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Water-Emulsified Diesel Fuel in a CI Engine Tested for Aeronautical Applications
Publication . Oliveira, Pedro Manuel Pimenta da Silva ; Brójo, Francisco Miguel Ribeiro Proença; Serôdio, Rogério Pedro Fernandes
Water-in-diesel emulsions as an alternative fuel are emerging as a viable option to fight global fossil fuel consumption and emissions restrictions without the need for engine modifications. In an emulsion, two immiscible liquids are mixed with the help of surfactants, where droplets of one liquid are dispersed in a continuous flow. This thesis aims to compare water-emulsified diesel fuel with European commercially available diesel (EN590) when it comes to engine performance and emissions. The tests were performed in a single-cylinder, direct injection diesel engine (Hatz 1B40) using an eddy current dynamometer, an exhaust gas analyser, and an opacimeter. With the objective of testing the engine and the alternative fuels for operating conditions often found in aerial vehicles equipped with diesel engines (same operating principles), the tests were performed for idle settings (simulating the taxiing movement of an aircraft), 100% engine load (simulating the take-off and climbing manoeuvres), 50% engine load (representing the descent, approach, and landing phases), and 75% engine load (representing the cruise component of an aircraft's flight profile) at different engine speeds. Mixtures of EN590 diesel fuel, deionised water, and surfactants were performed in laboratory by mechanical homogenization to obtain the ideal concentrations of the different reagents to be later replicated on a bigger scale using a low-energy mixing method. The emulsions were developed to be optimised at the engine’s operating temperature and heated above the diesel fuel operating temperature to reach similar viscosities during the test procedures. The results suggest that adding water as a dispersed phase in the fuel allows to obtain overall better thermal efficiency and lower emissions of nitrogen oxides and smoke in some conditions when compared to traditional diesel. On the other hand, carbon monoxide, hydrocarbons, and carbon dioxide emissions have increased, possibly due to differences in the injection delay between the fuels. By focusing on performance over stability through the adoption of a hydrophilic emulsion formulation in line with the difficulty of further optimisation in general aviation gasoline engines and the widespread availability of diesel fuel, it can be concluded that water-in-diesel emulsions can be a viable alternative towards the goal of improving fuel consumption rates, lowering emissions, and reducing costs when optimised for specific operating conditions of diesel-powered aerial vehicles.
Computational Resources and Infrastructures for a Novel Bioinformatics Laboratory: A Case Study
Publication . Maldonado, Emanuel Filipe Escaleira ; Lemos, Manuel; Manoj, Gupta; Dennis, Douroumis
Introduction: Bioinformatics is a relatively recent multidisciplinary research field continuously offering novel opportunities. Although many researchers are actively working in/with bioinformatics, some research centers still face difficulties in hiring bioinformaticians and establishing the appropriate (first) bioinformatics infrastructures and computational resources. In our research center, we started from scratch and established initial bioinformatics infrastructures for common use and also for the specific case of precision/personalized medicine. Case description: Here, we report a case study reflecting our specific needs and circumstances during the implementation of a novel bioinformatics laboratory. This involved the preparation of rooms, computer networks, computational resources novel designs, and upgrades to existing designs. Moreover, this work involved people from diverse areas and institutions, such as companies, institutional projects, informatics, and technical infrastructures services. Discussion and evaluation: The work resulted in the implementation of four novel designs dedicated to genomic medicine and in the adaptation of two existing designs dedicated to common use located in the dry-lab room. This is not an accurate and objective work, as it often depends on the available computer hardware and the target bioinformatics field(s). The four novel designs offered substantial improvements when compared to the upgraded designs, additionally corroborated by performance evaluations, which resulted in an overall highest performance of the novel designs. Conclusions: We present work that was developed over two years until completion with functioning infrastructure. This project enabled us to learn many novel aspects not only related to redundant disk technologies, but also related to computer networks, hardware, storage-management operating systems, file systems, performance evaluation, and also in the management of services. Moreover, additional equipment will be important to maintain and expand the potential and reliability of the bioinformatics laboratory. We hope that this work can be helpful for other researchers seeking to design their bioinformatics equipment or laboratories.
