Search Results (26)

This study presents a novel artificial intelligence-driven architecture for real-time sensor fusion in autonomous vehicles (AVs), leveraging Apache Kafka and MongoDB for synchronous and asynchronous data processing to enhance resilience against sensor failures and dynamic conditions. We introduce Chaotic AdaBoost (CAB), an advanced variant of AdaBoost that integrates a logistic chaotic map into its weight update process, overcoming the limitations of deterministic ensemble methods. CAB is evaluated alongside k-Nearest Neighbors (kNNs), Artificial Neural Networks (ANNs), standard AdaBoost (AB), Gradient Boosting (GBa), and Random Forest (RF) for speed and acceleration prediction using CARLA simulator data. CAB achieves a superior 99.3% accuracy (MSE: 0.018 for acceleration, 0.010 for speed; MAE: 0.020 for acceleration, 0.012 for speed; R²: 0.993 for acceleration, 0.997 for speed), a mean Time-To-Collision (TTC) of 3.2 s, and jerk of 0.15 m/s³, outperforming AB (98.5%, MSE: 0.15, TTC: 2.8 s, jerk: 0.22 m/s³), GB (99.1%), ANN (98.2%), RF (97.5%), and kNN (87.0%). This logistic map-enhanced adaptability, reducing MSE by 88% over AB, ensures robust anomaly detection and data fusion under uncertainty, critical for AV safety and comfort. Despite a 20% increase in training time (72 s vs. 60 s for AB), CAB’s integration with Kafka’s high-throughput streaming maintains real-time efficacy, offering a scalable framework that advances operational reliability and passenger experience in autonomous driving. Full article

In the past 5 years, the COVID-19 pandemic has experienced frequently changing variants contextualizing immune evasion. The emergence of Omicron with >30–50 mutations on the spike gene has shown a sharp divergence from its relative VOCs, such as WT, Alpha, Beta, Gamma, and Delta. The requisition of prime boosting was essential within 3–6 months to improve the Nab response that had been not lasted for longer. Omicron subvariant BA.1.1 was less transmissible, but with an extra nine mutations in next variant BA.2 made it more transmissible. This remarkable heterogeneity was reported in ORF1ab or TRS sites, ORF7a, and 10 regions in the genomic sequences of Omicron BA.2 and its evolving subvariants BA.4.6, BF.7, BQ.2, BF. 7, BA.2.75.2, and BA.5 (BQ.1 and BQ.1.1). The mutational stability of subvariants XBB, XBB 1, XBB 1.5, and XBB 1.6 conferred a similar affinity towards ACE-2. This phenomenon has been reported in breakthrough infections and after booster vaccinations producing hybrid immunity. The reduced pathogenic nature of Omicron has implicated its adaptation either through immunocompromised individuals or other animal hosts. The binding capacity of RBD and ACE-2, including the proteolytic priming via TMPRSS2, reveals its (in-vitro) transmissibility behavior. RBD mutations signify transmissibility, S1/S2 enhances virulence, while S2 infers the effective immunogenic response. Initial mutations D614G, E484A, N501Y, Q493K, K417N, S477N, Y505H, and G496S were found to increase the Ab escape. Some mutations such as, R346K, L452R, and F486Vwere seen delivering immune pressure. HR2 region (S2) displayed mutations R436S, K444T, F486S, and D1199N with altered spike positions. Later on, the booster dose or breakthrough infections contributed to elevating the immune profile. Several other mutations in BA.1.1-N460K, R346T, K444T, and BA.2.75.2-F486S have also conferred the neutralization resistance. The least studied T-cell response in SARS-CoV-2 affects HLA- TCR interactions, thus, it plays a role in limiting the virus clearance. Antigenic cartographic analysis has also shown Omicron’s drift from its predecessor variants. The rapidly evolving SARS-CoV-2 variants and subvariants have driven the population-based immunity escape in fully immunized individuals within short period. This could be an indication that Omicron is heading towards endemicity and may evolve in future with subvariants could lead to outbreaks, which requires regular surveillance. Full article

