Miracle-dz/UniFAR-FTUs huggingface dataset:
Circular Ring Shaped Polarization Reconfigurable Antenna for Wireless Communications

A single fed polarization reconfigurable antenna is presented. The antenna comprises of a circular ring-shaped radiating element along with reconfigurable feed network located at its center which eliminates the need for additional space for reconfigurable feed network. A separate biasing network is placed to bias the pin diodes in the feed network for polarization reconfiguration and achieves three polarization states (linear, left-hand and right-hand circularly polarization). The antenna is designed to operate at 2.4 GHz ISM band. The antenna parameters are simulated using Ansoft highfrequency structure simulator and are validated using Agilent network analyzer (N9925A) and antenna test systems. The antenna achieves a good −10 dB impedance bandwidth of 85 MHz (2.40–2.485) GHz in linear state and 85 MHz (2.41–2.495) GHz in the circularly polarization states along with better crosspolarization isolation (≥ 15 dB) in the operating bands and hence more suitable for modern wireless communications.
Flexible Robotic Grasping with Sim-to-Real Transfer based Reinforcement Learning

Robotic manipulation requires a highly flexible and compliant system. Task-specific heuristics are usually not able to cope with the diversity of the world outside of specific assembly lines and cannot generalize well. Reinforcement learning methods provide a way to cope with uncertainty and allow robots to explore their action space to solve specific tasks. However, this comes at a cost of high training times, sparse and therefore hard to sample useful actions, strong local minima, etc. In this paper we show a real robotic system, trained in simulation on a pick and lift task, that is able to cope with different objects. We introduce an adaptive learning mechanism that allows the algorithm to find feasible solutions even for tasks that would otherwise be intractable. Furthermore, in order to improve the performance on difficult objects, we use a prioritized sampling scheme. We validate the efficacy of our approach with a real robot in a pick and lift task of different objects.
Stereo Matching Algorithm Based on Improved Census Transform and Dynamic Programming

In order to reduce the mismatching rate of binocular stereo matching algorithm in the disparity discontinuity region and under noise disturbance, a stereo matching algorithm based on improved Census transform and dynamic programming is proposed. An improved Census transform with a noise margin is applied to compute the cost based on a cross shape support region. The reliability of single pixel matching cost is enhanced. The guided image filter is used to aggregate the cost volume fast and efficiently. In the disparity selecting step, an improved dynamic programming algorithm is designed to eliminate the scan-line effect and improve the matching speed and accuracy. The final disparity maps are gained after post-processing. The experimental results demonstrate that the proposed algorithm evaluated on the Middlebury benchmark achieves an average error rate of 5.31%, and the accurate disparity can be obtained in both low texture and disparity discontinuity regions with low computing complexity and strong robustness.
Oscillating activation functions and other recent progress in activation functions: A survey

Neural networks are at the heart of many autonomous intelligent systems now. Originally designed to mimic human neurons forming the brain, modern neural networks have their fundamental component in the perceptron[1][2], an analog to a single neuron in the brain. A perceptron boils down to a set of input, an equal number of weights, biases, and an activation function that maps the inputs to a bounded output. A perceptron can represent the basic logic gates, except the XOR gate, popularly referred to as the XOR problem[3]. Recent work [4] have proposed biologically inspired oscillatory functions that solve the XOR problem with a single perceptron. We highlight the shortcomings of existing activation functions with respect to the XOR problem and how oscillating functions can solve them in this paper.
IFN-α Mediates the Development of Autoimmunity both by Direct Tissue Toxicity and through Immune Cell Recruitment Mechanisms

