Evaluation of Cost-Effectiveness Criteria in Supply Chain Management: Case Study

The aim of this paper is to evaluate and prioritize the proposed cost-effectiveness criteria in supply chain management using fuzzy multiple attribute decision-making (MADM) approach. Over the past few years, the determination of suitable cost-effectiveness criteria in the supply chain has become a key strategic issue. However, the nature of these kinds of decisions is usually complex and unstructured. Many quantitative and qualitative factors must be considered to determine the suitable criteria. As the human decision-making process usually contains fuzziness and vagueness, a hierarchy of MADM model based on fuzzy-sets theory is used in this research. Using a fuzzy analytic hierarchy process (FAHP), the weights of criteria and subcriteria are determined and then the final ranking is determined by technique for order preference by similarity to ideal solution (TOPSIS). Finally, fuzzy TOPSIS (FTOPSIS) is employed to compare the results with classic TOPSIS. This paper concludes that the subcriteria in all the items are in the same rank

Motion Challenge of Thoracic Tumors at Radiotherapy by Introducing an Available Compensation Strategy

In this chapter a description is explained about radiotherapy as common available method in treatment of thoracic tumors located at thorax region of patient body and move mainly due to respiration. In radiotherapy of dynamic tumors, the correct and accurate information of tumor position during the therapeutic irradiation determine the degree of treatment success. In this chapter we investigate quantitatively the effect of tumor motion on treatment quality by considering to possible drawbacks and errors at external surrogate’s radiotherapy as clinical treatment modality. For this aim, tumor motion information of a group of real patients treated with Cybeknife Synchrony system (from Georgetown University Hospital) was taken into account. A fuzzy logic based correlation model was employed for tumor motion tracking. Final results represent graphically the amount of tumor motion estimated by our utilized correlation model on three dimensions with targeting error calculation. It’s worth mentioning that each strategy that can improve targeting accuracy of dynamic tumors may strongly enhance treatment quality by saving healthy tissues against additional high dose. In this chapter we just tried to introduce readers with thoracic tumor motion error as challenging issue in radiotherapy and motion compensation solutions, implemented clinically up to now

Evaluation of cost-effectiveness criteria in supply chain management : case study

The aim of this paper is to evaluate and prioritize the proposed cost-effectiveness criteria in supply chain management using fuzzy multiple attribute decision-making (MADM) approach. Over the past few years, the determination of suitable cost-effectiveness criteria in the supply chain has become a key strategic issue. However, the nature of these kinds of decisions is usually complex and unstructured. Many quantitative and qualitative factors must be considered to determine the suitable criteria. As the human decision-making process usually contains fuzziness and vagueness, a hierarchy of MADM model based on fuzzy-sets theory is used in this research. Using a fuzzy analytic hierarchy process (FAHP), the weights of criteria and subcriteria are determined and then the final ranking is determined by technique for order preference by similarity to ideal solution (TOPSIS). Finally, fuzzy TOPSIS (FTOPSIS) is employed to compare the results with classic TOPSIS. This paper concludes that the subcriteria in all the items are in the same rank

Calculation of Inter- and Intra-Fraction Motion Errors at External Radiotherapy Using a Markerless Strategy Based on Image Registration Combined with Correlation Model

Introduction: A new method based on image registration technique and an intelligent correlation model to calculate. The present study aimed to propose inter- and intra-fraction motion errors in order to address the limitations of conventional Patient positioning methods. Material and Methods: The configuration of the markerless method was accomplished by using four-dimensional computed tomography (4DCT) datasets. Firstly, the MeVisLab software package was used to extract a three-dimensional (3D) surface model of the patient and determine the tumor location. Then, the patient-specific 3D surface model which also included the breathing phases was imported into the MATLAB software package in order to define several control points on the thorax region as virtual external markers. Finally, based on the correlation of breathing signals/patient position with breathing signals/tumor coordinate, an adaptive neuro fuzzy inference system was proposed to both verify and align the inter- and intra-fraction motion errors in radiotherapy, if needed. In order to validate the proposed method, the 4DCT data acquired from four real patients was considered. Results: Final results revealed that our hybrid configuration method was capable of aligning patient setup with lower uncertainties, compared to other available methods. In addition, the 3D root-mean-square error has been reduced from 5.26 to 1.5 mm for all patients. Conclusion: In this study, a markerless method based on the image registration technique in combination with a correlation model was proposed to address the limitations of the available methods, including dependence on operator’s attention, use of passive markers, and rigid-only constraint for patient setup

Self-scheduling approach to coordinating wind power producers with energy storage and demand response

The uncertainty of wind energy makes wind power producers (WPPs) incur profit loss due to balancing costs in electricity markets, a phenomenon that restricts their participation in markets. This paper proposes a stochastic bidding strategy based on virtual power plants (VPPs) to increase the profit of WPPs in short-term electricity markets in coordination with energy storage systems (ESSs) and demand response (DR). To implement the stochastic solution strategy, the Kantorovich method is used for scenario generation and reduction. The opti-mization problem is formulated as a Mixed-Integer Linear Programming (MILP) problem. From testing the proposed method for a Spanish WPP, it is inferred that the proposed method en-hances the profit of the VPP compared to previous models.fi=vertaisarvioitu|en=peerReviewed

Do comprehensive deep learning algorithms suffer from hidden stratification? A retrospective study on pneumothorax detection in chest radiography

ObjectivesTo evaluate the ability of a commercially available comprehensive chest radiography deep convolutional neural network (DCNN) to detect simple and tension pneumothorax, as stratified by the following subgroups: the presence of an intercostal drain; rib, clavicular, scapular or humeral fractures or rib resections; subcutaneous emphysema and erect versus non-erect positioning. The hypothesis was that performance would not differ significantly in each of these subgroups when compared with the overall test dataset.DesignA retrospective case–control study was undertaken.SettingCommunity radiology clinics and hospitals in Australia and the USA.ParticipantsA test dataset of 2557 chest radiography studies was ground-truthed by three subspecialty thoracic radiologists for the presence of simple or tension pneumothorax as well as each subgroup other than positioning. Radiograph positioning was derived from radiographer annotations on the images.Outcome measuresDCNN performance for detecting simple and tension pneumothorax was evaluated over the entire test set, as well as within each subgroup, using the area under the receiver operating characteristic curve (AUC). A difference in AUC of more than 0.05 was considered clinically significant.ResultsWhen compared with the overall test set, performance of the DCNN for detecting simple and tension pneumothorax was statistically non-inferior in all subgroups. The DCNN had an AUC of 0.981 (0.976–0.986) for detecting simple pneumothorax and 0.997 (0.995–0.999) for detecting tension pneumothorax.ConclusionsHidden stratification has significant implications for potential failures of deep learning when applied in clinical practice. This study demonstrated that a comprehensively trained DCNN can be resilient to hidden stratification in several clinically meaningful subgroups in detecting pneumothorax.</jats:sec

Application of polymeric nanoparticles in food sector

Nanotechnology presents opportunities to create new and better products. Nano technology has huge impact in many applications including food industry. Product of nanotechnology, such as polymeric nanoparticle, can be utilized to improve food quality by extending food shelf life, increase food safety, lower the cost and enhance the nutritional benefits. This chapter provides an overview of the properties of polymeric nanoparticle, preparation techniques, as well as the role polymeric nano-particles in the food industr