Wildfires are increasingly frequent and severe, posing substantial risks to ecosystems, communities, and infrastructure. Accurately mapping wildfire severity (WSM) using remote sensing and machine learning (ML) is critical for evaluating damages, informing recovery efforts, and guiding long-term mitigation strategies. Stacking ensemble ML (SEML) enhances predictive accuracy and robustness by combining multiple diverse models into a single meta-learned predictor. This approach leverages the complementary strengths of individual base learners while reducing variance, ultimately improving model reliability. This study aims to optimize a SEML framework to (1) identify the most effective ML models for use as base learners and meta-learners, and (2) determine the optimal number of base models needed for robust and accurate wildfire severity predictions. The study utilizes six ML models—Random Forests (RF), Support Vector Machines (SVM), k-Nearest Neighbors (KNN), Linear Regression (LR), Adaptive Boosting (AB), and Multilayer Perceptron (MLP)—to construct an SEML. To quantify wildfire impacts, we extracted 118 spectral indices from post-fire Landsat-8 data and incorporated four additional predictors (land cover, elevation, slope, and aspect). A dataset of 911 CBI observations from 18 wildfire events was used for training, and models were validated through cross-validation and bootstrapping to ensure robustness. To address multicollinearity and reduce computational complexity, we applied Linear Discriminant Analysis (LDA) and condensed the dataset into three primary components. Our results indicated that simpler models, notably LR and KNN, performed well as meta-learners, with LR achieving the highest predictive accuracy. Moreover, using only two base learners (RF and SVM) was sufficient to realize optimal SEML performance, with an overall accuracy and precision of 0.661, recall of 0.662, and F1-score of 0.656. These findings demonstrate that SEML can enhance wildfire severity mapping by improving prediction accuracy and supporting more informed resource allocation and management decisions. Future research should explore additional meta-learning approaches and incorporate emerging remote sensing data sources such as hyperspectral and LiDAR. Full article

Unreinforced masonry buildings are highly vulnerable to earthquake damage due to their limited ability to withstand lateral loads, compared to other structures. Therefore, a detailed assessment of the seismic response and resultant damage associated with such buildings becomes necessary. The present study employs machine learning models to effectively predict the seismic response and classify the damage level for a benchmark unreinforced masonry building. In this regard, eight regression-based models, namely, Linear Regression (LR), Stepwise Linear Regression (SLR), Ridge Regression (RR), Support Vector Machine (SVM), Gaussian Process Regression (GPR), Decision Tree (DT), Random Forest (RF), and Neural Networks (NN), were used to predict the building’s responses. Additionally, eight classification-based models, namely, Naïve Bayes (NB), Discriminant Analysis (DA), K-Nearest Neighbours (KNN), Adaptive Boosting (AB), DT, RF, SVM, and NN, were explored for the purpose of categorizing the damage states of the building. The material properties of the masonry and the earthquake intensity were considered as the input parameters. The results from the regression models indicate that the GPR model efficiently predicts the seismic response with larger coefficients of determination and smaller root mean square error values than other models. Among the classification-based models, the RF, AB, and NN models effectively classify the damage states with accuracy levels of 92.9%, 91.1%, and 92.6%, respectively. In conclusion, the overall performance of the non-parametric models, such as GPR, NN, and RF, was found to be better than that of the parametric models. Full article

The emergence of SARS-CoV-2 variants escaping immunity challenges the efficacy of current vaccines. Here, we investigated humoral recall responses and vaccine-mediated protection in Syrian hamsters immunized with the third-generation Comirnaty^® Omicron XBB.1.5-adapted COVID-19 mRNA vaccine, followed by infection with either antigenically closely (EG.5.1) or distantly related (JN.1) Omicron subvariants. Vaccination with the YF17D vector encoding a modified Gamma spike (YF-S0*) served as a control for SARS-CoV-2 immunity restricted to pre-Omicron variants. Our results show that both Comirnaty^® XBB.1.5 and YF-S0* induce robust, however, poorly cross-reactive, neutralizing antibody (nAb) responses. In either case, total antibody and nAb levels increased following infection. Intriguingly, the specificity of these boosted nAbs did not match the respective challenge virus, but was skewed towards the primary antigen used for immunization, suggesting a marked impact of antigenic imprinting, confirmed by antigenic cartography. Furthermore, limited cross-reactivity and rapid decline in nAbs induced by Comirnaty^® XBB.1.5 with EG.5.1 and, more concerning, JN.1, raises doubts about sustained vaccine efficacy against recent circulating Omicron subvariants. In conclusion, we demonstrate that antigenic imprinting plays a dominant role in shaping humoral immunity against emerging SARS-CoV-2 variants. Future vaccine design may have to address two major issues: (i) overcoming original antigenic sin that limits the breadth of a protective response towards emerging variants, and (ii) achieving sustained immunity that lasts for at least one season. Full article

