Modern Models Research Articles

John Cross, epidemic theory, and mathematically modeling the Norwich smallpox epidemic of 1819.

In this paper, we reintroduce Dr. John Cross' neglected and unusually complete historical data set describing a smallpox epidemic occurring in Norwich, England in 1819. We analyze this epidemic data in the context of early models of epidemic spread including the Farr-Evans-Brownlee Normal law, the Kermack-McKendrick square Hyperbolic Secant and SIR laws, along with the modern Volz-Miller random-network law. We show that Cross' hypothesis of susceptible pool limitation is sufficient to explain the data under the SIR law, but requires parameter estimates differing from the modern understanding of smallpox epidemiology or large errors in Cross' data collection. We hypothesize that these discrepancies are due to the mass-action hypothesis in SIR theory, rather than significant errors by Cross, and use Volz-Miller theory to support this. Our analysis demonstrates the difficulties arising in inference of attributes of the disease from death incidence data and how model hypotheses impact these inferences. Our study finds that, combined with Volz-Miller modeling theory, Cross' death incidence data and population observations give smallpox attributes which largely cohere to those used in modern smallpox models.

Quantitative assessment of neurodevelopmental maturation: a comprehensive systematic literature review of artificial intelligence-based brain age prediction in pediatric populations

IntroductionOver the past few decades, numerous researchers have explored the application of machine learning for assessing children’s neurological development. Developmental changes in the brain could be utilized to gauge the alignment of its maturation status with the child’s chronological age. AI is trained to analyze changes in different modalities and estimate the brain age of subjects. Disparities between the predicted and chronological age can be viewed as a biomarker for a pathological condition. This literature review aims to illuminate research studies that have employed AI to predict children’s brain age.MethodsThe inclusion criteria for this study were predicting brain age via AI in healthy children up to 12 years. The search term was centered around the keywords “pediatric,” “artificial intelligence,” and “brain age” and was utilized in PubMed and IEEEXplore. The selected literature was then examined for information on data acquisition methods, the age range of the study population, pre-processing, methods and AI techniques utilized, the quality of the respective techniques, model explanation, and clinical applications.ResultsFifty one publications from 2012 to 2024 were included in the analysis. The primary modality of data acquisition was MRI, followed by EEG. Structural and functional MRI-based studies commonly used publicly available datasets, while EEG-based studies typically relied on self-recruitment. Many studies utilized pre-processing pipelines provided by toolkit suites, particularly in MRI-based research. The most frequently used model type was kernel-based learning algorithms, followed by convolutional neural networks. Overall, prediction accuracy may improve when multiple acquisition modalities are used, but comparing studies is challenging. In EEG, the prediction error decreases as the number of electrodes increases. Approximately one-third of the studies used explainable artificial intelligence methods to explain the model and chosen parameters. However, there is a significant clinical translation gap as no study has tested their model in a clinical routine setting.DiscussionFurther research should test on external datasets and include low-quality routine images for MRI. T2-weighted MRI was underrepresented. Furthermore, different kernel types should be compared on the same dataset. Implementing modern model architectures, such as convolutional neural networks, should be the next step in EEG-based research studies.

ANALISIS PENYALURAN PEMBIAYAAN DAN MODEL PENGEMBANGAN KOPRASI IBNU AFFAN PATANI THAILAND

This study aims to examine the financing distribution system and the development model of the Ibnu Affan Cooperative in Thailand. A qualitative approach with descriptive methods was utilized, involving interviews and documentation as data collection techniques.Based on the analysis conducted, Koperasi Ibnu Affan applies Sharia principles with a focus on transparency. The transparency referred to involves the openness of information and decision-making processes carried out by the cooperative, where the cooperative is able to manage finances openly to both the management and all members, as well as the supervisory board. Furthermore, the welfare of the members is one of the cooperative's main programs aimed at improving the welfare of its members and the community in general. This is achieved through the use of a modern management model that focuses on the application of efficient and professional management principles while considering cooperative values based on democracy, transparency, and solidarity for operational success.

