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NEW FASTd* algorithm OF 2-d dISCRETE COSINE TRANSFORM

Journal of Electronics(China) 1995年第3期12卷 270-275页

作者：王新成周晓明卢颉朱维乐 Dept. of Elect. Eng. of Tsinghua University Beijing 100084 Rhode University USA Dept. of Elect. Eng. of University of Elect. Sci. and Tech. of China Chengdu 610054

In this paper, a newd* algorithm for the fast computation of a 2-d discrete cosine transform (dCT) is presented. It is shown that the N×N dCT, where N = 2m, can be computed using only N 1-d dCT’s and additions, instead of using 2N 1-d dCT’s as in the conventional row-column approach. Hence the total number of multiplications for the proposed* algorithm is only half of that required for the row-column approach, and is also less than that of most of other fastd* algorithms, while the number of additions is almost comparable to that of others.

关键词： Image processing discrete cosine transform Fast algorithm

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Greedyd* algorithms, ordering of variables, and d-degenerate instances

Greedy algorithms, ordering of variables, and d-degenerate i...

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42nd IEEE International Symposium on Multiple-Valued Logic (ISMVL)

作者： Wang, Cong Bulatov, Andrei A. Simon Fraser Univ Sch Comp Sci Burnaby BC V5A 1S6 Canada

ISBN: (纸本)9780769546735

We consider the MAX-2-SAT problem on d-degenerate formulas. This class of 2-CNFs is a generalization of 2-CNFs of bounded tree width. We first show that the class of d-degenerate formulas is very broad, since random formulas can be shown to be d-degenerate for an appropriate d. Then we test several heuristicd* algorithms on random d-degenerate formulas and compare results against similar tests on regular random 2-CNFs. Finally, we model the performance of one of thesed* algorithms, the greedy one, on both random and random d-degenerate formulas by systems of differential equations.

关键词： satisfiability algorithm analysis random satisfiability d-degenerate

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Two-stage robust co-optimization of energy and reserve scheduling for resilient distribution systems considering line reinforcement

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ENERGY 2025年 316卷

作者： Han, Haiteng Yu, Lichengzi Zhou, Yizhou Zang, Haixiang Cao, Yang Chen, Sheng Sun, Guoqiang Wei, Zhinong Hohai Univ Sch Elect & Power Engn Nanjing 211100 Peoples R China

In recent years, the resilience of distribution systems has faced challenges due to the increasing frequency of extreme weather events and the intensifying global effects of climate change. To mitigate the adverse effects of these weather occurrences, we propose a day-ahead energy scheduling and line reinforcement strategy designed to address day-ahead spare capacity scheduling during extreme weather conditions, while also accommodating the unpredictability associated with such events. Initially, we integrate a rapid power regulation model for photovoltaic (PV) power plants to enhance the swift regulation of new energy sources. Subsequently, we develop a two-stage robust planning approach aimed at minimizing both load shedding and dispatch costs amidst extreme weather uncertainties. It enhances the system resilience, lowering load shedding by 7.8 %. Finally, the proposed model is decomposed into master and subproblems and solved iteratively through the application of nested column-and-constraint generation (NC&CG)d* algorithm. Numerical results demonstrate that the proposed approach improves economic efficiency by reducing the total investment by 3.8 % and the day-ahead cost by 3.05 %, as well as enhancing the system resilience by mitigating potential load shedding under extreme conditions.

关键词： Two-stage robust optimization day-ahead dispatch distribution systems Extreme weather events Nested column-and-constraint generation algorithm

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Striking a Balance: Innovation, Equity, and Consistency in AI Health Technologies

JMIR AI

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JMIR AI 2025年 4卷

作者： Perakslis, Eric Nolen, Kimberly Fricklas, Ethan Tubb, Tracy Duke Univ Sch Med Duke Clin Res Inst 300 West Morgan St Durham NC 27701 USA Pluto Hlth Durham NC USA Pfizer Inc New York NY USA