The post-harvest process is important to increasing the market value of limes and requires focus. During this process, limes are graded and categorized based on size, weight, and volume. Therefore, identifying efficient means of estimating these properties is very important and remains an open research area. This study applies the concept of computer vision based on traditional machine learning algorithms (partial least square regression (PLS), epsilon-support vector regression (ε-SVR), decision tree (DT), random forest (RF), adaptive boosting (AB), gradient boosting (GB), Bagging meta-estimator (BME), and extremely randomized trees (ERTs)) and pre-trained deep learning (InceptionV3, MoblieNetV2, ResNet50, and VGG-16) for estimating the weight and volume of limes. Our findings showed that the BME and ResNet50 could yield the highest performance for estimating the weight and volume of limes. The BME produced ${\mathrm{R}}_{\mathrm{test}}^2$ values of 0.954 and 0.882 for weight and volume, respectively, while the ${\mathrm{R}}_{\mathrm{test}}^2$ values of ResNet50 models were between 0.951 and 0.957 for weight and volume, respectively. This study concluded that computer vision based on both traditional machine learning and deep learning could be used to estimate the weight and volume of limes. The approach proposed in this study can be adopted for applications related to computer vision in the post-harvest process. Full article

This paper presents a thorough robust Class-AB power amplifier design and its application in pressure-mode sensor-on-chip nonlinearity correction. Considering its use in piezoresistive sensing applications, a gain-boosting-aided folded cascode structure is utilized to increase the amplifier’s gain by a large amount as well as enhancing the power rejection ability, and a push–pull structure with miller compensation, a floating gate technique, and an adaptive output driving limiting structures are adopted to achieve high-efficiency current driving capability, high stability, and electronic environmental compatibility. This amplifier is applied in a real sensor nonlinearity correction on-chip system. With the help of a self-designed 7-bit + sign DAC and a self-designed two-stage operational amplifier, this system is compatible with nonlinear correction at different signal conditioning output values. It can also drive resistive sensors as small as 300 ohms and as high as tens of thousands of ohms. The designed two-stage operational amplifier utilizes the TSMC 0.18 um process, resulting in a final circuit power consumption of 0.183 mW. The amplifier exhibits a gain greater than 140 dB, a phase margin of 68°, and a unit gain bandwidth exceeding 199.76 kHz. The output voltage range spans from 0 to 4.6 V. The final simulation results indicate that the nonlinear correction system designed in this paper can correct piezoresistive sensors with a nonlinearity of up to ±2.5% under various PVT (Process–Voltage–Temperature) conditions. After calibration by this system, the maximum error in the output voltage is 4 mV, effectively reducing the nonlinearity to 4% of its original value in the worst-case scenario. Full article

The initial melting quality of a high-speed laser cladding layer has an important impact on its post-treatment and practical application. In this study, based on the repair of hydraulic support columns of coal mining machines, the influence of high-speed laser cladding process parameters on the quality of Fe-Cr-Ni alloy coatings was investigated to realize the accurate prediction of coating quality. The Taguchi orthogonal method was used to design the L₂₅(5⁶) test. The prediction models of the relationship between the cladding process and the coating quality were established using the Random Forest (RF) and AdaBoost (Adaptive Boosting, AB) algorithms, respectively. Then, the prediction accuracy of the two models was compared, and the process parameter features were screened for importance evaluation. The results show that the AB prediction model is more accurate than the RF prediction model and more sensitive to abnormal data. The importance evaluation based on the AdaBoost model shows that the scanning speed has a great influence on the height and surface roughness of the coating. On the other hand, the overlap rate is the most important factor in controlling the dilution ratio and near-surface grain size of high-speed laser melting coatings. In addition, the micro-hardness of the coating and the thermal effect of the substrate can be effectively enhanced by adjusting the laser power and scanning speed. Finally, it was verified that the AB prediction model could accurately estimate the quality indexes of the coating with a prediction error less than 6%. The results show that it is feasible to predict the quality of high-speed laser cladding with the AB algorithm. It provides a basis for the adjustment of process parameters in the subsequent quality control process of cladding. Full article

