Miracle-dz/UniFAR-FTUs huggingface dataset:
Benthic Prokaryotic Community Response to Polycyclic Aromatic Hydrocarbon Chronic Exposure: Importance of Emission Sources in Mediterranean Ports

The present study aimed to evaluate the impact of polycyclic aromatic hydrocarbons (PAHs) produced by multiple emission sources on prokaryotic communities in sediments chronically affected by anthropogenic pressures. In this context, surface sediments were investigated in three Mediterranean touristic ports over three sampling periods and in different port sectors. The levels of 16 priority PAHs varied over three orders of magnitude (25-49,000 ng g-1) covering the range of concentrations previously reported for Mediterranean harbors. Pyrogenic processes were found to be the dominant emission source of PAHs, with considerable differences among ports. The prokaryotic communities were identified by using the terminal restriction fragment length polymorphism, targeting the 16S rRNA gene for Bacteria and Archaea as well as the dsrAB gene for sulphate-reducing bacteria (SRB). The structure of the three benthic prokaryotic communities varied consistently among the ports. The structure of Bacteria and Archaea exhibited strong spatiotemporal variations that did not allow us to specifically link the observed differences in community structures with PAH sources. On the contrary, our study provided, for the first time, evidence that the PAH emission sources play a role in structuring benthic communities of sulphate-reducing bacteria (SRB). Our findings indicate that the SRB community can be used as a valuable candidate biotic descriptor for bioremediation monitoring in heavily impacted port sediments.
Detoxification of Deoxynivalenol by a Mixed Culture of Soil Bacteria With 3-epi-Deoxynivalenol as the Main Intermediate

Deoxynivalenol (DON) is a widely distributed mycotoxin that frequently occurs in various agricultural raw materials and feeds. DON acts as a virulence factor that accelerates the spread of plant diseases; moreover, its accumulation in grains causes yield loss and serious health problems to humans and livestock. Biodegradation of DON into less- or non-toxic substances using naturally existing microorganisms is considered the best approach for DON detoxification. Although various single isolates and mixed cultures capable of detoxifying DON have been reported, details of the metabolic pathways and the degrading enzymes/coding genes involved are scarce. In this study, we aimed to isolate DON-degrading bacteria from soil samples and explore the mechanisms. Toward this end, 85 soil samples collected from different provinces in China were enriched under aerobic conditions with mineral media containing 50 μg/ml of DON as the sole carbon source. The bacterial consortium LZ-N1 exhibited highly efficient and steady DON-transforming activity. High-throughput sequencing was used to characterize the composition of the involved microflora, and analysis of 16S rRNA sequences indicated that LZ-N1 was composed of at least 11 bacterial genera, with Pseudomonas accounting for nearly half the relative abundance. Coincubation of a mixed culture of two novel strains from the LZ-N1 consortium, namely Pseudomonas sp. Y1 and Lysobacter sp. S1, showed sustained transformation of DON into the metabolite 3-epi-deoxynivalenol, with no degradation products detected after 72 h. The cell-free supernatant, lysate, and cell debris of the mixed culture possessed DON-degrading ability, with the supernatant reaching a DON degradation rate of 100% within 48 h with 50 μg/ml of DON. This is the first report of two-step enzymatic epimerization of DON by a mixed culture, which may provide a new insight into this pathway for future applications in detoxification of DON-contaminated cereals and feed.
Supply of Methionine During Late-Pregnancy Alters Fecal Microbiota and Metabolome in Neonatal Dairy Calves Without Changes in Daily Feed Intake

