mdpi-ai/citelens huggingface dataset:
Digital learning in the 21st century: trends, challenges, and innovations in technology integration
Field: EDUCATION

The integration of digital technologies into education represents a significant evolution in the pedagogical landscape, with the potential to enhance accessibility, engagement, and personalization in learning. This review synthesizes current trends, challenges, and innovations within digital learning, emphasizing the impact of artificial intelligence (AI), virtual reality (VR), and online platforms on student achievement. It highlights the importance of addressing technical, pedagogical, and socioeconomic challenges to ensure equitable access to technology. Successful initiatives like the Open University illustrate digital learning's potential to improve educational outcomes. The review also anticipates future directions, including the expanding role of AI, VR, mobile learning, and blockchain in education. It concludes with strategic recommendations for educators and policymakers to adopt best practices, prioritize infrastructure development, and focus on continuous professional development to leverage the benefits of digital learning. As education enters an era of digital transformation, a collaborative approach among stakeholders will be essential in creating an inclusive and effective learning environment for the future.
Estimating the Morbidity of Robot-Assisted Radical Cystectomy Using the Comprehensive Complication Index: Data from the Asian Robot-Assisted Radical Cystectomy Consortium
Field: ONCOLOGY

Background/Objectives: The Clavien–Dindo classification (CDC) grades the most severe post-operative complication and may not comprehensively reflect cumulative surgical morbidity. Our objective was to investigate the potential incremental role of the comprehensive complication index (CCI) over the CDC in defining the quality of robot-assisted radical cystectomy (RARC). Methods: Data were extracted from the Asian RARC Consortium database. Complications were classified using the CCI (CCI = 0, CCI < 75th and ≥75th percentile) and CDC. Adverse peri-operative outcomes such as length of stay >14 days (LOS > 14 days), estimated blood loss >350 mL (EBL > 350 mL), time to solid food intake >4 days (TFI > 4 days) and 30-day readmission rates were analyzed. The area under the curve (AUC) of the receiver operating characteristic (ROC) curves for CCI and CDC were compared for the various adverse outcomes. Results: The peri-operative complication rate was 44.4%, comprising 11.6% with severe complications (CDC ≥ III). The mean CCI was 10.2 (±13.5) while median CCI was 0 (IQR 0–21). There were 7.6% of patients with >one perioperative complication. On adjusted analysis, CCI ≥ 75th percentile was significantly associated with greater LOS (>14 days) (OR 2.21, 95% CI 1.47–3.31, p < 0.001) compared to when CCI = 0. There were no significant differences in the AUC between CDC and CCI in predicting LOS > 14 days, TFI > 4 days, 30-day readmission or EBL > 350 mL. Conclusions: In our multi-institutional cohort, the CCI did not provide additional discrimination over CDC, and this is likely related to the limited number of complications that occurred per individual in the Asian RARC cohort. Hence, the perceived advantages of CCI over CDC are contextual.
Identification of MAD2L1 as a novel biomarker for hepatoblastoma through bioinformatics and machine learning approaches
Field: ONCOLOGY

Objective: This study aims to identify potential biomarkers for Hepatoblastoma (HB) using bioinformatics and machine learning, and to explore their underlying mechanisms of action.Methods: We analyzed the datasets GSE131329 and GSE133039 to perform differential gene expression analysis. Single-sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network analysis (WGCNA) were utilized to identify gene modules linked to gene set activity. Protein-protein interaction (PPI) networks were constructed to identify hub genes, while random forest and support vector machine models were employed to screen for key diagnostic genes. Survival and immune infiltration analyses were conducted to assess the prognostic significance of these genes. Additionally, the expression levels, biological functions, and mechanisms of action of the selected genes were validated in HB cells through relevant experimental assays.Results: We identified 1,377 and 1,216 differentially expressed genes in datasets GSE131329 and GSE133039, respectively. ssGSEA and WGCNA analyses identified 234 genes significantly linked to gene set activity. PPI analysis identified 20 core Hub genes. Machine learning highlighted three key diagnostic genes: CDK1, CCNA2, and MAD2L1. Studies have demonstrated that MAD2L1 is significantly overexpressed in HB and is associated with prognosis. WGCNA revealed that MAD2L1 is enriched in gene sets related to E2F_ TARGETS and G2M_CHECKPOINT. Experimental assays demonstrated that MAD2L1 knockdown significantly inhibits the proliferation, migration, and invasion of HB cell lines, and that MAD2L1 promotes cell cycle progression through the regulation of E2F.Conclusion: Our study identifies MAD2L1 as a novel potential biomarker for HB, providing new strategies for early diagnosis and targeted therapy in HB.
Inducing Targeted, Caspase-Independent Apoptosis with New Chimeric Proteins for Treatment of Solid Cancers
Field: ONCOLOGY

