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Objective To explore the protective effects and the underlying mechanisms of bone marrow mesenchymal stem cells (BMSCs) on the brain in microgravity environment. Methods The physiological effects of microgravity were simulated with the hindlimb unloading (HU) mouse model. The animals were divided into 3 groups: the control group (wild-type mice), the hindlimb unloading (HU) group, and the BMSCs treatment group, with six in each. RT-qPCR was adopted to determine the expression levels of pro-inflammatory cytokines (Il-6, Il-1β and Tnf-α) in the cerebral cortex; immunohistochemistry was performed to evaluate the proportions of microglia (IBA-positive) and astrocytes (GFAP-positive); Western blot was used to measure the expression levels of apoptosis-and senescence-related molecules (BAX, BCL-2, p21, and p53). Results Compared with wild-type mice, the expression levels of Il-6, Il-1β and Tnf-α in the cerebral cortex of HU mice were significantly increased (P＜0.05), the expression level of BAX, p21 and p53 was also significantly increased(P＜0.05). However, the expression level of BCL-2 protein were significantly decreased (P＜0.05). The proportion of microglia (IBA1 positive) and astrocytes (GFAP positive) was increased (P＜0.05); After BMSCs treatment, the expression level of Il-1β in the cerebral cortex of HU mice was significantly decreased (P＜0.05), the expression levels of BAX, p21 and p53 were significantly decreased (P＜0.05), and the expression level of BCL-2 protein was significantly increased (P＜0.05). The proportion of microglia (IBA1 positive) and astrocytes (GFAP positive) decreased (P＜0.05). Conclusions BMSCs potentially relieve the detrimental effects of simulated microgravity on mouse brain by inhibiting inflammation, apoptosis and cellular senescence.

Fe nanoparticle-functionalized ordered mesoporous carbon (Fe0/OMC) was synthesized using triblock copolymers as templates and through solvent evaporation self-assembly, followed by a carbothermal reduction. Fe0/OMC had three microstructures of two-dimensional hexagonal (space group, p6mm, Fe0/OMC-1), body centered cubic (Im3̄m, Fe0/OMC-2), and face centered cubic (Fm3̄m, Fe0/OMC-3) which were controlled by simply adjusting the template. All Fe0/OMC displayed paramagnetic characteristics, with a maximum saturation magnetization of 50.1 emu·g−1. This high magnetization is advantageous for the swift extraction of the adsorbent from the solution following the adsorption process. Fe0/OMC was used as an adsorbent for the removal of silver ions (Ag(i)) from aqueous solutions, and the adsorption capacity of Fe0/OMC-1 was enhanced by the functionalization of Fe0. Adsorption property of Fe0/OMC-1 was significantly higher than that of Fe0/OMC-2 and Fe0/OMC-3, indicating that the long and straight ordered pore channels were more favorable for adsorption, and the adsorption capacity of Ag(i) on Fe0/OMC-1 was 233 mg·g−1. The adsorption process exhibited conformity with both the pseudo-second-order kinetic model and the Freundlich model, suggesting that the dominant mechanism of adsorption involved multilayer adsorption on heterogeneous surfaces.

Comparing with cities, rural communities especially those declining rural communities have become vulnerable to natural disasters owing to their backward socioeconomic conditions. Taking Xun County of China's Henan Province as the study area, the paper aims to evaluate rural community resilience to flood by unveiling the connection between individuals' cognition, follow-up actions and the community resilience. Research results show that: (1) The logic chain exists as individual's cognition to disaster leads to their constructive actions to cope with disaster, which contribute to community resilience. (2) At the cognition dimension, individual's knowledge reserve of disaster prevention and their recognition to local authority are playing an important role in their decision making and follow-up behaviors when disaster occurs. (3) At the action dimension, individual's familiarity with the disaster preparedness, efficient information transmission when disaster occurs and villagers' following order and their unity of action all contribute to community resilience to disaster. The paper proposes ways to improve rural community resilience to disasters based on the research findings. [ABSTRACT FROM AUTHOR]

