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Student academic achievement is an important indicator for evaluating the quality of education, especially, the achievement prediction empowers educators in tailoring their instructional approaches, thereby fostering advancements in both student performance and the overall educational quality. However, extracting valuable insights from vast educational data to develop effective strategies for evaluating student performance remains a significant challenge for higher education institutions. Traditional machine learning (ML) algorithms often struggle to clearly delineate the interplay between the factors that influence academic success and the resulting grades. To address these challenges, this paper introduces the XGB-SHAP model, a novel approach for predicting student achievement that combines Extreme Gradient Boosting (XGBoost) with SHapley Additive exPlanations (SHAP). The model was applied to a dataset from a public university in Wuhan, encompassing the academic records of 87 students who were enrolled in a Japanese course between September 2021 and June 2023. The findings indicate the model excels in accuracy, achieving a Mean absolute error (MAE) of approximately 6 and an R-squared value near 0.82, surpassing three other ML models. The model further uncovers how different instructional modes influence the factors that contribute to student achievement. This insight supports the need for a customized approach to feature selection that aligns with the specific characteristics of each teaching mode. Furthermore, the model highlights the importance of incorporating self-directed learning skills into student-related indicators when predicting academic performance.

Overexpression of bacterial γ‐glutamylcysteine synthetase in the cytosol of Populus tremula × P. alba produces higher glutathione (GSH) concentrations in leaves, thereby indicating the potential for cadmium (Cd) phytoremediation. However, the net Cd²⁺influx in association with H⁺/Ca²⁺, Cd tolerance, and the underlying molecular and physiological mechanisms are uncharacterized in these poplars. We assessed net Cd²⁺influx, Cd tolerance and the transcriptional regulation of several genes involved in Cd²⁺transport and detoxification in wild‐type and transgenic poplars. Poplars exhibited highest net Cd²⁺influxes into roots at pH 5.5 and 0.1 mM Ca²⁺. Transgenics had higher Cd²⁺uptake rates and elevated transcript levels of several genes involved in Cd²⁺transport and detoxification compared with wild‐type poplars. Transgenics exhibited greater Cd accumulation in the aerial parts than wild‐type plants in response to Cd²⁺exposure. Moreover, transgenic poplars had lower concentrations of O₂˙⁻and H₂O₂; higher concentrations of total thiols, GSH and oxidized GSH in roots and/or leaves; and stimulated foliar GSH reductase activity compared with wild‐type plants. These results indicate that transgenics are more tolerant of 100 μM Cd²⁺than wild‐type plants, probably due to the GSH‐mediated induction of the transcription of genes involved in Cd²⁺transport and detoxification.

Stress state has significant influence on the initiation of suffusion, whereas the effect of deviator stress has not been investigated thoroughly. This paper presented a series of hydro-mechanical coupling suffusion tests on four internally unstable cohesionless soils to investigate the effect of deviator stress on the initiation of suffusion. A dimensionless parameter, shear stress ratio, which is the ratio of deviator stress to mean effective stress, was introduced to denote the variation of deviator stress under the same confining pressure. The results indicate that the deviator stress or shear stress ratio has significant influence on the initiation of suffusion. The relationship between shear stress ratio and critical hydraulic gradient initiating suffusion is piecewise linear, and there is a critical shear stress ratio for different internally unstable soils. The critical hydraulic gradient first linearly increases with the increase in shear stress ratio, and it reaches the maximum value at the critical shear stress ratio, then it decreases suddenly and significantly, finally it increases linearly again with the increase in shear stress ratio. The critical shear stress ratio is not the division between volumetric contraction and dilation, but it can be determined by a consolidated drained triaxial compression test. Based on the characteristic of the relationship between shear stress ratio and critical hydraulic gradient, an empirical method determining the critical hydraulic gradients under complex stress states was developed.

