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Enhancing student performance and academic planning can be greatly impacted by predicting students’ growth. It is possible to customize educational programs to each student’s performance by offering comprehensive insights into their needs and weaknesses. These methods can also aid in recognizing and averting academic issues, which will ultimately improve pupils’ academic performance. In this regard, we have presented a novel method that achieves these goals. The proposed method consists of four basic steps, in the first step, preprocessing of raw data is performed. In the next step, a fuzzy logic-based hybrid model is used to select features related to students’ academic performance. In this method, first, each of the preprocessed features is ranked using Mutual Information (MI) and Analysis of Variance (ANOVA) measures. Then, these rankings are combined with the help of a fuzzy inference model and the features are ranked based on the rules of the fuzzy model. Finally, using the backward elimination feature selection (BEFS) technique, irrelevant features are eliminated and relevant features are selected. In the third step, modeling is performed using three deep neural networks including Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), and Multilayer Perceptron (MLP), each of which independently tries to model the target variable. In the final step, a meta-model based on the MLP structure is used to extract the target variable based on the predictions from the three deep ensemble models. According to the results obtained through evaluating the model by a questionnaire-based dataset, the proposed methodology achieves significant improvements in predictive accuracy (RMSE 0.6%, MAPE 0.03%), offering a valuable tool for institutions seeking to implement data-driven, individualized academic planning and intervention strategies.

The urea oxidation reaction has attracted increasing attention. Here, porous rod-like Ni 2 P/Ni assemblies, which consist of numerous nanoparticle subunits with matching interfaces at the nanoscale have been synthesized via a simple phosphating approach. Density functional theory calculations and density of states indicate that porous rod-like Ni 2 P/Ni assemblies can significantly enhance the activity of chemical bonds and the conductivity compared with NiO/Ni toward the urea oxidation reaction. The optimal catalyst of Ni 2 P/Ni can deliver a low overpotential of 50 mV at 10 mA·cm −2 and Tafel slope of 87.6 mV·dec −1 in urea oxidation reaction. Moreover, the constructed electrolytic cell exhibits a current density of 10 mA·cm −2 at a cell voltage of 1.47 V and an outstanding durability in the two-electrode system. This work has provided a new possibility to fabricate metal phosphides-metal assemblies with advanced performance.

The triboelectric nanogenerator (TENG), as a novel energy harvesting technology, has garnered widespread attention. As a relatively young field in nanogenerator research, investigations into various aspects of the TENG are still ongoing. This review summarizes the development and dissemination of the fundamental principles of triboelectricity generation. It outlines the evolution of triboelectricity principles, ranging from the fabrication of the first TENG to the selection of triboelectric materials and the confirmation of the electron cloud overlapping model. Furthermore, recent advancements in TENG application scenarios are discussed from four perspectives, along with the research progress in performance optimization through three primary approaches, highlighting their respective strengths and limitations. Finally, the paper addresses the major challenges hindering the practical application and widespread adoption of TENGs, while also providing insights into future developments. With continued research on the TENG, it is expected that these challenges can be overcome, paving the way for its extensive utilization in various real-world scenarios.

The micro-turbojet engine (MTE) is especially suitable for unmanned aerial vehicles (UAVs). Because the rotor speed is proportional to the thrust force, the accurate speed tracking control is indispensable for MTE. Thanks to its simplicity, the proportional–integral–derivative (PID) controller is commonly used for rotor speed regulation. However, the PID controller cannot guarantee superior performance over the entire operation range due to the time-variance and strong nonlinearity of MTE. The gain scheduling approach using a family of linear controllers is recognized as an efficient alternative, but such a solution heavily relies on the model sets and pre-knowledge. To tackle such challenges, a single neural adaptive PID (SNA-PID) controller is proposed herein for rotor speed control. The new controller featuring with a single-neuron network is able to adaptively tune the gains (weights) online. The simple structure of the controller reduces the computational load and facilitates the algorithm implementation on low-cost hardware. Finally, the proposed controller is validated by numerical simulations and experiments on the MTE in laboratory conditions, and the results show that the proposed controller achieves remarkable effectiveness for speed tracking control. In comparison with the PID controller, the proposed controller yields 54% and 66% reductions on static tracking error under two typical cases.

