Miracle-dz/UniFAR-FTUs huggingface dataset:
LayerPump: Rapid Prototyping of Functional 3D Objects with Built-in Electrohydrodynamics Pumps Based on Layered Plates

The development and widespread use of digital fabrication techniques has accelerated the fabrication of three-dimensional (3D) objects, enabling not only to design their shapes but also to customize their internal structures and materials to realize various functions. In this research we propose a novel design method to embed internal pumps and liquid paths inside 3D objects. We utilized a pump driven by electrohydrodynamics (EHD) of fluid migrating in response to an applied electric field. To arrange the EHD pumps and fluid paths in appropriate positions inside 3D objects, we stacked layered acrylic plates cut with digital fabrication tools such as laser cutters and cutting plotters. Using these embedded pumps, we can control the flow of fluid to change color, shape, and motion of the objects. In this paper, we detailed our fabrication techniques named LayerPump, and demonstrated its design space and application scenarios.
Plasma Metabolic Profiling of Pediatric Sepsis in a Chinese Cohort

Sepsis represents one of the most pressing problems in pediatrics, characterized by pathogenic bacteria invading the blood, growing and multiplying in the blood circulation, and ultimately causing severe infections. Most children with sepsis have a rapid disease onset and frequently exhibit sudden high fever or first chills. Here we performed comprehensive metabolomic profiling of plasma samples collected from pediatric sepsis patients to identify specific metabolic alterations associated with these patients (n = 84, designated as case subjects) as compared to healthy cohorts (n = 59, designated as control subjects). Diagnostic models were constructed using MetaboAnalyst, R packages, and multiple statistical methods, such as orthogonal partial least squares-discriminant analysis, principal component analysis, volcano plotting, and one-way ANOVA. Our study revealed a panel of metabolites responsible for the discrimination between case and control subjects with a high predictive value of prognosis. Moreover, significantly altered metabolites in sepsis survivors versus deceased patients (non-survivors) were identified as those involved in amino acids, fatty acids, and carbohydrates metabolism. Nine metabolites including organic acids and fatty acids were also identified with significantly higher abundance in sepsis patients with related microbes, implicating greater potentials to distinguish bacterial species using metabolomic analysis than blood culture. Pathway enrichment analysis further revealed that fatty acid metabolism might play an important role in the pathogenesis of sepsis.
Worldlikeness: a Web crowdsourcing platform for typological psycholinguistics

Abstract Worldlikeness, hosted online at https://worldlikeness.org, is a free Web crowdsourcing experimental tool and database that seeks to make it easier to generate and share cross-linguistic processing data. Adopting HTML5 technology and a responsive Web design using the JavaScript-based Meteor package, Worldlikeness supports the use of text and multimedia stimuli in a user interface optimized for participants using either desktops or mobile devices. Worldlikeness allows individual psycholinguists to collect data on individual languages, store the data in theWorldlikeness server, and share them to help build a cross-linguistic database. As the database grows, outside researchers can freely conduct their own typological cross-linguistic analyses and contribute to our understanding of language-specific and universal properties of language processing. In this paper, we introduce the key features of Worldlikeness and compare online wordlikeness judgments to in-lab data to demonstrate its reliability.
Real-Time Cost Minimization of Fog Computing in Mobile-Base-Station Networked Disaster Areas

The use of big data has led to many technologies that were previously thought to be impossible. We are now able to analyze the spread of a disaster automatically through the use of social networking analysis, which is effectively served by internet or cloud services. One problem with using such algorithms in these cases is that internet services and connections to the cloud can often be damaged. In order to combat this issue, mobile base stations can be deployed, allowing for an emergency internet network to be used until the landlines can be repaired. These emergency networks have limitations in speed and cost, but seem to be the most promising technology for the future. Forwarding all of the data through the network results in the lowest cost but yields a large amount of data overflow, forcing the system to cache data, thus increasing the delay. Fully processing data in the edge resources results in a higher cost. A genetic algorithm was used to find the ideal balance between processing and sending the data, which allowed for the most data to be transmitted without causing data overflow. Results show that the proposed algorithm closely matched the results of the genetic algorithm, while being executable with minimal clock cycles.
A Low-Cost Light-Weight Ultrawideband Wide-Angle Scanning Tightly Coupled Dipole Array Loaded With Multilayer Metallic Strips

