TANGLED: A Cooperative Anycast Testbed

Lifelong learners are people who display an attitude and ability that prompts them to learn across their life spans. Even in the testing environment of United States schools today, a goal of library media specialists is to help children become lifelong learners. The Self-Determination Theory (SDT, Deci & Ryan, 1985) provides a framework for examining a positive disposition toward learning, or the motivation to learn: the key attribute of a lifelong learner. Additional areas explored include contributions of school library research toward an understanding of student motivation, suggestions for further areas of study, and implications for school libraries.

A survey on consumers empowerment, communication technologies, and renewable generation penetration within Smart Grid

Further reducing greenhouse gas and pollutant emissions from road vehicles is a major task for the automotive industry. Stricter regulations regarding emissions and fleet fuel consumption require the continuous development of new powertrains and methods. In particular, the combination of hybrid powertrains on the technical side and the focus on real driving emissions (RDE) on the legislative side pose significant challenges to the vehicle calibration process. Against this background, new test methods and environments are being investigated to counteract the high number of interactions between hybrid drive systems and quasi-infinite test conditions due to RDE. Complementary to new test environments, innovative methods for data analysis are needed that allow the exploitation of the complete potential of measurement data. The application of such a method in the field of emission calibration is presented in this paper. For this purpose, a clustering method (HDBSCAN) is applied to critical sequences from emission tests. Within this presentation, the clustering process is based on a single signal only. This paper shows how signals of various characteristics can be processed with dynamic time warping and generically structured with the clustering method used. Here, 959 single events are automatically categorized into 24 clusters. This provides a new basis for system evaluation, enabling the automatic identification, categorization, and prioritization of calibration weaknesses. Using twelve signals of different characteristics, the generic usability of the clustering method is demonstrated.

Collaboration in distributed settings has become a reality in organizational life, yet we still have much to learn. One important area of study is the integration of Collaboration Engineering (CE) in distributed, or virtual, teams. Collaboration Engineering offers promising guidelines for process structure, but its application in distributed environments is just beginning to be studied. We conducted a study in the design science tradition with the goal of examining whether and how the principles and techniques of Collaboration Engineering can be taken into a distributed setting. We report on the design, development, and feasibility test of a prototype environment that implements CE techniques for distributed teams. The study examined leadership and process structure effects on the development of shared understanding in student teams working in a simulated organizational environment. Content analysis of qualitative data was combined with descriptive statistics of quantitative data to gain insight into participants' activities. We discuss the challenges of Collaboration Engineering in distributed environments and offer lessons learned and opportunities for future research.

<abstract> <p>In this article, I discuss the problem of automatic detection of coarse measurements (outliers) in the time series of measurement data generated by technical devices. Solving this problem is of great importance to improve the accuracy of estimates of various physical quantities obtained in solving many applications in which the input data is observations. Since outliers adversely affect the accuracy of final results, they must be detected and removed from further calculations at the stage of data preprocessing and analysis. This can be done in various ways, since the concept of outliers does not have a strict definition in statistics. The author of the article previously formulated the problem of finding the optimal solution that satisfies the condition of maximizing the amount of measuring data that remained after removal of outliers and proposed a robust algorithm for finding such a solution. The complexity of this algorithm is estimated of the order of magnitude $ (N+{N}_{out}^{2}) $, where N is the number of source data and N<sub>out</sub> is the number of outliers detected. For highly noisy data, the number of outliers can be extremely large, for example, comparable to N. In this case, it will take about N<sup>2</sup> arithmetic operations to find the optimal solution using the algorithm developed earlier. I propose a new algorithm for finding the optimal solution, requiring the order of NlogN arithmetic operations, regardless of the number of outliers detected. The efficiency of the algorithm is manifested when cleaning from outliers large amounts of highly noisy measuring data containing a great many of outliers. The algorithm can be used for automated cleaning from outliers of observation data in information and measuring systems, in systems with artificial intelligence, as well as when solving various scientific, applied managerial and other problems using modern computer systems in order to obtain promptly the most accurate final result.</p> </abstract>

Water pollution o by agricultural and human activities has become a hot topic that must be taken into account. Its qualitative and quantitative evaluation has been well demonstrated by the SWAT model. The proposed research is an important step in the study of the agricultural and human pollution, in the various tributaries of Sidi Salem dam. We envisaged the use of the tool SWAT, for one of the main polluting tributaries, Wadi Beja, and planned a qualitative management of this watershed. The application of this model requires a prior study of the hydrological basin of interest and the calibration of a large number of intrinsic factors specific to the area of study. This model has been mainly tested on several watersheds in the Nordic countries, including Canada and France because the model contains a module specific to the glacial melt. Its adaptation to the Mediterranean context has rarely been carried out. In the case of Tunisia, the absence of a long ongoing series of measurement data and hydrodynamics of the soil hampers the application of the SWAT model. In a first step, we adapted this model to the specific context of the study area, in order to understand the hydrological functioning of this watershed. Subsequently, we studied the sensitivity of the model according to the key hydrological parameters, by performing the continuous simulation for more than 8 years. This allowed us to have a better choice of the shimming parameters, in order to properly shim the model. In a second step, we refined the model calibration settings according to 2 years of deep study: wet and dry, though opting for a seasonal cutting for the sake of better describing the pedoclimatic context of the study area. This approach should have a good match of the simulated and observed data, as confirmed by four accuracy measures. In addition, it has been shown that the contents of nitrate and orthophosphate were of concern because of the poor use of agricultural fertilizers. Of this fact, various scenarios of management have been developed and tested by the model in order to reduce the percolation rate of various forms of nitrogen and t phosphorus leaching. These approaches would enable policymakers to monitor the water pollution in the dam area while reducing the burden of various eutrophying elements.