The motion information of blades is a key reflection of the operation state of an entire wind turbine unit. However, the special structure and operation characteristics of rotating blades have become critical obstacles for existing contact vibration monitoring technologies. Digital image correlation performs powerfully in non-contact, full-field measurements, and has increasingly become a popular method for solving the problem of rotating blade monitoring. Aiming at the problem of large-scale rotation matching for blades, this paper proposes a modified speeded-up robust features (SURF)-enhanced digital image correlation algorithm to extract the full-field deformation of blades. Combining an angle compensation (AC) strategy, the AC-SURF algorithm is developed to estimate the rotation angle. Then, an iterative process is presented to calculate the accurate rotation displacement. Subsequently, with reference to the initial state of rotation, the relative strain distribution caused by flaws is determined. Finally, the sensitivity of the strain is validated by comparing the three damage indicators including unbalanced rotational displacement, frequency change, and surface strain field. The performance of the proposed algorithm is verified by laboratory tests of blade damage detection and wind turbine model deformation monitoring. The study demonstrated that the proposed method provides an effective and robust solution for the operation status monitoring and damage detection of wind turbine blades. Furthermore, the strain-based damage detection algorithm is more advantageous in identifying cracks on rotating blades than one based on fluctuated displacement or frequency change.

Digital image correlation (DIC) for displacement and strain measurement has flourished in recent years. There are integer pixel and subpixel matching steps to extract displacement from a series of images in the DIC approach, and identification accuracy mainly depends on the latter step. A subpixel displacement matching method, named the double-precision gradient-based algorithm (DPG), is proposed in this study. After, the integer pixel displacement is identified using the coarse-fine search algorithm. In order to improve the accuracy and anti-noise capability in the subpixel extraction step, the traditional gradient-based method is used to analyze the data on the speckle patterns using the computer, and the influence of noise is considered. These two nearest integer pixels in one direction are both utilized as an interpolation center. Then, two subpixel displacements are extracted by the five-point bicubic spline interpolation algorithm using these two interpolation centers. A novel combination coefficient considering contaminated noises is presented to merge these two subpixel displacements to obtain the final identification displacement. Results from a simulated speckle pattern and a painted beam bending test show that the accuracy of the proposed method can be improved by four times that of the traditional gradient-based method that reaches the same high accuracy as the Newton–Raphson method. The accuracy of the proposed method efficiently reaches at 92.67%, higher than the Newton-Raphon method, and it has better anti-noise performance and stability.

In remote sensing building detection tasks, data acquisition remains a critical bottleneck that limits both model performance and large-scale deployment. Due to the high cost of manual annotation, limited geographic coverage, and constraints of image acquisition conditions, obtaining large-scale, high-quality labeled datasets remains a significant challenge. To address this issue, this study proposes an automatic semantic labeling framework for remote sensing imagery. The framework leverages geospatial vector data provided by OpenStreetMap, precisely aligns it with high-resolution satellite imagery from Bing Maps through projection transformation, and incorporates a quality-aware sample filtering strategy to automatically generate accurate annotations for building detection. The resulting dataset comprises 36,647 samples, covering buildings in both urban and suburban areas across multiple cities. To evaluate its effectiveness, we selected three publicly available datasets—WHU, INRIA, and DZU—and conducted three types of experiments using the following four representative object detection models: SSD, Faster R-CNN, DETR, and YOLOv11s. The experiments include benchmark performance evaluation, input perturbation robustness testing, and cross-dataset generalization analysis. Results show that our dataset achieved a mAP at 0.5 intersection over union of up to 93.2%, with a precision of 89.4% and a recall of 90.6%, outperforming the open-source benchmarks across all four models. Furthermore, when simulating real-world noise in satellite image acquisition—such as motion blur and brightness variation—our dataset maintained a mean average precision of 90.4% under the most severe perturbation, indicating strong robustness. In addition, it demonstrated superior cross-dataset stability compared to the benchmarks. Finally, comparative experiments conducted on public test areas further validated the effectiveness and reliability of the proposed annotation framework.

