Bio-inspired design of hard-bodied mobile robots based on arthropod morphologies: a 10-year systematic review and bibliometric analysis.

This research presents a 10-year systematic review based on bibliometric analysis of the bio-inspired design of hard-bodied mobile robot mechatronic systems considering the anatomy of arthropods. These are the most diverse group of animals whose flexible biomechanics and adaptable morphology, thus, it can inspire robot development. Papers were reviewed from two international databases (Scopus and Web of Science) and one platform (Aerospace Research Central), then they were classified according to: year of publication (January 2013 to April 2023), arthropod group, published journal, conference proceedings, editorial publisher, research teams, robot classification according to the name of arthropod, limb's locomotion support, number of legs/arms, number of legs/body segments, limb's degrees of freedom, mechanical actuation type, modular system, and environment adaptation. During the screening, more than 33000 works were analyzed. Finally, a total of 174 studies (90 journal-type, 84 conference-type) were selected for in-depth study: Insecta - hexapod (53,8%), Arachnida - octopods (20.7%), Crustacea - decapods (16,1%), and Myriapoda - centipedes and millipedes (9,2%). The study reveals that the most active editorials are the Institute of Electrical and Electronics Engineers Inc., Springer, MDPI, and Elsevier, while the most influential researchers are located in the USA, China, Singapore, and Japan. Most works pertained to spiders, crabs, caterpillars, cockroaches, and centipedes. We conclude that "arthrobotics" research, which merges arthropods and robotics, is constantly growing and includes a high number of relevant studies with findings that can inspire new methods to design biomechatronic systems.

Response times are affected by mispredictions in a stochastic game.

Acting as a goalkeeper in a video-game, a participant is asked to predict the successive choices of the penalty taker. The sequence of choices of the penalty taker is generated by a stochastic chain with memory of variable length. It has been conjectured that the probability distribution of the response times is a function of the specific sequence of past choices governing the algorithm used by the penalty taker to make his choice at each step. We found empirical evidence that besides this dependence, the distribution of the response times depends also on the success or failure of the previous prediction made by the participant. Moreover, we found statistical evidence that this dependence propagates up to two steps forward after the prediction failure.

Transformers for Multi-Horizon Forecasting in an Industry 4.0 Use Case.

Recently, a novel approach in the field of Industry 4.0 factory operations was proposed for a new generation of automated guided vehicles (AGVs) that are connected to a virtualized programmable logic controller (PLC) via a 5G multi-access edge-computing (MEC) platform to enable remote control. However, this approach faces a critical challenge as the 5G network may encounter communication disruptions that can lead to AGV deviations and, with this, potential safety risks and workplace issues. To mitigate this problem, several works have proposed the use of fixed-horizon forecasting techniques based on deep-learning models that can anticipate AGV trajectory deviations and take corrective maneuvers accordingly. However, these methods have limited prediction flexibility for the AGV operator and are not robust against network instability. To address this limitation, this study proposes a novel approach based on multi-horizon forecasting techniques to predict the deviation of remotely controlled AGVs. As its primary contribution, the work presents two new versions of the state-of-the-art transformer architecture that are well-suited to the multi-horizon prediction problem. We conduct a comprehensive comparison between the proposed models and traditional deep-learning models, such as the long short-term memory (LSTM) neural network, to evaluate the performance and capabilities of the proposed models in relation to traditional deep-learning architectures. The results indicate that (i) the transformer-based models outperform LSTM in both multi-horizon and fixed-horizon scenarios, (ii) the prediction accuracy at a specific time-step of the best multi-horizon forecasting model is very close to that obtained by the best fixed-horizon forecasting model at the same step, (iii) models that use a time-sequence structure in their inputs tend to perform better in multi-horizon scenarios compared to their fixed horizon counterparts and other multi-horizon models that do not consider a time topology in their inputs, and (iv) our experiments showed that the proposed models can perform inference within the required time constraints for real-time decision making.