Microbiologically influenced corrosion (MIC) caused by sulfate‐reducing bacteria (SRB) is a major threat to buried pipelines. The crevices beneath disbonded coatings provide the environment for the survival and growth of SRB, accelerating the attachment of SRB and significantly reducing the effectiveness of buried pipelines. In this work, the influence of initial pH values (6.4, 8.0) and SRB concentrations (2.0 × 101, 4.5 × 102, 2.5 × 103 cells/mL) were investigated on the behaviors of L415 carbon steel. The results indicated that the overall uniform corrosion rate of L415 carbon steel decreased while the maximum pitting corrosion rate exhibited an increasing trend with the increasing of initial concentration of SRB. In comparison to the pH 6.4 environment, the overall risk of pitting corrosion in pH 8.0 environment was reduced due to the biomineralization of SRB, enhancing the protection of metal materials. [ABSTRACT FROM AUTHOR]

The occurrence of peritendinous adhesion, which hampers the quality and function of the healed tendon, is strongly linked to oxidative stress, inflammatory, and infection that occur after surgery. Tendon damage and repair provide a considerable obstacle for regenerative medicine owing to the absence of patches that possess comprehensive functionality, including self‐healing capacity, anti‐inflammatory and anti‐bacterial properties, as well as tissue repair guidance. A dual dynamic crosslinked network is created by coordination bonds between catechol groups in protocatechuic aldehyde (PA) and Fe3+, as well as a dynamic Schiff base reaction between amino groups in hyaluronic acid‐adipic acid dihydrazide (HA‐ADH) and aldehyde groups in PA. The HA‐ADH@PA/Fe hydrogel exhibits self‐healing ability, tissue adhesion, anti‐bacterial activity, regulation of macrophage polarization via the NF‐κB signaling, oxidative stress elimination, and anti‐inflammation. In addition, a dual‐layer Janus patch is created, taking inspiration from the anatomy and disease progression of tendon adhesion. The inner layer of the patch, which is in direct contact with the operated tendon, is a multifunctional HA‐ADH@PA/Fe hydrogel, encircled further by a poly(ɛ‐caprolactone) (PCL) electrospinning membrane outer layer facing the surrounding tissue. The PCL@HA‐ADH@PA/Fe hydrogel patch reduced tendon adhesion and supported tissue regeneration by stimulating macrophages polarization into an anti‐inflammatory phenotype and resolving both local and systemic inflammation. This research showcased the PCL@HA‐ADH@PA/Fe hydrogel patch as an alternative therapeutic method for preventing the development of adhesions around tendons. [ABSTRACT FROM AUTHOR]

Geopolymer foam concrete (GFC), an emerging thermal insulation material known for its environmentally friendly and low-carbon attributes, has gained prominence for its use in bolstering building energy efficiency. A critical challenge in GFC production is foam destabilization by the alkaline environment in which foam is supersaturated with salt. In this study, GFC was prepared by using triterpene saponin (TS), sodium dodecyl sulphate (SDS), and cetyltrimethylammonium bromide (CTAB) as blowing agents, with fly ash as the precursor and calcium carbide slag (CA) combined with Glauber's salt (GS, Na2SO4 ≥ 99%) as the activator. The effect of GFC on mechanical properties was analyzed by examining its fluidity, pore structure, dry density, and compressive strength. The results show that TS has a stable liquid film capable of adapting to the adverse effects of salt supersaturation and alkaline environments. TS is highly stable in the GFC matrix, and so the corresponding pore size is small, and the connectivity is low in the hardened GFC. In addition, the hydration products of GFC exhibit different morphologies depending on the surfactant used. TS has better water retention due to hydrogen bonding, which facilitates the hydration process. [ABSTRACT FROM AUTHOR]

