Knowledge in Sight: Toddlers Plan Efficient Epistemic Actions by Anticipating Learning Gains

Anticipating the learning consequences of actions is crucial to plan efficient information-seeking. Such a capacity is needed for learners to determine which actions are most likely to result in learning. Here, we tested the early ontogeny of the human capacity to anticipate the amount of learning gained from seeing. In Study 1, we tested infants’ capacity to anticipate the availability of sight. Fourteen-month-old infants (N = 72) were invited to search for a toy hidden inside a container. The participants were faster to attempt at opening a shutter when this action allowed them to see inside the container. Moreover, this effect was specifically observed when seeing inside the container was potentially useful to the participants’ goals. Thus, infants anticipated the availability of sight, and they calibrated their information-seeking behaviors accordingly. In Studies 2-3, we tested toddlers’ capacity to anticipate whether data would be cognitively useful for their goals. Two-and-a-half-year-olds (N = 72) had to locate a target character hidden among distractors. The participants flipped the characters more often, and were comparatively faster to initiate this action when it yielded access to visual data allowing them to locate the target. Thus, toddlers planned their information-seeking behaviors by anticipating the cognitive utility of sight. In contrast, toddlers did not calibrate their behaviors to the cognitive usefulness of auditory data. These results suggest that cognitive models of learning guide toddlers’ search for information. The early developmental onset of the capacity to anticipate future learning gains is crucial for active learning.

When community change theory meets practice: Exploring some of the challenges to information sharing networks for community change

Networked community change (NCC) efforts focus on building and supporting networks of stakeholders in communities to address complex problems, with a particular focus on tie formation among stakeholders and organizer facilitation of an information-sharing network (Lawlor & Neal, 2016). NCC approaches include, for example, systemic action research (Burns, 2007), Collective Impact (Kania & Kramer, 2011), and network action research (Foth, 2006) and can be understood as community-level interventions supporting change (Bess, 2015). Previous research demonstrates the promising nature of NCC efforts under ideal circumstances for efficient information sharing in community change efforts. However, these efforts frequently operate in non-ideal conditions, needing to adapt to a variety of community challenges. This article extends the agent-based simulation model presented in Lawlor and Neal (2016) to reflect the challenges that arise when stakeholders implement these approaches to community change and identify how they can impact information-sharing networks. First, we review literature to establish common challenges that arise in these efforts. Second, we employ the Lawlor and Neal (2016) model to examine these challenges and report on the resulting structure of the information-sharing networks that emerge from implementing them in the simulation model. We conclude with implications of common networked community change challenges on network formation and future directions for addressing these real-world challenges as communities work on complex problems.

Samsun İlinde Hayvansal Kaynaklı Kompost Potansiyeli ve Optimum Tesis Konumları

Compared to industrial wastes, the recycling of agricultural wastes is more efficient because they are organic. The low organic matter content of Anatolian agricultural lands makes the recycling of agricultural wastes even more important. In the Samsun region, compost centres are recommended in order to help increase productivity in agricultural production, develop agricultural activities and ensure rural development. It is very important to use natural fertilizers because it is to protect and improve the organic structure of the soil. The low production costs of organic fertilizers compared to chemical fertilizers provide an advantage to farmers and prevent foreign exchange loss. In this study, it is aimed to determine the animal existence and optimum facility locations for evaluation of animal wastes as compost in Samsun. For this purpose, the number of animals of the enterprises in the region was determined by using the data obtained from Samsun Provincial Directorate of Agriculture and Forestry. The coordinates of the settlements of 1,284 enterprises with bovine capacity of 40 or more in Samsun were determined according to the rectangular coordinate system. In terms of animal distribution in Samsun province, 27 cluster numbers were preferred through Elbow method and f(K) Function method. The location of most suitable compost production centres according to the distance and the total number of animals was determined by the K-Means clustering analysis method using geographical coordinates. For Samsun, the group size was taken as 2,000-10,000 cattle and the enterprises with a focal length less than 15 km were included in the compost production clusters. In line with these criteria, 10 compost production clusters for compost production have been determined in Samsun and the locations of these cluster centres are shown on the map in a way that will provide the most efficient information to investors and relevant institutions.

Applications of ANN for Agriculture Using Remote Sensed Data

Ever since the advent of modern geo information systems, tracking environmental changes due to natural and/or manmade causes with the aid of remote sensing applications has been an indispensable tool in numerous fields of geography, most of the earth science disciplines, defense, intelligence, commerce, economics, and administrative planning. Remote sensing is used in science and technology, and through it, an object can be identified, measured, and analyzed without physical presence for interpretation. In India remote sensing has been using since 1970s. One among these applications is the crop classification and yield estimation. Using remote sensing in agriculture for crop mapping, and yield estimation provides efficient information, which is mainly used in many government organizations and the private sector. The pivotal sector for ensuring food security is a major concern of interest in these days. In time, availability of information on agricultural crops is vital for making well-versed decisions on food security issues.

Multistate Active Combined Power and Message/Data Rate Adaptive Decentralized Congestion Control Mechanisms for Vehicular Ad Hoc Networks

Vehicular ad hoc networks (VANETs) have emerged in time to reduce on-road fatalities and provide efficient information exchange for entertainment-related applications to users in a well-organized manner. VANETs are the most instrumental elements in the Internet of Things (IoT). The objective lies in connecting every vehicle to every other vehicle to improve the user’s quality of life. This aim of continuous connectivity and information exchange leads to the generation of more information in the medium, which could congest the medium to a larger extent. Decentralized congestion control (DCC) techniques are specified to reduce medium congestion and provide various safety applications. This article presents two DCC mechanisms that adapt message rate and data rate combined with transmit power control mechanism. These mechanisms are developed under multi-state active design proposed by the standard. The proposed methods deliver better performance over other mechanisms in terms of power, channel load, and channel utilization using real-time-based scenarios by simulation in SUMO.

