Search Results (305)

This study responds to the increasing call for thoughtful theological and ethical engagement with Artificial Intelligence (AI) by examining the role of personal theological reflection using Generative Artificial Intelligence (GenAI) content in Catholic theological education. It investigates how both educators and students might utilize AI-generated imagery as a pedagogical resource with which to enrich theological insight and foster ethical discernment, particularly through the lens of theological virtue ethics. AI is not a substitute for all human tasks. However, the use of AI holds potential for theology and catechetical religious education. Following Gläser-Zikuda’s model of Self-Reflecting Methods of Learning Research, this study systematically engages in reflective observation to examine how the use of GenAI in theology classrooms has influenced personal theological thinking, pedagogical practices, and ethical considerations. It documents experiences using common generative AI tools such as ChatGPT, Canva, Meta AI, Deep AI, and Gencraft in theology classes. The principles of virtue ethics and Human-Centered Artificial Intelligence (HCAI) offer a critical framework for ethical, pedagogical, and theological engagement. The findings contribute to the emerging interdisciplinary discourse on AI ethics and theology, and religious pedagogy in the digital age. Full article

The present-day issues related to the cotton-growing industry, namely yield estimation, pest effect, and growth phase diagnostics, call for integrated, scalable monitoring solutions. This write-up reveals Cotton Multitask Learning (CMTL), a transformer-driven multitask framework that launches three major agronomic tasks from UAV pictures at one go: boll detection, pest damage segmentation, and phenological stage classification. CMTL does not change separate pipelines, but rather merges these goals using a Cross-Level Multi-Granular Encoder (CLMGE) and a Multitask Self-Distilled Attention Fusion (MSDAF) module that both allow mutual learning across tasks and still keep their specific features. The biologically guided Stage Consistency Loss is the part of the architecture of the network that enables the system to carry out growth stage transitions that occur in reality. We executed CMTL on a tri-source UAV dataset that fused over 2100 labeled images from public and private collections, representing a variety of crop stages and conditions. The model showed its virtues state-of-the-art baselines in all the tasks: setting 0.913 mAP for boll detection, 0.832 IoU for pest segmentation, and 0.936 accuracy for growth stage classification. Additionally, it runs at the fastest speed of performance on edge devices such as NVIDIA Jetson Xavier NX (Manufactured in Shanghai, China), which makes it ideal for deployment. These outcomes evoke CMTL’s promise as a single and productive instrument of aerial crop intelligence in precision cotton agriculture. Full article

In the current era, when civilizations are in constant conflict and humankind is facing a series of serious existential crises, there is an urgent need for universal love to unite humankind. As models of world religions, Christianity and Buddhism provide rich intellectual resources for the construction of such universal love. Regarding Thomistic Christianity, its philosophy of friendship has gradually achieved a dual transformation from virtue-oriented to love-oriented, and from God-centered to human-centered. In the case of Mahayana Buddhism, its philosophy of friendship has evolved with the “Humanistic Buddhism” movement, increasingly demonstrating a compassionate spirit of saving the world. By comparing Thomistic Christianity with Mahayana Buddhism, we can see that although they exhibit different models of friendship, their main developmental trends are consistent. Both are committed to demonstrating a human-centered model of friendship, both emphasize the value of self-reduction in friendship, and both demonstrate the unique and irreplaceable role of religion in friendship. The goal of the philosophy of friendship is universal love and harmonious development of civilizations, while its future development depends on the joint efforts of contemporary religious scholars and philosophers. Full article

Fairness is a fundamental virtue in machine learning systems, alongside with four other critical virtues: Accountability, Transparency, Ethics, and Performance (FATE + Performance). Ensuring fairness has been a central research focus, leading to the development of various mitigation strategies in the literature. These approaches can generally be categorized into three main techniques: pre-processing (modifying data before training), in-processing (incorporating fairness constraints during training), and post-processing (adjusting outputs after model training). Beyond these, an increasingly explored avenue is the direct modification of existing algorithms, aiming to embed fairness constraints into their design while preserving or even enhancing predictive performance. This paper presents a comprehensive survey of classical machine learning models that have been modified or enhanced to improve fairness concerning sensitive attributes (e.g., gender, race). We analyze these adaptations in terms of their methodological adjustments, impact on algorithmic bias and ability to maintain predictive performance comparable to the original models. Full article

