efficacy. Notably, the co-administration of UDCA and DOX was observed to attenuate DOX-induced autophagy via the modulation of the TGF-β/MAPK signaling axis, a pathway pivotal in mediating cellular survival and autophagic mechanisms. Such findings not only underscore the therapeutic potential of UDCA as a chemosensitizer but also illuminate the molecular underpinnings of its modulatory effects, thereby contributing to the corpus of knowledge necessary to surmount chemoresistance in NSCLC. The implications of this research are twofold: firstly, it offers a compelling evidence base for the clinical reevaluation of UDCA in combinatory chemotherapeutic regimens; secondly, it posits a novel mechanistic insight into the modulation of chemotherapeutic efficacy and resistance. Collectively, these insights advocate for the expedited clinical translation of UDCA-DOX synergy, potentially heralding a paradigm shift in the management of NSCLC, thereby addressing a critical lacuna in contemporary oncological therapy.

To achieve rapid detection of astaxanthin content in Antarctic krill meal, a determination method for astaxanthin content in krill meal was established using computer vision and convolutional neural networks. A total of 70 Antarctic krill meal samples were analyzed using high-performance liquid chromatography to determine their astaxanthin contents as label, and corresponding images of the samples were acquired using a computer vision system to form the dataset and the dataset was augmented. The model was built using the TensorFlow learning framework. The 5-fold cross-validation was used to tune and evaluate the model and select the optimal parameter model. The optimal parameter model was evaluated by randomly dividing the dataset, and 30 images from the dataset were randomly selected for model validation. The results showed that the optimal hyperparameters model with a root mean square error (RMSE) of 3.59 was preserved through a five-fold cross-validation. For model evaluation, the model was repeated three times. The mean values of the coefficient of determination (R2), mean absolute error (MAE), mean square error (MSE), and RMSE for the test set were 0.9626, 1.49, 4.22, and 2.05, respectively. For model validation, the relative errors ranged from 0.10% to 6.46%, indicating small deviations between the predictions and the observations. The astaxanthin content prediction model demonstrated high accuracy, enabling quick and nondestructive detection of astaxanthin content in krill meal samples.

Objective: The institutionalization of care for patients with dementia is becoming a trend. Understanding the burden on employed caregivers and exploring associated factors are of great importance in practice. Therefore, this study aimed to examine the relationship between basic attributes, caring ability, and caregiver burden in employed caregivers practicing in nursing homes. Methods: This cross-sectional study included 541 employed caregivers in 11 four-star nursing homes in Zhejiang Province from April to December 2022. Caregiver burden was assessed using the Zarit Burden Interview (ZBI). Demographic characteristics of participants, characteristics of the older patients with dementia, caring characteristics, training in dementia care, and caring abilities were collected for analysis of influencing factors. A hierarchical multiple regression analysis was conducted to explore the factors influencing the burden on employed caregivers in nursing homes. Results: The ZBI score of employed caregivers in nursing homes was 40.42 ± 10.18, representing a moderate caregiver burden. Factors such as age (U = 27.82, P

Abstract Industrial CO2 electrolysis via electrochemical CO2 reduction has achieved progress in alkaline solutions, while the same reaction in acidic solution remains challenging because of severe hydrogen evolution side reactions, acid corrosion, and low target product selectivity. Herein, an industrial acidic CO2 electrolysis to pure HCOOH system is realized in a proton-exchange-membrane electrolyzer using an acid-tolerant Bi-based metal-organic framework guided by a Pourbaix diagram. Significantly, the Faradaic efficiency of HCOOH synthesis reaches 95.10% at a large current density of 400 mA/cm2 with a high CO2 single-pass conversion efficiency of 64.91%. Moreover, the proton-exchange-membrane device also achieves an industrial-level current density of 250 mA/cm2 under a relatively low voltage of 3.5 V for up to 100 h with a Faradaic efficiency of 93.5% for HCOOH production, which corresponds to an energy consumption of 200.65 kWh/kmol, production rate of 12.1 mmol/m2/s, and an energy conversion efficiency of 38.2%. These results will greatly aid the contemporary research moving toward commercial implementation and success of CO2 electrolysis technology.

