Search Results (14)

Financial risk early warning systems provide critical corporate financial status information to stakeholders, including corporate managers, investors, regulatory agencies, and other interested parties, enabling informed decision-making. This study proposes a corporate financial risk early warning model based on a bagging–cascading–boosting architecture, which can be used to predict the financial risk of a firm. The model performance is improved by integrating the residual fitting characteristics of LightGBM, the variance suppression mechanism of bagging, and the adaptive expansion ability of the cascade framework. Evaluated on 46 financial indicators from 2826 A-share-listed companies, the model demonstrates superior performance in AUC and F1-score metrics, outperforming traditional statistical methods and standalone machine-learning models. The methodological innovation lies in its tripartite mechanism: LightGBM ensures low-bias prediction, bagging controls variance, and the cascading structure dynamically adapts to data complexity, maintaining 94.09% AUC robustness, even when training data is reduced to 50%. Empirical results confirm this “ensemble-of-ensembles” framework effectively identifies Special Treatment (ST) firms, delivering early risk alerts for management while supporting investment decisions and regulatory risk mitigation. Full article

Aquatic floating debris detection is a key technological foundation for ecological monitoring and integrated water environment management. It holds substantial scientific and practical value in applications such as pollution source tracing, floating debris control, and maritime navigation safety. However, this field faces ongoing challenges due to water surface polarization. Reflections of polarized light produce intense glare, resulting in localized overexposure, detail loss, and geometric distortion in captured images. These optical artifacts severely impair the performance of conventional detection algorithms, increasing both false positives and missed detections. To overcome these imaging challenges in complex aquatic environments, we propose a novel YOLOv8-based detection framework with integrated polarized light suppression mechanisms. The framework consists of four key components: a fisheye distortion correction module, a polarization feature processing layer, a customized residual network with Squeeze-and-Excitation (SE) attention, and a cascaded pipeline for super-resolution reconstruction and deblurring. Additionally, we developed the PSF-IMG dataset (Polarized Surface Floats), which includes common floating debris types such as plastic bottles, bags, and foam boards. Extensive experiments demonstrate the network’s robustness in suppressing polarization artifacts and enhancing feature stability under dynamic optical conditions. Full article

Dog activities recognition, especially dog motion status recognition, is an active research area. Although several machine learning and deep learning approaches have been used for dog motion states recognition, the use of ensemble learning methods is rather missing, as well as a comparison with deep learning ones. This paper focuses on the use of deep learning neural networks and ensemble classifiers in recognizing dog motion states and their comparison. A dataset from the Kaggle database, which includes measures by accelerometer and gyroscope and concerns seven dog motion states (galloping, sitting, standing, trotting, walking, lying on chest, and sniffing), was used for our experiments. Gaussian Naive Bayes, Decision Tree, k-Nearest Neighbors (kNN), Random Forest, a Bagging Tree-Based Classifier, a Stacking Classifier, a Compound Stacking Model (CSM), a Convolutional Neural Network (CNN), a Recurrent Neural Network (RNN), and a Hybrid Cascading Model (HCM) were used in our experiments. Results showed a 1.78% superiority in accuracy (92.64% vs. 90.86%) of deep learning (RNN) vs. stacking (CSTAM) best classifier, but at the cost of larger complexity and training time for the deep learning classifier, which makes ensemble techniques still attractive. Finally, HCM gave the best result (96.82% accuracy). Full article

In the Industry 4.0 era of smart grids, the real-world problem of blackouts and cascading failures due to cyberattacks is a significant concern and highly challenging because the existing Intrusion Detection System (IDS) falls behind in handling missing rates, response times, and detection accuracy. Addressing this problem with an early attack detection mechanism with a reduced missing rate and decreased response time is critical. The development of an Intelligent IDS is vital to the mission-critical infrastructure of a smart grid to prevent physical sabotage and processing downtime. This paper aims to develop a robust Anomaly-based IDS using a statistical approach with a machine learning classifier to discriminate cyberattacks from natural faults and man-made events to avoid blackouts and cascading failures. The novel mechanism of a statistical approach with a machine learning (SAML) classifier based on Neighborhood Component Analysis, ExtraTrees, and AdaBoost for feature extraction, bagging, and boosting, respectively, is proposed with optimal hyperparameter tuning for the early discrimination of cyberattacks from natural faults and man-made events. The proposed model is tested using the publicly available Industrial Control Systems Cyber Attack Power System (Triple Class) dataset with a three-bus/two-line transmission system from Mississippi State University and Oak Ridge National Laboratory. Furthermore, the proposed model is evaluated for scalability and generalization using the publicly accessible IEEE 14-bus and 57-bus system datasets of False Data Injection (FDI) attacks. The test results achieved higher detection accuracy, lower missing rates, decreased false alarm rates, and reduced response time compared to the existing approaches. Full article

