Stefano Alderighi, Paolo Landa, E. Tànfani, A. Testi
Molecular genetic techniques allow for the diagnosing of hereditary diseases and congenital abnormalities prenatally. A high variability of treatments exists, engendering an inappropriate clinical response, an inefficient use of resources, and the violation of the principle of the equality of treatment for equal needs. The proposed framework is based on modeling clinical pathways that contribute to identifying major causes of variability in treatments justified by the clinical needs’ variability as well as depending on individual characteristics. An electronic data collection method for high-risk pregnant women addressing genetic facilities and laboratories was implemented. The collected data were analyzed retrospectively with two aims. The first is to identify how the whole activity of genetic services can be broken down into different clinical pathways. This was performed by building a flow chart with the help of doctors. The second aim consists of measuring the variability, within and among, the different paths due to individual characteristics. A set of statistical models was developed to determine the impact of the patient characteristics on the clinical pathway and its length. The results show the importance of considering these characteristics together with the clinical information to define the care pathway and the use of resources.
{"title":"Assessing the Impact of Patient Characteristics on Genetic Clinical Pathways: A Regression Approach","authors":"Stefano Alderighi, Paolo Landa, E. Tànfani, A. Testi","doi":"10.3390/a17020075","DOIUrl":"https://doi.org/10.3390/a17020075","url":null,"abstract":"Molecular genetic techniques allow for the diagnosing of hereditary diseases and congenital abnormalities prenatally. A high variability of treatments exists, engendering an inappropriate clinical response, an inefficient use of resources, and the violation of the principle of the equality of treatment for equal needs. The proposed framework is based on modeling clinical pathways that contribute to identifying major causes of variability in treatments justified by the clinical needs’ variability as well as depending on individual characteristics. An electronic data collection method for high-risk pregnant women addressing genetic facilities and laboratories was implemented. The collected data were analyzed retrospectively with two aims. The first is to identify how the whole activity of genetic services can be broken down into different clinical pathways. This was performed by building a flow chart with the help of doctors. The second aim consists of measuring the variability, within and among, the different paths due to individual characteristics. A set of statistical models was developed to determine the impact of the patient characteristics on the clinical pathway and its length. The results show the importance of considering these characteristics together with the clinical information to define the care pathway and the use of resources.","PeriodicalId":502609,"journal":{"name":"Algorithms","volume":"52 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139857487","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Á. Muñoz-Zavala, J. Macías-Díaz, Daniel Alba-Cuéllar, José A. Guerrero-Díaz-de-León
This paper reviews the application of artificial neural network (ANN) models to time series prediction tasks. We begin by briefly introducing some basic concepts and terms related to time series analysis, and by outlining some of the most popular ANN architectures considered in the literature for time series forecasting purposes: feedforward neural networks, radial basis function networks, recurrent neural networks, and self-organizing maps. We analyze the strengths and weaknesses of these architectures in the context of time series modeling. We then summarize some recent time series ANN modeling applications found in the literature, focusing mainly on the previously outlined architectures. In our opinion, these summarized techniques constitute a representative sample of the research and development efforts made in this field. We aim to provide the general reader with a good perspective on how ANNs have been employed for time series modeling and forecasting tasks. Finally, we comment on possible new research directions in this area.
本文回顾了人工神经网络（ANN）模型在时间序列预测任务中的应用。首先，我们简要介绍了与时间序列分析相关的一些基本概念和术语，并概述了文献中考虑用于时间序列预测的一些最流行的人工神经网络架构：前馈神经网络、径向基函数网络、递归神经网络和自组织图。我们分析了这些架构在时间序列建模方面的优缺点。然后，我们总结了最近在文献中发现的一些时间序列 ANN 建模应用，主要侧重于前面概述的架构。我们认为，这些总结的技术构成了该领域研发工作的代表性样本。我们的目标是为普通读者提供一个良好的视角，让他们了解在时间序列建模和预测任务中是如何使用 ANN 的。最后，我们对这一领域可能的新研究方向进行了评论。
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One of the fundamental challenges in analyzing wind turbine performance is the occurrence of torque creep under load and without load. This phenomenon significantly impacts the proper functioning of torque transducers, thus necessitating the utilization of appropriate measurement data analysis algorithms. In this regard, employing the least squares method appears to be a suitable approach. Linear regression can be employed to investigate the creep trend itself, while visualizing the creep in the form of a non-linear curve using a third-degree polynomial can provide further insights. Additionally, calculating deviations between the measurement data and the regression curves proves beneficial in accurately assessing the data.