This article discusses the problem of trend detection in time series generated by technical devices. The solution to this problem is closely related to the problem of detecting coarse measurements (outliers), which negatively impact the accuracy of estimates of various physical quantities. These are crucial in many applications in various scientific fields in which the input data are observations, such as space geodynamics, geodesy, and others. Previously, the author proposed a trend-detecting method based on the condition of maximizing the amount of data cleared of outliers and used in further processing. The reference values used for trend construction are determined as a result of a completely convergent iterative process, the core of which is the minimizing sets method developed earlier by the author. This paper deals with the aspects of trend construction in the class of harmonic functions with unknown frequencies, phases and amplitudes.The main problem of trend construction in such a functional class is the nonlinear dependence of harmonics on the desired parameters, which does not allow to reduce the problem of trend search to the solution of a system of linear equations. The search for harmonics approximating the measurement data is carried out by the conjugate gradients method generalized to nonlinear problems. The efficiency of the method was tested on the test problem of trend construction in the data obtained by computer simulation.

The development, testing, and calibration of a new dust sensor require a controlled dust generating environment. A low cost, safe, and easily customizable dust generating system that uses only COTS (commercial off-the-shelf) parts was developed as an integral part of a feedlot dust study. The developed system is a dust generation and sampling chamber for testing wireless nephelometers. The system can generate suspended dust/air mixture with a high degree of density variations. The testing sensors are placed inside the sampling chamber from which the measurement data is wirelessly transmitted to a recording console, providing a safe testing environment. This system uses a kitchen blender to generate dust and controls the concentration through an adjustable dust feedline, an intake airflow valve, and vacuum control valve. A shop vacuum controls the airflow rate through the dust chamber and collects the dust onto a filter. The objective of this project was to develop low-cost, miniaturized, wireless nephelometers to study PM10 concentration of a feedlot under various environmental and weather conditions. The apparatus, theory of operation, construction steps, and experimental results of the dust chamber and wireless nephelometers are presented.

The Global Navigation Satellite Systems (GNSS) becomes the primary choice for device localization in outdoor situations. At the same time, many applications do not require precise absolute Earth coordinates, but instead, inferring the geometric configuration information of the constituent nodes in the system by relative positioning. The Real-Time Kinematic (RTK) technique shows its efficiency and accuracy in calculating the relative position. However, when the cycle slips occur, the RTK method may take a long time to obtain a fixed ambiguity value, and the positioning result will be a “float” solution with a low meter accuracy. The novel method presented in this paper is based on the Relative GNSS Tracking Algorithm (Regtrack). It calculates the changes in the relative baseline between two receivers without an ambiguity estimation. The dead reckoning method is used to give out the relative baseline solution while a parallel running Extended Kalman Filter (EKF) method reinitiates the relative baseline when too many validation failures happen. We conducted both static and kinematic tests to assess the performance of the new methodology. The experimental results show that the proposed strategy can give accurate millimeter-scale solutions of relative motion vectors in adjacent two epochs. The relative baseline solution can be sub-decimeter level with or without the base station is holding static. In the meantime, when the initial tracking point and base station coordinates are precisely obtained, the tracking result error can be only 40 cm away from the ground truth after a 25 min drive test in an urban environment. The efficiency test shows that the proposed method can be a real-time method, the time that calculates one epoch of measurement data is no more than 80 ms and is less than 10 ms for best results. The novel method can be used as a more robust and accurate ambiguity free tracking approach for outdoor applications.

This work describes an approach for strain determination at the “in-plane” torsional test using digital image correlation (DIC) without brushing a statistical pattern on the specimen. It is well known that the in-plane torsion test represents a appropriate test method for material characterization of sheet metal in terms of yielding and kinematic hardening (Wagner et al. in Application of the in-plane torsion test in an industrial environment—recent advances and remaining challenges. In: Conference Paper of the 12th Forming Technology Forum, Herrsching, Germany, 2019). However, the gained measurement data do not allow the exact reading of material models for direct use in numerical analysis. The determination of shear values represents thereby the main challenge. Besides well-established methods for stress–strain analysis mentioned in this contribution, the applicability of DIC methods for the direct reading of distortion on the specimen has been investigated. Therefore, a torsion test rig has been developed and torsion tests using specimen out of mild- and high-strength steel have been conducted. The in-plane situation allows the distortion tracking of single points on the specimen by using a 2D image correlation software. The needed pictures were taken by using a common digital lens reflex camera. It has been shown that the resolution of the inherent specimen pattern is sufficient for the use of common 2D image correlation software. Torsional test results in the form of computed yield curves of mild- and advanced high-strength steels are aimed to be presented in this contribution.