Background: Prostate cancer (PCa) is the most common cancer in men and the second leading cause of male cancer‐related death. Gleason score (GS) is the primary driver of PCa risk‐stratification and medical decision‐making, but can only be assessed at present via biopsy under anesthesia. Magnetic resonance imaging (MRI) is a promising non‐invasive method to further characterize PCa, providing additional anatomical and functional information. Meanwhile, the diagnostic power of MRI is limited by qualitative or, at best, semi‐quantitative interpretation criteria, leading to inter‐reader variability. Purposes: Computer‐aided diagnosis employing quantitative MRI analysis has yielded promising results in non‐invasive prediction of GS. However, convolutional neural networks (CNNs) do not implicitly impose a frame of reference to the objects. Thus, CNNs do not encode the positional information properly, limiting method robustness against simple image variations such as flipping, scaling, or rotation. Capsule network (CapsNet) has been proposed to address this limitation and achieves promising results in this domain. In this study, we develop a 3D Efficient CapsNet to stratify GS‐derived PCa risk using T2‐weighted (T2W) MRI images. Methods: In our method, we used 3D CNN modules to extract spatial features and primary capsule layers to encode vector features. We then propose to integrate fully‐connected capsule layers (FC Caps) to create a deeper hierarchy for PCa grading prediction. FC Caps comprises a secondary capsule layer which routes active primary capsules and a final capsule layer which outputs PCa risk. To account for data imbalance, we propose a novel dynamic weighted margin loss. We evaluate our method on a public PCa T2W MRI dataset from the Cancer Imaging Archive containing data from 976 patients. Results: Two groups of experiments were performed: (1) we first identified high‐risk disease by classifying low + medium risk versus high risk; (2) we then stratified disease in one‐versus‐one fashion: low versus high risk, medium versus high risk, and low versus medium risk. Five‐fold cross validation was performed. Our model achieved an area under receiver operating characteristic curve (AUC) of 0.83 and 0.64 F1‐score for low versus high grade, 0.79 AUC and 0.75 F1‐score for low + medium versus high grade, 0.75 AUC and 0.69 F1‐score for medium versus high grade and 0.59 AUC and 0.57 F1‐score for low versus medium grade. Our method outperformed state‐of‐the‐art radiomics‐based classification and deep learning methods with the highest metrics for each experiment. Our divide‐and‐conquer strategy achieved weighted Cohen's Kappa score of 0.41, suggesting moderate agreement with ground truth PCa risks. Conclusions: In this study, we proposed a novel 3D Efficient CapsNet for PCa risk stratification and demonstrated its feasibility. This developed tool provided a non‐invasive approach to assess PCa risk from T2W MR images, which might have potential to personalize the treatment of PCa and reduce the number of unnecessary biopsies. [ABSTRACT FROM AUTHOR]

In this study, the interaction between a diester amide Gemini surfactant (C12–E2Ph–C12) and the non-ionic polymer polyethylene glycol (PEG) with average molecular weights of 6000 g mol−1 and 10,000 g mol−1 was investigated using the surface tension method. The study showed no discernible interaction between them. Further examinations, using surface tension, micropolarity and turbidity methods were conducted to investigate the interaction between C12–E2Ph–C12 and the anionic polymer sodium alginate (NaAlg). C12–E2Ph–C12 and NaAlg molecules bind together to form aggregates with a pearl necklace-like structure, driven by electrostatic attraction. This interaction is evident in both the surface tension and micropolarity curves, where double plateaus appear. The inorganic salt NaCl exhibited a dual influence on the interaction between C12–E2Ph–C12 and NaAlg, manifesting as either salt enhancement or reduction, depending on their competitive effects. Additionally, the dyeing performance of the different surfactants on two types of dyes was investigated using a colorimeter. C12–E2Ph–C12 showed favorable effects on dyeing uniformity, color fastness, and improvement of hair tensile strength. Hair dyeing results were satisfactory when C12–E2Ph–C12 was included as a uniform coloring agent and NaAlg was used as a thickening agent in the hair dye formulation. [ABSTRACT FROM AUTHOR]

Effective collaboration and teamwork skills are critical in high‐risk sectors, as deficiencies in these areas can result in injuries and risk of death. To foster the growth of these vital skills, immersive learning spaces have been created to simulate real‐world scenarios, enabling students to safely improve their teamwork abilities. In such learning environments, multiple dialogue segments can occur concurrently as students independently organise themselves to tackle tasks in parallel across diverse spatial locations. This complex situation creates challenges for educators in assessing teamwork and for students in reflecting on their performance, especially considering the importance of effective communication in embodied teamwork. To address this, we propose an automated approach for generating teamwork analytics based on spatial and speech data. We illustrate this approach within a dynamic, immersive healthcare learning environment centred on embodied teamwork. Moreover, we evaluated whether the automated approach can produce transcriptions and epistemic networks of spatially distributed dialogue segments with a quality comparable to those generated manually for research objectives. This paper makes two key contributions: (1) it proposes an approach that integrates automated speech recognition and natural language processing techniques to automate the transcription and coding of team communication and generate analytics; and (2) it provides analyses of the errors in outputs generated by those techniques, offering insights for researchers and practitioners involved in the design of similar systems.Practitioner notesWhat is currently known about this topic Immersive learning environments simulate real‐world situations, helping students improve their teamwork skills.In these settings, students can have multiple simultaneous conversations while working together on tasks at different physical locations.The dynamic nature of these interactions makes it hard for teachers to assess teamwork and communication and for students to reflect on their performance.What this paper adds We propose a method that employs multimodal learning analytics for automatically generating teamwork‐related insights into the content of student conversations.This data processing method allows for automatically transcribing and coding spatially distributed dialogue segments generated from students working in teams in an immersive learning environment and enables downstream analysis.This approach uses spatial analytics, natural language processing and automated speech recognition techniques.Implications for practitioners Automated coding of dialogue segments among team members can help create analytical tools to assist in evaluating and reflecting on teamwork.By analysing spatial and speech data, it is possible to apply learning analytics advancements to support teaching and learning in fast‐paced physical learning spaces where students can freely engage with one another. [ABSTRACT FROM AUTHOR]

