Set Of Algorithms Research Articles

Faecal source apportionment using molecular methods: A proof of concept using the FEAST algorithm

Faecal contamination of freshwater and marine environments represents a significant risk for public health, recreational activity and food safety, and tools for evaluating complex multi-source contamination remain largely in the development phase. We evaluated the efficacy of the Fast Expectation Maximization (FEAST) microbial source tracking (MST) algorithm to apportion sources of faecal contamination among four mammalian species of interest in coastal waters in New Zealand. Using 16S ribosomal DNA metabarcoding of faecal samples from cows, fur seals, and sheep, as well as human wastewater, we aimed to differentiate and quantify the contribution of these sources in mixed faecal samples. Multivariate analysis confirmed significant differences in the microbial communities associated with each mammalian source, with specific bacterial classes indicative of different sources. The FEAST algorithm was tested using mixed DNA and mixed faecal samples, and we found that the algorithm correctly assigned the dominant source from all samples, but underestimated the dominant source's proportional contribution. This underestimation suggests the need for further refinement and validation to ensure accurate source apportionment in environmental samples where the faecal signal is likely to be a minor component. Despite these limitations, the findings of our study, in combination with the evidence from others who have tested the FEAST algorithm in environmental settings, indicates that it represents an advance on existing tools for microbial source tracking and may become a useful addition to the toolbox for environmental management.

New horizons in prediction modelling using machine learning in older people's healthcare research.

Machine learning (ML) and prediction modelling have become increasingly influential in healthcare, providing critical insights and supporting clinical decisions, particularly in the age of big data. This paper serves as an introductory guide for health researchers and readers interested in prediction modelling and explores how these technologies support clinical decisions, particularly with big data, and covers all aspects of the development, assessment and reporting of a model using ML. The paper starts with the importance of prediction modelling for precision medicine. It outlines different types of prediction and machine learning approaches, including supervised, unsupervised and semi-supervised learning, and provides an overview of popular algorithms for various outcomes and settings. It also introduces key theoretical ML concepts. The importance of data quality, preprocessing and unbiased model performance evaluation is highlighted. Concepts of apparent, internal and external validation will be introduced along with metrics for discrimination and calibration for different types of outcomes. Additionally, the paper addresses model interpretation, fairness and implementation in clinical practice. Finally, the paper provides recommendations for reporting and identifies common pitfalls in prediction modelling and machine learning. The aim of the paper is to help readers understand and critically evaluate research papers that present ML models and to serve as a first guide for developing, assessing and implementing their own.

PREDICTING RISK OF EMPLOYEE INJURY IN A PEDIATRIC HOSPITAL

PURPOSEHealthcare has one of the highest rates of non-fatal occupational injury as compared to other industries. Yet, evaluation of risk determinants and prediction algorithms in hospital settings remains limited. METHODSThis study examines risk factors for employee injuries in a large pediatric hospital and evaluates prediction algorithms using hospital surveillance data, incident reports, and work unit measures such as patient density and employee workload. We employed multiple statistical models and machine learning tools, including logistic regression (LR), random forest (RF), penalized logistic regression (PLR), Naïve Bayes, neural network (NN), XGBoost (XG), and mixed-effects logistic regression (GLMER) to predict employee injury risk for specific time periods. We used cross-validation and receiver-operator characteristic (ROC) curve analyses to assess model performance. RESULTSGLMER, LR, and PLR were superior to other models, with higher AUC values (∼0.76), indicating good discrimination ability, though accuracy and specificity varied across models. RF showed high accuracy and specificity and comparable AUC with the top performing models. Further analyses using GLMER revealed variability in employee injury risk across months, days, and hospital units, identifying peaks on Tuesdays and Saturdays and in April and July, with lows in March and June. CONCLUSIONOur findings highlight the significance of monitoring specific risk factors within pediatric hospital settings and pairing them with appropriate predictive algorithms to effectively predict and mitigate employee injuries. These insights indicate that continuous monitoring may help enhance employee safety. Future work should evaluate additional predictors that may be obtained from individual hospital units, which may inform targeted prevention strategies.

