Miracle-dz/UniFAR-FTUs huggingface dataset:
Quality comparison of published genomes: Definition of methods and techniques.

Based on recent versions of the genomes that are publicly available within the scientific community, we have decided to investigate the quality of such existing genomes. The aim of this analysis is to evaluate current methods and techniques to measure and compare the quality of assemblies and annotations, considering genomes from different origins and evolutionary lineages (both from model and non-model organisms). The aim of this study is to further evaluate the consequences of the use of a poor-quality genome and avoid inaccuracies which are intrinsic in order to minimize the range of error in research. Moreover, we want to assess if by using genomes with different quality standards in research, we can optimize results. The above-mentioned questions justify the development for new methods and techniques which will help us control the quality of genomes and improve investigation in the future. The main objective of this project is the identification of varied techniques using a range of existing software technologies to automate and make the quality control of genomes more accessible for researchers, as well as the possibility to compare genome quality as a result. Keywords— Genomics, Genomes quality, Methods, Techniques, Analysis, Genome assembly, Genome annotation
Validating complex agent behavior

Developing software agents that replicate human behavior, even within a narrow domain, is a time consuming and error prone process. The most widely used methodology for designing these agents is based on the complementary processes of knowledge acquisition and validation, both of which have been cited as significant bottlenecks. In this thesis, we identify two methods for comparing actors' behavior that have the potential to decrease the cost of validation. The first is a simple sequence-based approach that can be used to compare many different aspects of two actors' behavior. Although initially promising, our empirical and analytical analysis exposes significant limitations with this general class of approaches, especially as the complexity of the domain increases. As a result, we turn to a novel comparison approach that we call behavior bounding. Unlike the sequential approaches, behavior bounding uses a concise representation of an actor's aggregate behavior as a basis for performing its comparison. We show that behavior bounding requires minimal human effort to use and that its representation of behavior is efficient to construct and maintain even as the complexity of the environment increases. Furthermore, we show that behavior bounding outperforms the sequential comparison approach in two domains of distinct complexity. Finally, we provide empirical evidence that behavior bounding's summary of the differences in two actors' behavior can be used to significantly speed up the knowledge validation process.
Efficiency of the bio-inspired Leaky Integrate-and-Fire neuron for signal coding

The goal of this paper is to investigate whether purely neuro-mimetic architectures are more efficient for signal compression than architectures that combine neuroscience and state-of-the-art models. We are motivated to produce spikes, using the LIF model, in order to compress images. Seeking solutions to improve the efficiency of the LIF in terms of the memory cost, we compare two different quantization approaches; the Neuroinspired Quantization (NQ) and the Conventional Quantization (CQ). We present that when the LIF model and the NQ appear in the same architecture, the performance of the compression system is higher compared to an architecture that consists of the LIF model and the CQ. The main reason of this occurrence is the dynamic properties embedded in the neuro-mimetic models. As a consequence, we first study which are the dynamic properties of the recently released (NQ) which is an intuitive way of counting the number of spikes. Moreover, we show that some parameters of the NQ (i.e. the observation window and the resistance) strongly influence its behavior that ranges from non-uniform to uniform. As a result, the NQ is more flexible than the CQ when it is applied to real data while for the same bit rate it ensures higher reconstruction quality.
Bolchem: an On-Line Coupled Mesoscale Chemistry Model

Abstract. This work presents the on-line coupled meteorology-chemistry transport model BOLCHEM based on the hydrostatic meteorological BOLAM model, the gas chemistry module SAPRC90 and the aerosol dynamic module AERO3. It includes parameterizations to describe natural source emissions, dry and wet removal processes, as well as the transport and dispersion of air pollutants. The equations for different processes are solved on the same grid during the same integretation step, by means of operator splitting method. The paper describes the model and shows the model performances at continental scale for oneyear run (December 2009–November 2010). The results show that BOLCHEM reproduces both O3 and PM10 at surface. For O3, we found the best agreement in the summer, with a correlation coefficient R of 0.7 and mean bias of 4.0. On the contrary, PM10 is better reproduced in the winter, with a correlation coefficient R of 0.6 and the mean bias MB of 6.7.
Selective toluene disproportionation over ZSM-5 zeolite

