Hasil untuk "Science (General)"

F. Ludlam, B. J. Mason

M. Baker, Gilbert A. Churchill

R. Lent, Steven D. Brown, Kevin C. Larkin

D. Angluin, Carl H. Smith

J. Richards

P. Godfrey‐Smith

What makes science different from other ways of investigating the world? In "Theory and Reality" Peter Godfrey-Smith uses debates - such as the problem of confirmation, the new riddle of induction, and the problem of scientific realism - as a way to introduce, in a completely accessible way, the main themes in the philosophy of science. Intended for undergraduates and general readers with no prior background in philosophy, "Theory and Reality" starts by surveying the last hundred years of work in the field. It covers logical positivism; induction and confirmation; Karl Popper's theory of science; Thomas Kuhn and "scientific revolutions"; the radical views of Imre Lakatos, Larry Laudan and Paul Feyerabend; and challenges to the field from sociology of science, feminism and science studies. The book then looks in detail at some of the broader philosophical issues at stake, such as philosophical naturalism, scientific realism, theories of explanation in science, Bayesianism, and other modern theories of explanation in science. Throughout the text he points out connections between philosophical debates and wider discussions about science in recent decades, such as the infamous "science wars". Examples and asides engage the beginning student, a glossary of terms explains key concepts, and suggestions for further reading are included at the end of each chapter. Like no other text in this field, "Theory and Reality" combines a survey of recent history of the philosophy of science with current key debates in language that any beginning scholar or critical reader can follow.

K. Ariga, Jonathan P. Hill, Michael V. Lee et al.

Abstract The controlled fabrication of nanometer-scale objects is without doubt one of the central issues in current science and technology. However, existing fabrication techniques suffer from several disadvantages including size-restrictions and a general paucity of applicable materials. Because of this, the development of alternative approaches based on supramolecular self-assembly processes is anticipated as a breakthrough methodology. This review article aims to comprehensively summarize the salient aspects of self-assembly through the introduction of the recent challenges and breakthroughs in three categories: (i) types of self-assembly in bulk media; (ii) types of components for self-assembly in bulk media; and (iii) self-assembly at interfaces.

A. Morgan, J. Gilman

James T. Croasmun, L. Ostrom

Jun Zhang, Chuan Zhang, Mingtao Zhang

ABSTRACT This study integrates embodied intelligence (EI) with a two‐stage two‐sided Hotelling duopoly model to reveal how physical intelligence reshapes digital platform equilibrium in intelligent logistics. By embedding EI‐driven efficiency parameters into market cost functions, the model demonstrates that improved perception and coordination reduce the effective transportation cost and transform pricing dynamics between competing platforms. Experiments in a digital twin warehouse show that when EI strength η increases from 0 to 0.6, throughput rises by 37.5%, Dock‐to‐Stock time decreases by 30.9%, and unit energy consumption drops by 7%–8%, verifying that EI directly enhances operational and economic efficiency. Further analysis confirms that asymmetric advantages in action or data lead to discriminatory pricing as the optimal strategy. Complementary encryption experiments indicate that lightweight security algorithms such as SHA‐1/SHA‐256 add less than 3% latency overhead, maintaining real‐time performance.

Li Ke, T. Sadler, Laura Zangori et al.

Learning science in the context of socio-scientific issues (SSI) can promote scientific literacy that links science to everyday life and society. In this position paper, we argue that developing and using multiple models equip students with the appropriate knowledge and skills needed to deal with complex issues. We draw upon literature from science education and philosophy of science and advance our theoretical argument about why it is critical for students to develop and use multiple models as part of their science learning experiences in general, and how the practice benefits students in the context of SSI in particular. We posit that students should engage in both scientific and socio-scientific models as they explore a complex societal issue because (1) engagement in multiple scientific models promotes students’ understanding about the phenomena relevant to the focal issue, and (2) engagement in socio-scientific models helps students to use that scientific knowledge in the larger social contexts and reason about how interacting science and social factors may impact students’ positions on the complex issue. We take COVID-19 as the learning context and present exemplar models students can develop and use as they learn about the pandemic. We conclude the paper by discussing the teaching aspects of the proposed modeling approach for SSI-based instruction as well as identifying possible areas for future research.

