Development Of Model Research Articles

Unleash the Power of State Space Model for Whole Slide Image with Local Aware Scanning and Importance Resampling.

Whole slide image (WSI) analysis is gaining prominence within the medical imaging field. However, previous methods often fall short of efficiently processing entire WSIs due to their gigapixel size. Inspired by recent developments in state space models, this paper introduces a new Pathology Mamba (PAM) for more accurate and robust WSI analysis. PAM includes three carefully designed components to tackle the challenges of enormous image size, the utilization of local and hierarchical information, and the mismatch between the feature distributions of training and testing during WSI analysis. Specifically, we design a Bi-directional Mamba Encoder to process the extensive patches present in WSIs effectively and efficiently, which can handle large-scale pathological images while achieving high performance and accuracy. To further harness the local information and inherent hierarchical structure of WSI, we introduce a novel Local-aware Scanning module, which employs a local-aware mechanism alongside hierarchical scanning to adeptly capture both the local information and the overarching structure within WSIs. Moreover, to alleviate the patch feature distribution misalignment between training and testing, we propose a Test-time Importance Resampling module to conduct testing patch resampling to ensure consistency of feature distribution between the training and testing phases, and thus enhance model prediction. Extensive evaluation on nine WSI datasets with cancer subtyping and survival prediction tasks demonstrates that PAM outperforms current state-of-the-art methods and also its enhanced capability in modeling discriminative areas within WSIs. The source code is available at https://github.com/HKU-MedAI/PAM.

A REVIEW ONIN VITRO AND IN VIVO MODELADVANCEMENTS FOR THE STUDY OF ESCHERICHIACOLI-INDUCED URINARY TRACT INFECTIONS

Urinary tract infections (UTIs) are bacterial infections that affect public health and are caused more frequently due to Uropathogenic Escherichia coli (UPEC). UPEC uses a wide range of virulence factors like adhesins, biofilm formation, fimbriae, and immune evasion techniques for persistent, recurrent infection and antimicrobial resistance. The need for improved diagnostic methods, treatments, and protective actions is raised due to the emergence of Multidrug-resistant (MDR) UPEC strain which has complicated the present treatment plans. Traditional methods like Invitro & Invivo models helped in studying the UTI host-pathogen interaction & pathogenesis but have constraints in studying long-term infections and also in recreating human urinary tract conditions. The latest innovations in experimental models such as bladder organoids, dynamic microfluidic systems, and murine & zebrafish models improved physiological relevance and understanding of UPEC behaviour and newer treatment methods. These models help us to deepen our knowledge of antibiotic resistance, biofilm dynamics, and host immune responses which enable us to develop novel therapeutic approaches. UTI research has fastened after technological innovations in genome & transcriptomic analyses, imaging techniques, and high-throughput screening. To deal with MDR UPEC, newer treatment methods like vaccines, phage therapy and anti-virulence agents are being delved into along with antibiotics. In addition, improved invitro and invivo models are used to develop vaccines specific to UPEC. This review focuses on the developments in experimental models and methods to study E.coli-induced UTIs, mainly focusing on its purpose in studying pathogenesis, improving preventive measures, and overcoming treatment challenges. These integrated innovations are critical to tackling the rising MDR UPEC and the need for personalized treatment to decrease the worldwide burden of UTIs.