Leisure activities engagement could promote freshmen's well-being by providing social support and improving positive mood. Little is known about the day-to-day variability in leisure activities and stress and their links to daily well-being. Using a month-long daily diary design among 273 Canadian freshmen (M[subscript age] = 18, 73% female, 32% White), this study investigated the relations between daily leisure activities, stress, and well-being. Multilevel modeling revealed that daily leisure activities showed concurrent positive effects on daily well-being, whereas daily stress had negative effects. There were substantial inter-individual differences in these links, which were partly explained by coping orientations and person-average levels of stress. Engaging in more diverse leisure activities was related to better well-being. The findings demonstrate the immediate beneficial effects of leisure activities on well-being in everyday life and provide interventions with amendable targets to promote freshmen's positive socioemotional development during their transition to university.

This study presents an approach for the generation of a large-scale viscous mesh using a parallel advancing layer method based on distributed systems with multi-cores. A triangular surface mesh that conforms with the original geometrical model is prepared as input and decomposed into multiple subdomains. A coarser background mesh with high-quality viscous elements from the same model is employed for parallel computation. Instead of using a conventional approach to generate layer-wise prismatic elements, a normal calculation through linear interpolation is implemented to overcome the boundary problems arising in distributed systems. A novel strategy to deal with cross-processor transition elements is proposed to reduce frequent communications during viscous mesh generation. To improve the parallel efficiency, a fast domain decomposition method is introduced to speed up the parallel meshing procedure of tetrahedral elements. Numerical experiments are performed on high-performance computing systems with billions of mesh elements to demonstrate the effectiveness and efficiency of the proposed approach and numerical simulation results represent a promising applicability.

The presence of heterogeneous image disparities often leads to inferior quality in the generated difference images during change detection. This paper proposes a self-supervised change detection of heterogeneous images based on a difference algorithm. Firstly, a combination of phase consistency and a simplified pulse-coupled neural network (PC-SPCNN) is used to fuse the heterogeneous images, and the result is used to compute the difference image (DI). The new DI generation method can generate the standard and exponential difference images. Secondly, the hierarchical FCM clustering algorithm is improved to extract stable and correct self-supervised samples by difference images so that the clustering process is not overly dependent on thresholds. Then, the support vector machine classifier is trained based on the heterogeneous images, the fused images, and self-supervised sample sets, and the information from the fused images is utilized to increase the feature dimension for better detection of changes. Finally, the support vector machine classifier automatically detects whether the intermediate pixels are changed and produces the change detection results. The experimental results confirm the improvements made by the proposed method in difference image extraction, training sample selection, and clustering algorithm, and the stability of the method exceeds that of the state-of-the-art change detection methods.

Sensitivity, which is useful for evaluating the contributions of input variations to output changes, favors designers exploiting the most sensitive design parameters and completing design optimization in aerospace. This study introduces the turbulence adjoint method due to its low computational cost in calculating sensitivities with high accuracy and then solves the adjoint partial differential equations with respect to both the Reynolds-averaged Navier–Stokes and turbulence model equations with the assistance of an automatic differentiation tool. By utilizing the turbulence adjoint method, the sensitivities of aerodynamic performance to the blade profile modifications of two-dimensional and three-dimensional compressor cascades are calculated, allowing for the investigations of the impact of geometric variations on the changes in the flow field. Ultimately, aerodynamic optimization of the compressor cascades is conducted. After optimization, the adiabatic efficiency of the two-dimensional cascade increases by 3.11%, and it increases by 1.15% for the three-dimensional cascade. The variations in flow fields of both the original and optimized cascades are illustrated to discover the origins of performance improvements. The changes in blade profile are almost consistent with those forecasted from sensitivity analysis, demonstrating the potential superiority of the adjoint method for aerodynamic design.