Currently, biobased epoxy resins derived from plant oils and natural fibers are available on the market and are a promising substitute for fossil-based products. The purpose of this work is to investigate novel lightweight thermoset fiber-reinforced composites with extremely high biobased content. Paying attention to the biobased content, following a cascade pathway, many trials were carried out with different types of resins and hardeners to select the best ones. The most promising formulations were then used to produce flax fiber reinforced composites by vacuum bagging process. The main biocomposite properties such as tensile, bending, and impact properties as well as the individuation of their glass transition temperatures (by DSC) were assessed. Three biocomposite systems were investigated with biobased content ranging from 60 to 91%, obtaining an elastic modulus that varied from 2.7 to 6.3 GPa, a flexural strength from 23 to 108.5 MPa, and Charpy impact strength from 11.9 to 12.2 kJ/m². The properties reached by the new biocomposites are very encouraging; in fact, their stiffness vs. lightweight (calculated by the E/ρ³ ratio) is comparable to some typical epoxy–glass composites. Full article

In this study, we explore the integration of cascading and ensemble techniques in Deep Learning (DL) to improve prediction accuracy on diabetes data. The primary approach involves creating multiple Neural Networks (NNs), each predicting the outcome independently, and then feeding these initial predictions into another set of NN. Our exploration starts from an initial preliminary study and extends to various ensemble techniques including bagging, stacking, and finally cascading. The cascading ensemble involves training a second layer of models on the predictions of the first. This cascading structure, combined with ensemble voting for the final prediction, aims to exploit the strengths of multiple models while mitigating their individual weaknesses. Our results demonstrate significant improvement in prediction accuracy, providing a compelling case for the potential utility of these techniques in healthcare applications, specifically for prediction of diabetes where we achieve compelling model accuracy of 91.5% on the test set on a particular challenging dataset, where we compare thoroughly against many other methodologies. Full article

The environmental impact of food products is significantly affected by their packaging. Therefore, this study aimed to assess the effect of PLA (polylactic acid) film, as an alternative to petroleum-based bags, on the shelf-life of fresh-cut wild rocket and sea fennel grown in a cascade cropping system (CCS). To this end, wild rocket (main crop) was cultivated using either peat or compost as a growing medium. Sea fennel (secondary crop) was subsequently grown in a floating system with leachates from the primary crop as a nutrient solution. The leaves of both crops were harvested and packaged in OPP- (oriented polypropylene) or PLA-based bags and stored for 7 days at 4 °C. The leaves of wild rocket and sea fennel showed lower dehydration and lower respiration when compost was used as a growing medium or leachate. Wild rocket in compost increased in nitrate and vitamin C contents at harvest while leachates had scarce influence on their contents in sea fennel. After storage, regardless of the crop, no relevant detrimental changes were observed on leaves packaged with PLA, being a product microbiologically safer when compared to OPP. The bag type had almost no influence on most relevant phytochemical compounds. In conclusion, the use of a PLA-based film on minimally processed wild rocket and sea fennel leaves is a sustainable alternative to petroleum-based plastic for a short food supply chain. Full article

The mycotoxin citrinin, which can contaminate food, is a major global concern. Citrinin is regarded as an inevitable pollutant in foods and feed since fungi are widely present in the environment. To identify contentious toxicity and lessen its severity by understanding the targets of citrinin in the human body and the impacted biosynthetic pathways, we analyzed the production of citrinin from Aspergillus flavus and Penicillium notatum and used a thorough bioinformatics analysis to characterize the toxicity and predict genes and protein targets for it. The predicted median fatal dosage (LD₅₀) for citrinin was 105 mg/kg weight, and it belonged to toxicity class 3 (toxic if swallowed). Citrinin was found to be well absorbed by human intestinal epithelium and was a Pgp nonsubstrate (permeability glycoprotein), which means that once it is absorbed, it cannot be pumped out, hence leading to bioconcentration or biomagnification in the human body. The main targets of toxicity were casp3, TNF, IL10, IL1B, BAG3, CCNB1, CCNE1, and CDC25A, and the biological pathways implicated were signal transduction involved in DNA damage checkpoints, cellular and chemical responses to oxidative stress, DNA damage response signal transduction by P53, stress-activated protein kinase signaling cascade, netrin–UNC5B signaling, PTEN gene regulation, and immune response. Citrinin was linked to neutrophilia, squamous cell carcinoma, Fanconi anemia, leukemia, hepatoblastoma, and fatty liver diseases. The transcription factors E2F1, HSF1, SIRT1, RELA, NFKB, JUN, and MYC were found to be responsible. When data mining was performed on citrinin targets, the top five functional descriptions were a cell’s response to an organic cyclic compound, the netrin–UNC5B signaling pathway, lipids and atherosclerosis, thyroid cancer, and controlling the transcription of the PTEN gene. Full article