PENGEMBANGAN ALAT PERAGA KOTAK SIFAT CAHAYA SEBAGAI MEDIA PEMBELAJARAN IPAS SISWA SD

This study aims to develop and test the validity and practicality of a light properties box as a learning media for students at UPTD SDI Ngoramawo. This teaching aid is designed to help students understand concepts of light properties, such as light traveling in straight lines, being reflected, refracted, and passing through transparent objects. The research uses a Research and Development (R&D) approach with a 3D development model (Define, Design, and Develop). The study was conducted at UPTD SDI Ngoramawo, Bajawa Subdistrict, Ngada Regency, East Nusa Tenggara Province, involving 5 fourth-grade students and 1 teacher. Data analysis was performed by subject matter experts, design experts, and language experts, all of whom are IPAS teachers. The data used includes quantitative data from expert validation results and responses from students and teachers through questionnaires, as well as qualitative data in the form of suggestions and feedback from the evaluators. The validation results showed a percentage of 90% from the subject matter expert, indicating that the teaching aid is considered highly valid for helping students understand the concepts being taught. This result shows that the teaching aid aligns with the learning objectives and can be easily understood by students. A score of 87% from the design expert reflects the evaluation of the visual and functional aspects of the teaching aid. With this value, the teaching aid is deemed to have a good and effective design in delivering the material, although there may still be small aspects that can be improved. The language expert gave a score of 82%. Teacher responses through the questionnaire reached 90.7%, while student responses reached 88%. The developed light properties box demonstrates high validity and practicality for use in IPAS learning in fourth-grade classrooms. The light properties box developed shows high validity with validation percentages of 90% from the subject matter expert, 87% from the design expert, and 82% from the language expert, indicating that the teaching aid aligns with the learning objectives, has an effective design, and uses clear, easily understandable language. Its practicality is confirmed through positive responses from teachers (90.7%) and students (88%), showing that the teaching aid is easy to use in daily learning and effective in helping students understand the concepts being taught

Modelling of the Bolted Joint in Relation to the Working Load of the Bolt

The paper presents a piece of research carried out on the development of modelling and calculation methods for multi-bolted connections. The influence of the modelling of the separated bolted joint on the value of the working load in the bolt was considered. A stiffness analysis of the bolted joint for selected linear and non-linear models was carried out. Guidelines for the selection of the modelling method for the analysis of working loads occurring in a bolted joint are discussed.

Mapping neutron biological effectiveness for DNA damage induction as a function of incident energy and depth in a human sized phantom

We present new developments for an ab-initio model of the neutron relative biological effectiveness (RBE) in inducing specific classes of DNA damage. RBE is evaluated as a function of the incident neutron energy and of the depth inside a human-sized reference spherical phantom. The adopted mechanistic approach traces neutron RBE back to its origin, i.e. neutron physical interactions with biological tissues. To this aim, we combined the simulation of radiation transport through biological matter, performed with the Monte Carlo code PHITS, and the prediction of DNA damage using analytical formulas, which ground on a large database of biophysical radiation track structure simulations performed with the code PARTRAC. In particular, two classes of DNA damage were considered: sites and clusters of double-strand breaks (DSBs), which are known to be correlated with cell fate following radiation exposure. Within a coherent modelling framework, this approach tackles the variation of neutron RBE in a wide energy range, from thermal neutrons to neutrons of hundreds of GeV, and reproduces effects related to depth in the human-sized receptor, as well as to the receptor size itself. Besides providing a better mechanistic understanding of neutron biological effectiveness, the new model can support better-informed decisions for radiation protection: indeed, current neutron weighting (ICRP)/quality (U.S. NRC) factors might be insufficient for use in some radiation protection applications, because they do not account for depth. RBE predictions obtained with the reported model were successfully compared to the currently adopted radiation protection standards when the depth information is not relevant (at the shallowest depth in the phantom or for very high energy neutrons). However, our results demonstrate that great care is needed when applying weighting factors as a function of incident neutron energy only, not explicitly considering RBE variation in the target. Finally, to facilitate the use of our results, we propose look-up RBE tables, explicitly considering the depth variable, and an analytical representation of the maximal RBE vs. neutron energy.

Pathogenesis, Diagnosis, and Management of Trigeminal Neuralgia: A Narrative Review.

