Combination Of Features Research Articles

Molecular phylogeny and morphology reveal a new Eichleriella species (Auriculariales, Basidiomycota) from China

A new wood-inhabiting fungus Eichleriella yunnanensis is proposed based on a combination of the morphological features and molecular evidence. Eichleriella yunnanensis is characterized by the buff to clay-pink hymenial surface, a monomitic hyphal system with thin- to thick walled generative hyphae having the clamp connections, and thin-walled, subglobose to subclavate basidia with longitudinally septate, and thin-walled, curved-cylindrical to allantoid basidiospores. The phylogenetic tree inferred from ITS+nLSU sequences revealed that the new species was nested into the family Auriculariaceae within the order Auriculariales, in which it was closely related to the species E. discolor and E. xinpingensis. On the basis of ITS sequences indicated that the new species was nested into the genus Eichleriella, and also grouped with the clade comprising E. discolor and E. xinpingensis.

Circulating Tumor cells and multiple indicators combined to identify the risk of poorer prognosis in patients with resected non-small cell lung cancer

BackgroundSurgical resection is an important treatment option for patients with non-small cell lung cancer (NSCLC). However, recurrence and survival rates remain a cause of concern. To further improve prognosis, more studies have focused on liquid biopsy, which has significant value as a prognostic factor for defining the risk stratification of postoperative NSCLC patients. This study aimed to identify circulating tumor cells (CTCs) as biomarkers that indicate a poor prognosis, combined with multiple indicators to determine prognostic risks in advance and develop individualized treatment strategies.MethodsBetween November 2015 and August 2018, 65 radical resected patients with NSCLC were analyzed. Preoperative CTCs were collected, and follow-up lasted until August 2023. Overall survival (OS) and disease-free survival (DFS) were the primary outcomes.ResultsWith an 11 CTC unit threshold, the high preoperative CTC level group had worse OS and DFS than the low-level group, suggesting that preoperative CTC levels have prognostic value. Time-dependent receiver operating characteristic (ROC) curves also showed satisfactory predictive efficiency of CTCs. Univariate analysis revealed that preoperative CTC levels were significantly associated with increasing risks for OS and DFS. Moreover, we combined CTCs and multiple indicators to provide a reference for a group at high risk of adverse outcomes.ConclusionsCTCs serve as feasible biomarkers for predicting postoperative prognosis in NSCLC patients. The combination of hematological, radiological, and pathological features could be valuable tools to guide postoperative management and treatment decisions in these patients. A multimodal prognostic approach is important for the clinical evaluation of lung cancer.

Four new species of Phanerochaete (Polyporales, Basidiomycota) from China

Four new wood-inhabiting fungi viz. Phanerochaete castanea, P. citrinoalba, P. citrinorhizomorpha, and P. wuyiensisspp. nov. – are proposed based on a combination of morphological features and molecular evidence. Phanerochaete castanea is characterized by soft coriaceous basidiomata detachable from the substrate, becoming reddish brown in KOH, subulate cystidia with an obtuse apex. Phanerochaete citrinoalba is characterized by the coriaceous basidiomata with smooth, cracking hymenial surface, sterile margins with yellowish to whitish rhizomorphs, a monomitic hyphal system, generative hyphae mostly with simple septa and occasionally with clamp connections at basal hyphae. Phanerochaete citrinorhizomorpha is characterized by soft coriaceous basidiomata with a salmon to peach hymenial surface, a sterile margin with yellowish rhizomorphs, simple septate generative hyphae, and clavate to subfusiform or subulate cystidia with an obtuse apex. Phanerochaete wuyiensis is characterized by membranaceous basidiomata with smooth or locally tuberculate hymenial surface and the whitish rhizomorphs, generative hyphae with both simple septa and clamp connections at basal hyphae, cystidia projecting above hymenium. DNA sequences of the ITS and LSU markers of the studied samples were generated, and phylogenetic analyses were performed with Maximum Likelihood and Bayesian Inference methods. The phylogenetic tree inferred from the concatenated ITS+nLSU dataset highlighted the placement of the four new species in the genus Phanerochaete (Phanerochaetaceae, Polyporales). Phylogenetically related and morphologically similar species to these four new species are discussed. Furthermore, an identification key to accepted species of Phanerochaete in China is given.