Background: Most newly developed anticancer treatments trigger tumor cell death through apoptosis, for which involvement of caspases activity is essential. However, numerous mutations in apoptotic pathways that lead to cancer and favor resistance to apoptosis are known; most are related to caspase-dependent apoptosis pathways and thus have low efficacy. To overcome these limitations, we constructed a novel chimeric protein, GnRH-AIF, using a gonadotropin-releasing hormone (GnRH) analog as a targeting moiety and the apoptosis-inducing factor (AIF) in its cleaved form as a killing moiety, fused at the cDNA level. AIF has a crucial role in the caspase-independent apoptotic pathway. A wide variety of solid tumors overexpress GnRH-receptors (GnRH-R) that are targeted by the new GnRH-AIF chimeric protein. Methods and Results: In this study, we constructed, expressed, and highly purified GnRH-AIF chimeric proteins. We demonstrated the ability of the chimera to enter and specifically and very efficiently kill solid cancer cell lines overexpressing GnRH-R. Most importantly, upon its entry, GnRH-AIFs translocate to the nucleus where it causes DNA fragmentation leading to a direct caspase-independent apoptotic death. As AIFs lack nuclease activity, our findings also emphasize that cell death induced by GnRH-AIF is dependent on the presence of the ENDOG and PPIA proteins, known to participate in the formation of a DNA–degradosome complex. Finally, we demonstrated the high anti-tumor efficacy of the GnRH-AIF ex vivo, in a human, colon cancer organoid model. Conclusions: Our study shows the potential of using a GnRH-AIF chimeric protein as a novel approach to treat solid cancers that overexpress GnRH-R, via a caspase-independent apoptotic pathway.
Significance of 5-ALA-Guided Fluorescence in Resection of Invasive Intracranial Meningiomas: Findings from a Prospective Clinical Study
Field: ONCOLOGY

Background: In cases of intracranial meningiomas invading into surrounding tissues, determining the resection boundary can be challenging and often makes complete resection difficult. In such situations, the introduction of novel intraoperative techniques to identify infiltrative tumor components is desirable to improve the extent of tumor resection. Methods: A prospective clinical study was conducted on patients with intracranial meningiomas suspected of infiltration into the surrounding tissues. After completing the tumor resection under conventional white-light microscopy, intraoperative fluorescence diagnosis using 5-aminolevulinic acid (5-ALA) was performed to determine whether additional resection of the unintended residual tumor was feasible. Results: Intraoperative fluorescence diagnosis enabled additional resection of the residual tumor in 38.5% of the 13 enrolled cases and 45.5% of the 11 cases in which the tumor exhibited fluorescence positivity. Among the additional resected specimens, tumor infiltration was observed in all fluorescence-positive lesions of the bone and dura mater, whereas tumor cells were detected in only 33.3% of the fluorescence-positive areas in the adjacent brain parenchyma. Conclusions: Intraoperative fluorescence diagnosis using 5-ALA enhanced the extent of the resection of invasive meningiomas. Future large-scale studies are warranted to determine whether 5-ALA fluorescence diagnosis contributes to reducing tumor recurrence and improving overall survival in patients with invasive intracranial meningiomas.
The impact of study abroad social interactions on post-return relationships with international students: Japanese students’ perceptions of recategorization
Field: EDUCATION

