Miracle-dz/UniFAR-FTUs huggingface dataset:
A Model Template Green Environment Initiative for Recycling Plastic Bottles with Progressive Entrepreneurship Partnership

Plastic has been widely adopted by the global industry as the most common and adaptable material for marketing their products. Current levels of plastic usage and disposal is one of the biggest environmental challenge that we have to deal with. Collecting and Recycling plastic is one of the most important actions currently used to meet this challenge, but it represents one of the most demanding areas in the plastics industry today. A major percentage of recycled plastic produced each year is used to manufacture disposable packaging items or other short-lived items that are discarded within a year or so. This clearly means that our current use of plastics is not sustainable. In addition, because of the durability of the polymers involved, large amounts of discarded end-of-life plastics are accumulating as debris in landfills and in natural habitats worldwide [1]. Energy efficient plastic recycling process involves a complex system of machines and new manufacturing practices. This paper discusses a promising environmental friendly solution for reducing pollution by recycling plastic bottles. The process of recovering waste plastic and reprocessing into useful products would be implemented in three stages: a) Set up and organize a system for collecting plastics used for water and soda bottles. b) Design and manufacture production line for automatically processing plastic bottles to make plastic ribbon/wire of various sizes. c) Use the plastic ribbon/wire to manufacture prototypes of useful artifacts and products like table, chair, roofing material, decorations, etc. The project has both economic and social value. On the economical side, this will save money by minimizing all expenditure for waste processing related to destroying the plastic waste; the second important contribution of the project is the significant impact on employment creation especially in rural areas; lastly, the recycled plastic is used to make new plastic products with reasonable price for low-income families. On the Social side, this will improve environmental conditions for life; reduce landfill waste; prevent disease-spreading due to recycling plastic; eliminate contamination from burning and melting plastic material and increase public awareness of environmental protection for benefit of humanity. A prototype of the project designed and manufactured in Duy Tan University, Vietnam for cutting bottles into ribbon/wire and connecting them continuously is complete. We will present a pilot test result of our approach to use the plastic ribbon/wire to make new and useful products. A Progressive sustainable economic model of Entrepreneurship Partnership is presented for the developing economies.
Optimization-Oriented Method for Broadband Doherty Power Amplifier Designs Using Support Vector Regression

In this paper, an optimization-oriented method is presented to design a broadband Doherty power amplifier (DPA) using a technique known as support vector regression (SVR). By combining simulation software with a programming environment, the proposed method can be applied to optimize DPA designs automatically. For verification, a 20-W DPA design is optimized from 1.5 to 2.3 GHz. Measured results show that the optimized DPA achieves a 6 dB back-off efficiency of 46–55 % and a saturated efficiency of 48–68 % across the band, with saturated output power varying from 42.6 to 44.30 dBm.
Observability and Autonomous Navigation for Two Satellites Using Relative Position Measurements

Autonomous operation of multi-satellite is an enabling technology for a variety of future space missions. The main purpose of this paper is to investigate the observability of autonomous navigation system of two satellite system using relative position measurements only. K-order local weak observability as a new concept is proposed and introduced into analysis of nonlinear system. Sufficient and necessary conditions of 4, 5 and 6-order local weak observability of the autonomous navigation system are presented and mathematically proved respectively. The relationships between these three conditions as well as the sufficient and necessary condition of local weak observablity for the autonomous navigation system are given and proved. A navigation algorithm using an unscented Kalman filter for simultaneously estimating the two satellite orbits has been designed. Several simulations are shown to verify the result on observability analysis, and to demonstrate that the navigation algorithm can significantly improve the accuracy of the proposed system.
Based on Nonlinear Optimization and Keyframes Dense Mapping Method for RGB-D SLAM System

This paper presents a RGB-D SLAM system, which can simultaneous local every pose of a camera motion and then construct an visualizable pointcloud map for the mobile robots. The system includes four main modules:visual odometry, optimization, loop closing and mapping. With the help of this system, a mobile robot in an unknown environment can acquire a global map and local its own position. In order to satisfy the requirement of high-accuracy location, we have used nonlinear optimization method with Bundle Adjustment based on the local map in optimization. Moreover, a new filter method has been used to remove redundant information points, it will speed up the mapping procedure. Finally we acquire a dense pointcloud map that could help robots finish its own job. The result of our experiment demonstrated the performance of our system in the speed and accuracy under these methods.
$H_{\infty}$ Controller Design of Event-Triggered Networked Control Systems Under Quantization and Denial-of-Service Attacks

