Miracle-dz/UniFAR-FTUs huggingface dataset:
Two lateral RF MEMS varactors and an experimental switch made using a CMOS-MEMS process

An expanding wireless industry creates a demand for smaller and smarter devices, while maintaining the high performance requirements. Todays solutions often use off-chip components to meet these demands, which lead to additional area, cost and parasitic components. RF MEMS can beneficially replace a great number of these components, and in integration with CMOS offer small devices at low cost with on-chip signal processing. One important component in RF systems is the variable capacitor, or varactor. This thesis presents two varactor designs made using a CMOSMEMS process that enable monolithic integration of MEMS and CMOS. The varactors are electrothermal actuated and based on interdigitated combs moving lateral, parallel to the substrate. A latch mechanism is used so that the varactors only require power when switching. Since the fabricated circuit did not arrive in time for measurements only calculated and simulated results are presented. Bimetal actuator theory is used for theoretical understanding of the actuator and the various design parameters are thoroughly analyzed and discussed. The capacitances of the varactors is in the range of 133 − 897fF , with tuning ranges of 440% and 489% and Q-factors of ∼ 30 at 2GHz. In addition an experimental lateral DC series switch has been made. The purpose of this switch is to investigate how well defined the sidewalls of this CMOS-MEMS process are to determine if it is possible to obtain a good metal-to-metal connection. This work will be important for future work on CMOS-MEMS switches. All simulations have been done using Coventorware, while the layout of the chip has been made in Cadence. The chip has been fabricated in a 0. 25μm CMOS process from STMicroelectronics through the broker service Circuit Multi Projects (CMP), France. The post-process has been done at Carnegie Mellon University (CMU), USA and Sintef, Norway.
Balancing Fun and Seriousness Serious: Game Design
 Considerations

Much research has been done on the design procedure and principles for serious games. However, those studies rarely account for the balance between the fun elements and the serious purpose of the games or lack the specific steps for achieving such a goal. This paper outlines game design considerations in the development of a serious game aimed at enabling both hedonic and eudaimonic gaming experiences. A user study was conducted with 89 players to evaluate their gaming experiences. Results of the playtests showed that the game was able to achieve a good balance between hedonic (fun and pleasure) and eudaimonic (serious and purpose) experiences. Lessons learned and specific design recommendations for improving the current design are further discussed.
Complete mitochondrial genome of Stigmaeopsis miscanthi (Acari: Tetranychidae)

Abstract Complete mitochondrial genome of Stigmaeopsis miscanthi was first reported for the genus Stigmaeopsis in Tetranychidae. The circular mitochondrial genome of S. miscanthi is 15,171 bp in length, with 80.4% AT content, involving 13 protein-coding genes, 22 transfer RNA genes, two ribosomal RNA, and main one of non-coding regions. The length of 16SrRNA and 12SrRNA gene is 973 bp and 636 bp, respectively. Seven PCGs started with ATT codon, and other PCGs started with ATG and ATA codons. Five PCGs stopped with TAA codon, and the other eight PCGs ended with incomplete stop codons (T). Phylogenetic analysis indicated that S. miscanthi branched with the genus Panonychus.
Taxonomic delimitation and molecular identification of clusters within the species Zanthoxylumnitidum (Rutaceae) in China

