Miracle-dz/UniFAR-FTUs huggingface dataset:
Induction of a Peptide with Activity against a Broad Spectrum of Pathogens in the Aedes aegypti Salivary Gland, following Infection with Dengue Virus

The ultimate stage of the transmission of Dengue Virus (DENV) to man is strongly dependent on crosstalk between the virus and the immune system of its vector Aedes aegypti (Ae. aegypti). Infection of the mosquito's salivary glands by DENV is the final step prior to viral transmission. Therefore, in the present study, we have determined the modulatory effects of DENV infection on the immune response in this organ by carrying out a functional genomic analysis of uninfected salivary glands and salivary glands of female Ae. aegypti mosquitoes infected with DENV. We have shown that DENV infection of salivary glands strongly up-regulates the expression of genes that encode proteins involved in the vector's innate immune response, including the immune deficiency (IMD) and Toll signalling pathways, and that it induces the expression of the gene encoding a putative anti-bacterial, cecropin-like, peptide (AAEL000598). Both the chemically synthesized non-cleaved, signal peptide-containing gene product of AAEL000598, and the cleaved, mature form, were found to exert, in addition to antibacterial activity, anti-DENV and anti-Chikungunya viral activity. However, in contrast to the mature form, the immature cecropin peptide was far more effective against Chikungunya virus (CHIKV) and, furthermore, had strong anti-parasite activity as shown by its ability to kill Leishmania spp. Results from circular dichroism analysis showed that the immature form more readily adopts a helical conformation which would help it to cause membrane permeabilization, thus permitting its transfer across hydrophobic cell surfaces, which may explain the difference in the anti-pathogenic activity between the two forms. The present study underscores not only the importance of DENV-induced cecropin in the innate immune response of Ae. aegypti, but also emphasizes the broad-spectrum anti-pathogenic activity of the immature, signal peptide-containing form of this peptide.
Modeling the Distribution of Migratory Bird Stopovers to Inform Landscape-Scale Siting of Wind Development

Conservation of migratory birds requires understanding the distribution of and potential threats to their migratory habitats. However, although migratory birds are protected under international treaties, few maps have been available to represent migration at a landscape scale useful to target conservation efforts or inform the siting of wind energy developments that may affect migratory birds. To fill this gap, we developed models that predict where four groups of birds concentrate or stopover during their migration through the state of Wyoming, USA: raptors, wetland, riparian and sparse grassland birds. The models were based on existing literature and expert knowledge concerning bird migration behavior and ecology and validated using expert ratings and known occurrences. There was significant agreement between migratory occurrence data and migration models for all groups except raptors, and all models ranked well with experts. We measured the overlap between the migration concentration models and a predictive model of wind energy development to assess the potential exposure of migratory birds to wind development and illustrate the utility of migratory concentration models for landscape-scale planning. Wind development potential is high across 15% of Wyoming, and 73% of this high potential area intersects important migration concentration areas. From 5.2% to 18.8% of each group’s important migration areas was represented within this high wind potential area, with the highest exposures for sparse grassland birds and the lowest for riparian birds. Our approach could be replicated elsewhere to fill critical data gaps and better inform conservation priorities and landscape-scale planning for migratory birds.
Image Adaptive Reconstruction Based on Compressive Sensing and the Genetic Algorithm via ROMP

Compressive sensing (CS) effectively relieved the pressure of sensor network. CS proposed doing sampling and compression at the same time which avoided the waste of a large number of samples and large redundant data. In CS, reconstruction algorithm is a critical technology. In this paper, we use greedy algorithm Regularized Orthogonal Matching Pursuit (ROMP) to reconstruct signal. Because the recovery effect of the greedy algorithm depends on characteristics of the signal and the greedy algorithm needs a minimum measurement value, an adaptive image recovery method is proposed for the improvement of image recovery performance. In our method, we combine ROMP and the genetic algorithm (GA). The number of measurements N and the sparse of signal m in ROMP are respectively defined according to the GA and prior knowledge of signal. We compare the new method with other methods under the same test conditions, the new method can effectively reconstruct signal, reduce errors and reconstruction effects are superior to other methods.
KF-PSO: Hybridization of particle swarm optimization and kernel-based fuzzy C means algorithm

