Miracle-dz/UniFAR-FTUs huggingface dataset:
Unique Microbial Communities Inhabiting Underground Seawater in an Intertidal Area Utilized for Industrialized Aquaculture, as Compared with the Coastal Water

Abstract In recent decades, the decline of coastal water quality has promoted the birth of a new industrialized aquaculture mode in China, which involves the cultivation of organisms using underground seawater extracted from various depths below the intertidal zone. In view of the special physicochemical characteristics of underground seawater, the microbial community in this environment has attracted interest. In this study, the microbial community in the underground seawater of an intertidal area of the Qingdao coast of China was investigated. Compared with the upper coastal water, the underground seawater displayed lower numbers of microorganisms (2.7 ± 0.3 × 105 cells mL−1 in underground seawater vs. 5.3 ± 0.4 × 105 cells mL−1 in upper coastal seawater) but displayed much higher microbial diversity. At the phyla level, Proteobacteria, Bacteroidetes, Cyanobacteria, and Actinobacteria inhabited both environments, whereas bacteria in the phyla Planctomycetes, Deferribacteres, and Nitrospirae were recovered only from the underground seawater. Eighty-nine percent of the OTUs in the underground seawater were environmental specific. Furthermore, compared with coastal water, underground seawater displayed significant lower (p < 0.05) concentration of NH3-N, NO2-N, PO4-P, and DOC-C, and contained fewer potentially harmful pathogens (e.g., Verrucomicrobia/Opitutae) and more denitrifying bacteria (e.g., Shewanella denitrificans), thus making it more suitable for aquaculture.
Consensus-Based Formation Control of Networked Nonholonomic Vehicles With Delayed Communications

For a network of nonholonomic vehicles communicating according to an undirected connected graph, a consensus-based formation control problem is solved via a smooth time-varying, proportional-derivative, <inline-formula><tex-math notation="LaTeX">$\delta -$</tex-math></inline-formula>persistently-exciting, controller. It is assumed that the communication among agents is affected by time-varying, nondifferentiable, communication delays and uniform global asymptotic stability is demonstrated. This goes beyond the more-often encountered property of nonuniform convergence and, what is more, for the first time in the literature, a strict Lyapunov-Krasovskii functional is provided.
Optimization of Degraded Document Image Binarization Method Based on Background Estimation

Binarization of document images is one of the most relevant pre-processing operations, leading to a signiﬁcant decrease of the amount of information used during their further analysis. Since many document images, particularly historical, may be degraded over time, the application of some simple global thresholding methods usually lead to highly unsatisfactory results. A similar situation may occur for unevenly illuminated images, limiting the visibility of various shapes, representing not only the alphanumerical characters. A typical solution of this problem is the application of some adaptive thresholding methods, as well as more sophisticated solutions, proposed recently e.g. during Document Image Binarization Competitions (DIBCO) or TQ-DIB 2019 competition. Nevertheless, due to their relatively high computational demands, there is still a need of some faster methods, leading to high binarization accuracy for challenging benchmark datasets, such as DIBCO or Nabuco. Hence, the adaptation and optimization of the parameters of the fast thresholding method utilizing background estimation, proposed originally for the OCR purposes and veriﬁed for unevenly illuminated printed documents, is presented in this paper. The proposed solution has been optimized and veriﬁed using the state-of-the-art datasets containing 166 degraded document images together with their ground-truth binary equivalents, leading to better results, also in comparison to much slower adaptive thresholding methods. The performance of all methods used in comparisons has been determined using commonly accepted metrics, such as F-Measure, Accuracy, Distance Reciprocal Distortion (DRD) or Misclassiﬁcation Penalty Metric (MPM), and relative execution time, calculated for all used image datasets.
Decoding the translation initiation mechanism of maize chlorotic mottle virus

Maize chlorotic mottle virus (MCMV) is the key player of Maize Lethal Necrosis Disease (MLND). MLND is caused by the co-infection and synergistic interaction of MCMV and any potyvirus that infects grasses. Recent outbreaks of MLND have ravaged maize fields in Kenya and neighboring regions of East Africa. The catastrophic economic losses brought back the interest of stakeholders to learn more about the synergistic interaction between MCMV and potyviruses and find ways to use this knowledge to create MLND resistant lines. MCMV is a positive-sense single-stranded RNA virus from the Tombusviridae family. Similarly to other members of the Tombusviridae family, MCMV does not have a 5’-cap or a poly (A) tail and must use alternative mechanisms to translate viral proteins. The lack of a 5’-cap in the viral RNA does not prevent it from interacting with host translation factors. Instead of a 5’-cap, most tombusvirids are known to employ unconventional mechanisms to sequester translation factors to the viral RNA. One mechanism of recruiting host’s translation factors is 3’cap-independent translation elements (3’-CITEs). 3’-CITEs are secondary structures located at the 3’-end of the virus genome used to recruit the translation initiation machinery. The work presented in this dissertation revolves around finding out the translation initiation control elements of MCMV RNA. The unearthing of MCMV’s translation initiation process began with a series of deletions at the 3’-end of the virus genome. The genome sequence mapping indicated that there was an essential sequence for virus translation between nucleotides 4164 to 4333. Structural RNA probing of this region showed the presence of a panicum mosaic virus 3-CITE (PTE) -like structure located in the 3’-untranslated region (UTR) of the viral RNA. Similar to other PTEs, the MCMV 3’-CITE secondary structure was composed of a hammer-like helix structure that
Survey of indoor navigation solutions

