The ability to classify the traf c, based on the application or protocol that generated it, is essential for the effective management of computer networks. Although Internet applications were generally based on the client-server paradigm, generating traf c whose properties were well de ned and easily predictable, the advent of peer-to-peer (P2P) computing brought the power to the edges, facilitating the direct exchange of contents between hosts and modifying the behavior of the traf c load in the networks of Internet Service Providers (ISPs) and organizations. In that context, the ability to identify the nature of the traf c became increasingly important. Nonetheless, the early traf c classi cation methods, based on the association of port numbers of transport-level protocols to applications or protocols, became ineffective when many Internet applications started to use random port numbers or ports normally used by other applications. The natural alternative was to look deep into the contents of the packets to search for data strings that could be used as a signature of the traf c of a target application. However, this approach, usually called Deep Packet Inspection (DPI), requires more computational resources which may make it dif cult to be used for real-time monitoring in high-speed networks. Moreover, several applications have started to encrypt their traf c preventing the use of DPI. In order to overcome these limitations, researchers are proposing new classi cation approaches, sometimes called classi cation in the dark, which are based on the traf c behavior and do not rely on the payload data. Although their accuracy is generally lower, in most cases, they offer a good compromise between effectiveness and computational cost and are not affected by encryption techniques. Nevertheless, the search for more accurate behavioral methods is also leading to an increase in their complexity. This thesis is focused on the identi cation of P2P traf c and aims to propose a classi cation approach capable of identifying traf c generated by P2P applications in real-time, without relying on the payload data. Since one of the differences between client-server and P2P paradigms is the dual role played by P2P hosts, the research work described herein, after a literature review, started with the study of the properties of the traf c from several P2P and non-P2P applications at its source. Instead of collecting the experimental data in an aggregation point, the traf c from each individual host, running a single application or a prede ned set of applications, was captured immediately after its network connection. By doing so, it was possible to assure that the analyzed traf c was generated by the studied applications and that its properties were not affected by the aggregation of different types of traf c. The study included the statistical analysis of the following traf c features: the byte count per time unit, the inter-arrival time, and the packet length. The observation of the source traf c showed that the lengths of the packets generated by P2P and non-P2P applications present distinct patterns. The traf c from non-P2P applications usually results from connections with a stable behavior, mostly formed by small and large packets, used to send requests and acknowledgments and to receive contents, respectively. In these cases, both small and large packets generally present very homogeneous lengths. On the contrary, the P2P traf c is very heterogeneous in terms of packet lengths, as it results from the aggregation of several concurrent connections to different peers. Moreover, the distributed search mechanisms and the replies to requests from other peers also generate a large number of small packets with multiple lengths. Hence, a deeper study focused solely on packet length properties was performed and the set of analyzed applications was extended. The entropy was used to measure the heterogeneity of the packet lengths and the results showed it was possible to differentiate both kinds of traf c. To improve the results in speci c cases, the entropy of the packet lengths was also computed using slots of 200 bytes, which means that all the packet lengths within the same slot are used in the entropy computation as being similar lengths. Based on this approach, it was possible to propose a new behavioral classi er capable of identifying hosts running P2P applications, without using payload data. In order to make the method suitable for real-time analysis, the entropy is computed using a sliding window with a constant size of N packets. Although the proposed classi cation method was able to identify hosts running P2P applications by analyzing the heterogeneity of the packet lengths in the aggregated traf c of each host, it could not classify individual ows as being generated by P2P or non-P2P applications. In fact, the heterogeneity of the packet lengths observed in the traf c of each single host running P2P le-sharing or P2P media streaming applications resulted, mostly, from the aggregation of several connections with different properties, used to share contents with other peers. For this reason, the heterogeneity of individual ows is lower, even for P2P traf c. Nonetheless, in the case of P2P Voice over Internet Protocol (VoIP) traf c, the heterogeneity of the packet lengths results from the use of Variable Bit Rate (VBR) speech codecs and, thus, the heterogeneity is observable in the individual ow used to carry each VoIP session. Therefore, experimental traf c generated by P2P VoIP applications using several VBR and Constant Bit Rate (CBR) speech codecs was collected and used to study the lengths of the packets generated by VoIP sessions. The results of the analysis showed that the packet lengths depend on the speech codec used in each the session. Hence, the heterogeneity of the packet lengths from each VoIP session was measured using entropy, which was computed using a sliding window with a constant size of 500 packets. For each speech codec considered in the study, the intervals of packet lengths and entropy observed during the traf c analysis were compiled and, based on those intervals, a traf c classi er capable of identifying VoIP traf c using a single traf c feature was proposed. The classi er uses a set of behavioral signatures associated with each speech codec, formed by an interval of packet lengths and an interval of the entropy of the packet lengths. Besides of being able to recognize VoIP traf c in the dark, the classi er is also capable of identifying the speech codec used in that VoIP session. After proposing the P2P VoIP traf c classi er, the research work focused on the traf c from P2P le-sharing and P2P media streaming applications. Unlike VoIP, the traf c generated by a single host running one of these applications results from many parallel connections with several peers. Hence, in this thesis, P2P le-sharing and P2P media streaming traf c is also designated by one-to-many P2P traf c. The entropy of the packet lengths of individual ows from these applications is not suf ciently distinct from the entropy obtained from non-P2P individual ows. Therefore, several dimensions of the traf c were separately studied, including incoming, outgoing, or incoming and outgoing packets together, and also packets whose payload length is smaller or equal to 100 bytes, greater than 100 bytes and smaller or equal to 900 bytes, or greater than 900 bytes. The mean of the entropy of the packet lengths for each of these dimensions was computed for each ow of the analyzed applications, using a sliding window with a constant size of 100 packets. Additionally, the mean of the entropy of the inter-arrival times and of the remote host/port pairs to which a local host/port pair communicates was also computed. Based on the obtained results, a traf c classi er that does not rely on payload data was proposed. In the performance evaluation, the classi er was able to identify P2P traf c with an accuracy greater than 95%.