Trigeminal neuralgia (TN) is an excruciating neurological disorder characterized by intense, stimulus-induced, and transient facial stabbing pain. The classification of TN has changed as a result of new discoveries in the last decade regarding its symptomatology, pathogenesis, and management. Because different types of facial pain have different clinical therapy and neuroimaging interpretations, a precise diagnosis is essential. Diagnosis should include magnetic resonance imaging with specific sequences to rule out secondary causes and to identify possible neurovascular contact. The purpose of demonstrating a neurovascular contact is to aid in surgical decision making, not to validate a diagnosis. Microvascular decompression is the first-line procedure for individuals who do not respond to medical management, whereas carbamazepine and oxcarbazepine are the preferred medications for long-term care. New developments in animal models and neuroimaging methods will shed more light on the biology and etiology of TN. This paper reviews the pathogenesis, the clinical features, the diagnosis, and the management of TN. Furthermore, the potential role of low-intensity pulsed ultrasound in neurological disorders is discussed.

Chiral Effective Model of Cold and Dense Two-Color QCD: The Linear Sigma Model Approach

This review is devoted to summarizing recent developments of the linear sigma model (LSM) in cold and dense two-color QCD (QC2D), in which lattice simulations are straightforwardly applicable thanks to the disappearance of the sign problem. In QC2D, both theoretical and numerical studies derive the presence of the so-called baryon superfluid phase at a sufficiently large chemical potential (μq), where diquark condensates govern the ground state. The hadron mass spectrum simulated in this phase shows that the mass of an iso-singlet (I=0) and 0− state is remarkably reduced, but such a mode cannot be described by the chiral perturbation theory. Motivated by this fact, I have invented a LSM constructed upon the linear representation of chiral symmetry, more precisely Pauli–Gürsey symmetry. It is shown that my LSM successfully reproduces the low-lying hadron mass spectrum in a broad range of μq simulated on the lattice. As applications of the LSM, topological susceptibility and sound velocity in cold and dense QC2D are evaluated to compare with the lattice results. Additionally, the generalized Gell–Mann–Oakes–Renner relation and hardon mass spectrum in the presence of a diquark source are analyzed. I also introduce an extended version of the LSM incorporating spin-1 hadrons.

Human Formation in the Age of Automation

ABSTRACTThis chapter responds to the recent crisis surrounding developments in large language models (LLMs) and generative AI with a relational view of education informed by the emerging world‐centered approach to education and a synthesis of personalist character formation with feminist care ethics. It proposes that the instinct to manage student use of generative AI with carceral practices, characterized by surveillance and control, undermines the project of forming students as whole persons who can evaluate and use or reject emerging technologies in a mature way. The chapter concludes with practical suggestions for the classroom that can be aligned with institutional strategic plans.

Low-Cost, High-Fidelity 3D-Printed Temporomandibular Joint (TMJ) Dislocation Reduction Task Trainer.

Temporomandibular joint (TMJ) dislocation is an emergency that requires prompt reduction to relieve pain and restore function. However, practicing reduction techniques can be challenging due to the limited availability of real-world cases and high-fidelity task trainers. Hands-on TMJ dislocation reduction task trainers are not readily available, highlighting the need to improve traditional education methods to provide more effective and accessible hands-on learning opportunities. To address this gap, we developed a low-cost, high-fidelity three-dimensional (3D) printed TMJ dislocation reduction task trainer using open-source 3D computer-assisted design files. This innovative simulator provides a realistic, anatomically accurate model to teach and practice TMJ dislocation reduction techniques. The task trainer was designed using 3D printing technology and inexpensive, readily available materials, significantly reducing production costs while maintaining high anatomical fidelity. The model replicates key anatomical features of the TMJ, including the mandibular condyles and articular surfaces, allowing for realistic hand placement, joint manipulation, and reduction procedures. A stepwise approach was followed in the model's development, including iterative printing and validation through expert feedback from emergency medicine, oral and maxillofacial surgery, and simulation experts. The final product was evaluated in a pilot study, where resident physicians and medical students practiced reduction techniques on the trainer. Participants reported high satisfaction with the realism of the task trainer, particularly regarding tactile feedback and ease of manipulation. With a 3D printer already available, cost analysis demonstrated that the materials for each task trainer cost less than $20. This cost-effective solution has the potential to democratize access to quality TMJ reduction training worldwide. In addition, the modular design of the task trainer allows for customization, offering opportunities for further advancements in medical simulation education. Future areas of investigation include further validation studies to assess skill transfer to clinical settings and incorporation of this task trainer into emergency medicine and dental training programs. We believe that this low-cost, high-fidelity model will play a critical role in improving hands-on training for TMJ dislocation reduction, benefiting both learners and patients by enhancing clinical preparedness.