In the last decade, there is an increasing demand for indoor positioning and navigation. These systems are beneficial in different areas of life, helping the people to find their way in the shopping mall, in the hospital or in any other building. As technology is continuously improving, new indoor systems are appearing bringing less costs and better performance, while other ones are becoming deprecated. This survey is an evaluation of the recent systems, taking their benefits and drawbacks into consideration and analysing the future tendencies.
The DEI: tracking economic activity daily during the lockdown

The SARS-CoV-2 outbreak has spread worldwide causing unprecedented disruptions in the economies. These unparalleled changes in economic conditions made clear the urgent need to depart from traditional statistics to inform policy responses. Hence, the interest in tracking economic activity in a timely manner has led economic agents to rely on high-frequency data as traditional statistics are released with a lag and available at a lower frequency. Naturally, taking on board such a novel data involves addressing some of the complexities of highfrequency data (e.g. marked seasonal patterns or calendar effects). Herein, we propose a daily economic indicator (DEI), which can be used to assess the behavior of economic activity during the lockdown period in Portugal. The indicator points to a sudden and sharp drop of economic activity around mid-March 2020, when the highest level of alert due to the COVID-19 pandemic was declared in March 12. It declined further after the declaration of the State of Emergency in the entire Portuguese territory in March 18, reflecting the lockdown of several economic activities. The DEI also points to an unprecedented decline of economic activity in the first half of April, with some very mild signs of recovery at the end of the month.
Scheduling Mix-Coflows in Datacenter Networks

Data-parallel applications generate a mix of coflows with and without deadlines. Deadline coflows are mission-critical and must be completed within deadlines, while the non-deadline coflows desire to be completed as soon as possible. Scheduling such mix-coflows is an important problem in modern datacenters. However, existing solutions only focus on one of the two types of coflows: they either solely concentrate on meeting the deadlines of deadline-aware coflows or reducing the coflow completion times (CCTs) of non-deadline coflows. In this article, we study the problem of optimizing deadline and non-deadline coflows simultaneously. To this end, we present a new optimization framework, mixCoflow, to schedule deadline coflows to minimize and balance their bandwidth footprint, such that non-deadline coflows can be scheduled as early as possible. Specifically, we develop the mathematical model and formulate the scheduling problem for deadline coflows as a lexicographical min-max integer linear programming (ILP) problem. Through rigorous theoretical analysis, this ILP problem has been proved to be equivalent to a linear programming (LP) problem that can be solved with standard LP solvers. By solving this LP, mixCoflow is able to balance the bandwidth footprint of deadline coflows while guaranteeing their deadlines. As a result, non-deadline coflows can be scheduled as soon as possible whenever they arrive. To demonstrate the effectiveness of our work, we have conducted extensive simulations based on a widely used Facebook data trace. The simulation results verify that mixCoflow can achieve significant improvement on the average CCT of non-deadline coflows, at no expense of increasing the deadline miss rates of deadline coflows, when compared to the state-of-art solutions.
Alternative Methods of Vaccine Delivery: An Overview of Edible and Intradermal Vaccines

Vaccines are recognized worldwide as one of the most important tools for combating infectious diseases. Despite the tremendous value conferred by currently available vaccines toward public health, the implementation of additional vaccine platforms is also of key importance. In fact, currently available vaccines possess shortcomings, such as inefficient triggering of a cell-mediated immune response and the lack of protective mucosal immunity. In this regard, recent work has been focused on vaccine delivery systems, as an alternative to injectable vaccines, to increase antigen stability and improve overall immunogenicity. In particular, novel strategies based on edible or intradermal vaccine formulations have been demonstrated to trigger both a systemic and mucosal immune response. These novel vaccination delivery systems offer several advantages over the injectable preparations including self-administration, reduced cost, stability, and elimination of a cold chain. In this review, the latest findings and accomplishments regarding edible and intradermal vaccines are described in the context of the system used for immunogen expression, their molecular features and capacity to induce a protective systemic and mucosal response.
Decoupling of Antennas With Adjacent Frequency Bands Using Cascaded Decoupling Network

