Miracle-dz/UniFAR-FTUs huggingface dataset:
A 60-MS/s 5-MHz BW Noise-Shaping SAR ADC With Integrated Input Buffer Achieving 84.2-dB SNDR and 97.3-dB SFDR Using Dynamic Level-Shifting and ISI-Error Correction

This article presents a 60-MS/s 5-MHz BW noise-shaping (NS) successive-approximation-register (SAR) analog-to-digital converter (ADC) with an integrated highly linear input buffer in a 40-nm CMOS process. A dynamic level-shifting (DLS) technique is proposed to adaptively adjust the output common-mode voltage of the integrated input buffer for different operation phases, achieving the optimal linearity while alleviating the voltage breakdown problem at the capacitive digital-to-analog converter (CDAC) top plate. The mismatch-error-shaping (MES) technique is utilized to minimize the CDAC mismatch, which also generates undesired inter-symbol-interference (ISI) errors as a side effect. An ISI-error correction (IEC) technique is proposed to mitigate this problem and further improve the linearity. Both foreground and background calibrations are adopted in the subranging ADC and the NS filter to improve the gain accuracy. This SAR ADC prototype, with an integrated input buffer driving a 4.4-pF CDAC within 2.7 ns, achieves signal-to-noise ratio (SNR)/signal-to-noise-and-distortion ratio (SNDR)/spurious-free dynamic range (SFDR)/dynamic range (DR)/total harmonic distortion (THD) of 85.4 dB/84.2 dB/97.3 dBc/86.4 dB/−92.9 dBc while consuming 8.06 mW from 2.5- and 1.1-V dual supply voltages. The Walden FoM (FoM $_{\mathrm {W}}$ ) and figure-of-merit (FoM $_{\mathrm {S}}$ ) are 60.6 fJ/conv. -step and 172.1 dB, respectively.
Drought tolerance of elite soybean cultivars with the introgression of transgene AtAREB1

Abstract The objective of this work was to verify if the introgression of the AtAREB1 gene in the 'LS93-0375' and 'BMX Desafio RR' elite soybean germplasms increases the tolerance of these plants to water deficit. The F4 progenies of these two elite cultivars and of the AtAREB1 transgenic line (BR16-AtAREB1) and its background ('BR16') were subjected to water deficit assays. The water deficit bioassays were performed in a greenhouse using the following six soybean lines: the genetically modified BR16-AtAREB1 and its background 'BR16'; 'LS93' and its F4 progeny, LS93-AtAREB1; and 'BMX Desafio RR' and its F4 progeny, Desafio-AtAREB1. A randomized complete block experimental design was carried out in a 6x2 factorial arrangement, with the six soybean genotypes and two water conditions – control (C) and water deficit (WD) treatments – with nine replicates. Soybean genotypes containing the AtAREB1 gene showed better physiological performances under drought stress and altered expressions of drought-responsive genes. The intogression of AtAREB1 in soybean increases the plant drought tolerance, regardless of the genetic background in which the gene was introduced.
EUV stochastic defect monitoring with advanced broadband optical wafer inspection and e-beam review systems

As Extreme UltraViolet (EUV) lithography nears high volume manufacturing (HVM) adoption to enable the sub-7nm scaling roadmap, characterizing and monitoring defects that print at wafer level are of critical importance to yield. This is especially true for defects coming from the EUV mask, such as multi-layer defects, added particles or growth on mask, and for defects coming from the pattern formation process itself, also referred to as stochastic printing defects. A “Print Check” solution has been previously described.1 This technique uses full-wafer patterned optical inspection to monitor mask defects that print on the wafer. In this paper we focus on developing metrology solutions for stochastic printing defects, which are random local variations that occur between structures that should, in principle, print identically, but actually occur at significant frequencies with current state-of-the-art processes. Specifically, we discuss the importance of monitoring these defects using broadband plasma optical inspection and e-beam defect review systems. We show extensive characterizations of defects on line space patterns down to a pitch of 36nm, on contact holes at a pitch of 48nm and on logic blocks in a foundry equivalent N5 test vehicle. Analysis methods based on CD SEM and review SEM images have been described.
ZSI for PV systems with LVRT capability

