Towards Named Data Networking for Internet of Things Search Engines

The advance of internet of things (IoT) techniques enables a variety of smart-world systems in energy, transportation, home, and city infrastructure, among others. To provide cost-effective data-oriented service, internet of things search engines (IoTSE) have received growing attention as a platform to support efficient data analytics. There are a number of challenges in designing efficient and intelligent IoTSE. In this chapter, the authors focus on the efficiency issue of IoTSE and design the named data networking (NDN)-based approach for IoTSE. To be specific, they first design a simple simulation environment to compare the IP-based network's performance against named data networking (NDN). They then create four scenarios tailored to study the approach's resilience to address network issues and scalability with the growing number of queries in IoTSE. They implement the four scenarios using ns-3 and carry out extensive performance evaluation to determine the efficacy of the approach concerning network resilience and scalability. They also discuss some remaining issues that need further research.

Trombe Walls – Characteristic, Overview and Simple Case Study for Different Climate Conditions

Currently, when we spend a significant part of the day indoors, paying attention to indoor air quality and thermal comfort rise to prominence. Sometimes, improving these issues could be really simple and possible by using passive solar systems like Trombe walls. Because the implementation of solar walls is still problematic due to numerous barriers connecting with a system management or effectiveness in summer or winter period, many of researchers try to find the solutions, which could optimize them. This paper characterizes the main issues of Trombe walls, presents the current state of research on solar walls and provides a simple simulation of a building with a Trombe wall performed in TRNSYS software, for 3 variants of the system and 4 locations with different climatic conditions. It was estimated that system with Trombe wall and control strategies allows the building to maintain thermal comfort for more than 20% of the year, but effectiveness of Trombe walls depends largely on the climatic conditions and they should be considered only as an auxiliary support for HVAC systems.

On the use of neural networks for energy reconstruction in high-granularity calorimeters

We contrasted the performance of deep neural networks — Convolutional Neural Network (CNN) and Graph Neural Network (GNN) — to current state of the art energy regression methods in a finely 3D-segmented calorimeter simulated by GEANT4. This comparative benchmark gives us some insight to assess the particular latent signals neural network methods exploit to achieve superior resolution. A CNN trained solely on a pure sample of pions achieved substantial improvement in the energy resolution for both single pions and jets over the conventional approaches. It maintained good performance for electron and photon reconstruction. We also used the Graph Neural Network (GNN) with edge convolution to assess the importance of timing information in the shower development for improved energy reconstruction. We implement a simple simulation based correction to the energy sum derived from the fraction of energy deposited in the electromagnetic shower component. This serves as an approximate dual-readout analogue for our benchmark comparison. Although this study does not include the simulation of detector effects, such as electronic noise, the margin of improvement seems robust enough to suggest these benefits will endure in real-world application. We also find reason to infer that the CNN/GNN methods leverage latent features that concur with our current understanding of the physics of calorimeter measurement.

An Easily Installed Method of the Estimation of Soybean Yield Based on Meteorological Environments with Regression Analysis

A simple method for estimating soybean yield under ideal environments in Japan is proposed. Several models that simulate soybean yield have been proposed in other countries; however, direct adaptation to Japanese species is difficult in terms of climatic and regional characteristics. In addition, they often require variety-specific information or various environmental information, which is sometimes hard to simulate. Therefore, we attempted to create a simple simulation model with meteorological data as the main input to the model. The proposed model ignores the features that need setting for each cultivated field and is composed of a statistical model instead of a physiological analysis for the sake of brevity. Although the prediction accuracy of the model needs to be improved, we can use it as a decision support system for soybean cultivation because it requires only location information and can be easily introduced by many farmers.