IFN-α is known to play a key role in autoimmunity, but the mechanisms are uncertain. Although the induction of autoimmunity by IFN-α is consistent with primarily immunomodulatory effects, the high frequency of nonautoimmune inflammation suggests other mechanisms. We used thyroiditis as a model to dissect these possibilities. IFN-α treatment of cultured thyrocytes increased expression of thyroid differentiation markers, thyroglobulin, thyroid-stimulating hormone receptor, thyroid peroxidase, and sodium iodide transporter. RNAseq analysis demonstrated that pathways of Ag presentation, pattern recognition receptors, and cytokines/chemokines were also stimulated. These changes were associated with markedly increased nonapoptotic thyroid cell death, suggesting direct toxicity. To corroborate these in vitro findings, we created transgenic mice with thyroid-specific overexpression of IFN-α under control of the thyroglobulin promoter. Transgenic mice developed marked inflammatory thyroid destruction associated with immune cell infiltration of thyroid and surrounding tissues leading to profound hypothyroidism, findings consistent with our in vitro results. In addition, transgenic mice thyroids showed upregulation of pathways similar to those observed in cultured thyrocytes. In particular, expression of granzyme B, CXCL10, a subset of the tripartite motif-containing family, and other genes involved in recruitment of bystander cytotoxic immune responses were increased. Pathways associated with apoptosis and autophagy were not induced. Taken together, our data demonstrate that the induction of tissue inflammation and autoimmunity by IFN-α involves direct tissue toxic effects as well as provocation of destructive bystander immune responses.
Application of theory of planned behaviour to households’ source separation behaviour in Ghana

Purpose The purpose of this paper is to examine the factors that encourage households’ source separation behaviour in Accra and Tamale Metropolises in Ghana. Design/methodology/approach Using a cross-sectional design, 855 households of Ghana were interviewed based on the theoretical framework of the theory of planned behaviour (TPB). The ordered probit regression model was employed to examine the factors that influence households’ source separation intention. Findings The results indicated that educational attainment of head of household, total income of household, occupation type of household head, information, past experience with source separation, inconvenience in terms of time, space and availability of formal source separation scheme, attitude, subjective norm and the location of the respondents significantly predicted households’ solid waste separation intentions. Research limitations/implications The cross-sectional design does not determine causality but an association. Thus, future studies should examine actual household waste separation behaviour by using the experimental design to test the TPB model. Practical implications To promote solid waste separation at source, the public should be educated and provided with solid waste separation schemes that are efficient and compatible with households’ preference. Originality/value This study was partly motivated by the fact that despite the benefits associated with source separation, little attention has been given to formal source separation in Ghana. Moreover, there are limited studies on source separation behaviour in Ghana using the TPB as the theoretical framework.
The Role of Sexting in the Development of Romantic Relationships

iv Chapter 1: Introduction . 1 Chapter 2: Theories of Relationship Development . 6 Escalation . 8 De-escalation.14 Chapter 3: Communication Technologies in Relationships.20 Online Dating and Dating Applications . 24 Social Media . 26 Texting and Snapchat . 28 Sexting . 31 Chapter 4: Method . 36 Chapter 5: Results & Discussion . 39 Chapter 6: Conclusion.46 References . 48
Enzymatic Biotransformation of Gypenoside XLIX into Gylongiposide I and Their Antiviral Roles against Enterovirus 71 In Vitro

Biotransformation of specific saponins in the valuable medical plants to increase their bioavailability and pharmaceutical activities has attracted more and more attention. A gene encoding a thermophilic glycoside hydrolase from Fervidobaterium pennivorans DSM9078 was cloned and expressed in Escherichia coli. The purified recombinant enzyme, exhibiting endoglucanase cellulase activity, was used to transform gypenoside XLIX into gylongiposide I via highly selective and efficient hydrolysis of the glucose moiety linked to the C21 position in gypenoside XLIX. Under the optimal reaction conditions for large scale production of gylongiposide I, 35 g gypenoside XLIX was transformed by using 20 g crude enzyme at pH 6.0 and 80 °C for 4 h with a molar yield of 100%. Finally, 11.51 g of gylongiposide I was purified using a silica gel column with 91.84% chromatographic purity. Furthermore, inhibitory activities of gypenoside XLIX and gylongiposide I against Enterovirus 71 (EV71) were investigated. Importantly, the EC50 of gypenoside XLIX and gylongiposide I calculated from viral titers in supernatants was 3.53 μM and 1.53 μM, respectively. Moreover, the transformed product gylongiposide I has better anti-EV71 activity than the glycosylated precursor. In conclusion, this enzymatic method would be useful in the large-scale production of gylongiposide I, which would be a novel potent anti-EV71 candidate.
Providing Public Goods in Rural America: The Role of the Professional Public Administrator

