PEP4Django - A Policy Enforcement Point for Python Web Applications

Traditionally, access control mechanisms have been hard-coded into application components. Such approach is error-prone, mixing business logic with access control concerns, and affecting the flexibility of security policies, as is the case with IFRN SUAP Django-based system. The externalization of access control rules allows their decoupling from business logic, through the use of authorization servers where access control policies are stored and queried for computing access decisions. In this context, this paper presents an approach that allows a Django Web application to delegate access control decisions to an external authorization server. The approach has been integrated into an enterprise level system, which has been used for experimentation. The results obtained indicate a negligible overhead, while allowing the modification of access control policies without interrupting the system

Some theoretical aspects of immittance conversion and inversion in the context of active RC networks

Imperial Users onl

Applying and integer Linear Programming Model to an appointment scheduling problem

Dissertação de Mestrado, Ciências Económicas e Empresariais (Economia e Políticas Públicas), 28 de fevereiro de 2022, Universidade dos Açores.A gestão de consultas ambulatórias pode ser um processo complexo, uma vez que envolve vários stakeholders com diferentes objetivos. Para os utentes poderá ser importante minimizar os tempos de espera. Simultaneamente, para os trabalhadores do setor da saúde, condições de trabalho justas devem ser garantidas. Assim, é cada vez mais necessário ter em conta o equilíbrio de cargas horárias e a otimização dos recursos disponíveis como principais preocupações no agendamento e planeamento de consultas. Nesta dissertação, uma abordagem com dois modelos para a criação de um sistema de agendamento de consultas é proposta. Esta abordagem é feita em programação linear, com dois modelos que têm como objetivo minimizar as diferenças de cargas horárias e melhorar o seu equilíbrio ao longo do planeamento. Os modelos foram estruturados e parametrizados de acordo com dados gerados aleatoriamente. Para isso, o desenvolvimento foi feito em Java, gerando assim os dados referidos. O Modelo I minimiza as diferenças de carga horária entre os quartos disponíveis. O Modelo II, por outro lado, propõe uma nova função objetivo que minimiza a diferença máxima observada, com um processo de decisão minxmax. Os modelos mostram resultados eficientes em tempos de execução razoáveis para instâncias com menos de aproximadamente 10 quartos disponíveis. Os tempos de execução mais altos são observados quando as instâncias ultrapassam este número de quartos disponíveis. Em relação ao equilíbrio da carga horária, observou-se que o número de especialidades disponíveis para atendimento e a procura por dia foram o que mais influenciou a minimização da diferença da carga horária. Os resultados do Modelo II mostram melhor tempo de execução e um maior número de soluções ótimas. Uma vez que as diferenças entre os dois modelos não são consideráveis, o Modelo I poderá representar um melhor conjunto de soluções para os decisores já que minimiza a diferença da carga horária total entre quartos em vez de apenas minimizar o valor máximo da diferença de carga horária entre quaisquer dois quartos.ABSTRACT: Outpatient appointment management can be a complex process since it involves many conflicting stakeholders. As for the patients it might be important to minimize waiting time. Simultaneously, for healthcare workers, fair working conditions must be guaranteed. Thus, it is increasingly necessary to have workload balance and resource optimization as the main concerns in the scheduling and planning of outpatient appointments. In this dissertation, a two-model approach for designing an appointment scheduling is proposed. This approach is formulated as two mathematical Integer Linear Programming models that integrate the objective of minimizing workload difference and improving workload balance. The models were structured and parameterized according to randomly generated data. For this, the work was developed in Java, generating said data. Model I minimizes the workload differences among rooms. Model II, on the other hand, proposes a new objective function that minimizes the maximum workload difference, with a minxmax decision process. The computational models behaves efficiently in reasonable run times for numerical examples with less than approximately 10 rooms available. Higher run times are observed when numerical examples surpass these number of available rooms. Regarding workload balance, it was observed that the number of specialties available for appointments and the demand for each day were the most influential in the minimization of workload difference. Model II results show a shorter model run time and more optimal solutions. As the differences between both Models are not considerable, Model I might propose a better set of solution for decision makers since it minimizes the total workload difference amongst rooms instead of only minimizing the maximum workload difference between any two rooms

G-quadruplex ligands for cancer therapy

DNA may fold into a diversity of structures and topologies such as duplexes and triplexes. Some specific guanine-rich DNA sequences may even fold into a higher order structures denominated guanine G-quadruplexes (G4). These G-quadruplex forming sequences have shown biological interest since were found in telomeres and in promoter region of oncogenes. Thus, these G4 forming sequences have been explored as therapeutic targets for cancer therapy, since G4 formation was demonstrated to inhibit RNA-polymerase and telomerase activity. However, the G4 structures are transient and are only formed under specific conditions. Hence the main objective of this work is to develop new G4-specific ligands which may potentially find applications in the therapeutic area. Several potential G4-binding ligands were synthesized and characterized. The synthesis of these compounds consisted on a procedure based on van Leusen chemistry and a cross-coupling reaction through C-H activation, affording phenanthroline compounds (Phen-1, 50%; Phen-2, 20%), phenyl (Iso-1, 61%; Iso-2, 21%; Ter-1, 85%; Ter-2, 35%), and quinolyl (Quin-1, 85%; Quin-2, 45%) compounds. Screening assays for selecting the potential G4 compounds were performed by FRET-melting, G4-FID, CD-melting and DSF. Qualitative biophysical studies were performed by fluorescence and CD spectroscopy. Two high-specific G-quadruplex ligands, Phen-1 and Phen-2, were found to effectively bind telomeric and c-myc G4 structures. Phen-1 was found to stabilize parallel telomeric 22AG and c-myc sequence by 4.1 and 4.3 ˚C, respectively. Phen-2 also displayed high affinity towards 22AG (=9.56×109 −1) and to c-myc (=3.55×106 −1), increasing their thermal stability by 15.0 (in K+) and 31.0 ˚C, respectively. The compounds were evaluated concerning their anti-proliferative effects on three cancer cell lines (MCF-7, LNCaP and U87) and normal cell line (NHDF), by MTT assay. Phen-2 and Quin-2 displayed strong anti-proliferative effects on LNCaP (IC50 = 0.40 and 39.14 μM, respectively) and MCF-7 (IC50 = 0.64 and 4.17 μM, respectively) cancer cell lines. Furthermore Quin-2 did not display cytotoxic effects on U87 and normal NHDF cells. Overall, this work explored new possibilities for finding new G4 ligands for cancer therapy

Tomographic Image Reconstruction of Fan-Beam Projections with Equidistant Detectors using Partially Connected Neural Networks

We present a neural network approach for tomographic imaging problem using interpolation methods and fan-beam projections. This approach uses a partially connected neural network especially assembled for solving tomographic\ud reconstruction with no need of training. We extended the calculations to perform reconstruction with interpolation and to allow tomography of fan-beam geometry. The main goal is to aggregate speed while maintaining or improving the quality of the tomographic reconstruction process