A protocol for model intercomparison of impacts of marine cloud brightening climate intervention

Abstract. A modeling protocol (defined by a series of climate model simulations with specified model output) is introduced. Studies using these simulations are designed to improve the understanding of climate impacts using a strategy for climate intervention (CI) known as marine cloud brightening (MCB) in specific regions; therefore, the protocol is called MCB-REG (where REG stands for region). The model simulations are not intended to assess consequences of a realistic MCB deployment intended to achieve specific climate targets but instead to expose responses to interventions in six regions with pervasive cloud systems that are often considered candidates for such a deployment. A calibration step involving simulations with fixed sea surface temperatures (SSTs) is first used to identify a common forcing, and then coupled simulations with forcing in individual regions and combinations of regions are used to examine climate impacts. Synthetic estimates constructed by superposing responses from simulations with forcing in individual regions are considered a means of approximating the climate impacts produced when MCB interventions are introduced in multiple regions. A few results comparing simulations from three modern climate models (CESM2, E3SMv2, and UKESM1) are used to illustrate the similarities and differences between model behavior and the utility of estimates of MCB climate responses that were synthesized by summing responses introduced in individual regions. Cloud responses to aerosol injections differ substantially between models (CESM2 clouds appear much more susceptible to aerosol emissions than the other models), but patterns in precipitation and surface temperature responses were similar when forcing is imposed with similar amplitudes in the same regions. A previously identified La Niña-like response to forcing introduced in the Southeast Pacific is evident in this study, but the amplitude of the response was shown to markedly differ across the three models. Other common response patterns were also found and are discussed. Forcing in the Southeast Atlantic consistently (across all three models) produces weaker global cooling than that in other regions, and the Southeast Pacific and South Pacific show the strongest cooling. This indicates that the efficiency of a given intervention depends on not only the susceptibility of the clouds to aerosol perturbations, but also the strength of the underlying radiative feedbacks and ocean responses operating within each region. These responses were generally robust across models, but more studies and an examination of responses with ensembles would be beneficial.

Pythagoreanism and Modern Models of Cosmology

Turning to the accumulated philosophical knowledge, the scientist almost always discovers that there are ideas in it which are far ahead of their time, but which can help in solving scientific problems of quite another historical epoch. In this respect one of the most revealing examples are associated with the discovery of heliocentrism and the General Theory of Relativity. Philosophical ideas and principles immanent in one or another particular science and serving for the solution of some problem of it form the very philosophical foundations of the given science. These, in turn, are by no means identical with the whole body of knowledge which philosophy creates. By reflecting on the foundations of science and turning to the analysis of its arbitrary forms – namely cosmology – philosophical reflection elaborates ideas and principles that can play an essential role in a certain stage of scientific inquiry.

Time allocation among different sorts of activities: An overview of mathematical models

The history of time use research dates back more than 100 years. Russian scientists were among the first to conduct large-scale surveys in this field. Nowadays, this sphere has become interesting among scientists again. Rosstat has rebegun to conduct surveys on the daily time use, large-scale independent surveys have appeared, and media have increased their interest. The amount of empirical microdata that has emerged has made it possible to examine a complex concept as the household time use in ever more numbers. The purpose of this methodological overview is to describe the most significant mathematical models that have been used and are being used to study the time budgets of the population. This article overviews the first models in which time was considered as one of the means of production along with other goods, and households tried to maximize the utility function solely based on financial considerations. Then, modern models, where time is no less an important factor of well-being than material status, are represented. The article demonstrates how the new model classes that have recently emerged adapt to the data that is accessible in certain circumstances, modify themselves to accomplish fresh tasks, and become less costly when looking at analytical estimations. In order to categorize the population’s time budget structure under different research activities, the paper examines and systemsatizes a number of mathematical models

Bridging Tridosha Siddhanta and Biorhythm Theory: Uniting Ancient Dosha Principles with Modern Cyclic Patterns

This article explores the conceptual intersections between Tridosha Siddhanta, the foundational theory of Ayurveda, and the Biorhythm Theory, a modern psychological model of cyclical biological and emotional states. Tridosha Siddhanta posits that human health and wellness are governed by the balance of three fundamental Doshas Vata, Pitta, and Kapha each representing a combination of the five elements and their dynamic influences on physiological and psychological functions. In contrast, Biorhythm Theory suggests that individuals experience predictable cycles of physical, emotional, and intellectual states influenced by inherent rhythms. By analyzing both systems, this article seeks to elucidate how the cyclical nature of biorhythms might correlate with the doshic imbalances and fluctuations described in Ayurveda. It aims to provide a comprehensive understanding of how integrating these two paradigms can enhance approaches to personalized health and wellness, offering a holistic perspective that bridges ancient wisdom with contemporary scientific insights.