Human-Plant Coevolution: A modelling framework for theory-building on the origins of agriculture

The domestication of plants and the origin of agricultural societies has been the focus of much theoretical discussion on why, how, when, and where these happened. The 'when' and 'where' have been substantially addressed by bioarchaeology, thanks to advances in methodology and the broadening of the geographical and chronological scope of evidence. However, the 'why' and 'how' have lagged behind, holding on to relatively old models with limited explanatory power. Armed with the evidence now available, we can return to theory by revisiting the mechanisms allegedly involved, disentangling their connection to the diversity of trajectories, and identifying the weight and role of the parameters involved. We present the Human-Plant Coevolution (HPC) model, which represents the dynamics of coevolution between a human and a plant population. The model consists of an ecological positive feedback system (mutualism), which can be reinforced by positive evolutionary feedback (coevolution). The model formulation is the result of wiring together relatively simple simulation models of population ecology and evolution, through a computational implementation in R. The HPC model captures a variety of potential scenarios, though which conditions are linked to the degree and timing of population change and the intensity of selective pressures. Our results confirm that the possible trajectories leading to neolithisation are diverse and involve multiple factors. However, simulations also show how some of those factors are entangled, what are their effects on human and plant populations under different conditions, and what might be the main causes fostering agriculture and domestication.

The Intriguing Structure of Stripes in GRACE Geopotential Models

Geopotential models derived from Gravity Recovery and Climate Experiment (GRACE) mission measurements are significantly obscured by the presence of a systematic artifact, known as longitudinal stripes. Based on our previous work (Peidou and Pagiatakis, 2020) we provide an in-depth analysis of the latitudinal sampling characteristics of GRACE and we reveal the intriguing sampling mechanism that creates sub-Nyquist artifacts (stripes). Because the sub-Nyquist artifacts are poorly understood, we provide a simple simulation example to elucidate the mechanism of the sub-Nyquist artifact generation. Subsequently, we randomly select June 2009 daily GPS precise science orbits for GRACE-A to produce ground tracks to sample the low frequency disturbing potential (geoid) along the parallel of ϕ=10° N. The sampled geoid is then deinterlaced in space to produce a monthly data sequence whose detailed analysis shows that the sub-Nyquist artifacts (stripes) are produced from a critical sampling rate of the low degree gravitational field that is related to the ratio m/n of two mutually prime integers, where m is the number of days it takes to have a nearly repeat orbit and n is the number of complete orbits in one day. We perform extensive analyses of GRACE Level-2 data over a period of eight years to show the variability in the orbital characteristics that are directly linked to the orbit resonances (via integers m and n). It turns out that during short repeat cycle resonances the stripes are amplified. Finally, to minimize the presence of stripes in Level-2 data products, it is recommended that orbits of future missions should be designed to avoid the critical m/n ratios while appropriately monitoring and adjusting them during the mission. For completed missions, or missions that are already active, force modelling the latitudinal low frequency disturbing potential may be a viable and most preferred approach to filtering.

The Role of Chance in the Census Bureau Database Reconstruction Experiment

The Census Bureau plans a new approach to disclosure control for the 2020 census that will add noise to every statistic the agency produces for places below the state level. The Bureau argues the new approach is needed because the confidentiality of census responses is threatened by “database reconstruction,” a technique for inferring individual-level responses from tabular data. The Census Bureau constructed hypothetical individual-level census responses from public 2010 tabular data and matched them to internal census records and to outside sources. The Census Bureau did not compare these results to a null model to demonstrate that their success in matching would not be expected by chance. This is analogous to conducting a clinical trial without a control group. We implement a simple simulation to assess how many matches would be expected by chance. We demonstrate that most matches reported by the Census Bureau experiment would be expected randomly. To extend the metaphor of the clinical trial, the treatment and the placebo produced similar outcomes. The database reconstruction experiment therefore fails to demonstrate a credible threat to confidentiality.

Simulating the Effect of Friction on Drive Screw Using System-of-System Modeling with Predetermined Torque

In most mechanical systems, screw threads serve three main basic purposes: (i) to transmit power, (ii) to provide a clamping force, and finally (iii) to restrict or control motion. This chapter demonstrates the effects of friction and behavior which can occur in a bolted fastening (screw thread) for advanced design purposes. To model this behavior, other control components are attached to the bolted screw. The bolt preload is applied with a predetermined torque. For this case the preload depends on the friction under the head and in the thread. The friction prevents the loosing of the bolted fastening. This effect is termed as self-locking effect. We designed an algorithm that reproduces an exemplary simulation scenario, which determines friction and its effect on thread angle based on the strength of the coefficient of friction at a specific tension or clamp load value using the system-of-system approach. The result shows specific behavior on both the motion in threads and drive screw with predetermined torque. The chapter is limited to creating a simple simulation environment to demonstrate the effects.