The antisolvent‐assisted spin‐coating still lags behind the thermal evaporation method in fabricating perovskite films atop industrially textured silicon wafers in making monolithic perovskite/silicon solar cells (P/S‐TSCs). The inhomogeneity of hole‐selective self‐assembled monolayers (SAMs) often arises from the insufficient bonding between hygroscopic phosphonic acid anchors and metal oxide. To address this, a mixed‐SAM strategy (Mx‐SAM) is proposed to enhance the adsorption energy of SAMs on the ITO surface, facilitate the formation of dense and humidity‐resistant hole‐selective layer (HSL) on substrates, and improve hole transport capabilities. With the aid of the Mx‐SAM strategy, the optimized wide‐bandgap PSCs achieved an impressive power conversion efficiency (PCE) of 22.63% with an exceptionally high fill factor (FF) of 86.67% using the 1.68 eV perovskite. Moreover, they exhibited enhanced stability under damp‐heat conditions (ISOS‐D‐3, 85% RH, 85 °C) with a T90 of 900 h for encapsulated PSCs, representing one of the best performances for wide‐bandgap PSCs. When further extending the Mx‐SAM strategy to making P/S‐TSCs using silicon wafers from industry, a remarkable efficiency of 28.07% is reached while upholding outstanding reproducibility. This strategy holds significant promise for the feasibility of fabricating industrially‐compatible P/S‐TSCs. [ABSTRACT FROM AUTHOR]

The reconstruction of Cu catalysts during electrochemical reduction of CO2 is a widely known but poorly understood phenomenon. Herein, we examine the structural evolution of Cu nanocubes under CO2 reduction reaction and its relevant reaction conditions using identical location transmission electron microscopy, cyclic voltammetry, in situ X-ray absorption fine structure spectroscopy and ab initio molecular dynamics simulation. Our results suggest that Cu catalysts reconstruct via a hitherto unexplored yet critical pathway - alkali cation-induced cathodic corrosion, when the electrode potential is more negative than an onset value (e.g., −0.4 VRHE when using 0.1 M KHCO3). Having alkali cations in the electrolyte is critical for such a process. Consequently, Cu catalysts will inevitably undergo surface reconstructions during a typical process of CO2 reduction reaction, resulting in dynamic catalyst morphologies. While having these reconstructions does not necessarily preclude stable electrocatalytic reactions, they will indeed prohibit long-term selectivity and activity enhancement by controlling the morphology of Cu pre-catalysts. Alternatively, by operating Cu catalysts at less negative potentials in the CO electrochemical reduction, we show that Cu nanocubes can provide a much more stable selectivity advantage over spherical Cu nanoparticles. Copper catalysts are widely studied for electrochemical CO2 conversion, but their structural changes during reactions require further investigation. Here the authors propose that Cu undergoes transformation through alkali cation-induced cathodic corrosion when exposed to sufficiently negative potentials in the presence of alkali cations in the electrolyte. [ABSTRACT FROM AUTHOR]