Quantitative evaluation of internal cluster validation indices using binary data sets

AbstractAimsDifferent clustering methods often classify the same data set differently. Selecting the “best” clustering solution from alternatives is possible with cluster validation indices. Because of the large variety of cluster validation indices (CVIs), choosing the most suitable index concerning the data set and clustering algorithms is challenging. We aim to assess different internal clustering validation indices.MethodsArtificial binary data sets with equal‐ and unequal‐sized well‐separated a priori clusters were simulated and three levels of noise were then added. Twenty replications of each of the six types of data sets (two group sizes × three levels of noise) were created and analyzed by three clustering algorithms with Jaccard dissimilarity. Twenty‐seven clustering validation indices are evaluated including both geometric and non‐geometric indices.ResultsAlthough, in theory, all CVIs could differentiate between good and wrong classifications, only a few perform as expected with noisy data. Tau and silhouette widths proved to be the best geometric CVIs both for equal and unequal cluster sizes. Among non‐geometric indices, crispness and OptimClass performed best.ConclusionWe recommend using these best‐performing CVIs. We suggest plotting the CVI value against the number of clusters because the lack of a sharp peak means that the position of the maximum is uncertain.

ANN based optimization of nano-beam oscillations with intermolecular forces and geometric nonlinearity

In this study, we investigate the effect of Van der Waals and Casimir forces on the mathematical model of nano-electromechanical systems (NEMS) such as nano-beam actuators that contain cantilever and double cantilever beams. The singular nonlinear boundary value problem governing the beam-type actuators, including geometric nonlinearity is solved by using an intelligent strength of feedforward artificial neural networks (ANNs) and hybridization of optimization algorithms such as arithmetic optimization algorithm (AOA) and active set algorithm (ASA). The proposed ANN-AOA-AS algorithm is employed to quantify the effect of changes in applied voltage, dispersion forces, geometric nonlinearity parameters, and initial axial strain on the deflection of the beam. Furthermore, to validate the results obtained by the proposed algorithm, statistical analyses are conducted to compare the approximate solutions with state-of-the-art methodologies available in the latest literature. In addition, performance indicators are defined such as mean square error (MSE), Nash–Sutcliffe efficiency (NSE), mean absolute deviations (MAD), root mean square error (RMSE), and Error in Nash–Sutcliffe efficiency (ENSE) to study the accuracy and efficiency of the solutions. The results show that these indicators’ mean percentage values lie around 10−4 to 10−6 which reflects the perfect modeling of the approximate solutions.

Development and simulation of two novel indoor odor source localization methods using a modified shark smell optimization algorithm

Aiming to solve the problem of odor source localization (OSL) in the presence of interference sources, this paper presents two methods based on swarm intelligence algorithms. We initially introduced the shark smell optimization (SSO) algorithm and modified it for OSL tasks. Subsequently, mechanisms for collective information sharing and preventing falling into local minima were incorporated, leading to the development of the Improved Shark Smell Optimization (I-SSO) algorithm. We tested both algorithms in a computational fluid dynamics (CFD) simulated environment with a single interference source and compared them to the particle swarm optimization (PSO) algorithm and whale optimization algorithm (WOA). In scenarios with one and two interference sources. The results showed that the I-SSO algorithm outperformed the other three algorithms in both environment settings, demonstrating a higher success rate and superior search distance efficiency.

Universal methods for variational inequalities: Deterministic and stochastic cases

In this paper, we propose universal proximal mirror methods to solve the variational inequality problem with Hölder-continuous operators in both deterministic and stochastic settings. The proposed methods automatically adapt not only to the oracle’s noise (in the stochastic setting of the problem) but also to the Hölder continuity of the operator without having prior knowledge of either the problem class or the nature of the operator information. We analyzed the proposed algorithms in both deterministic and stochastic settings and obtained estimates for the required number of iterations to achieve a given quality of a solution to the variational inequality. We showed that, without knowing the Hölder exponent and Hölder constant of the operators, the proposed algorithms have the least possible in the worst-case sense complexity for the considered class of variational inequalities. We also compared the resulting stochastic algorithm with other popular optimizers for the task of image classification.

Do executive facial trustworthiness have impact on IPO underpricing in the Indonesia stock exchange?