Digital twin for human-robot collaboration enhancement in manufacturing systems: Literature review and direction for future developments

During the development of state-of-the-art driver assistance systems and highly autonomous driving functions, there is a demand for reliable research vehicle platforms that can be used in a variety of applications. Especially for data-driven machine learning approaches, a large amount of measurement data obtained from multimodal sensors is needed. This paper presents a Robot Operating System (ROS) based prototype vehicle that is built on a Porsche Cayenne, which provides a dedicated test environment for autonomous research. To bridge the gap between pure research and actual production vehicles, the platform features near-series placement of sensors and the use of the built-in camera and actuators. Open-source packages and a containerized software architecture make the system reusable and easy to extend in terms of hardware and algorithms. Furthermore, we describe our approach for data recording and long-term persistence.

In modern times, the collaboration between humans and machines increasingly rises, combining their respective benefits. The direct physical support causes interaction forces in human–machine interfaces, whereas their form determines both the effectiveness and comfort of the collaboration. However, their correct detection requires various sensor characteristics and remains challenging. Thus, this paper presents a developed low-cost sensor pad working with a silicone capsule and a piezoresistive pressure sensor. Its measurement accuracy is validated in both an isolated testing environment and a laboratory study with four test subjects (gender-balanced), and an application integrated in interfaces of an active upper-body exoskeleton. In the material-testing machine, it becomes apparent that the sensor pad generally features the capability of reliably determining normal forces on its surface until a certain threshold. This is also proven in the real application, where the measurement data of three sensor pads spatially embedded in the exoskeletal interface are compared to the data of an installed multi-axis load cell and a high-resolution flexible pressure map. Here, the consideration of three sensor pads potentially enables detection of exoskeletal support on the upper arm as well as “poor” fit conditions such as uneven pressure distributions that recommend immediate system adjustments for ergonomic improvements.

The precise management of time-dependent environmental data connected to soil moisture alongside temperature and humidity serves as a key component of precision agriculture systems. The research develops an approach to analyze wireless sensor network data obtained across multiple sensors deployed in a smart agriculture testing environment. The dataset was collected over five hours by eight sensor nodes while enriching the information with various environmental conditions specific to irrigation periods. Experimental data were divided into four successive phases that corresponded to different operational periods: pre-watering and immediate post-watering and short-term recovery and stabilization. The analysis system combines matrix-based statistics along with mean and standard deviation methods alongside visualization methods to reveal behavioral patterns with their associated variabilities. The confidence band defined by ±1 presented uncertainty levels in environmental measurement data. The collected sensor data maintained predictable behavior during irrigation by demonstrating both increasingly wet soil and higher humidity and momentary temperature reduction. A multi-part visual design successfully measured the active response patterns throughout all measurement channels. The research adds value to smart farming through its development of a statistical framework which examines sensor temporal patterns. The developed methodology improves environmental decision-making interpretability thereby enabling real-time irrigation control and enhanced resource management.

When dealing with plastics producing techniques such as injection molding and extrusion control of pressure is a decisive parameter influencing the quality and reproducibility of plastics products. Different types of pressure sensors are commonly used which most often transform mechanical deformation into electrical signals and these in turn are converted into the value of pressure. In some cases separate pressure chambers might be necessary, often the pressure range is limited, and usage of electrical components lead to constraints concerning temperature range. This in turn means extensive effort for installation and insulation of the pressure sensors for example when you want to control the pressure at any desired area of the injection system. This work is based on an approach of using a flexible element and determining the pressure solely by its deformation. This eliminates thermal insulation and electromagnetic shielding. But concurrently this demands some knowledge concerning the mechanical behavior of the flexible element. To determine the mechanical behavior of the flexible element especially its stiffness different approaches have been taken into account. First one is to regard the flexible element as Kirchhoff plate. This analytic approach implies a constant bending stiffness which means a linear dependency between working pressure and resulting deflection. This could be confirmed evaluating measurement data that has been taken using a test rig specially designed for that task. In the test rig as substitution of the pressure a variable but defined load can be applied. When carrying out some measurements with one certain flexible element the influence of assembling and disassembling the test rig was obvious. Only when not changing the assembled test rig measurements have been reproducible. Main reason was the radial variance of applied load and the center of the flexible element. In order to solve that a test rig was redesigned including a centering device to guarantee load transmission happening always at the same point. Additionally influence of certain linkages in the test rig has been investigated. Combination of both leads to a more reliable and better test rig with higher reproducibility.