This communication presents a novel decoupling device called a cascaded power dividing decoupling network (C-PDDN). It is cascaded with two antennas operating in adjacent or even contiguous frequency bands to reduce the mutual coupling. To achieve high isolations at <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula> desired frequency points, an <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>-order C-PDDN can be accordingly designed. Each order PDDN consists of two power dividers (PDs), two sections of transmission lines, and one reactive component. Explicit design formulas are presented to calculate the design parameters of the C-PDDN to meet the required isolation criteria. To validate the decoupling concept, a two-order C-PDDN is designed and applied to a testing array that consists of two base-station dipole antennas, one of which serves the time-division long-term evolution (2300–2400 MHz) and the other for wireless fidelity system (2400–2483.5 MHz). The simulated and measured results demonstrate that the proposed two-order C-PDDN could decrease the mutual coupling by over 20 dB, verifying the decoupling methodology.
SCLA-RTI: A Novel Device-Free Multi-Target Localization Method Based on Link Analysis in Passive UHF RFID Environment

RFID, a key enabling technology of Internet of Things (IoT), has a broad development prospect in the device-free localization (DFL). The RFID-based radio tomographic imaging (RTI) method has received great attention from scholars, owing to its properties of good real-time performance, low computational complexity, high localization accuracy. However, RTI suffers from the influence of multipath and noise, which results in the artifacts and false targets appearing in the imaging result and, in turn, decreases the positioning performance significantly, especially when there are multiple targets. Therefore, how to recognize the locations and number of true targets from the imaging result becomes an urgent problem in multi-target positioning. To solve this problem, we propose a novel scanning circle link analysis (SCLA-RTI) method to eliminate false targets and locate real targets in UHF RFID scenario. First, scanning circle method is introduced to detect the line-of-sight (LOS) links through the detecting region of the local maximum pixel in the imaging result. After feature analysis and selection of the RSS change of links, the random forest classifier is used to determine the location and number of real targets. Experiments demonstrate that SCLA-RTI method has superior performance in an indoor environment, which can accurately identify true and false targets and obtain high localization accuracy.
Multimodal Deception Detection Using Real-Life Trial Data

Hearings of witnesses and defendants play a crucial role when reaching court trial decisions. Given the high-stakes nature of trial outcomes, developing computational models that assist the decision-making process is an important research venue. In this article, we address the identification of deception in real-life trial data. We use a dataset consisting of videos collected from public court trials. We explore the use of verbal and non-verbal modalities to build a multimodal deception detection system that aims to discriminate between truthful and deceptive statements provided by defendants and witnesses. In particular, three complementary modalities (visual, acoustic and linguistic) are evaluated for the classification of deception at the subject level. The final classifier is obtained by combining the three modalities via score-level classification, achieving 83.05 percent accuracy in subject-level deceit detection. To place our results in perspective, we present a human deception detection study where we evaluate the human capability of detecting deception using different modalities and compare the results to the developed system. The results show that our system outperforms the average non-expert human capability of identifying deceit.
NSAIDs pattern of use in Nasiriya City-South of Iraq

Background: NSAIDs are group of drugs that was used for their properties as analgesic, antipyretic and anti-inflammatory and this group was divided into 2 groups according to their selectivity towards enzyme called COX as this enzyme have two isomers and these two groups named selective and non-selective COXII inhibitors. NSAIDs are include more than 20 agents that differs in their affinity toward this enzyme and thus differ in their analgesic, antipyretic, anti-inflammatory and adverse effects.1 Aim: Aim of this current study is to validate non-steroidal inti-inflammatory drugs (NSAIDs) pattern selection in Nasiriya city- South of Iraq. Method: 82 physicians were asked about the Non-steroidal anti-inflammatory drugs they prescribe for their patients, the dose, dosage form, dose frequency, concomitant drugs (objective from administration of these drugs with NSAIDs) and adverse effect. The study period was extended from January 2019 till July 2019. Results: The most prescribed NSAIDs was found Ibuprofen (32.9%) and Diclofenac sodium (28.1%) and the other NSAIDs including Mefenamic acid and others account for (39%) and in term the investigation of dosage form we found that the oral dosage form is the more dominant. Conclusion: We find out, Ibuprofen and Diclofenac sodium is suitable for self-medication with regards to its relatively wide spectrum of indication, good tolerance and safety. Overall, it has been rated respectively as the most prescribed conventional NSAIDs.
Mindset Messaging: Fostering Student Support and Confidence through Micro-Messaging in Advisor Communication

As academic advisors help students navigate academic challenges toward a degree, seemingly mundane interactions have the potential to shape students' beliefs about themselves and their abilities. This study examines whether subtle cues within messages from advisors may help students develop what Carol Dweck calls a growth mindset—the belief that ability is malleable through effort, strategy, and help-seeking—and lead to greater perceived support from advisors and student confidence. Drawing on focus groups and interviews with twenty undergraduate students at a large public university, this study offers empirical support for the positive impact that growth mindset language can have within advisor-student communication, as well as a set of practical recommendations for bringing these insights to day-to-day advising practice.
Design of Passivity-Based Damping Controller for Suppressing Power Oscillations in DC Microgrids