This research aimed at improving p-xylene selectivity in toluene disproportionation over ZSM-5 zeolite by exploring the effect of crystal size and various post synthetic modification methods. A comprehensive study of the effect of different modifications on the physicochemical properties of ZSM-5 was investigated using X-ray diffraction (XRD), pyridine adsorption, Fourier transform infrared (FTIR), Si magic-angle spinning nuclear magnetic resonance (MAS NMR), BET surface area by N2 adsorption, inductively coupled plasma (ICP) and scanning electron microscopy (SEM). The catalytic performance of each catalyst was studied in a fixed bed reactor at a temperature 475 °C, WHSV 3-83 h and two different pressures (1 and 10 bar). ZSM-5 zeolites with different crystal sizes (5, 50 and 100 μm) were synthesized in house and compared with the commercially obtained ZSM-5 having a crystal size of 0.5 μm. The increase in crystal size improved p-xylene selectivity which was attributed to the diffusion constraints imposed by the longer diffusion path lengths of large crystals. The highest p-xylene selectivity (58 %) was achieved over ZSM-5 with the largest crystal size 100 μm at the highest WHSV 83 h. However, it was accompanied by a low conversion (2 wt. %). ZSM-5 with crystal size of 5 μm delivered the best results in terms of the combination of para-selectivity (40 %) and toluene conversion (15 wt. %). The p-xylene produced in the channels of ZSM-5 can quickly isomerise to o-xylene and m-xylene on the external unselective acid sites. Different post modification methods were applied in this study in attempt to suppress the fast isomerization reaction by deactivating the external acid sites. This was achieved to some extent by depositing an inert silica layer using different silica agents, amounts and number of modification cycles and as a result p-xylene selectivity was significantly improved (84 %), especially over large crystals 5 μm. The decrease in Brønsted acidity (FTIR) suggested the success of the silylation method. Furthermore, impregnation of lanthanum and phosphorus on ZSM-5 improved p-xylene selectivity (40 %). FTIR measurements showed a drastic drop in the number of Brønsted and Lewis acid sites after loading phosphorus which led to a large reduction in toluene conversion. Lanthanum impregnation had less effect on conversion and increased selectivity with decreased Brønsted sites and pore volume reduction showed by N2 adsorption suggesting some pore narrowing. There are several approaches that can be considered in future to further improve p-xylene selectivity. Improving the synthesis of large crystals to balance acidity and crystal size can lead to the enhancement of p-xylene selectivity. Also, performing toluene disproportionation on optimised pre-coked ZSM-5 large crystals at high pressure can help to maintain the conversion while increasing p-xylene selectivity. Another approach would be to apply silylation modification to extruded large crystals ZSM-5.
Building an Improved Internet of Things Smart Sensor Network Based on a Three-Phase Methodology

In recent years, the Internet of Things (IoT) has allowed the easy, intelligent, and efficient connection of many devices used in daily life by means of numerous smart sensors which communicate with each other using wireless signals. The rapid development of the IoT has been a result of recent advances in sensing technology. This paper proposes a three-phase methodology to improve the quality of experience for IoT system technologies. The proposed method employs the concepts of simple routing and two well-known multi-criteria decision-making method (MCDM) techniques: The Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). First, all simple routings are obtained using the proposed depth-first search technology (DFS). AHP is applied to analyze the structure of the problem and to obtain weights for various selected criteria in the second phase. In the third phase, TOPSIS is utilized to rank the simple routings, which are simple paths. A case study example is provided to demonstrate the proposed three-phase methodology. The results from the numerical experiments show that the proposed methodology can successfully achieve the aim of this paper.
A simulation framework to evaluate urban logistics schemes

textabstractThe domain of urban freight logistics is becoming increasingly complex. Many urban supply chains are composed of small and independent actors; for such actors it is particularly difficult to efficiently organize the highly fragmented supply chains that they must deal with. As a consequence, a high number of transport movements is required to satisfy the high service level demands of receivers, which has negative effects on the environment as well as on the quality of life in urban areas. Both companies and local administrators try to improve transport efficiency and reduce external costs, but the effects of such interventions are difficult to predict, especially when applied in combination with each other (an urban logistics scheme). This paper proposes an agent-based simulation framework that quantifies the effects of urban logistics schemes on multiple actors. We provide a detailed mathematical representation of the framework. The working of the framework is demonstrated with numerical experiments that evaluate a variety of urban logistics schemes. We show that most schemes yield significant environmental improvements; achieving financial sustainability, however, is challenging. Also, interventions such as subsidies and access restrictions do not always have the intended effect. Our experiments underline the applicability of the framework to quantify the effects of urban logistics schemes.
A State of the Art Survey on Polymorphic Malware Analysis and Detection Techniques

Nowadays, systems are under serious security threats caused by malicious software, commonly known as malware. Such malwares are sophisticatedly created with advanced techniques that make them hard to analyse and detect, thus causing a lot of damages. Polymorphism is one of the advanced techniques by which malware change their identity on each time they attack. This paper presents a detailed systematic and critical review that explores the available literature, and outlines the research efforts that have been made in relation to polymorphic malware analysis and their detection.
Real-time and semantic energy management across buildings in a district configuration

