Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling of Soft Robots Actuated by Twisted-and-Coiled Actuators

Soft robots made from soft materials outperform traditional rigid robots in safety, maneuverability, and adaptability. Various methods have been proposed to actuate soft robots such as cables, pneumatic, or smart materials (e.g., shape memory alloys, dielectric elastomer, or ionic polymer-metal composites, etc.). In this paper, we propose to leverage a recently discovered artificial muscle—twisted-and-coiled actuators (TCAs)—to actuate soft robots. Compared with existing actuation methods, TCAs can be actuated electronically, can be embedded inside soft materials to enable distributed actuation, and are easy and low-cost to manufacture. We establish a general modeling framework for TCAs and TCA-actuated soft robots, including a physics-based model for TCAs as well as both the forward and inverse kinetostatics for TCA-actuated soft robots, where a coupling between TCA actuation and deformation of soft robots exists. Extensive experiments are conducted to verify the proposed models. The presented work will not only lay a theoretical foundation for using TCAs to actuate soft robots but also enable wider application for TCA-actuated soft robots (e.g., manipulation or locomotion).
Wireless Transmission of Sensor Data over UWB in Spacecraft Payload Networks

The work presented in this paper consists in the implementation and testing of a Wireless Sensor Network for wirelessly connecting intra-satellite payload sensors to a Payload Data Handling Unit by using a custom ISA100 industrial wireless communication protocol over an Ultra-Wide Band RF communication channel. All tests were performed on a laboratory test bed consisting of three wirelessly enabled camera sensors and a wireless Gateway. The Gateway is connected via RS422 to a Payload Data Handling Unit emulated by a PC application that can generate acquisition commands to the sensors and display the resulting images. Test results indicate the suitability of Ultra-Wide Band and ISA100 wireless protocol combination for implementing wireless communications inside a spacecraft payload network.
Term Extraction for a Single & Multi-word Based on Islamic Corpus English

While a wide range of methods has been conducted to single word English terminology extraction, relatively few studies have been applied to multi-word phrases in Islamic corpus. In this paper, we develop a single & multi-word terms (SMWTS) extraction program for Islamic terms using English language. We argue that this system provides much more detailed information about (SMWTS) meanings than can be obtained using standard lexical lookup procedures of semantic associations among words. the results will show a highly interconnected network of words by labeled with semantic relations such as Part_of, Location, Hypernym, and Purpose.
Efficient Information Sampling in Multi-Category RFID Systems

In RFID-enabled applications, when a tag is put into use and associated with a specific object, the category-related information (e.g., the brands of clothes) about this object might be preloaded into the tag’s memory for the purpose of live query. Since such information reflects category attributes, all tags in the same category carry identical category information. To collect this information, we do not need to repeatedly interrogate each tag; one tag’s response in a category is sufficient. In this paper, we investigate the problem of category information collection in a multi-category RFID system, which is referred to as information sampling. We propose two time-efficiency protocols. The first is a two-phase sampling protocol (TPS) that works in the case of knowing tag IDs. By quickly zooming into a category and isolating a tag from this category, TPS is able to sample a category with small overhead. The second protocol, called back-and-forth sampling protocol (BFS), relaxes a key assumption in TPS and performs the sampling task efficiently without knowing any tag IDs or category IDs. By carrying out a step-forward frame and using the step-backward scheme, BFS is able to interrogate only 1.45 tags (close to the lower bound of one tag) on average for each category. We theoretically analyze the protocol performance of TPS and BFS and discuss the optimal parameter settings that minimize the overall execution time. Extensive simulations show that both the protocols outperform the benchmark, greatly improving the sampling performance.
Wireless power supply for mobile aluminium furnaces

The metal casting industry is continuously pursuing improved energy efficiency of its processes. As a contribution to this effort, this paper describes a new concept of furnaces equipped with wireless power transfer (WPT) technology. The WPT system acts as a versatile power supply for small mobile aluminium transfer furnaces in order to maintain melt temperature during transfer and degassing. 100 kW system design and test results are presented, which were obtained in the frame of the NIWE Project, funded by European Union (FP7, grant agreement No. 296024). Two 50-kW grid-tied converter are implemented as a three-level NPC bridge with NTV modulation. HF converteres (which feeds WPT coils) are a three-leg interleaved half-bridge. Test results have shown high power transfer efficiency of over 92% and confirmed the possibility to apply the experience from wireless charging of electric vehicles to high-power industry applications.
Disease‐structured N‐mixture models: A practical guide to model disease dynamics using count data