Empowering model repair: a rule-based approach to graph repair without side effects—extended version

AbstractWorking with models can lead to inconsistencies, e.g., due to erroneous or contradictory actions during concurrent modeling processes. Modern modeling environments typically tolerate inconsistencies and support their detection. However, at a later stage of development, models are expected to be consistent, meaning their inconsistencies should be considered and resolved. The process of resolving model inconsistencies is commonly referred to as model repair. Our approach to model repair is semi-automatic in the sense that the repair tool computes appropriate repair plans and the modeler decides which path to take. The speciality of our approach is that the repair process can register any small improvement in the model. This allows the interaction with the user to be optimized, resulting in an approach with a high level of automation on the one hand and flexible configuration options on the other. The approach focuses on providing repair plans that do not have side effects, i.e., the computed repair plans do not inadvertently introduce a new inconsistency of already repaired constraints into the model. Since models often have a graph-like structure, we present our approach to model repair based on graphs. Our approach is completely formal—we use the algebraic graph transformation approach to prove its correctness. We also present a prototype implementation of our repair approach based on the Eclipse Modeling Framework and Henshin, a model transformation engine based on graph transformation, to perform the actual model repair. A first performance evaluation shows that graphs with up to 1000 nodes can be repaired in about 10 s.

Transforming Personal Finance Coaching through Artificial Intelligence

Artificial intelligence (AI) has bloomed in recent years and is gradually becoming an irreplaceable asset in finance,among other sectors. Personal finance is a subset of finance, which too is being revolutionized due to changing timesand technological advancements, much like AI. Security and proper financial guidance have never been moreimportant with such significant change. In this study, we use FinBERT, a modern large language model specializedin the financial domain, for our AI-powered personal finance coach. However, FinBERT, although a cut above therest, still has room for growth, so we aim to improve its flaws and enhance its efficiency. We established thatFinBERT succeeded in detecting sentiments in explicit sentiments, but was not usually successful in doing socorrectly for implicit sentiments. FinBERT, despite its limitations, has a high accuracy and is the best model to usein our study. This model can also be utilised to provide accurate results regarding the overall trend (positive ornegative) of the global stock market. Our results demonstrate that integrating AI in personal finances is feasible andcan successfully aid individuals in making decisions regarding their finances.

Advances and Challenges in Weather Nowcasting : A Comprehensive Review of Modern Techniques and Models

Weather nowcasting, the short-term forecasting of weather conditions, is a critical area of study due to its impact on safety and planning. Recent advances in machine learning (ML) and deep learning (DL) have significantly improved nowcasting accuracy, particularly in precipitation prediction. This review examines modern techniques and models used for weather nowcasting, focusing on their advancements and challenges. Motivation for this study arises from the need for more accurate and timely weather predictions to mitigate adverse effects on daily life and infrastructure. Despite advancements, current methods face limitations such as high computational costs, data quality issues, and the challenge of generalizing models across different geographic regions. The aim of this review is to provide a comprehensive overview of recent developments in nowcasting techniques, highlight their strengths and weaknesses, and discuss future directions for research. The objective is to synthesize the latest findings, assess the performance of various models, and propose potential improvements. This review underscores the importance of continued innovation and integration of new technologies, including transfer learning and hybrid models, to enhance the effectiveness of weather nowcasting systems.

