Miracle-dz/UniFAR-FTUs huggingface dataset:
Molecular markers for Nassella trichotoma (Poaceae) to study genetic variation in New Zealand

Premise of The Study Simple sequence repeat (SSR) markers were developed for the study of genetic diversity of New Zealand Nassella trichotoma (Poaceae) and to support future studies in its native range. Methods and Results Genomic DNA was extracted from N. trichotoma leaf material and subjected to Roche 454 sequencing. From a total of 745 putative SSRs, 48 with di‐ to pentanucleotide repeats were screened, 32 primer pairs were designed, and 15 polymorphic markers were optimized for multiplex PCR on 105 N. trichotoma samples from four New Zealand regions. Each locus resulted in two to six alleles per locus, and four of the loci cross‐amplified in N. tenuissima. The mean observed and expected heterozygosity ranged from 0.00 to 0.90 and 0.00 to 0.50 per locus, respectively. Conclusions The novel SSR markers are valuable for the study of genetic diversity of N. trichotoma and might also be useful for closely related species.
Physical Interpretation of the Orthogonality Sampling Method

The orthogonality sampling method has been recently introduced in inverse scattering literature as a way to retrieve morphological properties of unknown targets. Both the simplicity and the possibility of being exploited also for a single incidence make this method more appealing than other qualitative methods. However, notwithstanding these interesting features, no physical interpretation is attributed to the method. For this reason, in this contribution we firstly attempt to formulate a physical interpretation of the method, in order to take full advantage of its flexibility and to fully understand its limitations.
Automatic Lexical Access in Visual Modality: Eye-Tracking Evidence

Language processing has been suggested to be partially automatic, with some studies suggesting full automaticity and attention independence of at least early neural stages of language comprehension, in particular, lexical access. Existing neurophysiological evidence has demonstrated early lexically specific brain responses (enhanced activation for real words) to orthographic stimuli presented parafoveally even under the condition of withdrawn attention. These studies, however, did not control participants’ eye movements leaving a possibility that they may have foveated the stimuli, leading to overt processing. To address this caveat, we recorded eye movements to words, pseudowords, and non-words presented parafoveally for a short duration while participants performed a dual non-linguistic feature detection task (color combination) foveally, in the focus of their visual attention. Our results revealed very few saccades to the orthographic stimuli or even to their previous locations. However, analysis of post-experimental recall and recognition performance showed above-chance memory performance for the linguistic stimuli. These results suggest that partial lexical access may indeed take place in the presence of an unrelated demanding task and in the absence of overt attention to the linguistic stimuli. As such, our data further inform automatic and largely attention-independent theories of lexical access.
Modeling and Throughput Analysis of FSO Systems using GBN-ARQ and AR Transmission over Atmospheric Turbulence Channels

This paper addresses the error-control issue in free-space optical (FSO) communications. In particular, we model and analyze the throughput performance of FSO systems over atmospheric fading channel when the go-back-N (GBN) automatic repeat request (ARQ) and adaptive rate (AR) transmission schemes are employed. For this purpose, we first build the model to capture the time-varying behavior in atmospheric fading channels. We then use that channel model to develop the burst error model for GBN-ARQ with AR in order to analytically derive the system throughput. Numerical results quantitatively show the impacts of atmospheric turbulence on the throughput and confirm the effectiveness of the GBN/ARQ. Monte Carlo simulations are also performed to validate the analytical results.
Predictive Utilization of Energy Storage for Wind Power Fluctuation Mitigation

Deployment of battery energy storage system (BESS) is a viable option to alleviate the inherent power fluctuation of wind farms. In this paper, a novel model predictive control (MPC) scheme is presented in order to control the BESS dispatch in an integrated wind farm. The constraints of BESS are formulated and are taken into account in optimization problem. The predictive control is based on receding horizon which shows robustness and improved performance compared to the conventional BESS control strategies. The objective function is defined in order to minimize the deviation of wind power production relative to the scheduled production. The results showed the effectiveness of the proposed method and improved performance of the integrated wind farm.
Reputation-Aware Recruitment and Credible Reporting for Platform Utility in Mobile Crowd Sensing with Smart Devices in IoT

