13361 research outputs found
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Solving flood problems with deep learning technology: Research status, strategies, and future directions
No full textAs a frequent and devastating natural disaster worldwide, floods are influenced by complex factors. Building flood models for simulating, monitoring, and forecasting floods is crucial to reduce the risk of disasters and minimize damage to people and property. With advancements in computing power and the impressive capabilities of deep learning in such areas as classification and prediction, there has been growing interest in using this technology in flood research. There is also a growing body of research into building flood data-driven models with deep learning. Based on this, this study adopts a mixed-method approach of bibliometric and qualitative analyses to provide an overview of the research. The research status is revealed in a bibliometric visualization, where the research objects are defined from the flood perspective, and the research strategies are explained from the deep learning perspective to provide a comprehensive and in-depth understanding of the flood problem and how to apply deep learning to solve it. In addition, the study reflects on the future direction of improvement and innovation needed to promote the further development and exploration of deep learning in flood research.</p
Book Review: Philosophies, Puzzles and Paradoxes: A Statistician's Search for Truth, by Yudi Pawitan and Youngjo Lee. CRC Press. 2024. xxvii+ 323 pp., ISBN 978-1-032-37739-1
No full textPhilosophies, Puzzles and Paradoxes: A Statistician Search for Truth explores the three major approaches to statistical thinking—Bayesian, Frequentist, and Likelihood—through a series of engaging puzzles and paradoxes. The book offers a distinctive perspective for statisticians and academicians interested in the philosophical foundations underlying probability and statistical concepts
Socioeconomic Variation in the Association Between Participation in Cardiac Rehabilitation and Clinical Outcomes in Patients With Acute Coronary Syndrome
No full textPurpose: To investigate (1) the relationship between socioeconomic status of patients with acute coronary syndrome and participation in cardiac rehabilitation and (2) the relationship between patient participation stratified by socioeconomic status and their outcomes at 12 months.Methods: Analyzed data were from the CONCORDANCE registry. Patients were stratified (quintiles) according to the National Index of Relative Socio-Economic Disadvantage. The odds of a major adverse cardiovascular event (MACE; defined as heart failure, myocardial infarction, stroke, or cardiac-cause death) and separately all-cause death between hospital discharge and 12 months were analyzed using multilevel logistic regression models, adjusting for clinical history and hospital clustering.Results: Of 3787 patients referred to cardiac rehabilitation, followed up at 6 and 12 months, 1834 (48%) participated in cardiac rehabilitation. Participation rate was higher among patients in least socioeconomically disadvantaged quintiles (Q5 [least disadvantaged]: 61%, Q4: 53%, Q3: 42%, Q2: 47%, Q1 [most disadvantaged]: 42%). The odds of MACE were not different between participants and non-participants (6% vs 8%, OR = 0.87: 95% CI, 0.66-1.15). However, the odds of death were lower among participants than non-participants (0.4% vs 2%, OR = 0.35: 95% CI, 0.16-0.78). The association between participation and MACE and death did not differ by socioeconomic status (Pinteraction = .6943 and Pinteraction = .6339, respectively).Conclusions: Although patient socioeconomic status may influence their participation rates in cardiac rehabilitation, no significant differences were observed in the relationships between participation and MACE or mortality at 12 months across socioeconomic groups. Targeted strategies are needed to improve participation rates across all socioeconomic groups
Understanding researchers' perceptions and experiences in finance research replication studies: A pre-registered study
Full text linkThis pre-registered study executes the empirical design approved in the associated pre-registered report (Chai et al., 2024) to survey authors of replication studies at the Pacific-Basin Finance Journal (PBFJ). The survey aims to understand their motivations, challenges encountered, and perceived benefits, as well as their evaluations of the replication framework. The findings reveal strong support for replication studies, with participants emphasizing their role in fostering a more robust research culture in financial economics. However, challenges in reproduction and replication frequently arise due to issues with the accessibility and quality of open-source materials provided in the original studies. Our results also suggest that PBFJ's initiative offers a replicable model for other journals, providing actionable insights to address the replication crisis. Key recommendations include providing academic recognition for replication work, standardizing data transparency mandates, and promoting cross-disciplinary dialogue on robust research practices
Soldier load carriage: Does the type of pack matter?
