Enhancing the Sustainability of the Aviation Industry: Airlines’ Commitment to “Green” Practices

The aviation industry represents an important polluter, being responsible for increasing environmental impacts on global scale. Aiming to approach the adoption of suitable policies in the aviation industry towards the achievement of the national and international sustainability goals, the present research tackles airlines’ commitment to aviation-related environmental issues, as well as their willingness to adopt sustainable aviation fuel (i.e., bio jet fuel) and sustainable development strategies, focusing on those companies operating flights in the Karol Wojtyła Airport (Bari, Italy). The paper adopts the χ2 test and the logistic regression to investigate three different hypotheses related to airlines’ headquarters, carriers’ typology (i.e., low-cost or not, flag carriers or not) and years of service. Results outline that traditional airlines, either flag carriers or not, as well as South and North American companies, are more likely to be aware of aviation environmental consequences, publishing environmental reports and offering to passengers the chance to participate to climate change reduction (e.g., through online carbon offset programs or more expensive ticket to produce bio jet fuels). In addition, airlines transiting in Karol Wojtyła Airport show a small willingness to share information through environmental reports and are scarcely intentioned to make use of bio jet fuels, confirming that low-cost companies are still less attentive towards aviation environmental issues. The present research contributes to the empirical studies on sustainable aviation and carriers’ commitment to environmental strategies, highlighting the need to enhance carbon offsets programs and digital technologies as the online compensation of CO2 emissions

A generative neural network model for the quality prediction of work in progress products

© 2019 Elsevier B.V. One of the key challenges in manufacturing processes is improving the accuracy of quality monitoring and prediction. This paper proposes a generative neural network model for automatically predicting work-in-progress product quality. Our approach combines an unsupervised feature-extraction step with a supervised learning method. An autoencoding neural network is trained using raw manufacturing process data to extract rich information from production line recordings. Then, the extracted features are reformed as time-series and are fed into a multi-layer perceptron for predicting product quality. Finally, the outputs are decoded into a forecast quality measure. We evaluate the performance of the generative model on a case study from a powder metallurgy process. Our experimental results suggest that our method can precisely capture the defective products

Global patient outcomes after elective surgery: prospective cohort study in 27 low-, middle- and high-income countries.

BACKGROUND: As global initiatives increase patient access to surgical treatments, there remains a need to understand the adverse effects of surgery and define appropriate levels of perioperative care. METHODS: We designed a prospective international 7-day cohort study of outcomes following elective adult inpatient surgery in 27 countries. The primary outcome was in-hospital complications. Secondary outcomes were death following a complication (failure to rescue) and death in hospital. Process measures were admission to critical care immediately after surgery or to treat a complication and duration of hospital stay. A single definition of critical care was used for all countries. RESULTS: A total of 474 hospitals in 19 high-, 7 middle- and 1 low-income country were included in the primary analysis. Data included 44 814 patients with a median hospital stay of 4 (range 2-7) days. A total of 7508 patients (16.8%) developed one or more postoperative complication and 207 died (0.5%). The overall mortality among patients who developed complications was 2.8%. Mortality following complications ranged from 2.4% for pulmonary embolism to 43.9% for cardiac arrest. A total of 4360 (9.7%) patients were admitted to a critical care unit as routine immediately after surgery, of whom 2198 (50.4%) developed a complication, with 105 (2.4%) deaths. A total of 1233 patients (16.4%) were admitted to a critical care unit to treat complications, with 119 (9.7%) deaths. Despite lower baseline risk, outcomes were similar in low- and middle-income compared with high-income countries. CONCLUSIONS: Poor patient outcomes are common after inpatient surgery. Global initiatives to increase access to surgical treatments should also address the need for safe perioperative care. STUDY REGISTRATION: ISRCTN5181700

A Game-Theoretic Analysis of Grid Job Scheduling

Computational Grid is a well-established platform that gives an assurance to provide a vast range of heterogeneous resources for high performance computing. Efficient and effective resource management and Grid job scheduling are key requirements in order to optimize the use of the resources and to take full advantage from Grid systems. In this paper, we study the job scheduling problem in Computational Grid by using a game-theoretic approach. Grid resources are usually owned by different organizations which may have different and possibly conflicting concerns. Thus it is a crucial objective to analyze potential scenarios where selfish or cooperative behaviors of organizations impact heavily on global Grid efficiency. To this purpose, we formulate a repeated non-cooperative job scheduling game, whose players are Grid sites and whose strategies are scheduling algorithms. We exploit the concept of Nash equilibrium to express a situation in which no player can gain any profit by unilaterally changing its strategy. We extend and complement our previous work by showing whether, under certain circumstances, each investigated strategy is a Nash equilibrium or not. In the negative case we give a counter-example, in the positive case we either give a formal proof or motivate our conjecture by experimental results supported by simulations and exhaustive search