Seleção de Culturantes por dois tipos de metacontingências

Metacontingência do tipo 1 descreve relações condicionais entre culturantes – contingências comportamentais entrelaçadas (CCEs), consequência individual (CI), e produto agregado (PA) – e consequências culturais (CC). O tipo 2 envolve programações nas quais CC selecionam tanto respostas quanto culturantes. Portanto, não há CI. Este estudo verificou se a programação de diferentes tipos de metacontingências produziria diferentes padrões de respostas ou culturantes. Participaram duas tríades de universitários. Na condição A vigoraram metacontingências do tipo 1. Na condição B, metacontingências do tipo 2.  A Tríade 1 foi exposta ao delineamento ABAB. Já a Tríade 2 ao delineamento BABA. Na condição A, participantes emitiram respostas e engajaram em culturantes que produziam consequências. Na condição B houve queda na frequência de respostas e manutenção dos culturantes. Conclui-se que a programação de diferentes tipos de metacontingências produz diferentes efeitos sobre comportamentos. Não foram observados efeitos de ordem de exposição as condições. Discute-se ainda a interação entre operantes e culturantes tanto na aquisição quanto na manutenção do comportamento em grupo.

Deep neural networks for unsupervised damage detection on the Z24 bridge

During their life-cycle, civil infrastructures are continuously prone to significant functionality losses, primarily due to material's degradation and exposure to several natural hazards. Following these concerns, many researchers have attempted to develop reliable monitoring strategies, as integration to visual inspections, to efficiently ensure bridge maintenance and early-stage damage detection. In this framework, recent improvements in sensor technologies and data science have stimulated the use of Machine Learning (ML) algorithms for Structural Health Monitoring (SHM). Among unsupervised learning techniques, the potential of autoencoder networks has been attracting notable interest in the context of anomaly detection. In this light, the present paper proposes two different autoencoder-based damage detection techniques, focused on the Multi-Layer Perceptron (MLP) and the Convolutional Autoencoder (CAE) networks, respectively. During the training, the selected ML models learn how reconstructing raw acceleration sequences acquired from sound conditions. Unknown data, including both healthy and damaged bridge responses, are afterwards used to test the implemented networks and to detect damage occurrence. To this aim, a specific index of reconstruction loss is selected as a damage sensitive feature with the aim to quantify the errors between the original and reconstructed sequences. The performance exhibited by the two approaches is compared and evaluated by application to the Z24 benchmark bridge. Results demonstrate the effectiveness of the proposed methodology to perform feature classification and real time damage detection at the level of macro-sequences as new sensor data is collected, resulting suitable for continuous assessment of full-scale monitored bridges

Lie to Me: Shield Your Emotions from Prying Software

Deep learning approaches for facial Emotion Recognition (ER) obtain high accuracy on basic models, e.g., Ekman’s models, in the specific domain of facial emotional expressions. Thus, facial tracking of users’ emotions could be easily used against the right to privacy or for manipulative purposes. As recent studies have shown that deep learning models are susceptible to adversarial examples (images intentionally modified to fool a machine learning classifier) we propose to use them to preserve users’ privacy against ER. In this paper, we present a technique for generating Emotion Adversarial Attacks (EAAs). EAAs are performed applying well-known image filters inspired from Instagram, and a multi-objective evolutionary algorithm is used to determine the per-image best filters attacking combination. Experimental results on the well-known AffectNet dataset of facial expressions show that our approach successfully attacks emotion classifiers to protect user privacy. On the other hand, the quality of the images from the human perception point of view is maintained. Several experiments with different sequences of filters are run and show that the Attack Success Rate is very high, above 90% for every test

Stability of Glutaraldehyde in Biocide Compositions

Glutaraldehyde (GA) is used as biocide in hospitals. Recent public investigations on the chemical composition of biocides used in Romania have in some cases found GA, as a key ingredient, to be apparently diluted. However, these data did not explicitly consider the complex chemical equilibria inherent to GA. An investigation of experimental and theoretical data is reported here, assessing the stability of GA solutions relevant for biocide compositions. GA solutions of various chemical composition and under varying circumstances were analyzed using spectroscopy (UV-VIS, Raman, NMR) coupled with density functional theory (DFT) calculations, as well as chemically, such as via the formation of imines in reaction/titration with glycine monitored at 270 nm; using LC-MS; or using SDS-PAGE analysis with GA as reagent in the polymerization of two test proteins- hemoglobin and myoglobin. The spectral properties of GA changed significantly over time, in a temperature-dependent manner; titration with glycine confirmed the spectral data. SDS-PAGE experiments demonstrated a non-linear and apparently unpredictable change in the reactivity of GA over time. The results may be relevant for the determination of GA concentration in various settings such as biocide analysis, hospital wastewaters, and others

Bioactive Properties of Composites Based on Silicate Glasses and Different Silver and Gold Structures

Using an ideal biomaterial to treat injured bones can accelerate the healing process and simultaneously exhibit antibacterial properties; thus protecting the patient from bacterial infections. Therefore, the aim of this work was to synthesize composites containing silicate-based bioactive glasses and different types of noble metal structures (i.e., AgI pyramids, AgIAu composites, Au nanocages, Au nanocages with added AgI). Bioactive glass was used as an osteoconductive bone substitute and Ag was used for its antibacterial character, while Au was included to accelerate the formation of new bone. To investigate the synergistic effects in these composites, two syntheses were carried out in two ways: AgIAu composites were added in either one step or AgI pyramids and Au nanocages were added separately. All composites showed good in vitro bioactivity. Transformation of AgI in bioactive glasses into Ag nanoparticles and other silver species resulted in good antibacterial behavior. It was observed that the Ag nanoparticles remained in the Au nanocages, which was also beneficial in terms of antibacterial properties. The presence of Au nanoparticles contributed to the composites achieving high cell viability. The most outstanding result was obtained by the consecutive addition of noble metals into the bioactive glasses, resulting in both a high antibacterial effect and good cell viability