To our knowledge, most studies demonstrating the role of manipulating maternal nutrition on hindgut (i.e., large intestine) microbiota in the offspring have been performed in non-ruminants. Whether this phenomenon exists in cattle is largely unknown. Therefore, the objectives of the current study were to evaluate the impact of maternal post-ruminal supply of methionine during late-pregnancy in dairy cows on fecal microbiota and metabolome in neonatal calves, and their association with body development and growth performance during the preweaning period. To achieve this, heifer calves, i.e., neonatal female offspring, born to Holstein cows receiving either a control (CON) diet (n = 13) or CON plus rumen-protected methionine (MET; Evonik Nutrition & Care GmbH) during the last 28 days of pregnancy were used. Fecal samples from heifers were collected from birth until 6 weeks of age, i.e., the preweaning period. Fecal microbiota was analyzed with QIIME 2 whereas fecal metabolites were measured using an untargeted LC-MS approach. At birth, MET heifers had greater (P ≤ 0.05) BW, HH, and WH. During the preweaning period, no differences between groups were detected for starter intake (P = 0.77). However, MET heifers maintained greater (P ≤ 0.05) BW, HH and tended (P = 0.06) to have greater WH and average daily gain (ADG) (P = 0.10). Fecal microbiota and metabolome profiles through 42 days of age in MET heifers indicated greater capacity for hindgut production of endogenous antibiotics and enhanced hindgut functionality and health. Enhancing maternal post-ruminal supply of methionine during late-gestation in dairy cows has a positive effect on hindgut functionality and health in their offspring through alterations in the fecal microbiota and metabolome without affecting feed intake. Those alterations could limit pathogen colonization of the hindgut while providing essential nutrients to the neonate. Together, such responses contribute to the ability of young calves to achieve better rates of nutrient utilization for growth.
Extracellular Matrices to Modulate the Innate Immune Response and Enhance Bone Healing

Extracellular matrices (ECMs) have emerged as promising off-the-shelf products to induce bone regeneration, with the capacity not only to activate osteoprogenitors, but also to influence the immune response. ECMs generated starting from living cells such as mesenchymal stromal cells (MSCs) have the potential to combine advantages of native tissue-derived ECMs (e.g., physiological presentation of multiple regulatory factors) with those of synthetic ECMs (e.g., customization and reproducibility of composition). MSC-derived ECMs could be tailored by enrichment not only in osteogenic cytokines, but also in immunomodulatory factors, to skew the innate immune response toward regenerative processes. After reviewing the different immunoregulatory properties of ECM components, here we propose different approaches to engineer ECMs enriched in factors capable to regulate macrophage polarization, recruit host immune and mesenchymal cells, and stimulate the synthesis of other immunoinstructive cytokines. Finally, we offer a perspective on the possible evolution of the paradigm based on biological and chemico-physical design considerations, and the use of gene editing approaches.
Randomized Controlled Trials on Intracerebral Hemorrhage: A Cross Sectional Retrospective Analysis of CONSORT Item Adherence

Object: Intracranial hemorrhage (ICH) is the second most common cause of stroke but still there is little consolidated knowledge about the optimal treatment strategies (e.g., the benefit of surgical evacuation). We evaluated the current randomized controlled trials (RCTs) on primary ICH (01.2013–03.2017) according to their fulfillment of the CONSORT statement's criteria (published in 2010) –as a marker of transparency and quality of study planning and realization. Methods: A Pubmed and a Cochrane database (including clinicaltrials.gov) search was carried out (01.2014–3.2017, respectively 01.2013–12.2013). Abstracts were screened for inclusion. Eligible full text manuscripts were assessed for the implementation of the CONSORT criteria. Citation frequencies and impact factors of the journals were related to ratio of CONSORT criteria fulfillment. Further, the risk of bias according to the Risk of bias tool 2 (RoB 2) was assessed. Results: Overall 3097 abstracts were screened for inclusion; 39 studies were suitable for final analysis. A mean fulfillment ratio of 51% (±28%) was found. A high correlation between impact factor and adherence to CONSORT criteria was shown (r = 0.7664; p < 0.0001). Citation frequency per year was related to ratio of CONSORT item fulfillment (r = 0.6747; p < 0.0001) and to the impact factor of the publishing journal (r = 0.7310; p < 0.0001). Of note, the items 10 (randomization: implementation) and 21 (generalizability) showed particularly high rates of non-fulfillment (87 and 85%). The majority of studies (95%) complied with item 2b (specific objectives or hypotheses), but strikingly objectives were mostly described vaguely. Other essential criteria such as sample size determination, definition of outcome parameters, and participant flow were only fulfilled weakly (51, 54, and 39%). Conclusions: Over 20 years after its inception there is still weak adherence to the CONSORT statement. As a consequence, conclusions are hampered by inadequate planning and/or reporting. Particularly with respect to pathologies as ICH lacking clear, evidence-based guidelines adherence to the CONSORT statement might improve research quality in order to define valuable treatment strategies.
Wireless Coverage for Mobile Users in Dynamic Environments Using UAV

