Miracle-dz/UniFAR-FTUs huggingface dataset:
jmbReview Selection of High Laccase-Producing Coriolopsis gallica Strain T 906 : Mutation Breeding , Strain Characterization , and Features of the Extracellular Laccases

Laccase (p-diphenol oxidase, E.C. 1.10.3.2) is part of a group of copper-containing polyphenol oxidases of the blue oxidase family [27]. Laccases can modify organic and inorganic substrates via four-electron reduction of O2 to H2O with the concomitant oxidation of phenolic compounds [20]. Therefore, these enzymes have broad application prospects and potential value, such as in pulp biobleaching, dye decolorization, wastewater treatment, food processing, medical care, and biomass energy. In previous reports, the immobilization of laccase on several supports has proved to be promising for its biotechnological applications [10, 23, 24]. However, commercial application of laccases is often hampered by insufficient enzyme stocks, with very low yields obtained from natural sources [11]. Laccases are widely spread among fungi, bacteria, and plants. Fungal laccases usually show higher redox potential and yield than their bacterial counterparts [15], and their secretion patterns are affected by the fungal species, substrates, and eco-physiological factors [4]. The most important fungal laccase sources are white-rot fungi (WRF), which are involved in lignin degradation [16]. The potential applications of lignocellulolytic enzymes in industrial and environmental technologies require large amounts of biodegradation enzymes at low cost. Therefore, it is urgent to select new organisms with high synthesis of enzymes such as laccase, and develop strategies for their Received: April 5, 2016 Revised: May 26, 2016 Accepted: June 1, 2016
TOWARDS A HOLISTIC ANALYSIS OF TOURISM SECTOR IN TUNISIA : A SYSTEM DYNAMICS APPROACH

Tourism is a complex and dynamic industry. It involves several sectors, components and engages a variety of stakeholders, who have conflicting goals, which can make planning for tourism sector a challenging task. This study aims to investigate the role of system thinking to develop a participatory model for tourism sector in Tunisia. System dynamics offers a global understanding of the sector and helps analysing tourism policies. This paper proposes a system dynamics-based tourism model that can be used to analyse the potential problems and understand the causal interactions within the tourism sector in Tunisia. The model focuses on the complex interactions between three sustainability sectors that capture the dynamics of the tourism industry in Tunisia. Reviewing literature, analysing the current tourism strategy and group model building helped identifying the elements of the system, as well as the different interactions between them. The developed causal loop diagram represents the basis for further investigations, mainly simulating the tourism sector under different policy alternatives. The simulation results will be used in a further research to help policy makers achieving economic Sociocultural and environmental sustainability.
Application of the Apriori algorithm in the management of sports training

The objective of the study was to test the Apriori algorithm of classification for obtaining training components that are connected with one another in the strongest way, in athletic training of race walkers. The presented algorithm expansion of the selection process will also generalize and obtain a system for recognizing the effectiveness of athletic training. Furthermore, particular properties of the criteria of component sets will be specified in terms of general selection in order to combine the relative and non-relative criteria.
UAV 3 D Path Planning Simulation for Obstacle Avoidance Auburn REU on SMART UAVs 2019

Drone navigation is one of the hottest topics in Unmanned Aerial Vehicles. Simulating UAVs is a way to visualize how a drone can move without the risk of crashing a real drone. One way to navigate a drone in either a simulated or real-life environment is for the drone to have a path planning algorithm built into its programming. A path planning algorithm is a path detection system that can map out the most efficient way to get to a given target, before the drone actually begins moving. Up to this point, these path planning algorithms have only been created for a 2D environment. This report is attempting to create a 3D path planning algorithm using a drone in a pre-made obstacle course simulation. The main algorithms to be implemented in the simulation are A* and Dijkstra. Using a robotics framework, ROS [1], a simulation environment, Gazebo [2], and a sensor data visualizer, RViz [3], a simulated environment will be created which can run a 3D path planning script. The goal is to use the simulation to find the most efficient algorithm for the drone to find its way to its set destination in a 3D environment.
Are Women Perceived as Worse Magicians Than Men? Gender Bias When Evaluating Magic Tricks

We present two experiments investigating the effect of the perceived gender of a magician on the perception of the quality of magic tricks. In Experiment 1, tricks performed by an allegedly female magician were considered worse than those by an allegedly male magician. In Experiment 2, participants had to generate possible solutions to how the tricks were done. Under these conditions, male participants were better at explaining the tricks, but the gender effect found in Experiment 1 disappeared. We discuss the gender bias in Experiment 1 and the lack of bias in Experiment 2 in terms of specific social and cognitive mechanisms (e.g., cognitive dissonance).
MOBILE-BASED PERSONAL TOUR APPLICATION USING ARTIFICIAL BEE COLONY OPTIMIZATION ALGORITHM