This study examined the performance of random forest (RF), support vector machine (SVM) and adaptive boosting (AB) machine learning models used to estimate daily potato crop evapotranspiration adjusted (ET_c-adj) under full irrigation (I₁₀₀), 50% of full irrigation supply (I₅₀) and rainfed cultivation (I₀). Five scenarios of weather, crop and soil data availability were considered: (S1) reference evapotranspiration and precipitation, (S2) S1 and crop coefficient, (S3) S2, the fraction of total available water and root depth, (S4) S2 and total soil available water, and (S5) S3 and total soil available water. The performance of machine learning models was compared with the standard FAO56 calculation procedure. The most accurate ET_c-adj estimates were observed with AB4 for I₁₀₀, RF3 for I₅₀ and AB5 for I₀ with coefficients of determination (R²) of 0.992, 0.816 and 0.922, slopes of 1.004, 0.999 and 0.972, modelling efficiencies (EF) of 0.992, 0.815 and 0.917, mean absolute errors (MAE) of 0.125, 0.405 and 0.241 mm day⁻¹, root mean square errors (RMSE) of 0.171, 0.579 and 0.359 mm day⁻¹ and mean squared errors (MSE) of 0.029, 0.335 and 0.129 mm day⁻¹, respectively. The AB model is suggested for ET_c-adj prediction under I₁₀₀ and I₀ conditions, while the RF model is recommended under the I₅₀ condition. Full article

Purpose: In 2021, the WHO central nervous system (CNS) tumor classification criteria added the diagnosis of diffuse astrocytic glioma, IDH wild-type, with molecular features of glioblastoma, WHO grade 4 (DAG-G). DAG-G may exhibit the aggressiveness and malignancy of glioblastoma (GBM) despite the lower histological grade, and thus a precise preoperative diagnosis can help neurosurgeons develop more refined individualized treatment plans. This study aimed to establish a predictive model for the non-invasive identification of DAG-G based on preoperative MRI radiomics. Patients and Methods: Patients with pathologically confirmed glioma in Huashan Hospital, Fudan University, between September 2019 and July 2021 were retrospectively analyzed. Furthermore, two external validation datasets from Wuhan Union Hospital and Xuzhou Cancer Hospital were also utilized to verify the reliability and accuracy of the prediction model. Two regions of interest (ROI) were delineated on the preoperative MRI images of the patients using the semi-automatic tool ITK-SNAP (version 4.0.0), which were named the maximum anomaly region (ROI1) and the tumor region (ROI2), and Pyradiomics 3.0 was applied for feature extraction. Feature selection was performed using a least absolute shrinkage and selection operator (LASSO) filter and a Spearman correlation coefficient. Six classifiers, including Gauss naive Bayes (GNB), K-nearest neighbors (KNN), Random forest (RF), Adaptive boosting (AB), and Support vector machine (SVM) with linear kernel and multilayer perceptron (MLP), were used to build the prediction models, and the prediction performance of the six classifiers was evaluated by fivefold cross-validation. Moreover, the performance of prediction models was evaluated using area under the curve (AUC), precision (PRE), and other metrics. Results: According to the inclusion and exclusion criteria, 172 patients with grade 2–3 astrocytoma were finally included in the study, and a total of 44 patients met the diagnosis of DAG-G. In the prediction task of DAG-G, the average AUC of GNB classifier was 0.74 ± 0.07, that of KNN classifier was 0.89 ± 0.04, that of RF classifier was 0.96 ± 0.03, that of AB classifier was 0.97 ± 0.02, that of SVM classifier was 0.88 ± 0.05, and that of MLP classifier was 0.91 ± 0.03, among which, AB classifier achieved the best prediction performance. In addition, the AB classifier achieved AUCs of 0.91 and 0.89 in two external validation datasets obtained from Wuhan Union Hospital and Xuzhou Cancer Hospital, respectively. Conclusions: The prediction model constructed based on preoperative MRI radiomics established in this study can basically realize the prospective, non-invasive, and accurate diagnosis of DAG-G, which is of great significance to help further optimize treatment plans for such patients, including expanding the extent of surgery and actively administering radiotherapy, targeted therapy, or other treatments after surgery, to fundamentally maximize the prognosis of patients. Full article

