HUI Research (Sweden)

Abstract Accurate State of Charge (SOC) estimation is vital for ensuring the efficiency and reliability of lithium-ion battery systems. Accurately estimating SOC under complex environmental conditions remains a significant challenge. Electric forklifts offer advantages such as low operating costs and convenient charging, presenting broad application prospects. However, during year-round operation, they face issues like large temperature variations and high complexity in battery data acquisition and processing. This paper first analyzes the operational characteristics of forklift batteries. Pearson correlation coefficient analysis is employed for feature extraction from the data. These features are directly derived from the actual charge-discharge operational data of the batteries, forming the core inputs for subsequent SOC estimation. To enhance SOC estimation accuracy, a temperature correction method is applied to adjust the maximum available capacity at the current temperature. Subsequently, a neural network model combining Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Attention mechanism is utilized. Different optimization algorithms are applied to tune the model's hyperparameters. Validation is performed by comparing various deep learning frameworks and optimization methods across different datasets.Results demonstrate that the SOC estimation accuracy based on the CNN-BiLSTM-Attention neural network algorithm is high. After incorporating the temperature correction method and the Grey Wolf Optimizer (GWO), the Root Mean Square Error (RMSE) is reduced to 1.1%, and the overall relative error is decreased by 52%.

Metastable responses to illumination, temperature, electrical bias, and exposure history complicate the performance assessment and reliability in CdTe thin-film photovoltaics. Here, we demonstrate a synergistic light–heat–bias (L–H–B) activation that reproducibly boosts the performance of graded CdSeTe devices. L–H–B suppresses nonradiative recombination at the illuminated front interface and within the absorber, coincident with deactivation of dominant shallow traps near EV + 0.35 eV and reduced occupancy of midgap states around EV + 0.76–0.79 eV. These changes mitigate defect-mediated Shockley–Read–Hall losses and increase open-circuit voltage and fill factor, yielding a champion efficiency of 21.02%─among the highest reported for CdSeTe thin-film solar cells. Specifically, the response is reversible after dark storage, recoverable upon reactivation, and not Cu-specific, generalizing to group-V-doped devices. The protocol provides an operational, standardizable preconditioning route for performance activation and reliable benchmarking.

Feature selection plays an essential role in the field of computer vision. Current research involves the adjustment of feature subsets to minimize the dissimilarity between the feature space and selected feature subset, thus enhancing the quality of the chosen features. However, the optimization process encounters challenges related to extended computation times. To address this challenge, this study introduces an $$L_{2,0}$$ sparse constraint and presents a greedy feature selection approach utilizing a residual downhill strategy to enhance computational efficiency, while not compromising model accuracy. The residual downhill can quickly reduce the loss of the objective function, and the sparse regularization can filter out irrelevant features. This study aims to rigorously evaluate the effectiveness of the model by utilizing various data sets, including six publicly gene datasets related to diseases (e.g., Leukemia etc.), four additional classification challenge datasets (e.g., Arcene, etc.), and five image datasets (e.g., VOC-2007, etc.). KNN and SVM with five-fold cross validation are implemented as classifiers to assess their efficacy. The performance of the model is comprehensively assessed by three evaluation metrics such as classification accuracy. Finally, the results are tested for significance to verify that the improvement is significant based on P-value. Due to the efficacy of this method, it is necessary to further investigate it and explore its applications in other domains.

This article mainly describes a fermentation and purification method for expressing recombinant collagen protein in Escherichia coli. The method comprises constructing engineered bacteria expressing human type III collagen and adopting a strategy of feeding in batches for high-density fermentation. The rapid proliferation of bacterial cells is promoted at 37°C, and then the culture is inoculated into a fermentation tank with different carbon sources for growth. When glycerol is used as the main carbon source, the yield of recombinant collagen protein can reach 0.25 to 0.40 g/L. The method allows exploration of the differences in recombinant collagen production with different carbon sources in order to identify the most suitable fermentation medium component. The human type III collagen produced by the method has the typical structure of collagen, with high cell adhesion and the stability of tissue structure. Therefore, it can be used as the raw material for various collagen products, especially facial fillers, dressings, freeze-dried fibers, and gels. © 2025 Wiley Periodicals LLC. Basic Protocol: Fermentation and purification of recombinant human type III collagen expressed in Escherichia coli.

Abstract The increasing demand for radiation detection in applications like medical diagnostics and security inspection drives scintillator research. Traditional scintillators are limited by toxicity, crystallization challenges, and high production costs. A one‐step chemical vapor deposition (CVD) method is developed to produce high‐purity, large‐area, and uniform Cs 3 Cu 2 Cl 5 microcrystalline film. This material utilizes a unique self‐trapped exciton (STE) emission mechanism, resulting in significant Stokes shift of 241 nm and a high photoluminescence quantum yield (PLQY). It offers new opportunities for converting X‐ray and high‐energy radiation. It shows a linear RL intensity increase with dose rates (25–188 µGy s −1 ), excellent dose‐response linearity, and high absorption coefficients comparable to commercial scintillators. This X‐ray imaging system allows for high‐resolution visualization of internal chip structures even at low doses. This work establishes a controllable synthesis method for large‐area scintillator films and highlights the potential of Cs 3 Cu 2 Cl 5 microcrystalline film as a next‐generation scintillators materials.