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This article introduces a new type of C*-algebra valued modular G-metric spaces that is more general than both C*-algebra valued modular metric spaces and modular G-metric spaces. Some properties are also discussed with examples. A few common fixed point results in C*-algebra valued modular G-metric spaces are discussed using the “C*-class function”, along with some suitable examples to validate the results. Ulam–Hyers stability is used to check the stability of some fixed point results. As applications, the existence and uniqueness of solutions for a particular problem in dynamical programming and a system of nonlinear integral equations are provided.

African swine fever (ASF) has become the major threat for the global swine industry. Furthermore, the epidemiological situation of African swine fever virus (ASFV) in some endemic regions of Sub-Saharan Africa is worse than ever, with multiple virus strains and genotypes currently circulating in a given area. Despite the recent advances on ASF vaccine development, there are no commercial vaccines yet, and most of the promising vaccine prototypes available today have been specifically designed to fight the genotype II strains currently circulating in Europe, Asia, and Oceania. Previous results from our laboratory have demonstrated the ability of BA71∆CD2, a recombinant LAV lacking CD2v, to confer protection against homologous (BA71) and heterologous genotype I (E75) and genotype II (Georgia2007/01) ASFV strains, both belonging to same clade (clade C). Here, we extend these results using BA71∆CD2 as a tool trying to understand ASFV cross-protection, using phylogenetically distant ASFV strains. We first observed that five out of six (83.3%) of the pigs immunized once with 106 PFU of BA71∆CD2 survived the tick-bite challenge using Ornithodoros sp. soft ticks naturally infected with RSA/11/2017 strain (genotype XIX, clade D). Second, only two out of six (33.3%) survived the challenge with Ken06.Bus (genotype IX, clade A), which is phylogenetically more distant to BA71∆CD2 than the RSA/11/2017 strain. On the other hand, homologous prime-boosting with BA71∆CD2 only improved the survival rate to 50% after Ken06.Bus challenge, all suffering mild ASF-compatible clinical signs, while 100% of the pigs immunized with BA71∆CD2 and boosted with the parental BA71 virulent strain survived the lethal challenge with Ken06.Bus, without almost no clinical signs of the disease. Our results confirm that cross-protection is a multifactorial phenomenon that not only depends on sequence similarity. We believe that understanding this complex phenomenon will be useful for designing future vaccines for ASF-endemic areas.

The indoor location of individuals is a key contextual variable for commercial and assisted location-based services and applications. Commercial centers and medical buildings (e.g., hospitals) require location information of their users/patients to offer the services that are needed at the correct moment. Several approaches have been proposed to tackle this problem. In this paper, we present the development of an indoor location system which relies on the human activity recognition approach, using sound as an information source to infer the indoor location based on the contextual information of the activity that is realized at the moment. In this work, we analyze the sound information to estimate the location using the contextual information of the activity. A feature extraction approach to the sound signal is performed to feed a random forest algorithm in order to generate a model to estimate the location of the user. We evaluate the quality of the resulting model in terms of sensitivity and specificity for each location, and we also perform out-of-bag error estimation. Our experiments were carried out in five representative residential homes. Each home had four individual indoor rooms. Eleven activities (brewing coffee, cooking, eggs, taking a shower, etc.) were performed to provide the contextual information. Experimental results show that developing an indoor location system (ILS) that uses contextual information from human activities (identified with data provided from the environmental sound) can achieve an estimation that is 95% correct.

Among the current challenges of the Smart City, traffic management and maintenance are of utmost importance. Road surface monitoring is currently performed by humans, but the road surface condition is one of the main indicators of road quality, and it may drastically affect fuel consumption and the safety of both drivers and pedestrians. Abnormalities in the road, such as manholes and potholes, can cause accidents when not identified by the drivers. Furthermore, human-induced abnormalities, such as speed bumps, could also cause accidents. In addition, while said obstacles ought to be signalized according to specific road regulation, they are not always correctly labeled. Therefore, we developed a novel method for the detection of road abnormalities (i.e., speed bumps). This method makes use of a gyro, an accelerometer, and a GPS sensor mounted in a car. After having the vehicle cruise through several streets, data is retrieved from the sensors. Then, using a cross-validation strategy, a genetic algorithm is used to find a logistic model that accurately detects road abnormalities. The proposed model had an accuracy of 0.9714 in a blind evaluation, with a false positive rate smaller than 0.018, and an area under the receiver operating characteristic curve of 0.9784. This methodology has the potential to detect speed bumps in quasi real-time conditions, and can be used to construct a real-time surface monitoring system.