Search Results (1,011)

Given that student entrepreneurship contributes to the intensification of economic activities and the improvement of the social well-being of the parties involved, evaluating and fostering students’ entrepreneurial intentions can be a step in moving from intention to action in the entrepreneurial process. From this perspective, the present study assesses students’ entrepreneurial intentions and measures the impact of the most important determinants based on online questionnaires addressed to students from two different fields of study: economics and engineering. Using the collected data (N = 392 students) and analysis methods based on correlation and stratified multiple regression as well as non-parametric tests (Mann–Whitney U), the study reveals that students’ entrepreneurial intentions are influenced by mindset and resilience. The study indicates that the influences can vary significantly when the analyses include control variables, such as gender, field of study, year of study, professional experience, age, and country of origin. It is also important to note that the statistical significance of the results regarding the impact of resilience varies depending on the specifics of the control variables. This study considered both analyses of resilience (as a synthetic indicator) and its subcomponents. The results of this study have both theoretical and practical utility. Full article

The increasing demand for high-resolution subsurface imaging has driven significant advances in geophysical inversion methodologies. Despite the availability of various software packages for electrical resistivity tomography (ERT), time-domain induced polarization (TDIP), and seismic refraction tomography (SRT), significant challenges remain in selecting optimal regularization parameters and in the effective incorporation of prior information into the inversion process. In this study, we propose new strategies to address these critical issues by developing versatile and flexible tools for electrical and seismic tomographic data inversion. Specifically, we introduce two automated procedures for regularization parameter selection: a full loop method (fixed-λ optimization) where the regularization parameter is kept constant during the inversion process, and a single-inversion approach (automaticLam) where it varies throughout the iterations. Additionally, we present a novel constrained inversion strategy that effectively balances prior information, minimizes data misfit, and promotes model smoothness. This approach is thoroughly compared with the state-of-the-art methods, demonstrating its superiority in maintaining model reliability and reducing dependence on subjective operator choices. Applications to synthetic, laboratory, and real-world case studies validate the efficacy of our strategies, showcasing their potential to enhance the robustness of geophysical models and standardize the inversion process, ensuring its independence from operator decisions. Full article

Ground penetrating radar (GPR) is widely used for subsurface object detection, but manual interpretation of hyperbolic features in B-scan images remains inefficient and error-prone. In addition, traditional forward modeling methods suffer from low computational efficiency and strong dependence on field measurements. To address these challenges, we propose an unsupervised data augmentation framework that utilizes CycleGAN-based model to generates diverse synthetic B-scan images by simulating varying geological parameters and scanning configurations. This approach achieves GPR data forward modeling and enhances the scenario coverage of training data. We then apply the EfficientDet architecture, which incorporates a bidirectional feature pyramid network (BiFPN) for multi-scale feature fusion, to enhance the detection capability of hyperbolic signatures in B-scan images under challenging conditions such as partial occlusions and background noise. The proposed method achieves a mean average precision (mAP) of 0.579 on synthetic datasets, outperforming YOLOv3 and RetinaNet by 16.0% and 23.5%, respectively, while maintaining robust multi-object detection in complex field conditions. Full article

In recent years, deep learning (DL) has been demonstrated remarkable capabilities in hyperspectral unmixing (HU) due to its powerful feature representation ability. Convolutional neural networks (CNNs) are effective in capturing local spatial information, but limited in modeling long-range dependencies. In contrast, transformer architectures extract global contextual features via multi-head self-attention (MHSA) mechanisms. However, most existing transformer-based HU methods focus only on spatial or spectral modeling at a single scale, lacking a unified mechanism to jointly explore spatial and channel-wise dependencies. This limitation is particularly critical for multiscale contextual representation in complex scenes. To address these issues, this article proposes a novel Spatial-Channel Multiscale Transformer Network (SCMT-Net) for HU. Specifically, a compact feature projection (CFP) module is first used to extract shallow discriminative features. Then, a spatial multiscale transformer (SMT) and a channel multiscale transformer (CMT) are sequentially applied to model contextual relations across spatial dimensions and long-range dependencies among spectral channels. In addition, a multiscale multi-head self-attention (MMSA) module is designed to extract rich multiscale global contextual and channel information, enabling a balance between accuracy and efficiency. An efficient feed-forward network (E-FFN) is further introduced to enhance inter-channel information flow and fusion. Experiments conducted on three real hyperspectral datasets (Samson, Jasper and Apex) and one synthetic dataset showed that SCMT-Net consistently outperformed existing approaches in both abundance estimation and endmember extraction, demonstrating superior accuracy and robustness. Full article