﻿Abstract Zanthoxylumnitidum, known as Liang-Mian-Zhen in China, is a traditional Chinese medicinal plant used to treat traumatic injury, rheumatism, paralysis, toothache, stomach ache, and venomous snake bites. Two varieties of the species have been described and three morphological types have been reported within the original variety. However, taxonomic delimitation and molecular markers for distinguishing these varieties and types within this species remain unknown. Since different populations exhibit varying chemical compositions, easy identification of intraspecific taxa is crucial. We collected 420 individuals from 38 natural populations, 3 samples of standard medicinal material, and 17 folk-medicine samples to perform classification and identification within Zanthoxylumnitidum. Four distinct genetic clusters (A, B, C, and D) were highly supported by the nuclear barcode. Two distinct chloroplast clusters (A1 and A2) were further detected within A, and three others had one-to-one correspondence with the remaining nuclear clusters. Molecular identification showed that the 17 folk samples comprised A1, A2, B, and D, while the 3 standard samples belonged to A2. The internal transcribed spacer (ITS) region and rbcL gene are proposed as barcodes for rapid and accurate identification of the different Liang-Mian-Zhen lineages in China. This study highlights the importance of accurate taxonomic delimitation in combination with rapid and accurate molecular identification of medicinal plants.
A Frequency-Time Synchronization Scheme for Real-Time Wireless Sensor Networks

In a real-time wireless sensor network (RT-WSN), an unpredictable time length of the synchronization (or connection) process between nodes is generally a pity, though the communication after the connection may be controllable. The purpose of this paper is to solve this kind of pity based on the <italic>multiple-request-single</italic> occasion (multiple slave nodes request to send data to a single master node simultaneously before getting synchronized using the frequency channel hopping technique). Suppose that the master sends the <italic>synchronization packet</italic> (or <italic>beacon</italic>) and the slaves scan for this packet with different channels for connection. A slave <italic>getting synchronized</italic> with the master means that both nodes have just selected an identical <italic>frequency</italic> channel during a <italic>time</italic> region and the slave has received the synchronization packet successfully in this region, which is called <italic>frequency and time synchronization</italic>, abbreviated as <italic>FTS</italic>. For many existing wireless protocols, if they are directly adopted in this situation, two deficiencies exist as for real-time performances: First, the time length required for a slave to join the network is often not deterministic if one or more channels are disturbed. Second, when multiple slaves do their scanning simultaneously, which slave can synchronize with the master first is unpredictable so that a slave with a lower priority may be serviced prior to others. In this paper, two FTS examples with poor real-time performances are provided first. Then, a synchronization method named 1/<inline-formula> <tex-math notation="LaTeX">$2n$ </tex-math></inline-formula> FTS is presented and proved. With this method, a slave scans for the synchronization packet of the master with <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> different available channels repeatedly until it gets the packet while the master transmits the packet <inline-formula> <tex-math notation="LaTeX">$2n$ </tex-math></inline-formula> times in <inline-formula> <tex-math notation="LaTeX">$2n$ </tex-math></inline-formula> continuous timeslots. The width of the scan widow of the salve takes twice the width of the slot. In this way, every slave has the opportunity to get synchronized with the master at the end of the <inline-formula> <tex-math notation="LaTeX">$2n$ </tex-math></inline-formula> slots even if one or more (not all) channels are disturbed. Then, the slaves can send their requests to the master in different slots so that the master can schedule subsequent communications according to their requests and priorities. Also, if the mater broadcasts the beacon periodically, the time length for a slave to join or rejoin the master is not difficult to predict. The theorems associated with the 1/<inline-formula> <tex-math notation="LaTeX">$2n$ </tex-math></inline-formula> FTS method are demonstrated in experiments with NORDIC Semiconductor chips.
Machine Learning and Application in Terahertz Technology: A Review on Achievements and Future Challenges

Terahertz (THz) radiation ( $0.1\sim 10$ THz) shows great potential in agricultural products detection, biomedical, and security inspection in recent years. Machine learning methods are widely used to support the user demand of higher efficiency and high prediction accuracy. The technological and key challenges of machine learning methods are for THz spectroscopy and image data preprocessing, reconstruction algorithms, and qualitative and quantitative analysis. In this paper, an exhaustive review of recent related works of THz detection and imaging techniques and machine learning methods are presented. The application of machine learning methods combined with THz technology in quality inspection of agricultural products, biomedical, security inspection, and materials science are highlighted. Challenges of machine learning methods for these applications are addressed. The development trend and future perspectives of THz technology are also discussed.
Fingerprint Augment Based on Super-Resolution for WiFi Fingerprint Based Indoor Localization