AED-PADA:Improving Generalizability of Adversarial Example Detection via Principal Adversarial Domain Adaptation

Adversarial example detection, which can be conveniently applied in many scenarios, is important in the area of adversarial defense. Unfortunately, existing detection methods suffer from poor generalization performance, because their training process usually relies on the examples generated from a single known adversarial attack and there exists a large discrepancy between the training and unseen testing adversarial examples. To address this issue, we propose a novel method, named Adversarial Example Detection via Principal Adversarial Domain Adaptation (AED-PADA). Specifically, our approach identifies the Principal Adversarial Domains (PADs), i.e., a combination of features of the adversarial examples generated by different attacks, which possesses a large portion of the entire adversarial feature space. Subsequently, we pioneer to exploit Multi-source Unsupervised Domain Adaptation in adversarial example detection, with PADs as the source domains. Experimental results demonstrate the superior generalization ability of our proposed AED-PADA. Note that this superiority is particularly achieved in challenging scenarios characterized by employing the minimal magnitude constraint for the perturbations.

HER-2 expression is correlated with multimodal imaging features in breast cancer: a pilot study

Objective A pilot study to evaluate the correlation between multimodal imaging features and the expression of the human epidermal growth factor receptor type 2 (HER-2) in breast cancer to provide a basis for clinical treatment and prognosis evaluation. Methods We included a total of 62 patients with breast cancer admitted to the Affiliated Hospital of Hebei University between 2018 and 2022. All of them underwent the relevant investigations, including ultrasound, mammography, and enhanced magnetic resonance imaging (MRI), in the hospital within one month before surgery or biopsy. HER-2 expression level was divided into negative and positive by immunohistochemistry(IHC). Using SPSS 24.0 statistical software to analyze the differences in imaging features between the HER-2 positive and the HER-2 negative groups. Results There was a statistically significant difference between the HER-2 positive and the HER-2 negative groups (p = 0.005) in the hyperechoic halo sign around the lesion detected by ultrasonography as well as in the apparent diffusion coefficient (ADC) on MRI (p = 0.047). The sensitivity and specificity of the hyperechoic halo sign in predicting HER-2 positivity was 48.3% and 84.8% respectively, and the area under the curve (AUC) for the ADC value to predict HER-2 expression was 0.533. When b was equal to 800 and the ADC value (cutoff value) was 0.000888 mm2/s, the sensitivity and specificity were 65.5% and 51.5%, respectively. Conclusion A combination of multimodal imaging features and HER-2 gene expression can provide more valuable information for clinical diagnosis and therapeutic schedule in breast cancer.

Estimating actual crop evapotranspiration by using satellite images coupled with hybrid deep learning-based models in potato fields

Estimating actual crop evapotranspiration (ETc act) is a fundamental requirement for effective crop water management, aiming to achieve precision irrigation amidst changing environmental conditions. Despite the introduction of various methods for estimating ETc act values, these methods are still associated with several challenges and limitations. Motivated by the robustness of deep learning models, this study employed two hybrid models that integrate Convolution Neural Network with either Random Forests (CNN-RF) or Support Vector Machine (CNN-SVR) to estimate potato ETc act using a limited set of input features. Three models were configured and compared for each CNN-RF (CNN-RF1, CNNRF2, CNNRF3) and CNN-SVM (CNN-SVM1, CNN-SVM2, CNN-SVM3), by using different combinations of variable input features derived from meteorological data (air temperature (Ta), vapour pressure deficit (VPD), net radiation (Rn)) and MODIS satellite data (land surface temperature (LST), fraction of photosynthetically active radiation (Fpar), leaf area index (LAI)). The results demonstrated the outstanding performance of the CNN-RF models, showcasing their superiority over the CNN-SVM models. Notably CNN-RF1 model yielded the best results, achieving a normalized root mean squared error (nRMSE) of 11.7 and 31.5 %; Nash–Sutcliffe Efficiency (NSE) of 0.97 and 0.80; Correlation coefficient (CC) of 0.99 and 0.91 at training and testing phases respectively. While, both models exhibited lower performance when using remote sensing satellite-based inputs, CNN-RF2, managed to produce satisfactory estimates of nRMSE = 16.7, 40.9 %; NSE = 0.95, 0.66; CC = 0.98, 0.813; Bias = −0.41, −4.377 W/m2 during training and testing respectively. The ETc act of potato showed to have high correlation with LST (0.74–0.84) and using LST as proxy for Ta, resulted in improved model estimates compared to using satellite data exclusively. These findings suggest that, in situations where climatic data is limited, remote sensing can serve as a viable alternative for modelling potato ETc act when coupled with CNN-RF, and further improvement can be achieved by incorporating meteorological data fusion.

