Miracle-dz/UniFAR-FTUs huggingface dataset:
TIRF Microscope Image Sequences of Fluorescent IgE-FcεRI Receptor Complexes inside a FcεRI-Centric Synapse in RBL-2H3 Cells

Total internal reflection fluorescence (TIRF) microscope image sequences are commonly used to study receptors in live cells. The dataset presented herein facilitates the study of the IgE-FcεRI receptor signaling complex (IgE-RC) in rat basophilic leukemia (RBL-2H3) cells coming into contact with a supported lipid bilayer with 25 mol% N-dinitrophenyl-aminocaproyl phosphatidylethanolamine, modeling an immunological synapse. TIRF microscopy was used to image IgE-RCs within this FcεRI-centric synapse by loading RBL-2H3 cells with fluorescent anti-dinitrophenyl (anti-DNP) immunoglobulin E (IgE) in suspension for 24 h. Fluorescent anti-DNP IgE (IgE488) concentrations of this suspension increased from 10% to 100% and corresponding non-fluorescent anti-DNP IgE concentrations decreased from 90% to 0%. After the removal of unbound anti-DNP IgE, multiple image sequences were taken for each of these ten conditions. Prior to imaging, anti-DNP IgE-primed RBL-2H3 cells were either kept for a few minutes, for about 30 min, or for about one hour in Hanks buffer. The dataset contains 482 RBL-2H3 model synapse image stacks, dark images to correct for background intensity, and TIRF illumination profile images to correct for non-uniform TIRF illumination. After background subtraction, non-uniform illumination correction, and conversion of pixel units from analog-to-digital units to photo electrons, the average pixel intensity was calculated. The average pixel intensity within FcεRI-centric synapses for all three Hanks buffer conditions increased linearly at a rate of 0.42 ± 0.02 photo electrons per pixel per % IgE488 in suspension. RBL-2H3 cell degranulation was tested by detecting β-hexosaminidase activity. Prolonged RBL-2H3 cell exposure to Hanks buffer inhibited exocytosis in RBL-2H3 cells.
Exploring the uncharted territories of networked control systems : by scavenging for structure in dynamics and communication

• A submitted manuscript is the version of the article upon submission and before peer-review. There can be important differences between the submitted version and the official published version of record. People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website. • The final author version and the galley proof are versions of the publication after peer review. • The final published version features the final layout of the paper including the volume, issue and page numbers.
INVESTIGATION OF THE DYNAMIC LOADING OF A BODY OF PASSENGER CARS DURING TRANSPORTATION BY RAIL FERRY

To ensure the safety of passenger carriages by rail ferries, mathematical modeling of dynamic loading is performed. The accelerations are determined as components of the dynamic load acting on the body of a passenger car. This takes into account the actual hydrometeorological characteristics of the water area of the railway ferry. The calculations are made in relation to the railway ferry "Mukran", which moves the Baltic Sea. The model takes into account that the car body is rigidly fixed relative to the deck and during the oscillations of the railway ferry follows the trajectory of its movement. The solution of the mathematical model is implemented in the Mathcad software environment using the Runge-Kutta method. It is established that the maximum value of the acceleration acting on the car body is 1.8 m/s2. Determination of the dynamic loading of the passenger car body during transportation by sea is also carried out by computer simulation. The calculations were carried out in the CosmosWorks software package using the finite element method. Numerical values and acceleration distribution fields are obtained relative to the carriage body structure of a passenger car. A modal analysis of the car body during transportation by rail ferry is carried out. The numerical values of the critical frequencies and waveforms are obtained. To check the adequacy of the developed models, a calculation is made according to the Fisher criterion. It is established that the hypothesis of adequacy is not rejected. The research will contribute to the creation of recommendations on the safety of passenger carriages by railway ferries, as well as the manufacture of their modern structures in terms of car-building enterprises.
The Idea of Using Bayesian Networks in Forecasting Impact of Traffic-Induced Vibrations Transmitted through the Ground on Residential Buildings