Abstract Obtaining inferences on disease dynamics (e.g., host population size, pathogen prevalence, transmission rate, host survival probability) typically requires marking and tracking individuals over time. While multistate mark–recapture models can produce high‐quality inference, these techniques are difficult to employ at large spatial and long temporal scales or in small remnant host populations decimated by virulent pathogens, where low recapture rates may preclude the use of mark–recapture techniques. Recently developed N‐mixture models offer a statistical framework for estimating wildlife disease dynamics from count data. N‐mixture models are a type of state‐space model in which observation error is attributed to failing to detect some individuals when they are present (i.e., false negatives). The analysis approach uses repeated surveys of sites over a period of population closure to estimate detection probability. We review the challenges of modeling disease dynamics and describe how N‐mixture models can be used to estimate common metrics, including pathogen prevalence, transmission, and recovery rates while accounting for imperfect host and pathogen detection. We also offer a perspective on future research directions at the intersection of quantitative and disease ecology, including the estimation of false positives in pathogen presence, spatially explicit disease‐structured N‐mixture models, and the integration of other data types with count data to inform disease dynamics. Managers rely on accurate and precise estimates of disease dynamics to develop strategies to mitigate pathogen impacts on host populations. At a time when pathogens pose one of the greatest threats to biodiversity, statistical methods that lead to robust inferences on host populations are critically needed for rapid, rather than incremental, assessments of the impacts of emerging infectious diseases.
High Step-Up Nonisolated ZVS/ZCS DC–DC Converter for Photovoltaic Thin-Film Module Applications

Photovoltaic (PV) thin-film modules normally present higher voltages at the maximum power points than crystalline silicon ones. This feature makes the usage of low-voltage MOSFETs (≤100 V) unfeasible in high step-up boost-based converters. In order to solve this drawback, this paper proposes a novel high step-up nonisolated dc–dc converter based on the classical synchronous buck converter with coupled inductor and voltage multiplier techniques. Besides reducing the voltage stress on the active switches, the proposed converter also requires a coupled inductor with lower core-window product, allowing the use of a smaller magnetic device. The resonant operation mode of the proposed converter enables zero-voltage-switching turn-on of active switches and zero-current-switching turn-off of diodes, which increases its efficiency. A 150-W prototype, able to work with a large variety of commercial PV thin-film modules, with 55–85-V input and 400-V output voltages, is built to verify the developed analysis. Experimental results show that the maximum obtained efficiency is nearly 98%, and the weighted California Energy Commission efficiencies are greater than 96.7% over the whole input voltage range.
FastFI: Accelerating Software Fault Injections

Software Fault Injection (SFI) is a widely used technique to experimentally assess the dependability of software systems. To provide a comprehensive view on the dependability of a software under test, SFI typically requires large numbers of experiments, which leads to long test latencies. In order to reduce the overall test duration for SFI, we propose FASTFI, which (1) avoids redundant executions of common path prefixes for faults in the same injection location, (2) avoids test executions for faults that do not get activated, and (3) utilizes parallel processors by executing SFI tests concurrently. FASTFI takes patch files that specify source code mutations as an input, conducts an automated source code analysis to identify the function they target, and then automatically parallelizes the execution of all mutants that target the same function. Our evaluation of FASTFI on four PARSEC benchmarks shows a SFI test latency reduction of up to a factor of 26.
Immunomodulatory Functions of Mesenchymal Stem Cells in Tissue Engineering

The inflammatory response to chronic injury affects tissue regeneration and has become an important factor influencing the prognosis of patients. In previous stem cell treatments, it was revealed that stem cells not only have the ability for direct differentiation or regeneration in chronic tissue damage but also have a regulatory effect on the immune microenvironment. Stem cells can regulate the immune microenvironment during tissue repair and provide a good “soil” for tissue regeneration. In the current study, the regulation of immune cells by mesenchymal stem cells (MSCs) in the local tissue microenvironment and the tissue damage repair mechanisms are revealed. The application of the concepts of “seed” and “soil” has opened up new research avenues for regenerative medicine. Tissue engineering (TE) technology has been used in multiple tissues and organs using its biomimetic and cellular cell abilities, and scaffolds are now seen as an important part of building seed cell microenvironments. The effect of tissue engineering techniques on stem cell immune regulation is related to the shape and structure of the scaffold, the preinflammatory microenvironment constructed by the implanted scaffold, and the material selection of the scaffold. In the application of scaffold, stem cell technology has important applications in cartilage, bone, heart, and liver and other research fields. In this review, we separately explore the mechanism of MSCs in different tissue and organs through immunoregulation for tissue regeneration and MSC combined with 3D scaffolds to promote MSC immunoregulation to repair damaged tissues.
Joint Uplink and Downlink Resource Allocation for the Internet of Things