This paper focuses on the problem of $H_{\infty}$ controller design of event-triggered networked control systems (NCSs) under quantization and Denial-of-Service attacks. Firstly, a new event-triggering transmission scheme is proposed, in order to lighten the load of computing and communications, while offsetting DoS attacks. A unified model containing event-triggering mechanism, quantization and DoS attacks is established by using time delay modeling approach. Then the sufficient conditions can be obtained for ensuring the exponential stability of the system under quantization and DoS attacks by using the time-varying Lyapunov functional method. The co-design of the event-triggering parameters and the controller parameter of the controller are obtained. Finally a simulation example is used to prove the effectiveness and feasibility of the obtained results.
BRCT domains of the DNA damage checkpoint proteins TOPBP1/Rad4 display distinct specificities for phosphopeptide ligands

TOPBP1 and its fission yeast homologue Rad4, are critical players in a range of DNA replication, repair and damage signalling processes. They are composed of multiple BRCT domains, some of which bind phosphorylated motifs in other proteins. They thus act as multi-point adaptors bringing proteins together into functional combinations, dependent on post-translational modifications downstream of cell cycle and DNA damage signals. We have now structurally and/or biochemically characterised a sufficient number of high-affinity complexes for the conserved N-terminal region of TOPBP1 and Rad4 with diverse phospho-ligands, including human RAD9 and Treslin, and Schizosaccharomyces pombe Crb2 and Sld3, to define the determinants of BRCT domain specificity. We use this to identify and characterise previously unknown phosphorylation-dependent TOPBP1/Rad4-binding motifs in human RHNO1 and the fission yeast homologue of MDC1, Mdb1. These results provide important insights into how multiple BRCT domains within TOPBP1/Rad4 achieve selective and combinatorial binding of their multiple partner proteins. Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).
Allergymap: A Hybrid mHealth Mobile Crowdsensing System for Allergic Diseases Epidemiology : a multidisciplinary case study

Based on its large-scale, ubiquitous sensing ability of the physical world and its direct interaction with users, Mobile Crowd Sensing can be used for public health monitoring and improvement of the quality of life of patients. This paper presents Allergymap, a mHealth mobile crowdsensing system that aims to address several aspects of management of allergic diseases, like identification of allergens season onsets, patient stratification, control of allergy and monitoring treatment progress. Subjective data inputs from users, as well as objective environmental data from fixed stations, are inserted into the system (in a privacy-aware manner) for further processing and analysis, and finally exporting data visualization in distinct spatial maps The envisioned system attempts in a novel way to combine a hybrid architecture for analysis and visualization of allergens and irritants onsets, as well as new methods for increasing patient’s stratification to treatment. Also, some additional features are implemented for motivating users to become members of Allergymap community and enhance its potential. The system is currently deployed and validated in a multidisciplinary case study for Greece but is general enough to be used in any region.
Online Learning to Rank with Features

We introduce a new model for online ranking in which the click probability factors into an examination and attractiveness function and the attractiveness function is a linear function of a feature vector and an unknown parameter. Only relatively mild assumptions are made on the examination function. A novel algorithm for this setup is analysed, showing that the dependence on the number of items is replaced by a dependence on the dimension, allowing the new algorithm to handle a large number of items. When reduced to the orthogonal case, the regret of the algorithm improves on the state-of-the-art.
Strain Dependent Carrier Mobility in 8 − Pmmn Borophene: ab-initio study

Recently two dimensional layer of 8-Pmmn borophene has been successfully fabricated on single crystal Ag(111) substrate and shown to have a tilted anisotropic Diraccone. In this paper we investigate the strain dependent carrier mobility in 8 - Pmmn borophene. We calculate the electronic structure, group velocity and carrier mobility of 8 - Pmmn borophene using density functional theory coupled with Boltzmann transport method using constant relaxation time approximation. Our results show that bandstructure anisotropy results in anisotropic group velocity and mobility. The intrinsic group velocity and mobility at room temperature are calculated to be 2. $17 \times 10^{6ms^{-1}}$, 2. $42 \times 10^{6ms^{-1}}$ and 1. $82 \times 10^{6 cm^{2V^{-1s^{-1}}},2.26\times 10^{6 cm^{2V^{-1s^{-1}}}}}$ in $+x, +y$ direction as well as $1.56\times 10^{6ms^{-1}}, 2.43\times 10^{6ms^{-1}}$ and $9.42\times 10^{5} cm^{2}V^{-1}s^{-1}, 2.29\times 10^{6 cm^{2V^{-1s^{-1}}}}$, in - x, - y directions, respectively. Both of them are found to be highest under 1% compressive strain.
Inter laboratory comparison (ILC) of conducted emission measurements