Tourists that visit multiple locations need to have prior knowledge about interesting and attractive destinations. Selecting the most suitable tour plan for any tourist is not an easy task. Building a schedule of proposed destinations may allow the tourist to navigate between these destinations efficiently based on his/her time and budget. This paper applies the Artificial Bee Colony optimization algorithm (ABC) to build a tour plan by selecting the Points of Interest (POIs). The algorithm is applied to the user mobile phone. Tourist must specify the start place, the end place, the stay period (number of days) and the preferred POIs, if any, and some other preferences. The objective is to maximize the tourist satisfaction by selecting several POIs which satisfy the provided time and budget. The system is built on top of Android for its simplicity, popularity, and availability of web services. To evaluate the efficiency of the proposed technique, we build a dataset of 50 attractions in Jordan. The algorithm fitness function is tuned based on comparing the resulted tour plan with a tour plan generated by an expert. The evaluation shows that the proposed technique gives the maximum number of POI within the provided time and budget.
Non-destructive inspection & subwavelength characterization

Phased array transducers have in recent years allowed for ever increasing imaging capabilities in the field of nondestructive inspection. By capturing the maximum possible amount of acoustic information we are able to utilize imaging algorithms which bring unparalleled resolution and flexibility to industrial inspections. This talk will cover the background of acoustic array imaging and detail current trends and ongoing challenges being addressed at the University of Bristol.
Response of Different Value Added Briquettes with Reference to Yield Attributes and Nutrient Status of Cashew in Konkan Region of Maharashtra

The present investigation was conducted at Dr. Balasaheb Sawant Konkan Krishi Vidyapeeth, Cattle Breeding Farm, Nileli416 519, Tal. Kudal, Dist. Sindhudurg during the year 2015-2016 with a view to study the effect of different value added briquettes on flowering, fruiting, yield and nutrient status of cashew cv. Vengurla-4. The experiment was conducted in Randomized Block Design with three replications and seven treatments. The recommended package of practices was uniformly followed for all the treatments including control. The different value added briquettes were prepared at Regional Fruit Research Station, Vengurle and applied by ring method in June, 2015. The observations regarding flowering, fruiting and yield attributes were recorded at appropriate stages and the data were statistically analyzed. Among the different value added briquettes studied, soil application of Konkan Annapurna Briquettes (KAB) @ 75% + Zn + Cu +
Implicit High-Order Gas Kinetic Scheme for Turbulence Simulation

In recent years, coupled with the Reynolds averaged Navier-Stokes (RANS) models, the large eddy simulation (LES) models, and the hybrid RANS/LES models, the second-order gas-kinetic scheme (GKS) has been used in the turbulent flow simulations. At the same time, high-order GKS has been developed, such as the two-stage fourth-order scheme (S2O4), and used for laminar flow calculations. The S2O4 GKS achieves fourth-order accuracy in space and time and shows distinguishable robustness for the discontinuous flows. In this paper, targeting on the three-dimensional turbulent flows, an implicit high-order GKS with Lower-Upper Symmetric Gauss-Seidel (LU-SGS) technique is developed under the S2O4 framework. Based on Vremantype LES model and k − ω SST model, a turbulent relaxation time is obtained and used for an enlarged particle collision time in the implicit high-order GKS in the high-Reynolds number turbulent flows. Numerical experiments include Taylor-Green vortex problem, incompressible decaying homogeneous isotropic turbulence, incompressible high-Reynolds number flat plate turbulent flow, and transonic high-Reynolds number ARA M100 wing-body flow. Comparisons among the numerical solutions from current implicit high-order GKS, the explicit high-order GKS, the secondorder GKS, the second-order Navier-Stokes solver, and experimental measurements, have been conducted. Through these example, it is concluded that the current scheme has high accuracy in space and time, and significant acceleration on computational efficiency, as well as super robustness in simulating complex flows. This study also indicates that turbulence modeling plays a dominant role in the capturing physical solution. For example, the possible error in turbulent models can become the leading error in a numerical simulation, such as in the transonic three-dimensional complex RANS simulation, in comparison with numerical discretization errors. So, the development of appropriate turbulence models is still the dominant task in turbulence simulation.
Evolutionary Perspectives on Personality and Individual Differences

