Hasil untuk "Law"

A. Kaveh, S. Talatahari

T. Witten, L. Sander

E. Bonnet, O. Bour, N. Odling et al.

Scaling in fracture systems has become an active field of research in the last 25 years motivated by practical applications in hazardous waste disposal, hydrocarbon reservoir management, and earthquake hazard assessment. Relevant publications are therefore spread widely through the literature. Although it is recognized that some fracture systems are best described by scale‐limited laws (lognormal, exponential), it is now recognized that power laws and fractal geometry provide widely applicable descriptive tools for fracture system characterization. A key argument for power law and fractal scaling is the absence of characteristic length scales in the fracture growth process. All power law and fractal characteristics in nature must have upper and lower bounds. This topic has been largely neglected, but recent studies emphasize the importance of layering on all scales in limiting the scaling characteristics of natural fracture systems. The determination of power law exponents and fractal dimensions from observations, although outwardly simple, is problematic, and uncritical use of analysis techniques has resulted in inaccurate and even meaningless exponents. We review these techniques and suggest guidelines for the accurate and objective estimation of exponents and fractal dimensions. Syntheses of length, displacement, aperture power law exponents, and fractal dimensions are found, after critical appraisal of published studies, to show a wide variation, frequently spanning the theoretically possible range. Extrapolations from one dimension to two and from two dimensions to three are found to be nontrivial, and simple laws must be used with caution. Directions for future research include improved techniques for gathering data sets over great scale ranges and more rigorous application of existing analysis methods. More data are needed on joints and veins to illuminate the differences between different fracture modes. The physical causes of power law scaling and variation in exponents and fractal dimensions are still poorly understood.

A. Eringen, D. Edelen

D. Sornette

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R. Stubblefield

M. Savageau

A. Bejan

Yuda Bi, Vince D Calhoun

Scaling laws, a defining feature of deep learning, reveal a striking power-law improvement in model performance with increasing dataset and model size. Yet, their mathematical origins, especially the scaling exponent, have remained elusive. In this work, we show that scaling laws can be formally explained as redundancy laws. Using kernel regression, we show that a polynomial tail in the data covariance spectrum yields an excess risk power law with exponent alpha = 2s / (2s + 1/beta), where beta controls the spectral tail and 1/beta measures redundancy. This reveals that the learning curve's slope is not universal but depends on data redundancy, with steeper spectra accelerating returns to scale. We establish the law's universality across boundedly invertible transformations, multi-modal mixtures, finite-width approximations, and Transformer architectures in both linearized (NTK) and feature-learning regimes. This work delivers the first rigorous mathematical explanation of scaling laws as finite-sample redundancy laws, unifying empirical observations with theoretical foundations.

Minwei Zhao, Sanja Scepanovic, Stephen Law et al.

Understanding how urban socio-demographic and environmental factors relate with health is essential for public health and urban planning. However, traditional statistical methods struggle with nonlinear effects, while machine learning models often fail to capture geographical (nearby areas being more similar) and topological (unequal connectivity between places) effects in an interpretable way. To address this, we propose MedGNN, a spatio-topologically explicit framework that constructs a 2-hop spatial graph, integrating positional and locational node embeddings with urban characteristics in a graph neural network. Applied to MEDSAT, a comprehensive dataset covering over 150 environmental and socio-demographic factors and six prescription outcomes (depression, anxiety, diabetes, hypertension, asthma, and opioids) across 4,835 Greater London neighborhoods, MedGNN improved predictions by over 25% on average compared to baseline methods. Using depression prescriptions as a case study, we analyzed graph embeddings via geographical principal component analysis, identifying findings that: align with prior research (e.g., higher antidepressant prescriptions among older and White populations), contribute to ongoing debates (e.g., greenery linked to higher and NO2 to lower prescriptions), and warrant further study (e.g., canopy evaporation correlated with fewer prescriptions). These results demonstrate MedGNN's potential, and more broadly, of carefully applied machine learning, to advance transdisciplinary public health research.

Thijs L van der Plas, Stephen Law, Michael JO Pocock

The growing demand for scalable biodiversity monitoring methods has fuelled interest in remote sensing data, due to its widespread availability and extensive coverage. Traditionally, the application of remote sensing to biodiversity research has focused on mapping and monitoring habitats, but with increasing availability of large-scale citizen-science wildlife observation data, recent methods have started to explore predicting multi-species presence directly from satellite images. This paper presents a new data set for predicting butterfly species presence from satellite data in the United Kingdom. We experimentally optimise a Resnet-based model to predict multi-species presence from 4-band satellite images, and find that this model especially outperforms the mean rate baseline for locations with high species biodiversity. To improve performance, we develop a soft, supervised contrastive regularisation loss that is tailored to probabilistic labels (such as species-presence data), and demonstrate that this improves prediction accuracy. In summary, our new data set and contrastive regularisation method contribute to the open challenge of accurately predicting species biodiversity from remote sensing data, which is key for efficient biodiversity monitoring.

Kai Hong Law

A selection of new results from the CMS experiment is presented. These results focus on searches for dark-sector particles using Run 2 or Run 3 data. Dedicated data streams were utilised to explore the low-mass parameter space. Machine learning techniques were employed to discriminate between signal and background.

Matthew Aldridge

If you take a superposition of n IID copies of a point process and thin that by a factor of 1/n, then the resulting process tends to a Poisson point process as n tends to infinity. We give a simple proof of this result that highlights its similarity to the law of large numbers and to the law of thin numbers of Harremoës et al.

Nick Bansback, Mina Tadrous, Marie Paul Nisingizwe et al.