In this paper, the dynamic deployment of a single UAV as an aerial base station in providing wireless coverage for mobile outdoor and indoor users is studied. The problem of finding the efficient UAV trajectory is formulated with the objective to minimize the required UAV transmit power that satisfies the users’ minimum data rate. The proposed solution to the problem considers the users’ movement in a search and rescue (SAR) operation. More specifically, the outdoor rescue team members are considered to move in a group with the reference point group mobility (RPGM) model. Whilst, the indoor rescue team members are considered to move individually and in a group with random waypoint and RPGM models, respectively. The efficient UAV trajectory is developed using two approaches, namely, heuristic and optimal approaches. The employment of the heuristic approach, namely particle swarm optimization (PSO) and genetics algorithm (GA), to find the efficient UAV trajectory reduced the execution time by a factor of <inline-formula> <tex-math notation="LaTeX">$\simeq ~1/60$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$\simeq ~1/9$ </tex-math></inline-formula> compared to that when using the optimal approach of brute-force search space algorithm. Furthermore, the use of PSO algorithm reduced the execution time by a factor of <inline-formula> <tex-math notation="LaTeX">$\simeq ~1/7$ </tex-math></inline-formula> compared to that when the GA algorithm is invoked.The performance of the dynamic UAV deployment also outperformed the static UAV deployment in terms of the required transmit power. More specifically, the dynamic UAV deployment required less total transmit power by a factor of about 1/2 compared to the static UAV deployment, in providing wireless coverage for rescue team to perform SAR operation within a rectangular sub-region.
Evaluation of Fast, High-detail Projected Light 3D Sensing for Robots in Construction

Robots used on-site in construction need to perceive the surrounding environment to operate autonomously. This is challenging as the construction environment is often less than ideal due to changing lighting conditions, turbid air, and the need to detect fine details. In this work we evaluate a custom made projected light 3D sensor system for suitability and practicality in enabling autonomous robotics for construction. A series of tests are performed to evaluate the sensor based on ability to capture environmental details, operate robustly in challenging lighting conditions, and make accurate geometric measurements. Analysis shows that high fidelity measurements with accuracy in the order of millimeters can be obtained, making the technology a promising solution for robots operating in construction environments.
Trading Switching Setup Based on Reinforcement Learning Applied to a Multiagent System Simulation of Financial Markets

This work intends to replicate the interactions among intelligent agents in a double auction environment, leading to an artificial market. Aiming at making agents able to take their own decisions, different methodologies for agents intelligence are proposed, ranging from zero intelligence agents to reinforcement learning. Regarding the study of financial markets, agents can rely on a myriad of techniques in their making-decision process. In this manuscript, the Fundamental and/or Technical Analysis are the tools used by the agents to take their own decisions. A different set of parameters is assigned to each agent, thereby heterogeneity is included within each strategy. Based on these simple strategies some agents can also learn with their past experience, updating the probability to select a trading strategy according to switching rules as a function of the previous result. Several experiments are carried out. Results show that the time series produced by the interactions of those agents resembles to financial time series, reproducing some stylized facts encountered thereof. The performance of different methods for agent design are also compared.
Pneumonia Detection with Weighted Voting Ensemble of CNN Models