The objective of the present study is to detect chirped optical solitons of the perturbed Chen–Lee–Liu equation with full nonlinearity. By virtue of the traveling wave hypothesis, the discussed model is reduced to a simple form known as an elliptic equation. The latter equation, which is a second-order ordinary differential equation, is handled by the undetermined coefficient method of two forms expressed in terms of the hyperbolic secant and tangent functions. Additionally, the auxiliary equation method is applied to derive several miscellaneous solutions. Various types of chirped solitons are revealed such as W-shaped, bright, dark, gray, kink and anti-kink waves. Taking into consideration the existence conditions, the dynamical behaviors of optical solitons and their corresponding chirp are illustrated. The modulation instability of the perturbed CLL equation is examined by means of the linear stability analysis. It is found that all solutions are stable against small perturbations. These entirely new results, compared to previous works, can be employed to understand pulse propagation in optical fiber mediums and dynamic characteristics of waves in plasma. Full article

This study explores the use of Large Language Models (LLMs), specifically GPT, for different safety management applications in the construction industry. Many studies have explored the integration of GPT in construction safety for various applications; their primary focus has been on the feasibility of such integration, often using GPT models for specific applications rather than a thorough evaluation of GPT’s limitations and capabilities. In contrast, this study aims to provide a comprehensive assessment of GPT’s performance based on established key criteria. Using structured use cases, this study explores GPT’s strength and weaknesses in four construction safety areas: (1) delivering personalized safety training and educational content tailored to individual learner needs; (2) automatically analyzing post-accident reports to identify root causes and suggest preventive measures; (3) generating customized safety guidelines and checklists to support site compliance; and (4) providing real-time assistance for managing daily safety tasks and decision-making on construction sites. LLMs and NLP have already been employed in each of these four areas for improvement, making them suitable areas for further investigation. GPT demonstrated acceptable performance in delivering evidence-based, regulation-aligned responses, making it valuable for scaling personalized training, automating accident analyses, and developing safety protocols. Additionally, it provided real-time safety support through interactive dialogues. However, the model showed limitations in deeper critical analysis, extrapolating information, and adapting to dynamic environments. The study concludes that while GPT holds significant promise for enhancing construction safety, further refinement is necessary. This includes fine-tuning for more relevant safety-specific outcomes, integrating real-time data for contextual awareness, and developing a nuanced understanding of safety risks. These improvements, coupled with human oversight, could make GPT a robust tool for safety management. Full article

This philosophical essay reconsiders and redefines the moral attributes, virtues, and duties of an excellent secondary school teacher, emphasizing their pivotal role in the moral development of adolescents during secondary socialization. Grounded in Kantian deontological ethics, it formulates 15 maxims as categorical imperatives to guide morally acceptable teacher behavior, focusing on their function as role models in shaping students’ moral consciousness, as informed by Kohlberg’s theory of moral development. Through a multidisciplinary approach integrating philosophy, psychology, pedagogy, sociology, and anthropology, the essay provides a comprehensive framework for understanding the complexity of the teaching profession. The results offer a foundation for future empirical studies and the development of teacher training programs to enhance educational quality. Full article

The recent birth of genetically modified canids phenotypically resembling the extinct dire wolf (Aenocyon dirus) was hailed as a landmark in synthetic biology. Using genome editing and cloning, the biotech company Colossal Biosciences created three such animals from gray wolf cells, describing the project as an effort in “functional de-extinction”. This case raises significant questions regarding animal welfare, moral justification, and regulatory governance. We used the five domains model framework to assess the welfare risks for the engineered animals, the surrogate mothers used in reproduction, and other animals potentially affected by future reintroduction or escape scenarios. Ethical implications are examined through utilitarian, deontological, virtue, relational, and environmental ethics. Our analysis suggests that the project suffers from ontological ambiguity: it is unclear whether the animals created are resurrected species, hybrids, or novel organisms. While the current welfare of the engineered animals may be manageable, their long-term well-being, particularly under rewilding scenarios, is likely to be compromised. The moral arguments for reviving long-extinct species are weak, particularly in cases where extinction was not anthropogenic. Legally, the current EU frameworks lack the clarity and scope to classify, regulate, or protect genetically engineered extinct animals. We recommend that functional de-extinction involving sentient beings be approached with caution, supported by revised welfare tools and regulatory mechanisms. Full article