With the explosion of innovation driven by generative and traditional artificial intelligence (AI), comes the necessity to understand and regulate products that often defy current regulatory classification. Tradition, and lack of regulatory expediency, imposes the notion of force-fitting novel innovations into pre-existing product classifications or into the essentially unregulated domains of wellness or consumer electronics. Further, regulatory requirements, levels of risk tolerance, and capabilities vary greatly across the spectrum of technology innovators. For example, currently unregulated information and consumer electronic suppliers set their own editorial and communication standards without extensive federal regulation. However, industries like biopharma companies are held to a higher standard in the same space, given current direct-to-consumer regulations like the Sunshine Act (also known as Open Payments), the federal Anti-Kickback Statute, the federal False Claims Act, and others. Clear and well-defined regulations not only reduce ambiguity but facilitate scale, showcasing the importance of regulatory clarity in fostering innovation and growth. To avoid highly regulated industries like health care and biopharma from being discouraged from developing AI to improve patient care, there is a need for a specialized framework to establish regulatory evidence for AI-based medical solutions. In this paper, we review the current regulatory environment considering current innovations but also pre-existing legal and regulatory responsibilities of the biopharma industry and propose a novel, hybridized approach for the assessment of novel AI-based patient solutions. Further, we will elaborate the proposed concepts via case studies. This paper explores the challenges posed by the current regulatory environment, emphasizing the need for a specialized framework for AI medical devices. By reviewing existing regulations and proposing a hybridized approach, we aim to ensure

关键词： artificial intelligence algorithm regulatory landscape predictive model predictive analytics predictive system practical model machine learning large language model natural language processing deep learning digital health regula-tory health technology

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Validation of and* algorithm to Identify End-Stage Kidney disease in Electronic Health Records data

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American Journal of Kidney diseases 2025年

作者： Chenxi Gao Yunwen Xu Sneha Mehta Yingying Sang Carina Flaherty Aditya Surapaneni Krutika Pandit Alexander R. Chang Jamie Alton Green Morgan E. Grams Jung-Im Shin Department of Epidemiology Johns Hopkins Bloomberg School of Public Health Baltimore MD New York University Grossman School of Medicine New York NY Departments of Nephrology and Population Health Sciences Geisinger Health System Danville PA Danville PA Department of Population Health Sciences Geisinger Health System Danville PA Danville PA

Rationale & Objectives Accurate ascertainment of end-stage kidney disease (ESKd) in electronic health records (EHRs) data is important for much epidemiological research. This study aimed to develop and validate and* algorithm using diagnosis and procedure codes to identify patients with ESKd (treated with maintenance dialysis or kidney transplantation) in EHRs data. Study design Study of diagnosticd* algorithms. Setting & Participants The development cohort included 559,615 patients treated at the Geisinger Health System (January 1996-June 2018). The validation cohort included 767,186 patients treated at New York University Langone Health System (January 2018-december 2020).d* algorithms Compared And* algorithm developed using diagnosis and procedure codes compared to a nominal gold standard designation within the United States Renal data System (USRdS) data. The performance of thed* algorithm was characterized by sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). The dates of incident ESKd between thed* algorithm and USRdS were compared in a subset of cases. Outcomes ESKd (maintenance dialysis, prior recipient of a kidney transplant, or kidney transplantation surgery) cases. Results In Geisinger, we developed the ESKd* algorithm that identified 4,766 (0.85%) ESKd cases, while there were 5,155 (0.92%) ESKd cases reported by the USRdS. The sensitivity, specificity, PPV, and NPV of thed* algorithm were 73.9% (95% CI, 72.7-75.1%), 99.83% (99.82-99.84%), 79.9% (78.9-81.0%), and 99.76% (99.75-99.77%), respectively. When applying thed* algorithm to New York University Langone Health System data, the sensitivity, specificity, PPV, and NPV were 71.8% (95% CI, 70.7-73.0%), 99.95% (99.95-99.96%), 91.6% (90.8-92.4%), and 99.79 (99.78-99.80%), respectively. The median (interquartile range) difference between dates of incident ESKd (algorithms minus USRdS) were -3 (-21 to 83) days in Geisinger and 0 (-12 to 69) days in New York University Langone Heal

关键词： EHRs ESKd End-stage kidney disease algorithm dialysis electronic health records kidney transplant

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degradation prediction of PEM fuel cell using LSTM based on Gini gamma correlation coefficient and improved sand cat swarm optimization under dynamic operating conditions

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APPLIEd ENERGY 2025年 392卷

作者： Huang, Ruike Zhang, Xuexia dong, Sidi Huang, Lei Li, Yuan Southwest Jiaotong Univ Sch Elect Engn Chengdu 611756 Sichuan Peoples R China Southwest Jiaotong Univ Natl Rail Transit Electrificat & Automat Engn Tech Chengdu 611756 Peoples R China

