Classical Approach Research Articles

Explicit Topology Optimization of Voronoi Foams.

Topology optimization can maximally leverage the high DOFs and mechanical potentiality of porous foams but faces challenges in adapting to free-form outer shapes, maintaining full connectivity between adjacent foam cells, and achieving high simulation accuracy. Utilizing the concept of Voronoi tessellation may help overcome the challenges owing to its distinguished properties on highly flexible topology, natural edge connectivity, and easy shape conforming. However, a variational optimization of the so-called Voronoi foams has not yet been fully explored. In addressing the issue, a concept of explicit topology optimization of open-cell Voronoi foams is proposed that can efficiently and reliably guide the foam's topology and geometry variations under critical physical and geometric requirements. Taking the site (or seed) positions and beam radii as the DOFs, we explore the differentiability of the open-cell Voronoi foams w.r.t. its seed locations, and propose a highly efficient local finite difference method to estimate the derivatives. During the gradient-based optimization, the foam topology can change freely, and some seeds may even be pushed out of shape, which greatly alleviates the challenges of prescribing a fixed underlying grid. The foam's mechanical property is also computed with a much-improved efficiency by an order of magnitude, in comparison with benchmark FEM, via a new material-aware numerical coarsening method on its highly heterogeneous density field counterpart. We show the improved performance of our Voronoi foam in comparison with classical topology optimization approaches and demonstrate its advantages in various settings.

Fermatean Fuzzy MOORA-based Approach for Hazard Analysis in An Aluminium Company

Introduction/Objective: Hazard analysis as one of the main study subjects in ergo-nomics and occupational health and safety (OHS) risk assessment is a critical requirement for ensuring the health and safety of workers in work environments. Current hazard analysis ap-proaches in the literature may have some shortcomings. This study aims to provide more relia-ble assessments in the hazard analysis process by overcoming the shortcomings of classical ap-proaches. Methods: This study proposes a new hazard analysis approach based on the integration of the Fermatean Fuzzy set and the multi-criteria decision-making (MCDM) method MOORA. Results: The proposed approach is used to perform a hazard analysis of a company operating in the metal industry, which is one of the sectors where occupational accidents and occupational diseases most often occur. As a result of the application, the hazards and associated risks in the aluminum metal company are prioritized. Conclusion: This study provides an advanced risk assessment technique for ergonomists and OHS professionals to make better decisions in hazard analysis studies.

Closing the Gap Between Theory and Practice During Alternating Optimization for GANs.

Synthesizing high-quality and diverse samples is the main goal of generative models. Despite recent great progress in generative adversarial networks (GANs), mode collapse is still an open problem, and mitigating it will benefit the generator to better capture the target data distribution. This article rethinks alternating optimization in GANs, which is a classic approach to training GANs in practice. We find that the theory presented in the original GANs does not accommodate this practical solution. Under the alternating optimization manner, the vanilla loss function provides an inappropriate objective for the generator. This objective forces the generator to produce the output with the highest discriminative probability of the discriminator, which leads to mode collapse in GANs. To address this problem, we introduce a novel loss function for the generator to adapt to the alternating optimization nature. When updating the generator by the proposed loss function, the reverse Kullback-Leibler divergence between the model distribution and the target distribution is theoretically optimized, which encourages the model to learn the target distribution. The results of extensive experiments demonstrate that our approach can consistently boost model performance on various datasets and network structures.

Improving Depth Perception in Immersive Media Devices by Addressing Vergence-Accommodation Conflict.

Recently, immersive media devices have seen a boost in popularity. However, many problems still remain. Depth perception is a crucial part of how humans behave and interact with their environment. Convergence and accommodation are two physiological mechanisms that provide important depth cues. However, when humans are immersed in virtual environments, they experience a mismatch between these cues. This mismatch causes users to feel discomfort while also hindering their ability to fully perceive object distances. To address the conflict, we have developed a technique that encompasses inverse blurring into immersive media devices. For the inverse blurring, we utilize the classical Wiener deconvolution approach by proposing a novel technique that is applied without the need for an eye-tracker and implemented in a commercial immersive media device. The technique's ability to compensate for the vergence-accommodation conflict was verified through two user studies aimed at reaching and spatial awareness, respectively. The two studies yielded a statistically significant 36% and 48% error reduction in user performance to estimate distances, respectively. Overall, the work done demonstrates how visual stimuli can be modified to allow users to achieve a more natural perception and interaction with the virtual environment.

