Miracle-dz/UniFAR-FTUs huggingface dataset:
Volcanic Lateral Collapse Processes in Mafic Arc Edifices: A Review of Their Driving Processes, Types and Consequences

Volcanic cones are frequently near their gravitational stability limit, which can lead to lateral collapse of the edifice, causing extensive environmental impact, property damage, and loss of life. Here, we examine lateral collapses in mafic arc volcanoes, which are relatively structurally simple edifices dominated by a narrow compositional range from basalts to basaltic andesites. This still encompasses a broad range of volcano dimensions, but the magma types erupted in these systems represent the most abundant type of volcanism on Earth and rocky planets. Their often high magma output rates can result in rapid construction of gravitationally unstable edifices susceptible both to small landslides but also to much larger-scale catastrophic lateral collapses. Although recent studies of basaltic shield volcanoes provide insights on the largest subaerial lateral collapses on Earth, the occurrence of lateral collapses in mafic arc volcanoes lacks a systematic description, and the features that make such structures susceptible to failure has not been treated in depth. In this review, we address whether distinct characteristics lead to the failure of mafic arc volcanoes, or whether their propensity to collapse is no different to failures in volcanoes dominated by intermediate (i.e., andesitic-dacitic) or silicic (i.e., rhyolitic) compositions? We provide a general overview on the stability of mafic arc edifices, their potential for lateral collapse, and the overall impact of large-scale sector collapse processes on the development of mafic magmatic systems, eruptive style and the surrounding landscape. Both historical accounts and geological evidence provide convincing proofs of recurrent (and even repetitive) large-scale (>0.5 km3) lateral failure of mafic arc volcanoes. The main factors contributing to edifice instability in these volcanoes are: (1) frequent sheet-like intrusions accompanied by intense deformation and seismicity; (2) shallow hydrothermal systems weakening basaltic rocks and reducing their overall strength; (3) large edifices with slopes near the critical angle; (4) distribution along fault systems, especially in transtensional settings, and; (5) susceptibility to other external forces such as climate change. These factors are not exclusive of mafic volcanoes, but probably enhanced by the rapid building of such edifices.
Expanding the Scope of Non-invasive Prenatal Testing to Detect Fetal Chromosomal Copy Number Variations

Non-invasive prenatal testing (NIPT) for common fetal trisomies is effective. However, the usefulness of cell-free DNA testing to detect other chromosomal abnormalities is poorly understood. We analyzed the positive rate at different read depths in next-generation sequencing (NGS) and identified a strategy for fetal copy number variant (CNV) detection in NIPT. Pregnant women who underwent NIPT by NGS at read depths of 4–6 M and fetuses with suspected CNVs were analyzed by amniocentesis and chromosomal microarray analysis (CMA). These fetus samples were re-sequenced at a read depth of 25 M and the positive detection rate was determined. With the increase in read depth, the positive CNV detection rate increased. The positive CNV detection rates at 25 M with small fragments were higher by NGS than by karyotype analysis. Increasing read depth in NGS improves the positive CNV detection rate while lowering the false positive detection rate. NIPT by NGS may be an accurate method of fetal chromosome analysis and reduce the rate of birth defects.
Trade-Offs and Synergies Between Food and Fodder Production and Other Ecosystem Services in an Actively Restored Forest, Natural Forest and an Agroforestry System in Ghana

Active restoration of degraded areas with multi-purpose tree species has been proposed as a measure to counter the losses from deforestation and mitigate consequences for local human communities. In a restoration project at a former mining site in Ghana, ecosystem services (ES) proxies in an actively restored forest were compared to a local agroforestry system and a natural forest. The results provide information about trade-offs and synergies between proxies of multiple ES(s). ES proxies were assessed according to the following categories: (a) food-tree ES: biomass of food and fodder trees, (b) other trees ES: biomass of fuelwood, medicine or mulch trees, (c) ES-providing arthropods: the number of detritivorous and predaceous arthropods, (d) carbon storage, and (e) tree diversity. Eight replicated plots with sizes of 20 m × 20 m were established in each forest type, and the following ES proxies were quantified: tree diversity was estimated as taxonomic richness of all trees with a diameter at breast height ≥ 10 cm in each plot. Tree species were then classified into ES categories (food, fodder, fuelwood, medicine, or mulch). Ground-dwelling arthropods were sampled for 10 weeks with five pitfall traps in each plot and categorized as decomposers and predators. Tree above-ground biomass was estimated based on the measured tree diameter, height, and specific wood density using an improved allometric equation. The above-ground biomass was later converted into carbon storage by assuming 50% of the above-ground biomass of each tree. ES proxies based on tree biomass were highest in the natural forest. Fodder, medicine, fuelwood, and mulch ES proxies were significantly higher in the restored forest than the agroforestry system. Decomposer arthropods were most dominant in the natural forest, followed by the restored forest and the agroforestry system. Predacious arthropods were more dominant in the restored forest than in the other forest types. Carbon storage was highest in the natural forest, followed by the agroforestry system and the restored forest. The actively restored forest took an intermediate position between the agroforestry system and the natural forest regarding values for all nine ES proxies. Out of the 14 possible relationships between food or fodder and other ES proxies, five were significantly positive (synergies) with no observed trade-offs (significant negative relationships). High fodder production in the restored and natural forests went along with higher values of other biomass ES proxies and numbers of beneficial arthropods, while higher food biomass also correlated positively with numbers of decomposing arthropods. Our results document that active restoration of degraded sites provides a valuable framework to promote ES provision to local communities compared to agroforestry system, but at the cost of lower food and fuelwood biomass and carbon storage compared to natural forests.
Decidual Natural Killer Cells: A Good Nanny at the Maternal-Fetal Interface During Early Pregnancy