PurposeThis study aims to investigate the impact of facial-based perceived trustworthiness on stock valuation, particularly, in the initial public offering (IPO). IPO settings provide the opportunity to investigate whether information asymmetry resulting from company newness in the market would influence the incorporation of soft information in the form of executive facial trustworthiness in stock valuation.Design/methodology/approachWe use a recent machine learning algorithm to detect facial landmarks and then calculate a composite facial trustworthiness measure using several facial features that have previously been observed in neuroscience and psychological studies to be the most determining factor of perceived trustworthiness. We then regress the facial trustworthiness of IPO firm executives to IPO underpricing.FindingsUtilizing machine learning algorithms, we find that the facial trustworthiness of the company executive negatively impacts the extent of IPO underpricing. This result implies that investors incorporate the facial trustworthiness of company executives into stock valuation. The IPO underpricing also shows that the cost of equity is higher when perceived trustworthiness is low. With regard to the higher information asymmetry in IPO transactions, such a negative impact implies the role of facial trustworthiness in alleviating information asymmetry.Originality/valueThis study provides evidence of the impact of top management personal characteristics on firms’ financial transactions in the Indonesian context. From the perspective of investors and other fund providers, this study shows evidence that heuristics still play an important role in financial decision-making. This is also an indication of investor reliance on soft information. Our research method also provides a new opportunity for the use of machine-learning algorithms in processing non-conventional types of data in finance research, which is still relatively rare in emerging markets like Indonesia. To the best of our knowledge, our study is the first to use personalized measures of trust generated through machine-learning algorithms in IPO settings in Indonesia.

Assessing the impact of Machine Learning Algorithms on Portfolio Optimization and Risk Management in the Era of Sustainable Investing

The contemporary financial landscape is rapidly evolving, with sustainable investing emerging as a pivotal dimension. Against this backdrop, this research aimed to assess the impact of machine learning algorithms on portfolio optimization and risk management in the era of sustainable investing. Utilizing data from 90 FTSE100 companies that possess an ESG score, multiple machine learning models were employed to understand their efficacy. The Lasso regression model emerged as the top performer, underscoring the potential of machine learning in this domain. A conspicuous gap was identified in the literature, highlighting a limited implementation of machine learning techniques in the financial sector despite their proven capabilities. In an era characterized by the proliferation of big data, the rise of sustainable investing, and unparalleled computational power, relying solely on traditional methods becomes impractical. As manual calculations become increasingly untenable, the seamless integration of machine learning becomes imperative. However, while the preliminary findings are promising, further studies are essential to navigate potential pitfalls and to refine the application of these algorithms in real-world financial settings.

A hybrid FEM-IBM-level set algorithm for water entry of deformable body

Water entry of deformable bodies has been investigated extensively despite the major challenges in precise simulations of the whole physical process. Inspired by the basic idea of immersed boundary method (IBM), a hybrid FEM-IBM-level set algorithm is proposed where structural compliance is considered. The flow is described by Navier-Stokes equations with an added body force while the drastically moving object of arbitrary geometry is represented by several Lagrangian points on solid boundary. The direct forcing IBM is enhanced and coupled with finite element method (FEM) to facilitate simulation of severe fluid-structure interactions. Consequently, the added body force is calculated implicitly based on the exact velocity boundary condition with divergence-free condition ensured simultaneously. The improved conservative level set technique is adopted for accurate depiction of instantaneously distorted water surface, and the strongly coupled system is resolved iteratively through a partitioned scheme. Examples including sedimentation of a disk, water entry of deformable wedge and cylindrical shell are performed for validation. Specifically, hydroelastic impact of a three-dimensional conical shell is investigated, and the influence factors of entry dynamics are discussed. The results demonstrate the reliability and great potential of present algorithm for practical applications in ocean engineering, naval architecture and military engineering, etc.