Individual differences are essential to the process of evolution by natural selection. Natural selection is the only known process capable of creating and maintaining adaptations. The properties of inheritance, variation, and differential reproduction are required for evolution of adaptations (see Sela and Shackelford, 2015, for a review). Physiological and psychological traits must be genetically heritable and reliably passed from parent to offspring in order for natural selection to act upon them. Some individuals propagate their genes more successfully than do others, resulting in differential reproduction. Ultimately, the number of successful offspring (and the number of their successful offspring, etc.) determines gene frequency of particular traits in a population. Survival therefore determines fitness to the extent that it contributes to successful gene propagation. Fitness is a term that applies in the context of a specific environment, however, and therefore an individual that is ‘fit’ in one environment may not necessarily be ‘fit’ in another. Certain traits may be differentially beneficial in different environments. Natural selection of physiological and psychological traits is dependent upon organisms’ interaction with the environment. Relative consistency of environmental inputs is crucial for significant allele frequency shifts (i.e., evolution) and the construction of adaptations. Consistent environmental variables that pose survival and reproductive challenges over many generations are referred to as selection pressures. Selection pressures vary across geographic areas and ecological systems, and consist of both the animate and inanimate natural environments. The idea that heritable variations of traits provide the raw material upon which selection can act has long been acknowledged in the biological life sciences. Natural selection is typically viewed as a homogenizing force that results in species-typical characteristics. Indeed, application of an evolutionary framework to the study of humans – via a relatively new metatheoretical paradigm called evolutionary psychology (Tooby and Cosmides, 9
Revisiting Population Structure and Particle Swarm Performance

Population structure strongly affects the dynamic behavior and performance of the particle swarm optimization (PSO) algorithm. Most of PSOs use one of two simple sociometric principles for defining the structure. One connects all the members of the swarm to one another. This strategy is often called gbest and results in a connectivity degree = , where is the population size. The other connects the population in a ring with = 3. Between these upper and lower bounds there are a vast number of strategies that can be explored for enhancing the performance and adaptability of the algorithm. This paper investigates the convergence speed, accuracy, robustness and scalability of PSOs structured by regular and random graphs with 3 ≤ ≤ . The main conclusion is that regular and random graphs with the same averaged connectivity may result in significantly different performance, namely when is low.
Trade and Conflict: The Case of the Arab Maghreb Union

-This paper explores the links between trade, conflict and peace in the Arab Maghreb countries. Since its creation in 1989, the Arab Maghreb Union (AMU) has been in a lethargic state. We assume that reducing inter and intra-state conflicts would be a sound argument for the reactivation of the Arab Maghreb Union. In this paper, we will discuss trade flows between the Maghreb Union countries and highlight the pending political issues and conflicts. The results confirm that countries which are more likely to create regional blocks are naturally more open to trade and even more subject to interstate disputes. Elimination of all trade barriers and putting into effect multilateral trading systems would enable AMU countries the creation of a regional security framework that leverages diplomatic, political, economic and even military resources. Regional integration can be built and strengthened in order to promote peace and stability.
DESIGN AND IMPLEMENTATION OF EMBEDDED TRUE PARALLELISM JAMMER SYSTEM USING FPGA-SoC FOR LOW DESIGN COMPLEXITY

The condition that drives a system to complete the processing of a number of functions within a given amount of time is called the real-time system. A projective missile system’s processing platforms face two major issues: high cost and structural complexity. The system structure’s complexity is a result of various reasons that include the mechanism utilised in the system in order to perform the system functionality. This mechanism can lead to delays in data processing because various factors, such as the synchronisation of the system modules’ signals, the processing unit’s architecture, and the unit’s computational power. In order to lessen system complexity and system cost, true parallelism mechanism is applied over the embedded system, along with a concurrent structure. The FPGA platform (DE1-SoC) was used as the implementation environment for this system. This led to an enriched implemented system that had low costs. Furthermore, the system complexity is lessened since the system uses a concurrent structure. Some of the modules that are closely related to the system are implemented to support main processing module. In this system, the signals covered were in four directions. The total logic element was (5032) and total registers was (5180). The Phase Locked Loop up to (1.6) GHz was manipulated in order to allow the system cover a wide spectrum of signals with high accuracy of computing process. Furthermore, the laser projective frequency jamming system is capable of processing multiple frequencies at a time. The implementation was able to obtain acceptable levels of throughput and it also lowered the complexity. Furthermore, the structural design methodology also makes it possible for the embedded concurrent computing architecture to be scalable while the entire system grows.
Introducing S-Index into Factoring RSA Modulus via Lucas Sequences

At any instance in the factoring algorithm, the accumulative result stands independently. In e ect, there is no clear direction to manoeuvre whether to go left or right. General Lucas sequences are practically useful in cryptography. In the past quarter century, factoring large RSA modulo into its primes is one of the most important and most challenging problems in computational number theory. A factoring technique on RSA modulo is mainly hindered by the strong prime properties. The success of factoring few large RSA modulo within the last few decades has been due to computing prowess overcoming one strong prime of RSA modulo. In this paper, some useful properties of Lucas sequences shall be explored in factoring RSA modulo. This paper will also introduces the S-index formation in solving quadratic equation modulo N . The S-index pattern is very useful in designing an algorithm to factor RSA modulo. The S-index will add another comparative tool to better manoeuvre in a factoring process. On one hand, it shall remain a theoretical challenge to overcome the strong prime properties. On the other hand, it shall remain a computational challenge to achieve a running time within polynomial time to factor RSA modulo. This paper will propose an avenue to do both using general Lucas sequences.
Feature Selection via Sequential Parallel LASSO for eQTL Analysis

