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Behavior analysts have long recognized the benefits of closely following their data; however, the data we are following may be moving faster than the tools we have to accurately analyze and predict future behaviors. This problem even saturates behavior-analytic investigations that focus on the evaluation of complex data related to public policy issues in areas such as poverty, geriatrics, and child welfare practice. In the face of this research enigma, there exists a more powerful and precise set of classification and prediction platforms for researchers in the behavioral sciences. In this article, we describe a combination of neural network strategies that predict child welfare professionals' decision making. Extending the data analysis from the Yelick and Thyer (2019) study, we employed our current version of the Kohonen self-organizing map in conjunction with our deep neural network as a strategy for identifying participants who were at high probability for making heuristic decisions. [ABSTRACT FROM AUTHOR]

In this paper, we proposed a plant leaf disease identification model based on a Pretrained deep convolutional neural network (Deep CNN). The Deep CNN model is trained using an open dataset with 10 different classes of tomato leaves We observed that overall architectures which can increase the best performance of the model. The proposed model was trained using different training epochs, batch sizes and dropouts. The Xception has attained maximum accuracy compare with all other approaches. After an extensive simulation, the proposed model achieves classification accuracy better. This accuracy of the proposed work is greater than the accuracy of all other Pretrained approaches. The proposed model is also tested with respect to its consistency and reliability.The set of data used for this work was collected from the plant village dataset, including sick and healthy images. Models for detection of plant disease should predict the disease quickly and accurately in the early stage itself .so that a proper precautionary measures can be applied to avoid further spread of the diseases. So, to reduce the main issue about the leaf diseases, we can analyze distinct kinds of deep neural network architectures in this research. From the outcomes, Xception has a constantly improving more to enhance the accuracy by increasing the number of epochs, without any indications of overfitting and decrease in quality. And Xception also generated a fine 99.45% precision in less computing time. [ABSTRACT FROM AUTHOR]

Machine learning applications, especially deep neural networks (DNNs) have seen ubiquitous use in computer vision, speech recognition, and robotics. However, the growing complexity of DNN models have necessitated efficient hardware implementations. The key compute primitives of DNNs are matrix vector multiplications, which lead to significant data movement between memory and processing units in today's von Neumann systems. A promising alternative would be colocating memory and processing elements, which can be further extended to performing computations inside the memory itself. We believe in-memory computing is a propitious candidate for future DNN accelerators, since it mitigates the memory wall bottleneck. In this article, we discuss various in-memory computing primitives in both CMOS and emerging nonvolatile memory (NVM) technologies. Subsequently, we describe how such primitives can be incorporated in standalone machine learning accelerator architectures. Finally, we analyze the challenges associated with designing such in-memory computing accelerators and explore future opportunities. [ABSTRACT FROM AUTHOR]

Despite the volume of data generated, scientists cannot accurately predict how climate change will manifest itself locally and what measures should be applied to mitigate it effectively. On the other hand, Big Data is a new technology that faces the challenge of collecting, characterizing and analyzing a large amount of data, taking into account data from multiple sources, multiple variables and multiple scales with different spatial and temporal attributes. To do this, we review and synthesize the current state of research of Big Data architectures that help solve the problems caused by climate change in health (16%), agriculture(8%), biodiversity(16%), energy(8%), water resources(4%) and clima(48%). To achieve the objective, we have carried out a systematic mapping study, which includes four research questions, including 25 studies, published from 2013 to 2019. The architectures found have been classified according to their use, which can be for statistical analysis, monitoring and simulations; helping researchers to integrate knowledge into the practical use of Big Data in the context of climate change. [ABSTRACT FROM AUTHOR]

Contrary to using distant and centralized cloud data center resources, employing decentralized resources at the edge of a network for processing data closer to user devices, such as smartphones and tablets, is an upcoming computing paradigm, referred to as fog/edge computing. Fog/edge resources are typically resourceconstrained, heterogeneous, and dynamic compared to the cloud, thereby making resource management an important challenge that needs to be addressed. This article reviews publications as early as 1991, with 85% of the publications between 2013 and 2018, to identify and classify the architectures, infrastructure, and underlying algorithms for managing resources in fog/edge computing. [ABSTRACT FROM AUTHOR]

Breakthroughs in additive manufacturing (AM), particularly direct ink write 3D printing, have allowed for greater control of material performance by manipulation of architecture and/or spatial composition. Herein, the range of control over the dynamic energy release rate in 3D‐printed Al/CuO thermite is quantified by substituting random porous pathways present in powder beds or compacts with printed void channels to modulate the energy transport during a reaction. The thermite is produced via on‐the‐fly static mixing of constituent Al and CuO inks, which offers a safe way to handle these materials. By reducing the fundamental burn unit size (i.e., filament size) and introducing small amounts of engineered porosity for gas flow, it is shown that energy release rates can be increased by more than 100 times that of maximum print density strips. Unique channel structures, which display propagation velocities of over 100 m s−1 due to confinement of gas flow, are reported. Ashby plots that show the reactivity design space for 3D‐printed thermites are presented as a function of the effective print/energy density. Architecting with AM tailors an object to release its energy in a prescribed way, and this control adds much‐needed versatility by allowing a single formulation to satisfy multiple applications. [ABSTRACT FROM AUTHOR]