High-efficiency utilization of resources has always been the major concern and design objective in the Internet of Things (IoT). In this paper, an orthogonal frequency division multiplexing-based wireless system model for IoT is proposed, where the simultaneous wireless information and power transfer and the non-orthogonal multiple access (NOMA) are jointly used to improve the energy and spectrum utilization. Specifically, terminals can decode information and harvest energy simultaneously in the downlink and transmit signals to the access point (AP) in the uplink. Then, the AP decodes terminals’ information based on NOMA. A joint uplink and downlink resource allocation scheme is proposed with the optimization objective to maximize the sum information decoding rate in the uplink under the condition that the target sum information decoding rate in the downlink is guaranteed. To solve the optimization problem, a two-step algorithm is proposed. For the first step, a joint subcarrier and power allocation algorithm is designed to maximize the harvested energy of terminals in the downlink. For the second step, a subslot optimization algorithm is designed to increase the sum information decoding rate in the uplink. Simulation results demonstrate that, compared with the conventional algorithms, the proposed algorithm can not only achieve the maximization of harvested energy but also support higher sum information decoding rate in the uplink.
A Multi-MHz IPT-link Developed for Load Characterisation at Highly Variable Coupling Factor

This paper presents the development and characterisation of an inductive link to assess and compare inductive power transfer (IPT) systems that operate at 6.78 or 13.56 MHz. First, the properties of two equal air-core coils were obtained from simulations and corroborated experimentally. Then, the coupling factor between the coils was calculated in function of separation and misalignment. A receiving-end circuit, comprised of a capacitance and a resistive load, was also characterised in order to reflect different loads to the transmitter at different tunings and couplings, and therefore represent the effects produced by changes in coupling and variations in the rectifier’s input impedance. The link was tested, firstly using a Class E inverter and then a load-independent Class EF inverter, both at power levels lower than 200 W. The reflected load was changed by altering coupling, and the tuning capacitance. A comparison between these inverter topologies handling highly reactive loads is shown here for the first time.
Comparison of Electrical and Sensing Properties of Pure, Sn- and Zn-Doped CuO Gas Sensors

In this paper, the evaluation of the gas sensing performance of chemoresistive sensor devices based on M-doped CuO (M = Sn, Zn) nanoparticles prepared by a simple wet chemical method is reported. The morphology of samples has been assessed by scanning electron microscope and transmission electron microscope. The sensors were fabricated, depositing by drop coating the nanoparticles on the sensor substrate platform, and the electrical performance upon exposure to different gases in the air was recorded. The CuO-based gas sensors present a p-type semiconductor behavior. The results of electrical tests have shown that Sn and Zn effectively act as promoters improving the gas sensing properties of CuO toward some volatile organic compounds such as acetone and ethanol. Zn–CuO sensor resulted the most sensitive and selective to acetone, revealing that the appropriate choice of dopant plays a key role in the design of CuO-based sensors.
FireFlow - High Performance Hybrid SDN-Firewalls with OpenFlow

Today, the most widely distributed type of firewalls are software firewalls, running as applications on standard systems. Dedicated networking hardware like SDN switches has also been used to implement firewall functionality, although their native classification capabilities are restricted to relatively simple checks, such as subnet tests. However, native SDN hardware can satisfy high performance requirements that would be challenging for standard software firewalls. This motivates to build a hybrid combination of fast SDN hardware with a standard software firewall. Our approach directly offloads simple rule policies instead of flows to the SDN switch, in order to exploit the limited storage capacity more efficiently. An effective packet diversion algorithm based on header space analysis avoids expensive communication with the back-end. This way, it can achieve the throughput of a native SDN switch while still being able to resort decisions to the full extent of a software firewall. Our evaluation demonstrates a 23-fold classification performance increase over a standard software firewall.
Outdoor Insulator Surface Condition Evaluation using Image Classification

Outdoor high voltage insulators play a key role in power transmission systems. Due to adverse meteorological conditions, the surface of outdoor overhead insulators frequently covered with snow and ice and exposed to mechanical and electrical stress. This condition leads to insulation breakdowns and power transmission line outages. This paper studies the surface of glass insulator covered with snow, ice and wet condition using image processing techniques. Image filtering methods and state-of-the-art classification algorithms are utilized to discriminate the states of insulator surface using real images of high voltage glass insulator. The obtained results showed that four different insulator surface conditions classified correctly with the accuracy of 99%. An Unnamed Aerial Vehicle (UAV) application is proposed as a prospective data acquisition system.
Time-Energy Optimal Trajectory Planning for Variable Stiffness Actuated Robot