This thesis explores the challenges presented by an ageing workforce in modern production systems, focusing on the evolving context from Industry 4.0 to the human-centred paradigm of Industry 5.0. The study investigates the physical, cognitive, and well-being conditions of operators through four key perspectives: demographic ageing, technological developments, individual operator characteristics, and workplace ergonomic considerations. Using a mixed-methods design, including qualitative and quantitative methodologies, systematic literature reviews, and case studies in two Portuguese metal polishing industries, the research collects sociodemographic, anthropometric, and musculoskeletal symptom data via the Nordic Musculoskeletal Questionnaire (NMQ), and assesses ergonomic risks through sensorless, AI-driven Rapid Entire Body Assessment (REBA) applications. The research reveals a high prevalence of work-related musculoskeletal disorders (WRMSDs), particularly affecting the spine and upper limbs, with significant variation across gender and age groups. Particularly, female operators and ageing workers exhibit an increased risk. Significant correlations were found between age, company seniority, health perception, and WRMSD symptoms, with perceived health declining and symptoms increasing with age and seniority. The methodologies applied were validated in the second company, an independent industrial setting, confirming the reliability, reproducibility, and transferability of findings across different polishing companies. In addition, the study introduces AgErgoVSM, an integrative Lean tool embedding ergonomic principles for distinct age clusters and applies it systematically to map and improve current work practices. The outcomes of this study underscore the need to integrate digital and supportive technologies, such as AI-based ergonomic analysis, to sustainably adapt workplaces to diverse and ageing workforces.