Interpretable Multiscale Machine Learning‐Based Parameterizations of Convection for ICON

AbstractMachine learning (ML)‐based parameterizations have been developed for Earth System Models (ESMs) with the goal to better represent subgrid‐scale processes or to accelerate computations. ML‐based parameterizations within hybrid ESMs have successfully learned subgrid‐scale processes from short high‐resolution simulations. However, most studies used a particular ML method to parameterize the subgrid tendencies or fluxes originating from the compound effect of various small‐scale processes (e.g., radiation, convection, gravity waves) in mostly idealized settings or from superparameterizations. Here, we use a filtering technique to explicitly separate convection from these processes in simulations with the Icosahedral Non‐hydrostatic modeling framework (ICON) in a realistic setting and benchmark various ML algorithms against each other offline. We discover that an unablated U‐Net, while showing the best offline performance, learns reverse causal relations between convective precipitation and subgrid fluxes. While we were able to connect the learned relations of the U‐Net to physical processes this was not possible for the non‐deep learning‐based Gradient Boosted Trees. The ML algorithms are then coupled online to the host ICON model. Our best online performing model, an ablated U‐Net excluding precipitating tracer species, indicates higher agreement for simulated precipitation extremes and mean with the high‐resolution simulation compared to the traditional scheme. However, a smoothing bias is introduced both in water vapor path and mean precipitation. Online, the ablated U‐Net significantly improves stability compared to the non‐ablated U‐Net and runs stable for the full simulation period of 180 days. Our results hint to the potential to significantly reduce systematic errors with hybrid ESMs.

Small signal stability analysis of a wind power system integrated with a multi-band supplementary damping controller

Abstract With the integration of wind turbines represented by doubly-fed induction generators (DFIG) into the power system, the inertia level of the system decreases sharply, and small disturbances can easily cause low-frequency oscillation (LFO) in the system. In this paper, we propose a multi-band supplementary damping controller (MBSDC) directly applied to the active power control link of the rotor-side converter in the wind power generation system. We establish a linearized model for DFIG integrated with the power system, calculate eigenvalues, and screen out multiple electromechanical oscillation modes to be suppressed. Based on the calculation results, an MBSDC is designed to suppress LFO, and we propose a parameter-setting algorithm to enhance the suppression effect. Taking the New England 10-generator 39-bus system containing DFIG as an example for simulation verification, the results show that the MBSDC and parameter setting algorithm designed in this paper can effectively suppress LFO.

An Intelligent Parameter Identification Algorithm of Linear Fuzzy Fractional Differential Equation

Abstract The traditional linear generation fuzzy fractional differential equation parameter identification algorithm lacks the update of parameter identification process, has large amount of calculation, slow convergence speed of parameter identification and strong dependence on initial values. In this paper, a new intelligent recognition algorithm for linearly generated fuzzy fractional differential equations is proposed. The parameters of the equation are re expressed by the constant level set. The piecewise constant level set algorithm based on equation parameter intelligent identification is used to solve the steady-state solution of fractional differential equation, and the non convergence problem caused by too much calculation is solved. A new algorithm scheme for linearly generating fuzzy fractional differential equations is established, the constraints of the level set of the differential equations are calculated, and the updated algorithm for parameter identification of the equation is obtained. The evolutionary algorithm is used to solve the updating algorithm to realize the intelligent identification algorithm of linear fractional fuzzy differential equations. Experimental results show that the algorithm has the advantages of fast convergence speed, high calculation accuracy and low initial value.

Data-driven neural network model for maximum speed profile based on driver risk

In the field of vehicle control systems, the primary objective is to ensure the safety of road users. The intricate dynamics of road vehicles necessitate a high level of precision. Vehicle safety encompasses a multitude of considerations, including vehicle trajectory, prevailing traffic conditions, road structure, and meteorological factors. This study employs an Artificial Neural Network (ANN) trained with human driver data using SCANeR Studio software to evaluate the risk for the driver. The risk has been defined as a five-level parameter, which depends on the potential danger of a situation, where speed and direction play a crucial role. The system incorporates a simulator, an ANN, and a display interface to present the surroundings and communicate important information to the driver. This research employs a simulated driving scenario comprising a multi-lane roundabout with vehicles travelling in different directions to simulate real-world challenges. Risk estimation is achieved through a Time Delay Neural Network (TDNN) trained with various information about the environment in relation to the driven vehicle. The research employs a Jackknife technique for overall evaluation and introduces an adaptive algorithm for speed limit setting. The findings demonstrate the stability, generality, and practical applicability of the ANN in enhancing road safety.

Machine Learning Methods to Estimate Individualized Treatment Effects for Use in Health Technology Assessment.