Traumatic non-compressible hemorrhage and subsequent wound management remain critical challenges in military and civilian settings to this day. Cryogels have emerged as promising hemostatic materials for non-compressible hemorrhage due to their blood-triggered shape recovery. In this study, a biodegradable and mechanically resilient cryogel (CF/PD) was produced via cryopolymerization, employing methacrylated recombinant collagen as a macromolecular crosslinker alongside poly (ethylene glycol) diacrylate (PEGDA) and dopamine methacrylate (DMA). With its interpenetrating macro-porous structure and high hydrophilicity, the CF/PD rapidly absorbs blood and returns to its original shape within 1.5 s. In a rat liver defect model, CF/PD outperformed commercially available gelatin sponges, reducing hemostasis time by 74.4% and blood loss by 76.5%. Moreover, CF/PD cryogels facilitate in situ tissue regeneration by virtue of the bioactivity and degradability of recombinant collagen. This work establishes a bioactive recombinant collagen-driven cryogel platform, offering a transformative solution for managing non-compressible hemorrhage while enabling tissue regeneration. Full article

As the development of oilfields in China enters its middle-to-late stage, the old oilfields still occupy a dominant position in the production structure. The seepage process of reservoirs in the high Water Content Period (WCP) presents significant nonlinear and non-homogeneous evolution characteristics, and the traditional seepage-modeling methods are facing the double challenges of accuracy and adaptability when dealing with complex dynamic scenarios. In recent years, Deep Learning technology has gradually become an important tool for reservoir seepage field prediction by virtue of its powerful feature extraction and nonlinear modeling capabilities. This paper systematically reviews the development history of seepage field prediction methods and focuses on the typical models and application paths of Deep Learning in this field, including FeedForward Neural networks, Convolutional Neural Networks, temporal networks, Graphical Neural Networks, and Physical Information Neural Networks (PINNs). Key processes based on Deep Learning, such as feature engineering, network structure design, and physical constraint integration mechanisms, are further explored. Based on the summary of the existing results, this paper proposes future development directions including real-time prediction and closed-loop optimization, multi-source data fusion, physical consistency modeling and interpretability enhancement, model migration, and online updating capability. The research aims to provide theoretical support and technical reference for the intelligent development of old oilfields, the construction of digital twin reservoirs, and the prediction of seepage behavior in complex reservoirs. Full article

In this study, we examined the role of intellectual humility (IH) as an antecedent of individual attitude toward sustainable tourism viewed from the lens of personality trait theory, virtue ethics theory, and regenerative tourism principles within a stakeholder framework. Data were collected via Qualtrics in an online survey of 233 adults in the United States. A series of confirmatory factor analyses (CFA) were applied to the data to test the measurement model. In addition, a bifactor CFA was found to have acceptable fit and appropriate in controlling for common method variance. A series of covariance-based structural equations models (SEMs) was estimated to test the hypothesized model while controlling for common method variance in addition to individual age and gender. Using the chi-square difference test for nested model comparison, we found that intellectual humility was a significant antecedent of the negative ecological impact of tourism (β = 0.14, p < 0.01) while its relationships with economic and social impacts of travel became non-significant after controlling for common method variance. Pro-social tendency, operationalized as HEXACO Honesty–Humility, was also a significant antecedent of the negative ecological impact (β = 0.17) and positive economic impact (β = −0.34) of tourism, after controlling for common method variance. Despite its limitations due to its cross-sectional design and use of self-report data in the U.S., this study was novel in introducing intellectual humility as an important virtue to be cultivated at the individual level to achieve a holistic approach to sustainable tourism, especially in shaping destination choices. In addition, the study highlights the need to detect common method variance in self-report data via bifactor CFA to avoid erroneous reporting of significant findings, hampering our collective research efforts to address climate change and its impact. Full article