Traffic–induced vibrations may constitute a considerable load to buildings. In this paper, vibrations transmitted through the ground caused by wheeled vehicles are considered. This phenomenon may cause cracking of plaster, cracks in load-bearing elements or even, in extreme cases, collapse of the whole structure. Measurements of vibrations of real structures are costly and laborious. Therefore, the aim of the present paper is to propose a method of using Bayesian networks combined with implementation of geoscience for assessment of impact of traffic–induced vibrations on residential buildings. Firstly, the experimental tests were performed on different buildings using specialized equipment taking into account five factors: Distance from the building to the edge of the road, condition of road surface, condition of the building, the absorption of soil and the type of vehicle. Then, probabilistic analyses applying Bayesian networks were conducted and two methods of assessing the information value (EVSI method and entropy method) were compared. Finally, the developed diagnostic–decision support model was tested, so as to verify the most important parameter, affecting the possibility of structural vibrations to occur. The results of the study clearly showed that the use of Bayesian networks was a very effective approach to assess the impact of traffic-induced vibrations. The developed algorithm could be successfully applied both to existing and planned buildings, for which the source of vibration is already present or may appear in the future.
A Design Process Centric Application of State Space Modeling as a Function of Communications and Cognitive Skills Assessments.

Modern engineering projects rely on three critical components: People – Process – Products. The goal of this paper is to document the development of an objective mathematical framework that is capable of accounting for the inherit communication and technical skill of a design team. This technique will begin to apply rigor and consistency to this traditional, ‘soft science’.
Secure Computing Enclaves Using FPGAs

A new scheme for securing users’ data and applications in public clouds and data centers using Field Programmable Gate Arrays (FPGAs) has been developed. This scheme incorporates all necessary protocols, hardware, and software components to provide protection against many known potential attacks including internal attacks. It achieves perfect forward secrecy, provides FPGA authentication and integrity checks, and securely establishes a symmetric session key between the user and the FPGA. A complete prototype has been implemented to show the feasibility of the proposed scheme with current FPGAs. Experimental results showed that an FPGA-based compute node can be set up in a cloud in 3.36s; 12.6 times faster than booting a medium-size conventional Virtual Machine (VM) on the same cloud. Based on the average global Internet speed, the time it takes to set up the FPGA-based machine from anywhere in the world was estimated to be 15s. Also, running an experimental secure image processing application on the FPGA took 50 percent less time than running the same application on a conventional state-of-the art processor (without a secure container).
Certificate-Based Anonymous Device Access Control Scheme for IoT Environment

As the “Internet communications infrastructure” develops to encircle smart devices, it is very much essential for designing suitable methods for secure communications with these smart devices, in the future Internet of Things (IoT) applications context. Due to wireless communication among the IoT smart devices and the gateway node (GWN), several security threats may arise in the IoT environment, including replay, man-in-the-middle, impersonation, malicious devices deployment, and physical devices capture attacks. In this article, to mitigate such security threats, we design a new certificate-based device access control scheme in IoT environment which is not only secure against mentioned attacks, but it also preserves anonymity property. A detailed security analysis using the widely accepted real-or-random (ROR) model-based formal security analysis, informal security analysis, and also formal security verification based on the broadly accepted automated validation of Internet security protocols and applications (AVISPAs) tool has been performed on the proposed scheme to show that it is secure against various known attacks. In addition, a comprehensive comparative analysis among the proposed scheme and other relevant schemes shows that a better tradeoff among the security and functionality attributes, communication, and computational costs is achieved for the proposed scheme as compared to other schemes.
PENGEMBANGAN KUIS INTERAKTIF BERBASIS E-LEARNING DENGAN MENGGUNAKAN APLIKASI WONDERSHARE QUIZ CREATOR PADA MATA KULIAH BELAJAR DAN PEMBELAJARAN MATEMATIKA