Previous research has focused on how students adapt to the host country during study abroad. However, less is known about how these experiences influence students’ social engagement upon returning home. This study explores how Japanese students’ social interactions abroad influence their relationships with international students in Japan after their return. Using a qualitative approach based on grounded theory, semi-structured interviews were conducted with 24 Japanese students who had studied abroad for one academic year. The findings suggest that social interactions abroad facilitate recategorization, a process in which individuals redefine group boundaries and develop a broader shared identity. This process was influenced by four key factors: language and social skills, motivation, opportunities, and perceived fit. Through this process, Japanese students expanded their group boundaries and formed a shared identity with international students in Japan as individuals with study abroad experience. As a result, they developed more positive attitudes toward international students, heightened empathy, and a stronger motivation to engage with and help international students. These findings indicate that recategorization can occur through the formation of a new social identity based on shared experiences rather than direct intergroup contact, highlighting the long-term impact of study abroad on students’ intercultural engagement. This study underscores Japanese students’ tendency to identify with international students in Japan rather than with host nationals upon their return.
Evaluating the Effects of Sensorimotor Training on the Physical Capacities of Older People
Field: PSYCHOLOGY

Background: Physical activity (PA) plays a crucial role in improving the quality of life (QoL) in older people, particularly by enhancing their balance and movement coordination. Objective: This study aimed to assess the effects of sensorimotor training intervention in older adults. Methods: A total of 90 participants, divided into a Control Group (n = 44) and Experimental Group (n = 46) were involved in a 24-week sensorimotor training program. The physical capacities of the participants were assessed both before and after the intervention program. Strength and flexibility were measured using the “Rikli and Jones” protocol (1999), while agility and speed were assessed through “Timed-up-and-go” tests. Taking into account the participants’ gender, a descriptive analysis of the sample was conducted to describe the data using the mean and standard deviation. Student’s T test was performed to compare the differences between the groups according to the first and second data collection moments (before and after the intervention). Jamovi software (v. 2.5.2.0) was used to develop the statistical analysis, using a p-value of less than 0.05 to assess the statistical significance. Results: The Experimental Group showed significant improvements across all the analyzed variables following the intervention (p < 0.005), indicating substantial gains in physical capacities. In contrast, the Control Group in the “sitting and reaching” test did not show a significant difference between the groups highlighting the lack of improvement without intervention. According to the effect size of the sample, it was observed that the parameters “reach behind your back (right)” and “reach behind your back (left)” showed the highest effect size comparing the Control Group and Experimental Group (ES: 0.60, 0.71). Conclusions: The findings highlight the practical clinical impact of implementing tailored physical activity programs for older adults. Such interventions are critical for enhancing QoL, reducing the risk of falls, injuries, and chronic illnesses, and promoting overall health, independence, and well-being. Integrating sensorimotor training into the routine care for older people can support healthy aging and functional independence.
An optimized system for predicting energy usage in smart grids using temporal fusion transformer and Aquila optimizer
Field: AI

This research presents an optimized system for predicting energy usage in smart grids by integrating the Temporal Fusion Transformer (TFT) with the Aquila Optimizer (AO). The study addresses the growing need for accurate energy consumption forecasts in smart grids, driven by the increasing adoption of renewable energy and real-time data collection through smart meters. The TFT model leverages self-attention mechanisms to handle complex time-series data, improving forecasting accuracy across various time horizons. To enhance predictive performance, the Aquila Optimizer, a nature-inspired algorithm, is employed to fine-tune critical hyperparameters, ensuring optimal model convergence and performance. The proposed AO-TFT model is evaluated against traditional models like LSTM and CNN-BiLSTM, demonstrating superior accuracy, lower RMSE, and faster computation times. The research also analyses the impact of various factors, including building types, weather conditions, and load variations on energy prediction. The proposed AO-TFT model achieved a significantly lower RMSE of 0.48 and MAE of 0.31, demonstrating superior accuracy compared to traditional models. Future work is suggested to explore hybrid optimization techniques and real-time adaptive models for dynamic grid management.
Multimodal Data Fusion for Tabular and Textual Data: Zero-Shot, Few-Shot, and Fine-Tuning of Generative Pre-Trained Transformer Models
Field: AI