Accurate longevity prediction is crucial for the optimal functioning of Proton Exchange Membrane Fuel Cells (PEMFC). This paper proposes the relative voltage loss rate (RVLR) as a novel aging indicator to overcome the limitations of traditional static indicators like voltage and power, which fall short under dynamic operating conditions. An innovative approach using an LSTM neural network model enhanced by Improved Sand Cat Swarm Optimization (ISCSO) and the Gini Gamma Correlation Coefficient method (GG) is presented for predicting PEMFC degradation across variable conditions. This method first conducts a Gini Gamma correlation analysis, then employs the LSTM network to forge a degradation prediction model for the PEMFC, optimizing the number of neurons in the hidden layer, initial weights, and iteration count through ISCSO. Comparative discussions with eight alternative methods and validations through aging experiments on PEMFC under two different conditions underscore the accuracy of this new approach in various application environments. Specifically, the ISCSO-LSTM model achieves R2 values of 0.9713 and 0.9822, MAE values of 0.0026 and 0.0019, and RMSE values of 0.0050 and 0.0032 across the two different datasets, respectively. These results demonstrate the robustness, accuracy, and reliability of the proposed method for accurate degradation prediction.

关键词： Proton exchange membrane fuel cells Relative voltage loss rate Gini gamma correlation coefficient method Improved sand cat swarm optimization algorithm Long short-term memory degradation prediction

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Broken time- reversal symmetry in visual motion detection

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PROCEEdINGS OF THE NATIONAL ACAdEMY OF SCIENCES OF THE UNITEd STATES OF AMERICA 2025年第10期122卷 e2410768122页

作者： Wu, Nathan Zhou, Baohua Agrochao, Margarida Clark, damon A. Yale Coll New Haven CT 06511 USA Yale Univ Dept Mol Cellular & Dev Biol New Haven CT 06511 USA Yale Univ Dept Phys New Haven CT 06511 USA Yale Univ Dept Neurosci New Haven CT 06511 USA Yale Univ Quantitat Biol Inst New Haven CT 06511 USA Yale Univ Wu Tsai Inst New Haven CT 06511 USA

Our intuition suggests that when a movie is played in reverse, our perception of motion at each location in the reversed movie will be perfectly inverted compared to the original. This intuition is also reflected in classical theoretical and practical models of motion estimation, in which velocity flow fields invert when inputs are reversed in time. However, here we report that this symmetry of motion perception upon time reversal is broken in real visual systems. We designed a set of visual stimuli to investigate time reversal symmetry breaking in the fruit fly drosophila's well-studied optomotor rotation behavior. We identified a suite of stimuli with a wide variety of properties that can uncover broken time reversal symmetry in fly behavioral responses. We then trained neural network models to predict the velocity of scenes with both natural and artificial contrast distributions. Training with naturalistic contrast distributions yielded models that broke time reversal symmetry, even when the training data themselves were time reversal symmetric. We show analytically and numerically that the breaking of time reversal symmetry in the model responses can arise from contrast asymmetry in the training data, but can also arise from other features of the contrast distribution. Furthermore, shallower neural network models can exhibit stronger symmetry breaking than deeper ones, suggesting that less flexible neural networks may be more prone to time reversal symmetry breaking. Overall, these results reveal a surprising feature of biological motion detectors and suggest that it could arise from constrained optimization in natural environments.

关键词： motion detection symmetry symmetry breaking algorithm drosophila

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Information and Analytical System for Monitoring Gender Equality among university lecturers

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INTERNATIONAL JOURNAL OF ELECTRONICS ANd TELECOMMUNICATIONS 2025年第1期71卷 189-196页

作者： Makulov, Kaiyrbek Chernyshova, daryna Bogatyrova, Kateryna Geseleva, Nataliia Miniailo, Viktoriia Karpunin, Ihor Caspian Univ Technol & Engn Alma Ata Kazakhstan State Univ Trade & Econ Kyiv Ukraine

The authors offer the structure of the information-analytical system (IAS) for monitoring gender equality (MGE) among university staff (lecturers). The proposed architecture of the IAS MGE represents an integrated approach to collecting, analyzing and monitoring personnel data considering gender aspects. An additional criterion for monitoring gender equality in the selection of candidates for vacant positions in universities is proposed, which will reduce the likelihood of prejudiced decisions at the recruitment stage and create a more equitable university environment.