Effects of physical exercise concomitant with chemotherapy on cardiac functionality of women with breast cancer
Publication . Antunes, Pedro Miguel da Silva; Esteves, Maria Dulce Leal; Ascensão, António Alexandre Moreira Ribeiro de; Sampaio, Francisco Pedro Morais Dias de Almeida
Anthracycline-containing chemotherapy is commonly used in the treatment of hematologic cancers and solid tumors, including breast cancer. Despite its wellrecognized clinical efficacy, the use of anthracyclines is limited by dose-dependent and cumulative cardiotoxic risk. Additionally, breast cancer treatment is often multimodal, which can further increase the cardiac damage. The cardiac toxicity poses significant challenges, influencing therapeutic decision-making and exacerbate patients’ morbidity and mortality. Longitudinal studies have shown that cardiovascular diseases are one of the leading causes of death among women with breast cancer. As a result, various international agencies, such as the American Society of Clinical Oncology and the European Society of Cardiology, emphasize the importance of implementing primary prevention interventions. Exercise training is an accessible and cost-effective intervention with well-documented benefits for overall health. Preclinical studies have shown promising results in mitigating the cardiotoxic impact of anthracyclines. However, whether this potential cardioprotective effect translates to humans is a question that remains to be clarified. The objectives of this thesis were to conduct a literature review on this topic (Study 1 and 2) and, as the core of this doctoral program, to design a single-center, randomized controlled trial with two arms (exercise group vs. usual care group) (Study 3). The main objective of this trial was to analyze the impact of a supervised exercise training program on established markers of cardiac toxicity (i.e., left ventricular ejection fraction, global longitudinal strain, and cardiac biomarkers) in women with breast cancer undergoing curative anthracycline-containing chemotherapy. Secondary objectives included analyzing the safety of exercise training and its effect on cardiorespiratory fitness, physical functionality, and health-related quality of life. The program consisted of three sessions per week of combined training (i.e., aerobic and resistance training) during each participant's chemotherapy length (approximately 5- months). Through the literature review, supported by a systematic review with metaanalysis of randomized controlled trials (Study 2), we found that evidence on the effect of physical exercise on established parameters of cardiac toxicity was limited, as only four eligible studies were identified. Most of these studies had significant limitations, including designs not specifically aimed at evaluating the efficacy of physical exercise on the parameters of interest, small sample sizes, short training programs, and brief followup periods. The results of the meta-analysis did not show a significant effect of exercise training compared to non-exercise. However, an exploratory analysis, which included studies with training programs consisting of at least 36 sessions (n=3), revealed a superior effect of exercise training on resting left ventricular ejection fraction. This suggests that the length of the exercise training programs might be associated with the effect of exercise training on cardiac function. The results of the randomized controlled trial demonstrated that exercise training did not significantly prevent the decline in established markers of cardiac toxicity compared to control (Study 4). However, it was found that exercising was safe—no serious adverse events were reported during the exercise training sessions—and it significantly prevented the decline in cardiorespiratory fitness, physical functionality, and health-related quality of life (Study 4 and 6). Additionally, we found that the response of cardiorespiratory fitness among participants randomized to the exercise group was heterogeneous, which justified the analysis of potential predictors through an exploratory study (Study 5). The results revealed that the parameters associated with the response in cardiorespiratory fitness to training were baseline body mass index and total aerobic training metabolic equivalent of task per hour (MET-hour). In summary, the results of this thesis suggest that adding exercise training to standard breast cancer care does not provide benefits on established markers of cardiac toxicity in women undergoing anthracycline-containing chemotherapy. However, the findings of experimental works indicate that implementing a supervised exercise training program during chemotherapy is safe and can be a viable and effective approach for improving cardiorespiratory fitness, preventing the decline in physical functionality and healthrelated quality of life in this challenging patient population. Additionally, our study suggests that the total energy expenditure of aerobic exercise (i.e., MET-hour) may be a predictor of cardiorespiratory response.