Existing building and district energy management strategies are in urgent need of an overhaul to meet the energy and environmental challenges of the 21st Century. The immense growth in the availability of data through the Internet of Things (IoT), the decentralisation of energy generation, and the increasing power of Artificial Intelligence (AI) presents an opportunity to achieve a paradigm shift in the way energy is controlled and managed. To contribute to this field, this PhD project undertook a thorough literature review combined with a participatory, action research approach to identify and understand the key challenges faced by facility managers and to identify potential areas of improvement. Following this, the PhD thesis aims to tackle three key research areas using simulated case study experiments. These aim to optimise thermal energy management within buildings at a zone-level, control energy generation at a district-level, and combine the learnings from these two experiments with a holistic energy management solution that controls both the energy supply and demand at a building and district-level. At a building-level, a model predictive control approach combining a genetic algorithm and surrogate artificial neural network is used. A predictive and context aware controller is able to produce 24 hour heating set point schedules for each zone within a building. This approach achieved an energy saving of 18% whilst maintaining thermal comfort for users. The methodology also had the capability to adapt to dynamic energy pricing tariffs and capable of optimising for energy cost by shifting load to cheaper periods. At a district-level, a predictive, optimisation-based approach was developed to determine the operation of a multi-vector, district heating, energy centre. When thermal storage and several generation sources are available, alongside variable renewable energy generation and building demand, static, rulebased controllers cannot perform adequately in all conditions. Instead, the optimisation-based approach, developed in this thesis, was able to increase profit to the energy centre by 45% as well as decrease CO2 emissions whist adapting to errors in energy demand and supply forecasting. Finally, the most significant contribution of this thesis was provided by ef-
Quantum dynamics and spectra of the iodine atom in a strong laser field as calculated with the URIMIR package

ABSTRACT We report computations for the quantum dynamics and spectra of the iodine atom in the ground state () subjected to a strong laser field at wavenumbers in the range of CO laser emission. The computations include the hyperfine structure of the magnetic dipole transitions between the fine structure levels ( and ) of the iodine atom near cm−1 important for the iodine atom laser and for the kinetic spectroscopy of iodine atoms generated in infrared multiphoton excitation and dissociation of organic iodides, where simultaneous high time and frequency resolution including hyperfine structure can be achieved, that is only limited by the Heisenberg uncertainty principle. Accurate numerical results from calculations with the URIMIR package are compared with estimates from perturbation theory and they agree well. Possibilities for experimental tests of the theoretical predictions are outlined and the URIMIR package is included in an appendix of supplementary material. GRAPHICAL ABSTRACT
Improving students’ motivation and collaborative skills through Remap Jigsaw learning combined with modelling activities

Students’ motivation and their collaborative skills play a crucial role in determining the learning quality. The lower the motivation and collaborative skills the lower the learning quality. This lesson study-based classroom action research aimed at improving the students’ learning motivation and collaborative skills by implementing a Remap Jigsaw learning model. The subjects of this research were the fifth semester undergraduate students of Biology Education of Universitas Negeri Malang, who were taking the Basic Skills of Teaching course. The research was conducted in four cycles in which the each cycle consisted of two meetings. The instruments of data collection were the observation sheets and assessment sheets of students’ self-report. The data was analyzed qualitatively and quantitatively. The results showed that students’ learning motivation increased from 66.25% in the cycle I to 84.37% in the cycle IV; while the students’ collaborative skills enhanced from 50% to 70.83% in the cycle IV. In the other words, the Remap Jigsaw combined with modelling activities can improve students’ motivation and their collaborative skills.
Electrospun PCL/cefuroxime scaffolds with custom tailored topography

Abstract The study focuses on the fabrication and properties evaluation of dual functional electrospun polycaprolactone scaffolds incorporating antibiotic drug cefuroxime and having custom tailored topography for the support of human cells to be used in tissue regeneration. The scaffolds were evaluated in regard to their morphology, total porosity, thermal properties, tensile properties and cells adhesion/growth, thus to define the effect of both drug content and physical architecture on the scaffolds properties. Both the addition of the drug and the scaffolds custom topography resulted in higher total porosity (up to 90%), while the fibers diameter highest reduction was up to 73%. The type of 3 D printed collector influenced scaffolds tensile properties, thus the increase of the tensile strength was due to the change of the central cylinder height in the hexagonal geometry of the collectors. Cells distribution and spreading on the electrospun scaffolds after 72 hours, showed that scaffolds with custom topography have more evenly scattered cells than the ones with random electrospun fibers. The study has demonstrated that both drug concentration and scaffolds target topography influenced the scaffolds properties and both can be optimized depending on scaffolds demands.
Voice Gender Recognition Using Deep Learning