This study proposes a power electronics interface (PEI) for photovoltaic (PV) applications with a wide range of ancillary services. As the penetration of distributed generation systems is booming, the PEI for renewable energy sources should be capable of providing ancillary services such as reactive power compensation and low-voltage ride through (LVRT). This study proposes a robust model predictive-based control strategy for grid-tied Z -source inverters (ZSIs) for PV applications with LVRT capability. The proposed system has two operation modes: normal grid condition and grid fault condition modes. In normal grid condition mode, the maximum available power from the PV panels is injected into the grid. In this mode, the system can provide reactive power compensation as a power conditioning unit for ancillary services from DG systems to main ac grid. In case of grid faults, the proposed system changes the behaviour of reactive power injection into the grid for LVRT operation according to the grid requirements. Thus, the proposed controller for ZSI is taking into account both the power quality issues and reactive power injection under abnormal grid conditions. The proposed system operation is verified experimentally, the results demonstrate fast dynamic response, small tracking error in steady-state, and simple control scheme.
Visualizing the knowledge domain of anti-poverty research between 2011 and 2020: a bibliometric analysi in CiteSpace

ABSTRACT: The elimination of poverty and hunger in all its forms and dimensions is one of the key objective of global sustainable development (GSD). This paper analyzed the characteristics of literature release, research progress and frontier trends in the field of anti-poverty in the past ten years by means of bibliometrics, and information visualization. Using CiteSpace to analyze the literature on anti-poverty in the core journals of Web of Science and Scopus (W&S), and China National Knowledge Infrastructure (CNKI). The results showed that:(1) The heat of anti-poverty research is on the rise in both W&S and CNKI, the growth rate of Chinese literature published is significantly higher than that of English literature after 2015, but the international influence is not enough. (2) Researchers and institutions in W&S are more independent than in CNKI. The Chinese research team is more stable and larger, but it needs to be further strengthened in cross-institutional and interdisciplinary research. (3)The research content in W&S mainly focuses on influencing factors and poverty reduction mechanisms, ecosystem services, maternal health and sustainable development. According to the China National Knowledge Infrastructure (CNKI), China paid more attention to concrete ways to reduce poverty, and after completing the fight against absolute poverty by 2020, China will pay more attention to reducing relative poverty and solve multi-dimensional poverty problems by improving the security of medical care, education and housing conditions.
Efficient Detection and Recovery of Malicious PowerShell Scripts Embedded into Digital Images

Due to steady improvements in defensive systems, malware developers are turning their attention to mechanisms for cloaking attacks as long as possible. A recent trend exploits techniques like Invoke-PSImage, which allows embedding a malicious script within an innocent-looking image, for example, to smuggle data into compromised devices. To address such a class of emerging threats, new mechanisms are needed, since standard tools fail in their detection or offer poor performance. To this aim, this work introduces Mavis, an efficient and highly accurate method for detecting hidden payloads, retrieving the embedded information, and estimating its size. Experimental results collected by considering real-world malicious PowerShell scripts showcase that Mavis can detect attacks with a high accuracy (100%) while keeping the rate of false positives and false negatives very low (0.01% and 0%, respectively). The proposed approach outperforms other solutions available in the literature or commercially through “as a service” model.
Effect of Using Steel Bar Reinforcement on Concrete Quality by Ultrasonic Pulse Velocity Measurements