Decidual natural killer (dNK) cells are the tissue-resident and major subpopulation of NK cells at the maternal-fetal interface. It has been demonstrated that dNK cells play pivotal roles in pregnancy, including keeping maternal-fetal immune tolerance, promoting extravillous trophoblast (EVT) cell invasion, and driving uterine spiral artery remodeling. However, the molecular mechanisms haven’t been elucidated until recent years. In this review, we systemically introduce the generation, subsets, and surface or soluble molecules of dNK cells, which are critical for maintaining the functions of dNK cells. Further, new functions of dNK cells including well-controlled cytotoxicity, immunosurveillance and immunotrophism supporting via the cell-cell interaction between dNK cells and EVT cells are mainly focused. The molecular mechanisms involved in these functions are also illustrated. Moreover, pregnancy-associated diseases caused by the dNK cells abnormalities are discussed. It will be important for future investigations about the mechanism of maintenance of pregnancy and parturition and potential clinical applications of dNK cells.
Long Noncoding RNA: Regulatory Mechanisms and Therapeutic Potential in Sepsis

Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection and is characterized by a hyperinflammatory state accompanied by immunosuppression. Long noncoding RNAs (lncRNAs) are noncoding RNAs longer than 200 nucleotides and have important roles in mediating various biological processes. Recently, lncRNAs were found to exert both promotive and inhibitory immune functions in sepsis, thus participating in sepsis regulation. Additionally, several studies have revealed that lncRNAs are involved in sepsis-induced organ dysfunctions, including cardiovascular dysfunction, acute lung injury, and acute kidney injury. Considering the lack of effective biomarkers for early identification and specific treatment for sepsis, lncRNAs may be promising biomarkers and even targets for sepsis therapies. This review systematically highlights the recent advances regarding the roles of lncRNAs in sepsis and sheds light on their use as potential biomarkers and treatment targets for sepsis.
Fairness-Oriented User Scheduling for Bursty Downlink Transmission Using Multi-Agent Reinforcement Learning

In this work, we develop practical user scheduling algorithms for downlink bursty traffic with emphasis on user fairness. In contrast to the conventional scheduling algorithms that either equally divides the transmission time slots among users or maximizing some ratios without physcial meanings, we propose to use the 5%-tile user data rate (5TUDR) as the metric to evaluate user fairness. Since it is difficult to directly optimize 5TUDR, we first cast the problem into the stochastic game framework and subsequently propose a Multi-Agent Reinforcement Learning (MARL)-based algorithm to perform distributed optimization on the resource block group (RBG) allocation. Furthermore, each MARL agent is designed to take information measured by network counters from multiple network layers (e.g. Channel Quality Indicator, Buffer size) as the input states while the RBG allocation as action with a proposed reward function designed to maximize 5TUDR. Extensive simulation is performed to show that the proposed MARL-based scheduler can achieve fair scheduling while maintaining good average network throughput as compared to conventional schedulers.
An Efficient Communication Intrusion Detection Scheme in AMI Combining Feature Dimensionality Reduction and Improved LSTM