The expansion of British steeplechasing in the Grand Duchy of Posen

ABSTRACT In the nineteenth century, British culture and sport were held in high esteem abroad as models of progress and modernity. British-organised sports gained prestige in European societies, and their practice became a symbol of social class development and progress. In contrast to the Polish population living under Austrian and Russian rule, the Polish population of the Prussian-controlled Grand Duchy of Posen had the opportunity to practise, or at least passively enjoy, steeplechasing, which originated in the British Isles. This was made possible by the Society for the Improvement of Horse, Cattle and Sheep Breeding in the Grand Duchy of Posen. Unlike the British steeplechase, the Society's jump races were free of betting and focused more on utilitarian aspects by promoting the improvement of horse breeding and the benefits of horse ownership. Despite the socio-political differences, they were very similar to their British counterparts. This makes the steeplechase races in the western Polish territories annexed by Prussia significant events and, in some ways, pioneering contributions to the development of the sport in Continental Europe. With the establishment of English sporting fashions, the steeplechase became one of the components of a long-term process of introducing various British sports to Poland.

DSCIMABNet: A novel multi-head attention depthwise separable CNN model for skin cancer detection

Skin cancer is a common type of cancer worldwide. Early diagnosis of skin cancer can reduce the risk of death by increasing treatment success. However, it is challenging for dermatologists or specialists because the symptoms are vague in the early stages and cannot be noticed by the naked eye. This study examines digital diagnostic techniques supported by artificial intelligence, focusing on early skin cancer detection and two methods have been proposed. In the first method, DSCIMABNet deep learning architecture was developed by combining multi-head attention and depthwise separable convolution techniques. This model provides flexibility in learning the dataset's local features, abstract concepts, and long-term relationships. The DSCIMABNet model and modern deep learning models trained on ImageNet are proposed to be combined with the ensemble learning method in the second method. This approach provides a comprehensive feature extraction process that will increase the performance of the classification process with ensemble learning. The proposed approaches are trained and evaluated on the ISIC 2018 dataset with image enhancement applied in preprocessing. In the experimental results, DSCIMABNet achieved 84.28% accuracy, while the proposed hybrid method achieved 99.40% accuracy. Moreover, on the Mendeley dataset (CNN for Melanoma Detection Data), DSCIMABNet achieved 92.58% accuracy, while the hybrid method achieved 99.37% accuracy. This study may significantly contribute to developing new and effective methods for the early diagnosis and treatment of skin cancer.

Perspectives Toward an Integrative Structural Biology Pipeline With Atomic Force Microscopy TopographicImages.

After the recent double revolutions in structural biology, which include the use of direct detectors for cryo-electron microscopy resulting in a significant improvement in the expected resolution of large macromolecule structures, and the advent of AlphaFold which allows for near-accurate prediction of any protein structures, the field of structural biology is now pursuing more ambitious targets, including several MDa assemblies. But complex target systems cannot be tackled using a single biophysical technique. The field of integrative structural biology has emerged as a global solution. The aim is to integrate data from multiple complementary techniques to produce a final three-dimensional model that cannot be obtained from any single technique. The absence of atomic force microscopy data from integrative structural biology platforms is not necessarily due to its nm resolution, as opposed to Å resolution for x-ray crystallography, nuclear magnetic resonance, or electron microscopy. Rather a significant issue was that the AFM topographic data lacked interpretability. Fortunately, with the introduction of the AFM-Assembly pipeline and other similar tools, it is now possible to integrate AFM topographic data into integrative modeling platforms. The advantages of single molecule techniques, such as AFM, include the ability to confirm experimentally any assembled molecular models or to produce alternative conformations that mimic the inherent flexibility of large proteins or complexes. The review begins with a brief overview of the historical developments of AFM data in structural biology, followed by an examination of the strengths and limitations of AFM imaging, which have hindered its integration into modern modeling platforms. This review discusses the correction and improvement of AFM topographic images, as well as the principles behind the AFM-Assembly pipeline. It also presents and discusses a series of challenges that need to be addressed in order to improve the incorporation of AFM data into integrative modeling platform.