Providing sufficient driving force for charge separation and transfer (CST) is a critical issue in photoelectrochemical (PEC) energy conversion. Normally, the driving force is derived mainly from band bending at the photoelectrode/electrolyte interface but negligible in the bulk. To boost the bulky driving force, we report a rational strategy to create effective electric field via controllable lattice distortion in the bulk of a semiconductor film. This concept is verified by the lithiation of a classic TiO 2 (Li-TiO 2 ) photoelectrode, which leads to significant distortion of the TiO 6 unit cells in the bulk with well-aligned dipole moment. A remarkable internal built-in electric field of ~2.1 × 10 2  V m −1 throughout the Li-TiO 2 film is created to provide strong driving force for bulky CST. The photoelectrode demonstrates an over 750% improvement of photocurrent density and 100 mV negative shift of onset potential upon the lithiation compared to that of pristine TiO 2 film. The driving force for charge transfer in photoelectrochemical systems is typically derived from band bending at a surface-electrolyte interface. In this work, battery-type lithiation of TiO 2 generates a built-in electric field in the bulk material, giving a 750% enhancement in photocurrent density.

Single-image super-resolution (SR) is an effective approach to enhance spatial resolution for numerous applications such as object detection and classification when the resolution of sensors is limited. Although deep convolutional neural networks (CNNs) proposed for this purpose in recent years have outperformed relatively shallow models, enormous parameters bring the risk of overfitting. In addition, due to the different scale of objects in images, the hierarchical features of deep CNN contain additional information for SR tasks, while most CNN models have not fully utilized these features. In this paper, we proposed a deep yet concise network to address these problems. Our network consists of two main structures: (1) recursive inference block based on dense connection reuse of local low-level features, and recursive learning is applied to control the model parameters while increasing the receptive fields; (2) a bidirectional convolutional LSTM (BiConvLSTM) layer is introduced to learn the correlations of features from each recursion and adaptively select the complementary information for the reconstruction layer. Experiments on multispectral satellite images, panchromatic satellite images, and nature high-resolution remote-sensing images showed that our proposed model outperformed state-of-the-art methods while utilizing fewer parameters, and ablation studies demonstrated the effectiveness of a BiConvLSTM layer for an image SR task.

The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has become a global crisis. There is no therapeutic treatment specific for COVID-19. It is highly desirable to identify potential antiviral agents against SARS-CoV-2 from existing drugs available for other diseases and thus repurpose them for treatment of COVID-19. In general, a drug repurposing effort for treatment of a new disease, such as COVID-19, usually starts from a virtual screening of existing drugs, followed by experimental validation, but the actual hit rate is generally rather low with traditional computational methods. Here we report a virtual screening approach with accelerated free energy perturbation-based absolute binding free energy (FEP-ABFE) predictions and its use in identifying drugs targeting SARS-CoV-2 main protease (Mpro). The accurate FEP-ABFE predictions were based on the use of a restraint energy distribution (RED) function, making the practical FEP-ABFE–based virtual screening of the existing drug library possible. As a result, out of 25 drugs predicted, 15 were confirmed as potent inhibitors of SARS-CoV-2 Mpro. The most potent one is dipyridamole (inhibitory constant Ki = 0.04 μM)which has shown promising therapeutic effects in subsequently conducted clinical studies for treatment of patients with COVID-19. Additionally, hydroxychloroquine (Ki = 0.36 μM) and chloroquine (Ki = 0.56 μM) were also found to potently inhibit SARS-CoV-2 Mpro. We anticipate that the FEP-ABFE prediction-based virtual screening approach will be useful in many other drug repurposing or discovery efforts.

Skin-like sensors capable of detecting multiple stimuli simultaneously have great potential in cutting-edge human-machine interaction. However, realizing multimodal tactile recognition beyond human tactile perception still faces significant challenges. Here, an extreme environments-adaptive multimodal triboelectric sensor was developed, capable of detecting pressure/temperatures beyond the range of human perception. Based on triboelectric nanogenerator technology, an asymmetric structure capable of independently outputting dual signals was designed to improve perception sensitivity. By converting the signals and the stimuli into feature matrices, parallel perception of complex objects (with a recognition rate of 94%) and temperature at high temperatures was achieved. The proposed multimodal triboelectric tactile sensor represents progress in maximum detection range and rapid response, realizing the upper limit of human skin’s high-temperature sensing (60 °C) with a working temperature of 200 °C. The proposed self-powered multimodal sensing system offers a wider range of possibilities for human/robot/environment interaction applications. Existing tactile sensors struggle with high-temperature environments. Here, authors developed a triboelectric tactile sensor with an asymmetric structure and heat-resistant materials, enabling 94% object recognition rate, fast response times, and stable performance up to 200 °C.