Communication intrusion detection in Advanced Metering Infrastructure (AMI) is an eminent security technology to ensure the stable operation of the Smart Grid. However, methods based on traditional machine learning are not appropriate for learning high-dimensional features and dealing with the data imbalance of communication traffic in AMI. To solve the above problems, we propose an intrusion detection scheme by combining feature dimensionality reduction and improved Long Short-Term Memory (LSTM). The Stacked Autoencoder (SAE) has shown excellent performance in feature dimensionality reduction. We compress high-dimensional feature input into low-dimensional feature output through SAE, narrowing the complexity of the model. Methods based on LSTM have a superior ability to detect abnormal traffic but cannot extract bidirectional structural features. We designed a Bi-directional Long Short-Term Memory (BiLSTM) model that added an Attention Mechanism. It can determine the criticality of the dimensionality and improve the accuracy of the classification model. Finally, we conduct experiments on the UNSW-NB15 dataset and the NSL-KDD dataset. The proposed scheme has obvious advantages in performance metrics such as accuracy and False Alarm Rate (FAR). The experimental results demonstrate that it can effectively identify the intrusion attack of communication in AMI.
Data Quality Strategy Selection in CRIS: Using a Hybrid Method of SWOT and BWM

Data quality has been considerably faced with more attention in recent years. While improving the quality of any type of information system needs to apply data quality dimensions, this process is a strategic decision of any organization. Current Research Information System (CRIS) is a state of the art information system which manages different processes for acquisition, indexing, and dissemination of researches funded by research funders. In this paper, quality improvement programs for a CRIS are strategically defined using Strength, Weakness, Opportunity and Threaten (SWOT) approach. According to examined SWOT method, weaknesses (such as failure to evaluate the quality of information contained in the research), strengths (such as the accuracy of information classification), opportunities (such as the presence of university representatives in the process of thesis/dissertation registration) and threats (such as transfer of incorrect information by other systems) are identified and categorized. Besides, data quality dimensions are considered for determining all strategies for improving CRIS. An advanced multi-criteria decision-making method called Best-Worst Method (BWM) is applied for prioritizing obtained strategies. Results of proposed methodology indicated that the development and classification of the appropriate space for recording, controlling, indexing and disseminating the received information is obtained the first rank among the other strategies. Also, the creation of a comprehensive knowledge database for all researches in different universities is another main strategy that is ranked in second priority.
Road signs recognition: state-of-the-art and perspectives

: Making cars safer is a crucial element of saving lives on roads. In case of inattention or distraction, drivers need a performant system that is capable of assisting and alerting them when a road sign appears in their field of vision. To create such type of systems, we need to know first the major difficulties that still face traffic signs recognition, as presented in the first and second sections of this paper. We should also study the different methods proposed by researchers to overcome each of these challenges, as proposed in the third section. Evaluation metrics and criteria for proving the effectiveness of these approaches represents also an important element which section three of this article presents. Ameliorating the existing methods is crucial to ensure the effectiveness of the recognition process, especially by using deep learning algorithms and optimisation techniques, as discussed in the last section of this paper.
Impact of Banking Sector wise differences on High-Performance Work Practices

The objective of this research paper is to comprehend the aspects of High-Performance Work-Practices pertinent to Government Undertaking public sector banks vis-à-vis their counterpart of private sector banks. The sample consists of 800 people, out of which 400 were public sector bank and 400 were from private sector banks. High-Performance Work Practices was measured through a four-dimensioned scale developed by Delery and Doty (1996). Significant differences regarding High-Performance Work Practices after data collection were tested using t-test. The findings reported noteworthy variance in the demonstration of many of the influencing factors of High-Performance Work Practices between the studied groups. The insights gained through this paper can be utilized to improve the level of High-Performance Outcome WorkPractices among the Government owned-public sector bank and privately owned-private sector banks. This study represents the data and delivers insights of the dimensions of HighPerformance Outcome Work-Practices of the workforce. Keywords—High-Performance Outcome Work-Practices, Sector-wise differences, Public sector bank and Private sector banks
Enhancement of Images Taken in Fog Condition: A Review

— Images captured using camera systems in foggy weather conditions often suffer from poor visibility and can be seriously degraded due to atmospheric conditions , which creates a lot of impacts on the outdoor computer vision systems. To solve this problem, image enhancement is very important as this process is used for enhancing the quality of an image, and for this purpose numerous visibility enhancement techniques have been used and applied. In this paper, we have tried to describe how to enhance an image using different techniques and methods. For this, various techniques and methods have been studied for image enhancement used in different research and review papers. The main goal of this paper is to understanding and reviewing the techniques used for image enhancement
PUF-Based Mutual-Authenticated Key Distribution for Dynamic Sensor Networks

Because of the movements of sensor nodes and unknown mobility pattern, how to ensure two communicating (static or mobile) nodes authenticate and share a pairwise key is important. In this paper, we propose a mutual-authenticated key distribution scheme based on physical unclonable functions (PUFs) for dynamic sensor networks. Compared with traditional key predistribution schemes, the proposal reduces the storage overhead and the key exposure risks and thereby improves the resilience against node capture attacks. Mutual authentication is provided by the PUF challenge-response mechanism. However, the PUF response is not transmitted in plain forms so as to resist the modelling attacks, which is vulnerable in some existing PUF-based schemes. We demonstrate the proposed scheme to improve the secure connectivity and other performances by analysis and experiments.
Verifiable Location-Encrypted Spatial Aggregation Computing for Mobile Crowd Sensing