In this article, a novel damping control scheme for V/I droop-controlled direct current (dc) microgrids is proposed to attenuate the oscillatory components in the power and current. At first, the model of the dc microgrid is introduced providing stability analysis. Then, the damping controller is designed using the interconnection and damping assignment passivity-based control (IDA-PBC) methodology. In addition, the gain selection technique for the IDA-PBC is developed. The proposed control scheme ensures that the oscillations of the output power and current are effectively attenuated. The validity of the proposed control algorithm has been verified by the results from both hardware-in-the-loop simulation and proto-type experiments.
Intelligent Evaluation System of Government Emergency Management Based on BP Neural Network

With the deepening of the global economic community, various emergencies emerge in endlessly, and the risks gradually increase. People’s lives and property are threatened, which also causes a great burden on the social economy. Hitherto unknown novel coronavirus events occurred in China after the outbreak of the new coronavirus in 2019. The emergency management system is not perfect, so we start to study and improve the deficiencies of the emergency management system, but it is still difficult to effectively prevent and deal with all kinds of sudden and frequent social problems. Therefore, this paper puts forward the research of intelligent evaluation system of government emergency management based on BP neural network. In this paper, an intelligent evaluation system of government emergency management based on Internet of things environment is established, and then the system is deepened by BP neural network algorithm to avoid the interference of human factors. An objective intelligent evaluation system of government emergency management is constructed and verified by an example. We applied the system in a province, and proved that the system has strong executive ability, outstanding big data computing ability, and can objectively evaluate and analyze the government emergency management. The operability and accuracy of the intelligent evaluation system are verified. The effective evaluation content provides a new idea and method for government emergency management. And then continuously improve the emergency management measures to achieve the effect of dealing with things smoothly without panic.
Occurrence of alien spirlin (Alburnoidessp.) in the Neretva river basin

Northern Mediterranean region is characterised by an exceptional richness of the freshwater ichthyofauna. Many fish species of this region are endemic to a single or a few river basins. This is also the case of the Neretva river basin (Adriatic Sea slope), where 17 out of 34 native species are endemic solely to this river basin. However, these unique Mediterranean freshwater ecosystems are fragile and are susceptible to human-induced changes, including introduction of alien fish species. We report here a finding of the 32ndalien fish species in the Neretva river basin, spirlinAlburnoidessp. , which was found at two localities in Bosnia and Herzegovina. The spirlin species was identified by molecular means as so far unnamed species with the native range in the Sava river basin (Danube river basin, Black Sea slope). Based on the comparison ofcytochrome bsequences, the introduced population originated most probably from nearby rivers of the Danube basin in Bosnia and Herzegovina (Vrbas and Bosna river basins). Such a high number of alien fish species reported in a single river basin is alarming and pointing to a necessity of raising public awareness, especially among local fishermen.
Percutaneous Nephrostomy: Is Ultrasound Alone Sufficient as Imaging Guidance?

*Corresponding Author: Çağrı Damar, Gaziantep University, Faculty of Medicine, Department of Radiology, Ünivers ite Bulvarı, 27310, Şehitkamil/Gaziantep E-mail: cagridamar@hotmail.com ORCID ID: Çağrı Damar: https://orcid.org/0000-0001-7208-1290, Mehmet Onay: https://orcid.org/0000-0001-6220-3207, Ali Burak Binboğa: https://orcid.org/0000-0001-5129-3067 Received: 10.02. 2020, Accepted: 17.06. 2020 ORIGINAL ARTICLE
A Realistic Image Generation of Face From Text Description Using the Fully Trained Generative Adversarial Networks

Text to face generation is a sub-domain of text to image synthesis. It has a huge impact on new research areas along with the wide range of applications in the public safety domain. Due to the lack of dataset, the research work focused on the text to face generation is very limited. Most of the work for text to face generation until now is based on the partially trained generative adversarial networks, in which the pre-trained text encoder has been used to extract the semantic features of the input sentence. Later, these semantic features have been utilized to train the image decoder. In this research work, we propose a fully trained generative adversarial network to generate realistic and natural images. The proposed work trained the text encoder as well as the image decoder at the same time to generate more accurate and efficient results. In addition to the proposed methodology, another contribution is to generate the dataset by the amalgamation of LFW, CelebA and locally prepared dataset. The dataset has also been labeled according to our defined classes. Through performing different kinds of experiments, it has been proved that our proposed fully trained GAN outperformed by generating good quality images by the input sentence. Moreover, the visual results have also strengthened our experiments by generating the face images according to the given query.