The increasing complexity and scale of Internet of Vehicles (IoV) networks pose significant security challenges, necessitating the development of advanced intrusion detection systems (IDS). Traditional IDS approaches, such as rule-based and signature-based methods, are often inadequate in detecting novel and sophisticated attacks due to their limited adaptability and dependency on predefined patterns. To overcome these limitations, machine learning (ML) and deep learning (DL)-based IDS have been introduced, offering better generalization and the ability to learn from data. However, these models can still struggle with zero-day attacks, require large volumes of labeled data, and may be vulnerable to adversarial examples. In response to these challenges, Generative AI-based IDS—leveraging models such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformers—have emerged as promising solutions that offer enhanced adaptability, synthetic data generation for training, and improved detection capabilities for evolving threats. This survey provides an overview of IoV architecture, vulnerabilities, and classical IDS techniques while focusing on the growing role of Generative AI in strengthening IoV security. It discusses the current landscape, highlights the key challenges, and outlines future research directions aimed at building more resilient and intelligent IDS for the IoV ecosystem. Full article

Modern energy storage systems for both power and transportation are highly related to lithium-ion batteries (LIBs). However, their safety depends on a potentially hazardous failure mode known as thermal runaway (TR). Predicting and classifying TR causes can widely enhance the safety of power and transportation systems. This paper presents an advanced machine learning method for forecasting and classifying the causes of TR. A generative model for synthetic data generation was used to handle class imbalance in the dataset. Hyperparameter optimization was conducted using Optuna for four classifiers: Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), tabular network (TabNet), and Extreme Gradient Boosting (XGBoost). A three-fold cross-validation approach was used to guarantee a robust evaluation. An open-source database of LIB failure events is used for model training and testing. The XGBoost model outperforms the other models across all TR categories by achieving 100% accuracy and a high recall (1.00). Model results were interpreted using SHapley Additive exPlanations analysis to investigate the most significant factors in TR predictors. The findings show that important TR indicators include energy adjusted for heat and weight loss, heater power, average cell temperature upon activation, and heater duration. These findings guide the design of safer battery systems and preventive monitoring systems for real applications. They can help experts develop more efficient battery management systems, thereby improving the performance and longevity of battery-operated devices. By enhancing the predictive knowledge of temperature-driven failure mechanisms in LIBs, the study directly advances thermal analysis and energy storage safety domains. Full article

This paper presents a modern spectroscopic characterization of the synthetic oil from oil sands of Beke, Munaily-Mola, and Dongeleksor. The pyrolysis process was carried out at temperatures up to 580 °C with a controlled heating rate, and the products obtained were analyzed using Fourier transform infrared spectroscopy (FTIR), gas chromatography–mass spectrometry (GC–MSD), and nuclear magnetic resonance (NMR) spectroscopy. The FTIR spectra showed a predominance of aliphatic hydrocarbons in the sample from Munaily-Mola synthetic oil, while the content of aromatic compounds was higher in the sample from Beke. GC–MSD analysis revealed significant differences in the distribution of hydrocarbons between the samples, with the Munaily-Mola sample containing a higher proportion of heavy hydrocarbons. NMR spectroscopy provided additional information about the structural composition of the extracted oil. The results indicate the potential of pyrolysis as an effective method for processing oil sands, while the composition of the product varies depending on the geological origin of the raw materials. These findings provide valuable information for optimizing oil sands processing technologies and improving the efficiency of synthetic oil production. Full article

