Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling, analysis and reduction of biological systems

This thesis deals with modeling, analysis and reduction of various biological models, with a focus on gene regulatory networks in the bacterium E. coli. Different mathematical approaches are used. In the first part of the thesis, we model, analyze and reduce, using classical tools, a high-dimensional transcription-translation model of RNA polymerase in E. coli. In the second part, we introduce a novel method called Principal Process Analysis (PPA) that allows the analysis of high-dimensional models, by decomposing them into biologically meaningful processes, whose activity or inactivity is evaluated during the time evolution of the system. Exclusion of processes that are always inactive, and inactive in one or several time windows, allows to reduce the complex dynamics of the model to its core mechanisms. The method is applied to models of circadian clock, endocrine toxicology and signaling pathway; its robustness with respect to variations of the initial conditions and parameter values is also tested. In the third part, we present an ODE model of the gene expression machinery of E. coli cells, whose growth is controlled by an external inducer acting on the synthesis of RNA polymerase. We describe our contribution to the design of the model and analyze with PPA the core mechanisms of the regulatory network. In the last part, we specifically model the response of RNA polymerase to the addition of external inducer and estimate model parameters from single-cell data. We discuss the importance of considering cell-to-cell variability for modeling this process: we show that the mean of single-cell fits represents the observed average data better than an average-cell fit.
Comparative Study of Model-Free Predictive Control and Its Database Maintenance for Unstable Systems

: This study provides a comparison of three methods, i.e., standard locally weighted averaging (LWA), least-norm solutions, and (cid:2) 1 -minimization, for model-free predictive control based on Just-In-Time modeling and database maintenance for an unstable system. In contrast to conventional model predictive control, the model-free predictive control method does not use any mathematical model; rather, it uses the past input / output data stored in a database. Although conventional stabilizing feedback is used to obtain the input / output data of an unstable system, model-free predictive control is assumed to be used without it. Three methods based on standard LWA, least-norm solutions, and (cid:2) 1 minimization are statistically compared using a simple model. The results show that the methods of least-norm solutions and (cid:2) 1 -minimization are superior to that of LWA. The method by (cid:2) 1 -minimization yields tracking errors smaller than that by least-norm solutions; however, the method by (cid:2) 1 -minimization requires a long computational time. In addition, the e ﬀ ectiveness of a method of database maintenance is illustrated by numerical simulations. (cid:2)
Colorimetric Sensing of Pb2+ Ion by Using Ag Nanoparticles in the Presence of Dithizone

Colorimetric analysis of heavy metal ions can be realized by the aid of Ag nanoparticles to improve the analytical characteristics. The method is based on the localized surface plasmon resonance (LSPR) properties of the Ag nanoparticles (AgNPs). In this work, we applied the AgNPs with the addition of dithizone to further improve the selectivity and sensitivity of Pb2+ analysis. Colorimetric sensing of Pb2+ ions based on the polyvinyl alcohol (PVA)-stabilized-colloidal AgNPs in the presence of dithizone is reported. A linear decrease in the AgNPs LSPR absorbance at 421 nm was observed along with the increase in the Pb2+ concentration in the range of 0.50–10 µg/L. The other ions give a minor change in the LSPR absorbance of colloidal AgNPs. The Pb2+ limit of detection, the limit of quantification, and sensitivity were found to be 0.64 ± 0.04 µg/L, 2.1 ± 0.15 µg/L, 0.0282 ± 0.0040 L/µg (n = 5), respectively. The obtained sensitivity is comparable with that of the immunosensing method. The proposed method could offer a good alternative for colorimetric analysis of Pb2+ ions by using nanoparticles in the presence of ligands, which can improve selectivity.
Deploying Millimetre Wave Indoor Wireless Systems

Rapid growth in wireless communication technologies and the introduction of smart phones has resulted in an unprecedented increase in the demand for data. Present systems operating at sub-6 GHz frequencies are not capable of addressing this increased data demand from future wireless communication systems, owing to the limited bandwidth available and spectral congestion at these frequencies. The potential for using largely unallocated frequency spectrum available at millimetre wave frequencies to address this increased data demand has been studied recently. To overcome the increased propagation and penetration losses at millimetre wave frequencies, integrated circuit technologies and signal processing techniques are being developed in order to make millimetre wave communication a reality. In wireless system deployment, the system planner must deploy base station antennas at the most appropriate locations to maximize system performance. In this research, the effect of transmitting antenna deployments on system performance in single room office environments at millimetre wave frequencies is investigated using a three-dimensional implementation of Geometrical Optics and the Uniform Theory of Diffraction (GO/UTD). Multiple antenna deployments and artificial environmental modifications (such as planar and non-planar reflectors, hemispherical array reflectors and reflectarrays) are also investigated in order to quantify how such techniques could improve system performance. A quantitative estimate of shadowing inside the office volume indicated that there are significant shadow regions that cannot be eliminated by simply increasing the transmitted power. It is shown that such regions can be significantly reduced in size if an appropriate deployment of multiple antennas or passive reflectors is adopted.
Probiotic soft sheep's cheese: evaluation of probiotic survival and its influence on proteolysis and organoleptic characteristics.