This article presents a new ultrawideband wide-angle scanning tightly coupled dipole array loaded with multilayer metallic strips. The loaded multilayer metallic strips, which is composed of several narrow strips with different lengths, are used to equivalent the metal patch which has a specific shape. It can help achieve smoother impedance transform from the dipole to the free space. An equivalent circuit model (ECM) of the unit cell is proposed to help analyze and design the tightly coupled dipole array. Thanks to the loaded multilayer metallic strips, the array can be fed by a simple feeding structure, avoiding a matching network located below the ground plane. Also, all components of the proposed array are printed on a single dielectric substrate, resulting in a low-cost and light-weight array. The proposed tightly coupled dipole array can achieve 6.2:1 bandwidth with voltage stand wave ratio (VSWR) < 3 while scanning to ±60° in all planes. The total profile of the array is 0.13 wavelength at 1.4 GHz. Finally, an $8\times 8$ prototype is fabricated and measured. The measured results prove the feasibility of the design.
Towards Sustainable Energy Efficiency With Intelligent Electricity Theft Detection in Smart Grids Emphasising Enhanced Neural Networks

In smart grids, electricity theft is the most significant challenge. It cannot be identified easily since existing methods are dependent on specific devices. Also, the methods lack in extracting meaningful information from high-dimensional electricity consumption data and increase the false positive rate that limit their performance. Moreover, imbalanced data is a hurdle in accurate electricity theft detection (ETD) using data driven methods. To address this problem, sampling techniques are used in the literature. However, the traditional sampling techniques generate insufficient and unrealistic data that degrade the ETD rate. In this work, two novel ETD models are developed. A hybrid sampling approach, i.e., synthetic minority oversampling technique with edited nearest neighbor, is introduced in the first model. Furthermore, AlexNet is used for dimensionality reduction and extracting useful information from electricity consumption data. Finally, a light gradient boosting model is used for classification purpose. In the second model, conditional wasserstein generative adversarial network with gradient penalty is used to capture the real distribution of the electricity consumption data. It is constructed by adding auxiliary provisional information to generate more realistic data for the minority class. Moreover, GoogLeNet architecture is employed to reduce the dataset’s dimensionality. Finally, adaptive boosting is used for classification of honest and suspicious consumers. Both models are trained and tested using real power consumption data provided by state grid corporation of China. The proposed models’ performance is evaluated using different performance metrics like precision, recall, accuracy, F1-score, etc. The simulation results prove that the proposed models outperform the existing techniques, such as support vector machine, extreme gradient boosting, convolution neural network, etc., in terms of efficient ETD.
Classification and forecasting of real-time server traffic flows employing long short-term memory for hybrid E/O data center networks

Long short-term memory neural networks demonstrate a classification accuracy larger than 99% for highly variable and bursty, real-time server traffic flows. Their performance in terms of forecasting precision displays promising results, both for one-step as well as multi-step predictions. These capabilities make the a priori detection of heavy data streams possible, thus enabling the employment of optical circuit switching.
A Service Redundancy Strategy and Ant Colony Optimization Algorithm for Multiservice Fog Nodes

Fog computing has been widely deployed at the edge of the network to provide instant computing services for terminal devices to alleviate the transmission problems from the cloud to the terminal. However, when the fog node's service is single, it will increase latency and power consumption when the fog node processes data, affecting the reliability of fog computing. We propose a fog node service redundancy strategy based on ensuring fog computing reliability, minimizing optimization latency and power consumption. Simultaneously, based on this strategy, an ant colony optimization algorithm is proposed, which realizes the collaborative optimization between latency and power consumption. Furthermore, the experimental results show that our proposed strategy and algorithm have lower power consumption, efficient energy-saving and higher reliability than before.
Reconstruction Bias U-Net for Road Extraction From Optical Remote Sensing Images

Automatic road extraction from remote sensing images plays an important role for navigation, intelligent transportation, and road network update, etc. Convolutional neural network (CNN)-based methods have presented many achievements for road extraction from remote sensing images. CNN-based methods require a large dataset with high quality labels for model training. However, there is still few standard and large dataset, which is specially designed for road extraction from optical remote sensing images. Besides, the existing end-to-end CNN models for road extraction from remote sensing images are usually with symmetric structure, studying on asymmetric structure between encoding and decoding is rare. To address the above problems, this article first provides a publicly available dataset LRSNY for road extraction from optical remote sensing images with manually labelled labels. Second, we propose a reconstruction bias U-Net for road extraction from remote sensing images. In our model, we increase the decoding branches to obtain multiple semantic information from different upsamplings. Experimental results show that our method achieves better performance compared with other six state-of-the-art segmentation models when testing on our LRSNY dataset. We also test on Massachusetts and Shaoshan datasets. The good performances on the two datasets further prove the effectiveness of our method.
Secure Multiple-Mode OFDM With Index Modulation

