A Modern Approach to Classifying Medieval Latin Scripts

Paleography, the study of historical handwriting, is essential for preserving societal understanding of cultural, social, and legal frameworks from the past. Medieval manuscripts, often exhibiting refined craftsmanship, present unique challenges to modern readers due to differences in handwriting conventions and the absence of standardized punctuation and spaces. These texts hold valuable insights into the evolution of written communication, literacy, and language development. However, interpreting them requires specialized knowledge and technological solutions. Convolutional Neural Networks (CNNs) can be leveraged to classify scripts, an important step in Historical Document analysis. These models extract and analyze hierarchical features from images, addressing inconsistencies in script style and document quality. Furthermore, transfer learning and data augmentation techniques, like rotation and random cropping, enhance model robustness by mimicking the natural variability of handwritten texts

Quantity vs. Value: Divergent Innovation Outcomes Under Chinese Import Pressure

We explore the impact of Chinese import competition on innovation in U.S. manufacturing firms. Our analysis uncovers a striking divergence in innovation outcomes: although firms boost their patent output, the economic value and technological significance of these patents decline. More specifically, the firms transition from groundbreaking inventions to a series of incremental advancements aimed at differentiating their product lines. Our findings reconcile two foundational views on competition and innovation-one emphasizing rent erosion and the other innovation stimulation-by showing that competitive pressure can simultaneously encourage more innovation while reducing its economic value

‘Alpha’ as a Tool of Evangelism for Creating Christian Community in the Horizon Texas Conference of The United Methodist Church among South Asian Indians Living in Coppell, Texas: Challenges and Recommendations

Alpha is a tool of evangelism that has been popular in its current form in the West, especially in the United Kingdom where it originated and moreover in the United States, after its global launch in 1993. It has been used by churches of various traditions and denominations over several decades to reach the ‘unchurched,’ has been translated into different languages, and over time the content has been repackaged for a modern and postmodern generation. However, it is found that Alpha is not an effective tool of evangelism to invite those of faith backgrounds and worldviews other than Christian to explore the Christian faith and be discipled into belonging to a Christian community, particularly the South Asian Indian community in the United States who mostly adhere to the Hindu faith tradition. This dissertation aims at exploring the basis of evangelism and then evaluating the effectiveness of Alpha as a tool of evangelism to communicate the Gospel of Jesus Christ by analyzing its journey from its beginnings to the present and considering its strengths and its criticisms, along with exploring the religious and cultural identity of South Asian Indians living in the United States, especially in Coppell, Texas. For achieving this, qualitative data was examined to study about the Ministry of Evangelism from a biblical, theological, and historical perspective; about Alpha as a tool of evangelism from a historical and theological perspective; about the religious and cultural identity and worldview of South Asian Indians living in the United States, especially in and around Coppell, Texas, from a historical and sociological perspective; and about the plausible unsuitability of Alpha, in its current form, as a tool of evangelism to reach the South Asian Indian community in the United States from a sociological and theological, particularly missiological, perspective. The kind of evidence that was examined included biblical and theological arguments from the viewpoint of mission and evangelism, historical records of events, and socio-narrative accounts. The results of this research showed that Alpha is more suitable as a tool for revival of Christian faith and iv practice for those coming from Christian backgrounds rather than for evangelism to lead those from other faiths to Christianity because Alpha originated and developed in the West where most people have some level of exposure to Christian faith and practice because of its Christian roots. Moreover, before its global launch, Alpha was a course for discipleship for new Christians from the time it first started in 1977 and was later repositioned in 1991 as a tool for evangelism and therefore its content generally presupposes some background or exposure to Christian faith from those who attend its sessions. The results also suggest that unless Alpha is modified and adapted to take into consideration the religious and cultural identity and worldview of those from faith backgrounds other than Christian, it will be an ineffective and unsuitable tool of evangelism for them and will continue to face the challenge of contextualization. As I have attempted to make a few recommendations for modification and adaptation of Alpha for it to be an effective tool of evangelism for South Asian Indians in the United States who mostly have a Hindu faith background, I believe and hope that it will provide useful insight to those in the field of Missiology in understanding and effectively using Western tools of evangelism by contextualizing them for reaching people who adhere to non-Christian faiths, particularly Hinduism, and hold to non-western worldviews

Topology Optimization Based on Micropolar Elasticity and Enhanced by Machine Learning: Structure Generation and Material Design