Flash floods are natural disasters that have significant impacts on human life and economic damage. The detection of flash floods using remote-sensing techniques provides essential data for subsequent flood-risk assessment through the preparation of flood inventory samples. In this research, a new deep-learning approach for bi-temporal flash-flood detection in Synthetic Aperture Radar (SAR) is proposed. It combines a U-Net convolutional network with a Transformer model using a compact Convolutional Tokenizer (CCT) to improve the efficiency of long-range dependency learning. The hybrid model, namely CCT-U-ViT, naturally combines the spatial feature extraction of U-Net and the global context capability of Transformer. The model significantly reduces the number of basic blocks as it uses the CCT tokenizer instead of conventional Vision Transformer tokenization, which makes it the right fit for small flood detection datasets. This model improves flood boundary delineation by involving local spatial patterns and global contextual relations. However, the method is based on Sentinel-1 SAR images and focuses on Erbil, Iraq, which experienced an extreme flash flood in December 2021. The experimental comparison results show that the proposed CCT-U-ViT outperforms multiple baseline models, such as conventional CNNs, U-Net, and Vision Transformer, obtaining an impressive overall accuracy of 91.24%. Furthermore, the model obtains better precision and recall with an F1-score of 91.21% and mIoU of 83.83%. Qualitative results demonstrate that CCT-U-ViT can effectively preserve the flood boundaries with higher precision and less salt-and-pepper noise compared with the state-of-the-art approaches. This study underscores the significance of hybrid deep-learning models in enhancing the precision of flood detection with SAR data, providing valuable insights for the advancement of real-time flood monitoring and risk management systems. Full article

Accurate gas prediction is crucial for identifying gas-bearing zones in tight sandstone reservoirs. Traditional seismic techniques, primarily grounded in elastic theory, often overlook inelastic dispersion effects inherent to such formations. To overcome this limitation, we introduce a gas prediction approach utilizing a dispersion attribute derived from frequency-dependent inversion based on an AVO equation parameterized by a gas indicator and related properties. Rock physics modeling, based on multi-scale fracture theory, reveals the frequency-dependent gas indicator is highly responsive to variations in porosity and gas saturation. Seismic AVO simulations exhibit distinguishable signatures corresponding to these variations, supporting the potential to estimate reservoir properties from pre-stack seismic data. Synthetic data tests confirm that the values of the proposed dispersion attribute increase with increasing porosity and gas saturation. Additionally, the calculated dispersion attribute exhibits a strong positive correlation with gas content, validating its effectiveness for gas evaluation. Field application results further demonstrate that the proposed dispersion attribute shows prominent anomalies in sandstone reservoirs with high gas content. Compared to the conventional P-wave dispersion attribute, the proposed dispersion attribute exhibits superior reliability in detecting gas-rich zones. These results demonstrate the utility of the method in predicting gas-bearing regions in tight sandstone reservoirs. Full article