The effective acquisition of clean water from atmospheric water offers a potential sustainable solution for increasing global water and energy shortages. In this study, an asymmetric amphiphilic surface incorporating self-driven triboelectric adsorption was developed to obtain clean water from the atmosphere. Inspired by cactus spines and beetle elytra, the asymmetric amphiphilic surface was constructed by synthesizing amphiphilic cellulose ester coatings followed by coating on laser-engraved spines of fluorinated ethylene propylene. Notably, the spontaneous interfacial triboelectric charge between the droplet and the collector was exploited for electrostatic adsorption. Additionally, the droplet triboelectric nanogenerator converts the mechanical energy generated by droplets falling into electrical energy through the volume effect, achieving an excellent output performance, and further enhancing the electrostatic adsorption by means of external charges, which achieved a water harvesting efficiency of 93.18 kg/m 2 h. This strategy provides insights for the design of water harvesting system. The effective acquisition of clean water from atmospheric water offers a potential sustainable solution for increasing global water shortages. Here, authors developed a bioinspired asymmetric amphiphilic surface incorporating self-driven triboelectric adsorption to obtain clean water.

To investigate N metabolism of two contrasting Populus species in acclimation to low N availability, saplings of slow-growing species (Populus popularis, Pp) and a fast-growing species (Populus alba × Populus glandulosa, Pg) were exposed to 10, 100, or 1000 μM NH4NO3. Despite greater root biomass and fine root surface area in Pp, lower net influxes of NH4 + and NO3 – at the root surface were detected in Pp compared to those in Pg, corresponding well to lower NH4 + and NO3 – content and total N concentration in Pp roots. Meanwhile, higher stable N isotope composition (δ15N) in roots and stronger responsiveness of transcriptional regulation of 18 genes involved in N metabolism were found in roots and leaves of Pp compared to those of Pg. These results indicate that the N metabolism of Pp is more sensitive to decreasing N availability than that of Pg. In both species, low N treatments decreased net influxes of NH4 + and NO3 –, root NH4 + and foliar NO3 – content, root NR activities, total N concentration in roots and leaves, and transcript levels of most ammonium (AMTs) and nitrate (NRTs) transporter genes in leaves and genes involved in N assimilation in roots and leaves. Low N availability increased fine root surface area, foliar starch concentration, δ15N in roots and leaves, and transcript abundance of several AMTs (e.g. AMT1;2) and NRTs (e.g. NRT1;2 and NRT2;4B) in roots of both species. These data indicate that poplar species slow down processes of N acquisition and assimilation in acclimation to limiting N supply.

Ammonia, a vital component in the synthesis of fertilizers, plastics, and explosives, is traditionally produced via the energy‐intensive and environmentally detrimental Haber–Bosch process. Given its considerable energy consumption and significant greenhouse gas emissions, there is a growing shift toward electrocatalytic ammonia synthesis as an eco‐friendly alternative. However, developing efficient electrocatalysts capable of achieving high selectivity, Faraday efficiency, and yield under ambient conditions remains a significant challenge. This review delves into the decades‐long research into electrocatalytic ammonia synthesis, highlighting the evolution of fundamental principles, theoretical descriptors, and reaction mechanisms. An in‐depth analysis of the nitrogen reduction reaction (NRR) and nitrate reduction reaction (NitRR) is provided, with a focus on their electrocatalysts. Additionally, the theories behind electrocatalyst design for ammonia synthesis are examined, including the Gibbs free energy approach, Sabatier principle, d‐band center theory, and orbital spin states. The review culminates in a comprehensive overview of the current challenges and prospective future directions in electrocatalyst development for NRR and NitRR, paving the way for more sustainable methods of ammonia production. This review delves into the decades‐long research into electrocatalytic ammonia synthesis, highlighting the evolution of fundamental principles, theoretical descriptors, and reaction mechanisms and paving the way for more sustainable methods of ammonia production.