Multiple-mode orthogonal frequency division multiplexing with index modulation (MM-OFDM-IM) is a promising IM technique for OFDM systems to improve the spectral efficiency. In this paper, a secure index and data symbol modulation (DSM) scheme based on MM-OFDM-IM systems is proposed by employing the notion of the channel reciprocity assuming time division duplexing mode. The channel state information of the legitimate link is adopted as a secret key for protecting data transmission. We investigate randomized mapping rules for both IM and DSM, based on which even though an eavesdropper has estimated the permutations of modes and their carrying symbols, he cannot decode the message correctly. Particularly, the randomized mapping rules for IM are designed by exploiting the redundancy of mode permutations. Both numerical simulations and theoretical analysis for achievable secure rate and bit error rate are processed, whose results demonstrate that the proposed scheme can enhance the physical-layer security of the considered communication system.
Comprehensive Modeling of Zonalized Autonomous Microgrid with Improved Voltage Profiles in the Renewable Generation Dominant Region

This paper presents results from the comprehensive modeling of autonomous microgrid with both conventional synchronous generator based generations and inverter based renewable generations. Zonalization or regionalization has been adopted in which the conventional generations are installed in one region while inverter based renewable generations are installed in another region. The former takes well-established droop control: $\pmb{f}-\mathbf{P};\pmb{V}-\mathbf{Q}$ to ensure proper power sharing among multiple conventional generators while constant frequency method is adopted to operate the latter. Back to back converter is used to join two regions, through which a planned power exchange is fulfilled. A modified method is adopted to improve voltage profiles in the renewable generation region through a combination of proportional controller, bounded integral controller and a bias. By doing so, bus voltage deviation from its rated value can be kept well within $\pm \mathbf{5}\%$.
Parallel Route Optimization and Service Assurance in Energy-Efficient Software-Defined Industrial IoT Networks

In recent years, the Industrial world has been embracing new digital technology, including the internet of things (IoT) paradigm that promises revolutionizing-prospects in numerous industrial applications. However, many deployment challenges related to real-time big data analytics, service assurance, resource optimization, energy consumption, and security awareness are raised. In this work, we focus on service assurance and resource optimization, including energy consumption challenges over Industrial Internet of Things (IIoT)-based environments since the existing network routing algorithms cannot meet the strict heterogeneous quality of service (QoS) requirements of industrial communications while optimizing resources. We take advantage of the flexibility and programmability offered by the promising software-defined networking paradigm, and we propose a centralized route optimization and service assurance scheme, named ROSA, over a multi-layer programmable industrial architecture. The proposed solution supports a wide range of heterogeneous flows, such as ultra-reliable low-latency communications (URLLC) and bandwidth-sensitive services. The routing optimization problems are formulated as multi-constrained shortest path problems. The Lagrangian Relaxation approach is used to solve the NP-hard complexity. Hence, we deploy a pair of parallel routing algorithms run according to the flow type to ensure QoS requirements, efficiently allocate constrained resources, and enhance the overall network energy consumption. We conduct extensive simulations to validate the proposed ROSA scheme. The experimental results show promising performance in terms of reducing bandwidth utilization by up to 22%, end-to-end delay at least by 21%, packet loss by more than 19%, flow violation by about 16%, and energy consumption up to 14% as compared to well-known benchmarks in QoS provisioning and energy-aware routing problem.
Threats and Security Issues in Cloud Storage and Content Delivery Networks: Analysis

The rapid growth of technology tends to improve human's life, to save time and to impose a rhythm to human day to day activity. More than ever, the world is facing tremendous difficulties to acquire, ingest, process, save and visualize information. This is how big companies like AWS Amazon, Microsoft Azure, Google Cloud and Alibaba are coming into the game by providing cloud computing to remediate to such difficulties. To help companies, organizations, corporates and users to store huge amounts of data, Cloud Service Providers provide storage in their data centers and even Content Delivery Network service to keep data to the closest location of users. Among those companies, some are using cloud storage as Content Delivery Network. Beside Cloud Service Providers, other big companies like Akamai or Cloudflare are specialized in Content Delivery Network. Therefore, which kind of threats are facing companies which use cloud storage as Content Delivery Network? Are those threats similar to companies specialized only on Content Delivery Networks? And what can be the impact of Content Delivery Network attacks on cloud storage used as Content Delivery Networks? The aim of this paper is to propose a new threat model of cloud storage used as Cloud Delivery Network and determine the level of risk on this last one.
On the Performance of SWIPT MEC Systems in the Presence of Spatially Correlated Shadowing

In this paper we analyze the performance of a mobile edge computing (MEC) system in the presence of spatially correlated shadowing. To achieve this goal, obstacles like walls are modelled by a Manhattan Poisson line Process (MPLP). Simultaneous wireless information and power transfer (SWIPT) is used to send command messages as well as to deliver the power to low power devices (LPDs). The energy harvested by the LPDs in the downlink phase is then used to either locally execute the assigned computation task or to offload it to the MEC server. By leveraging stochastic geometry, we derive analytical expressions for the success probability of the system which is defined as the joint probability of SINR downlink coverage and of harvesting enough energy to execute the computation task. Moreover, the impact of different system parameters on the trade-off between the harvested energy and the computational task admission rate is analyzed.
Robust Prediction of Traffic Flow Based on Multi-Task Graph Convolution Network