WiFi fingerprint based indoor localization has become a key research direction in the field of indoor localization due to its high positioning accuracy and low equipment deployment cost. Increasing the number of reference point collected offline can improve the positioning accuracy, however it yields excessive cost of offline collection. Fingerprint augment is an effective solution to reduce the cost while ensuring the positioning accuracy. In this paper, we are pioneering to propose a fingerprint augment framework based on super-resolution (FASR), which achieves the fusion of fingerprint augment and super-resolution based on mutual conversion between fingerprint data and fingerprint image. The processing framework of FASR is formulated and the implementation of three modules in FASR are given, including Fingerprint-To-Image Conversion module, Super-Resolution module and Image-To-Fingerprint Conversion module. Simulated and real data experiments reveal the feasibility and effectiveness of the FASR. In addition, we explore the impact of two key engineering parameters on the performance of the FASR method. Our work demonstrates the new application of super-resolution in image processing field in wireless indoor localization topics.
The Active Microbiota of the Eggs and the Nauplii of the Pacific Blue Shrimp Litopenaeus stylirostris Partially Shaped by a Potential Vertical Transmission

The many ecological niches present in an organism harbor distinct microorganisms called microbiota. Different factors can influence the establishment of these commensal microbial communities. In a previous article, we have concluded that some bacterial lineages associated with the early larval stages of the Pacific blue shrimp Litopenaeus stylirostris could be acquired from the breeders via a potential vertical transmission. The present study was conducted in order to investigate this hypothesis. Using HiSeq sequencing of the V4 region of 16S rRNA gene, we analyzed the active microbiota associated with the eggs and the nauplii of L. stylirsotris as well as with the reproductive organs of their breeders. Microbial communities associated with the rearing water were also considered to discriminate environmental microbial lineages. Using these analyses, we highlight a set of core bacterial families present in all samples and composed of members of Colwelliaceae, Alteromonadaceae, Pseudoalteromonadaceae, Saccharospirillaceae, Oceanospirillaceae, Vibrionaceae, Burkholderiaceae, Rhodobacteraceae, Flavobacteraceae, and Corynebacteriaceae; showing the importance of the environment in the establishment of the larval microbiota. We also present specific bacteria affiliated to the Arcobacteraceae, Rhodobacteraceae, Comamonadaceae, and Colwelliaceae families, which were only found in the breeders and their offspring strengthening the hypothesis of a potential vertical transmission shaping the active microbiota of the eggs and the nauplii of L. stylirostris.
Controlled outputs, full data: A privacy-protecting infrastructure for MOOC data

Learning analytics research presents challenges for researchers embracing the principles of open science. Protecting student privacy is paramount, but progress in increasing scientific understanding and improving educational outcomes depends upon open, scalable and replicable research. Findings have repeatedly been shown to be contextually dependent on personal and demographic variables, so how can we use this data in a manner that is ethical and secure for all involved? This paper presents ongoing work on the MOOC Replication Framework (MORF), a big data repository and analysis environment for Massive Open Online Courses (MOOCs). We discuss MORF's approach to protecting student privacy, which allows researchers to use data without having direct access. Through an open API, documentation and tightly controlled outputs, this framework provides researchers with the opportunity to perform secure, scalable research and facilitates collaboration, replication, and novel research. We also highlight ways in which MORF represents a solution template to issues surrounding privacy and security in the age of big data in education and key challenges still to be tackled.
Rubus ellipticus Sm. Fruit Extract Mediated Zinc Oxide Nanoparticles: A Green Approach for Dye Degradation and Biomedical Applications