This paper presents a comprehensive Inter Laboratory Comparison (ILC) of conducted emission measurements as per CISPR 22. The proposed ILC, an important requirement of International Standard ISO/IEC 17025 as part of Quality Assurance is accomplished by utilizing Comparison Noise Emitter (CNE III) source (150 kHz–30 MHz) which is widely used for ILC measurements of EMC Tests facilities. The ILC for five NABL (National Accreditation Board for Testing and Calibration Laboratories) accredited EMC laboratories in India is presented. The measurements data are presented in peak and average detection modes over the frequency range of interest under controlled environment. The measured emission is in the range of 65 dBμV to 80 dBμV. Z-score is calculated for all the laboratories over 0.165 MHz–30MHz range using Classical Method as well as Robust Method. The measurements at all 5 EMC laboratories exhibited a Z-score ≤ 2. This paper intends to report that the performance evaluation of participant Laboratories can be done using either by Classical Method or Robust method. A comparative analysis is presented for all the measured data for both Classical and Robust methods.
A hybrid positioning method for multi-robot simultaneous location and mapping

Navigating in a GPS-disabled environment is one of the basic tasks of autonomous mobile robots. Our research focuses on the topic that using a multi-robot simultaneous location and mapping (SLAM) to detect the surrounding environment. A new multi-robot SLAM framework is proposed. Firstly, each mobile robot is positioned with six degrees of freedom using cooperative identification, and then the data correlation between sub-maps is used to optimize the result of mutual positioning. Finally, sub-maps are merged for a consistent global map. Experimental results show that this method can achieve global map reconstruction, and can get more accurate results than single-robot SLAM.
Dendritic Cell Algorithm with Optimised Parameters Using Genetic Algorithm

Intrusion detection systems are developed with the abilities to discriminate between normal and anomalous traffic behaviours. The core challenge in implementing an intrusion detection systems is to determine and stop anomalous traffic behavior precisely before it causes any adverse effects to the network, information systems, or any other hardware and digital assets which forming or in the cyberspace. Inspired by the biological immune system, Dendritic Cell Algorithm (DCA) is a classification algorithm developed for the purpose of anomaly detection based on the danger theory and the functioning of human immune dendritic cells. In its core operation, DCA uses a weighted sum function to derive the output cumulative values from the input signals. The weights used in this function are either derived empirically from the data or defined by users. Due to this, the algorithm opens the doors for users to specify the weights that may not produce optimal result (often accuracy). This paper proposes a weight optimisation approach implemented using the popular stochastic search tool, genetic algorithm. The approach is validated and evaluated using the KDD99 dataset with promising results generated.
Circularly Polarized Dielectric Rod Waveguide Antenna for Millimeter-Wave Applications

This paper presents a novel wideband circularly polarized dielectric rod waveguide (DRW) antenna for millimeter-wave applications. The rotated tapering rod that consists of a matching taper and a radiation taper is the first time adopted to realize circular polarization in the DRW antenna design. The circular polarization is realized by generating two orthogonal electric fields through rotating the radiation taper to an angle of $\varphi$ relative to the matching taper. The proposed antenna achieves a wide impedance bandwidth throughout the whole Ka-band as well as a wide 3 dB axial ratio bandwidth (ARBW) of 37% from 27.5 to 40 GHz. The main advantages for this antenna design are its wide impedance bandwidth, 3 dB ARBW, simple structure, and easy fabrication.
Consistent trophic amplification of marine biomass declines under climate change

The impact of climate change on the marine food web is highly uncertain. Nonetheless, there is growing consensus that global marine primary production will decline in response to future climate change, largely due to increased stratification reducing the supply of nutrients to the upper ocean. Evidence to date suggests a potential amplification of this response throughout the trophic food web, with more dramatic responses at higher trophic levels. Here we show that trophic amplification of marine biomass declines is a consistent feature of the Coupled Model Intercomparison Project Phase 5 (CMIP5) Earth System Models, across different scenarios of future climate change. Under the business‐as‐usual Representative Concentration Pathway 8.5 (RCP8.5) global mean phytoplankton biomass is projected to decline by 6.1% ± 2.5% over the twenty‐first century, while zooplankton biomass declines by 13.6% ± 3.0%. All models project greater relative declines in zooplankton than phytoplankton, with annual zooplankton biomass anomalies 2.24 ± 1.03 times those of phytoplankton. The low latitude oceans drive the projected trophic amplification of biomass declines, with models exhibiting variable trophic interactions in the mid‐to‐high latitudes and similar relative changes in phytoplankton and zooplankton biomass. Under the assumption that zooplankton biomass is prey limited, an analytical explanation of the trophic amplification that occurs in the low latitudes can be derived from generic plankton differential equations. Using an ocean biogeochemical model, we show that the inclusion of variable C:N:P phytoplankton stoichiometry can substantially increase the trophic amplification of biomass declines in low latitude regions. This additional trophic amplification is driven by enhanced nutrient limitation decreasing phytoplankton N and P content relative to C, hence reducing zooplankton growth efficiency. Given that most current Earth System Models assume that phytoplankton C:N:P stoichiometry is constant, such models are likely to underestimate the extent of negative trophic amplification under projected climate change.
A distributed decision mechanism for controller load balancing based on switch migration in SDN