In traffic safety analysis, previous research has often focused on tabular data or textual crash narratives in isolation, neglecting the potential benefits of a hybrid multimodal approach. This study introduces the Multimodal Data Fusion (MDF) framework, which fuses tabular data with textual narratives by leveraging advanced Large Language Models (LLMs), such as GPT-2, GPT-3.5, and GPT-4.5, using zero-shot (ZS), few-shot (FS), and fine-tuning (FT) learning strategies. We employed few-shot learning with GPT-4.5 to generate new labels for traffic crash analysis, such as driver fault, driver actions, and crash factors, alongside the existing label for severity. Our methodology was tested on crash data from the Missouri State Highway Patrol, demonstrating significant improvements in model performance. GPT-2 (fine-tuned) was used as the baseline model, against which more advanced models were evaluated. GPT-4.5 few-shot learning achieved 98.9% accuracy for crash severity prediction and 98.1% accuracy for driver fault classification. In crash factor extraction, GPT-4.5 few-shot achieved the highest Jaccard score (82.9%), surpassing GPT-3.5 and fine-tuned GPT-2 models. Similarly, in driver actions extraction, GPT-4.5 few-shot attained a Jaccard score of 73.1%, while fine-tuned GPT-2 closely followed with 72.2%, demonstrating that task-specific fine-tuning can achieve performance close to state-of-the-art models when adapted to domain-specific data. These findings highlight the superior performance of GPT-4.5 few-shot learning, particularly in classification and information extraction tasks, while also underscoring the effectiveness of fine-tuning on domain-specific datasets to bridge performance gaps with more advanced models. The MDF framework’s success demonstrates its potential for broader applications beyond traffic crash analysis, particularly in domains where labeled data are scarce and predictive modeling is essential.
Ethical Climate, Intrinsic Motivation, and Affective Commitment: The Impact of Depersonalization
Field: PSYCHOLOGY

Although affective commitment has been the focus of numerous studies, we know relatively little about certain factors that drive or hinder its progress. In this sense, this study contributes to the knowledge on the subject by establishing a relationship between a benevolent ethical climate and affective commitment, taking into account the mediating effect of intrinsic motivation. Furthermore, we highlight depersonalization as an aspect that can hinder these relationships when it assumes a moderating function. The sample was established through 448 employees of the Colombian electrical sector. The mediating effect was confirmed through a four-step method. The moderated mediation model was examined using SEM structural equations. The results show that a benevolent ethical climate is positively related to affective commitment and that intrinsic motivation is a mediating factor that justifies this relationship. However, depersonalization moderates the mediation between benevolent ethical climate, intrinsic motivation, and affective commitment. Specifically, the positive effect of the benevolent ethical climate on affective commitment is halted when depersonalization is high. The positive relationship between intrinsic motivation and affective commitment is interrupted when depersonalization is medium or high. Finally, as depersonalization progresses, the positive relationship between a benevolent ethical climate and intrinsic motivation is reduced. Therefore, organizations in the Colombian electrical sector must take measures that, in addition to avoiding social isolation, behave as indicators that warn when employees’ behaviors change significantly.
Evaluation of the effect of fatigue on the coping behavior of international truck drivers
Field: PSYCHOLOGY

Background: Fatigue can affect the behavior of drivers. While the driver must be able to respond and cope appropriately to the critical situations, which is known as the ability to cope with a crisis. It is likely that the fatigue can change the people’s coping style and thereby increase the chance of the crashes. Therefore, the present study aimed to investigate the effects of fatigue on the coping behavior of international truck drivers. Methods: This study was conducted on 239 of international truck drivers employed in Iran. The Endler and Parker coping strategies questionnaire (CISS) and Persian version of the Fatigue Multidimensional Fatigue Inventory (MFI) were used to evaluate the coping styles of the drivers and the drivers’ fatigue, respectively. Results: The mean values of the total fatigue before and after traveling were 36.77 and 76.13, respectively. The mean values of coping styles of the problem-oriented, emotion-oriented, and avoidance before traveling were 53.66, 40.91, and 38.17, respectively, and those after traveling were 45.59, 51.18, and 36.45, respectively. The chi-square test demonstrated that there was a significant difference in the coping style of drivers before and after the trip (P < 0.001), and the percent of individuals with emotion-oriented increased. Conclusions: In general, the results showed that fatigue due to traveling could change the coping styles of subjects from problem-oriented to emotion-oriented and avoidance. This can increase the statistics of driving accidents.
Emotional prompting amplifies disinformation generation in AI large language models
Field: AI