Protein CREATE enables closed-loop design of de novo synthetic protein binders.

Proteins have proven to be useful agents in a variety of fields, from serving as potent therapeutics to enabling complex catalysis for chemical manufacture. However, they remain difficult to design and are instead typically selected for using extensive screens or directed evolution. Recent developments in protein large language models have enabled fast generation of diverse protein sequences in unexplored regions of protein space predicted to fold into varied structures, bind relevant targets, and catalyze novel reactions. Nevertheless, we lack methods to characterize these proteins experimentally at scale and update generative models based on those results. We describe Protein CREATE (Computational Redesign via an Experiment-Augmented Training Engine), an integrated computational and experimental pipeline that incorporates an experimental workflow leveraging next generation sequencing and phage display with single-molecule readouts to collect vast amounts of quantitative binding data for updating protein large language models. We use Protein CREATE to generate and assay thousands of designed binders to IL-7 receptor α and insulin receptor with parallel positive and negative selections to identify on-target binders. We discover not only individual novel binders but also features of ligand-receptor binding, including preservation of the IL7Rα - ligand hydrophobic interface specifically and existence of multiple approaches to contact the insulin receptor. We also demonstrate the importance of structural features, such as the lack of unpaired cysteine residues, toward design fidelity and find computational pre-screening metrics, such as interchain predicted TM scoring (iPTM), while useful, are imperfect predictors as they neither guarantee experimental binding nor rule it out. We use the data collected from Protein CREATE to score designs from the initial generative models. Globally, Protein CREATE will power future closed-loop design-build-test cycles to enable fine-grained design of protein binders.

Graphical User Interface for Handwritten Mathematical Expression Employing RNN-based Encoder-decoder Model

Aim: Scientific, technical, and educational research domains all heavily rely on handwritten mathematical expressions. The extensive use of online handwritten mathematical expression recognition is a consequence of the availability of strong computational touchscreen appliances, such as the recent development of deep neural networks as superior sequence recognition models. Background: Further investigation and enhancement of these technologies are vital to tackle the contemporary obstacles presented by the widespread adoption of remote learning and work arrangements as a result of the global health crisis. Objective: Handwritten document processing has gained more attention in the last ten years due to notable developments in deep neural network-based computer vision models and sequence recognition, as well as the widespread proliferation of touch and pen-enabled smartphones and tablets. It comes naturally to people to write by hand in daily interactions. Method: In this patent article, authors implemented Hand written expressions using RNNbased encoder for the CROHME dataset. Later, the proposed model was validated using CNNbased encoder and End-to-end encoder decoder techniques. The proposed model is also validated on other datasets. Results: The RNN-based encoder model yields 82.78%, while the CNN-based encoder model and end-to-end encoder-decoder technique yield 81.38% and 80.73%, respectively. Conclusion: 1.6% accuracy improvement was attained over CNN-based encoder while 2.4% accuracy improvement over end-to-end encoder-decoder. CROHME dataset 2019 version results in better accuracy than other datasets.