The importance of solar power generation facilities, as one of the renewable energy types, is increasing daily. This study proposes a two-way validation approach to verify the validity of the forecast data by integrating solar energy production quantity with machine learning (ML) and I-MR statistical process control (SPC) charts. The estimation data for the amount of solar energy production were obtained by using random forest (RF), linear regression (LR), gradient boosting (GB), and adaptive boost or AdaBoost (AB) algorithms from ML models. Data belonging to eight independent variables consisting of environmental and geographical factors were used. This study consists of approximately two years of data on the amount of solar energy production for 636 days. The study consisted of three stages: First, descriptive statistics and analysis of variance tests of the dependent and independent variables were performed. In the second stage of the method, estimation data for the amount of solar energy production, representing the dependent variable, were obtained from AB, RF, GB, and LR algorithms and ML models. The AB algorithm performed best among the ML models, with the lowest RMSE, MSE, and MAE values and the highest R² value for the forecast data. For the estimation phase of the AB algorithm, the RMSE, MSE, MAE, and R² values were calculated as 0.328, 0.107, 0.134, and 0.909, respectively. The RF algorithm performed worst with performance scores for the prediction data. The RMSE, MSE, MAE, and R² values of the RF algorithm were calculated as 0.685, 0.469, 0.503, and 0.623, respectively. In the last stage, the estimation data were tested with I-MR control charts, one of the statistical control tools. At the end of all phases, this study aimed to validate the results obtained by integrating the two techniques. Therefore, this study offers a critical perspective to demonstrate a two-way verification approach to whether a system’s forecast data are under control for the future. Full article

Parkinson’s disease (PD) is a neurological disorder affecting the nerve cells. PD gives rise to various neurological conditions, including gradual reduction in movement speed, tremors, limb stiffness, and alterations in walking patterns. Identifying Parkinson’s disease in its initial phases is crucial to preserving the well-being of those afflicted. However, accurately identifying PD in its early phases is intricate due to the aging population. Therefore, in this paper, we harnessed machine learning-based ensemble methodologies and focused on the premotor stage of PD to create a precise and reliable early-stage PD detection model named PDD-ET. We compiled a tailored, extensive dataset encompassing patient mobility, medication habits, prior medical history, rigidity, gender, and age group. The PDD-ET model amalgamates the outcomes of various ML techniques, resulting in an impressive 97.52% accuracy in early-stage PD detection. Furthermore, the PDD-ET model effectively distinguishes between multiple stages of PD and accurately categorizes the severity levels of patients affected by PD. The evaluation findings demonstrate that the PDD-ET model outperforms the SVR, CNN, Stacked LSTM, LSTM, GRU, Alex Net, [Decision Tree, RF, and SVR], Deep Neural Network, HOG, Quantum ReLU Activator, Improved KNN, Adaptive Boosting, RF, and Deep Learning Model techniques by the approximate margins of 37%, 30%, 20%, 27%, 25%, 18%, 19%, 27%, 25%, 23%, 45%, 40%, 42%, and 16%, respectively. Full article

Machine learning (ML)-based models are popular for complex physical system simulation and prediction. Lake is the important indicator in arid and semi-arid areas, and to achieve the proper management of the water resources in a lake basin, it is crucial to estimate and predict the lake dynamics, based on hydro-meteorological variations and anthropogenic disturbances. This task is particularly challenging in arid and semi-arid regions, where water scarcity poses a significant threat to human life. In this study, a typical arid area of China was selected as the study area, and the performances of eight widely used ML models (i.e., Bayesian Ridge (BR), K-Nearest Neighbor (KNN), Gradient Boosting Decision Tree (GBDT), Extra Trees (ET), Random Forest (RF), Adaptive Boosting (AB), Bootstrap aggregating (Bagging), eXtreme Gradient Boosting (XGB)) were evaluated in predicting lake area. Monthly lake area was determined by meteorological (precipitation, air temperature, Standardised Precipitation Evapotranspiration Index (SPEI)) and anthropogenic factors (ET_c, NDVI, LUCC). Lake area determined by Landsat satellite image classification for 2000–2020 was analysed side-by-side with the Standardised Precipitation Evapotranspiration Index (SPEI) on 9 and 12-month time scales. With the evaluation of six input variables and eight ML algorithms, it was found that the RF models performed best when using the SPEI-9 index, with R² = 0.88, RMSE = 1.37, LCCC = 0.95, and PRD = 1331.4 for the test samples. Furthermore, the performance of the ML model constructed with the 9-month time scale SPEI (SPEI-9) as an input variable (ML_SPEI-9) depended on seasonal variations, with the average relative errors of up to 0.62 in spring and a minimum of 0.12 in summer. Overall, this study provides valuable insights into the effectiveness of different ML models for predicting lake area by demonstrating that the right inputs can lead to a remarkable increase in performance of up to 13.89%. These findings have important implications for future research on lake area prediction in arid zones and demonstrate the power of ML models in advancing scientific understanding of complex natural systems. Full article