{"title":"Algorithms Utilized for Creep Analysis in Torque Transducers for Wind Turbines","authors":"Jacek G. Puchalski, J. Fidelus, Paweł Fotowicz","doi":"10.3390/a17020077","DOIUrl":"https://doi.org/10.3390/a17020077","url":null,"abstract":"One of the fundamental challenges in analyzing wind turbine performance is the occurrence of torque creep under load and without load. This phenomenon significantly impacts the proper functioning of torque transducers, thus necessitating the utilization of appropriate measurement data analysis algorithms. In this regard, employing the least squares method appears to be a suitable approach. Linear regression can be employed to investigate the creep trend itself, while visualizing the creep in the form of a non-linear curve using a third-degree polynomial can provide further insights. Additionally, calculating deviations between the measurement data and the regression curves proves beneficial in accurately assessing the data.","PeriodicalId":502609,"journal":{"name":"Algorithms","volume":"113 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139794738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Stefano Alderighi, Paolo Landa, E. Tànfani, A. Testi
Molecular genetic techniques allow for the diagnosing of hereditary diseases and congenital abnormalities prenatally. A high variability of treatments exists, engendering an inappropriate clinical response, an inefficient use of resources, and the violation of the principle of the equality of treatment for equal needs. The proposed framework is based on modeling clinical pathways that contribute to identifying major causes of variability in treatments justified by the clinical needs’ variability as well as depending on individual characteristics. An electronic data collection method for high-risk pregnant women addressing genetic facilities and laboratories was implemented. The collected data were analyzed retrospectively with two aims. The first is to identify how the whole activity of genetic services can be broken down into different clinical pathways. This was performed by building a flow chart with the help of doctors. The second aim consists of measuring the variability, within and among, the different paths due to individual characteristics. A set of statistical models was developed to determine the impact of the patient characteristics on the clinical pathway and its length. The results show the importance of considering these characteristics together with the clinical information to define the care pathway and the use of resources.
{"title":"Assessing the Impact of Patient Characteristics on Genetic Clinical Pathways: A Regression Approach","authors":"Stefano Alderighi, Paolo Landa, E. Tànfani, A. Testi","doi":"10.3390/a17020075","DOIUrl":"https://doi.org/10.3390/a17020075","url":null,"abstract":"Molecular genetic techniques allow for the diagnosing of hereditary diseases and congenital abnormalities prenatally. A high variability of treatments exists, engendering an inappropriate clinical response, an inefficient use of resources, and the violation of the principle of the equality of treatment for equal needs. The proposed framework is based on modeling clinical pathways that contribute to identifying major causes of variability in treatments justified by the clinical needs’ variability as well as depending on individual characteristics. An electronic data collection method for high-risk pregnant women addressing genetic facilities and laboratories was implemented. The collected data were analyzed retrospectively with two aims. The first is to identify how the whole activity of genetic services can be broken down into different clinical pathways. This was performed by building a flow chart with the help of doctors. The second aim consists of measuring the variability, within and among, the different paths due to individual characteristics. A set of statistical models was developed to determine the impact of the patient characteristics on the clinical pathway and its length. The results show the importance of considering these characteristics together with the clinical information to define the care pathway and the use of resources.","PeriodicalId":502609,"journal":{"name":"Algorithms","volume":"72 11","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139797401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Adding-Doubling (AD) algorithm is a general analytical solution of the radiative transfer equation (RTE). AD offers a favorable balance between accuracy and computational efficiency, surpassing other RTE solutions, such as Monte Carlo (MC) simulations, in terms of speed while outperforming approximate solutions like the Diffusion Approximation method in accuracy. While AD algorithms have traditionally been implemented on central processing units (CPUs), this study focuses on leveraging the capabilities of graphics processing units (GPUs) to achieve enhanced computational speed. In terms of processing speed, the GPU AD algorithm showed an improvement by a factor of about 5000 to 40,000 compared to the GPU MC method. The optimal number of threads for this algorithm was found to be approximately 3000. To illustrate the utility of the GPU AD algorithm, the Levenberg–Marquardt inverse solution was used to extract object parameters from optical spectral data of human skin under various hemodynamic conditions. With regards to computational efficiency, it took approximately 5 min to process a 220 × 100 × 61 image (x-axis × y-axis × spectral-axis). The development of the GPU AD algorithm presents an advancement in determining tissue properties compared to other RTE solutions. Moreover, the GPU AD method itself holds the potential to expedite machine learning techniques in the analysis of spectral images.