Enzymatically derived selenium-enriched peptides from Cardamine violifolia (CV) can serve as valuable selenium supplements. However, the industrial application of free enzyme is impeded by its limited stability and reusability. Herein, this study explores the application of co-immobilized enzymes (Alcalase and Dispase) on amino resin for hydrolyzing CV proteins to produce selenium-enriched peptides. The successful enzyme immobilization was confirmed through scanning electron microscopy (SEM), energy dispersive X-ray (EDX), and Fourier-transform infrared spectroscopy (FTIR). Co-immobilized enzyme at a mass ratio of 5:1 (Alcalase/Dispase) exhibited the smallest pore size (7.065 nm) and highest activity (41 U/mg), resulting in a high degree of hydrolysis of CV protein (27.2%), which was obviously higher than the case of using free enzymes (20.7%) or immobilized Alcalase (25.8%). In addition, after a month of storage, the co-immobilized enzyme still retained a viability level of 41.93%, showing fairly good stability. Encouragingly, the selenium-enriched peptides from co-immobilized enzyme hydrolysis exhibited uniform distribution of selenium forms, complete amino acid fractions and homogeneous distribution of molecular weight, confirming the practicality of using co-immobilized enzymes for CV protein hydrolysis. [ABSTRACT FROM AUTHOR]

Despite the remarkable progress of perovskite solar cells (PSCs), challenges remain in terms of finding effective and viable strategies to enhance their long‐term stability while maintaining high efficiency. In this study, a new insulating and hydrophobic fluorinated polyimide (FPI: 6FDA‐6FAPB) was used as the interface layer between the perovskite layer and the hole transport layer (HTL) in PSCs. The functional groups of FPI play a pivotal role in passivating interface defects within the device. Due to its high work function, FPI demonstrates field‐effect passivation (FEP) capabilities as an interface layer, effectively mitigating non‐radiative recombination at the interface. Notably, the FPI insulating interface layer does not impede carrier transmission at the interface, which is attributed to the presence of hole tunneling effects. The optimized PSCs achieve an outstanding power conversion efficiency (PCE) of 24.61 % and demonstrate excellent stability, showcasing the efficacy of FPI in enhancing device performance and reliability. [ABSTRACT FROM AUTHOR]

Background and purpose: Magnetic resonance imaging (MRI)‐based synthetic computed tomography (sCT) simplifies radiation therapy treatment planning by eliminating the need for CT simulation and error‐prone image registration, ultimately reducing patient radiation dose and setup uncertainty. In this work, we propose a MRI‐to‐CT transformer‐based improved denoising diffusion probabilistic model (MC‐IDDPM) to translate MRI into high‐quality sCT to facilitate radiation treatment planning. Methods: MC‐IDDPM implements diffusion processes with a shifted‐window transformer network to generate sCT from MRI. The proposed model consists of two processes: a forward process, which involves adding Gaussian noise to real CT scans to create noisy images, and a reverse process, in which a shifted‐window transformer V‐net (Swin‐Vnet) denoises the noisy CT scans conditioned on the MRI from the same patient to produce noise‐free CT scans. With an optimally trained Swin‐Vnet, the reverse diffusion process was used to generate noise‐free sCT scans matching MRI anatomy. We evaluated the proposed method by generating sCT from MRI on an institutional brain dataset and an institutional prostate dataset. Quantitative evaluations were conducted using several metrics, including Mean Absolute Error (MAE), Peak Signal‐to‐Noise Ratio (PSNR), Multi‐scale Structure Similarity Index (SSIM), and Normalized Cross Correlation (NCC). Dosimetry analyses were also performed, including comparisons of mean dose and target dose coverages for 95% and 99%. Results: MC‐IDDPM generated brain sCTs with state‐of‐the‐art quantitative results with MAE 48.825 ± 21.491 HU, PSNR 26.491 ± 2.814 dB, SSIM 0.947 ± 0.032, and NCC 0.976 ± 0.019. For the prostate dataset: MAE 55.124 ± 9.414 HU, PSNR 28.708 ± 2.112 dB, SSIM 0.878 ± 0.040, and NCC 0.940 ± 0.039. MC‐IDDPM demonstrates a statistically significant improvement (with p < 0.05) in most metrics when compared to competing networks, for both brain and prostate synthetic CT. Dosimetry analyses indicated that the target dose coverage differences by using CT and sCT were within ± 0.34%. Conclusions: We have developed and validated a novel approach for generating CT images from routine MRIs using a transformer‐based improved DDPM. This model effectively captures the complex relationship between CT and MRI images, allowing for robust and high‐quality synthetic CT images to be generated in a matter of minutes. This approach has the potential to greatly simplify the treatment planning process for radiation therapy by eliminating the need for additional CT scans, reducing the amount of time patients spend in treatment planning, and enhancing the accuracy of treatment delivery. [ABSTRACT FROM AUTHOR]