Enhancing Skin Cancer Diagnosis through the Integration of Deep Learning and Machine Learning Approaches

Skin cancer is a disease characterized by the uncontrolled proliferation of skin cells, typically manifesting as lesions or abnormal growths. Early diagnosis is critical for improving treatment outcomes. This study proposes an innovative approach to skin cancer diagnosis by integrating modern deep learning models with traditional machine learning algorithms. A three-phase methodology was developed. In the first phase, meaningful features were extracted from skin lesion images using various transfer learning models, including Xception, VGG16, ResNet152V2, InceptionV3, InceptionResNetV2, MobileNetV2, EfficientNetB2, and DenseNet201. In the second phase, dimensionality reduction was performed using Principal Component Analysis (PCA). In the final phase, the reduced feature sets were classified using K-Nearest Neighbors (KNN) and Random Forest (RF) algorithms. Experimental results demonstrated that the highest accuracy of 91.28% was achieved through the combination of DenseNet201 for feature extraction, PCA for dimensionality reduction, and Random Forest for classification. These findings highlight the effectiveness of integrating transfer learning models, dimensionality reduction techniques, and machine learning algorithms in enhancing the accuracy of skin cancer diagnosis.

How to Learn More? Exploring Kolmogorov–Arnold Networks for Hyperspectral Image Classification

Convolutional neural networks (CNNs) and vision transformers (ViTs) have shown excellent capability in complex hyperspectral image (HSI) classification. However, these models require a significant number of training data and are computational resources. On the other hand, modern Multi-Layer Perceptrons (MLPs) have demonstrated a great classification capability. These modern MLP-based models require significantly less training data compared with CNNs and ViTs, achieving state-of-the-art classification accuracy. Recently, Kolmogorov–Arnold networks (KANs) were proposed as viable alternatives for MLPs. Because of their internal similarity to splines and their external similarity to MLPs, KANs are able to optimize learned features with remarkable accuracy, in addition to being able to learn new features. Thus, in this study, we assessed the effectiveness of KANs for complex HSI data classification. Moreover, to enhance the HSI classification accuracy obtained by the KANs, we developed and proposed a hybrid architecture utilizing 1D, 2D, and 3D KANs. To demonstrate the effectiveness of the proposed KAN architecture, we conducted extensive experiments on three newly created HSI benchmark datasets: QUH-Pingan, QUH-Tangdaowan, and QUH-Qingyun. The results underscored the competitive or better capability of the developed hybrid KAN-based model across these benchmark datasets over several other CNN- and ViT-based algorithms, including 1D-CNN, 2DCNN, 3D CNN, VGG-16, ResNet-50, EfficientNet, RNN, and ViT.

Advancing Pain Understanding and Drug Discovery: Insights from Preclinical Models and Recent Research Findings

Despite major advancements in our understanding of its fundamental causes, pain—both acute and chronic—remains a serious health concern. Various preclinical investigations utilizing diverse animal, cellular, and alternative models are required and frequently demanded by regulatory approval bodies to bridge the gap between the lab and the clinic. Investigating naturally occurring painful disorders can speed up medication development at the preclinical and clinical levels by illuminating molecular pathways. A wide range of animal models related to pain have been developed to elucidate pathophysiological mechanisms and aid in identifying novel targets for treatment. Pain sometimes drugs fail clinically, causing high translational costs due to poor selection and the use of preclinical tools and reporting. To improve the study of pain in a clinical context, researchers have been creating innovative models over the past few decades that better represent pathological pain conditions. In this paper, we provide a summary of traditional animal models, including rodents, cellular models, human volunteers, and alternative models, as well as the specific characteristics of pain diseases they model. However, a more rigorous approach to preclinical research and cutting-edge analgesic technologies may be necessary to successfully create novel analgesics. The research highlights from this review emphasize new opportunities to develop research that includes animals and non-animals using proven methods pertinent to comprehending and treating human suffering. This review highlights the value of using a variety of modern pain models in animals before human trials. These models can help us understand the different mechanisms behind various pain types. This will ultimately lead to the development of more effective pain medications.