Despite technological advances, two-dimensional electrophoresis (2DE) of biological fluids, such as vitreous, remains a major challenge. In this study, artificial neural network was applied to optimize the recovery of vitreous proteins and its detection by 2DE analysis through the combination of several solubilizing agents (CHAPS, Genapol, DTT, IPG buffer), temperature, and total voltage. The highest protein recovery (94.9% ± 4.5) was achieved using 4% (w/v) CHAPS, 0.1% (v/v) Genapol, 20 mM DTT, and 2% (v/v) IPG buffer. Two iterations were required to achieve an optimized response (580 spots) using 4% (w/v) CHAPS, 0.2% (v/v) Genapol, 60 mM DTT, and 0.5% (v/v) IPG buffer at 35 kVh and 25 °C, representing a 2.4-fold improvement over the standard initial conditions of the experimental design. The analysis of depleted vitreous using the optimized protocol resulted in an additional 1.3-fold increment in protein detection over the optimal output, with an average of 761 spots detected in vitreous from different vitreoretinopathies. Our results clearly indicate the importance of combining the appropriate amount of solubilizing agents with a suitable control of the temperature and voltage to obtain high-quality gels. The high-throughput of this model provides an effective starting point for the optimization of 2DE protocols. This experimental design can be adapted to other types of matrices. Graphical abstract.

The design of nanocarriers for the delivery of drugs and nucleic-acids remains a very challenging goal due to their physicochemical differences. In addition, the reported accelerated clearance and immune response of pegylated nanomedicines highlight the necessity to develop carriers using new materials. Herein, we describe the synthesis of amphiphilic triblock poly(2-ethyl-2-oxazoline)–PLA-g–PEI (PEOz–PLA-g–PEI) micelles for the delivery of minicircle DNA (mcDNA) vectors. In this copolymer the generally used PEG moieties are replaced by the biocompatible PEOz polymer backbone that assembles the hydrophilic shell. The obtained results show that amphiphilic micelles have low critical micellar concentration, are hemocompatible and exhibit stability upon incubation in serum. The uptake in MCF-7 cells was efficient and the nanocarriers achieved 2.7 fold higher expression than control particles. Moreover, mcDNA-loaded micelleplexes penetrated into 3D multicellular spheroids and promoted widespread gene expression. Additionally, to prove the concept of co-delivery, mcDNA and doxorubicin (Dox) were simultaneously encapsulated in PEOz–PLA-g–PEI carriers, with high efficiency. Dox–mcDNA micelleplexes exhibited extensive cellular uptake and demonstrated anti-tumoral activity. These findings led us to conclude that this system has a potential not only for the delivery of novel mcDNA vectors, but also for the co-delivery of drug–mcDNA combinations without PEG functionalization.

The transport sector plays a crucial aspect in society and economic evolution. However, improper energy management has negatively impacted health and the environment. Thus, the use of sustainable and green fuels in the aeronautical industry has been implemented due to environmental concerns and the depletion of fossil fuels. The introduction of biofuels, a renewable energy source in the transportation sector, has shown advantages in terms of pollutant reduction. Recently, the addition of nanoparticles in the combustion of biofuel has been studied with the purpose of enhancing its combustion characteristics. Consequently, the present work evaluates nanofuel single droplet in a falling droplet method. In this way, the fiber suspension effect was neglected, and droplets in a size of 250 μm were evaluated. To this end, a comparison between pure biofuel and a nanofuel at two furnace temperatures (T = 800 ºC and T = 1000 ºC) was performed. The results reveal that disruptive burning phenomena occur when aluminum nanoparticles are added to the biofuel. Consequently, a micro-explosion determines the end of the droplet lifetime, mainly affected by the furnace temperature.

The present work is dedicated to the study of cryogenic nitrogen jets under supercritical chamber conditions with the objective of simulating the process of fuel injection inside a combustion engine. In order to do so, a numerical simulation using a RANS model was performed over two case studies previously analyzed by other authors both in experimental and numerical studies. The result obtained by the present numerical approach were then compared with previous results and this way accessed the capabilities of RANS approach using a k-ε turbulence model in which the density is calculated through the mixture fraction value to correctly model cryogenic jets at supercritical conditions. The results show the ability to achieve good agreement with other studies for the axial density distribution however for other parameters like the jet spreading angle the same agreement was not found.