Due to their weight, the seismic response control of buildings needs a large-scale damper. To reduce the consumption of shape memory alloys (SMAs), this study proposed a dual self-centering pattern accomplished by the coil springs and SMA, which could drive the energy dissipation device to recenter. Combined with the friction energy dissipation device (FD), the dual self-centering friction damper (D-SCFD) was designed, and the motivation and parameters were described. The mechanical properties of D-SCFD, including the simplified D-SCFD mechanical model, theoretical index calculations of recentering, and energy dissipation performance, were then investigated. The seismic response mitigation of the steel frame adopting the D-SCFDs under consecutive strong earthquakes was finally analyzed. The results showed that a decrease in the consumption of SMA by the dual self-centering pattern was feasible, especially in the case of low demand for the recentering performance. Reducing the D-SCFD's recentering performance hardly affected the steel frame's residual inter-story drift ratios when the residual deformation rate was less than 50%, which can help strengthen the controls on the steel frame's peak seismic responses. It is recommended to utilize the D-SCFD with not too high a recentering performance to mitigate the seismic response of the structure. [ABSTRACT FROM AUTHOR]

Peanut southern blight is a soil-borne fungal disease caused by Agroathelia rolfsii (syn. Sclerotium rolfsii) Sacc, which seriously affects peanut yield. The disease mainly affects the stem, pod, and root of the plant, and it is difficult to detect the disease by visual interpretation. Detecting peanut southern blight using existing technology is an urgent problem that needs to be solved. To address this issue, field experiments were conducted in September 2022 to determine whether hyperspectral techniques could be used to assess the severity of peanut southern blight. In this study, we obtained 610 canopy-scale spectral data through field experiments. Firstly, 18 traditional spectral features were calculated. Then, wavelengths of 544 nm, 678 nm, and 769 nm were selected as sensitive by the Relief-F algorithm, and the NDSISB and NSISB were constructed using normalization and ratio calculation methods. Finally, Support Vector Machine (SVM), Light Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), and ANN were used to evaluate the diagnostic ability of all spectral features to assess disease severity levels. The results showed that the NSISB had the highest association with peanut southern blight (R2 = 0.817), exceeding the other spectral features. Compared to the other three models, CatBoost demonstrated superior accuracy, with an overall accuracy (OA) and Kappa coefficient of 84.18% and 78.31%, respectively. The findings of this study can serve as a reference for estimating the severity levels of peanut southern blight using ground-based hyperspectral data. [ABSTRACT FROM AUTHOR]

There are two types of land systems in China: collective-owned and state-owned. Under both systems, differences in land ownership have led to different land-use and management systems, resulting in urban–rural dual structures under the jurisdiction of local governments and urban–rural unitary structures in reclamation areas of the state-owned land system. This has significantly changed the relationship between the rural population and land, which has been intensified by rapid urbanization and industrialization. Therefore, based on the rural population and remote sensing data, this study uses the Tapio decoupling model to determine the relationships between the rural population and residential land under different land systems. The main results indicate that the evolution of the rural population and residential land area under different land systems presents an obvious inverse relationship. From 1995 to 2020, under the jurisdiction of the local government, the land area and population of rural dwellers declined, with the decline in population being faster than that in residential areas. At the same time, the spatial agglomeration of residential areas from the middle to the south of the province expanded. In contrast, the population and residential land area showed significant reverse changes in state-owned domains. Rural residential land area declined, and the rural population increased, most notably in the eastern region. Furthermore, strong negative decoupling between the rural population and rural residential land was observed in areas under local government jurisdiction (accounting for 89.61%), meaning that the population declined while the living area increased, which is not conducive to increasing intensive land-use and productivity. Simultaneously, in state-owned domains, only 33.33% of the rural population and residential land areas presented the above-mentioned negative decoupling, with the remaining 66.67% being coordinated. After 2010, due to the socio-economic gap, the proportion of coordination in local government jurisdiction areas continued to decrease, whereas coordination in reclamation areas remained stable. Therefore, the data suggest that a single land allocation and governance regime across urban and rural domains under the state-owned land system is more helpful in enabling populations and efficient land-use. This suggests that, in the future, it will be helpful to consider promoting the effective integration of urban and rural land markets, optimizing the allocation of urban and rural land resources, and enhancing the reform of the rural land system in order to realize synergistic interactions between the urban and rural populations and construction land in Heilongjiang Province. [ABSTRACT FROM AUTHOR]