Rubus ellipticus fruits aqueous extract derived ZnO-nanoparticles (NPs) were synthesized through a green synthesis method. The structural, optical, and morphological properties of ZnO-NPs were investigated using XRD, FTIR, UV-vis spectrophotometer, XPS, FESEM, and TEM. The Rietveld refinement confirmed the phase purity of ZnO-NPs with hexagonal wurtzite crystalline structure and p-63-mc space group with an average crystallite size of 20 nm. XPS revealed the presence of an oxygen chemisorbed species on the surface of ZnO-NPs. In addition, the nanoparticles exhibited significant in vitro antioxidant activity due to the attachment of the hydroxyl group of the phenols on the surface of the nanoparticles. Among all microbial strains, nanoparticles’ maximum antibacterial and antifungal activity in terms of MIC was observed against Bacillus subtilis (31.2 µg/mL) and Rosellinia necatrix (15.62 µg/mL), respectively. The anticancer activity revealed 52.41% of A549 cells death (IC50: 158.1 ± 1.14 µg/mL) at 200 μg/mL concentration of nanoparticles, whereas photocatalytic activity showed about 17.5% degradation of the methylene blue within 60 min, with a final dye degradation efficiency of 72.7%. All these results suggest the medicinal potential of the synthesized ZnO-NPs and therefore can be recommended for use in wastewater treatment and medicinal purposes by pharmacological industries.
Learning Robust Deep State Space for Unsupervised Anomaly Detection in Contaminated Time-Series

Anomalies are ubiquitous in real-world time-series data which call for effective and timely detection, especially in an unsupervised setting for labeling cost saving. In this paper, we develop an unsupervised density reconstruction model for multi-dimensional time-series anomaly detection. In particular, it directly handles an important realistic setting that the detection is achieved towards raw time-series contaminated with noise for training, in contrast to most existing anomaly detection works that assume the training data is in general clean i.e., not contaminated with anomaly. It extends recent advancements in deep generative models and state space models to achieve robust anomaly detection. Our approach comprises of a novel state space based generative model, a filtering based inference model, together with a carefully-designated emission model based on robust statistics theory. Extensive experimental results are conducted to show that our approach can adapt to complex patterns even given severely contaminated training data. We also develop visualization techniques to help better understand the behavior of the anomaly detection models. Empirical results show that our method outperforms state-of-the-arts on both synthetic and real-world datasets.
Interpreting consequences of inadequate sampling of oceanic motions

Inadequate sampling of oceanic motions, which commonly occurs for both oceanic measurements and simulations, can cause peculiar spectral features and potentially leads to misinterpretations. Here, we combine an extremely high‐frequency moored velocity record and a high‐resolution numerical simulation with the basic signal‐processing theory to quantitatively explore how varying sampling rates affect the ability to represent oceanic motions, especially internal gravity waves (IGWs). The moored measurements and simulations demonstrate that hourly sampling is sufficient to capture horizontal internal tidal velocities, but inadequate to faithfully characterize the vertical velocity for nearly all frequencies. The daily‐sampled model simulation shows a complicated frequency‐wavenumber spectral pattern of IGWs. Due to contrasting periodicities in time and space, temporal subsampling tends to retain the total variance of original IGWs, while spatial subsampling directly induces spectral energy loss. This study sheds light on data applications of the upcoming satellite altimetry missions.
Performance Analysis of Heterogeneous Wireless Network

Energy efficiency in cellular networks is a growing concern for the cellular operators to not only maintain profitability, but also to reduce the overall environment effect. This emerging trend of achieving energy efficiency in cellular network is motivating the standardization authorities and network operators to continuously explore future technologies in order to bring improvements in the entire network infrastructure. Heterogeneous wireless technologies deployed in environment to support user’s requirements, which adversely affect on society by increasing energy consumption of network and by increasing electromagnetic radiation on the environment. The mobiles are connected to their nearest BS. The nearest BS has been considered by the coverage range. With this, we analyzed the performance of the wireless system in heterogeneous environment in terms of outage probability and energy consumption by changing coverage range.
Energy Consumption Scheduling as a Fog Computing Service in Smart Grid