Penelitian ini bertujuan untuk mengembangkan kuis interaktif berbasis e-learning pada mata kuliah Belajar dan Pembelajaran Matematika dengan menggunakan aplikasi Wondershare Quiz Creator . Jenis penelitian ini adalah penelitian pengembangan modifikasi antara Rowntree dan Tessmer. Terdapat 3 tahap dalam penelitian ini yaitu tahap perencanaan, tahap pengembangan, dan tahap evaluasi. Dalam proses pengembangannya, penelitian ini melibatkan ahli sebagai validator kuis interaktif yang dikembangkan. Subjek penelitian ini adalah seluruh mahasiswa yang menempuh mata kuliah Belajar dan Pembelajaran Matematika tahun ajaran 2016/2017. Hasil dari penelitian ini menunjukan bahwa kuis interaktif yang dikembangkan oleh peneliti ini dinyatakan valid oleh para ahli yang menguji 3 aspek yaitu aspek materi, tampilan media serta design pembelajaran; dinyatakan praktis berdasarkan hasil penelitian pada skala satu satu yaitu mengujicobakan produk pada 3 orang siswa yang berkemampuan berbeda dan pada skala kelompok kecil yang berjumlah 6 orang; serta efektif dari segi waktu dan ekonomi berdasarkan hasil uji coba lapangan.
Beamforming Designs Robust to Propagation Model Estimation Errors for Binaural Hearing Aids

This work introduces new binaural beamforming algorithms for hearing aids, with a good robustness to errors in the estimated target source propagation model. Two different binaural beamforming designs are proposed. One design is based on an adaptive null positioning scheme, and the second is based on a combination of the adaptive null positioning scheme and wider beampatterns. Simulations are performed using signals and propagation models obtained from multichannel binaural hearing aids recordings, including some in a mildly reverberant environment. Evaluations are done in terms of noise reduction and target distortion. Evaluation results illustrate the robustness of the two proposed designs to errors between the true and estimated directions of arrival for the target source, and to mismatch between the anechoic propagation models used for the beamformers designs and the reverberant propagation models used to generate the signals at the sensors or beamformer inputs. Both designs surpass the performance of standard binaural Minimum Variance Distortionless Response (MVDR) and binaural Generalized-Side Lobe Canceler (GSC) beamformers.
Enhancement to BGPSEC for Protection against Route Leaks

This document enumerates different types of route leaks based on observed events on the Internet. It illustrates how BGPSEC in its current form (as described in draft-ietf-sidr-bgpsec-protocol-09) already provides protection against all but one of these route-leaks scenarios. The document further discusses a design enhancement to the BGPSEC protocol that will extend protection against this one remaining type of route-leak attack as well. With the inclusion of this enhancement, BGPSEC is expected to provide protection against all types of route-leaks. The document also includes a stopgap method for detection and mitigation of route leaks for the phase when BGPSEC (path validation) is not yet deployed but only origin validation is deployed.
Unsupervised Anomaly Detection Based on Minimum Spanning Tree Approximated Distance Measures and its Application to Hydropower Turbines