Supply chain model having stochastic lead time demand with variable production rate and demand dependent on price and advertisement

In this present study, a single-manufacturer and single-retailer supply chain management model are formulated for a single product. This study specifically looks at a supply chain with variable production rate, stochastic lead time demand, and price- and advertisement-dependent demand. By incorporating these complex aspects into a model, which enables to examine their combined effects on supply chain performance, this study adds to the body of knowledge. The study reveals unique insights into the complex interplay between pricing tactics, advertising efforts, production dynamics, and the variability brought on by stochastic lead times through meticulous study and modelling. Finally, the total system profit is calculated and optimized with all the decision variables. A classical approach is performed to obtain the optimized solution of the joint profit function along with the decision variables. Two models are discussed in the study: (1) the model with normally distributed lead time demand and (2) the model with distribution-free lead time demand. The joint profit of the supply chain is found to be lesser by 1% for the normally distributed lead time demand than the distribution free pattern. The comparison of the shipment policies and the safety factors for the different distribution patterns of the lead time demand are shown. Though the huge increment in the safety factor for unknown leadtime demand distribution may help in reducing the uncertainity factor and disruptions in the supply chain, but also it may unnecessarily tie up more capital which can be invested in other sectors of the supply chain.

Frequency beating and damping of breathing oscillations of a harmonically trapped one-dimensional quasicondensate

We study the breathing (monopole) oscillations and their damping in a harmonically trapped one-dimensional (1D) Bose gas in the quasicondensate regime using a finite-temperature classical field approach. By characterising the oscillations via the dynamics of the density profile's rms width over long time, we find that the rms width displays beating of two distinct frequencies. This means that 1D Bose gas oscillates not at a single breathing mode frequency, as found in previous studies, but as a superposition of two distinct breathing modes, one oscillating at frequency close to $\simeq\!\sqrt{3}\omega$ and the other at $\simeq\!2\omega$, where $\omega$ is the trap frequency. The breathing mode at $\sim\!\sqrt{3}\omega$ dominates the beating at lower temperatures, deep in the quasicondensate regime, and can be attributed to the oscillations of the bulk of the density distribution comprised of particles populating low-energy, highly-occupied states. The breathing mode at $\simeq\!2\omega$, on the other hand, dominates the beating at higher temperatures, close to the nearly ideal, degenerate Bose gas regime, and is attributed to the oscillations of the tails of the density distribution comprised of thermal particles in higher energy states. The two breathing modes have distinct damping rates, with the damping rate of the bulk component being approximately four times larger than that of the tails component.

Synthesis and Biological Activity of Pyrrolizidine Alkaloids: A Review

Abstract: Pyrrolizidine alkaloids (PAs) belong to a structurally varied class of natural products that have garnered significant attention due to their intriguing chemical properties and broad range of biological activities. This review aims to provide a comprehensive overview of the synthesis and biological activity of pyrrolizidine alkaloids. An in-depth exploration of the various synthetic methodologies employed in the construction of these complex molecules, including classical and modern synthetic approaches, is presented in this review. Moreover, the potential therapeutic applications of these alkaloids are discussed, emphasizing their promising role in drug discovery and development. By synthesizing the current knowledge, this paper aims to facilitate further research in the field and inspire the discovery of novel bioactive compounds with therapeutic potential.

Enhancing awareness of industrial robots in collaborative manufacturing

The diffusion of Human-Robot Collaborative cells is prevented by several barriers. Classical control approaches seem not yet fully suitable for facing the variability conveyed by the presence of human operators beside robots. The capabilities of representing heterogeneous knowledge representation and performing abstract reasoning are crucial to enhance the flexibility of control solutions. To this aim, the ontology SOHO (Sharework Ontology for Human-Robot Collaboration) has been specifically designed for representing Human-Robot Collaboration scenarios, following a context-based approach. This work brings several contributions. This paper proposes an extension of SOHO to better characterize behavioral constraints of collaborative tasks. Furthermore, this work shows a knowledge extraction procedure designed to automatize the synthesis of Artificial Intelligence plan-based controllers for realizing flexible coordination of human and robot behaviors in collaborative tasks. The generality of the ontological model and the developed representation capabilities as well as the validity of the synthesized planning domains are evaluated on a number of realistic industrial scenarios where collaborative robots are actually deployed.