Accurate traffic prediction is indispensable to realize an intelligent transportation system in a smart city. However, the traffic network has a complex spatial structure, and short-term / long-term time dependence. Besides, the traffic network is faced with the various external factors, including the instability of hardware equipment in data collection and network time delay in the process of data transmission. And most of the existing methods predict the traffic flow under the traffic data is complete, which lose the tolerance in the case of abnormal data. In this paper, to improve the robustness of the traffic flow prediction model, we propose a multi-task graph convolution network (MTGCN) structure, which combines multi-task learning (MTL) with graph convolution network (GCN). Firstly, the GCN method is used to obtain the spatiotemporal correlation of traffic network. Secondly, we use a model that is able to do MTL via multiple outputs, each corresponding to the same traffic network at different adjacent time durations. Experiments on real traffic flow data sets demonstrate the effectiveness and robustness of the proposed method in the presence of abnormal patterns such as Gaussian noise and random missing.
Pharaoh Cuttlefish, Sepia pharaonis, Genome Reveals Unique Reflectin Camouflage Gene Set

Sepia pharaonis, the pharaoh cuttlefish, is a commercially valuable cuttlefish species across the southeast coast of China and an important marine resource for the world fisheries. Research efforts to develop linkage mapping, or marker-assisted selection have been hampered by the absence of a high-quality reference genome. To address this need, we produced a hybrid reference genome of S. pharaonis using a long-read platform (Oxford Nanopore Technologies PromethION) to assemble the genome and short-read, high quality technology (Illumina HiSeq X Ten) to correct for sequencing errors. The genome was assembled into 5,642 scaffolds with a total length of 4.79 Gb and a scaffold N50 of 1.93 Mb. Annotation of the S. pharaonis genome assembly identified a total of 51,541 genes, including 12 copies of the reflectin gene, that enable cuttlefish to control their body coloration. This new reference genome for S. pharaonis provides an essential resource for future studies into the biology, domestication and selective breeding of the species.
Shear Wave Dispersion as a Potential Biomarker for Cervical Remodeling During Pregnancy: Evidence From a Non-Human Primate Model

Shear wave dispersion (variation of phase velocity with frequency) occurs in tissues with layered and anisotropic microstructure and viscous components, such as the uterine cervix. This phenomenon, mostly overlooked in previous applications of cervical Shear Wave Elasticity Imaging (SWEI) for preterm birth risk assessment, is expected to change drastically during pregnancy due to cervical remodeling. Here we demonstrate the potential of SWEI-based descriptors of dispersion as potential biomarkers for cervical remodeling during pregnancy. First, we performed a simulation-based pre-selection of two SWEI-based dispersion descriptors: the ratio R of group velocities computed with particle-velocity and particle-displacement, and the slope S of the phase velocity vs. frequency. The pre-selection consisted of comparing the contrast-to-noise ratio (CNR) of dispersion descriptors in materials with different degrees of dispersion with respect to a low-dispersive medium. Shear waves induced in these media by SWEI were simulated with a finite-element model of Zener viscoelastic solids. The pre-selection also considered two denoising strategies to improve CNR: a low-pass filter with automatic frequency cutoff determination, and singular value decomposition of shear wave displacements. After pre-selection, the descriptor-denoising combination that produced the largest CNR was applied to SWEI cervix data from 18 pregnant Rhesus macaques acquired at weeks 10 (mid-pregnancy stage) and 23 (late pregnancy stage) of the 24.5-weeks full pregnancy. A maximum likelihood linear mixed-effects model (LME) was used to evaluate the dependence of the dispersion descriptor on pregnancy stage, maternal age, parity and other experimental factors. The pre-selection study showed that descriptor S combined with singular value decomposition produced a CNR 11.6 times larger than the other descriptor and denoising strategy combinations. In the Non-Human Primates (NHP) study, the LME model showed that descriptor S significantly decreased from mid to late pregnancy (−0.37 ± 0.07 m/s-kHz per week, p < 0.00001) with respect to the base value of 15.5 ± 1.9 m/s-kHz. This change was more significant than changes in other SWEI features such as the group velocity previously reported. Also, S varied significantly between the anterior and posterior portions of the cervix ( p = 0.02) and with maternal age ( p = 0.008). Given the potential of shear wave dispersion to track cervical remodeling, we will extend its application to ongoing longitudinal human studies.