Non-destructive tests (NDTs) represent one of the solutions that aid engineers in evaluating the strength of materials. However, the results obtained using such tests are still questionable as they may be affected by different factors. One of these factors is the presence of steel reinforcement in concrete. An experimental investigation is presented in this study to investigate the effect of the single reinforcement steel bar on ultrasonic pulse velocity (UPV). Seven concrete beams, one containing no steel and the other six beams containing varying bar diameter and cover thicknesses, were tested. UPV measurements were obtained using the indirect method and then modified to eliminate the effect of the steel bar. To provide the scientific evidence to give a reliable and reasonable solution, a statistical analysis was also conducted. The results show that a large bar diameter and a small cover thickness significantly influence the measured UPV. Measured UPV with a spacing between transducers up to 500 mm can effectively be used to predict the compressive strength of concrete after the modification of the initial UPV.
The Strategy of Paratransgenesis for the Control of Malaria Transmission

Insect-borne diseases are responsible for important burdens on health worldwide particularly in Africa. Malaria alone causes close to half a million deaths every year, mostly in developing, tropical and subtropical countries, with 94% of the global deaths in 2019 occurring in the WHO African region. With several decades, vector control measures have been fundamental to fight against malaria. Considering the spread of resistance to insecticides in mosquitoes and to drugs in parasites, the need for novel strategies to inhibit the transmission of the disease is pressing. In recent years, several studies have focused on the interaction of malaria parasites, bacteria and their insect vectors. Their findings suggested that the microbiota of mosquitoes could be used to block Plasmodium transmission. A strategy, termed paratransgenesis, aims to interfere with the development of malaria parasites within their vectors through genetically-modified microbes, which produce antimalarial effectors inside the insect host. Here we review the progress of the paratransgenesis approach. We provide a historical perspective and then focus on the choice of microbial strains and on genetic engineering strategies. We finally describe the different steps from laboratory design to field implementation to fight against malaria.
The Use of Nanoparticles in Otoprotection

The inner ear can be insulted by various noxious stimuli, including drugs (cisplatin and aminoglycosides) and over-acoustic stimulation. These stimuli damage the hair cells giving rise to progressive hearing loss. Systemic drugs have attempted protection from ototoxicity. Most of these drugs poorly reach the inner ear with consequent ineffective action on hearing. The reason for these failures resides in the poor inner ear blood supply, the presence of the blood-labyrinthine barrier, and the low permeability of the round window membrane (RWM). This article presents a review of the use of nanoparticles (NPs) in otoprotection. NPs were recently used in many fields of medicine because of their ability to deliver drugs to the target organs or cells. The studies included in the review regarded the biocompatibility of the used NPs by in vitro and in vivo experiments. In most studies, NPs proved safe without a significant decrease in cell viability or signs of ototoxicity. Many nano-techniques were used to improve the drugs' kinetics and efficiency. These techniques included encapsulation, polymerization, surface functionalization, and enhanced drug release. In such a way, it improved drug transmission through the RWM with increased and prolonged intra-cochlear drug concentrations. In all studies, the fabricated drug-NPs effectively preserved the hair cells and the functioning hearing from exposure to different ototoxic stimuli, simulating the actual clinical circumstances. Most of these studies regarded cisplatin ototoxicity due to the wide use of this drug in clinical oncology. Dexamethasone (DEX) and antioxidants represent the most used drugs in most studies. These drugs effectively prevented apoptosis and reactive oxygen species (ROS) production caused by ototoxic stimuli. These various successful experiments confirmed the biocompatibility of different NPs and made it successfully to human clinical trials.
Deep learning applied to automatic polyp detection in colonoscopy images : master thesis in System Engineering with Embedded Systems