A 1D-FDM Transmission Line Model for Partial Discharge Current Simulations Validated Against Needle-Plate HVDC Laboratory Experiments: Calculations of Ionized Air Conductivity

Abstract In this paper, we present a novel numerical model for simulating partial discharges (PDs) transient currents excited by high-voltage DC (HVDC) systems. The model is based on the telegraph equations, which are solved in the time domain via the finite-difference method (FDM). With the proposed model, one can simulate air ionization through its distinct phases based on the electric field’s magnitude. This study specifically provides effective electrical conductivity of ionized air over time and space, calculated in the discharge channel also as a function of voltage and gap distance in a needle-plate setup. Validation is performed against experimental results from our laboratory experiments, demonstrating agreement despite the model’s one-dimensional nature. The simplicity of the model leads to much smaller simulation times (up to two minutes) compared to more complex three-dimensional models (typically requiring hours to complete), highlighting its potential for efficient PD analysis and future developments of general PD models based on effective plasma conductivity.

Circular economy model and sustainable development nexus in Bangladesh

Circular economy model and sustainable development nexus in Bangladesh

USING A LARGE LANGUAGE MODEL TO CLASSIFY DEMENTIA CAREGIVERS’ HEALTH INFORMATION WANTS

Abstract Many caregivers of persons living with dementia use social media to express dementia care-related health information wants (HIWs), i.e., types of information caregivers seek that can help them manage challenges and concerns. Recent developments in large language models (LLMs) hold promise in automatic classification of HIWs expressed in large quantities of social media posts. As a first step, we evaluated GPT-3.5 Turbo’s performance in classifying 60 social media posts based on our previously developed HIW framework, which includes 7 first-level categories, including daily care. Under daily care, there are 7 second-level categories and a total of 32 third-level subcategories. All 60 posts were previously verified by 3 experienced clinicians as being in the daily care category; 40 posts were in the second-level category of mood and behavior management and 20 in the second-level category of safety; and 20 posts in each of the 3 third-level categories: agitation; memory loss and confusion; and driving safety. We asked GPT-3.5 Turbo to classify the posts on the first level; 14 (23%) were accurately classified to daily care. Next, we asked GPT-3.5 Turbo to classify those 14 posts into one of the second-level categories in daily care; 10 (71%) were accurately classified into mood and behavior management or safety. Finally, we asked GPT-3.5 Turbo to classify the 10 remaining posts into the third-level subcategories. Seven (70%) were accurately classified: agitation 5/5 (100%), driving safety 2/2 (100%) and memory loss and confusion 0/3 (0%). Disappointed by GPT-3.5 Turbo’s performance, we discuss plausible reasons and solutions.

A Review of Skin Disease Detection Using Deep Learning

Amid increasing concerns about skin diseases exacerbated by climate change or lifestyle, some diseases are undiagnosed or misdiagnosed due to limited healthcare facilities. The worldwide health burden emphasizes the need for innovative diagnostics. This study explores the evolutionary role of deep learning in skin disease detection, providing the most advanced and effective research approaches, model achievements, and dataset usage exclusively. The review adapts data from 30 research papers and many datasets to address imbalanced class and various efficiency factors. The developments in CNN models like MobileNet or EfficientNet, have strengthened computational potential, while hybrid models have accommodated local and global features. Furthermore, Explainable AI (EXI) and augmented datasets have overcome the challenges including noisy, biased datasets and the less interpretable AI models. This study declares the innovative capacity of deep learning in dermatological analysis, highlighting its scalability and performance. Future research is required to consider dataset diversity, interpretability, and incorporating medical metadata to enhance model performances.

Getting democracy wrong

Recent developments in large language models and computer automated systems more generally (colloquially called ‘artificial intelligence’) have given rise to concerns about potential social risks of AI. Of the numerous industry-driven principles put forth over the past decade to address these concerns, the Future of Life Institute’s Asilomar AI principles are particularly noteworthy given the large number of wealthy and powerful signatories. This paper highlights the need for critical examination of the Asilomar AI Principles. The Asilomar model, first developed for biotechnology, is frequently cited as a successful policy approach for promoting expert consensus and containing public controversy. Situating Asilomar AI principles in the context of a broader history of Asilomar approaches illuminates the limitations of scientific and industry self-regulation. The Asilomar AI process shapes AI’s publicity in three interconnected ways: as an agenda-setting manoeuvre to promote longtermist beliefs; as an approach to policy making that restricts public engagement; and as a mechanism to enhance industry control of AI governance.