In this article, a Multilayer Perceptron (MLP) deep learning model has been described to recognize voice gender. The data set have 3,168 recorded samples of male and female voices. The samples are produced by using acoustic analysis. An MLP deep learning algorithm has been applied to detect genderspecific traits. Our model achieves 96.74% accuracy on the test data set. Also the interactive web page has been built for recognition gender of voice. Keywords-deep learning; voice recognition; multilayer perceptron networks
Visualization of Scanned Cave Data with Global Illumination

3D scans of caves are acquired for virtual exploration, quantitative analysis, and communication purposes. Especially when presented to a non-professional audience, near realistic visualization is important because it improves acceptance and familiarity. A major part of realistic visualizations is the reasonable simulation of light transport, including global effects such as diffuse inter-reflections. In this paper, we present a direct visualization method for cave data that are represented as unstructured point clouds. Global light transport is approximated with a volumetric scene representation. Using hierarchical data structures, our system achieves interactive frame rates.
Fusion of Artificial Intelligence in Neuro-Rehabilitation Video Games

In this paper, an intuitive neuro-rehabilitation video game has been developed employing the fusion of artificial neural networks (ANNs), inverse kinematics (IK), and fuzzy logic (FL) algorithms. The embedded algorithms automatically adjust the game difficulty level based on the player’s interaction with the game. Moreover, it is manifested as an alternative approach for possible movements to improve incorrect positioning through real-time visual feedback on the screen; 52 participants volunteered to engage in the program. Motor assessment scale (MAS) was determined to assess the participants’ functional ability pre- and post-treatments. The system input is received via the Microsoft Kinect, a foot Pedal (Saitek), and the Thalmic Myo armband. The ANN classifier integrates the limb joints orientation, angular velocity, lower arms’ muscle activity, hand gestures, feet sole (plantar) pressure parameters, and the MAS scores to learn from data and predict the improvement following the intervention. The fuzzy input generates a crisp output and provides a personalized rehabilitation program with the potential to be integrated into clinical protocols. Experiments to obtain the input signals and desired outputs were conducted for the learning and validation of the network. The networks pattern recognition, self-organizing map, and non-linear auto-regression analysis performed using feed-forward and Levenberg–Marquardt backpropagation (LMBP) procedure. The results showed the effectiveness of the non-linear auto-regression using the optimized LMBP algorithm to classify and visualize the target categories. Furthermore, the state of the network demonstrates the prediction accuracy exceeding 94%. Clustering algorithm grouped the data based on the similarity. Self-organizing map trained the network to learn the topology of samples with high correlation, presented outputs with high achievement.
Exploiting Backscatter-Aided Relay Communications With Hybrid Access Model in Device-to-Device Networks

The backscatter and active RF radios can complement each other and bring potential performance gain. In this paper, we envision a dual-mode radio structure that allows each device to make smart decisions on mode switch between backscatter communications (i.e., the passive mode) or RF communications (i.e., the active mode), according to the channel and energy conditions. The flexibility in mode switching also makes it more complicated for transmission control and network optimization. To exploit the radio diversity gain, we consider a wireless powered device-to-device network of hybrid radios and propose a sum throughput maximization by jointly optimizing energy beamforming and transmission scheduling in two radio modes. We further exploit the user cooperation gain by allowing the passive radios to relay for the active radios. As such, the sum throughput maximization is reformulated into a non-convex. We first present a sub-optimal algorithm based on successive convex approximation, which optimizes the relays’ reflection coefficients by iteratively solving semi-definite programs. We also devise a set of heuristic algorithms with reduced computational complexity, which are shown to significantly improve the sum throughput and amenable for practical implementation.
ADAPTIVE KALMAN FILTER FORECASTING FOR ROAD MAINTAINERS

The article considers the road monitoring weather-stations which collects raw observations that are processed to be able to make the necessary forecasting for future decisions. For the road maintainers those predictions are crucial to make decisions daily. When it comes to the winter season when road safety is very important; however, the road condition is also affected by the snow and icing. In order to improve safety on the road network the road maintainers are trying to use every possible way to be able to provide it. A number of methods have been studied and compared to clarify the parameter required by Kalman filter, which can be improved by making forecasting more accurate. Several road monitoring weather-stations are merged into one region because they are relatively close to each other and it is assumed that there are common conditions in one region that may indicate changes in road conditions. The corresponding algorithms are applied for each region and then compared to each other. Adaptive Kalman filter is generalized in the relevant article in order to have a general understanding of how to correctly apply the approach. The main result of this article is a comparison with the different methods, which are finally compiled in a single table.