Enhancing soil organic carbon prediction of LUCAS soil database using deep learning and deep feature selection

The main terrestrial carbon (C) fraction is soil organic carbon (SOC), which has a considerable effect on climate change and greenhouse gas emissions through the absorption and sequestration of carbon dioxide (CO2). This has made SOC assessment very important from both economic and environmental viewpoints. The growing count of soil spectral libraries (SSLs) from regional to global scales has brought a tremendous opportunity for the quantification of SOC through developing spectral-based prediction models. Hence, there is a need to take advantage of big data analytics for spectral data processing. The unique ability of deep learning (DL) techniques to leverage important features of high-dimensional large-scale SSLs has made them top-demanding for more sophisticated modeling. The core objective of the present study was to assess the ability of two different DL algorithms, i.e., one-dimensional convolutional neural network (1DCNN) and fully connected neural network (FCNN) coupled with stacked autoencoder (SAE) feature extraction for SOC prediction based on the data from the land use/cover area frame statistical survey (LUCAS) database. SAE extracted the high-level deep features from the visible–near-infrared–shortwave infrared (Vis–NIR–SWIR) spectra of 11441 soil samples, which were then considered as inputs to the 1DCNN and FCNN models for predicting the SOC content. Both SAE-DL feature-selected models yielded higher accuracy than those the DL developed on the entire spectra and a random forest (RF) model was constructed for comparison. The best prediction was achieved by SAE-1DCNN (R2= 0.78, RMSE = 3.94%, RPD = 4.88, RPIQ = 3.91) followed by 1DCNN (R2= 0.73, RMSE = 5.43%, RPD = 3.67, RPIQ = 2.84) proving the superiority of 1DCNN over FCNN in this study. These results supported the applicability of combined deep features extraction and regression methods for predicting SOC using high dimensional large-scale SSLs.

Deep learning prediction of novel hollow core photonic crystal fiber with tuned As2S3 − LiNbO3 ring for multimode applications

Multimode capability in photonic crystal fiber (PCF) is an important feature in fiber development. This study presents an innovative Dual Ring Hollow core PCF (DRH-PCF), designed to support a large number of multimode operations with minimal loss and flat dispersion optimized in the wavelength range of 1.26 µm to 1.66 µm. The DRH-PCF structure comprises a high-purity silica matrix, featuring a unique configuration that includes a hollow core and high indexed Lithium niobate (LiNbO3)-Arsenic trisulfide (As2S3) dual rings of thickness 0.6 µm. This arrangement is complemented by a pattern of strategically placed air holes encircling the fiber. Our PCF is engineered to support an impressive 200 and more orbital angular momentum modes, with low confinement loss (∼10−5 dB/m) and near-flat dispersion (∼3 ps/mm/km) and very large power fraction (∼0.99), while maintaining high mode purity of >95 %. Furthermore, the fiber exhibits a near-flat dispersion profile over a wide spectral range, with a measured dispersion of ∼6 ps/(nm·km) around the operational wavelength. Stability due to fiber bend is also investigated for extreme bend tolerance (Bend radii RB=1cm to 80 cm). Deep Learning prediction is used to train and predict the performance of the fiber and obtained remarkably close results (within 5 %). This combination of features makes our PCF an excellent candidate for diverse applications in high-capacity optical communication systems, sensing technologies, and nonlinear optics.