Background: Data accessibility remains a significant barrier in healthcare AI due to privacy constraints and logistical challenges. Synthetic data, which mimics real patient information while remaining both realistic and non-identifiable, offers a promising solution. Large Language Models (LLMs) create new opportunities to generate high-fidelity clinical conversations between patients and physicians. However, the value of this synthetic data depends on careful evaluation of its realism, accuracy, and practical relevance. Objective: To assess the performance of four leading LLMs: ChatGPT 4.5, ChatGPT 4o, Claude 3.7 Sonnet, and Gemini Pro 2.5 in generating synthetic transcripts of patient–physician interactions in plastic surgery scenarios. Methods: Each model generated transcripts for ten plastic surgery scenarios. Transcripts were independently evaluated by three clinically trained raters using a seven-criterion rubric: Medical Accuracy, Realism, Persona Consistency, Fidelity, Empathy, Relevancy, and Usability. Raters were blinded to the model identity to reduce bias. Each was rated on a 5-point Likert scale, yielding 840 total evaluations. Descriptive statistics were computed, and a two-way repeated measures ANOVA was used to test for differences across models and metrics. In addition, transcripts were analyzed using automated linguistic and content-based metrics. Results: All models achieved strong performance, with mean ratings exceeding 4.5 across all criteria. Gemini 2.5 Pro received mean scores (5.00 ± 0.00) in Medical Accuracy, Realism, Persona Consistency, Relevancy, and Usability. Claude 3.7 Sonnet matched the scores in Persona Consistency and Relevancy and led in Empathy (4.96 ± 0.18). ChatGPT 4.5 also achieved perfect scores in Relevancy, with high scores in Empathy (4.93 ± 0.25) and Usability (4.96 ± 0.18). ChatGPT 4o demonstrated consistently strong but slightly lower performance across most dimensions. ANOVA revealed no statistically significant differences across models (F(3, 6) = 0.85, p = 0.52). Automated analysis showed substantial variation in transcript length, style, and content richness: Gemini 2.5 Pro generated the longest and most emotionally expressive dialogues, while ChatGPT 4o produced the shortest and most concise outputs. Conclusions: Leading LLMs can generate medically accurate, emotionally appropriate synthetic dialogues suitable for educational and research use. Despite high performance, demographic homogeneity in generated patients highlights the need for improved diversity and bias mitigation in model outputs. These findings support the cautious, context-aware integration of LLM-generated dialogues into medical training, simulation, and research. Full article

Background: Cyclin-dependent kinases 4 and 6 (CDK4/6) are pivotal regulators of the cell cycle, whose dysregulation is closely linked to cancer progression. While synthetic CDK4/6 inhibitors such as Palbociclib and Ribociclib are clinically effective, their use is limited by significant adverse effects. Methods: In this study, the aqueous root extract of Thottea siliquosa, a traditionally used medicinal plant, was evaluated for its potential as a natural CDK4/6 inhibitor. Phytochemical profiling using GC-MS identified bioactive compounds, which were subsequently subjected to molecular docking, ADME prediction, and in vitro cell-based assays using HCT116 and L929 cells. Results: The docking results revealed that Isocorydine (−7.4 kcal/mol for CDK4 and −7.2 kcal/mol for CDK6) and Thunbergol (−6.5 kcal/mol for CDK4 and −7.0 kcal/mol for CDK6) exhibited promising binding affinities comparable to standard CDK inhibitors, Palbociclib (−7.2, −8.3 kcal/mol) and Ribociclib (−7.1, −8.1 kcal/mol). Among the other tested natural compounds, Squalene (−7.1 kcal/mol for CDK4) and 2-palmitoylglycerol (−5.2 kcal/mol for CDK4, −4.9 kcal/mol for CDK6) demonstrated moderate binding affinities. ADME analysis confirmed favorable drug-like properties with minimal toxicity alerts. The extract displayed dose-dependent cytotoxicity with an IC₅₀ of 140 μg/mL and reduced cell migration in HCT116 cells, indicating potential anti-proliferative effects. These findings suggest that T. siliquosa root extract, through synergistic phytochemical interactions, holds promise as a multi-targeted, plant-based therapeutic candidate for CDK4/6-associated cancers, warranting further in vitro and in vivo validation. Full article

New psychoactive substances (NPSs) are emerging narcotics or psychotropics that pose a public health risk. The most commonly reported NPSs are synthetic cannabinoids and synthetic cathinones. Synthetic cannabinoids mimic the effects of Δ9-tetrahydrocannabinol (Δ9-THC), often with greater potency, while synthetic cathinones act as stimulants, frequently serving as cheaper alternatives to amphetamines, 3,4-methylenedioxymethamphetamine (MDMA) and cocaine. While some synthetic cannabinoids exhibit chirality depending on their synthesis precursors, synthetic cathinones are intrinsically chiral. Biotargets can recognize and differentiate between enantiomers, leading to distinct biological responses (enantioselectivity). Understanding these differences is crucial; therefore, the development of enantioresolution methods to assess the biological and toxicological effects of enantiomer is necessary. This work systematically compiles enantioselectivity studies and enantioresolution methods of synthetic cannabinoids and synthetic cathinones, following PRISMA guidelines. The main aim of this review is to explore the impact of chirality on these NPSs, improving our understanding of their toxicological behavior and evaluating advances in analytical techniques for their enantioseparation. Key examples from both groups are presented. This review highlights the importance of continuing research in this field, as demonstrated by the differing properties of synthetic cannabinoid and synthetic cathinone enantiomers, which are closely linked to variations in biological and toxicological outcomes. Full article