Purpose: The accurate preoperative histopathological grade diagnosis of adult gliomas is of great significance for the formulation of a surgical plan and the implementation of a subsequent treatment. The aim of this study is to establish a predictive model for classifying adult gliomas into grades 2–4 based on preoperative conventional multimodal MRI radiomics. Patients and Methods: Patients with pathologically confirmed gliomas at Huashan Hospital, Fudan University, between February 2017 and July 2019 were retrospectively analyzed. Two regions of interest (ROIs), called the maximum anomaly region (ROI1) and the tumor region (ROI2), were delineated on the patients’ preoperative MRIs utilizing the tool ITK-SNAP, and Pyradiomics 3.0 was applied to execute feature extraction. Feature selection was performed utilizing a least absolute shrinkage and selection operator (LASSO) filter. Six classifiers, including Gaussian naive Bayes (GNB), random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM) with a linear kernel, adaptive boosting (AB), and multilayer perceptron (MLP) were used to establish predictive models, and the predictive performance of the six classifiers was evaluated through five-fold cross-validation. The performance of the predictive models was evaluated using the AUC and other metrics. After that, the model with the best predictive performance was tested using the external data from The Cancer Imaging Archive (TCIA). Results: According to the inclusion and exclusion criteria, 240 patients with gliomas were identified for inclusion in the study, including 106 grade 2, 68 grade 3, and 66 grade 4 gliomas. A total of 150 features was selected, and the MLP classifier had the best predictive performance among the six classifiers based on T2-FLAIR (mean AUC of 0.80 ± 0.07). The SVM classifier had the best predictive performance among the six classifiers based on DWI (mean AUC of 0.84 ± 0.05); the SVM classifier had the best predictive performance among the six classifiers based on CE-T1WI (mean AUC of 0.85 ± 0.06). Among the six classifiers, based on ROI1, the MLP classifier had the best prediction performance (mean AUC of 0.78 ± 0.07); among the six classifiers, based on ROI2, the SVM classifier had the best prediction performance (mean AUC of 0.82 ± 0.07). Among the six classifiers, based on the multimodal MRI of all the ROIs, the SVM classifier had the best prediction performance (average AUC of 0.85 ± 0.04). The SVM classifier, based on the multimodal MRI of all the ROIs, achieved an AUC of 0.81 using the external data from TCIA. Conclusions: The prediction model, based on preoperative conventional multimodal MRI radiomics, established in this study can conveniently, accurately, and noninvasively classify adult gliomas into grades 2–4, providing certain assistance for the precise diagnosis and treatment of patients and optimizing their clinical management. Full article

Calcium (Ca), a secondary messenger, plays an essential role in improving drought resistance. We used the Fast Chlorophyll Fluorescence Induction Dynamics technique to investigate the effects of exogenous calcium on electron transport and energy fluxes in an 8-year-old Mongolian pine to investigate the mechanism of action of Ca in regulating drought adaptation in Pinus sylvestris var. mongolica. We found water stress significantly decreased Pn and Gs, but exogenous calcium significantly improved photosynthesis under water stress. The chlorophyll a fluorescence transient (OJIP) analysis revealed that water stress increased Fo and decreased Fm, inactivating reaction centers. Water stress reduced V_I and V_J while increasing Mo, destroying the electron transport chain. Exogenous calcium increased Sm while decreasing V_I and Mo under water stress, enhancing electron transport from QA to QB. Furthermore, 5 mM Ca²⁺ increased I-P phase and ψPo, δRo, and φRo, decreasing the drought-induced reduction in electron accepters of PSⅠ. The increase in ABS/RC, TRo/RC, ETo/RC, and DIo/RC caused by 5 mM Ca²⁺ demonstrated that calcium can regulate photoprotection to promote photosynthetic activity. Thus, exogenous calcium alleviated drought-induced reductions in photosynthetic activity by regulating photoprotection and boosting the electron transport efficiency at the acceptor side of PSⅡ and PSⅠ. Full article