Use of the Win Ratio Analysis in Critical Care Trials.

Composite outcomes are commonly used in critical care trials to estimate the treatment effect of an intervention. A significant limitation of classical analytic approaches is that they assign equal statistical importance to each component in a composite, even if these do not have the same clinical importance (i.e., in a composite of death and organ failure, death is clearly more important). The win ratio (WR) method has been proposed as an alternative for trial outcomes evaluation, as it effectively assesses events based on their clinical relevance (i.e., hierarchical order) by comparing each patient in the intervention group with their counterparts in the control group. This statistical approach is increasingly used in cardiovascular outcome trials. However, WR may be useful to unveil treatment effects also in the critical care setting, because these trials are typically moderately sized, thus limiting the statistical power to detect small differences between groups, and often rely on composite outcomes that include several components of different clinical importance. Notably, the advantages of this approach may be offset by several drawbacks (such as ignoring ties and difficulties in selecting and ranking endpoints) and challenges in appropriate clinical interpretation (i.e., establishing clinical meaningfulness of the observed effect size). In this perspective article, we present some key elements to implementing WR statistics in critical care trials, providing an overview of strengths, drawbacks, and potential applications of this method. To illustrate, we conduct a reevaluation of the HYPO-ECMO (Hypothermia during Venoarterial Extracorporeal Membrane Oxygenation) trial using the WR framework as a case example.

An optimistic-pessimistic game cross-efficiency method based on a Gibbs entropy model for ranking decision making units

The game cross-efficiency method, a commonly utilized approach for ranking decision-making units in tie-breaking scenarios, is based on secondary goals. However, in certain data envelopment analysis ranking problems, the classical game cross-efficiency method may fail to differentiate all decision-making units effectively. To address this limitation, it is prudent to explore the development of a new method that can enhance the ranking performance of the classical game cross-efficiency approach. In this study, we propose a novel Gibbs entropy linear programming model that integrates both optimistic and pessimistic perspectives of the classical game cross-efficiency method for data envelopment analysis ranking problems. To validate the reliability and utility of our proposed method, we present three examples: the six nursing homes problem, numerical example 2, and an application involving twenty Thai provinces with cash crop data. The reliability of the proposed method is assessed using Spearman’s correlation coefficient (rs) on the numerical examples. The results demonstrate that the rs values for both the proposed method and the classical game crossefficiency method, specifically for the six nursing homes problem, numerical example 2, and the application involving twenty Thai provinces, are determined to be rs=0.998, 0.998, and 0.986 respectively.

Unsupervised Fusion Feature Matching for Data Bias in Uncertainty Active Learning.

Active learning (AL) aims to sample the most valuable data for model improvement from the unlabeled pool. Traditional works, especially uncertainty-based methods, are prone to suffer from a data bias issue, which means that selected data cannot cover the entire unlabeled pool well. Although there have been lots of literature works focusing on this issue recently, they mainly benefit from the huge additional training costs and the artificially designed complex loss. The latter causes these methods to be redesigned when facing new models or tasks, which is very time-consuming and laborious. This article proposes a feature-matching-based uncertainty that resamples selected uncertainty data by feature matching, thus removing similar data to alleviate the data bias issue. To ensure that our proposed method does not introduce a lot of additional costs, we specially design a unsupervised fusion feature matching (UFFM), which does not require any training in our novel AL framework. Besides, we also redesign several classic uncertainty methods to be applied to more complex visual tasks. We conduct rigorous experiments on lots of standard benchmark datasets to validate our work. The experimental results show that our UFFM is better than the similar unsupervised feature matching technologies, and our proposed uncertainty calculation method outperforms random sampling, classic uncertainty approaches, and recent state-of-the-art (SOTA) uncertainty approaches.