Supersonic and hypersonic flows have gained considerable attention in the aerospace industry in recent years. Flow control is crucial for refining the quality of these high-speed flows and improving the performance and safety of fast aircraft. This paper discusses the distinctive characteristics of supersonic flows compared to low-speed flows, including phenomena such as boundary layer transition, shock waves, and sonic boom. These traits give rise to significant challenges related to drag, noise, and heat. Therefore, a review of several active and passive control strategies is provided, highlighting their significant advancements in flow transitions, reducing drag, minimizing noise, and managing heat. Furthermore, we provide a comprehensive analysis of various research methodologies used in the application of flow control engineering, including wind tunnel testing, flight testing, and computational fluid dynamics (CFD). This work gives an overview of the present state of flow control research and offers insights into potential future advancements.

Since random uncertainty may cause severe aerodynamic performance fluctuations for the wing-mounted aircraft, the Gaussian process regression (GPR) surrogate model method based on was proposed. The strategy of adding sample points by active-learning method can effectively reduce model uncertainty and improve the accuracy of uncertainty prediction. Focusing on incoming flow with uncertainty, the polynomial chaos expansion (PCE) method based on Smolyak sparse grid and the GPR surrogate model method based on the strategy of adding sample points by active-learning method were used to analyze the uncertainty of the wing-body-nacelle-pylon geometry combined with the Sobol sensitivity analysis method. Results show that the uncertainty of angle of attack and Mach number will cause dramatic fluctuation in lift coefficient and drag coefficient of the wing-mounted aircraft under transonic condition. The fluctuation of lift coefficient is affected by angle of attack and Mach number, and the fluctuation of drag coefficient is mainly determined by Mach number.

Abstract The statistical model for automatic flow recognition is significant for public place management. However, the current model suffers from insufficient statistical accuracy and low lightweight. Therefore, in this study, the structure of the lightweight object detection model "You Only Live Once v3" is optimized, and the "Deep Simple Online Real-Time Tracking" algorithm with the "Person Re-Identification" module is designed, so as to construct a statistical model for people flow recognition. The results showed that the median PersonAP of the designed model was 94.2%, the total detection time was 216 ms, the Rank-1 and Rank-10 were 87.2% and 98.6%, respectively, and the maximum occupied memory of the whole test set was 2.57 MB, which was better than all comparison models. The results indicate that the intelligent identification statistical model for public crowd flow obtained through this design and training has higher statistical accuracy, less computational resource consumption, and faster computing speed. This has certain application space in the management and guidance of crowd flow in public places.

Abstract Background Lovastatin has widespread applications thanks to its multiple pharmacological effects. Fermentation by filamentous fungi represents the major way of lovastatin production. However, the current lovastatin productivity by fungal fermentation is limited and needs to be improved. Results In this study, the lovastatin-producing strains of Aspergillus terreus from marine environment were screened, and their lovastatin productions were further improved by genetic engineering. Five strains of A. terreus were isolated from various marine environments. Their secondary metabolites were profiled by metabolomics analysis using Ultra Performance Liquid Chromatography–Mass spectrometry (UPLC–MS) with Global Natural Products Social Molecular Networking (GNPS), revealing that the production of secondary metabolites was variable among different strains. Remarkably, the strain of A. terreus MJ106 could principally biosynthesize the target drug lovastatin, which was confirmed by High Performance Liquid Chromatography (HPLC) and gene expression analysis. By one-factor experiment, lactose was found to be the best carbon source for A. terreus MJ106 to produce lovastatin. To improve the lovastatin titer in A. terreus MJ106, genetic engineering was applied to this strain. Firstly, a series of strong promoters was identified by transcriptomic and green fluorescent protein reporter analysis. Then, three selected strong promoters were used to overexpress the transcription factor gene lovE encoding the major transactivator for lov gene cluster expression. The results revealed that compared to A. terreus MJ106, all lovE over-expression mutants exhibited significantly more production of lovastatin and higher gene expression. One of them, LovE-b19, showed the highest lovastatin productivity at a titer of 1512 mg/L, which represents the highest production level reported in A. terreus. Conclusion Our data suggested that combination of strain screen and genetic engineering represents a powerful tool for improving the productivity of fungal secondary metabolites, which could be adopted for large-scale production of lovastatin in marine-derived A. terreus.