Anomalies are data points or a cluster of data points that lie away from the neighboring points or clusters and are inconsistent with the overall pattern of the data. Anomaly detection techniques help distinguish the anomalous observations from the regular ones, and thus provide the basis for developing a standard performance guideline for process control. The process of identifying anomalies becomes complicated in the absence of labeled training data as in supervised learning. Moreover, Euclidean distance between two points is less likely able to reflect the intrinsic structural distance imposed by the underlying manifold structure. In this paper, the authors propose a minimum spanning tree (MST)-based anomaly detection method. The merit of the method is that an MST provides a new distance measure, capable of capturing the relative connectedness of data points/clusters in a complicated manifold, and could be a better (dis)similarity metric, than the simple Euclidean distance, to identify anomalies in unsupervised learning settings. The proposed method is compared with 13 popular anomaly detection methods on 20 benchmark data sets, demonstrating a considerable improvement in its ability of identifying anomalies. Furthermore, the MST-based anomaly detection is applied to the data set from a hydropower turbine and demonstrates remarkable detection competence. Note to Practitioners—This paper is motivated by the problem of unsupervised anomaly detection in a hydropower generation plant, which operates with turbine systems that are instrumented with dozens of sensors. Each turbine has subcomponents or functional areas such as several bearing systems, a generator, and so on. Sensors collect various types of data in real time such as temperature of oil inside the bearing systems, temperature of the bearings, ambient temperature, vibrations in each functional areas, a variety of harmonics in functional areas, temperature of the coil in the generator, and many more. In total, each turbine collects more than 200 attributes from its sensors. The sensor data are then stored in a control system and kept as time stamped historical data points. When a service/maintenance engineer suspects that there is a malfunction in a turbine, she/he extracts a data set from the control system that contains the collected sensor data for that turbine for the selected period of time (few weeks to few months), and then stores this data in a relational databases or simply in a comma separate value (csv) file for further analysis. The objective is to efficiently identify and isolate anomalies in the turbines. Toward this goal, we propose a new solution for tackling this challenging problem, which is an unsupervised method based on the concept of MST. The proposed method can be used as a competitive tool to aid the practitioners in their search of anomalies for making their systems better.
Mobility Modeling, Prediction and Resource Allocation in Wireless Networks

Rapid increase in the number of mobile and wireless users has created greater loads on legacy networks. Also, increased miniaturization of computing devices has led to an influx of handheld gadgets. Mobile users now wish to have seamless roaming across networks and not experience fluctuations in the quality of service. These wireless devices can overwhelm existing networks since these were traditionally not built to handle heavy user mobility across networks. User mobility influences the performance seen by a mobile device in a wireless network. Prior knowledge of mobility patterns can be exploited to properly allocate network resources and enhance the performance and quality of service experienced by a mobile device for applications and services. Hence, mobility prediction plays an important role in the efficient operation of wireless networks such as WANs and WLANs. In this work we propose a probabilistic model to effectively predict user mobility using wireless trace sets using a Hidden Markov Model (HMM). Access to mobility related information such as user movement provides an opportunity for networks to efficiently manage resources to satisfy user needs. Also, mobility models for simulation and their performance analysis are investigated rigorously. Adaptive network resource management based on user mobility can reduce network loads. Towards this goal, we propose a generic methodology based on a control theoretic framework. The feedback based controller effectively uses the predicted mobility to allocate resources effectively for mobile users. Incorporation of this engine in a control theoretic framework with feedback from an adaptive controller permits the efficient allocation of network resources to various applications. The effectiveness of the approach using a prediction engine based on HMM is evaluated through simulation experiments. All simulations use real-world wireless traces. The proposed framework is quite general and the HMM based
Developing a self-learning model based on open-ended questions to increase the students’ creativity in calculus

This article reports on research that is aimed at developing and producing a self-learning model based on open-ended questions or the PMBST (accronym form Indonesian) model to increase students’ creativity in a calculus subject. The main product generated is a book on the PMBST model. It consists of: a) introduction; b) theoretical rationality; c) empirical rationality; d) supporting theories; e) components of the model; f) instructions for the model; and g) bibliography. Furthermore, to assess the quality of this model, the authors used product quality assessment criteria created by Nieveen, which covers hypothetical validity and practicality. The instrument, which is developed for supporting the PMBST model development cover: an assessment sheet of the PMBST model, and a validation format of the PMBST model assessment sheet. The subjects of the PMBST model in this research are Master’s programme students in the Mathematics Department at Universitas Negeri Makassar who have finished or are learning the calculus subject. The result of this research is the PMBST model developed with high theoretical rationality as indicated by experts’ assessments.
Predicting Human Discretion to Adjust Algorithmic Prescription: A Large-Scale Field Experiment in Warehouse Operations