Accurate segmentation of soil pore structure is crucial for studying soil water migration, nutrient cycling, and gas exchange. However, the low-contrast and high-noise CT images in complex soil environments cause the traditional segmentation methods to have obvious deficiencies in accuracy and robustness. This paper proposes a hybrid model combining a Multi-Modal Low-Frequency Reconstruction algorithm (MMLFR) and UNet (MMLFR-UNet). MMLFR enhances the key feature expression by extracting the image low-frequency signals and suppressing the noise interference through the multi-scale spectral decomposition, whereas UNet excels in the segmentation detail restoration and complexity boundary processing by virtue of its coding-decoding structure and the hopping connection mechanism. In this paper, an undisturbed soil column was collected in Hainan Province, China, which was classified as Ferralsols (FAO/UNESCO), and CT scans were utilized to acquire high-resolution images and generate high-quality datasets suitable for deep learning through preprocessing operations such as fixed-layer sampling, cropping, and enhancement. The results show that MMLFR-UNet outperforms UNet and traditional methods (e.g., Otsu and Fuzzy C-Means (FCM)) in terms of Intersection over Union (IoU), Dice Similarity Coefficients (DSC), Pixel Accuracy (PA), and boundary similarity. Notably, this model exhibits exceptional robustness and precision in segmentation tasks involving complex pore structures and low-contrast images. Full article

Traditional pest and disease identification methods mainly rely on manual detection or traditional machine learning techniques, but they have obvious deficiencies in terms of their accuracy and generalisation ability. In recent years, deep learning has gradually become the preferred solution for the intelligent identification of pests and diseases by virtue of its powerful automatic feature extraction and complex data-processing capabilities. In this paper, we systematically present the application of traditional machine learning methods in pest and disease identification and their limitations, and focus on the research progress of deep learning methods, covering three mainstream architectures: convolutional neural network (CNN), Vision Transformer model and CNN–Transformer hybrid model. In addition, this paper provides an in-depth analysis of the key challenges currently faced in the field of pest recognition, including the problems of small-sample learning, complex background interference and model lightweighting, and further propose solutions for future research to provide theoretical references and technical guidance for the development of related fields. Full article

Compared with traditional immunoassay methods, microfluidic immunoassay restricts the immune response in confined microchannels, significantly reducing sample consumption and improving reaction efficiency, making it worthy of widespread application. This paper proposes an exciting multi-frequency electrothermal flow (MET) technique by applying combined standing-wave and traveling-wave voltage signals with different oscillation frequencies to a three-period quadra-phase discrete electrode array, achieving rapid immunoreaction on functionalized electrode surfaces within straight microchannels, by virtue of horizontal pumping streamlines and transverse stirring vortices induced by nonlinear electrothermal convection. Under the approximation of a small temperature rise, a linear model describing the phenomenon of MET is derived. Although the time-averaged electrothermal volume force is a simple superposition of the electrostatic body force components at the two frequencies, the electro-thermal-flow field undergoes strong mutual coupling through the dual-component time-averaged Joule heat source term, further enhancing the intensity of Maxwell–Wagner smeared structural polarization and leading to mutual influence between the standing-wave electrothermal (SWET) and traveling-wave electrothermal (TWET) effects. Through thorough numerical simulation, the optimal working frequencies for SWET and TWET are determined, and the resulting synthetic MET flow field is directly utilized for microfluidic immunoassay. MET significantly promotes the binding kinetics on functionalized electrode surface by simultaneous global electrokinetic transport along channel length direction and local chaotic stirring of antigen samples near the reaction site, compared to the situation without flow activation. The MET investigated herein satisfies the requirements for early, rapid, and precise immunoassay of test samples on-site, showing great application prospects in remote areas with limited resources. Full article

Social sustainability that starts from the workplace is a relevant factor for the achievement of the Sustainable Development Goals. Based on this need, this study analyzes the role of virtuous leadership as facilitator of health and inclusive work environments that integrate followers’ psychological well-being and their attitudes towards people with disabilities. An exploratory design was used with latent variables to assess the proposed virtue-based ethical leadership adjustment model for social sustainability, which presented efficient absolute, comparative, and parsimonious adjustments for its operationalization. In conclusion, virtuous leadership plays a relevant role in the development of followers’ psychological well-being, and attitudes towards people with disabilities in the workplace, contributing to the social sustainability criteria from the work environment. Full article