Multiple partition Markov model for B.1.1.7, B.1.351, B.1.617.2, and P.1 variants of SARS-CoV 2 virus.

With tools originating from Markov processes, we investigate the similarities and differences between genomic sequences in FASTA format coming from four variants of the SARS-CoV 2 virus, B.1.1.7 (UK), B.1.351 (South Africa), B.1.617.2 (India), and P.1 (Brazil). We treat the virus' sequences as samples of finite memory Markov processes acting in A = { a , c , g , t } . We model each sequence, revealing some heterogeneity between sequences belonging to the same variant. We identified the five most representative sequences for each variant using a robust notion of classification, see Fernández et al. (Math Methods Appl Sci 43(13):7537-7549. 10.1002/mma.5705 ). Using a notion derived from a metric between processes, see García et al. (Appl Stoch Models Bus Ind 34(6):868-878. 10.1002/asmb.2346), we identify four groups, each group representing a variant. It is also detected, by this metric, global proximity between the variants B.1.351 and B.1.1.7. With the selected sequences, we assemble a multiple partition model, see Cordeiro et al. (Math Methods Appl Sci 43(13):7677-7691. 10.1002/mma.6079), revealing in which states of the state space the variants differ, concerning the mechanisms for choosing the next element in A. Through this model, we identify that the variants differ in their transition probabilities in eleven states out of a total of 256 states. For these eleven states, we reveal how the transition probabilities change from variant (group of variants) to variant (group of variants). In other words, we indicate precisely the stochastic reasons for the discrepancies.

Perfil nutricional según niveles socio-económicos y menús proporcionados en un comedor social de México / Nutritional profiles according to socioeconomic levels and the menu offered in a community restaurant from Mexico

Resumen Introducción: México es de los principales países que enfrenta una problemática nutricional provocada por un déficit o exceso de nutrientes, que ocasiona desnutrición o sobrepeso en su población. Objetivo: Identificar los hábitos alimenticios entre tres distintos estratos sociales y compararlos con el perfil nutricional en un comedor social que brinda desayuno y comida en la Ciudad de Tepic, Nayarit. Materiales y métodos: Se realizaron encuestas en los diferentes estratos sociales que correspondieron a un recordatorio de un día de desayuno y comida con sus respectivos refrigerios y bebidas. Resultados: Se encontró consumos de proteína que excedían la ingesta recomendada diaria, bajo consumo de fibra dietética (asociado con la baja ingesta de frutas y verduras) y alta incidencia en consumo de bebidas carbonatadas. Además, la mayor ingesta calórica total fue en el estrato socioeconómico alto (atribuido a su mayor poder adquisitivo), seguido del comedor social; sin embargo, en ningún estrato, ni el comedor social se encontró una dieta balanceada. Conclusiones: El poder adquisitivo de los distintos estratos socioeconómicos no fue el principal factor que limita una adecuada alimentación, si no la falta de información y malos hábitos alimenticios de la población, debido a la pérdida de cultura alimentaria.

Abstract Introduction: Mexico is among the main countries that faces nutritional problems caused by a deficit or excess of nutrients, which is causing malnutrition or overweight in its population. Objective: To identify the eating habits of people belonging to three different socioeconomic strata and compare them to nutritional profiles of a community restaurant in the City of Tepic, Nayarit. Materials and methods: The participants were surveyed about what they ate for breakfast and lunch the day before. Results: Protein consumption was found to exceed the recommended daily intake. Also, there was a low ingestion of dietary fiber (associated with low intake of fruits and vegetables) and a high incidence of carbonated drink consumption. Finally, the largest total caloric intake was recorded in people from the highest SS (attributed to their higher income), followed by those who eat at the community restaurant. However, none of the participants followed a balanced diet. Conclusions: The purchasing power of people belonging to the different socioeconomic strata is not the main factor that limits a balanced diet, since additional factors are the lack of information and poor nutritional habits of the Mexican population due to the loss of their food culture.