Software Defined Networking (SDN) provides flexible network management by decoupling control plane from data plane. And multiple controllers are deployed to improve the scalability and reliability of the control plane, which could divide the network into several subdomains with separate controllers. However, such deployment introduces a new problem of controller load imbalance due to the dynamic traffic and the static configuration between switches and controllers. To address this issue, this paper proposes a Distribution Decision Mechanism (DDM) based on switch migration in the multiple subdomains SDN network. Firstly, through collecting network information, it constructs distributed migration decision fields based on the controller load condition. Then we choose the migrating switches according to the selection probability, and the target controllers are determined by integrating three network costs, including data collection, switch migration and controller state synchronization. Finally, we set the migrating countdown to achieve the ordered switch migration. Through verifying several evaluation indexes, results show that the proposed mechanism can achieve controller load balancing with better performance.
To Tender or Not to Tender? Deliberate and Exogenous Sunk Costs in a Public Good Game

In an experimental study, we compare individual willingness to cooperate in a public good game after an initial team contest phase. While players in the treatment setup make a conscious decision on how much to invest in the contest, this decision is exogenously imposed on players in the control setup. As such, both groups of players incur sunk costs and enter the public good game with different wealth levels. Our results indicate that the way these sunk costs have been accrued matters especially for groups on the losing side of the contest: Given the same level of sunk costs, contributions to the public good are lower for groups which failed to be successful in the preceding between-group contest. Furthermore, this detrimental effect is more pronounced for individuals who play a contest with deliberate contributions before.
Deep Generative Network and Regression Network for Fishing Nets Detection in Real-time

The problem that the underwater robot is winded by fishing nets is the most concerned. At present, there are still no effective methods which can detect fishing nets in real time to avoid obstacles. As fishing net images taken with traditional detection technique are blurry and have low definition, it is hard to identify fishing nets. In view of this phenomenon, we proposed a new underwater detection method in this paper and used underwater laser scanning system to collect clear fishing net images. Due to the high cost of data acquisition and the single amount of experiment data, we took advantage of a deep generative network to amplify original data. In fishing nets detection network, we used a residual network as new base net to deepen the network layers, then combined the method of regression with some region suggestion to detect fishing nets, which can improve the accuracy of fishing nets detection without affecting the real-time performance. Finally, the contrast experiment of laser fishing net images is carried out to verify the effectiveness of the proposed method.
A Biased Random-Key Genetic Algorithm for the Rescue Unit Allocation and Scheduling Problem

The occurrence of a disaster brings about damages, destruction, ecological disruption, loss of human life, human suffering, deterioration of health and health service of sufficient magnitude to require external assistance, demanding the mobilization and deployment of emergency rescue units within the affected area, in order to reduce casualties and economic losses. The scheduling of those units is one of the key issues in the emergency response phase and can be seen as a generalization of the unrelated parallel machine scheduling problem with sequence and machine dependent setup. The objective is to minimize the total weighted completion time of the incidents to be attended, where the weight correspond to its severity level. We propose a biased random-key genetic algorithm to tackle this problem, considering fuzzy required processing times for the incidents, and compare the solutions with those generated by a constructive heuristic, from the literature, developed to deal with this problem. Our results show that the genetic algorithm's solutions are 2.17% better than those obtained with the constructive heuristic when applied to instances with up to 40 incidents and 40 rescue units.
P-Type Iterative Learning Control for a Class of Fractional Order Distributed Parameter Switched Systems

This paper presents a P-type iterative learning control (ILC) scheme for a class of fractional order distributed parameter switched systems. First, by analyzing of the control and learning processes, a discrete system for P-type ILC is established and the ILC design problem is then converted to a stabilization problem for such a discrete system. Next, a sufficient condition for the convergence of the tracking errors is obtained, which is less conservative than the existing one. Finally, the validity of the proposed method is verified by a numerical example.
Reliable Loop Closure Detection Using 2-channel Convolutional Neural Networks for Visual SLAM

To improve the reliability of identifying revisited places in cross-session visual SLAM, a reliable loop closure detection method is proposed in this paper. The proposed approach is based on 2-channel convolutional neural network for estimating image-to-image similarity. Transfer learning is employed, which enables a small dataset of specific scenes to be used to fine-tune the pre-trained 2-channel convolutional neural network. The posterior probability of continuous loop closure detection is estimated using Bayes filters with memory management to improve the real-time performance. Experiments on the cross-season and cross-time datasets are also demonstrated to evaluate the performance of the proposed method.