Benefiting from the development of smart urban computing, the mobile crowd sensing (MCS) network has emerged as momentous communication technology to sense and collect data. The users upload data for specific sensing tasks, and the server completes the aggregation analysis and submits to the sensing platform. However, users’ privacy may be disclosed, and aggregate results may be unreliable. Those are challenges in the trust computation and privacy protection, especially for sensitive data aggregation with spatial information. To address these problems, a verifiable location-encrypted spatial aggregation computing (LeSAC) scheme is proposed for MCS privacy protection. In order to solve the spatial domain distributed user ciphertext computing, firstly, we propose an enhanced-distance-based interpolation calculation scheme, which participates in delegate evaluator based on Paillier homomorphic encryption. Then, we use aggregation signature of the sensing data to ensure the integrity and security of the data. In addition, security analysis indicates that the LeSAC can achieve the IND-CPA indistinguishability semantic security. The efficiency analysis and simulation results demonstrate the communication and computation overhead of the LeSAC. Meanwhile, we use the real environment sensing data sets to verify availability of proposed scheme, and the loss of accuracy (global RMSE) is only less than 5%, which can meet the application requirements.
Developing Value Networks for Game 2048 with Reinforcement Learning

: The game 2048 is a stochastic single-player game and several computer players have been developed in not only research work but also student projects. Among them, the most successful approach is based on N-tuple networks trained by reinforcement learning methods. Though there have been several works on computer players with deep neural networks, their performance were not as good in most cases. In our previous work, we designed policy networks and applied supervised learning, which resulted in an average score of 215,802. In this study, we tackle the problem with value networks and reinforcement learning methods, since value networks are important to combine with game-tree search methods. We investigate the training methods in several aspects, including batches of training, use of symmetry, network structures, and use of game-speciﬁc tricks. We then conduct a training for 240 hours with the best conﬁguration. With the best value network obtained, we achieved an average score of 228,100 with the greedy (1-ply search) play, and furthermore an average score of 406,927 by combining it with the 3-ply expectimax search.
A Novel Transfer Enhanced α -Expansion Move Learning Model for EEG Signals

In this paper, we focus on recognizing epileptic seizure from scant EEG signals and propose a novel transfer enhanced α -expansion move (TrEEM) learning model. This framework implants transfer learning into the exemplar-based clustering model to improve the utilization rate of EEG signals. Starting from Bayesian probability theory, by leveraging Kullback-Leibler distance, we measure the similarity relationship between source and target data. Furthermore, we embed this relationship into the calculation of similarity matrix involved in the exemplar-based clustering model. Then we sum up a new objective function and study this new TrEEM scheme earnestly. We optimize the proposed TrEEM model by borrowing the mechanism utilized in EEM. In contrast to other machine learning models, experiments based on synthetic and real-world EEG datasets show that the performance of the proposed TrEEM is very promising.
Sentiment Analysis on Twitter by Using TextBlob for Natural Language Processing

— The Internet has become an innovative platform regarding online learning, exchanging content, sharing views. In this paper, we will use Twitter as our social networking platform. Sentiment analysis on Twitter is based on opinion mining on posts to obtain the user’s point of view. The leading goal deals with how opinion mining techniques can be accessed to analyze some of the tweets in many reports involving various types of tweet languages on Twitter and classify its polarity. Practical implication shows that the proposed machine learning classifiers are efficient and highly accurate.
Truth Detection in Social Media Posts using Jaccard Algorithm with SRTD and Word Net Concept

—Counterfeit news has gotten an essential subject of exploration in an assortment of solicitations including semantics and programming building. In this work, clarification of how the issue is drawn nearer from the point of view of fundamental language managing, with the objective of building a framework to subsequently see misdirection in news. The rule challenge in this line of examination is gathering quality information, i.e., occasions of phony and true reports on a sensible dispersing of subjects. In this paper, a novel truth acknowledgment system with near words thoughts is added to the versatile and overwhelming truth disclosure structure used previously. By the use of practically identical words thoughts, the controlled fake news can be recognized with much basic and snap-pier. The features add up same meaning words which are compared using Jaccard algorithm in the main algorithm to detect a greater number of fake news with reliability score. The reliability score is calculated by combining independent score, attitude score and uncertainty score. The implemented software is found to be having better accuracy and results compared to existing truth detection methods.