Rural local governments, similar to their larger and more urban counterparts, provide many of the public services their citizens encounter on a daily basis. As would be expected, most dedicate the majority of their expenditures to services such as road maintenance, fire protection, and a handful of administrative functions. Some, however, devote significant resources to a more robust collection that includes parks, cultural institutions, and public health programs, for example. Using data from rural governments in Wisconsin, this research examines public expenditures, with an emphasis on the role of the professional administrator and the degree to which the position is correlated with expenditures on public goods. While existing scholarship has examined overall expenditures, this research provides a more nuanced focus on nine specific expenditure categories. Results indicate that population, partisanship, county-level expenditures, and fiscal capacity are related to various expenditure categories, but the effect of the professional administrator is much more limited.
A Regional View of the Response to Climate Change: A Meta-Analysis of European Benthic Organisms’ Responses

Climate change is impacting organisms in every region of the world ocean by acting though on individuals in response to their local environments. Given projected future risks derived from these changes, it is becoming increasingly important to understand regional signals of how organisms respond to facilitate their governance and protection. Benthic organisms structure ecological compositions and ecosystem dynamics, therefore not only providing insights into their own response to climate change but also how ecosystems might respond to future conditions. European seas are transitional areas including boreal, warm-temperate, and subarctic waters with organisms frequently at limits of their distributions. Here, we use a meta-analytical approach to assess how calcification, growth, metabolism, photosynthesis, reproduction, and survival in European benthic organisms respond to ocean acidification and warming. Using meta-regression, we examine how study design factors influence effect-size outcomes. Longer experimental periods generally amplified the effects of climate change on taxonomic groupings and related physiological traits and against expectation do not result in acclimation. In agreement with global studies, we find that impacts vary considerably on different taxonomic groupings and their physiological traits. We found calcifying organisms are an at-risk taxon in European waters, with climate stressors decreasing growth rates, reproduction, and survival rates. Fleshy algal species demonstrate resilience to climate stressors, suggesting future European benthic ecosystems will undergo restructuring based on current climate emission pathways.
Co-Optimization of Communication and Sensing for Multiple Unmanned Aerial Vehicles in Cooperative Target Tracking

Abstract: In this paper, we consider motion-planning for multiple unmanned aerial vehicles (UAVs) that oversee cooperative target tracking in realistic communication environments. We present a novel multi-UAVs cooperative target tracking algorithm based on co-optimization of communication and sensing strategy, which can generate information-gathering trajectories considering the multi-hops communication reliability. Firstly, a packet-erasure channel model is used to describe the realistic wireless communication links, in which the probability of a successful information transmission is modeled as a function of the signal-to-noise ratio (SNR). Secondly, the Fisher information matrix (FIM) is used to quantify the information gained in target tracking. Thirdly, a scalar metric is used for trajectories panning over a finite time horizon. This scalar metric is a utility function of the expected information gain and the probability of a successful information transmission. With the combining of the sensing and communication into a utility function, the co-optimization of communication and sensing is reflected in the tradeoffs between maximizing information gained and improving communication reliability. The results of comparison simulations show that the proposed algorithm effectively improved estimation performance compared to the method that does not consider communication reliability.
Comparative Transcriptomic, Anatomical and Phytohormone Analyses Provide New Insights Into Hormone-Mediated Tetraploid Dwarfing in Hybrid Sweetgum (Liquidambar styraciflua × L. formosana)