加倍（AD）算法是辐射传递方程（RTE）的通用解析解。AD 在精度和计算效率之间取得了良好的平衡，在速度方面超过了蒙特卡罗（MC）模拟等其他 RTE 解法，而在精度方面则优于扩散逼近法等近似解法。虽然 AD 算法传统上是在中央处理器（CPU）上实现的，但本研究侧重于利用图形处理器（GPU）的功能来提高计算速度。在处理速度方面，GPU AD 算法比 GPU MC 方法提高了约 5000 到 40000 倍。该算法的最佳线程数约为 3000。为了说明 GPU AD 算法的实用性，我们使用 Levenberg-Marquardt 逆解法从各种血液动力学条件下的人体皮肤光学光谱数据中提取对象参数。在计算效率方面，处理一幅 220 × 100 × 61（x 轴 × y 轴 × 光谱轴）的图像大约需要 5 分钟。与其他 RTE 解决方案相比，GPU AD 算法的开发在确定组织属性方面取得了进步。此外，GPU AD 方法本身也具有在光谱图像分析中加速机器学习技术的潜力。
{"title":"GPU Adding-Doubling Algorithm for Analysis of Optical Spectral Images","authors":"M. Milanič, Rok Hren","doi":"10.3390/a17020074","DOIUrl":"https://doi.org/10.3390/a17020074","url":null,"abstract":"The Adding-Doubling (AD) algorithm is a general analytical solution of the radiative transfer equation (RTE). AD offers a favorable balance between accuracy and computational efficiency, surpassing other RTE solutions, such as Monte Carlo (MC) simulations, in terms of speed while outperforming approximate solutions like the Diffusion Approximation method in accuracy. While AD algorithms have traditionally been implemented on central processing units (CPUs), this study focuses on leveraging the capabilities of graphics processing units (GPUs) to achieve enhanced computational speed. In terms of processing speed, the GPU AD algorithm showed an improvement by a factor of about 5000 to 40,000 compared to the GPU MC method. The optimal number of threads for this algorithm was found to be approximately 3000. To illustrate the utility of the GPU AD algorithm, the Levenberg–Marquardt inverse solution was used to extract object parameters from optical spectral data of human skin under various hemodynamic conditions. With regards to computational efficiency, it took approximately 5 min to process a 220 × 100 × 61 image (x-axis × y-axis × spectral-axis). The development of the GPU AD algorithm presents an advancement in determining tissue properties compared to other RTE solutions. Moreover, the GPU AD method itself holds the potential to expedite machine learning techniques in the analysis of spectral images.","PeriodicalId":502609,"journal":{"name":"Algorithms","volume":"2 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139797480","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Á. Muñoz-Zavala, J. Macías-Díaz, Daniel Alba-Cuéllar, José A. Guerrero-Díaz-de-León
This paper reviews the application of artificial neural network (ANN) models to time series prediction tasks. We begin by briefly introducing some basic concepts and terms related to time series analysis, and by outlining some of the most popular ANN architectures considered in the literature for time series forecasting purposes: feedforward neural networks, radial basis function networks, recurrent neural networks, and self-organizing maps. We analyze the strengths and weaknesses of these architectures in the context of time series modeling. We then summarize some recent time series ANN modeling applications found in the literature, focusing mainly on the previously outlined architectures. In our opinion, these summarized techniques constitute a representative sample of the research and development efforts made in this field. We aim to provide the general reader with a good perspective on how ANNs have been employed for time series modeling and forecasting tasks. Finally, we comment on possible new research directions in this area.
本文回顾了人工神经网络（ANN）模型在时间序列预测任务中的应用。首先，我们简要介绍了与时间序列分析相关的一些基本概念和术语，并概述了文献中考虑用于时间序列预测的一些最流行的人工神经网络架构：前馈神经网络、径向基函数网络、递归神经网络和自组织图。我们分析了这些架构在时间序列建模方面的优缺点。然后，我们总结了最近在文献中发现的一些时间序列 ANN 建模应用，主要侧重于前面概述的架构。我们认为，这些总结的技术构成了该领域研发工作的代表性样本。我们的目标是为普通读者提供一个良好的视角，让他们了解在时间序列建模和预测任务中是如何使用 ANN 的。最后，我们对这一领域可能的新研究方向进行了评论。
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Giorgio Lazzarinetti, Riccardo Dondi, Sara Manzoni, I. Zoppis
Solving combinatorial problems on complex networks represents a primary issue which, on a large scale, requires the use of heuristics and approximate algorithms. Recently, neural methods have been proposed in this context to find feasible solutions for relevant computational problems over graphs. However, such methods have some drawbacks: (1) they use the same neural architecture for different combinatorial problems without introducing customizations that reflects the specificity of each problem; (2) they only use a nodes local information to compute the solution; (3) they do not take advantage of common heuristics or exact algorithms. Following this interest, in this research we address these three main points by designing a customized attention-based mechanism that uses both local and global information from the adjacency matrix to find approximate solutions for the Minimum Vertex Cover Problem. We evaluate our proposal with respect to a fast two-factor approximation algorithm and a widely adopted state-of-the-art heuristic both on synthetically generated instances and on benchmark graphs with different scales. Experimental results demonstrate that, on the one hand, the proposed methodology is able to outperform both the two-factor approximation algorithm and the heuristic on the test datasets, scaling even better than the heuristic with harder instances and, on the other hand, is able to provide a representation of the nodes which reflects the combinatorial structure of the problem.