In recent times, clustering has been well known for various researchers due to various applications in most of the fields like, telecommunication, networking, biomedical domain and so on. So, various attempts have been already made by the researchers to develop a better algorithm for clustering. One of the procedures well known among the researchers is optimization that has been effectively utilized for clustering. In most of the clustering, the objective function is to minimize the intra cluster distance among the data points. Here, we have made significant trial in developing optimization based clustering algorithm utilizing kernel-based FCM and PSO algorithm. The objective function for minimization is taken from kernel-based FCM and the same objective is solved using PSO algorithm. The algorithm developed based on these scenarios are named as, KF-PSO. At first, the input data is given to PSO algorithm and the final best cluster centers are chosen from PSO algorithm useful for grouping based on the objective of the kernel clustering. Finally, the experimentation have been performed in various datasets and from the results, we have showed that the proposed hybrid algorithm achieved 98% and 90.5 % accuracy in iris and wine dataset.
Antisymmetric PT-photonic structures with balanced positive-and negative-index materials

We propose a class of synthetic optical materials in which the refractive index satisfies n(−x) = −n∗(x). We term such systems antisymmetric parity-time (APT ) structures. Unlike PT -symmetric systems, which require balanced gain and loss, i.e., n(−x) = n∗(x), APT systems consist of balanced positiveand negative-index materials. Despite the seemingly PT -symmetric optical potential V (x) ≡ n(x)2ω2/c2, APT systems are not invariant under combinedPT operations due to the discontinuity of the spatial derivative of the wave function. We show that APT systems can display intriguing properties, such as spontaneous phase transition of the scattering matrix, a flat total transmission band, and a continuous lasing spectrum.
Sensor fault diagnosis of dynamic positioning ship based on finite impulse response filter

Sensors are important components of the dynamic positioning system, take compass for instance, which measures the course of a ship. Based on the Finite Impulse Response (FIR) filter, a simple and efficient sensor fault diagnosis scheme was proposed to improve the ability for judging whether sensor is out work or not. This method improved the capability of diagnosing when and what faults occur by using double criterions of fault detection method, and solved the sensor fault diagnosis problem in output mutation. The double criterions consist of state deviation and signal-to-noise ratio (SNR). Only the output signal of both state deviation and SNR changed suddenly can prove a fault of sensor has occurred. If the output signal of state deviation transforms but the SNR does not, it just indicates that the sensor is disturbed by the external environment. Applying this method, an explicit solution was obtained, and achieved a greater capacity and efficiency in the implementation process. The validity and excellent performance of this algorithm was proved by the simulation results.
A Scalable Set-Union Counting Approach to Pushing Back DDoS Attacks

It is a pressing task to fight off DDoS (Distributed Denial-of-Service) attacks effectively on the Internet. However, despite that there has been a plethora of efforts suggested in the literature to combat DDoS attacks, we are yet to see a really practicable efficient solution. First of all, such a solution has to be scalable because we need to inspect and process a gigantic amount of traffic flows in order to accurately detect an attack in its incipient stage. Thus, maintaining per flow state or simple per-packet based book-keeping schemes will not work in practice. Secondly, such a solution has to be simple to implement such that the subsequent action—pushing the attack back—can be launched in a swift manner. In this paper, we propose a novel technique for detecting incipient DDoS attacks and pushing the attack flows back to the perimeter ingress routers of an autonomous system. Our technique is based on probabilistic counting of the cardinality of the union of two crucial packet sets: ingress packet set and the destination packet set. In a high speed core network, these packet sets are huge, even if we just keep track of them over a short period of time. Our technique is of very low time- and space-complexity. Indeed, the amount of space needed to keep track of accurate traffic matrix entries is O(loglogn) (where n is the number of packets to be counted) so that the pushback mechanism is highly efficient because the amount of information to be communicated among routers is very small. Our NS-2 simulation results indicate that our proposed new technique is highly effective in combating DDoS attacks with an accuracy rate at around 90%. This can be achieved with a small amount of counting storage, say 2 KBytes, at each router. Furthermore, the amount of legitimate packets dropped is less than 10% when the pushback mechanism is in action. In summary, the new technique is both time- and space-efficient, demand low-cost to implement and low-overhead to operate, results in high accuracy and low error rate, and is highly scalable in defending against DDoS attacks over an increasing number of routers.
Impact of Single Amino Acid Substitution Upon Protein Structure