The Internet of things (IoT) comprises a huge collection of electronic devices connected to the Internet to ensure the dependable exchange of sensing information. It involves mobile workers (MWs) who perform various activities to support enormous online services and applications. In mobile crowd sensing (MCS), a massive amount of sensing data is also generated by smart devices. Broadly, in the IoT, verifying the credibility and truthfulness of MWs’ sensing reports is needed for MWs to expect attractive rewards. MWs are recruited by paying monetary incentives that must be awarded according to the quality and quantity of the task. The main problem is that MWs may perform false reporting by sharing low-quality reported data to reduce the effort required. In the literature, false reporting is improved by hiring enough MWs for a task to evaluate the trustworthiness and acceptability of information by aggregating the submitted reports. However, it may not be possible due to budget constraints, or when malicious reporters are not identified and penalized properly. Recruitment is still not a refined process, which contributes to low sensing quality. This paper presents Reputation, Quality-aware Recruitment Platform (RQRP) to recruit MWs based on reputation for quality reporting with the intention of platform profit maximization in the IoT scenario. RQRP comprises two main phases: filtration in the selection of MWs and verifying the credibility of reported tasks. The former is focused on the selection of suitable MWs based on different criteria (e.g., reputation, bid, expected quality, and expected platform utility), while the latter is more concerned with the verification of sensing quality, evaluation of reputation score, and incentives. We developed a testbed to evaluate and analyze the datasets, and a simulation was performed for data collection scenario from smart sensing devices. Results proved the superiority of RQRP against its counterparts in terms of truthfulness, quality, and platform profit maximization. To the best of our knowledge, we are the first to study the impact of truthful reporting on platform utility.
A New Low-Power and High-Linearity CMOS Bulk-Injection Mixer in $0.13\ \mu m$ Technology

This paper presents a low-power, high-linear and ultra-wideband (UWB) mixer using the bulk-injection technique. The proposed mixer is realized with a cross-coupled pair transistor as the transconductance stage, a Gilbert-cell to modulate the RF current with LO signal and resistors RL at the output IF port. In this circuit, the RF signal is applied to the bulk terminals of the first stage for low-power consumption and high conversion gain. Using bulk-injection technique allows more flat conversion gain over a wide range of frequencies from 3.1 GHz to 8.5 GHz. Also, a distortion analysis of the proposed bulk-driven mixer is presented based on Volterra series. To compare with the simulation results, the effects of the second- and third-order intermodulation distortions of the circuit are provided. In the designing of the circuit, the bulk-injection transistors are set to operate in the subthreshold region to reduce the power dissipation as low as possible. According to the simulation results, the proposed mixer dissipates 0.448 mW power from a 1.6V supply voltage and also, achieves 7dBm to 10dBm IIP3, 85dBm IIP2. Also, the proposed mixer offers the simulated conversion gain from 12.65 dB to 13.7 dB and DSB noise figure from 13.7 to 17.4 dB. The proposed mixer is designed using a TSMC $0.13 \mathbf{\mu m}$ RF CMOS technology.
Five Driving Forces of Multi-Access Edge Computing

The emergence of Multi-Access Edge Computing (MEC) technology aims at extending cloud computing capabilities to the edge of the wireless access networks. MEC provides real-time, high-bandwidth, low-latency access to radio network resources, allowing operators to open their networks to a new ecosystem and value chain. Moreover, it will provide a new insight to the design of future 5th Generation (5G) wireless systems. This paper describes five key technologies, including Network Function Vitalization (NFV), Software Defined Networking (SDN), Network Slicing, Information Centric Networking (ICN) and Internet of Things (IoT), that intensify the widespread of MEC and its adoption. Our goal is to provide the associativity between MEC and these five driving technologies in 5G context while identifying the open challenges, future directions, and tangible integration paths.
Extracellular Vesicles From Sporothrix brasiliensis Are an Important Virulence Factor That Induce an Increase in Fungal Burden in Experimental Sporotrichosis

Sporotrichosis is a mycosis that affects the skin, lymphatic system and other organs in humans and animals. The disease has a worldwide distribution, with endemic areas in Brazil, and is caused by a complex of species, including Sporothrix brasiliensis. Some fungi release extracellular vesicles (EVs) that can interact with the host cell and modulate the host immune response. The aim of this study was to analyze the participation of S. brasiliensis EVs in the modulation of dendritic cells (DCs) and in the control of infection in vivo. Our results showed that in vitro, the EVs isolated from S. brasiliensis induced an increase in the phagocytic index and fungal burden in DCs. In addition, we observed a significant increase in IL-12p40 and TNF-α cytokine production. Then, the EVs were inoculated into BALB/c mice before subcutaneous infection with yeast, and the lesion was analyzed after 21, 35, and 42 days. An increase in fungal burden and lesion diameter were observed after 21 days in mice inoculated with a high concentration of EVs. However, after 35 days, we observed a regression of the lesion, which persisted until 42 days after infection. Interestingly, we observed an increase in fungal burden in these mice. In addition, we observed the presence of immunogenic components and proteins that could be related with virulence in EVs. These results suggest that EVs can play an important role in virulence and modulation of the host immune system during experimental S. brasiliensis infection.
What's going on in my city?: recommender systems and electronic participatory budgeting