Recent developments in causal inference and machine learning (ML) allow for the estimation of individualized treatment effects (ITEs), which reveal whether treatment effectiveness varies according to patients' observed covariates. ITEs can be used to stratify health policy decisions according to individual characteristics and potentially achieve greater population health. Little is known about the appropriateness of available ML methods for use in health technology assessment. In this scoping review, we evaluate ML methods available for estimating ITEs, aiming to help practitioners assess their suitability in health technology assessment. We present a taxonomy of ML approaches, categorized by key challenges in health technology assessment using observational data, including handling time-varying confounding and time-to event data and quantifying uncertainty. We found a wide range of algorithms for simpler settings with baseline confounding and continuous or binary outcomes. Not many ML algorithms can handle time-varying or unobserved confounding, and at the time of writing, no ML algorithm was capable of estimating ITEs for time-to-event outcomes while accounting for time-varying confounding. Many of the ML algorithms that estimate ITEs in longitudinal settings do not formally quantify uncertainty around the point estimates. This scoping review may not cover all relevant ML methods and algorithms as they are continuously evolving. Existing ML methods available for ITE estimation are limited in handling important challenges posed by observational data when used for cost-effectiveness analysis, such as time-to-event outcomes, time-varying and hidden confounding, or the need to estimate sampling uncertainty around the estimates. ML methods are promising but need further development before they can be used to estimate ITEs for health technology assessments. Estimating individualized treatment effects (ITEs) using observational data and machine learning (ML) can support personalized treatment advice and help deliver more customized information on the effectiveness and cost-effectiveness of health technologies.ML methods for ITE estimation are mostly designed for handling confounding at baseline but not time-varying or unobserved confounding. The few models that account for time-varying confounding are designed for continuous or binary outcomes, not time-to-event outcomes.Not all ML methods for estimating ITEs can quantify the uncertainty of their predictions.Future work on developing ML that addresses the concerns summarized in this review is needed before these methods can be widely used in clinical and health technology assessment-like decision making.

A low-storage synchronization framework for blockchain systems

The advent of blockchain technology has brought major changes to traditional centralized storage. Therefore, various fields have begun to study the application and development of blockchain. However, blockchain technology has a serious shortcoming of data bloating. The reason is that blockchain technology achieves decentralization by storing complete blockchain data at each node, incurring a significant amount of blockchain data. Therefore, each node must spend significant amount of storage space and initialization synchronization time. To solve the above problems, in this research, we propose a secure and agile synchronization framework for low storage blockchains. First, we design a K-extreme segment algorithm, which reduces the synchronization time by returning only the first and last k blocks of each block segment at once to the local storage. Next, we decentrally store the block data of the blockchain by IPFS and establish a backup mechanism by IPFS-cluster. Finally, due to use of distributed storage, the nodes must request un-stored block data from IPFS, causing an increase in the throughput of the blockchain network. To avoid network congestion, we propose the working set algorithm to improve the hit ratio of the local storage and reduce the number of requests to decrease throughput. In summary, our experiments demonstrate that the ratio of full nodes to low storage nodes is significantly lower for nodes with higher storage limits compared to those with lower storage limits. In other words, a higher storage limit results in more low storage nodes which can be permitted to ensure that the blockchain network is robust and reliable. Therefore, our proposed framework can provide reliable low storage nodes for the blockchain. The node can reduce the local storage pressure and can still maintain the full functionality of blockchains.