In the biological sciences, one of the most fundamental operations is that of comparison. As we strive to further understand the constituent parts of living tissue, we need to examine proteins and their many mutations. Indeed, characterising mutations is an important part of proteomics, because a seemingly trivial mutation can sometimes stand between creating a life-saving drug on one hand, or blocking a vital receptor inactivating that same drug on the other. In this work we examined single point mutations to characterise their effects on outwardly expanding neighbourhood ranges. As the shape of a protein is very important, we examined how mutations can make subtle changes to the protein shape as well as investigated the implications both for backbone and side-chain residues. Our findings suggest that structural changes upon a mutation are significantly influenced by the protein shape, which allows for the prediction of the impact brought about the mutation by looking only into the protein shape. Surprisingly, we found that there was very little variation between wild type and mutant protein structures close to the mutation site. Also, in contrast with what was expected, the largest structural variations were found when deleted and introduced residues had similar hydrophobicity.
Compressive-signal annotation driven by a supervised topic-clustering BoF model

This paper presents a new Bag-of-Features model (BoF) to enhance the efficiency of automatic image annotation. Since the traditional BoF ignores the semantic of its vocabularies, it cannot be seen as descriptive representation of images in many image applications. To handle this critical limitation, firstly, we propose the RGB compressive texton. By using compressive sensing theory, the image can be compressed and its key information can be kept. Secondly, according to the topic of images, we extract RGB compressive texton from image of the same topic. Thirdly, the cluster algorithm is use to form clustering centers of each topic. Finally, using all topics cluster center to form new visual vocabularies of BoF model. Therefore each vocabulary has its semantics, which includes the topic information of images. We refer to such new BoF model as supervised topic-clustering BoF model. Experiments on automatic image annotation with a benchmark datasets Corel-5K show promising performance.
Neonatal Eye Infections due to Mycoplasma hominis

In one year Mycoplasma hominis was isolated from 8 out of 250 clinically infected eyes of newborn infants. This infection occurred in only a small proportion of babies whose mothers carried the organism in the vagina. Probably mycoplasma infection of the eye in neonates is commoner than is realized.
Analog Memristive Synapse in Spiking Networks Implementing Unsupervised Learning

Emerging brain-inspired architectures call for devices that can emulate the functionality of biological synapses in order to implement new efficient computational schemes able to solve ill-posed problems. Various devices and solutions are still under investigation and, in this respect, a challenge is opened to the researchers in the field. Indeed, the optimal candidate is a device able to reproduce the complete functionality of a synapse, i.e., the typical synaptic process underlying learning in biological systems (activity-dependent synaptic plasticity). This implies a device able to change its resistance (synaptic strength, or weight) upon proper electrical stimuli (synaptic activity) and showing several stable resistive states throughout its dynamic range (analog behavior). Moreover, it should be able to perform spike timing dependent plasticity (STDP), an associative homosynaptic plasticity learning rule based on the delay time between the two firing neurons the synapse is connected to. This rule is a fundamental learning protocol in state-of-art networks, because it allows unsupervised learning. Notwithstanding this fact, STDP-based unsupervised learning has been proposed several times mainly for binary synapses rather than multilevel synapses composed of many binary memristors. This paper proposes an HfO2-based analog memristor as a synaptic element which performs STDP within a small spiking neuromorphic network operating unsupervised learning for character recognition. The trained network is able to recognize five characters even in case incomplete or noisy images are displayed and it is robust to a device-to-device variability of up to ±30%.
Intelligent Decision Making Based on Data Mining Using Differential Evolution Algorithms and Framework for ETL Workflow Management

Business decision-making is not a simple task. There are many reasons for that but one of the main reason might be that data comes from different operational sources of an organization and it is difficult to organize and maintain especially if a huge volume of data is involved. A Data warehouse is helpful in this regard as it can assist in business decision-making by collecting data from different operational sources of the organization. To collect the data from different sources and then loading it into a warehouse is also difficult to manage the data on these sources is not in a consistent format. It is usually a three step process that involves extraction, transformation and loading. The extraction of data from these different sources, then transform it into a consistent format and consequently load it into the warehouse, by using ETL (Extract, transform and load) tools. Nowadays, the majority of ETL tools organize workflow. An ETL workflow can be considered as a group of ETL jobs with dependencies between them. In this research different considerations are discussed which are necessary for the proper management of workflow in context of efficient business decision-making process. The purpose of this research paper is to give a novel idea about the possibility of designing an intelligent decision support system by using the techniques of data mining as well as the differential evolution algorithm of artificial neural networks. Artificial neural networks are being used to enhance the capabilities of the intelligent decision support system. A preexisting differential evolution algorithm with slight modification is being used within the DSS environment. It has been assumed that this merger will lead towards more development and advancement within the concept of DSS.
Activity Analysis of Expert and Novice Operators in a Semi-Automated Manufacturing Process