S. Duane, Hansen bullet, Benjamin B. Dunford et al.

F. Spellman

The climate agreement reached in Paris last December establishes a global commitment to address climate change by reducing emissions of CO 2 and other greenhouse gasses in the coming decades. While long-term solutions to the challenge of sustainable energy generation rely on the direct or indirect conversion of solar energy, many of these solutions may be years from implementation. Meanwhile carbon emissions from existing fossil fuel infrastructure continue. Carbon Capture and Sequestration (CCS) employed on a global scale can mitigate the alarmingly high CO 2 levels in the atmosphere. This course will introduce students in science and engineering disciplines to energy and climate considerations in general and to state-of-the art research in CCS specifically. The participants will be made aware of current energy consumption, understanding the role of CO 2 in the earth and its atmosphere as a system, the science and technology of capturing CO 2 , the geological storage of CO 2 , alternative approaches to reducing CO 2 in the atmosphere

Xiuwen Chen, Jianming Chen, Dengsheng Wu et al.

Abstract In this paper, a co-word method based on keywords from funded project is proposed to map the research trends. Firstly, the keywords of funded project are used to describe the rsearch topic statistically. Then, co-word analysis, including cluster analysis, social network analysis, is adopted to study the relationship of each research topic. The projects of Management Science and Engineering in National Natural Science Foundation of China during 2011-2015 are collected as the empirical data. The data is composed of General Project, Youth Project, and Regional Project. The results show that the focus of researches are Game Theory, Supply Chain Management, Complex Network, Data Mining, Optimize, Risk Management, and Data Envelopment Analysis. Moreover, Game Theory, Supply Chain Management, and Data Mining are hot topics. The research fields in Management Science and Engineering in China are varied, and the well developed and core research fields are fewer.

Husson Ara, Nasreen Naz, Ayesha Walid et al.

Background: Adenomyosis is an important benign gynecological condition among females with variable signs and symptoms. Prompt detection of suspicious cases is important for the effective management of the disease. The objective of the current study was to determine the frequency of adenomyosis on transvaginal ultrasound (TVS), its diagnostic accuracy, and the identification of associated factors in women with symptoms of adenomyosis. Methods: This cross-sectional study was carried out at the radiology department of Dow University Hospital, Karachi, Pakistan from January 2022 to March 2023. All married females of reproductive age group presented with symptoms of adenomyosis for more than 7 days were included. Adenomyosis on TVS and histopathology were noted. Moreover, associated factors of adenomyosis were also studied. Results: Of 280 patients, adenomyosis on TVS was observed in 180 (64.3%) patients whereas on histopathology in 176 (62.9%) patients.  Diagnostic accuracy of adenomyosis on TVS showed that sensitivity was 89.20%, specificity 77.88%, positive predicted value 87.22%, negative predicted value 81.00%, and accuracy was found to be 85.00%. A significantly higher proportion of adenomyosis was observed among women who had infertility (p<0.001), symptoms of dysmenorrhea (p <0.001), dyspareunia (p<0.002), urinary symptoms (p <0.001), and GI symptoms (p<0.001). Conclusion: TVS is a valuable imaging modality for identifying adenomyosis, especially in patients with clinical symptoms. Furthermore, there is a significant association between adenomyosis and various clinical symptoms, including infertility, dysmenorrhea, dyspareunia, urinary symptoms, and gastrointestinal symptoms.

