102,381 research outputs found
Evaluation of deep learning with long short-term memory networks for time series forecasting in supply chain management
No full textPerformance analysis and forecasting the evolution of complex systems are two challenging tasks in manufacturing. Time series data from complex systems capture the dynamic behaviors of the underlying processes. However, non-linear and non-stationary dynamics pose a major challenge for accurate forecasting. To overcome statistical complexities through analyzing time series, we approach the problem with deep learning methods. In this paper, we mainly focus on the long short-term memory (LSTM) networks for demand forecasts in supply chain management, where the future demand for a certain product is the basis for the respective replenishment systems. This study contributes to the literature by conducting experiments on real data to investigate the potential of using LSTM networks for final customer demand forecasting, and hence for increasing the overall value generated by a supply chain. Both forward LSTM and bidirectional LSTM (forward-backward) for short-and long-term demand prediction in supply chain management are considered in this study
Optimizing Healthcare Ecosystem Performance-A Computational Study of Integrated Patient Assistance in Primary Care
No full textHome health care professionals provide medical services to patients in their homes. With rising demand, it’s crucial to manage operational costs effectively while ensuring satisfaction for patients. This study presents a bi-objective optimization model aimed at resolving routing and scheduling challenges in home health care, with a focus on both system efficiency and patient accessibility. A Mixed-Integer Linear Programming Model (MILP) is developed. To tackle computational time challenges, we propose a Non-dominated Sorting Genetic Algorithm II to solve the multi-objective optimization problems. The evaluation of Pareto fronts demonstrates the method’s efficiency. We apply the method in a real-world case study to provide managerial implications
Fault diagnosis by multisensor data: A data-driven approach based on spectral clustering and pairwise constraints
No full textThis paper deals with clustering based on feature selection of multisensor data in high-dimensional space. Spectral clustering algorithms are efficient tools in signal processing for grouping datasets sampled by multisensor systems for fault diagnosis. The effectiveness of spectral clustering stems from constructing an embedding space based on an affinity matrix. This matrix shows the pairwise similarity of the data points. Clustering is then obtained by determining the spectral decomposition of the Laplacian graph. In the manufacturing field, clustering is an essential strategy for fault diagnosis. In this study, an enhanced spectral clustering approach is presented, which is augmented with pairwise constraints, and that results in efficient identification of fault scenarios. The effectiveness of the proposed approach is described using a real case study about a diesel injection control system for fault detection
Finite Mixture Models for Clustering Auto-Correlated Sales Series Data Influenced by Promotions
No full textThe focus of the present paper is on clustering, namely the problem of finding distinct groups in a dataset so that each group consists of similar observations. We consider the finite mixtures of regression models, given their flexibility in modeling heterogeneous time series. Our study aims to implement a novel approach, which fits mixture models based on the spline and polynomial regression in the case of auto-correlated data, to cluster time series in an unsupervised machine learning framework. Given the assumption of auto-correlated data and the usage of exogenous variables in the mixture model, the usual approach of estimating the maximum likelihood parameters using the Expectation–Maximization (EM) algorithm is computationally prohibitive. Therefore, we provide a novel algorithm for model fitting combining auto-correlated observations with spline and polynomial regression. The case study of this paper consists of the task of clustering the time series of sales data influenced by promotional campaigns. We demonstrate the effectiveness of our method in a case study of 131 sales series data from a real-world company. Numerical outcomes demonstrate the efficacy of the proposed method for clustering auto-correlated time series. Despite the specific case study of this paper, the proposed method can be used in several real-world application fields
Biopolymeric Carriers for Nanomedical Applications
No full textNanomedicine offers the opportunity to develop novel nano and microcarriers for targeted drug therapy, reducing the undesirable side effects. This communication will discuss about multifunctional, highly biodegradable and biocompatible, (natural) biopolymeric drug carriers, having the following functionalities: 1) protection of drugs from chemical and biological degradation; 2) control of in vivo and in vitro distribution; 3) specific cell targeting. Several type of carriers will be examined. The first type is composed of biodegradable polyelectrolyte pairs (e.g. polysaccharides) with opposite charges, subsequently dissolved to obtain hollow capsules ready to encapsulate drugs [1, 2]. Selected fluorophores and magnetic nanoparticles have been included in the coating to allow in vitro and in vivo monitoring of microcapsule fate. In order to obtain cellular targeting, specific ligands can also be conjugated to the coating layers. The second type of carriers is represented by novel monodisperse micro and nanogels [3]. These are challenging drug delivery systems for the release of bioactive molecules, able to undergo reversible volume transition, when triggered by environmental factors such as temperature, ionic strength, or pH [4]. Nanogels, internalised inside cells through phagocytic pathways, release natural or synthetic drugs inside endosomes by pH-induced polymer removal. Biomolecules and biopolymers are therefore protected and released in a controlled manner. The third category of carriers discussed is the naturally “green” and cytocompatible halloysite nanotubes [5], used as novel hydrophobic drug carriers for targeted delivery into neoplastic cells. The internalisation and cytotoxicity of drug-loaded carriers has been evaluated in vitro on different cell lines.