This work presents a novel topology optimization (TO) framework that integrates the micropolar elasticity theory with machine learning (ML) techniques to design high-performance structures and metamaterials. Traditional TO approaches rooted in classical elasticity neglect microstructural effects such as size-dependent behaviors and microrotations, which limits their accuracy for advanced materials (e.g., composites and metamaterials). To address this limitation, a new TO model based on micropolar (Cosserat) elasticity is developed, which introduces the rotational degrees of freedom and the associated couple stresses to more accurately capture microstructure-dependent mechanical responses. The framework is further enhanced with ML algorithms – including feedforward neural networks (FFNN), convolutional neural networks (CNN), and generative adversarial networks (GAN) – to accelerate the optimization process. By training these models on intermediate designs from iterative TO, the ML-assisted approach can predict near-optimal material layouts with greatly reduced computational effort. Compared to conventional methods, the integrated approach achieves an 80–85% reduction in iteration count, about 80% faster convergence, and approximately 70% lower computational energy consumption, while maintaining a high level of accuracy (with a root-mean-square error ≤ 0.007). The proposed methodology is validated through both 2D and 3D structural examples under diverse loading conditions. Results show that incorporating micropolar parameters (such as a coupling coefficient and a characteristic length) into the TO significantly enhances structural stiffness – improvements of up to 18.5% are observed – by enabling better load distribution and increased bending resistance. For mechanical metamaterials, the framework optimizes periodic structures for target properties (e.g., bulk or shear modulus and micropolar coupling effects), with the ML models effectively capturing design trends under periodic boundary conditions. In case studies, the deep learning-based predictors (CNN and GAN) outperformed the FFNN in accurately generating spatially complex optimal topologies. Overall, this work bridges advanced continuum mechanics with data-driven optimization techniques, offering a robust tool for designing next-generation materials and lightweight structures in fields such as aerospace, automotive, and biomedical engineering. The findings demonstrate the potential of combining physics-based modeling with machine learning to efficiently solve high-resolution topology optimization problems that were previously computationally prohibitive

Modeling Droplet Levitation Over a Heated Liquid Surface

Research into the interactions between droplets and liquid surfaces is of importance for a number of practical applications. These applications range from spray cooling techniques in engineering, to infectious disease transmission, to the distribution of aerosolized pharmaceuticals and various challenges in particle transport in multiphase flows. In this study, we focus on the dynamics of a slowly condensing droplet, suspended above an evaporating liquid layer. The key objective of the present study is to formulate comprehensive mathematical models that describe the phenomena of diffusion and heat transfer occurring within this system. Using this, we model the flow around the droplet and the force on the droplet. We employ the method of separation of variables in bipolar coordinates for both fluid flow and heat transfer models. We derive series expansions that describe the temperature distribution within the droplet itself and around it, as well as the vapor concentration in the air surrounding the droplet. This framework allows us to obtain the temperature profiles and condensation rates both at the surface of the droplet and along the surface of the liquid layer. Using a similar methodology, we find analytical expressions for the Stokes stream function and force on the droplet, and are able to make conclusions about the levitation height as a function of the droplet radius. The analytical method is then improved upon by considering the temperature distribution in the liquid layer as spatially variable. A coupled numerical and analytical approach is used to model the heat and mass transfer in the system. Above the liquid layer, we use separation of variables in bipolar coordinates. However, below the layer surface, the geometry is not suitable for the use of bipolar coordinates, so we employ a finite difference scheme in polar coordinates. The two solution methods are coupled at the boundary via the interface boundary conditions. The modification of the original analytical model leads to more accurate predictions for the force on the droplet and levitation height

Strategic Claim Payment Delays? Evidence from Property and Casualty Insurance

It is well-known that insurers raise premiums after adverse events. We show that they also slow the pace of claim payments, potentially imposing high state-contingent costs on loss-making clients. Unlike premium adjustments, payment adjustments also occur after adverse shocks in unrelated business lines. These shifts increase unpaid losses—a substantial liability on insurers’ balance sheets augmenting liquidity analogously to interest-free credit. Slowdowns are more prevalent among insurers with lower capital or liquidity who serve clients less likely to file regulatory complaints. This evidence aligns with insurers’ strategic financial considerations, though whether they constitute formal delays in the legal sense remains an open question