关键词： digital educational environment digital footprints log files data mining (or intellectual data analysis) algorithm model

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Is the burden of diabetes in Australia underestimated? Comparison of diabetes ascertainment using linked administrative health data and an Australian diabetes registry

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dIABETES RESEARCH ANd CLINICAL PRACTICE 2025年 222卷 112113-112113页

作者： Cox, Emma Gale, Joanne Falster, Michael O. Costa, Juliana de Oliveira Colagiuri, Stephen Nassar, Natasha Gibson, Alice A. Univ Sydney Fac Med & Hlth Leeder Ctr Hlth Policy Sch Publ Hlth Sydney Australia Univ New South Wales Sch Populat Hlth Med Intelligence Res Program Sydney Australia Univ Sydney Fac Med & Hlth Sydney Australia Univ Sydney Charles Perkins Ctr Sydney Australia Univ Sydney Childrens Hosp Westmead Fac Med & Hlth Child Populat & Translat Hlth ResClin Sch Sydney Australia

Aims: To compare and* algorithm for identifying individuals with diabetes using linked administrative health data with an Australian diabetes registry (National diabetes Services Scheme, NdSS). Methods: This prospective cohort study linked baseline survey data for 266,414 individuals aged >= 45 years from the 45 and Up Study, Australia, to administrative health data sets. And* algorithm for identifying individuals with diabetes was developed based on a combination of claims for dispensed insulin and glucose lowering medicines, diabetes-related hospital admissions, and diabetes-specific Medicare claims. Using thed* algorithm, participants were classified as 'certain', 'uncertain' or 'no' diabetes. Thed* algorithm was compared to NdSS registrations as the reference standard. Results: Amongst the 45 and Up Study cohort, there were 53,669 individuals with certain diabetes identified by thed* algorithm, and 35,900 NdSS registrants. Compared with the NdSS, the sensitivity of thed* algorithm was 96.9% (95%CI 96.7-97.1) and specificity 91.8% (95%CI 91.7-91.9). Of the 53,699 individuals with diabetes identified by thed* algorithm, 34,864 were registered to the NdSS (PPV = 64.9%, 95%CI: 64.6-65.2). Conclusions: This study demonstrates the value in using linked administrative data for diabetes monitoring and surveillance. National estimates using the NdSS alone may underestimate the diabetes burden by up to 35%.

关键词： diabetes algorithm Administrative data data linkage population health disease surveillance epidemiology

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A novel framework for uncertainty quantification of rainfall-runoff models based on a Bayesian approach focused on transboundary river basins

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JOURNAL OF HYdROLOGY-REGIONAL STUdIES 2025年 57卷

作者： Nguyen, Thi-duyen Nguyen, duc Hai Kwon, Hyun-Han Bae, deg-Hyo Sejong Univ Dept Civil & Environm Engn 98 Gunja Dong Seoul 143747 South Korea Vinh Univ Dept Civil Engn Vinh 461010 Vietnam Univ Saskatchewan Dept Civil Geol & Environm Engn 57 Campus Dr Saskatoon SK S7N 5A9 Canada Thuyloi Univ Fac Water Resources Engn 175 Tay Son St Hanoi 116705 Vietnam

Study region: The transboundary Imjin River basin, Korea. Study focus: The primary aim is to propose and validate a novel framework for assessing the uncertainty in hydrological models, particularly rainfall-runoff models (RRMs), considering transboundary river basins with limited data accessibility. By utilizing an adaptive Markov chain Monte Carlo (MCMC) simulation method combined with three comprehensive uncertainty assessment measures, the developed framework focuses on evaluating the uncertainty inherent in RRMs. A key component of this framework is the delayed rejection adaptive Metropolis (dRAM)d* algorithm, which is employed to explore behavioral simulations defined by four likelihood functions (LFs). The proposed methodology was applied to the transboundary Imjin River basin using the Sejong University rainfall-runoff (SURR) model, a case study that involves a database of five-year extreme flood events. New hydrological insights for the region: The application of this framework in the transboundary Imjin basin demonstrated its effectiveness in quantifying and addressing the uncertainty in RRM predictions. The integration of the dRAMd* algorithm with uncertainty indices provided a robust mechanism for evaluating and improving the reliability of RRM outputs for transboundary basins. Effects of LFs in blending with the dRAMd* algorithm were confirmed by uncertainty measures and the behavior of the upper and lower uncertainty bounds. These insights could provide an approach to develop more accurate and reliable water resource management strategies in global transboundary contexts.

关键词： Uncertainty qualification Hydrological model delayed rejection adaptive Metropolis algorithm Assessment indices Likelihood function Transboundary river basin

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