Heterogeneous Cubic Multidimensional Integrated Circuit for Water and Food Security in Fish Farming Ponds

Among major food production sectors, world aquaculture shows the highest growth rate, providing more than 50% of the global seafood market. However, water pollution in fish farming ponds is regarded as the leading cause of fish death and financial losses in the market. Here, an Internet of Things system based on a cubic multidimensional integration of circuit (MD‐IC) is demonstrated for water and food security applications in fish farming ponds. Both faces of the silicon substrate are used for thin‐film‐based device fabrication. The devices are interconnected via through‐silicon‐vias, resulting in a bifacial complementary metal‐oxide‐semiconductor‐compatible electronics system. The demonstrated cubic MD‐IC is a complete, small, and lightweight system that can be easily deployed by farmers with no need for specialists. The system integrates on its outer sides simultaneous air and water quality monitoring devices (temperature, electrical conductivity, ammonia, and pH sensors), solar cells for energy‐harvesting, and antenna for real‐time data‐transfer, while data‐management circuitry and a solid‐state battery are integrated on its internal faces. Microfluidic cooling technology is used for thermal management in the MD‐IC. Finally, a biofriendly polymeric encapsulation is used to waterproof the embedded electronics, improve the mechanical robustness, and allow the system to float on the surface of the water.     

The use of augmented reality in the diagnosis and treatment of autistic children: a review and a new system

This paper consists of two parts. The first presents a review of the literature on the use of augmented reality (AR) in the diagnosis and treatment of autistic children with a particular focus on the efficacy of AR in assisting autistic children who have communicative, social, sentiment, and attention deficit disorders. The review also investigated interactions between AR systems and children, taking into consideration the target behaviors that are selected from the child during treatment. Such modes were fully explored by taking into account the needs of the individual child in terms of achieving an improvement in their condition. Most significantly, the empirical information that was obtained from the reviewed works was evaluated according to some specific targeted attitudes and how each AR solution was utilized during treatment to achieve the fostering of such attitudes in order to identify the requirements for building an effective AR system. In addition, the review revealed the essential design features that can enable AR systems to achieve a high level of effectiveness in autism therapy. The review also covered the instruction that AR systems were noticed to execute, and focused on the significant characteristics that allow AR systems to accomplish degrees of efficacy in autism treatment. The review ends by classifying the various AR systems based on different criteria. The second part of the paper focuses on our new AR system as a case study. It covers the design considerations and decisions as well as the key features and appearance of the system. The paper concludes by making some recommendations for the further development of an AR system for application in the domain of child autism.

     

Vibrating Particles System Algorithm for Solving Classification Problems

Big data is a term that refers to a set of data that, due to its largeness or complexity, cannot be stored or processed with one of the usual tools or applications for data management, and it has become a prominent word in recent years for the massive development of technology. Almost immediately thereafter, the term “big data mining” emerged, i.e., mining from big data even as an emerging and interconnected field of research. Classification is an important stage in data mining since it helps people make better decisions in a variety of situations, including scientific endeavors, biomedical research, and industrial applications. The probabilistic neural network (PNN) is a commonly used and successful method for handling classification and pattern recognition issues. In this study, the authors proposed to combine the probabilistic neural network (PPN), which is one of the data mining techniques, with the vibrating particles system (VPS), which is one of the metaheuristic algorithms named “VPS-PNN”, to solve classification problems more effectively. The data set is eleven common benchmark medical datasets from the machine-learning library, the suggested method was tested. The suggested VPS-PNN mechanism outperforms the PNN, biogeography-based optimization, enhanced-water cycle algorithm (E-WCA) and the firefly algorithm (FA) in terms of convergence speed and classification accuracy.     

Isomer-selective adsorption of amino acids by components of natural sediments

We present evidence that under circumstances of low pH and organic-free surfaces an ordinary estuarine sediment can exhibit strong optical isomer selectivity in its absorption of a number of amino acids. This selectivity can also be seen to a lesser degree in the minerals quartz, montmorillonite, and kaolin. Adsorption reactions were performed with racemic amino acid mixtures, and after equilibrium, deviations from a D/L ratio of 1 were measured and in many cases were found to be significant. This was particularly pronounced at pH 4.0, where selective removal of the L isomers by adsorption onto sedimentfractions was almosttotal. Changes in both the nature and degree of selectivity were also observable in different sediment size fractions. While we are at this stage unable to identify the mode of primary selectivity, adsorption experiments with these candidate sediment components, quartz, kaolin, and montmorillonite do exhibit some selective behavior. We believe that the existence of natural chirally selective components in sediment may indicate a new approach to the development of chiral catalysis and synthesis.