Coopetition Technology-Enabled Service Ecosystems: A Framework to Improve the Portuguese Ornamental Stone Sector
Publication . Silva, Agostinho Manuel Antunes da; Cardoso, António João Marques
This doctoral thesis critically examines the design, implementation, and effectiveness of coopetition networks among small and medium-sized enterprises (SMEs) within the dynamic and complex realms of digital and global supply chains. The study addresses the challenges that SMEs face, including financial constraints, operational inefficiencies, scalability issues, customer satisfaction demands, and pressures related to ecological sustainability. To address these challenges, the research introduces and empirically tests the Coopetition Network for Value Cocreation (CN-VCC) framework, subsequently operationalized through the Industrial Internet of Things (IIoT) as the Coopetition Technology-enabled Service Ecosystem (CNEcoTech). The Design Science Research (DSR) methodology, integrated with Service-Dominant (S-D) Logic, underpins the framework's development and validation, with a specific focus on the Portuguese ornamental stone sector (OS-PT), an industry of significant economic importance that is yet to be fully challenged by digital transformation and global market integration. Following the DSR stages of designing and developing an artefact, implementing a Proof-ofConcept (PoC), and an Experimental Pilot Project, the thesis assesses the CN-EcoTech artefacts's impact across multiple dimensions: operational efficiency, scale, customer satisfaction, and ecological sustainability. The findings reveal that engagement levels within the CN-EcoTech framework correlate with significant improvements in OS.PT SMEs. Specifically, SMEs fully engaged in the framework demonstrated enhanced deal closures, robust customer relationships, and superior operational outcomes compared to their less-engaged counterparts. This differential highlights the potential of deep integration into technology-based coopetition frameworks to overcome traditional and emerging market challenges. The thesis confirms that the CN-EcoTech framework facilitates long-term collaboration among SMEs and effectively addresses the challenges of digital and global supply chains. It extends beyond the immediate empirical setting of the OS-PT sector, suggesting broader applicability to similar SME-dominated industries facing digital integration challenges. Academically, this research augments the understanding of coopetition dynamics and provides a structured approach to evaluating and implementing technology-enabled service ecosystems. However, more importantly, it offers actionable insights for industry stakeholders and policymakers, empowering them with strategies to enhance SME competitiveness and sustainability in line with the United Nations Sustainable Development Goals (SDGs). This thesis lays a foundation for future research to explore the scalability of the CN-EcoTech framework across diverse sectors. It offers a robust model for enhancing SME resilience and innovation capacity in a globally interconnected economy.
Portable, multi-task, on-the-edge and low-cost computer vision framework based on deep learning: Precision agriculture application
Publication . Assunção, Eduardo Timóteo; Proença, Hugo Pedro Martins Carriço; Gaspar, Pedro Miguel de Figueiredo Dinis Oliveira
Precision agriculture is a new concept that has been introduced worldwide to increase production, reduce labor and ensure efficient management of fertilizers and irrigation processes. Computer vision is an essential component of precision agriculture and plays an important role in many agricultural tasks. It serves as a perceptual tool for the mechanical interface between robots and environments or sensed objects, as well as for many other tasks such as crop yield prediction. Another important consideration is that some vision applications must run on edge devices, which typically have very limited processing power and memory. Therefore, the computer vision models that are to run on edge devices must be optimized to achieve good performance. Due to the significant impact of Deep Learning and the advent of mobile devices with accelerators, there has been increased research in recent years on computer vision for general purpose applications that have the potential to increase the efficiency of precision agriculture tasks. This thesis explore how deep learning models running on edge devices are affected by optimizations, i.e., inference accuracy and inference time. Lightweight models for weed segmentation, peach fruit detection, and fruit disease classification are cases of studies. First, a case study of peach fruit detection with the well-known Faster R-CNN object detector using the breakthrough AlexNet Convolutional Neural Network (CNN) as the image feature extractor is performed. A detection accuracy of 0.90 was achieved using metric Average Precision (AP). The breakthrough AlexNet CNN is not an optimized model for use in mobile devices. To explore a lightweight model, a case study of peach fruit disease classification is next conducted using the MobineNet CNN. The MobileNet was trained on a small dataset of images of healthy, rotten, mouldy, and scabby peach fruit and achieved a performance of 0.96 F1. Lessons learned from this work led to using this model as a baseline CNN for other computer vision applications (e.g., fruit detection and weed segmentation). Next, a study was conducted on robotic weed control using an automated herbicide spot sprayer. The DeepLab semantic segmentation model with the MobileNet backbone was used to segment weeds and determine spatial coordinates for the mechanism. The model was optimized and deployed on the Jetson Nano device and integrated with the robotic vehicle to evaluate real-time performance. An inference time of 0.04 s was achieved, and the results obtained in this work provide insight into how the performance of the semantic segmentation model of plants and weeds degrades when the model is adapted through optimization for operation on edge devices. Finally, to extend the application of lightweight deep learning models and the use of edge devices and accelerators, the Single Shot Detector (SSD) was trained to detect peach fruit in three different varieties and was deployed in a Raspberry Pi device with an integrated Tensor Unity Processor (TPU) accelerator. Some variations of MobileNet as a backbone were explored to investigate the tradeoff between accuracy and inference time. MobileNetV1 yielded the best inference time with 21.01 Frame Per Second (FPS), while MobileDet achieved the best detection accuracy (88.2% AP). In addition, an image dataset of three peach cultivars from Portugal was developed and published. This thesis aims to contribute to future steps in the development of precision agriculture and agricultural robotics, especially when computer vision needs to be processed on small devices.