Fusion dynamical systems with machine learning in imitation learning: A comprehensive overview

Fusion dynamical systems with machine learning in imitation learning: A comprehensive overview

X-FSPMiner: A Novel Algorithm for Frequent Similar Pattern Mining

Frequent similar pattern mining (FSP mining) allows for finding frequent patterns hidden from the classical approach. However, the use of similarity functions implies more computational effort, necessitating the development of more efficient algorithms for FSP mining. This work aims to improve the efficiency of mining all FSPs when using Boolean and non-increasing monotonic similarity functions. A data structure to condense an object description collection, named FV-Tree , and an algorithm for mining all FSPs from the FV-Tree , named X-FSPMiner , are proposed. The experimental results reveal that the novel algorithm X-FSPMiner vastly outperforms the state-of-the-art algorithms for mining all FSPs using Boolean and non-increasing monotonic similarity functions.

Classical stochastic approach to quantum mechanics and quantum thermodynamics

We derive the equations of quantum mechanics and quantum thermodynamics from the assumption that a quantum system can be described by an underlying classical system of particles. Each component φ j of the wave vector is understood as a stochastic complex variable whose real and imaginary parts are proportional to the coordinate and momentum associated with a degree of freedom of the underlying classical system. From the classical stochastic equations of motion, we derive a general equation for the covariance matrix of the wave vector, which turns out to be of the Lindblad type. When the noise changes only the phase of φ j , the Schrödinger and the quantum Liouville equations are obtained. The component ψ j of the wave vector obeying the Schrödinger equation is related to the stochastic wave vector by |ψj|2=⟨|ϕj|2⟩ .

Deep-unrolling architecture for image-domain least-squares migration

Deep-image prior (DIP) is a novel approach to solving ill-posed inverse problems whose solution is parameterized with an untrained deep neural network and cascaded with the forward modeling operator. A key component to the success of such a method is represented by the choice of the network architecture, which must act as a natural prior to the inverse problem at hand and provide a strong inductive bias toward the desired solution. Inspired by the close link between neural networks and iterative algorithms in classical optimization, we apply an unrolled version of the gradient descent (GD) algorithm as our DIP network architecture, denoted as the deep-unrolling (DU) architecture. Each layer of the unrolled network is comprised of two parts: the first part corresponds to the GD step of the data-fidelity term, whereas the second part, formed by a six-layer convolutional neural network (CNN), plays the role of a regularizer in the associated objective function. The developed DU architecture is applied to the problem of image-domain least-squares migration (IDLSM) to invert migrated seismic images for their underlying reflectivity and is denoted as DU-IDLSM. As such, the DU architecture parameterizes the reflectivity, and the input of each layer of the unrolled network is the reflectivity at the previous layer. Similar to the classical DIP approach, the parameters of the DU architecture are optimized in an unsupervised fashion by minimizing the data misfit function itself. Through experiments with a part of the Sigsbee2A model and a marine field data set, we test the effectiveness of the DU-IDLSM approach and highlight two key benefits. First, the DU architecture can effectively regularize the inversion process, resulting in reflectivity estimates with fewer artifacts and higher image resolution than those produced by conventional IDLSM approaches. Second, we indicate that by including dropout layers in the CNN architecture, DU-IDLSM can produce a qualitative measure of the uncertainty associated with the least-squares migration process.

Comparative Analysis of Linear Models and Artificial Neural Networks for Sugar Price Prediction

Sugar is an important commodity that is used beyond the food industry. It can be produced from sugarcane and sugar beet, depending on the region. Prices worldwide differ due to high volatility, making it difficult to estimate their forecast. Thus, the present work aims to predict the prices of kilograms of sugar from four databases: the European Union, the United States, Brazil, and the world. To achieve this, linear methods from the Box and Jenkins family were employed, together with classic and new approaches of artificial neural networks: the feedforward Multilayer Perceptron and extreme learning machines, and the recurrent proposals Elman Network, Jordan Network, and Echo State Networks considering two reservoir designs. As performance metrics, the MAE and MSE were addressed. The results indicated that the neural models were more accurate than linear ones. In addition, the MLP and the Elman networks stood out as the winners.