• Tri-metallic fluorides supported on reduced graphene architectures are constructed by a facile hydrothermal method and fluoridation treatment. • The hierarchical structure of CoFeNiF-rGAs can expose abundant active sites and accelerate the electrolyte ion diffusion. • The co-existence of metal-F and metal-O bonds are favorable to improve the electrocatalytic OER performance. • The CoFeNiF-rGAs own a low overpotential, small Tafel slope and charge transfer resistance, and excellent catalytic stability. Exploring the transition metal fluoride-based catalysts with highly catalytic activity is of great importance to satisfy the electrochemical oxygen evolution reaction (OER). Herein, the tri-metallic (cobalt, iron, and nickel) fluorides immobilizing on reduced graphene oxide architectures (CoFeNiF-rGAs) are constructed by hydrothermal and low-temperature fluoridation. The hierarchical structure assembled by the π-π configuration of graphene can provide abundant channels for fast ion diffusion and guarantee the uniform loading of metal fluorides. Owing to the hierarchical morphology and the formation of metal fluorides/graphene interfaces, CoFeNiF-rGAs can show excellent OER performance. Only 238 mV of overpotential is required to achieve a current density of 10 mA cm−2 in alkaline electrolyte, and the Tafel slope (78.8 mV dec-1) and charge transfer resistance (13.6 Ω) are smaller than the control samples, respectively, implying a fast kinetic behavior during OER. The highly catalytic stability of CoFeNiF-rGAs is confirmed by 1000 cyclic voltammetry and chronoamperometry test for 20 h, and the boosted catalytic mechanism is elucidated by density function theory calculation. [ABSTRACT FROM AUTHOR]

Direct current (DC) microgrids (MG) constitute a research field that has gained great attention over the past few years, challenging the well-established dominance of their alternating current (AC) counterparts in Low Voltage (LV) (up to 1.5 kV) as well as Medium Voltage (MV) applications (up to 50 kV). The main reasons behind this change are: (i) the ascending amalgamation of Renewable Energy Sources (RES) and Battery Energy Storage Systems (BESS), which predominantly supply DC power to the energy mix that meets electrical power demand and (ii) the ascending use of electronic loads and other DC-powered devices by the end-users. In this sense, DC distribution provides a more efficient interface between the majority of Distributed Energy Resources (DER) and part of the total load of a MG. The early adopters of DC MGs include mostly buildings with high RES production, ships, data centers, electric vehicle (EV) charging stations and traction systems. However, the lack of expertise and the insufficient standards’ framework inhibit their wider spread. This review paper presents the state of the art of LV and MV DC MGs in terms of advantages/disadvantages over their AC counterparts, their interface with the AC main grid, topologies, control, applications, ancillary services and standardization issues. Overall, the aim of this review is to highlight the possibilities provided by DC MG architectures as well as the necessity for a solid/inclusive regulatory framework, which is their main weakness.

Development boards, Single-Board Computers (SBCs) and Single-Board Microcontrollers (SBMs) integrating sensors and communication technologies have become a very popular and interesting solution in the last decade. They are of interest for their simplicity, versatility, adaptability, ease of use and prototyping, which allow them to serve as a starting point for projects and as reference for all kinds of designs. In this sense, there are innumerable applications integrating sensors and communication technologies where they are increasingly used, including robotics, domotics, testing and measurement, Do-It-Yourself (DIY) projects, Internet of Things (IoT) devices in the home or workplace and science, technology, engineering, educational and also academic world for STEAM (Science, Technology, Engineering and Mathematics) skills. The interest in single-board architectures and their applications have caused that all electronics manufacturers currently develop low-cost single board platform solutions. In this paper we realized an analysis of the most important topics related with single-board architectures integrating sensors. We analyze the most popular platforms based on characteristics as: cost, processing capacity, integrated processing technology and open-source license, as well as power consumption (mA@V), reliability (%), programming flexibility, support availability and electronics utilities. For evaluation, an experimental framework has been designed and implemented with six sensors (temperature, humidity, CO2/TVOC, pressure, ambient light and CO) and different data storage and monitoring options: locally on a μSD (Micro Secure Digital), on a Cloud Server, on a Web Server or on a Mobile Application.