Obesity is considered a global pandemic. In Mexico, 7/10 adults, 4/10 adolescents, and 1/3 children are overweight or obese, and it is estimated that 90% of cases of type 2 diabetes (T2D) are attributable to these pathologies. Visceral adipose tissue (VAT) presents increased lipolysis, lower insulin sensitivity, and greater metabolic alterations. Glucagon-like peptide-1 (GLP-1) is a polypeptide incretin hormone that stimulates insulin secretion dependent on the amount of oral glucose consumed, reduces plasma glucagon concentrations, slows gastric emptying, suppresses appetite, improves insulin synthesis and secretion, and increases the sensitivity of β cells to glucose. Liraglutide is a synthetic GLP-1 analog that reduces VAT and improves the expression of Glucose transporter receptor type 4 (GLUT 4R), Mitogen-activated protein (MAP kinases), decreases Fibroblast growth factor type β (TGF-β), reactivates the peroxisome proliferator-activated receptor type ɣ (PPAR-ɣ) pathway, and decreases chronic inflammation. Currently, there are many studies that explain the decrease in VAT with these medications, but there are no studies that compare the decrease in patients with obesity and prediabetes vs. obesity and type 2 diabetes to know which population obtains a greater benefit from treatment with this pharmacological group; this is the reason for this study. The primary objective was to compare the difference in the determination of visceral adipose tissue in people with obesity and type 2 diabetes vs. obesity and prediabetes treated with liraglutide. Methods: A quasi-experimental, analytical, prolective, non-randomized, non-blinded study was conducted over a period of 6 months in a tertiary care center. A total of 36 participants were divided into two arms; group 1 (G1: Obesity and prediabetes) and group 2 (G2: Obesity and type 2 diabetes) for 6 months. Inclusion criteria: men and women ≥18 years with type 2 diabetes, prediabetes, and obesity. Exclusion criteria: Glomerular filtration rate (GFR) < 60 mL/min/1.73 m² elevated transaminases (>5 times the upper limit of normal), and use of non-weight-modifying antidiabetic agents. Conclusions: No statistically significant difference was found in the decrease in visceral adipose tissue when comparing G1 (OB and PD) with G2 (OB and T2D). When comparing intragroup in G2 (OB and T2D), greater weight loss was found [(−3.78 kg; p = 0.012) vs. (−3.78 kg; p = 0.012)], as well differences in waist circumference [(−3.9 cm; p = 0.049) vs. (−3.09 cm; p = 0.017)], and glucose levels [(−1.75 mmol/L; p = 0.002) vs. (−0.56 mmol/L; p = 0.002)], A1c% [(−1.15%; p = 0.001) vs. (−0.5%; p = 0.000)]. Full article