In this paper, we present electronic participatory budgeting (ePB) as a novel application domain for recommender systems. On public data from the ePB platforms of three major US cities - Cambridge, Miami and New York City-, we evaluate various methods that exploit heterogeneous sources and models of user preferences to provide personalized recommendations of citizen proposals. We show that depending on characteristics of the cities and their participatory processes, particular methods are more effective than others for each city. This result, together with open issues identified in the paper, call for further research in the area.
Single- And Double-Mode Light Generation in DFB Fiber Laser: Wavelength-Scale Electromagnetic Modelling by the Method of Single Expression

Distributed feedback (DFB) semiconductor and fiber lasers are widely used in different area of science, technology, medicine, etc. Single- and double-mode operation of fiber lasers is very advantageous for telecommunication, spectroscopy, sensors applications, microwave generation, etc. However, there is a lack of a wavelength-scale electromagnetic analysis of such fiber lasers at the single- and double-mode radiation. In the current work the results of correct wavelength-scale electromagnetic analysis of radiation of DFB fiber lasers operating in single- and double-mode are presented. For wavelength-scale analysis the non-traditional method of single expression is used. In accordance with the known information, single- and double-mode radiation from DFB fiber laser is observed at single π-phase-shift in the middle of permittivity sinusoidal modulation of an amplifying fiber and equally spaced two π-phase-shifts, correspondingly. Numerically obtained wavelength-scale distributions of electric field component and power flow density within the DFB amplifying fiber will be useful in designing single- and double-mode radiating DFB fiber lasers.
Real Time Temperature Prediction Using IoT

Accurate temperature predictions play a crucial role on planning our day-to-day tasks. Farmers need information about the weather condition in order to get ready for planting as well as harvesting of their yields. This study is done to make an endeavor to predict accurate temperature by utilizing potential deep learning approach; Long Term Memory (LSTM). DHT11 digital temperature and humidity sensor were used to gather real time data for undertaking the experiment. For the contextual investigation, the prediction of the temperature in different places of Vadodara is considered.
Metabolomic Investigations on Nesterenkonia flava Revealed Significant Differences between Marine and Terrestrial Actinomycetes

Marine microorganisms are an important source of natural products with potent bioactivities. Unlike the land, the ocean, especially the deep-sea, is characterized by high pressure, high salinity, low nutrition, and no light among others. Therefore, the biodiversity of marine microorganisms is supposed to be very different from that of the terrestrial ones. Yet, many marine microorganisms can find their counterparts in terrestrial environments. To evaluate their differences, a comparative metabolomics investigation was performed on four strains of Nesterenkonia flava isolated from terrestrial and marine environments. As a result, marine strains were clearly distinguished from terrestrial ones on the principal components analysis (PCA) score plot. Furthermore, by partial least squares discrimination analysis (PLS-DA) and univariate analysis, the characteristic metabolites were figured out and found to be involved in osmotic regulation, redox balancing, and energy metabolism. Our results demonstrated that marine actinomycetes could produce novel secondary metabolites different from their terrestrial relatives because they have special metabolic patterns closely related to the unique features of their living environment.
Recommendation Systems in a Conversational Web

In this paper we redefine the concept of Conversation Web in the context of hyper-personalization. We argue that hyper-personalization in the WWW is only possible within a conversational web where websites and users continuously“discuss” (interact in any way). We present a modular system architecture for the conversational WWW, given that adapting to various user profiles and multivariate websites in terms of size and user traffic is necessary, especially in e-commerce. Obviously there cannot be a unique fit-to-all algorithm, but numerous complementary personalization algorithms and techniques are needed. In this context, we propose PRCW, a novel hybrid approach combining offline and online recommendations using RFMG, an extension of RFM modeling. We evaluate our approach against the results of a deep neural network in two datasets coming from different online retailers. Our evaluation indicates that a) the proposed approach outperforms current state-of-art methods in small-medium datasets and can improve performance in large datasets when combined with other methods, b) results can greatly vary in different datasets, depending on size and characteristics, thus locating the proper method for each dataset can be a rather complex task, and c) offline algorithms should be combined with online methods in order to get optimal results since offline algorithms tend to offer better performance but online algorithms are necessary for exploiting new users and trends that turn up.
A 60-GHz Transmission Line Phase Shifter Using Varactors and Tunable Inductors in 65-nm CMOS Technology