LIU Shuxian, XU Huan, WANG Wei, DENG Le

Social network provides a medium for information dissemination,leading to the rapid development of public opinion.Controlling the development direction of public opinion is one of the core issues of public opinion dynamics.However,the public opinion dynamics model mainly studies the way of updating the opinions of the subject so as to deduce the law of public opinion evolution.This paper classifies the current public opinion dynamics models,analyzes their advantages and disadvantages,and their applications in different fields,and summarizes the future research direction of public opinion dynamics.It is helpful to understand the law of the evolution of public opinion,so as to provide better guidance for the government and other institutions to control the direction of public opinion.

Wenqiang Liu, Li Yan, Ningning Ma et al.

With the rapid development of deep learning, researchers are actively exploring its applications in the field of industrial anomaly detection. Deep learning methods differ significantly from traditional mathematical modeling approaches, eliminating the need for intricate mathematical derivations and offering greater flexibility. Deep learning technologies have demonstrated outstanding performance in anomaly detection problems and gained widespread recognition. However, when dealing with multivariate data anomaly detection problems, deep learning faces challenges such as large-scale data annotation and handling relationships between complex data variables. To address these challenges, this study proposes an innovative and lightweight deep learning model—the Attention-Based Deep Convolutional Autoencoding Prediction Network (AT-DCAEP). The model consists of a characterization network based on convolutional autoencoders and a prediction network based on attention mechanisms. The AT-DCAEP exhibits excellent performance in multivariate time series data anomaly detection without the need for pre-labeling large-scale datasets, making it an efficient unsupervised anomaly detection method. We extensively tested the performance of AT-DCAEP on six publicly available datasets, and the results show that compared to current state-of-the-art methods, AT-DCAEP demonstrates superior performance, achieving the optimal balance between anomaly detection performance and computational cost.

David Barrett, Robert Falconer

Ferenc Sztanek, László Imre Tóth, Attila Pető et al.

Guidelines for the management of obesity and type 2 diabetes (T2DM) emphasize the importance of lifestyle changes, including a reduced-calorie diet and increased physical activity. However, for many people, these changes can be difficult to maintain over the long term. Medication options are already available to treat obesity, which can help reduce appetite and/or reduce caloric intake. Incretin-based peptides exert their effect through G-protein-coupled receptors, the receptors for glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic polypeptide (GIP), and glucagon peptide hormones are important regulators of insulin secretion and energy metabolism. Understanding the role of intercellular signaling pathways and inflammatory processes is essential for the development of effective pharmacological agents in obesity. GLP-1 receptor agonists have been successfully used, but it is assumed that their effectiveness may be limited by desensitization and downregulation of the target receptor. A growing number of new agents acting on incretin hormones are becoming available for everyday clinical practice, including oral GLP-1 receptor agonists, the dual GLP-1/GIP receptor agonist tirzepatide, and other dual and triple GLP-1/GIP/glucagon receptor agonists, which may show further significant therapeutic potential. This narrative review summarizes the therapeutic effects of different incretin hormones and presents future prospects in the treatment of T2DM and obesity.

Jongho Lee, Jiuk Shin, Jaewook Lee et al.

Large fires in factories cause severe human casualties and property damage. Thus, preparing more economical and efficient management strategies for fire prevention can significantly improve fire safety. This study deals with property damage grade prediction by fire based on simplified building information. This paper’s primary objective is to propose and verify a framework for predicting the scale of property damage caused by fire using machine learning (ML). Korean public datasets are collected and preprocessed, and ML algorithms are trained with only 15 input data using building register and fire scenario information. Four models (artificial neural network (ANN), decision tree (DT), k-nearest neighbor (KNN), and random forest (RF)) are used for ML. The RF model is the most suitable for this study, with recall and precision of 74.2% and 73.8%, respectively. Structure, floor, causes, and total floor area are the critical factors that govern the fire size. This study proposes a novel approach by utilizing ML models to accurately and rapidly predict the size of fire damage based on basic building information. By analyzing domestic fire incident data and creating fire scenarios, a similar ML model can be developed.