The advent of smart grid technologies provides new tools and services to optimally manage the electricity grids. One of the most interesting services that emerged with the development of Information and Communication Technologies (ICTs) is energy demand management. This service permits us to face the issues caused by the ever-increasing energy demand such as grid congestion during peak hours, increasing energy generation costs, and even blackouts. In this paper, we investigate the problem of consumer-side optimization of residential energy demand. Our main aim is to better distribute the energy consumption over a day to avoid or reduce the demand during peak hours. Hence, we propose a fog computing-based model for energy demand scheduling using energy consumption cost as an incentive. In this model, the fog nodes schedule the appliances’ operations in order to reduce the individual and global energy bills whilst respecting consumers’ preferences. The proposed approach performs a multi-agent system-based cooperative scheduling game with minimal interactions between the nodes. Moreover, we present a fog nodes’ assignment scheme to decide which node will handle which appliances’ schedules. The nodes’ assignment strategy aims to optimize the use of fog nodes’ resources whilst reducing the scheduling process latency. The performance evaluation shows that the use of fog computing can achieve interesting results in terms of the reduction of energy consumption cost. For instance, the energy consumption during peak hour decreases by more than 25% from 670 kWh to 500 kWh when the scheduling game is performed. As a consequence, the energy consumption cost decreases by 7% from 806 € to 750 € .
Subchronic Toxicity of Microcystin-LR on Young Frogs (Xenopus laevis) and Their Gut Microbiota

Although toxic effects of microcystins (MCs) in mammals and fish have been extensively studied, the effects of MCs on the immune system and gut microbiota of amphibians have not received sufficient attention. As MCs cause general damage to the vertebrate liver and immune system and trigger an inflammatory response, and the gut microbiota is closely related to host metabolism and immunity, we speculated that MCs can cause changes in the immune system and gut microbiota of amphibians. To verify this, we examined the intestinal and liver injury of Xenopus laevis exposed to different microcystin-leucine-arginine (MC-LR) concentrations and the effects on the gut microbiota through high-throughput sequencing of 16S rDNA of the gut microbiota combined with histopathological analysis, enzyme activity determination, and qRT-PCR. Our results showed that MC-LR caused focal infiltration of inflammatory cells and increased the number of T cells and local congestion and vacuolization in X. laevis liver, but reduced the number, density, height, and regularity of villi. These liver and intestinal injuries became more obvious with an increase in MC-LR concentration. MC-LR significantly decreased the activities of malondialdehyde and alkaline phosphatase and the expression of TGF-β in the liver. Moreover, MC-LR significantly altered the gut microbiota of X. laevis. The relative abundance of Firmicutes and Bacteroidetes in high-concentration MC-LR groups was significantly reduced compared to that in low-concentration MC-LR groups, whereas Fusobacteria was significantly enriched. The metabolic gene composition of the gut microbiota in low-concentration MC-LR (≤5 μg/L) groups was significantly different from that in high-concentration MC-LR (≥20 μg/L) groups. These results deepen our understanding of the toxicity of MCs to aquatic organisms and assessment of the ecological risk of MCs in amphibians.
Few-Shot Instance Grasping of Novel Objects in Clutter

Instance grasping, which aims to grasp a specific object out of clutter, is a fundamental task within robotics. However, allowing a robot to quickly learn to perform instance grasping for new, previously unseen objects remains challenging. In this work, we present an instance grasping meta-learning framework (IGML), a simple yet effective end-to-end approach that not only teaches robots to identify novel objects but also how to grasp them. Given only a few examples to specify the grasping point of the target object, our IGML can quickly learn to recognize the target object and grasp it at the demonstrated grasping point by leveraging prior experience. Experimental results on the test sets show that IGML achieves decent success rates in cluttered environments, significantly surpassing state-of-the-art methods. Then we deployed IGML on a UR5 robot arm to handle pick-and-place scenarios and achieved a precision rate of 93.4% and a recall rate of 87.1%.