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Shakey the robot was a milestone of autonomous robots and artificial intelligence. Its design principles have dominated research until now. Tacit philosophical and architectural assumptions have impoverished the space of research topics and methods. I point out ways to overcome this impasse with sideglances to other scientific fields. [ABSTRACT FROM AUTHOR]

Novel CoFe-layered double hydroxide (LDH) coupled with NiFe-LDH nanosheet array supported on nickel foam (denoted as CoFe@NiFe/NF) is developed for the high-performance OER/HER bifunctional electrocatalyst. An efficient and stable alkali-electrolyzer using CoFe@NiFe/NF achieves a voltage of 1.59 V at the current density of 10 mA cm−2, superior to many other state-of-the-art earth-abundant electrocatalysts. • Novel CoFe-LDH@NiFe-LDH/NF nanosheet heterostructure was constructed. • The heterostructure showed high OER/HER bifunctional activities and stabilities. • An electrolyzer required a low cell voltage of only 1.59 V to drive 10 mA cm−2. • The unique nanosheet heterostructure contributes to the enhanced activities. • The interplay of the active components also contributes to the enhanced activities. Developing active, stable and cost-effective bifunctional electrocatalysts with earth-abundant metals (Ni, Fe, Co) is a prerequisite to achieve overall water splitting. In this work, a novel CoFe-layered double hydroxide (LDH) coupled with NiFe-LDH nanosheet array supported on nickel foam (denoted as CoFe@NiFe/NF) is developed through a facile hydrothermal and electrodeposition method. Remarkably, benefiting from strong synergistic effect between CoFe-LDH and NiFe-LDH and the unique structural features, the resulting CoFe@NiFe/NF architectures catalyst exhibits excellent activities and stabilities for oxygen evolution reaction (OER) and hydrogen evolution reaction (HER). Furthermore, an efficient and stable alkali-electrolyzer using CoFe@NiFe/NF as both the cathode and anode achieve a voltage of 1.59 V at the current density of 10 mA cm−2, which is superior to many other state-of-the-art earth-abundant electrocatalysts. This work provides a facile method for enhancing the electrocatalytic activity by constructing hierarchical core-shell architectures using the LDH nanosheet materials. [ABSTRACT FROM AUTHOR]

To meet the fast-growing energy demand and, at the same time, tackle environmental concerns resulting from conventional energy sources, renewable energy sources are getting integrated in power networks to ensure reliable and affordable energy for the public and industrial sectors. However, the integration of renewable energy in the ageing electrical grids can result in new risks/challenges, such as security of supply, base load energy capacity, seasonal effects, and so on. Recent research and development in microgrids have proved that microgrids, which are fueled by renewable energy sources and managed by smart grids (use of smart sensors and smart energy management system), can offer higher reliability and more efficient energy systems in a cost-effective manner. Further improvement in the reliability and efficiency of electrical grids can be achieved by utilizing dc distribution in microgrid systems. DC microgrid is an attractive technology in the modern electrical grid system because of its natural interface with renewable energy sources, electric loads, and energy storage systems. In the recent past, an increase in research work has been observed in the area of dc microgrid, which brings this technology closer to practical implementation. This paper presents the state-of-the-art dc microgrid technology that covers ac interfaces, architectures, possible grounding schemes, power quality issues, and communication systems. The advantages of dc grids can be harvested in many applications to improve their reliability and efficiency. This paper also discusses benefits and challenges of using dc grid systems in several applications. This paper highlights the urgent need of standardizations for dc microgrid technology and presents recent updates in this area.

The objective of this paper is to present the software architecture used for the OPERANDO privacy platform, funded by the European Union in a Horizon 2020 project. For integration, OPERANDO is using SwarmESB, an open source Enterprise Service Bus (ESB) based on executable choreographies. In this paper we are presenting the concept of service transformations, presented as a bridge between the world of REST web services and the world of services implemented with executable choreographies. These transformations are improving the heterogeneity aspects when we are analysing SwarmESB as a distributed system. Five types of transformations that have been analysed and implemented as open source software have been integrated. This proposal is shaped around a common language capable of expressing all these five transformation types we have identified working for OPERANDO. Therefore, the Domain Specific Language proposed, renders the essential elements for transformations among functions, web services and executable choreographies. This unification will trigger a quantitative effect on the productivity of the teams creating or integrating web services in a federated service bus environment which is a key architectural component in the future Internet-of-Things and cloud systems.

The distribution of services on multi-agent systems allows it to reduce to the agents their computational load. The functionality of the system does not reside in the agents themselves, however it is ubiquitously distributed so that allows you to perform tasks in parallel avoiding an additional computational cost to the elements in the system. The distribution of services that offers SCODA (Distributed and Specialized Agent Communities) allows an intelligent management of these services provided by agents of the system and the parallel execution of threads that allow to respond to requests asynchronously, which implies an improvement in the performance of the system at both the computational level as the level of quality of service in the control of these services. The comparison carried out in the case of study that is presented in this paper demonstrates the existing improvement in the distribution of services on systems based on SCODA.