A spectroscopic study of the structure of amorphous hydrogenated carbon

A range of amorphous hydrogenated carbon (a-C:H) samples have been studied using inelastic neutron spectroscopy (INS) and Fourier transform infrared (FTIR) spectroscopy. Using these complementary techniques, the bonding environments of both carbon and hydrogen can be probed in some detail, with the INS data providing not only qualitative but also quantitative information. By comparing the data from each of the samples we have been able to examine the effects of different deposition conditions, i.e. precursor gas, deposition energy and deposition method, on the atomic-scale structure of a-C:H

Coordination in closed-loop supply chain with price-dependent returns

This paper proposes two Closed-loop Supply Chain (CLSC) games in which a manufacturer sets some green activity programs efforts and a retailer sets the selling price. Both strategies influence the return rate, which is a state variable. The pricing strategy plays a key role in the identification of the best contract to achieve coordination as well as in achieving environmental objectives. The pricing strategy influences the return rate negatively, as consumers delay the return of their goods when the purchasing (and repurchasing) price is high. We then compare a wholesale price contract (WPC) and a revenue sharing contract (RSC) mechanism as both have interesting pricing policy implications. Our result shows that firms coordinate the CLSC through a (WPC) when the sharing parameter is too low while the negative effect of pricing on returns is too severe. In that case, the low sharing parameter deters the manufacturer to accept any sharing agreements. Further, firms coordinate the CLSC when the sharing parameter is medium independent of the negative impact of pricing on returns. When the sharing parameter is too high the retailer never opts for an RSC. We find that the magnitude of pricing effect on returns determines the contract to be adopted: For certain sharing parameter, firms prefer an RSC when the price effect on return is low and a WPC when this effect is high. In all other cases, firms do not have a consensus on the contract to be adopted and coordination is then not achieved

Methodology and Historical Perspective of a Hall Thruster Efficiency Analysis

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/77214/1/AIAA-38092-814.pd

Managing Product Returns Within the Customer Value Framework

Customers can create value to the firm by purchasing products, not returning these products, recommending products to other potential customers, influencing other customers, and providing feedback to the company. In this chapter, we first discuss how product returns and engagement behaviors can be included in the customer value framework. Second, we discuss the antecedents of a customer’s product return decision, namely, return policies, information at the moment of purchase, and customer and product characteristics. Third, we focus on the consequences of product returns: the effects on future purchase and product return behavior, as well as on customer engagement behaviors. Thus, this chapter provides a comprehensive synthesis of current knowledge on antecedents and consequences of product returns and how this relates to measuring and managing customer value

Myo-Guide: A Machine Learning-Based Web Application for Neuromuscular Disease Diagnosis With MRI

\ua9 2025 The Author(s). Journal of Cachexia, Sarcopenia and Muscle published by Wiley Periodicals LLC.Background: Neuromuscular diseases (NMDs) are rare disorders characterized by progressive muscle fibre loss, leading to replacement by fibrotic and fatty tissue, muscle weakness and disability. Early diagnosis is critical for therapeutic decisions, care planning and genetic counselling. Muscle magnetic resonance imaging (MRI) has emerged as a valuable diagnostic tool by identifying characteristic patterns of muscle involvement. However, the increasing complexity of these patterns complicates their interpretation, limiting their clinical utility. Additionally, multi-study data aggregation introduces heterogeneity challenges. This study presents a novel multi-study harmonization pipeline for muscle MRI and an AI-driven diagnostic tool to assist clinicians in identifying disease-specific muscle involvement patterns. Methods: We developed a preprocessing pipeline to standardize MRI fat content across datasets, minimizing source bias. An ensemble of XGBoost models was trained to classify patients based on intramuscular fat replacement, age at MRI and sex. The SHapley Additive exPlanations (SHAP) framework was adapted to analyse model predictions and identify disease-specific muscle involvement patterns. To address class imbalance, training and evaluation were conducted using class-balanced metrics. The model\u27s performance was compared against four expert clinicians using 14 previously unseen MRI scans. Results: Using our harmonization approach, we curated a dataset of 2961 MRI samples from genetically confirmed cases of 20 paediatric and adult NMDs. The model achieved a balanced accuracy of 64.8% \ub1 3.4%, with a weighted top-3 accuracy of 84.7% \ub1 1.8% and top-5 accuracy of 90.2% \ub1 2.4%. It also identified key features relevant for differential diagnosis, aiding clinical decision-making. Compared to four expert clinicians, the model obtained the highest top-3 accuracy (75.0% \ub1 4.8%). The diagnostic tool has been implemented as a free web platform, providing global access to the medical community. Conclusions: The application of AI in muscle MRI for NMD diagnosis remains underexplored due to data scarcity. This study introduces a framework for dataset harmonization, enabling advanced computational techniques. Our findings demonstrate the potential of AI-based approaches to enhance differential diagnosis by identifying disease-specific muscle involvement patterns. The developed tool surpasses expert performance in diagnostic ranking and is accessible to clinicians worldwide via the Myo-Guide online platform