Since the impoundment of the Three Gorges Reservoir area in 2003, the potential risks of geological disasters in the reservoir area have increased significantly, among which the hidden dangers of landslides are particularly prominent. To reduce casualties and damage, efficient and precise landslide susceptibility evaluation methods are important. Multiple ensemble models have been used to evaluate the susceptibility of the upper part of Badong County to landslides. In this study, EasyEnsemble technology was used to solve the imbalance between landslide and nonlandslide sample data. The extracted evaluation factors were input into three bagging, boosting, and stacking ensemble models for training, and landslide susceptibility mapping (LSM) was drawn. According to the importance analysis, the important factors affecting the occurrence of landslides are altitude, terrain surface texture (TST), distance to residences, distance to rivers and land use. The influences of different grid sizes on the susceptibility results were compared, and a larger grid was found to lead to the overfitting of the prediction results. Therefore, a 30 m grid was selected as the evaluation unit. The accuracy, area under the curve (AUC), recall rate, test set precision, and kappa coefficient of a multi-grained cascade forest (gcForest) model with the stacking method were 0.958, 0.991, 0.965, 0.946, and 0.91, respectively, which a significantly better than the values produced by the other models. Full article

Hyperspectral image (HSI) analysis generally suffers from issues such as high dimensionality, imbalanced sample sets for different classes, and the choice of classifiers for artificially balanced datasets. The existing conventional data imbalance removal techniques and forest classifiers lack a more efficient approach to dealing with the aforementioned issues. In this study, we propose a novel hybrid methodology ADASYN-enhanced subsampled multi-grained cascade forest (ADA-Es-gcForest) which comprises four folds: First, we extracted the most discriminative global spectral features by reducing the vast dimensions, i.e., the redundant bands using principal component analysis (PCA). Second, we applied the subsampling-based adaptive synthetic minority oversampling method (ADASYN) to augment and balance the dataset. Third, we used the subsampled multi-grained scanning (Mg-sc) to extract the minute local spatial–spectral features by adaptively creating windows of various sizes. Here, we used two different forests—a random forest (RF) and a complete random forest (CRF)—to generate the input joint-feature vectors of different dimensions. Finally, for classification, we used the enhanced deep cascaded forest (CF) that improvised in the dimension reduction of the feature vectors and increased the connectivity of the information exchange between the forests at the different levels, which elevated the classifier model’s accuracy in predicting the exact class labels. Furthermore, the experiments were accomplished by collecting the three most appropriate, publicly available his landcover datasets—the Indian Pines (IP), Salinas Valley (SV), and Pavia University (PU). The proposed method achieved 91.47%, 98.76%, and 94.19% average accuracy scores for IP, SV, and PU datasets. The validity of the proposed methodology was testified against the contemporary state-of-the-art eminent tree-based ensembled methods, namely, RF, rotation forest (RoF), bagging, AdaBoost, extreme gradient boost, and deep multi-grained cascade forest (DgcForest), by simulating it numerically. Our proposed model achieved correspondingly higher accuracies than those classifiers taken for comparison for all the HS datasets. Full article

Protein quality control allows eukaryotes to maintain proteostasis under the stress of constantly changing conditions. In this review, we discuss the current literature on PQC, highlighting flaws that must exist for malignancy to occur. At the nidus of PQC, the expression of BAG1-6 reflects the cell environment; each isoform directs proteins toward different, parallel branches of the quality control cascade. The sum of these branches creates a net shift toward either homeostasis or apoptosis. With an established role in ALP, Bag3 is necessary for cell survival in stress conditions including those of the cancerous niche (i.e., hypoxia, hypermutation). Evidence suggests that excessive Bag3–HSP70 activity not only sustains, but also propagates cancers. Its role is anti-apoptotic—which allows malignant cells to persist—and intercellular—with the production of infectious ‘oncosomes’ enabling cancer expansion and recurrence. While Bag3 has been identified as a key prognostic indicator in several cancer types, its investigation is limited regarding glioblastoma. The cochaperone HSP70 has been strongly linked with GBM, while ALP inhibitors have been shown to improve GBM susceptibility to chemotherapeutics. Given the highly resilient, frequently recurrent nature of GBM, the targeting of Bag3 is a necessary consideration for the successful and definitive treatment of GBM. Full article

