Abstract
Objective: The present investigation aims to identify the explanatory factors for academic satisfaction, as well as the influence that the identified factors have on the degree of academic satisfaction in its global context and on the chances of the institution's recommendation to friends and family.   Method: The investigation was conducted with a sample of 290 students from the Federal Institute of Education, Science, and Technology of Piauí (FIEPI-Campus Oeiras). The instrument used for data collection was a questionnaire. The statistical techniques used were of a descriptive, exploratory, and multivariate nature for the treatment, analysis, and validation of the results.   Results and Discussion: The results indicate that Academic Satisfaction is determined by the factors (1) Teaching Environment and (2) Structural Environment and that there is a significant decrease in Academic Satisfaction when students move from the group from High School to Higher Education due to the determinants satisfaction.   Research Implications: The development of strategies for the maintenance and attraction of new students by FIEPI-Campus Oeiras and other educational institutions must be based on academic satisfaction considering their specificities, namely, concerning the different perceptions and expectations generated by students about educational services.   Originality/Value: The relevance of this investigation is based on the fact that the measurement of student satisfaction by a given educational institution becomes essential for it to understand the perception of its students about its products and services, enabling the identification of new ways of generating satisfaction and loyalty.

Abstract
The progressive and complex nature of Parkinson's disease (PD) may largely benefit from regular and personalised monitoring, which is beyond the current clinical practice and routinely available systems. This paper proposes a simple and effective system to address this issue by using a wearable device to quantify a key PD's motor symptom - gait impairment as a proof-of-concept for a future broader approach. In this study, 60 inertial signals were collected from the ankle in 41 PD patients during a clinical standard gait assessment exercise. Each exercise iteration was classified by a specialised neurologist based on the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS). A signal processing and feature extraction pipeline was employed to characterise gait, followed by a statistical analysis of their ability to differentiate between the 5 levels of impairment. The results revealed that 4 of the 8 studied features exhibited high discriminatory power between different severity levels of gait impairment, with statistical significance. The distinguishing capability of these 4 extracted features - gait consistency, rotation angle, mean height and length of steps - holds great promise for the development of a gait severity quantification remote monitoring for PD patients at home or on the move, proving the concept of the usefulness of wearable devices for regular and personalised PD symptom monitoring.

Abstract
Manufacturing industry is experiencing another revolution towards the digitalization of industrial processes. Different value chain actors must share specific and sensitive data according to business and data requirements. Digital architectures must ensure seamless and comprehensive communications between actors according to agreed-upon vocabularies. The digital representation of machines and other types of equipment, including crucial information about their static and dynamic operational data, is made possible by the ontological modelling of Asset Administration Shells (AAS), which is proposed in this paper as modular and semantically interoperable resources. These Cognitive Digital Twins are herein defined with de facto domain ontologies that model the semantics of the current operation, status and configurations of assets. This paper reports a proof-of-concept technical implementation that demonstrates an innovative digital architecture that connects and communicates active and modular Digital Twin of a machine in a bi-directional, connecting this asset to a digital manufacturing service provider.

Abstract
The pandemic marked the beginning of a succession of events on a global scale (not the least of which is a greater concern for the environment and for quality of life/distance work) with a major impact on the economy. Innovation plays a key role in meeting the challenges of the future, but despite investment in innovation, global economic growth has fallen short of the expected performance. The aim of this study is to identify the factors with the greatest impact on the performance of innovation ecosystems based on the performance of the innovation ecosystems of 64 countries assessed by the Global Innovation Index 2022. The methodology consists of multiple hierarchical linear regressions, in which the impact factors on innovation ecosystems, measured through indicators, are the independent variables and innovation performance, in knowledge and technology and in creativity, are the dependent variables in an iterative process, using STATA/MP 18.0 data analysis software. The results indicate that human capital and research (the basis of business and innovative products aimed at filling gaps in the market are people with a good higher education, which is also linked to local university rankings) and business sophistication (highly qualified work, leveraging strategic partnerships, and with knowledge absorption capacity) are the main pillars determining innovation performance at a global level. Education (an educated workforce is of growing importance in the knowledge era), R&D investment (including support from the state in the form of tax incentives for whoever invests in R&D), innovation partnerships (for a faster, more open innovation effort), ecological sustainability (a new reinforced priority after COVID-19) and knowledge absorption (to absorb one must first detain valuable knowledge in the area) are the variables with the greatest impact on innovation performance. The work provides guidance on which areas should be prioritized in the development of policies and strategies to accelerate innovation in countries. The study is limited by the time frame and reveals, by comparison with pre-pandemic studies, that the determinants of innovation can be dynamic, varying according to the countries and, consequently, the global context of the analysis.

Abstract
Green ammonia production stands as a pivotal component in the transition towards sustainable energy and agriculture, poised to revolutionize numerous industries. This paper presents an optimization control framework for industrial green ammonia fuel hubs to engage in electricity, hydrogen, and oxygen markets, addressing both economic and technical considerations. By evaluating scenarios with and without battery storage, this study demonstrates the potential for increased profitability and energy independence through secondary reserve market participation, alongside insights into the economic viability of photovoltaic investments. These findings underscore the importance of considering market dynamics and technological integration in the sustainable operation of green ammonia production hubs.

Abstract
This paper emphasizes the need to broaden organizational perspectives through Open X, which promotes sharing and collaboration over selfishness and competition, instead of that industrial intellectual protection through patents can divert resources essential for the growth of organizations. Faced with new realities, organizations need different management approaches with the potential to transform the reindustrialization resulting from deindustrialization into a Neoindustrialization 2.0. It does not mean tearing down or creating new boundaries but an open culture where organizational efforts have social relevance. In the face of economic interests, Open X can make organizational outcomes more plentiful and robust. © 2024 Published by Faculty of Engineering.