Acknowledgments: “Con il contributo del Ministero degli Affari Esteri, Direzione Generale per la promozione e la Cooperazione Sociale ” (Large Scale ITA –USA Bilateral Project “Nano-trasportatori per la terapia del cancro”). G.L. thanks Regione Puglia for "Ritorno al Futuro" Fellowship INCANTO.
References
[1] V. Vergaro, F. Scarlino, C. Bellomo, R. Rinaldi, D. Vergara, M. Maffia, F. Baldassarre, G. Giannelli, X. Zhang, Y. M. Lvov and S. Leporatti Advanced Drug Delivery Review (2011), 63, 847-864.
[2] I.E Palamà, S. Leporatti, E. de Luca, N. Di Renzo, M. Maffia, C. Gambacorti-Passerini, R.Rinaldi, G. Gigli, R. Cingolani, A.ML Coluccia Nanomedicine Future Medicine Lt (2010) 5(3), 419-431.
[3] S. Argentiere, L. Blasi, G. Ciccarella, G. Barbarella, A. Cazzato, R. Cingolani, G. Gigli Small (2009) 4101-4103.
[4] S. Argentiere, L. Blasi, G. Ciccarella, G.Barbarella, R.Cingolani, G. Gigli Journal of Applied Polymer Science (2010), 116(5), 2808-2815.
[5] V.Vergaro, E.Abdullayev, Y.M. Lvov, A.Zeitoun, R. Cingolani, R. Rinaldi and S. Leporatti Biomacromolecules (2010) 11, 820–826
A numerical procedure for machining distortions simulation on a SAF 2507 casting workpiece
No full textThe workpiece distortion that occurs during machining, can lead to a large increase in the number of the scrap parts. Residual stresses are the main cause of these distortions and they are generally present in both forging and casting products. In order to obtain the desired microstructure and mechanical properties, the workpiece is subjected to heat treatment before being worked. Quenching produces residual stresses that exist throughout a large percentage of the casting or forging part. Distortion occurs as a result of removing stressed material from the workpiece. The component will re-equilibrate and distort as each layer of stressed material is machined away. This paper describes a procedure development for distortions numerical analysis on a SAF2507 casting bulk workpiece. A solubilization heat treatment has been simulated, in order to predict the bulk residual stresses distribution. Different metal cutting processes have been considered to measure the numerical distortions induced in the workpiece
Utilizing Mixture Regression Models for Clustering Time-Series Energy Consumption of a Plastic Injection Molding Process
No full textConsidering the issue of energy consumption reduction in industrial plants, we investigated a clustering method for mining the time-series data related to energy consumption. The industrial case study considered in our work is one of the most energy-intensive processes in the plastics industry: the plastic injection molding process. Concerning the industrial setting, the energy consumption of the injection molding machine was monitored across multiple injection molding cycles. The collected data were then analyzed to establish patterns and trends in the energy consumption of the injection molding process. To this end, we considered mixtures of regression models given their flexibility in modeling heterogeneous time series and clustering time series in an unsupervised machine learning framework. Given the assumption of autocorrelated data and exogenous variables in the mixture model, we implemented an algorithm for model fitting that combined autocorrelated observations with spline and polynomial regressions. Our results demonstrate an accurate grouping of energy-consumption profiles, where each cluster is related to a specific production schedule. The clustering method also provides a unique profile of energy consumption for each cluster, depending on the production schedule and regression approach (i.e., spline and polynomial). According to these profiles, information related to the shape of energy consumption was identified, providing insights into reducing the electrical demand of the plant
IC.IDO as a tool for displaying machining processes. The logic interface between computer-aided-manufacturing and virtual reality
No full textThis scientific communication investigates the logic interface of a CAM solver, i.e., MasterCAM, into a Virtual Reality (VR) environment. This integration helps in displaying machining operations in virtual reality. Currently, to partially visualize the results of a simulation in an immersive environment, an import/export procedure must be done manually. Here, a software plugin integrated into IC.IDO (by ESI Group) has been realized and fully described. This application allows the complete integration of CAM solver into the VR environment. In particular, the VERICUT solver has been integrated into VR. This kind of integration has never been done yet
Thermal characterization methodology for dry finishing turning of SAF 2507 stainless steel based on finite element simulations and surrogate models
No full textThis paper addresses the numerical thermal characterization of a 3D turning process of a SAF 2507 stainless steel. A thermographic test campaign was conducted to measure the temperature distribution at the tool-workpiece interface. The campaign was accommodated by means of a L18 fractional factorial design of experiment. The 3D turning process was simulated using the software TWS Advantedge. The heat transfer numerical coefficients were calibrated against experimental measures to obtain temperature values as accurate as possible. A statistical methodology framework was adopted to study the dependence of the coefficients from the machining parameters. A heat transfer surrogate model was then built and next experimentally validated
- …