Designing a human-machine interface for a manufacturing process requires a good knowledge of both the work domain and the operators' representation. Ecological Interface Design (EID) offers some interesting tools that can be of help in the design process. The literature on cognitive control also offers a good understanding of operators' cognitive resources. Analysing the activity of both expert and novice operators through these two frameworks may help us to better understand the differences between them. A three-step protocol was followed: 1. the elaboration of a means-end hierarchy, 2. the extraction of schemes via interviews, and 3. the evaluation of the behavioural manifestation of schemes. In the present case study, interviews revealed that both the novice and expert operators of a manufacturing process shared a representation of the global process. However, in contrast with the expert operators, the novice operators did not develop an operative scheme that related to the machine. The results will be used as a basis for the design of a human-machine interface that will aid them to do so.
A CONCEPTUAL MODEL FOR ONTOLOGY BASED LEARNING

Utilizing learning features by many fields like education, artificial intelligence, and multi-agent systems, leads to generation of various definitions for this concept. In this article, these field’s significant definitions for learning will be presented, and their key concepts in each field will be described. Using the mentioned features in different learning definitions, ontology will get presented for the concept of learning. In the ontology, the main ontological concepts and their relations have been represented. Also a conceptual model for learning based on presented ontology will be proposed by means of model and modeling description. Then concepts of presented definitions are going to be shown in proposed model and after that, the model’s functionality will be discuss. Twelve main characteristics have been used to describe the proposed model’s functionality. Utilizing learning ontology to improve the proposed conceptual model can be used also as a guide to model learning and also can be useful in different learning models’ comparison. So that the key concepts which can be used for considered learning model will be determined. Furthermore, an example based on proposed ontology and definition features is explained.
Quantum moduli spaces of N=1 string theories.

Generically, string models with $N=1$ supersymmetry are not expected to have moduli beyond perturbation theory; stringy non-perturbative effects as well as low energy field-theoretic phenomena such as gluino condensation will lift any flat directions. In this note, we describe models where some subspace of the moduli space survives non-perturbatively. Discrete $R$ symmetries forbid any inherently stringy effects, and dynamical considerations control the field-theoretic effects. The surviving subspace is a space of high symmetry; the system is attracted to this subspace by a potential which we compute. Models of this type may be useful for considerations of duality and raise troubling cosmological questions about string theory. Our considerations also suggest a mechanism for fixing the expectation value of the dilaton.
DC Link Capacitors Voltage Balancing in Diode-Clamped Multilevel Inverter

voltage balancing problem of DC capacitors is a key problem. This paper proposes a new topology of chopper circuit for dc capacitor voltage balancing in diode clamped multilevel inverters (DCMLI). In the presented topology the number of switches is reduced respect to other proposed topology in literatures. Suggested topology is modular and has high reliability. Based on presented control system for chopper switches, only one switch is turn on at each time. Therefore the conduction and switching losses of chopper system are reduced. The presented simulation results show fast dynamic response of suggested chopper and reduction the voltage stress on its switches. Computer aided simulations are performed through MATLAB/Simulink software. The simulations results are presented to verify the performance of proposed chopper circuit and its control system for DC capacitor voltage balancing in diode clamped multilevel inverter.
Cross-Product Sentiment Analysis via Ensemble SVM Classifiers

Sentiment analysis in context of product reviews involves automatically classifying whether users have expressed positive or negative opinions regarding a product. In this paper we define the novel problem of cross-product sentiment analysis, which involves predicting the sentiment polarity of a new product’s reviews using training data available for a different set of products. Our work has high practical utility, since while learning user feedback of newly released products is often most important, training data for them is seldom available. Our approach based on an ensemble of SVM classifiers combined using a vocabulary overlap heuristic, outperforms a baseline SVM based method.
TRANSFORMATIONS IN RISK MANAGEMENT OF CURRENCY EXCHANGE IN LITHUANIAN COMMERCIAL BANKS

Abstract After the adoption of International Convergence of Capital Measurement and Capital Standards (widely known as Basel II requirements) in 2004 the risk management in commercial banks has changed dramatically. Lithuanian commercial banks are in transitional period now adapting their risk management systems to Basel II requirements. Market risk is considered one of the key risks in bank risk management structure, so proper management of market risk is essential for a modern bank. Currency exchange risk usually is the main component of market risk. Currency exchange risk management in Lithuanian commercial banks was not good enough; also the Central Bank's regulatory limits were liberal. But after the adoption of Basel II requirements, the entire risk management system is transforming and currency exchange risk management is affected. The objective of this paper is to demonstrate the transformations of currency exchange in Lithuanian commercial banks and propose an effective model for commercial banki.