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Amalia Moutsopoulou, M. Petousis, G. Stavroulakis, A. Pouliezos, N. Vidakis
In this study, we created an accurate model for a homogenous smart structure. After modeling multiplicative uncertainty, an ideal robust controller was designed using μ-synthesis and a reduced-order H-infinity Feedback Optimal Output (Hifoo) controller, leading to the creation of an improved uncertain plant. A powerful controller was built using a larger plant that included the nominal model and corresponding uncertainty. The designed controllers demonstrated robust and nominal performance when handling agitated plants. A comparison of the results was conducted. As an example of a general smart structure, the vibration of a collocated piezoelectric actuator and sensor was controlled using two different approaches with strong controller designs. This study presents a comprehensive simulation of the oscillation suppression problem for smart beams. They provide an analytical demonstration of how uncertainty is introduced into the model. The desired outcomes were achieved by utilizing Simulink and MATLAB (v. 8.0) programming tools.
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Giorgio Lazzarinetti, Riccardo Dondi, Sara Manzoni, I. Zoppis
Solving combinatorial problems on complex networks represents a primary issue which, on a large scale, requires the use of heuristics and approximate algorithms. Recently, neural methods have been proposed in this context to find feasible solutions for relevant computational problems over graphs. However, such methods have some drawbacks: (1) they use the same neural architecture for different combinatorial problems without introducing customizations that reflects the specificity of each problem; (2) they only use a nodes local information to compute the solution; (3) they do not take advantage of common heuristics or exact algorithms. Following this interest, in this research we address these three main points by designing a customized attention-based mechanism that uses both local and global information from the adjacency matrix to find approximate solutions for the Minimum Vertex Cover Problem. We evaluate our proposal with respect to a fast two-factor approximation algorithm and a widely adopted state-of-the-art heuristic both on synthetically generated instances and on benchmark graphs with different scales. Experimental results demonstrate that, on the one hand, the proposed methodology is able to outperform both the two-factor approximation algorithm and the heuristic on the test datasets, scaling even better than the heuristic with harder instances and, on the other hand, is able to provide a representation of the nodes which reflects the combinatorial structure of the problem.
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Amalia Moutsopoulou, M. Petousis, G. Stavroulakis, A. Pouliezos, N. Vidakis
In this study, we created an accurate model for a homogenous smart structure. After modeling multiplicative uncertainty, an ideal robust controller was designed using μ-synthesis and a reduced-order H-infinity Feedback Optimal Output (Hifoo) controller, leading to the creation of an improved uncertain plant. A powerful controller was built using a larger plant that included the nominal model and corresponding uncertainty. The designed controllers demonstrated robust and nominal performance when handling agitated plants. A comparison of the results was conducted. As an example of a general smart structure, the vibration of a collocated piezoelectric actuator and sensor was controlled using two different approaches with strong controller designs. This study presents a comprehensive simulation of the oscillation suppression problem for smart beams. They provide an analytical demonstration of how uncertainty is introduced into the model. The desired outcomes were achieved by utilizing Simulink and MATLAB (v. 8.0) programming tools.
{"title":"μ-Analysis and μ-Synthesis Control Methods in Smart Structure Disturbance Suppression with Reduced Order Control","authors":"Amalia Moutsopoulou, M. Petousis, G. Stavroulakis, A. Pouliezos, N. Vidakis","doi":"10.3390/a17020073","DOIUrl":"https://doi.org/10.3390/a17020073","url":null,"abstract":"In this study, we created an accurate model for a homogenous smart structure. After modeling multiplicative uncertainty, an ideal robust controller was designed using μ-synthesis and a reduced-order H-infinity Feedback Optimal Output (Hifoo) controller, leading to the creation of an improved uncertain plant. A powerful controller was built using a larger plant that included the nominal model and corresponding uncertainty. The designed controllers demonstrated robust and nominal performance when handling agitated plants. A comparison of the results was conducted. As an example of a general smart structure, the vibration of a collocated piezoelectric actuator and sensor was controlled using two different approaches with strong controller designs. This study presents a comprehensive simulation of the oscillation suppression problem for smart beams. They provide an analytical demonstration of how uncertainty is introduced into the model. The desired outcomes were achieved by utilizing Simulink and MATLAB (v. 8.0) programming tools.","PeriodicalId":502609,"journal":{"name":"Algorithms","volume":"48 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139800129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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