Polyploid breeding is an effective approach to improve plant biomass and quality. Both fast growth and dwarf types of in vitro or ex vitro plants are produced after polyploidization. However, little is known regarding the dwarf type mechanism in polyploids grown in vitro. In this study, the morphological and cytological characteristics were measured in tetraploid and diploid hybrid sweetgum (Liquidambar styraciflua × L. formosana) with the same genetic background. RNA sequencing (RNA-Seq) was used to analyse shoot and root variations between tetraploid and diploid plants; important metabolites were validated. The results showed that the shoot and root lengths were significantly shorter in tetraploids than in diploids after 25 d of culture. Most tetraploid root cells were wider and more irregular, and the length of the meristematic zone was shorter, while tetraploid cells were significantly larger than diploid cells. Differentially expressed genes (DEGs) were significantly enriched in the plant growth and organ elongation pathways, such as plant hormone biosynthesis and signal transduction, sugar and starch metabolism, and cell cycles. Hormone biosynthesis and signal transduction genes, such as YUCCA, TAA1, GH3, SAUR, CPS, KO, KAO, GA20ox, GA3ox, BAS1 and CYCD3, which help to regulate organ elongation, were generally downregulated. The auxin, gibberellin, and brassinolide (BL) contents in roots and stems were significantly lower in tetraploids than in diploids, which may greatly contribute to slow growth in the roots and stems of tetraploid regenerated plants. Exogenous gibberellic acid (GA3) and indole-3-acetic acid (IAA), which induced plant cell elongation, could significantly promote growth in the stems and roots of tetraploids. In summary, comparative transcriptomics and metabolite analysis showed that the slow growth of regenerated tetraploid hybrid sweetgum was strongly related to auxin and gibberellin deficiency. Our findings provide insights into the molecular mechanisms that underlie dwarfism in allopolyploid hybrid sweetgum.
GoogleNet CNN Classifications for Diabetics Retinopathy

In the process of this work, an implementation of the CNN architecture that was powered by GoogleNet was created for the purpose of identifying cases of retinal diabetes. In order to categorize retinal pictures, the 144 layers of the GoogleNet CNN algorithm were utilized; these images were then trained utilizing a wide range of various training settings. Based on the findings of the comparison, the ADAM GoogleNet CNN option appears to be the most effective method for the diagnosis of diabetic retinal illness. It shows a 96% as accuracy in 3 min and 11 seconds with a good fit result.
Identification of Differentially Expressed Genes and Key Pathways Associated with PTPN11 Mutation in Acute Myeloid Leukemia

PTPN11 is a prominent oncogene in many tumors, which linked with poor prognosis. But the gene expression profile associated with this gene mutation in AML is not yet fully elucidated. So, the current study was intended to explore the key genes and pathways associated with AML with PTPN11 mutation. Using the TCGA database, we investigated the gene expression profile for PTPN11 mutation and wild type. EdgeR, an R platform, was applied to categorize the differentially expressed genes. GO and KEGG enrichment analysis was accomplished using DAVID and GSEA. PPI network is investigated with STRING database and Cytoscape software. A statistical investigation was employed to examine the expression levels of genes and prognostic values. As a result, in total 3268 gene s were differentially expressed. GO, KEGG pathway and GSEA analysis of up and down-regulated genes represented that gene were enriched in apoptosis and metabolic signaling pathways. Furthermore, ten hub genes GAPDH, HSP90AA, MYC, UBB, HSPA4, EP300, CCDS, POLR2A, EFTUD2, RPL11. GAPDH was identified from the PPI network. A statistical investigation revealed that PTPN11 gene mutation associated with AML patients had a shorter median survival time with an increased risk of death compared with those without mutations.
Noncooperative Event-Triggered Control Strategy Design With Round-Robin Protocol: Applications to Load Frequency Control of Circuit Systems