There is a clear correlation between the diagnosis of pneumonia and abnormalities on CXR. This association can be trained through performing object detection by a deep neural network. Therefore, applying deep learning to the detection of pneumonia can be a very effective diagnostic solution. This paper proposes a method to detect lung opacities, which can be identified as pneumonia, on chest radiographs (CXR) using an ensemble of deep convolutional neural networks. Furthermore, this paper applied an ensemble model using Mask R-CNN and RetinaNet to RSNA Pneumonia Detection Challenge on Kaggle and demonstrate that this method can effectively achieve high accuracy of prediction. Applying our voting ensemble methodologies outperformed all the individual models.
A Mainlobe Interference Suppression Algorithm Based on Bistatic Airborne Radar Cooperation

Mainlobe interference suppression has been a challenge in radar and communication systems for the last few decades. Here, a new anti-interference approach based on a bistatic radar system is proposed, and mainlobe interference is suppressed through cooperation of the two radars. The interference direction information of a single radar is extracted from the radar scene using multiple signal classification, and the location of the interference is obtained by the cross location method, which uses two different azimuthal angles of the interference. According to the azimuthal information of interference, the blocking matrix method is used to block mainlobe interference and protect the effective echo signal, and adaptive processing technology is adopted to suppress sidelobe interference. Finally, mainlobe interference can be cancelled by the received data of the two radars. A simulation verifies the performance of the model.
A Control and Power Management Scheme for Photovoltaic/Fuel Cell/Hybrid Energy Storage DC Microgrid

With the development of renewable energy such as hydrogen energy, renewable energy supplies have been an important part of DC microgrid. Related control and power management has become the focus of current research. The Photovoltaic /fuel cell/hybrid energy storage DC microgrid in this paper consists of Photovoltaic (PV), fuel cells, lithium-ion batteries, and supercapacitors, along with associated DC/DC and DC/AC converters. To guarantee the stability of the PV/fuel cell/hybrid energy storage DC microgrid, in this paper, a comprehensive control and energy management system is proposed. The proposed control and power management system stably regulate the bus voltage and well balance the power, mainly through automatic control of each module's voltage and power. Under the control and Power management system, when the load changes suddenly, the bus voltage remain stable and power remain balanced. The simulation is carried out to verify the performance of the proposed method.
Measuring Information Dissemination Mechanism on Retweet Network for Marketing Communication Effort : Case Study: Samsung Galaxy S10 Launch Event

Social media has changed human social interaction. Human activities such as giving opinions, sharing experiences, and reviewing products are done in social media. In this digital era, social media become an effective platform for promoting products and disseminating information. The majority of information dissemination studies are seen from the entire network perspective. In this research, we look at the points when information disseminated. As a case study, we use one of the viral events in 2019 i.e. new product launch of Samsung Galaxy S10. The dissemination process is modeled using Social Network Analysis (SNA). Our finding is fanbase account become the most dominant account in spreading information. The results allow us to understand the mechanism of information dissemination in social media. It is also advantageous for marketing study in order to get more effective communication with the consumer. We recommend business organizations to recognize objects which have vigorous fans as an aspect of their promotional media.
Efficient Motion Parameter Estimation for Maneuvering Target based on Segment Processing

The problems of range cell migration (RCM) and Doppler frequency migration (DFM) occurred during the long time integration may severely affect the integration performance for maneuvering targets. In this paper, the RCM is corrected through dividing the range frequency spectrum into several segments, which decreases the range resolution based on the relationship between the size of the range cell and the signal bandwidth. After that, a well-focused image of a maneuvering target with third-order motion is obtained via segmental dual-scaled correlation transform (SDSCT), and three motion parameters, i.e., velocity, acceleration, and acceleration rate, are effectively estimated. The proposed method can detect multiple maneuvering targets with third-order motion with low computational complexity. Compared with several representative methods, the proposed method achieves a good balance between the computational cost and the parameter estimation performance. Finally, simulated results are used to validate the effectiveness of the proposed method.
Minimum Detectable Velocity (MDV) for ATI and STAP for High Speed Platforms