Objectives: We wished to evaluate the safety profile of the Janus kinase (JAK) inhibitors used in the Spanish population; to study the onset of major adverse cardiovascular events (MACEs) and thrombotic events (arterial and venous); and to analyze the factors associated with the onset of these events. Methods: We conducted a retrospective observational study of a cohort of patients with rheumatoid arthritis (RA), spondyloarthritis (SpA), and psoriatic arthritis (PsA) included in the biological therapy registry of the Rheumatology Department of Virgen Macarena University Hospital (HUVM), Seville, Spain, who started targeted treatment between 2019 and late 2024. We collected data on disease activity, traditional cardiovascular risk factors, the Charlson comorbidity index, previous synthetic or biologic drug therapy, the use of corticosteroids (and their dose), severity data (structural damage, extra-articular manifestations), and adverse events at the end of follow-up (e.g., MACEs, infections, neoplasms, and herpes zoster). We performed a descriptive bivariate analysis and a multivariate logistic regression analysis (dependent variable: MACEs) to identify factors that were independently associated with MACEs. Results: The study population comprised 137 patients (110 with RA, 18 with PsA, and 9 with SpA) who were followed up for a mean of 3.9 (2.6) years. Most patients had received JAK inhibitors as their second-line or subsequent treatment. At the end of the follow-up, 82 patients (66.7%) continued their treatment. Nine patients (6.6%) experienced a MACE, and five experienced a heart attack. All of these patients had RA. We found no differences between JAK inhibitors in terms of the incidence of the adverse events studied. Patients who experienced MACEs were more often male and smokers (current or former) and more often had hypertension and diabetes. No significant differences were found in the association with disease activity or previous or concomitant treatment. The factors that were independently associated with MACEs were a previous cardiovascular event (OR, 10.74; 95%CI, 1.05–113.7; p = 0.036), male sex (OR, 9.7; 95%CI, 1.6–76.5; p = 0.016), diabetes mellitus (OR, 10.3; 95%CI, 1.75–83; p = 0.013), and the duration of treatment with JAK inhibitors (OR, 1.47; 95%CI, 1.13–2.01; p = 0.005). Conclusions: We found no differences in the onset of adverse events, specifically MACEs, between the different JAK inhibitors analyzed. These events are more common in patients who already have cardiovascular risk factors, such as diabetes mellitus, or who have already experienced a cardiovascular event. JAK inhibitors broadly suppress cytokines in patients whose disease is refractory to other treatments. However, we must continue to evaluate their long-term safety in real-world studies. Full article

Background/Objectives: ALK+ Anaplastic Large Cell Lymphoma (ALCL) is an aggressive T-cell lymphoma that is characterized by expression of the Anaplastic Lymphoma Kinase (ALK), which is induced by the t(2;5) chromosomal rearrangement, leading to the expression of the NPM-ALK fusion oncogene. Most previous preclinical models of ALK+ ALCL were based on overexpression of the NPM-ALK cDNA from heterologous promoters. Due to the enforced expression, this approach is prone to artifacts arising from synthetic overexpression, promoter competition and insertional variation. Methods: To improve the existing ALCL models and more closely recapitulate the oncogenic events in ALK+ ALCL, we employed CRISPR/Cas-based chromosomal engineering to selectively introduce translocations between the Npm1 and Alk gene loci in murine cells. Results: By inducing precise DNA cleavage at the syntenic loci on chromosome 11 and 17 in a murine IL-3-dependent Ba/F3 reporter cell line, we generated de novo Npm-Alk translocations in vivo, leading to IL-3-independent cell growth. To verify efficient recombination, we analyzed the expression of the NPM-ALK fusion protein in the recombined cells and could also show the t(11;17) in the IL-3 independent Ba/F3 cells. Subsequent functional testing of these cells using an Alk-inhibitor showed exquisite responsiveness towards Crizotinib, demonstrating strong dependence on the newly generated ALK fusion oncoprotein. Furthermore, a comparison of the gene expression pattern between Ba/F3 cells overexpressing the Npm-Alk cDNA with Ba/F3 cells transformed by CRISPR-mediated Npm-Alk translocation indicated that, while broadly overlapping, a set of pathways including the unfolded protein response pathway was increased in the Npm-Alk overexpression model, suggesting increased reactive changes induced by exogenous overexpression of Npm-Alk. Furthermore, we observed clustered expression changes in genes located in chromosomal regions close to the breakpoint in the new CRISPR-based model, indicating positional effects on gene expression mediated by the translocation event, which are not part of the older models. Conclusions: Thus, CRISPR-mediated recombination provides a novel and more faithful approach to model oncogenic translocations, which may lead to an improved understanding of the molecular pathogenesis of ALCL and enable more accurate therapeutic models of malignancies driven by oncogenic fusion proteins. Full article