Human gene expression is subjected to multiple layers of controls including the impact of genetic variants. Expression quantitative trait loci (eQTL) analysis has emerged as a powerful tool for investigating this genetic impact. In eQTL analysis, the data is typically high-dimensional whereas the number of observations exceed the number of samples. Therefore, sparse learning models are suitable for eQTL analysis. Sparse learning models have the ability to reflect the underlying biology in cases where only a small subset of genetic variants significantly affect gene expression. Therefore, sparse learning models such as LASSO, have shown their strengths to select associated features in high dimensional data. However, classical LASSO performs poorly when dealing with extremely high dimensional datasets in human genomic data. In this study, we introduce two novel methods named sequential LASSO and parallel LASSO. These methods allow efficient learning for datasets of ultra-high dimension. We theoretically prove the consistency of these methods when exact recovery is achieved in each sub-procedure. We further provided a multi-round process to address the sample size limitation in real applications. In addition to our theoretical analysis, we also perform extensive simulations on synthetic data to validate our methods. When applying to a real human genomics data set, we identify a set of SNP eQTLs that affect genes previously reported to be associated with human traits and diseases. Our methods are not limited to classical LASSO models and can be extended to variations of LASSO and many other machine learning models. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. ACM BCB ’2015 Atlanta, Georgia USA Copyright 20XX ACM X-XXXXX-XX-X/XX/XX . $15.00.
Automatic Melody Composition Using Enhanced GAN

In traditional music composition, the composer has a special knowledge of music and combines emotion and creative experience to create music. As computer technology has evolved, various music-related technologies have been developed. To create new music, a considerable amount of time is required. Therefore, a system is required that can automatically compose music from input music. This study proposes a novel melody composition method that enhanced the original generative adversarial network (GAN) model based on individual bars. Two discriminators were used to form the enhanced GAN model: one was a long short-term memory (LSTM) model that was used to ensure correlation between the bars, and the other was a convolutional neural network (CNN) model that was used to ensure rationality of the bar structure. Experiments were conducted using bar encoding and the enhanced GAN model to compose a new melody and evaluate the quality of the composition melody. In the evaluation method, the TFIDF algorithm was also used to calculate the structural differences between four types of musical instrument digital interface (MIDI) file (i.e., randomly composed melody, melody composed by the original GAN, melody composed by the proposed method, and the real melody). Using the TFIDF algorithm, the structures of the melody composed were compared by the proposed method with the real melody and the structure of the traditional melody was compared with the structure of the real melody. The experimental results showed that the melody composed by the proposed method had more similarity with real melody structure with a difference of only 8% than that of the traditional melody structure.
1 Computational Tools in the Discovery of FABP 4 2 Ligands : A Statistical and Molecular Modeling 3 Approach 4

Small molecule inhibitors of adipocyte fatty-acid binding protein 4 (FABP4) have got 16 interest following the recent publication of their pharmacologically beneficial effects. Recently it 17 comes out that FABP4 is an attractive molecular target for the treatment of type 2 diabetes, other 18 metabolic diseases, and some type of cancers. In the past years, hundreds of effective FABP4 19 inhibitors have been synthesized and discovered but, unfortunately, none of them is in the clinical 20 research phase. The field of computer-aided drug design seems to be promising and useful for the 21 identification of FABP4 inhibitors; hence, different structureand ligand-based computational 22 approaches were already performed for their identification. In this paper, we searched for new 23 potentially active FABP4 ligands in the Marine Natural Products (MNP) database. 14,492 24 compounds were retrieved from this database and filtered through a statistical and computational 25 filter. Seven compounds were suggested by our methodology to possess a potential inhibitory 26 activity upon FABP4 in the range of 79–245 nM. ADMET properties prediction were performed to 27 validate the hypothesis of the interaction with the intended target and to assess the drug-likeness of 28 these derivatives; from these analyses, three molecules resulted as excellent candidates for 29 becoming new drugs. 30
Naive Bayesian Networks in Intrusion Detection Systems

In this paper, we present and analyze an approach to intrusion detection using naive Bayes network. We have used a set of benchmark data from KDD’99 which are appropriate to evaluate an intrusion detection system. We consider three levels of attack granularities depending on whether dealing with whole attacks, or grouping them in four main categories or just focusing on normal and abnormal behaviours. Moreover, we consider different ways for handling continuous variables. The different experimental results show that naive Bayesian networks, even having a simple structure, can provide efficient and accurate classifiers to detect intrusions. keywords: Intrusion detection, Naive Bayesian networks, Learning