Front stability of infinitely steep travelling waves in population biology

Reaction–diffusion models are often used to describe biological invasion, where populations of individuals that undergo random motility and proliferation lead to moving fronts. Many reaction–diffusion models of biological invasion are extensions of the well–known Fisher–Kolmogorov–Petrovskii–Piskunov model that describes the spatiotemporal evolution of a 1D population density, u(x,t) , as a result of linear diffusion with flux J=−∂u/∂x , and logistic growth source term, S=u(1−u) . In 2020 Fadai introduced a new reaction–diffusion model of biological invasion with a nonlinear degenerate diffusive flux, J=−u∂u/∂x , and the model was formulated as a moving boundary problem on 0<x<L(t) , with u(L(t),t)=0 and dL(t)/dt=−κu∂u/∂x at x=L(t) (Fadai and Simpson 2020 J. Phys. A: Math. Theor. 53 095601). Fadai’s model leads to travelling wave solutions with infinitely steep, well–defined fronts at the moving boundary, and the model has the mathematical advantage of being analytically tractable in certain parameter limits. In this work we consider the stability of the travelling wave solutions presented by Fadai. We provide general insight by first presenting two key extensions of Fadai’s model by considering: (i) generalised nonlinear degenerate diffusion with flux J=−um∂u/∂x for some constant m > 0; and, (ii) solutions describing both biological invasion with dL(t)/dt>0 , and biological recession with dL(t)/dt<0 . After establishing the existence of travelling wave solutions for these two extensions, our main contribution is to consider stability of the travelling wave solutions by introducing a lateral perturbation of the travelling wavefront. Full 2D time–dependent level–set numerical solutions indicate that invasive travelling waves are stable to small amplitude lateral perturbations, whereas receding travelling waves are unstable. These preliminary numerical observations are corroborated through a linear stability analysis that gives more formal insight into short time growth/decay of wavefront perturbation amplitude. Julia–based software, including level–set algorithms, is available on Github to replicate all results in this study.

Comparative Analysis and Characteristic Verification of the Quality Standard of Milk Thistle Extract

Introduction:: Milk thistle extract is widely used in dietary supplements due to its strong hepatoprotective effect. There are some differences in the quality characteristics and standard values of milk thistle extract set by pharmacopoeias and standards in various countries and regions. Method:: To promote the standardization of quality standards for milk thistle extract, this paper summarized and compared the standard characteristics and related values settings in four different standards. At the same time, actual samples were tested for the characteristics that appeared more frequently in standard. We found that the ratio algorithm of silymarin set in the pharmacopoeia standards was not reasonable and could blur the quality of related products from the test results, especially without limiting the total amount of silymarin in the standard. Results:: We suggested setting standard values for all six silymarin characteristics, establishing more universal detection methods, and preparing relevant reference materials to match the detection methods. Based on the comparison of standards and test data, the characteristics of the ethanol content, ethyl acetate content, methanol insoluble substances, heavy metal content, and microbial characteristics should draw more attention and should be considered in the future for setting newly formulated standards. Conclusion:: This paper lays the data foundation for developing higher quality standards for milk thistle extract, which plays an important role in promoting the international trade of milk thistle extract.

The marriage of operations research and reinforcement learning: Integration of NEH into Q-learning algorithm for the permutation flowshop scheduling problem

The permutation flowshop scheduling problem (PFSP) attracted much interest from the operations research (OR) community, resulting in various heuristic and metaheuristic methods over the past half-century. However, given the hard nature of the PFSP, efficient algorithms rely on the configuration of initial solutions and sophisticated heuristics. Combining OR and artificial intelligence (AI), this paper investigates the marriage of OR and reinforcement learning for the PFSP. A novel method integrating NEH into the Q-learning algorithm is proposed for solving the PFSP with makespan criterion. With the refinement of action selection by strengthening good actions and weakening bad actions, the proposed Q-NEH algorithm shows a better learning and convergence rate, and outperforms the Q-learning algorithm for all 120 instances in Taillard’s benchmark dataset, with a significantly decreased average relative learning error (RLE) from 13.05 % to 1.73 %. Furthermore, by comparing the performances of the Q-NEH algorithm with related state-of-art benchmark algorithms in a wide range set of instances, it further confirms the superiority and stability of the Q-NEH algorithm in statistics. With a total of 2570 independent tests in two phases of experimental evaluations, the superiority of the Q-NEH algorithm for the PFSP suggests that the integration of OR and AI is a promising research direction for solving complex scheduling problems.

Alternating generalized projection method applied to phase-only synthesis process of satellite reflectarray antennas

Promising algorithm of alternating generalized projection method (AGP) is proposed for phase-only synthesis process of satellite reflectarray antennas under intersection approach. This promising algorithm is a hybridized algorithm of two specialized algorithms for non-convex sets rescued in the literature: algorithm based on separating hyperplanes and algorithm based on decomposition method in polar cones. Since the sets involved in phase-only synthesis process of satellite reflectarray antennas are non-convex, the conventional von Neumann alternating projection method proposed in the literature does not guarantee convergence to the point in the intersection of the involved sets. In addition, the results of the phase-only synthesis obtained by the different algorithms were compared and promising improvements produced by the proposed hybridized algorithm were shown.