Azov sea fishes stock assessment by swept area method through bootstrap

Recently driven changes in habitat conditions in the Azov Sea make classic applications of swept area method inconsistent. Swept area stock assessment method modification for stock estimation of aquatic biological resources through nonparametric bootstrap is presented. New approach is tested against classic calculated results of swept area method for next fish species: Azov anchovy, tulka, turbot and Black-Azov sea shad according to data collected during 2022. Results comparison of classic and modified through bootstrap stock assessment methods leads no significant deviations in mean value estimation. Maximum difference between mean estimates of stock size was 3,75%. Confidential intervals width difference between reviewed methods, in general, was not significant. Most valuable difference in confidential intervals was detected in BCa approach against classic method with quantile approach (due to methodological features of BCa in bias correction procedure). The new stock assessment approach for swept area method though bootstrap permit to calculate statistically valid estimates, their confidential intervals and perform input data validation. The absence of significant differences in the results by the classical and proposed methods indicates the further applicable possibility of classic method even in normality violation in source samples. The normality requirements in methodology of classic approach, probably, is redundant.

Inference of the Paramentes of a Bioreactor via Approximate Bayesian Evidence

In the context of the recent water crisis, the development and improvement of technologies for efficiently treating wastewater becomes increasingly necessary. Aligned with this, the activated sludge process is widely used in biological wastewater treatment plants to remove carbon and nutrients from wastewater. Although significant advancements have been achieved in recent years in modelling these processes to improve their performance and energy efficiency, the models are still complex and difficult to calibrate using data from real plants. In this work, we use Bayesian inference frameworks to estimate the parameters of the activated sludge process. We employ Gaussian processes to estimate the solution to the differential equations of the activated sludge process model and we fit this estimation to manufactured data of the observations of the wastewater. We find that the use of the Bayesian inference framework outperforms the classical approach in scenarios where measurements are more strongly affected by noise.

Post-institutionalism versus economic science: Critical analysis

The article analyzes post­institutionalism, highlighting its inability to present a “new mainstream” in institutional studies in economics. Pointing out some real problems of institutional studies of modern society, post­institutionalism does not provide what economic theory needs. The rejection of functionalism, rationalism and efficiency and optimality criteria sidetracks this approach to the field of sociology and cultural studies; de­economization is also effected on account of target substitution of institution transplanting. Another essential problem is the confusion of notions through their “unsealing” that enables an “alternate” interpretation of blockchain and allows to oppose against the transaction costs minimization principle, basing on the criticisms of the Coase Theorem. Rejecting this interpretation, the article claims that the studies of the institutional complexity of modern society does not require refusal of classical approaches, but their clarification. The paper also criticizes the call for “postdisciplinarity” and raises the question of the quality of interdisciplinary institution researches.

Predicting dental anxiety in young adults: classical statistical modelling approach versus machine learning approach

ObjectivesTo predict and identify the key demographic and clinical exposure factors associated with dental anxiety among young adults, and to compare if the traditional statistical modelling approach provides similar results to the machine learning (ML) approach in predicting factors for dental anxiety.MethodsA cross-sectional study of Western Illinois University students. Three survey instruments (sociodemographic questionnaire, modified dental anxiety scale (MDAS), and dental concerns assessment tool (DCA)) were distributed via email to the students using survey monkey. The dependent variable was the mean MDAS scores, while the independent variables were the sociodemographic and dental concern assessment variables. Multivariable analysis was done by comparing the classical statistical model and the machine learning model. The classical statistical modelling technique was conducted using the multiple linear regression analysis and the final model was selected based on Akaike information Criteria (AIC) using the backward stepwise technique while the machine learining modelling was performed by comparing two ML models: LASSO regression and extreme gradient boosting machine (XGBOOST) under 5-fold cross-validation using the resampling technique. All statistical analyses were performed using R version 4.1.3.ResultsThe mean MDAS was 13.73 ± 5.51. After careful consideration of all possible fitted models and their interaction terms the classical statistical approach yielded a parsimonious model with 13 predictor variables with Akaike Information Criteria (AIC) of 2376.4. For the ML approach, the Lasso regression model was the best-performing model with a mean RMSE of 0.617, R2 of 0.615, and MAE of 0.483. Comparing the variable selection of ML versus the classical statistical model, both model types identified 12 similar variables (out of 13) as the most important predictors of dental anxiety in this study population.ConclusionThere is a high burden of dental anxiety within this study population. This study contributes to reducing the knowledge gap about the impact of clinical exposure variables on dental anxiety and the role of machine learningin the prediction of dental anxiety. The predictor variables identified can be used to inform public health interventions that are geared towards eliminating the individual clinical exposure triggers of dental anxiety are recommended.