The co-delivery of multiple chemotherapeutics by micellar delivery systems is a valuable approach to improve cancer treatment since various disease hallmarks can be targeted simultaneously. However, the delivery of multiple drugs requires a nanocarrier structure that can encapsulate various bioactive molecules. In this study, we evaluate the simultaneous encapsulation of a novel triple drug combination in D-α-tocopheryl polyethylene glycol 1000 succinate-poly(lactic acid) (TPGS-PLA) amphiphilic micelles for cancer therapy. The drug mixture involves two anti-tumoral drugs, Crizotinib and Palbociclib combined with Sildenafil, a compound that is capable of increasing drug accumulation in the intracellular compartment. Such combination aims to achieve an enhanced cytotoxic effect in cancer cells. Our results demonstrated that TPGS-PLA copolymers self-assembled into stable nanosized micelles (158.3 nm) capable of co-encapsulating the three drugs with high loading efficiency. Triple drug loaded TPGS-PLA micelles were internalized in A549 non-small lung cancer cells and exhibited an improved cytotoxic effect in comparison with single (Crizotinib) or dual (Crizotinib–Palbociclib) drug loaded micelles, indicating the therapeutic potential of the triple co-delivery strategy. These findings demonstrate that TPGS-PLA micelles are suitable carriers for multiple drug delivery and also that this particular drug combination may have potential to improve cancer treatment.

Person-centered care aims to increase and guarantee the quality of care at residential care facilities for older adults. The implementation and development of this approach requires validated assessment tools, which are still lacking in Portugal. This study aims to adapt and validate for the Portuguese population the internationally and widely used essential instrument that is the Staff Assessment Person-Directed Care (SAPDC). The adaptation of the SAPDC included its translation, back translation, and a pilot-study. For validation, staff members were recruited by distributing the study via email and on social media. Respondents included 546 native Portuguese-speaking staff members working at residential care facilities for over 6 months. The mean score of SAPDC was 165.74 (SD = 36.78). The exploratory factor analysis showed eight conceptually distinct dimensions, considered adequate by the expert team. The total scale showed a very good internal consistency (α = .96) and excellent temporal stability assessed by Intraclass Correlation Coefficient (> .90). Providing a Portuguese version of the SAPDC is useful to substantiate technical and scientific advancements and define policies with implications on evolving care approaches. This tool helps optimize the quality and dignification of gerontological practices, which is urgent at Portuguese residential care facilities.

Strategies combining nanomaterials’ chemotherapy and photothermal therapy hold an enormous potential for improving cancer treatment. Still, the translation of this modality has been hindered by the immunogenicity triggered by some of the polymers used for coating nanomaterials as well as by the nanostructures’ poor tumor uptake after systemic administration. To address this bottleneck, the formulation of injectable polymeric matrices capable of delivering/confining chemotherapeutics and nanomaterials into the tumor site has been gathering a great interest. In this work, ionotropically crosslinked chitosan-based injectable in situ forming hydrogels co-incorporating Dopamine-reduced graphene oxide (DOPA-rGO; photothermal nano-agent) and Resveratrol (RES; chemotherapeutic drug), were prepared for the first time, to be applied in cancer chemo- photothermal therapy. The formulated hydrogels displayed injectability and in situ gelation as well as suitable physicochemical properties and good cytocompatibility. In vitro, the hydrogels’ photothermal therapy (DOPA- rGO@Gel +NIR light) only diminished the breast cancer cells’ viability to 72%. Moreover, cancer cells exposed to the hydrogels’ chemotherapy (RES+DOPA-rGO@Gel) still displayed a viability of 75%. In stark contrast, the hydrogels’ chemo-photothermal therapy (RES+DOPA-rGO@Gel +NIR light) was capable of decreasing cancer cells’ viability to just 31%. Overall, RES+DOPA-rGO@Gel presents an enormous potential for the chemo- photothermal therapy of breast cancer cells.