Ultraviolet‐induced degradation has emerged as a critical stability concern impeding the widespread adoption of perovskite solar cells (PSCs), particularly in the context of phase‐unstable wide‐band gap perovskite films. This study introduces a novel approach by employing a fully aromatic carbazole‐based self‐assembled monolayer, denoted as (4‐(3,6‐dimethoxy‐9H‐carbazol‐9‐yl)phenyl)phosphonic acid (MeO‐PhPACz), as a hole‐selective layer (HSL) in inverted wide‐band gap PSCs. Incorporating a conjugated linker plays a pivotal role in promoting the formation of a dense and highly ordered HSL on substrates, facilitating subsequent perovskite interfacial interactions, and fostering the growth of uniform perovskite films. The high‐quality film could effectively suppress interfacial non‐radiative recombination, improving hole extraction/transport efficiency. Through these advancements, the optimized wide‐band gap PSCs, featuring a band gap of 1.68 eV, attain an impressive power conversion efficiency (PCE) of 21.10 %. Remarkably, MeO‐PhPACz demonstrates inherent UV resistance and heightened UV absorption capabilities, substantially improving UV resistance for the targeted PSCs. This characteristic holds significance for the feasibility of large‐scale outdoor applications. [ABSTRACT FROM AUTHOR]

Educational technology innovations leveraging large language models (LLMs) have shown the potential to automate the laborious process of generating and analysing textual content. While various innovations have been developed to automate a range of educational tasks (eg, question generation, feedback provision, and essay grading), there are concerns regarding the practicality and ethicality of these innovations. Such concerns may hinder future research and the adoption of LLMs‐based innovations in authentic educational contexts. To address this, we conducted a systematic scoping review of 118 peer‐reviewed papers published since 2017 to pinpoint the current state of research on using LLMs to automate and support educational tasks. The findings revealed 53 use cases for LLMs in automating education tasks, categorised into nine main categories: profiling/labelling, detection, grading, teaching support, prediction, knowledge representation, feedback, content generation, and recommendation. Additionally, we also identified several practical and ethical challenges, including low technological readiness, lack of replicability and transparency and insufficient privacy and beneficence considerations. The findings were summarised into three recommendations for future studies, including updating existing innovations with state‐of‐the‐art models (eg, GPT‐3/4), embracing the initiative of open‐sourcing models/systems, and adopting a human‐centred approach throughout the developmental process. As the intersection of AI and education is continuously evolving, the findings of this study can serve as an essential reference point for researchers, allowing them to leverage the strengths, learn from the limitations, and uncover potential research opportunities enabled by ChatGPT and other generative AI models. Practitioner notesWhat is currently known about this topic Generating and analysing text‐based content are time‐consuming and laborious tasks.Large language models are capable of efficiently analysing an unprecedented amount of textual content and completing complex natural language processing and generation tasks.Large language models have been increasingly used to develop educational technologies that aim to automate the generation and analysis of textual content, such as automated question generation and essay scoring.What this paper adds A comprehensive list of different educational tasks that could potentially benefit from LLMs‐based innovations through automation.A structured assessment of the practicality and ethicality of existing LLMs‐based innovations from seven important aspects using established frameworks.Three recommendations that could potentially support future studies to develop LLMs‐based innovations that are practical and ethical to implement in authentic educational contexts.Implications for practice and/or policy Updating existing innovations with state‐of‐the‐art models may further reduce the amount of manual effort required for adapting existing models to different educational tasks.The reporting standards of empirical research that aims to develop educational technologies using large language models need to be improved.Adopting a human‐centred approach throughout the developmental process could contribute to resolving the practical and ethical challenges of large language models in education. [ABSTRACT FROM AUTHOR]