Conventional optimization algorithms that prescribe order packing instructions (which items to pack in which sequence in which box) focus on box volume utilization yet tend to overlook human behavioral deviations. We observe that packing workers at the warehouses of the Alibaba Group deviate from algorithmic prescriptions for 5.8% of packages, and these deviations increase packing time and reduce operational efficiency. We posit two mechanisms and demonstrate that they result in two types of deviations: (1) information deviations stem from workers having more information and in turn better solutions than the algorithm; and (2) complexity deviations result from workers’ aversion, inability, or discretion to precisely implement algorithmic prescriptions. We propose a new “human-centric bin packing algorithm” that anticipates and incorporates human deviations to reduce deviations and improve performance. It predicts when workers are more likely to switch to larger boxes using machine learning techniques and then proactively adjusts the algorithmic prescriptions of those “targeted packages. ” We conducted a large-scale randomized field experiment with the Alibaba Group. Orders were randomly assigned to either the new algorithm (treatment group) or Alibaba’s original algorithm (control group). Our field experiment results show that our new algorithm lowers the rate of switching to larger boxes from 29.5% to 23.8% for targeted packages and reduces the average packing time of targeted packages by 4.5%. This idea of incorporating human deviations to improve optimization algorithms could also be generalized to other processes in logistics and operations. This paper was accepted by Charles Corbett, operations management.
Joint Computation Offloading and Coin Loaning for Blockchain-Empowered Mobile-Edge Computing

The blockchain-empowered mobile-edge computing (MEC) is a promising solution for enhancing the computation capabilities of mobile equipments (MEs) to process computation-intensive tasks such as the real-time data processing tasks and mining tasks. However, because of the “cold start” and “long return” problems, efficient computation offloading cannot be achieved in blockchain-empowered MEC because the MEs do not always have enough coins to afford the offloading service cost. In this article, we study the joint computation-offloading and coin-loaning problem for blockchain-empowered MEC to minimize the total cost of all MEs. We introduce the banks that can provide loan services to the MEs to address the above two issues. We formulate the problem as a noncooperative game to model the competitions between the myopic MEs. By using a potential game method, we prove the existence of a pure-strategy Nash equilibrium (NE) and design a distributed algorithm to achieve the NE point with low computational complexity. We also provide an upper bound on the price of anarchy of the game by theoretical proof. Besides, two smart contracts are designed to automatically perform the computing resource trading and coin loaning processes. Lastly, our simulation results show that our proposed algorithm can significantly reduce the total cost of all MEs, has better performance compared with other solutions, and scales well as the number of MEs increases. Moreover, the financial cost for executing the two smart contracts on the Ethereum network is low.
Nonlinear transient dynamics of on-board rotors supported by Active Magnetic Bearings

Turbomachines are rotating machines enabling the conversion of the different types of energy. The latter are composed of a rotating mechanical part, called rotor, interacting with a fluid. Therefore, rotation play a key role in these machines and the mechanical link between the fixed and the rotating parts, called bearing, is essential for reliable and optimal operations. Turbomachines supported by active magnetic bearings (AMBs) are increasingly used by industrial companies, especially thanks to the absence of direct contact between fixed and rotating parts, enabling energy savings and reduction of CO2 emissions. Most of the time, these machines are "on-board" and are fixed on mobile supports. The motions generated by these supports must be considered in the prediction of the dynamic behaviour of turbomachinery in order to improve the designs accordingly. This PhD is a contribution to the study of turbomachines supported by AMBs subjected to strong external base motions. The approach is numerical and experimental. The use of an academic scale test rig comprising a rotor-AMB system, with the properties of an industrial turbomachine, allowed to test the developed models for cases of external solicitations such as earthquake and shock, thanks to the 6-axis shaker of the equipex PHARE. It is shown that the model provides good predictions of the behaviour of the machine for the tested cases. This tool can therefore be used for industrial designs.