The Wnt signaling pathway is ubiquitous in animals, playing a crucial role in embryonic development and adult tissue homeostasis in multicellular organisms. Wnt proteins act as ligands in this pathway, and their gene family encodes secreted signaling proteins involved in regulating vital physiological processes such as cell proliferation, migration, differentiation, polarity establishment, and maintenance. Based on the third-generation genome of the forest musk deer (FMD, Moschus berezovskii ), this study aimed to identify members of the Wnt gene family, elucidate their physicochemical properties, analyze their chromosomal localization and gene structure, construct a systematic evolutionary tree, and predict the two-dimensional and three-dimensional structures of the Wnt protein family. Genome-wide identification was performed using BLASTP searches. Phylogenetic relationships were reconstructed using the Maximum Likelihood method, and 3D structures were predicted using SWISS-MODEL. Results revealed 18 Wnt ( MbWnt ) gene family members distributed across 8 chromosomes, with cDNA lengths ranging from 819 to 1900 base pairs in the FMD. The number of amino acids in the Wnt family proteins of the FMD ranged from 349 to 586aa, with isoelectric points (pI) between 7.96 and 10.24, and molecular weights concentrated between 30 and 62 kilodaltons. Signal peptide analysis showed that only MbWnt1 , MbWnt2 , MbWnt4 , MbWnt6 , and MbWnt9b contained signal peptides. Subcellular localization analysis indicated that the Wnt gene family is primarily located in the nucleus and cytoplasm. MbWnt proteins shared six conserved motifs. Phylogenetic analysis demonstrated high consistency in Wnt conservation and grouping in the genomes of cattle ( Bos taurus ), goat ( Capra hircus ), sheep (Ovis aries) , and red deer ( Cervus elaphus ). Genome collinearity analysis demonstrated substantial chromosomal correspondence and orthologous conservation between the FMD and the three species of Bovinae and Ce. elaphus. The level of chromosome homology is relatively low, but the level of genome homology is relatively high among these species. Musk gland transcriptomic datasets indicated that the expression of MbWnt genes were stage-specific at musk secretion stage and non-secretory stage. RT-qPCR analysis indicated that six MbWnt genes were significantly highly expressed during the musk secretion stage, whereas two genes showed no significant differential expression.

Background: The global rise in ultra-processed food (UPF) consumption and the persistent burden of mental disorders have raised growing public health concerns. Emerging evidence suggests that unfavorable dietary patterns, particularly with high UPF intake, contribute to the development of mental disorders. Objective: To assess the associations of UPF-related metabolic signatures and mental disorders. Methods: In this population-based cohort study of 30,059 participants from the UK Biobank, we first identified a plasma metabolic signature associated with UPF intake leveraging nuclear magnetic resonance metabolomics. We then applied Cox and logistic regression models to investigate the associations of both UPF consumption and its metabolic signature with incident mental disorders and specific psychological symptoms, respectively. Results: Higher UPF intake was significantly associated with increased risks of overall mental disorder (hazard ratio per 10% increment [95% confidence interval]: 1.04 [1.02, 1.06]), depressive disorder (1.14 [1.08, 1.20]), anxiety disorder (1.12 [1.06, 1.18]), and substance use disorder (1.06 [1.01, 1.11]), as well as several psychological symptoms including suicidal ideation (odds ratios [95% confidence interval]: 1.12 [1.03, 1.16]) and anxiety feeling (1.05 [1.01, 1.09]). Similarly, the UPF-related metabolic signature was independently associated with elevated risks of these mental health outcomes and partially mediated the associations between UPF intake and mental disorders. Conclusions: These findings highlighted the potential metabolic pathways underlying the neuropsychiatric risks of UPF consumption and underscored the importance of dietary quality in mental health.

Osseointegration for implants, especially bioinert implants, poses significant clinical challenges. Overcoming fibrotic encapsulation and promoting osseointegration at the implant interface are critical for successful bone repair, which highly expected biomaterials with osteoblast over fibroblast selectivity. However, few materials possess the function. β-amino acid polymers have demonstrated cell adhesion property, easy preparation, and robust stability to resist proteolysis as emerging biomaterials. Here, we develop amphiphilic β-amino acid polymers that demonstrate exceptional osteoblast vs fibroblast selectivity, outperforming the natural osteoblast-selective KRSR peptide. The optimal polymer selectively supports osteoblast adhesion by manipulating the adsorption of serum proteins and the presentation of RGD motifs on polymer-modified surfaces. In vivo study using polymer-modified titanium-implants in female rat maxillary bone reveals that the optimal polymer substantially promotes osseointegration of titanium-implants compared to uncoated titanium-implants, which tend to develop fibrous encapsulation. This study demonstrates the effectiveness of our strategy in designing osteoblast-selective biomaterials and implies the promising application of β-amino acid polymer as emerging osteoblast-selective biomaterials to promote osseointegration. Biomaterials exerting osteoblast over fibroblast selectivity are promising for overcoming fibrotic encapsulation and promoting osseointegration at the implant interface, but remain underdeveloped. Here, the authors report amphiphilic β-amino acid polymers that exhibit selectivity for osteoblasts over fibroblasts, outperforming the natural osteoblast-selective peptide.