Is taxation a curse or a blessing? The case of Turkiye

This study aims to reveal whether taxes are blessings or curses based on the distinction between direct and indirect taxes. As in the past, taxation is an indispensable instrument of government policy in today’s modern state administration models. Since taxation is an inevitable phenomenon, designing an effective and efficient tax policy is an important decision process in economic development. This study will serve as a source for formulating an effective fiscal policy. Within the scope of the study, 1998/Q1 period and 2023/Q2 quarterly data for Turkiye are used. The tax curse hypothesis is analyzed using Prais-Winsten and Cumulative Fourier-frequency Toda & Yamamoto Test methods. In addition, the long-run performance of the relationship between the variables used in the model was evaluated by Wavelet Transform Coherence (WTC) analysis. The results of the analysis reveal that direct taxes have a negative impact on economic growth and thus the tax curse hypothesis is valid, while there is no tax curse in terms of indirect taxes. The study also reveals that direct taxes have a devastating effect in economies that are in the middle of the economic growth process and have a high growth rate, but this effect is not permanent and disappears after a while. The results are important in terms of guiding the formulation of government policies and the formulation of effective tax policies, especially for developing countries.

Eco-Tech Odyssey: The Political Craftsmanship of a Green Technostate in the United Arab Emirates (UAE)

The United Nations’s annual climate conference in Dubai in late 2023 provided the United Arab Emirates (UAE) with an opportunity to market their interpretation of a technology- and innovation-focused approach to addressing the climate crisis. Simultaneously, this perspective also helps ensure the future and prosperity of the oil state. This article illustrates that the behavior of the technostate is problematic because it has the potential to expedite environmental damage and endorse a carbon-intensive business as usual. A significant concern lies in the fact that the techno-utopian mode with a green makeover reinforces the autocratic nature of the state, as well as its promotion to the outside world. By using ethnographic methods, this article depicts the UAE’s ecological (super-)modernization model as an inherently political process, as it discloses facets of authority, dominance, legitimacy, and power hierarchy. Ultimately, this article emphasizes the importance of practices and techniques in the conceptual debate, challenging the prevailing focus on a simplified regime-type dichotomy in the discourse on sustainability transformation.

Цивилизационный анализ в исторической социологии и объяснения «советского коллапса»

The article deals with attempts to explain the collapse of the Soviet version of modernity –and the disintegration of the USSR—in (post)-Sovietology and historical sociology. It is argued that (post)-Sovietological studies were often characterized by a certain degree of ideological bias. In these studies, the example of “Soviet collapse” was generally used for a confirmation of earlier approaches to Soviet history. At the same time, they mostly focused on the immediate preconditions of that event rather than on long-term historical processes. The shortcomings of this approach can be overcome if we draw on historical sociology. The article considers the prediction of disintegration of the USSR offered by Randall Collins and Johann Arnason’s analysis of the Soviet version of modernity. Both sociologists characterized the Soviet collapse in the context of long-term historical dynamics. While Collins’s approach demonstrates a peculiar geopolitical determinism, Arnason singles out various factors of the dynamics of the alternative version of modernity. In the first half of the 1990s, Arnason applied his model of imperial modernization to the Soviet system, but later emphasized the civilizational aspects of communist modernity. This can also be seen in his evaluation of the Soviet collapse. In the literature on Soviet collapse, a contrast between the USSR and China is often highlighted, with structural and particularly economic factors being commonly highlighted. In contrast, the article argues that—for a comprehensive understanding of transformation processes in China—we need to consider the cultural sphere by drawing on sociological civilizational analysis.

The essence of contextual therapy, its place in the field of family therapy, and its role in the future.

This article aims to present the basic tenets of contextual therapy and its treatment goals, methodology, and theory of change. It provides an orientation for readers who are new to the approach and should serve as an updated frame of reference for readers already familiar with it. An important contribution of this article is that it clarifies the place of contextual therapy in the vast field of family therapy and dispels some common misunderstandings about the path of its founder, Ivan Boszormenyi-Nagy (1920-2007). Additionally, it demonstrates why contextual therapy should be counted among the postmodern, collaborative approaches to family therapy, which contradicts the usual view that it belongs to the earlier modern models of family therapy. Furthermore, it shows that contextual therapy is well-equipped to incorporate the current and future contributions of human sciences and offers unique therapeutic resources to address the individual and relational consequences of injustices that should remain relevant for the foreseeable future.