Full text linkHow a soldier's load is carried can elicit different physical and physiological costs on the carrier. As such, this study aimed to profile and compare the impacts of three different load carriage backpack systems on physical and physiological outcomes during and following a load carriage march. Twelve soldiers were randomly allocated to one of three pack variants (Variant A, B, or C) using a Latin Square design and completed three 5 km load carriage marches (30 kg at 5.5 km/h) over three separate sessions wearing the different Variants for each march. Outcome measures during the march were heart rate and oxygen consumption and pre and post march were a counter movement jump, grip strength, and postural sway. There were no significant differences (p > 0.01) in any of the objective outcome measures across pack Variants. These results suggest that load weight will impact on the physical and physiological costs associated with load carriage to a greater extent than military backpack design
Highway construction cost index forecasting: a hybrid VMD–LSTM–GRU method
No full textThe Highway Construction Cost Index (HCCI) is a crucial metric for monitoring price trends in the highway construction industry, where accurate forecasting is essential for effective budgeting and resource allocation. However, the inherent volatility and complexity of HCCI data present significant challenges to predictive accuracy. This study addresses these challenges by proposing a novel hybrid method that integrates variational mode decomposition (VMD) with long short-term memory (LSTM) and gated recurrent unit (GRU) networks to enhance forecasting performance. The VMD technique decomposes the HCCI time series into intrinsic mode functions (IMFs), representing various signal frequency components. The LSTM model is employed to predict smooth IMF components while the GRU model handles the more volatile IMF components, ensuring robust performance across different data characteristics. The proposed VMD–LSTM–GRU framework was applied to the Texas HCCI dataset, demonstrating superior forecasting accuracy compared to conventional time series or deep learning approaches. The study advances the application of hybrid models in construction cost time series forecasting and introduces a new methodology for enhancing budget estimations and financial planning within the construction industry. By improving prediction accuracy, the VMD-LSTM-GRU framework offers significant potential for more reliable financial management and strategic planning in highway construction projects.</p
Advancement in the automation of paved roadways performance patrolling: A review
No full textThis review critically analyzes available tools and techniques for road damage assessment and monitoring and identifies further research areas. After the review, several are found for image and sensor data collection, such as smartphones, cameras, accelerometers, and Unnamed Aerial Vehicles (UAVs). Of these, smartphones are the most frequently used among the data processing tools, including Machine Learning (ML) algorithms, Convolution Neural Networks (CNNs), and Support Vector Machines (SVMs): they outperform in detecting and measuring various forms of road surface damage, including potholes, bumps, ruts, and patches. It is also found that the study of crack identification is comparatively lacking. Moreover, some studies attempt to capitalize on crowdsourcing or road users in data collection connected to a server while driving over road networks. However, physical observation and smartphone-based image posting to the server backed by appropriate ML tools for automatic and dynamic road damage detection are yet to be demonstrated or developed and are, therefore, of further research potential. Finally, from a national perspective, India and China are ahead of other countries. Thus, future research can focus on image processing-based automatic road performance monitoring in any country with a transportation system that is highly dependent on paved road
Letter to the editor
No full textDear Editor,The US Government reports that it is conducting historically unprecedented intensive monitoring of COVID-19 vaccine safety [1], [2]. However, there are major shortcomings in the FDA’s recent publication of its first “near real-time surveillance” study [3]. The analysis was not sensitive enough to detect safety signals for known adverse reactions: myocarditis was not detected for Moderna vaccine in all data sources, and only detected for Pfizer vaccine in two of three data sources. This raises serious concerns about whether the surveillance system is fit for its purpose