Breast cancer is currently one of the main causes of death and tumoral diseases in women. Even if early diagnosis processes have evolved in the last years thanks to the popularization of mammogram tests, nowadays, it is still a challenge to have available reliable diagnosis systems that are exempt of variability in their interpretation. To this end, in this work, the design and development of an intelligent clinical decision support system to be used in the preventive diagnosis of breast cancer is presented, aiming both to improve the accuracy in the evaluation and to reduce its uncertainty. Through the integration of expert systems (based on Mamdani-type fuzzy-logic inference engines) deployed in cascade, exploratory factorial analysis, data augmentation approaches, and classification algorithms such as k-neighbors and bagged trees, the system is able to learn and to interpret the patient’s medical-healthcare data, generating an alert level associated to the danger she has of suffering from cancer. For the system’s initial performance tests, a software implementation of it has been built that was used in the diagnosis of a series of patients contained into a 130-cases database provided by the School of Medicine and Public Health of the University of Wisconsin-Madison, which has been also used to create the knowledge base. The obtained results, characterized as areas under the ROC curves of 0.95–0.97 and high success rates, highlight the huge diagnosis and preventive potential of the developed system, and they allow forecasting, even when a detailed and contrasted validation is still pending, its relevance and applicability within the clinical field. Full article

Background: Autism spectrum disorder (ASD) is a neurodevelopmental disorder with deficits in social communication ability and repetitive behavior. The pathophysiological events involved in the brain of this complex disease are still unclear. Methods: In this study, we aimed to profile the gene expression signatures of brain cortex of ASD patients, by using two publicly available RNA-seq studies, in order to discover new ASD-related genes. Results: We detected 1567 differentially expressed genes (DEGs) by meta-analysis, where 1194 were upregulated and 373 were downregulated genes. Several ASD-related genes previously reported were also identified. Our meta-analysis identified 235 new DEGs that were not detected using the individual RNA-seq studies used. Some of those genes, including seven DEGs (PAK1, DNAH17, DOCK8, DAPP1, PCDHAC2, and ERBIN, SLC7A7), have been confirmed in previous reports to be associated with ASD. Gene Ontology (GO) and pathways analysis showed several molecular pathways enriched by the DEGs, namely, osteoclast differentiation, TNF signaling pathway, complement and coagulation cascade. Topological analysis of protein–protein interaction of the ASD brain cortex revealed proteomics hub gene signatures: MYC, TP53, HDAC1, CDK2, BAG3, CDKN1A, GABARAPL1, EZH2, VIM, and TRAF1. We also identified the transcriptional factors (TFs) regulating DEGs, namely, FOXC1, GATA2, YY1, FOXL1, USF2, NFIC, NFKB1, E2F1, TFAP2A, HINFP. Conclusion: Novel core genes and molecular signatures involved with ASD were identified by our meta-analysis. Full article

Groundwater potential maps are one of the most important tools for the management of groundwater storage resources. In this study, we proposed four ensemble soft computing models based on logistic regression (LR) combined with the dagging (DLR), bagging (BLR), random subspace (RSSLR), and cascade generalization (CGLR) ensemble techniques for groundwater potential mapping in Dak Lak Province, Vietnam. A suite of well yield data and twelve geo-environmental factors (aspect, elevation, slope, curvature, Sediment Transport Index, Topographic Wetness Index, flow direction, rainfall, river density, soil, land use, and geology) were used for generating the training and validation datasets required for the building and validation of the models. Based on the area under the receiver operating characteristic curve (AUC) and several other validation methods (negative predictive value, positive predictive value, root mean square error, accuracy, sensitivity, specificity, and Kappa), it was revealed that all four ensemble learning techniques were successful in enhancing the validation performance of the base LR model. The ensemble DLR model (AUC = 0.77) was the most successful model in identifying the groundwater potential zones in the study area, followed by the RSSLR (AUC = 0.744), BLR (AUC = 0.735), CGLR (AUC = 0.715), and single LR model (AUC = 0.71), respectively. The models developed in this study and the resulting potential maps can assist decision-makers in the development of effective adaptive groundwater management plans. Full article