Nowadays, Agile and Global Software Development (AGSD) has brought benefits and new challenges to the software industry. Among the main challenges is Architecture Knowledge Management (AKM), due to the following reasons: (i) in Agile Software Development team members prefer to convey knowledge in a face-to-face manner, over transmitting it through formal documents; and (ii) an efficient AKM in Global Software Development involves managing explicit knowledge. These opposite paradigms turn AKM into an unsolved issue in AGSD. In this study, the authors present a systematic mapping review about AKM in AGSD. From this review, they identified nine approaches that AGSD companies use to overcome the AKM challenge, which are grouped in three areas: (i) documentation artefact-based, (ii) communication-based, and (iii) methodological-based. Also, they found that the selected papers evenly support the three phases of the integrated knowledge management cycle (creation/capture, sharing/dissemination and acquisition/application), although only 7% of them support the capture of architectural knowledge in a formalised way. Finally, they conclude proposing critical points to consider in the implementation of AKM solutions in AGSD, and presenting their directions of future work.

In recent years, due to significant developments in online shopping and the widespread use of e-commerce, competition among companies has increased considerably. As a result, product reviews have become a primary factor in consumers' decision making, which has given rise to a market for fraudulent reviews about real products and services. In this study, the authors propose a model using a multiple classifier system to identify deceptive negative customer reviews, which they validated with a dataset of hotel reviews from TripAdvisor. The proposed model used five classifiers by following the majority voting combination rule – namely, libLinear, libSVM, sequential minimal optimisation, random forest, and J48 – the first two of which represent different implementations of support vector machines. Ultimately, the model provided remarkable results that demonstrate improvement upon approaches reported in the literature.

Leptospirosis is a potentially life-threatening disease primarily affecting low-income populations, with an estimated annual incidence of 1.03 million infections worldwide. This disease has symptoms often confused with other febrile syndromes, such as dengue fever, influenza and viral hepatitis, often making diagnosis challenging. Improving the accuracy of early diagnosis of patients with leptospirosis will increase the speed of appropriate antibiotic treatment delivery, and both will improve clinical outcomes for this potentially fatal disease. The authors conducted an analysis of clinically and epidemiologically defined leptospirosis cases to predict disease using data mining classification algorithms. They conducted four sets of experiments to evaluate the performance of the algorithms, assessing their predictive accuracy of using different training and test datasets. The JRIP algorithm achieved 84% sensitivity using a dataset of only confirmed leptospirosis cases, and a specificity of 99% using a dataset of only confirmed dengue cases. Therefore, the approach successfully predicted leptospirosis cases, differentiated them from similar febrile illnesses, and may represent a new tool to assist health professionals, particularly in endemic areas for leptospirosis, accelerating targeted treatment and minimising disease exacerbation and mortality.

The production of medical imaging data has grown tremendously in the last decades. Nowadays, even small institutions produce a considerable amount of studies. Furthermore, the general trend in new imaging modalities is to produce more data per examination. As a result, the design and implementation of tomorrow's storage and communication systems must deal with big data issues. The research on technologies to cope with big data issues in large scale medical imaging environments is still in its early stages. This is mostly due to the difficulty of implementing and validating new technological approaches in real environments, without interfering with clinical practice. Therefore, it is crucial to create test bed environments for research purposes. This study proposes a methodology for creating simulated medical imaging repositories, based on the indexing of model datasets, extraction of patterns and modelling of study production. The system creates a model from a real-world repository's representative time window and expands it according to on-going research needs. In addition, the solution provides distinct approaches to reducing the size of the generated datasets. The proposed system has already been used by other research projects in validation processes that aim to assess the performance and scalability of developed systems.

The manufacturing cell design problem (MCDP) aims to minimise the movements of parts between the production cells. The MCDP is an NP-Hard optimisation problem with a binary domain. For the resolution of the MCDP, the authors employ the firefly algorithm (FA) metaheuristic. FA is a metaheuristic with a real domain; therefore, an efficient method for transfer and discretisation from the real domain to the binary domain has been used. The second metaheuristic used is Egyptian vulture optimisation algorithm (EVOA). EVOA is a recent metaheuristic inspired by the behaviour of the Egyptian vulture bird. EVOA uses a set of operators which must be adapted to the MCDP optimisation problem. Two types of experiments have been performed. The first experiment consists of solving the MCDP with a set of 90 homogeneous incidence matrices. In the tests, FA and EVOA have been used obtaining good results. Subsequently, the obtained results have been compared versus other eight metaheuristics. The second experiment consists in a set of 35 inhomogeneous incidence matrices. The global optimum value for 13 problems has been obtained using constraint programming. Finally, for the other 22 problems, the authors have reported the best values found using FA and EVOA.

Transportation cyber-physical system (T-CPS) is a spatiotemporal discrete-continuous hybrid system. However, the hybrid T-CPSs have some analysis and modelling problems, especially the problems related to the spatiotemporal characteristics. Existing coloured Petri net approaches of traffic control cannot effectively analyse dynamic changes of cyber systems and physical entities in space and time. Moreover, some problems, related to the state space explosion and the large complex T-CPSs, have not been well solved. This study develops the innovative methods for T-CPS design and modelling via the development and application of coloured spatiotemporal Petri nets (CSTPNs). The proposed research ideas involve the CSTPN theory creation, the development of traffic intersection coordination control system using CSTPNs, the traffic simulation analysis and the implementation of T-CPS-based CSTPNs. The experimental results show that the new spatiotemporal theories and approaches of the traffic coordination control based on CSTPNs have excellent potentials to address the issues related to the spatiotemporal discrete-continuous characteristics of T-CPS. Moreover, these innovative methods with a good validity can be easily applied into practise for the development of T-CPS.

It is a hot research topic today to find out the potential knowledge from the scattered urban data and take advantage of the relationship between the knowledge to solve the challenges of urban governance and smart city construction. The urban knowledge graph is an effective way to establish the relationship between the knowledge and address the urban issues. This study proposes the methodology to create an urban knowledge graph and its framework. It elaborates the approaches of urban knowledge acquisition, reasoning and expression. Furthermore, a hybrid reasoning algorithm known as EG is given based on expectation-maximisation algorithm and Gibbs algorithm, which has the complementary advantages of the both methods. Through a study case, this study illustrates constructing and working process of an urban knowledge graph. The case shows that the urban knowledge graph has a good application prospect.

Therapeutics is a very complex subject for every pharmacy student, since it requires the application of knowledge from several other disciplines. The study of therapeutics is often done in case-based learning in order to promote reflective thinking and give a scenario as real as possible. The objective of this study was to compare student performance between face-to-face (n = 54) and blended learning (n = 56) approaches to the teaching of therapeutics. They can confirm that there are statistically significant differences (p < 0.05) between the final exam scores from both groups, being that the b-learning group achieved higher scores. Blended learning seems to be an effective way to teach therapeutics, following pre-established teaching methods, and above all, does not negatively affect student performance. It also provides new learning environments and strategies, and promotes the development of new skills such as learning and collaborating online, which may be relevant in a networked knowledge society.