Predicting the Victims of Hate Speech on Microblogging Platforms

Hate speech constitutes a major problem on microblogging platforms, with automatic detection being a growing research area. Most existing works focus on analyzing the content of social media posts. Our study shifts focus to predicting which users are likely to become targets of hate speech. This paper proposes a novel Hate-speech Target Prediction Framework (HTPK) and introduce a new Hate Speech Target Dataset (HSTD), which contains tweets labeled for targets and non-targets of hate speech. Using a combination of Term Frequency-Inverse Document Frequency (TFIDF), N-grams, and Part-of-Speech (PoS) tags, we tested various machine learning algorithms, Naïve Bayes (NB) classifier performs best with an accuracy of 93%, significantly outperforming other algorithms. This research identifies the optimal combination of features for predicting hate speech targets and compares various machine learning algorithms, providing a foundation for more proactive hate speech mitigation on social media platforms.

Machine learning-based performance prediction for energy storage medium-deep borehole ground source heat pump systems

With the rapid development of artificial intelligence, data-driven prediction models play an important role in energy demand forecasting and performance prediction. This study established performance prediction models for medium-deep borehole ground source heat pump (MDB-GSHP) systems to predict the coefficient of performance (COP) of GSHP, utilizing back propagation neural network (BPNN), extreme learning machine (ELM), support vector machine (SVM), and particle swarm optimization-support vector machine (PSO-SVM) method that optimizes the penalty parameter (C), insensitive loss parameter (ε), and kernel parameter (γ) of SVM using PSO. Operational data were collected through field experiments, and typical parameters were selected as features to establish the feature set. The feature set was then optimized using Pearson correlation analysis and recursive feature elimination (RFE), resulting in the creation of five distinct feature sets. Error analysis and trend evaluation results indicate that the combined feature set (FS5), which incorporates the advantages of multiple feature selection methods, demonstrates the best performance. The PSO-SVM model exhibits outstanding performance under various input conditions, achieving an accuracy of over 90% within the error range. When using FS5, the PSO-SVM model achieves a mean absolute percentage error (MAPE) of 0.037, mean absolute error (MAE) of 0.103, root mean square error (RMSE) of 0.125, coefficient of multiple determinations (R2) of 0.970, and explained variance score (EVS) of 0.967, showcasing excellent predictive performance. The research results can provide a basis for the formulation of storage-related operational parameters and the optimization of system operation for energy storage in MDB-GSHP heating systems.

Facile Preparation of Polymerizable Deep Eutectic Solvents Under Mild Conditions for Multisensory Ionic Skins

AbstractPolymerizable deep eutectic solvents (PDESs) have emerged as promising building blocks for next‐generation eutectic gels, offering new opportunities for the development of advanced electronic devices. Traditional PDES fabrication typically involves heating and extended processing time. In this study, a facile method where a solid‐solid mixture of lithium bis(trifluoromethane) sulfonimide (LiTFSI) and acrylamide (AAm) rapidly forms a PDES at room temperature, significantly simplifying the ionogel preparation is presented. By combining this PDES with another system, comprising 3‐[dimethyl‐[2‐(2‐methylprop‐2‐enoyloxy)ethyl]azaniumyl]propane‐1‐sulfonate (SBMA) and ethylene glycol (EG), an eutectic gel is successfully fabricated with exceptional mechanical and conductive properties. This gel exhibits a fracture stress of 0.8 MPa, fracture energy of 3.7 kJ m−2, adhesion strength of 60 kPa, transmittance over 90%, high conductivity (0.113 S m−1), and an outstanding temperature tolerance ranged from ‐50 to 50 °C. Notably, the absence of chemical crosslinkers and the presence of reversible interactions such as hydrogen bonding and ion‐dipole electrostatic forces impart excellent self‐healing capabilities. These combined features position the gel a promising candidate for multisensory ionic skins, capable of detecting pressure, temperature, humidity, and sweat. This work pioneers the rapid, mild‐condition synthesis of PDESs, paving the way for new techniques in ionic skin development.