AI-Assisted Dynamic Modeling for Data Management in a Distributed System

Data management is an administrative mechanism that involves the acquisitions, validations, storage, protection, and processing of data needed by its users to ensure that data are accessible, reliable, and timely. It is a challenging task to manage protections for information properties. With the emphasis on distributed systems and Internet-accessible systems, the need for efficient information security management is increasingly important. In the paper, artificial intelligence-assisted dynamic modeling (AI-DM) is used for data management in a distributed system. Distributed processing is an effective way to enhance the efficiency of database systems. Therefore, each distributed database structure’s functionality depends significantly on its proper architecture in implementing fragmentation, allocation, and replication processes. The proposed model is a dynamically distributed internet database architecture. This suggested model enables complex decision-making on fragmentation, distribution, and duplication. It provides users with links from anywhere to the distributed database. AI-DM has an improved allocation and replication strategy where no query performance information is accessible at the initial stage of the distributed database design. AI-DM findings show that the proposed database model leads to the reliability and efficiency of the enhanced system. The final results are obtained by analyzing the dynamic modeling ratio is 87.6%, increasing decision support ratio is 88.7%, the logistic regression ratio is 84.5%, the data reliability ratio is 82.2%, and the system ratio is 93.8%.

A Research on Producer Reasons for Participation into Agricultural Fairs

Ever-developing agricultural technologies and progress in communication science increase competition and globalization in converge countries. Agricultural fairs offer ambient to bring producers and input-suppliers of agriculture together at certain places and times. They offer direct and efficient information about new technologies to producers. German Agricultural Society and Leader Farmer Association have jointly organized outdoor fairs in Turkey since 2010. Agriculture Days Fair is one of the most important events among these jointly organized fairs. This study was conducted to determine the reasons for producers’ participation in these fairs held in 2015 and 2017 and to determine the effects of socio-economic characteristics of the producers on their reasons for participation in these fairs. A total of 589 questionnaires were made in this study (250 in May 2015 fair and 339 in August 2017 fair). The same questionnaire forms were used in both years. CHAID analysis technique was employed in analyzing data gathered from the producers. It was observed that producers generally participated in agricultural fairs just for the excursion or spent time with their families at weekends. However, it was also determined that the producers were informed about the agricultural fairs by producer organizations and Leader Farmer Association participated in the fairs to promote a new products or to give information about new technologies and inputs. It can be suggested that beyond informing producers about the fairs, Leader Farmer Association formed within the scope of Leader Farmer Project initiated with the support of German Agricultural Society (DLG) should convey information about how important the fairs are in the acquaintance with introducing new information and technologies.

Emergency department physicians’ distribution of time in the fast paced-workflow-a novel time-motion study of drug-related activities

Background In the emergency department physicians are forced to distribute their time to ensure that all admitted patients receive appropriate emergency care. Previous studies have raised concerns about medication discrepancies in patient’s drug lists at admission to the emergency department. Thus, it is important to study how emergency department physicians distribute their time, to highlight where workflow redesign can be needed.Aim to quantify how emergency department physicians distribute their time between various task categories, with particular focus on drug-related tasks.Method Direct observation, time-motion study of emergency department physicians at Diakonhjemmet Hospital, Oslo, Norway. Physicians’ activities were categorized in discrete categories and data were collected with the validated method of Work Observation Method By Activity Timing between October 2018 to January 2019. Bootstrap analysis determined 95% confidence intervals for proportions and interruption rates.Results During the observation time of 91.4 h, 31 emergency department physicians were observed. In total, physicians spent majority of their time gathering information (36.5%), communicating (26.3%), and documenting (24.2%). Further, physicians spent 17.8% (95% CI 16.8%, 19.3%) of their time on drug-related tasks. On average, physicians spent 7.8 min (95% CI 7.2, 8.6) per hour to obtain and document patients’ drug lists.Conclusion Emergency department physicians are required to conduct numerous essential tasks and distributes a minor proportion of their time on drug-related tasks. More efficient information flow regarding drugs should be facilitated at transitions of care. The presence of healthcare personnel dedicated to obtaining drug lists in the emergency department should be considered.

ProxyFL: Decentralized Federated Learning through Proxy Model Sharing

Institutions in highly regulated domains such as finance and healthcare often have restrictive rules around data sharing. Federated learning is a distributed learning framework that enables multi-institutional collaborations on decentralized data with improved protection for each collaborator’s data privacy. In this paper, we propose a communication-efficient scheme for decentralized federated learning called ProxyFL, or proxy-based federated learning. Each participant in ProxyFL maintains two models, a private model, and a publicly shared proxy model designed to protect the participant’s privacy. Proxy models allow efficient information exchange among participants using the PushSum method without the need of a centralized server. The proposed method eliminates a significant limitation of canonical federated learning by allowing model heterogeneity; each participant can have a private model with any architecture. Furthermore, our protocol for communication by proxy leads to stronger privacy guarantees using differential privacy analysis. Experiments on popular image datasets, and a pan-cancer diagnostic problem using over 30,000 high-quality gigapixel histology whole slide images, show that ProxyFL can outperform existing alternatives with much less communication overhead and stronger privacy.