A variable stiffness actuator is inspired by the human motor control and is the most popular actuator used to exploit the human performance and human-like motion. However, these actuators are typically highly non-linear and redundant not only in their kinematics but also in their dynamics due to their capability to modulate their stiffness and positions simultaneously. It is not trivial to generate the trajectory for a strongly non-linear and redundant dynamic system equipped with variable stiffness actuators. In this paper, a trajectory planning method for a variable stiffness actuated robot via a time-energy optimal control policy is proposed. The simulation studies demonstrate the effectiveness of the trajectory planning method through the case studies of a two degree of freedom variable stiffness actuated robot. Furthermore, the results show that the proposed method could be able to generate the motion which is similar to the human arm motion strategy.
Detecting and Mitigating Points of Failure in Community Networks: A Graph-Based Approach

A community network (CN) is a bottom-up network created by a community of people with the goal of gaining control of their communications and overcoming digital divide. CNs are blooming, they range from small ones (tens of nodes) to gigantic ones (tens of thousands of nodes). They are made primarily of wireless links but in some cases, they mix wired and wireless technologies. CNs are generally unplanned and nonlayered, and the community tries to mirror the same approach in its governance, avoiding unnecessary management structures and relying on self-organization and spontaneous interactions. CNs are peer production platforms, a community of people that pools resources and contributes to build a shared value. While this value is generally immaterial (as in WikiPedia) CNs instead realize a distributed, peer-to-peer physical communication network. This paper analyses ninux.org, the largest CN in Italy, and one of the eldest in Europe. The goal of this paper is to understand if the spontaneous growth of the network and the community leads to a technically robust network and a socially robust community, or it hides the presence of (potentially interdependent) points of failure. We will show that, in spite of the original motivations of the ninux community, the network is fragile under several aspects, and we suggest ways to improve it.
Large Margin Graph Construction for Semi-Supervised Learning

Graph-based semi-supervised learning (GSSL) has gained increased interests in the last few years. A large number of empirical results show that the performance of GSSL methods heavily depends on the graph construction approach. Although great efforts have been devoted to construct good graphs, it remains challenging to construct a good graph in general situations. To alleviate this problem, this paper presents a novel graph construction approach. Unlike previous approaches that typically optimize a kNN-type loss on the unlabeled data, the proposed approach further enforces that the prediction of unlabeled data has a large margin separation so as to help exclude low-quality graphs. We formulate the problem as an optimization and present an efficient algorithm. Experimental results on benchmark data sets show that the proposed approach has a stronger ability to construct good graphs comparing with several representative graph construction approaches.
MODEL PERILAKU PENCARIAN INFORMASI GUNA MEMENUHI KEBUTUHAN INFORMASI (STUDI LITERATUR)

The existence of information lately so abundant. This is in accordance with the needs of human information increasing. We can get information all over the place. Information can also be a useful information or useless. This is because many people had confusion about how find information as needed. As a seeker of information , must first be aware of the information needs which was continued by the search information stage. There are some characteristic search information according to Ellis, namely starting, chaining, browsing, differentiating, monitoring, extracting, verifying, and ending. Eisenberg dan Berkowitz also introduced stage search information that is famous for its Big Six Skills Model. Levels to begin with the definition of the task, search information strategy, the location and access, the use of information, synthesis, and evaluation. Stage or model search information about can be applied in the search information. Fulfill the need for give complacency , so are fulfill needs our information.
Adapting MobileNets for mobile based upper body pose estimation

Human pose estimation through deep learning has achieved very high accuracy over various difficult poses. However, these are computationally expensive and are often not suitable for mobile based systems. In this paper, we investigate the use of MobileNets, which is well-known to be a light-weight and efficient CNN architecture for mobile and embedded vision applications. We adapt MobileNets for pose estimation inspired by the hourglass network. We introduce a novel split stream architecture at the final two layers of the MobileNets. This approach reduces over-fitting, resulting in improvement in accuracy and reduction in parameter size. We also show that by maintaining part of the original network we are able to improve accuracy by transferring the learned features from ImageNet pre-trained MobileNets. The adapted model is evaluated on the FLIC dataset. Our network out-performed the default MobileNets for pose estimation, as well as achieved performance comparable to the state of the art results while reducing inference time significantly.