Introduction: The emergence of artificial intelligence (AI) large language models (LLMs), which can produce text that closely resembles human-written content, presents both opportunities and risks. While these developments offer significant opportunities for improving communication, such as in health-related crisis communication, they also pose substantial risks by facilitating the creation of convincing fake news and disinformation. The widespread dissemination of AI-generated disinformation adds complexity to the existing challenges of the ongoing infodemic, significantly affecting public health and the stability of democratic institutions.Rationale: Prompt engineering is a technique that involves the creation of specific queries given to LLMs. It has emerged as a strategy to guide LLMs in generating the desired outputs. Recent research shows that the output of LLMs depends on emotional framing within prompts, suggesting that incorporating emotional cues into prompts could influence their response behavior. In this study, we investigated how the politeness or impoliteness of prompts affects the frequency of disinformation generation by various LLMs.Results: We generated and evaluated a corpus of 19,800 social media posts on public health topics to assess the disinformation generation capabilities of OpenAI’s LLMs, including davinci-002, davinci-003, gpt-3.5-turbo, and gpt-4. Our findings revealed that all LLMs efficiently generated disinformation (davinci-002, 67%; davinci-003, 86%; gpt-3.5-turbo, 77%; and gpt-4, 99%). Introducing polite language to prompt requests yielded significantly higher success rates for disinformation (davinci-002, 79%; davinci-003, 90%; gpt-3.5-turbo, 94%; and gpt-4, 100%). Impolite prompting resulted in a significant decrease in disinformation production across all models (davinci-002, 59%; davinci-003, 44%; and gpt-3.5-turbo, 28%) and a slight reduction for gpt-4 (94%).Conclusion: Our study reveals that all tested LLMs effectively generate disinformation. Notably, emotional prompting had a significant impact on disinformation production rates, with models showing higher success rates when prompted with polite language compared to neutral or impolite requests. Our investigation highlights that LLMs can be exploited to create disinformation and emphasizes the critical need for ethics-by-design approaches in developing AI technologies. We maintain that identifying ways to mitigate the exploitation of LLMs through emotional prompting is crucial to prevent their misuse for purposes detrimental to public health and society.
Legal regulation of AI-assisted academic writing: challenges, frameworks, and pathways
Field: AI

Introduction: The widespread application of artificial intelligence in academic writing has triggered a series of pressing legal challenges.Methods: This study systematically examines critical issues, including copyright protection, academic integrity, and comparative research methods. We establishes a risk assessment matrix to quantitatively analyze various risks in AI-assisted academic writing from three dimensions: impact, probability, and mitigation cost, thereby identifying high-risk factors.Results: The findings reveal that AI-assisted writing challenges fundamental principles of traditional copyright law, with judicial practice tending to position AI as a creative tool while emphasizing human agency. Regarding academic integrity, new risks, such as “credibility illusion” and “implicit plagiarism,” have become prominent in AI-generated content, necessitating adaptive regulatory mechanisms. Research data protection and personal information security face dual challenges in data security that require technological and institutional innovations.Discussion: Based on these findings, we propose a three-dimensional regulatory framework of “transparency, accountability, technical support” and present systematic policy recommendations from institutional design, organizational structure, and international cooperation perspectives. The research results deepen understanding of legal attributes of AI creation, promote theoretical innovation in digital era copyright and academic ethics, and provide practical guidance for academic institutions in formulating AI usage policies.