In this paper, the noncooperative optimal control problem is investigated for a class of discrete time-varying networked control systems subject to exogenous nonlinear disturbances. To relive the transmission burden in the sensor-to-controller channel, the Round-Robin protocol is adopted to schedule the sensor transmissions. On the other hand, the controller operates in an event-triggered manner so as to reduce the transmission frequency and thereby preserving the energy in the controller-to-actuator channel. In the presence of the underlying scheduling and triggering mechanism, it is literally impossible to acquire the accurate value of the individual cost function for each controller and, as an effective alternative, a certain upper bound is derived on the individual cost function. Then, in virtue of the completing-the-square technique and the Moore–Penrose pseudo inverse, such an upper bound is minimized at each time instant. Furthermore, a sufficient condition is established to guarantee the boundedness of the derived upper bound over the infinite horizon. Finally, a numerical example on the power grid is provided to verify the validity of the proposed methodology.
A Deep Learning Based Android Malware Detection System with Static Analysis

In recent years, smart mobile devices have become indispensable due to the availability of office applications, the Internet, game applications, vehicle guidance or similar most of our daily lives applications in addition to traditional services such as voice calls, SMSs, and multimedia services. Due to Android's open source structure and easy development platforms, the number of applications on Google Play, the official Android app store increased day by day. This also brig some security related issues for the end users. The increased popularity of Android operating system on mobile devices, and the associated financial benefits attracted attackers for developing some malware for these devices, which results a significant increase in the number of Android malware applications. To detect this type of security threats, signature based detection (static detection) in generally preferred due to its easy applicability and fast identification ability. Therefore in this study it is aimed to implement an up-to-date, effective, and reliable malware detection system with the help of some deep learning algorithms. In the proposed system, RNN-based LSTM, BiLSTM and GRU algorithms are evaluated on CICInvesAndMal2019 data set which contains 8115 static features for malware detection. Experimental results show that the BiLSTM model outperforms other proposed RNN-based deep learning methods with an accuracy rate of 98.85 %.
Microwave Photonic Signal Processing and Sensing Based on Optical Filtering

Microwave photonics, based on optical filtering techniques, are attractive for wideband signal processing and high-performance sensing applications, since it brings significant benefits to the fields by overcoming inherent limitations in electronic approaches and by providing immunity to electromagnetic interference. Recent developments in optical filtering based microwave photonics techniques are presented in this paper. We present single sideband modulation schemes to eliminate dispersion induced power fading in microwave optical links and to provide high-resolution spectral characterization functions, single passband microwave photonic filters to address the challenges of eliminating the spectral periodicity in microwave photonic signal processors, and review the approaches for high-performance sensing through implementing microwave photonics filters or optoelectronic oscillators to enhance measurement resolution.
Perfect Shuffles through Dynamical Systems

A perfect shufﬂe on a deck of 2 n cards is performed by splitting the deck into two packets of n cards each and then interleaving the cards in the two packets together perfectly. This can be done in two ways. If the shufﬂe moves the top and bottom cards one position into the deck, it is called an in shufﬂe . If, on the other hand, the shufﬂe leaves the top and bottom cards in their original positions, it is called an out shufﬂe . There are several ways to model perfect shufﬂes mathematically, and there is a good deal of mathematical research on perfect shufﬂes and their applications. We know that they were studied as far back as 1726 [ 3 ]. Many articles on perfect shufﬂes are of a recreational-mathematics nature [ 1, 10, 15 ], and some are directed towards undergraduate mathematics education [ 4, 14, 19 ]. Authors as renowned as Persi Diaconis, Ron Graham, and Martin Gardner have written on the subject [ 7, 8, 9 ]. You can ﬁnd applications to computer science [ 2, 5, 16, 20, 21, 22 ] as well as magic [ 13, 23, 24 ]. The best single reference on all of these aspects of perfect shufﬂes is the book Magic Tricks, Card Shufﬂing, and Dynamic Computer Memories by S. Brent Morris and published by the MAA. It also contains the most complete bibliography the author has found on the subject.