Data-Driven Fault Detection and Positioning of Eccentric Rolls in Roll-to-Roll Systems Using Wrap Angle and Sensor Proximity

This paper presents a novel approach for detecting and positioning eccentricity defects in roll-to-roll (R2R) slot-die coating systems, which is critical for maintaining high production quality and efficiency. It introduces the feature combination matrix (FCM) method, which improves fault detection and precise positioning of eccentric rolls through focused feature engineering. The study employs the FCM method to enhance defect detection accuracy, using Support Vector Machine (SVM) as the classifier to consistently evaluate the effectiveness of selected feature sets in identifying and positioning eccentricity in R2R systems. By leveraging tension data, optimal feature sets are identified, emphasizing the Mean, Fast Fourier Transform, and other key variables that capture crucial system dynamics, including wrap angles and sensor proximities. Classification algorithms were used to compare the performance of models employing these optimized FCM-based features against traditional models utilizing a full suite of 40 statistical features. The FCM methodology achieved a notable improvement in eccentric roll detection and positioning accuracy, reaching 97.3%, while also reducing data capacity requirements and processing time. These outcomes highlight the effectiveness of selective feature optimization and strategic combinations, demonstrating that high-impact features enhance both detection accuracy and efficiency. Conclusively, the FCM is positioned as a pivotal tool for advancing industrial diagnostic processes, with future work suggested in the areas of adaptive feature extraction techniques and real-time model integration to improve the reliability and adaptability of R2R manufacturing.

Multimodal AI Combining Clinical and Imaging Inputs Improves Prostate Cancer Detection.

Deep learning (DL) studies for the detection of clinically significant prostate cancer (csPCa) on magnetic resonance imaging (MRI) often overlook potentially relevant clinical parameters such as prostate-specific antigen, prostate volume, and age. This study explored the integration of clinical parameters and MRI-based DL to enhance diagnostic accuracy for csPCa on MRI. We retrospectively analyzed 932 biparametric prostate MRI examinations performed for suspected csPCa (ISUP ≥2) at 2 institutions. Each MRI scan was automatically analyzed by a previously developed DL model to detect and segment csPCa lesions. Three sets of features were extracted: DL lesion suspicion levels, clinical parameters (prostate-specific antigen, prostate volume, age), and MRI-based lesion volumes for all DL-detected lesions. Six multimodal artificial intelligence (AI) classifiers were trained for each combination of feature sets, employing both early (feature-level) and late (decision-level) information fusion methods. The diagnostic performance of each model was tested internally on 20% of center 1 data and externally on center 2 data (n = 529). Receiver operating characteristic comparisons determined the optimal feature combination and information fusion method and assessed the benefit of multimodal versus unimodal analysis. The optimal model performance was compared with a radiologist using PI-RADS. Internally, the multimodal AI integrating DL suspicion levels with clinical features via early fusion achieved the highest performance. Externally, it surpassed baselines using clinical parameters (0.77 vs 0.67 area under the curve [AUC], P < 0.001) and DL suspicion levels alone (AUC: 0.77 vs 0.70, P = 0.006). Early fusion outperformed late fusion in external data (0.77 vs 0.73 AUC, P = 0.005). No significant performance gaps were observed between multimodal AI and radiologist assessments (internal: 0.87 vs 0.88 AUC; external: 0.77 vs 0.75 AUC, both P > 0.05). Multimodal AI (combining DL suspicion levels and clinical parameters) outperforms clinical and MRI-only AI for csPCa detection. Early information fusion enhanced AI robustness in our multicenter setting. Incorporating lesion volumes did not enhance diagnostic efficacy.