Deep learning is an improvement to the neural network that contains more computational layers that allow for higher levels of abstraction and prediction in the data. So far, it is becoming a leading machine learning tool for general imaging and computer vision. Current trends in research have also demonstrated that deep convolutional neural networks (DCNNs) are very effective in automatically analyzing images. However, the requirement of large number of annotated samples prohibits its wide use in medical image analysis, since collecting and labeling a large amount of data is difficult due to the challenges in obtaining the data from the medical domain. Polyps are known as possible colorectal cancer precursors, and their early detection is of great importance, but highly challenging from an image processing standpoint. In this work, we evaluate several state-of-the-art machine learning techniques and deep learning methods in the medical image processing domain and research solutions about how they can be more efficiently utilized for automatic detection of polyps in endoscopy and colonoscopy images. This work proposes an effective transfer learning (TL) framework relying on pretrained DCNNs using a large collection of natural ImageNet images. This has been achieved by evaluating various kinds of cutting edge techniques including both traditional machine learning methods by training feature-based classifiers from scratch and modern DCNNs algorithms with (TL) and fine tuning pre-trained models. We transfer learned ImageNet weights as initial weights, and then fine-tune this model combined with a new deep classifier called fully connected networks (FCNs) with data augmentation and patch-extraction of colonoscopy images to automatically detect polyps. In case of insufficient colonoscopy images, patch-based data augmentation and deep features extracted using TL strategy can provide sufficient and balanced classification information. With the proposed TL framework with our optimized hyper-parameters, the system achieved overall 96.00% polyp detection precision and sensitivity, which outperformed the traditional machine learning classification methods in each defined performance metric. Moreover, the TL framework proposed is scalable and flexible so that it can easily be extended to include other types of disease detection in the future and also be able to integrate one more DCNNs model to boost its generalizing capabilities.
The Technological Alternatives for Energy and Hydraulic Improvements

The problem of converting attenuated hydraulic energy into power energy in an artificial local resistance unit placed on a gravity drinking water pipeline (outlet control valve, pressure regulator) without disrupting the hydraulic regime of the water pipeline is examined. It is recommended to replace the regulator with the same resistance hydraulic turbine, and thus, with its corrective device, automatically adjust the consumption outlet of the water pipe. The energy and economic data of the hydraulic turbine unit to be built on the «Arzakan-Yerevan» drinking water main pipelines have been presented as an example of proposal implementation. According to estimations, the small hydroelectric power plant on the Yerevan water pipeline could produce 90 million kWh of electricity per year. It should be noted that the water supply system in Yerevan has around 300 half-open valves and pressure control devices, and in case of conversion of many of them, it is possible to apply the suggestion given in the article.
PENGARUH KEMAMPUAN TECHNOLOGICAL PEDAGOGICAL CONTENT KNOWLEDGE (TPACK) TERHADAP KESIAPAN MENJADI GURU

Guru merupakan salah satu pihak yang berperan untuk mewujudkan pendidikan yang berkualitas. Oleh karena itu kualitas guru sangat menentukan kualitas pelaksanaan pembelajaran di sekolah. Perguruaan tinggi memiliki peran yang besar dalam memberikan bekal berbagai kompetensi kepada para calon guru untuk dapat memasuki dunia kerja. Dengan kompetensi yang cukup, calon guru akan lebih siap memasuki dunia sekolah. Penelitian ini bertujuan untuk menganalisis pengaruh kemampuan teknologi, kemampuan pedagogik, dan pengetahuan bidang akuntansi terhadap kesiapan mahasiswa menjadi guru. Penelitian ini menggunakan pendekatan kuantitatif dengan metode eksplanasi. Responden penelitian berjumlah 211 mahasiswa pendidikan akuntansi Universitas Negeri Malang. Teknik pengambilan sampel menggunakan proportional stratified random sampling. Teknik pengumpulan data menggunakan kuesioner yang disebarkan secara online kemudian dianalisis menggunakan regresi linier berganda. Hasil penelitian menunjukkan bahwa kemampuan teknologi, kemampuan pedagogik, dan pengetahuan bidang akuntansi berpengaruh positif signifikan terhadap kesiapan mahasiswa menjadi guru. Artinya semakin baik kemampuan teknologi, kemampuan pedagogik, dan pengetahuan bidang akuntansi mahasiswa calon guru membuat kesiapan mahasiswa untuk menjadi guru semakin baik. Oleh karena itu penelitian ini diharapkan mampu menjadi bahan literature dan wawasan bagi mahasiswa mengenai kemampuan TPACK terhadap kesiapan untuk menjadi guru.
Relative condition factor and predictive model for the presence of the invasive snail Achatina (Lissachatina) fulica in Sergipe, Northeast Brazil