Researchers have demonstrated that dialogue‐based intelligent tutoring systems (ITS) can be effective in assisting students in learning. However, little research has attempted to explore the necessity of equipping dialogue‐based ITS with one of the most important capabilities of human tutors, that is, maintaining polite interactions with students, which is essential to provide students with a pleasant learning experience. In this study, we examined the role of politeness by analysing a large‐scale real‐world dataset consisting of over 14K online human–human tutorial dialogues. Specifically, we employed linguistic theories of politeness to characterise the politeness levels of tutor–student‐generated utterances, investigated the correlation between the politeness levels of tutors' utterances and students' problem‐solving performance and quantified the power of politeness in predicting students' problem‐solving performance by applying Gradient Tree Boosting. The study results showed that: (i) in the effective tutorial sessions (ie, sessions in which students successfully solved problems), tutors tended to be very polite at the start of a tutorial session and become more direct to guide students as the session progressed; (ii) students with better performance in solving problems tended to be more polite at the beginning and the end of a tutorial session than their counterparts who failed to solve problems; (iii) the correlation between tutors' polite expressions and students' performance was not evident in non‐instructional communication; and (iv) politeness alone cannot adequately reveal students' problem‐solving performance, and thus other factors (eg, sentiment contained in utterances) should also be taken into account. Practitioner notesWhat is already known about this topic Human–human tutoring is acknowledged as an effective instructional method.Polite expression can help strengthen the relationship between tutors and students.Polite expression can promote students' learning achievements in many educational contexts.What this paper adds By considering the students' prior progress on a problem‐based learning task, we demonstrated the extent to which tutors and students display politeness in tutoring dialogues.Tutors' polite expressions might not correlate with students' problem‐solving performance in online human–human tutoring dialogues.Politeness alone was insufficient to predict the students' performance.Implications for practice Tutors might consider using words with positive sentiment values to express politeness to students with prior progress, which might encourage those students to make a further effort.The polite strategy of expressing indirect requests could help tutors mitigate the sense of directness, but this strategy should be carefully used in delivering instructional hints, especially for students without prior progress.To better assist students without prior progress, tutors might consider using more direct expression to explicitly guide students. [ABSTRACT FROM AUTHOR]

The performance of perovskite solar cells (PSCs) is often constrained by significant open‐circuit voltage (VOC) losses attributed to non‐radiative recombination processes induced by detrimental trap states. Surface treatments using passivating ligands typically involve single active binding sites on perovskite, posing challenges for effective passivation. Here, an aromatic donor‐acceptor (D‐A) configured phthalocyanine treatment is proposed to aim at dual‐site passivation of uncoordinated lead ions and effective mitigation of shallow and deep‐level defects on the perovskite surface. The resulting benign p‐type surface facilitates a more favorable energy level alignment and reduces energetic mismatches at the perovskite/Spiro‐OMeTAD interface. Pc‐BTBC, with its aromatic D‐A configuration, demonstrated compatibility with various perovskite compositions. Optimized PSCs achieves a power conversion efficiency (PCE) of 25.15% and reduces the VOC deficit to 0.379 V. Furthermore, encapsulated devices exhibited enhanced stability under damp‐heat conditions (ISOS‐D‐2, 50% RH, 65°C) with a T92 of 1000 h and maintained maximum power point tracking under continuous light in ambient air at 65°C (ISOS‐L‐2). Notably, fabricated wide‐bandgap semitransparent PSCs (ST‐PSCs) achieved a PCE of 20.29%, while four‐terminal perovskite/silicon tandem solar cells (4T‐P/STSCs) demonstrated an efficiency of 29.38%. This study provides insights into minimizing VOC losses and represents significant progress toward commercializing perovskite photovoltaics. [ABSTRACT FROM AUTHOR]

Additive engineering has emerged as a promising strategy to address the inherent instability challenges of perovskite solar cells (PSCs) in the pursuit of commercial viability. However, achieving multifunctionality using a singular additive remains a considerable challenge. In this study, a novel comb‐like multifunctional perfluoroalkyl‐g‐polyethylenimmonium iodide (FPEI·HI) as additives to the PbI2 precursor solution to facilitate the formation of high‐quality and water‐resistant perovskite films is presented. FPEI·HI establishes robust interactions with both formamidinium iodide (FAI) and PbI2, mediated by hydrogen bonding and Lewis acid‐base interactions. These interactions play a pivotal role in simultaneously passivating negative and positive charged defects within the perovskite structure. Furthermore, the inclusion of perfluoroalkyl chains serves as resilience against moisture intrusion. As a consequence of these effects, a notably high device efficiency of 24.29% is achieved, demonstrating comprehensive improvement in various photovoltaic parameters compared to the control device (22.51%). Notably, unencapsulated devices exhibit remarkable stability in high‐humidity environments, retaining 90% of their initial efficiency even after 2500 h of storage. This work underscores the efficacy of FPEI·HI as a critical enabler for enhancing the stability and efficiency of perovskite solar cells, marking a significant stride toward their commercialization. [ABSTRACT FROM AUTHOR]