Virtual Tourism Android-Based Application

The Virtual Tourism- Android-based Application revolutionizes the tourism indus try by offering users a unique opportunity to explore global destinations from the comfort of their homes. Leveraging cutting-edge technologies such as 360-degree panoramic views, augmented reality (AR), and interactive multimedia, the app cre ates an immersive and engaging travel experience that brings famous landmarks, historical sites, and natural wonders to life. This application is especially valuable for individuals who face physical, financial, or time-related constraints, allowing them to virtually visit locations they may oth erwise be unable to access. It also serves as an educational tool, enabling students and culture enthusiasts to learn about the world in a highly interactive and accessible manner. The real-time tour guides and interactive maps add a dynamic layer to the virtual exploration, enhancing user engagement. In addition to these features, the app integrates social media sharing, user reviews, and offline access, fostering a community-driven platform where users can share their experiences, rate destinations, and download content for use without internet connectivity. This comprehensive combination of innovative features positions the Virtual Tourism app as a game-changer in the future of travel and education.

Social Media and Mental Health: An IoT Approach to Identifying Suicide Mentation Using Deep Learning

Suicidal ideation sufferers post their opinions and ideas on social media frequently. As a result, research from multiple studies has shown that posts on social media can be utilized to spot people who are contemplating suicide. However, it can be challenging to recognize and comprehend patterns of suicidal thought. It is crucial to develop a machine learning system that can immediately spot suicidal ideas or any abrupt changes in a user's behavior by looking at their social media posts. In this article, we suggest a technique for developing a suicidal ideation detection system based on experimental research that makes use of publicly accessible Reddit datasets, word-embedding methods, classification- focused machine learning technology, or deep learning. Through Reddit, the study finds that wise feature selection and combination lead to improved performance.

Orthogonal Progressive Network for Few-shot Object Detection

Few-Shot Object Detection (FSOD) is a significant application of few-shot learning in object detection tasks. Its primary objective is to enable the model to quickly acquire the ability to detect novel categories through a limited number of annotated samples. However, such a small number of training samples is insufficient for the model to be fully trained, which leads to difficulties in completely decoupling the regression and classification tasks, and confusion among categories. To address this issue, this paper proposes to remap features into location and class spaces. By imposing task orthogonality and class orthogonality constraints on the fine-tuning process, task decoupling and class decoupling are achieved. Furthermore, we design and implement a progressive fine-tuning strategy that iteratively optimizes in a lagged and progressive manner through the combination of training samples and historical features, mitigating the risks of catastrophic forgetting and overfitting. Experimental results demonstrate that our method effectively improves the model’s performance in FSOD tasks, achieving state-of-the-art metrics on the PASCAL VOC and MS COCO benchmarks.

Exploring the Evidence to Interpret Differential Item Functioning via Response Process Data.

Evaluating differential item functioning (DIF) in assessments plays an important role in achieving measurement fairness across different subgroups, such as gender and native language. However, relying solely on the item response scores among traditional DIF techniques poses challenges for researchers and practitioners in interpreting DIF. Recently, response process data, which carry valuable information about examinees' response behaviors, offer an opportunity to further interpret DIF items by examining differences in response processes. This study aims to investigate the potential of response process data features in improving the interpretability of DIF items, with a focus on gender DIF using data from the Programme for International Assessment of Adult Competencies (PIAAC) 2012 computer-based numeracy assessment. We applied random forest and logistic regression with ridge regularization to investigate the association between process data features and DIF items, evaluating the important features to interpret DIF. In addition, we evaluated model performance across varying percentages of DIF items to reflect practical scenarios with different percentages of DIF items. The results demonstrate that the combination of timing features and action-sequence features is informative to reveal the response process differences between groups, thereby enhancing DIF item interpretability. Overall, this study introduces a feasible procedure to leverage response process data to understand and interpret DIF items, shedding light on potential reasons for the low agreement between DIF statistics and expert reviews and revealing potential irrelevant factors to enhance measurement equity.