This paper describes a 60-GHz artificial transmission line phase shifter designed in 65-nm CMOS technology. To increase the phase shift range and preserve the matching over the entire phase shift range, the fixed series inductors in the standard line cell are substituted by tunable inductors. The required tunable inductors are constructed using a transformer with the transformer secondary loaded by a varactor. To verify the operation principle, the phase shifters, including one-, two- and three-cell, are manufactured. The experimental phase shift range is 45° for one-cell, 92° for two-cell, and 133° for three-cell line shifters. These figures are nearly 80% larger than the corresponding phase shifts in the lines consisting of cells with fixed inductors. The input/output return loss is less than 10 dB for all cases over the entire phase shift range. The average insertion loss is 3.2 dB for one-cell, 5.6 dB for two-cell, and 7.8 dB for three-cell transmission line phase shifters. The proposed continuous phase shifter achieves the highest phase shift range per area among the millimeter-wave transmission line and switch-type phase shifters reported to date.
A Faster Technique on Rice Disease Detectionusing Image Processing of Affected Area in Agro-Field

Plant disease is defined as an abnormal physiological process that distorts the plant's normal structure, growth and function. Disease reduces quality as well as quantity of the crops which in turn affects the economy of country like Bangladesh where agriculture is the main occupation. Since Rice is the major crop, classification of disease in paddy is very important as it prevents the losses in the yields and quantity. Classification of rice disease includes visually observable patterns and color of the affected portion. Manual observation of patterns and colors to classify the diseases require excessive work and appears to be less useful while dealing with non-native diseases. This paper presents a new technique to detect and classify the diseases based on percentage of RGB value of the affected portion using image processing. Once the percentage of RGB from the affected region is extracted and grouped into various classes, they are fed to a simple classifier called Naive Bayes which classifies the disease into various categories. This technique has successfully detected and identified three rice diseases namely rice brown spot, rice bacterial blight, and rice blast. This technique is efficient and faster because it uses only one feature i.e. RGB values of the affected portion which requires minimum computation time to identify and classify the diseases. Rather than processing the whole leaf, this technique even successfully detects the diseases using only a small sample of leaf containing the affected portion for rice disease.
Measures of single- versus multiple-round translation argue against a mechanism to ensure coupling of transcription and translation

Significance It has been appreciated for decades that translation can strongly impact transcription in bacteria. A popular view is that RNA polymerase and the lead ribosome tend to be physically coupled, explaining how translation can influence transcription. Here, we investigate this model by comparing single- versus multiple-round translation of genes in six representative operons of Escherichia coli. Our data lend no support for a mechanism to ensure or promote coupling between RNA polymerase and the lead ribosome, implying that in these cases any physical coupling occurs stochastically. This work provides important insight on a fundamental aspect of gene expression in bacteria. In prokaryotes, the synthesis of RNA and protein occurs simultaneously in the cytoplasm. A number of studies indicate that translation can strongly impact transcription, a phenomenon often attributed to physical coupling between RNA polymerase (RNAP) and the lead ribosome on the nascent mRNA. Whether there generally exists a mechanism to ensure or promote RNAP–ribosome coupling remains unclear. Here, we used an efficient hammerhead ribozyme and developed a reporter system to measure single- versus multiple-round translation in Escherichia coli. Six pairs of cotranscribed and differentially translated genes were analyzed. For five of them, the stoichiometry of the two protein products came no closer to unity (1:1) when the rounds of translation were severely reduced in wild-type cells. Introduction of mutation rpoB(I572N), which slows RNAP elongation, could promote coupling, as indicated by stoichiometric SspA and SspB products in the single-round assay. These data are consistent with models of stochastic coupling in which the probability of coupling depends on the relative rates of transcription and translation and suggest that RNAP often transcribes without a linked ribosome.
A Framework for Development of Useful Metabolomic Biomarkers and Their Effective Knowledge Translation

Despite the significant advantages of metabolomic biomarkers, no diagnostic tests based on metabolomics have been introduced to clinical use. There are many reasons for this, centered around substantial obstacles in developing clinically useful metabolomic biomarkers. Most significant is the need for interdisciplinary teams with expertise in metabolomics, analysis of complex clinical and metabolomic data, and clinical care. Importantly, the clinical need must precede biomarker discovery, and the experimental design for discovery and validation must reflect the purpose of the biomarker. Standard operating procedures for procuring and handling samples must be developed from the beginning, to ensure experimental integrity. Assay design is another challenge, as there is not much precedent informing this. Another obstacle is that it is not yet clear how to protect any intellectual property related to metabolomic biomarkers. Viewing a metabolomic biomarker as a natural phenomenon would inhibit patent protection and potentially stifle commercial interest. However, demonstrating that a metabolomic biomarker is actually a derivative of a natural phenomenon that requires innovation would enhance investment in this field. Finally, effective knowledge translation strategies must be implemented, which will require engagement with end users (clinicians and lab physicians), patient advocate groups, policy makers, and payer organizations. Addressing each of these issues comprises the framework for introducing a metabolomic biomarker to practice.