Current Developments and Innovations in Early Detection and Subsequent Treatment of Cancer

Objective: The study aimed to identify key trends in modern oncology by analysing developments and innovations in early cancer diagnosis and treatment methods. Using a comparative analysis of scientific and healthcare systems in Albania, Bulgaria, Kyrgyzstan, and Uzbekistan, the study examined innovative diagnostic approaches such as liquid biopsy, biomarker discovery, genetic testing, advanced imaging techniques, and artificial intelligence algorithms. Methods: For treatment, it highlighted immunotherapy, personalised medicine, cellular, targeted, and combination therapies, as well as the development of radiopharmaceuticals and 3D modelling for surgical planning. Results: Key findings revealed that the lack of economic support for research is the primary barrier to innovation in all four countries. Bulgaria, benefiting from European Union membership, demonstrated the highest potential for advancing oncology due to its stronger scientific, technical, regulatory, and social indicators. In contrast, Albania's transition economy and Kyrgyzstan’s social and geographical challenges significantly hinder progress. The findings underline the need for enhanced economic investment, international cooperation, and regulatory support to address disparities and foster the implementation of innovative oncology practices globally. Conclusion: This regional analysis provides insights into how tailored approaches can bridge the gap between low- and high-income countries in advancing cancer care.

Beograd: kulturna baština i inspiracija u likovnoj radionici

The paper analyzes the development and application of distance learning as a modern educational model that uses information and communication technologies. It presents the stages of this model's development, from correspondence learning to modern online platforms. It describes different forms of distance learning, their challenges and advantages, and the impact of the COVID-19 pandemic on increasing the application of this model. The paper highlights the importance of distance learning in improving the quality of education and adapting it to the individual needs of students.

Priroda i osnovne karakteristike obrazovanja na daljinu

The paper analyzes the development and application of distance learning as a modern educational model that uses information and communication technologies. It presents the stages of this model's development, from correspondence learning to modern online platforms. It describes different forms of distance learning, their challenges and advantages, and the impact of the COVID-19 pandemic on increasing the application of this model. The paper highlights the importance of distance learning in improving the quality of education and adapting it to the individual needs of students.

The role of 4IR technologies and organizational culture in enhancing manufacturing supply chain performance

This study seeks to create a model for the management of the steel production supply chain in South Africa amid the Fourth Industrial Revolution (4IR), tackling persistent interruptions and delays within the supply chain despite current models. A mixed methodology was utilised in the model's development. The qualitative component entailed a Delphi research corroborated by literature to ascertain pertinent hypotheses, whereas the quantitative component validated the model through structural equation modelling (SEM) utilising SmartPLS software. The research delineated a seven-construct framework comprising cooperation, integration, communication, supply chain structure, Fourth Industrial Revolution technology, organisational culture, and trust. The amalgamation of these designs proved enough for improving the efficacy of the steel production supply chain. The research revealed that the use of Fourth Industrial Revolution technologies and the cultivation of a conducive organisational culture are essential for effective supply chain management in the steel manufacturing industry. The research presents a detailed model that facilitates the planning and organisation of steel industry supply networks in South Africa. This approach underscores the necessity of a cooperative organisational culture, client contentment, and the incorporation of Fourth Industrial Revolution technologies. Steel industry stakeholders are urged to implement adaptable supply chain frameworks (lean, agile, leagile) based on project goals. This study enhances theoretical frameworks by highlighting essential features that have not been previously incorporated into supply chain management models for the steel sector, including the integration of Fourth Industrial Revolution technology and organisational culture. It provides actionable insights for stakeholders in steel manufacture, enhancing supply chain performance via the proposed paradigm. The research methodology improves comprehension of supply chain management within the framework of the Fourth Industrial Revolution (4IR).