Los Antecedentes literarios y paremiológicos de la minificción hispanoamericana

Microfiction stands out as the most hybrid literary genre; a characteristic that is not only due to the author's writing art and aesthetic touch, but also to the diversity of his origins. This research seeks to demonstrate the polygenesis of minifiction, showing that its emergence is not limited to a single literary model, as some critical voices maintained, but that it has been nourished by dissimilar influences. The micro-story is the result of the combination of features belonging to archaic, modernist and extra-literary literary genres, especially paremiological. At a methodological level, a contrastive analysis is proposed between the microstory and these genres to identify similarities and differences in terms of structure, style, language, themes and narrative techniques. This analysis will allow us to identify the chromosomes inherited from each ancestor and define the DNA of the microstory as an independent narrative genre. Microfictions will be examined along with representative examples of the genres that have influenced their development.

Retinal imaging based glaucoma detection using modified pelican optimization based extreme learning machine

Glaucoma is defined as progressive optic neuropathy that damages the structural appearance of the optic nerve head and is characterized by permanent blindness. For mass fundus image-based glaucoma classification, an improved automated computer-aided diagnosis (CAD) model performing binary classification (glaucoma or healthy), allowing ophthalmologists to detect glaucoma disease correctly in less computational time. We proposed learning technique called fast discrete curvelet transform with wrapping (FDCT-WRP) to create feature set. This method is entitled extracting curve-like features and creating a feature set. The combined feature reduction techniques named as principal component analysis and linear discriminant analysis, have been applied to generate prominent features and decrease the feature vector dimension. Lastly, a newly improved learning algorithm encompasses a modified pelican optimization algorithm (MOD-POA) and an extreme learning machine (ELM) for classification tasks. In this MOD-POA+ELM algorithm, the modified pelican optimization algorithm (MOD-POA) has been utilized to optimize the parameters of ELM’s hidden neurons. The effectiveness has been evaluated using two standard datasets called G1020 and ORIGA with the \\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$10 \ imes 5$$\\end{document}-fold stratified cross-validation technique to ensure reliable evaluation. Our employed scheme achieved the best results for both datasets obtaining accuracy of 93.25% (G1020 dataset) and 96.75% (ORIGA dataset), respectively. Furthermore, we have utilized seven Explainable AI methodologies: Vanilla Gradients (VG), Guided Backpropagation (GBP ), Integrated Gradients ( IG), Guided Integrated Gradients (GIG), SmoothGrad, Gradient-weighted Class Activation Mapping (GCAM), and Guided Grad-CAM (GGCAM) for interpretability examination, aiding in the advancement of dependable and credible automation of healthcare detection of glaucoma.

2.6 GW, mJ-class high-energy femtosecond laser system based on Yb:YAG single-crystal fiber amplifier

High-peak-intensity ultrafast fiber lasers show excellent prospect for ultrafast science and industrial applications. For simplicity as well as efficiency, chirped-pulse amplification (CPA) is an effective technique for the generation of high-energy sources and single crystal fiber (SCF) also shows great potential due to its convenient configuration. In this work, a high-peak-power hybrid CPA pulsed laser system based on a three-stage single-pass end-pumped Yb:YAG SCF amplifier is experimentally demonstrated. The amplification system emitted pulses with the maximum power of 103.2 W at 100 kHz repetition rate and we obtained the compressed output power of 84.2 W, corresponding to the pulse energy of 0.84 mJ. Considering the third order dispersion that induced by the stretcher and the accurate tuning effect for higher-order dispersion compensation of chirped fiber Bragg grating, we have demonstrated a nearly transform limited output pulse duration of 323 fs with the peak power exceeding 2.6 GW. It can be said that we present the results for the first implementation of the shortest pulse duration and highest peak power in such multi-stage Yb:YAG SCF amplifier. The well-preserved beam quality with the measured M2 value of 1.22 and 1.29 for the horizontal and vertical directions at the maximum achieved average power. With such outstanding combined features, the demonstrated high-energy ultrafast fiber lasers would enable broad applications.