To develop a rapid and non-destructive method for assessing the freshness of large yellow croaker, a computer vision technique combined with a convolutional neural network (CNN) was utilized. Sixty fish were stored on ice, and images were captured using a smartphone at intervals of 0, 12, 24, 36, 48, 72, and 96 h. A modified ResNeXt architecture was applied to automatically extract features and establish a freshness classification model. The CNN model was able to identify imperceptible visual changes, and achieved classification accuracies of 84.0 % and 72.0 % for 24- and 12 h intervals, respectively. Furthermore, potential mechanisms for the model's performance were discussed, indicating that changes in skin, eyes, and other image features contribute to the freshness classification. In summary, this method is effective for real-time, non-destructive, low-cost, and environmentally friendly fish freshness evaluation, particularly during the early stages of storage.

Fish exhibit varying responses to polystyrene microplastics (MPs) depending on particle size. Previous studies suggested that microorganisms adhering to the surface of MPs can induce toxic effects. In this study, Tilapia were exposed to MPs of control (group A), 75 nm (B), 7.5 μm (C), 750 μm (D), as well as combinations of all sizes (E) and 75 nm MPs with Chlorella vulgaris addition (F) for 7, 10 and 14 days. Histopathological changes in liver of tilapia were assessed using enzyme activities, transcriptomics and proteomics. The results showed that in groups combined MPs of different particle sizes and those supplemented with chlorella, MPs were localized on the surface of goblet cells, leading to vacuoles, constricted hepatic sinuses and nuclei displacement. Exposure to 7.5 and 750 μm MPs significantly increased the contents of fatty acid synthase (FAS), adenosine triphosphate (ATP), acetyl-CoA carboxylase (ACC), lipoprotein lipase (LPL), total cholesterol (TC), total triglyceride (TG) contents at 7 and 10 days. In particular, cytochrome p450 1a1 (EROD), reactive oxygen species (ROS) and superoxide dismutase (SOD) were markedly elevated following exposure to MPs. Apoptotic markers caspase-3, and inflammatory markers, including tumor necrosis factor α (TNF-α) and interleukin-1β (IL-1β), had a similar upward trend in comparisons of group C vs A at 7 d, group D vs A at 14 d. The peroxisome proliferator activated receptor (PPAR) signaling pathway, spliceosome, was highly enriched during the 7-day exposure of medium sized MPs, while largest MPs in the comparison of group D vs A at 14 d activated pathways such as phagosome, apoptosis, salmonella infection. Transcriptomic analysis revealed that after 14 days, the kyoto encyclopedia of genes and genomes (KEGG) pathways associated with protein processing in endoplasmic reticulum and the PPAR signaling has been significantly enriched in the Chlorella-supplemented group, which was further confirmed via the proteomic analysis. Overall, the findings highlight the size-dependent effects of MPs on histopathological changes, gene and protein expression in the liver of tilapia, and C. vulgaris effectively attenuated liver damages, likely through modulation of endoplasmic reticulum protein processing and PPAR signaling pathways.

IntroductionLong-term care assistants are taking on more important roles in the healthcare system. The purpose of this study was to investigate what demographic factors influence the core competencies of nursing assistants, as well as to investigate the levels of organizational support, self-efficacy, and core competencies among nursing assistants in China, to explore the relationship between them.MethodsThis is a cross-sectional study with prospective data collection based on a self-report questionnaire. A total of 320 long-term care assistants from two healthcare institutions. We collected socio-demographic characteristics and measured their perceived organizational support, self-efficacy, and core competency levels of the participants. Pearson correlation tests were conducted to examine the relationships among three variables, and a structural equation model was developed to test the interrelationships among these variables.ResultsThe results indicated that age, employment type, licensing status, monthly income, pre-job training, and training methods were associated with core competency, with nursing knowledge identified as a weak area in core competencies. There were significant associations among each dimension of perceived organizational support, self-efficacy, and core competencies (p