In this paper, the GMTI performance and limitation in terms of minimum detectable velocity (MDV) using ATI and STAP are investigated from a high-speed platform. Statistical characterizations of the ATI phase variable and the STAP output power are used to derive probability of detection vs. probability of false alarm for moving targets as a function of their velocity, SNR and CNR. The MDV for moving targets are computed for a certain probability of detection and false alarm rate under different conditions. Finally, the comparison between the MDV performances for ATI and STAP are carried out by computing the MDVs as functions of key parameters of the problem.
Voltage Balancing Control Strategy for MMC Based on NLM Algorithm

DC/DC converters are a key part of flexible DC distribution networks and have developed rapidly in recent years. In order to expand the voltage level of the original side of the converter, reduce the disadvantages of high manufacturing cost and large unit volume, this paper studies a modular multilevel converter structure. For the new structure, an optimized double phase-shift voltage balancing control strategy is proposed. Under the double phase-shift control, the NLM algorithm is adopted for the high switching frequency of the voltage balancing control. The 5-level single-phase structure MMC simulation model is built by the simulation platform to verify the feasibility of the proposed structure and control scheme for voltage balancing, and the IGBT switching frequency is compared under the two voltage balancing control methods before and after optimization. The results show that the optimized double phase-shift control strategy can effectively reduce the number of IGBT switching and reduce the switching loss under the condition of modular multilevel converter capacitor voltage balancing.
Track Control of Complex-valued Recurrent Neural Networks Based on the Hanaly’s Inequality

This paper discusses the track control problem of complex-valued neural networks (CVNNs) with time delay. By defining the suitable norm, we directly investigate globally asymptotically stable of CVNNs. With the Hanaly’s inequality and analysis techniques, we derive a sufficient condition which can make the state of CVNNs globally exponentially track the given reference trace. At last, one illustrative example verifies our result.
Dense Depth Completion Based on Piecewise Planar Model

A new method is proposed to predict a complete and dense depth map from very sparse depth measurements. Previous state-of-the-art methods tackle the problem mainly by regular sparse depth distribution and exploiting the learning-based framework, which cannot guarantee effectiveness in various scenes. To handle these drawbacks, we propose a piecewise planar model based method, which models the depth map and corresponding color images as a collection of 3D planar, then transforms the task to the optimization of the planar parameters with the energy minimization formulation. Thus, the depth values can be computed through the fitting of the planar. The method can preserve the boundaries well and get high quality visible dense depth maps. In addition, our method doesn't need the training phase thus could robustly work on any scenarios, even if the sparse depth samples are irregular distributed. Apparently, the proposed new method has two applications: depth completion for Lidar sensors and converting irregular sparse depth samples computed from Simultaneous Localization and Mapping (SLAM) to dense depth maps. The method has been tested on the KITTI dataset and achieved the competitive results, proving its effectiveness in the real problem.
Analysis of the Word2Vec Model for Semantic Similarities in Indonesian Words

This paper examines the calculation of similarity between words in Indonesian by using the Word2Vec representation technique. Word2Vec is a model used to represent words into vector shapes. The model in this experiment was formed using the 4GB Indonesian Wikipedia corpus and then used the cosine similarity calculation method to determine its similarity value. This model is then tested with the gold standard set WordSim-353 and SimLex-999 which have been labeled with similarity values according to human ratings. To find out the accuracy of the correlation using the Pearson correlation. The results of the correlation of this study are 0.5663 for WordSim-353 test data using window size 14 and dimensions vector 150, and 0.3472 for SimLex-999 test data using window size 2 and dimensions vector 300. The results of the experiments show that the correlation between the gold standard and system techniques is still relatively weak.Our source code and data are available at : https://github.com/louistenmanalu/Word2Vec-Analysis-for-Indonesia-Languange.git