Background The introduction of direct-acting antivirals (DAAs) has allowed countries to reduce the health and economic burden of hepatitis C virus (HCV). However, access to DAAs remains expensive and limited in many countries globally due to wide disparities in HCV drug pricing. We assessed how global use of HCV drugs has changed over time and the effect that COVID-19 might have had on DAA utilisation.Methods We assessed longitudinal changes in DAA sales by country income group, geographical region and drug type. We also conducted an interrupted time series analysis to assess COVID-19-related changes in the trend of DAA units sold globally. Our analysis used DAA sales data from the IQVIA multinational integrated data analysis database of 52 countries and two regions and HCV prevalence data from Polaris from 2014 to 2020. Our primary outcome was the monthly rate of DAAs sold per 100 000 people living with HCV per country, country income group and geographic region. We then compared the pre-post change in DAA units by drug type and country income group. We fitted autoregressive moving average models with a ramp function to assess the impact of COVID-19 on monthly DAA units sold.Results Across all countries, from August 2014 to August 2020, a monthly average of 44 219 DAA units per 100 000 HCV cases was sold. High-income countries purchased more units than other groups. In terms of geographic location, North America (124 144 per 100 000 HCV cases) and Europe (81 001 per 100 000 HCV cases) had the highest DAA sales over time; the newer generation of combination DAAs was mainly used in high-income countries. In contrast, first-generation and second-generation DAAs were the predominant types of DAAs sold in lower middle-income countries (LMICs). The pre-post analysis showed a 23% (p<0.001) average decrease in global sales of DAAs during the first phase of COVID-19. The decrease in LMICs (69%, p<0.001) was approximately double that of high-income countries (33%, p<0.001), while upper middle-income countries (UMICs) had a 34% (p<0.001) increase in DAA sales. The pandemic was associated with an immediate and sustained decrease of 9263 units per month (95% CI −14 668 to −3857.46) in high-income countries, a 73.14 (−850.96 to 997.24) unit increase in UMICs and a 742.58 (95% CI −5505.91 to 4020.75) unit decrease in LMICs.Conclusion Our study showed uneven access to DAAs globally, with higher prevalence-adjusted utilisation in high-income countries compared with lower-income countries. Our study also found that the COVID-19 pandemic has significantly decreased DAA sales in many countries. To counter these trends, additional strategies, such as price reductions, increased competition among manufacturers and licensing agreements, may help to improve access and utilisation of DAAs globally.

Petrović Mila

Even though menstruation is a normal part of female biology, it still represents a taboo, a source of stigma, prejudice and practices that create further rifts in already heavily divided society. Not only is such a situation unjust and offensive, but it also creates problems in the work environment. The mere fact that a woman is menstruating puts her at risk of being perceived as incompetent, creates potential hygienic and logistical issues, and, in certain cases, fairly aggravates the possibility of female employees performing at work. Such a situation calls for policies that can accommodate the needs of menstruating employees, as well as for their protection from any unwanted behavior that could weaponize an individual's innate biological feature. Hence, the purpose of this paper is to analyze the position of menstruating employees at work, as well as possible policies that can be introduced by the employer in order to facilitate their position.

Gabriel da Silveira Matos

M. Davison, D. Mccarthy

Ravinder Jangra, Etender Singh, Sunil Manglaw et al.

پیشینه: ارزیابی ظرفیت ، جزء مهمی در حفظ پایداری در بخش گردشگری است. تمام نگرانی‌ها در گردشگری به تعداد گردشگرانی که از یک مکان خاص بازدید می‌کنند، مرتبط است. منطقه مورد مطالعه دارای مناظر زیبا در اکوسیستم بیابان سرد و همچنین ویژگی‌های متمایز بودایی است که گردشگری انبوه را جذب می‌کند. امروزه، توسعه گردشگری نگرانی‌هایی را در مورد پایداری و ایجاد استانداردهایی برای قابلیت‌های مقصد گردشگری ایجاد کرده است. اهداف: مطالعه حاضر با هدف تجزیه و تحلیل اهداف زیر انجام می‌شود: 1) ارزیابی ظرفیت برد فیزیکی (PCC)، ظرفیت برد واقعی (RCC) و ظرفیت برد مؤثر (ECC) نقاط گردشگری منتخب در روستای ناکو و 2) محاسبه ظرفیت پارکینگ صومعه. روش شناسی: روش‌های مشخص‌شده در اتحادیه بین‌المللی حفاظت از طبیعت و منابع طبیعی (IUCN) برای اندازه‌گیری ظرفیت برد مقاصد گردشگری خاص در ناکو استفاده شد. تکنیک‌های سه سطحی برای ارزیابی ظرفیت برد فیزیکی (۲۸۱۶۱ نفر)، ظرفیت برد واقعی (۴۱۶۲ نفر) و ظرفیت برد مؤثر (۲۹۶۸ نفر) به کار گرفته شد. نتایج: نتایج نشان می‌دهد که ظرفیت برد مؤثر (ECC) مناسب‌ترین روش برای تخمین است و وضعیت فعلی گردشگری در منطقه مورد مطالعه کمتر از ظرفیت خود بهره‌برداری شده است. نتیجه‌گیری: سیستم‌های بسیار کوچک تا بزرگ در ناکو یافت می‌شوند و این سیستم‌ها از انواع مختلف فعالیت‌ها نیز پشتیبانی می‌کنند. گردشگری یک فعالیت بسیار رایج است و تأثیرات زیست‌محیطی، اجتماعی، فرهنگی و اقتصادی دارد. این تأثیرات به پارامترهای مختلفی وابسته بوده و با تغییر ماهیت تعامل نیز تغییر می‌کنند. مشخص شده است که وضعیت فعلی فعالیت گردشگری در منطقه مورد مطالعه در مقایسه با ظرفیت برد آن، بسیار کمتر از حد بهره‌برداری قرار گرفته است.

Jan Beránek, Tereza Blažková

Conference report