Abstract: Achatina fulica is among the world’s 100 most impactive invasive species, and is now found in almost all Brazilian states, including Sergipe. This exotic snail is known to have negative impacts, not only on the environment, due primarily to the rapid growth of its populations, but also on public health, given that it is an intermediate host of nematodes that cause zoonotic diseases. However, relatively little is known of the development of this snail, including its relative condition factor. We investigated the occurrence of A. fulica in 24 municipalities distributed in the eight subregions of the state of Sergipe in the dry and rainy season. Furthermore, we present here a predictive model for the occurrence of A. fulica based on the variation in climate and soil chemistry. This snail was more frequent on soils with a pH of 6.5-7.5. A negative correlation was found between the growth of A. fulica and the soil pH, then, the more acidic the soil, the more allometric the growth of A. fulica. The relative condition factor indicated differences in the development pattern of A. fulica among the eight subregions. The influence of rain in increasing the frequency of A. fulica showed a significant correlation. As well, higher temperatures influenced the resting behavior of A. fulica. The mathematical model used to identify the potential presence of A. fulica presented a high degree of agreement. This is the first ecological study of A. fulica to verify the association between the body mass-length relationship and the relative condition factor, and the results indicate that the development of this exotic land snail in Sergipe is influenced by climatic factors and principally, the soil pH. The predictive mathematical model provides valuable insights into the biotic and abiotic factors associated with the presence of A. fulica, and the influence of climatic variables and the chemical parameters of the soil on the occurrence of this species. These findings provide important guidelines for the development of measures for the control of A. fulica populations, which will contribute to both public and environment health.
Tomato fruit blotch virus cytopathology strengthens evolutionary links between plant blunerviruses and insect negeviruses

: Tomato fruit blotch virus (ToFBV) is a blunervirus that causes blotches on mature tomato ( Solanum lycopersicon L.) fruits in Italy and Australia in 2020, and was newly detected in Brazil. A cytological study on pericarp tissues from the blotched areas of infected fruits collected in Brasília, Brazil, revealed characteristic cell alterations. Small and slender bacilliform particles (ca. 25 nm wide × 100 nm long) were found accumulating in the perinuclear space and the lumen of the endoplasmic reticulum of the epidermis, peri- and mesocarp cells. No viroplasm-like inclusion was observed either in the nuclei or in the cytoplasm. Such cell alterations are reminiscent of those described in cultured mosquito cells infected by negeviruses, an unofficial group of insect viruses. Negeviruses and some other arthropod-borne viruses shared a common ancestor in the RdRp gene with kitavirids, including blunerviruses. Although additional detailed studies are required, we show evidence that ToFBV particles are enveloped and bacilliform, and that such similarity in cytopathology seems to support the evolutionary relationship between plant kitavirids and insect negeviruses.
An Attention-Based Recommender System to Predict Contextual Intent Based on Choice Histories across and within Sessions

Recent years have witnessed the growth of recommender systems, with the help of deep learning techniques. Recurrent Neural Networks (RNNs) play an increasingly vital role in various session-based recommender systems, since they use the user’s sequential history to build a comprehensive user profile, which helps improve the recommendation. However, a problem arises regarding how to be aware of the variation in the user’s contextual preference, especially the short-term intent in the near future, and make the best use of it to produce a precise recommendation at the start of a session. We propose a novel approach named Attention-based Short-term and Long-term Model (ASLM), to improve the next-item recommendation, by using an attention-based RNNs integrating both the user’s short-term intent and the long-term preference at the same time with a two-layer network. The experimental study on three real-world datasets and two sub-datasets demonstrates that, compared with other state-of-the-art methods, the proposed approach can significantly improve the next-item recommendation, especially at the start of sessions. As a result, our proposed approach is capable of coping with the cold-start problem at the beginning of each session.