In the present work, the survival of two commercial strains of probiotic bacteria: Bifidobacterium animalis subsp. lactis BB-12 and Lactobacillus acidophilus LA-5, and their influence on the composition, proteolysis and sensory characteristics of soft sheep cheese were studied. Three different types of cheeses were made: control cheese (QT) without addition of probiotic bacteria, and two experimental cheeses with addition of BB-12 (QBb) and LA-5 (QLA). Gross composition, pH, counts of starter and probiotic bacteria and proteolysis were evaluated during ripening at 4, 15 and 30 days, and a descriptive sensory analysis was conducted at the end of ripening (30 d). The initial pH in all cheeses was 5.15±0.02 and was slightly increased at 30 days at a value of 5.31 ± 0.02; pH values were similar in all cheeses. Gross composition was similar between control and probiotic cheeses; mean values of moisture and content of fat and protein (expressed in dry matter) were 47.61±0.8 (%w/w), 53.38±1.52 (%w/w) and 42.87±1.15 (%w/w) respectively. The counts of BB-12 and LA-5 in the curd were ~108 CFU/g and were maintained at this level in the cheeses during all ripening time. The addition of BB-12 and LA-5 produced an increase in the levels of secondary proteolysis, being the effect more marked for LA-5. The descriptive sensory analysis of cheeses showed that the addition of LA-5 and BB-12 did not produce defects in flavor and taste but rather significantly improved (p<0.05) the elasticity, appearance of mass and mouthfeel of cheeses. Soft sheep cheese demonstrated to be a suitable carrier for these probiotic cultures, and the characteristics of these cheeses were slightly improved by the addition of probiotic bacteria.
How does a flipped classroom course affect the affective domain toward science course?

ABSTRACT Teaching science in higher education has traditionally consisted of delivering knowledge as lectures to students following a teacher-centered methodology. The flipped methodology known as inverted instruction can be used as an alternative approach providing a student-centered class. This contribution presents a comparison of two different instruction methodologies, Traditional Instruction Methodology (TIM) and Inverted Instruction Methodology (IIM), in terms of students’ perceptions and emotions in a science course. The results after applying various data analytics showed that the IIM had a significant influence in students’ perceptions toward the course and made classes more interactive. Besides, the IIM had a positive impact in students’ emotions self-reported during the course. In fact, the IIM made a promotion of positive emotions toward the course, particularly when students did not have a prior science educational background. That is especially relevant since the majority of students enrolled had not taken science subjects in the previous educational levels.
Yield and quality of sunflower oil in Ultisol and Oxisol under water regimes

ABSTRACT Extreme natural events influencing soil water availability are major factors limiting yield and oil quality in oilseed crops. The aim of the present study was to assess the effect of water deficit and surplus water on yield, oil content and on the fatty acid profile of sunflower plants sown in main and second crop seasons in two soil classes in Rio Grande do Sul State. The experiment was carried out in Santa Maria, RS (Ultisol) and Panambi, RS state, Brazil (Oxisol), Brazil, and comprised three water conditions (water deficit, water surplus and control) in a randomized block design. The hybrid Helio 250 was sown in early September (main crop season) and in early January (second crop season) in both soil classes. Yield, yield components, oil content, oil yield and fatty acid profile were herein analyzed. Yield and yield components were affected by water conditions, sowing dates and soil class. The soil class had no significant effect on oil content, yield or quality. Water deficit was more harmful to oil yield and quality than water surplus. Water deficit was responsible for increased oleic fatty acid contents and for linoleic content reduction.
Boosting the performance of visible light‐driven WO3/g‐C3N4 anchored with BiVO4 nanoparticles for photocatalytic hydrogen evolution

In this article, a ternary WO3/g‐C3N4@ BiVO4 composites were prepared using eco‐friendly hydrothermal method to produce efficient hydrogen energy through water in the presence of sacrificial agents. The prepared samples were characterized by scanning electron microscopy (SEM), scanning transmission electron microscopy (STEM), transmission electron microscopy (TEM), X‐ray diffraction (XRD), ultraviolet‐visible (UV‐vis), Brunauer‐Emmett‐Teller (BET) surface area, and photoluminescence spectroscopy (PL) emission spectroscopy. The experimental study envisages the formation of 2‐D nanostructures and observed that such kinds of nanostructures could provide more active sites for photocatalytic reduction of water and their inherent reactive‐species mechanism. The results showed the excellent photocatalytic performance (432 μmol h−1 g−1) for 1.5% BiVO4 nanoparticles in WO3/g‐C3N4 composite when compared with pure WO3 and BiVO4. The optical properties and photocatalytic activity measurement confirmed that BiVO4 nanoparticles in WO3/g‐C3N4 photocatalyst inhibited the recombination of photogenerated electron and holes and enhanced the reduction reactions for H2 production. The enhanced photocatalytic efficiency of the composite nanostructures may be attributed to wide absorption region of visible light, large surface area, and efficient separation of electrons/holes pairs owing to synergistic effects between BiVO4 and WO3/g‐C3N4. The prepared samples would be a precise optimal photocatalyst to increase their suppliers for worldwide applications especially in energy harvesting.
Pharmacognostic study of the stem of Caesalpinia bahamensis and characterization of its aqueous and hydroalcoholic extracts

Caesalpinia bahamensis Lam. is a medicinal plant widely used by the Cuban population to treat hepatic and renal diseases, peptic ulcer and diabetes. However, the scientific knowledge on this species is limited. The aim of this study is to determine the quality parameters and the preliminary chemical composition of the stem of Caesalpinia bahamensis Lam. and its aqueous and hydroalcoholic extracts. The quality parameters of the stem of C. bahamensis and its aqueous and hydroalcoholic extracts were determined according to WHO recommendations. The chemical composition of the extracts was studied by phytochemical screening and their TLC and UV profiles. Flavonoids and phenolic compounds were quantified by the aluminium trichloride and Folin-Ciocalteu methods, respectively. In this study, the morphological characteristics and physicochemical parameters of the stem of C. bahamensis and its extracts were determined for first time. Flavonoids were higher for the hydroalcoholic extract and phenols for the aqueous extract.
Molecular Basis for Strain Variation in the S. Cerevisiae adhesion Flo11p

FLO11 encodes a yeast cell wall flocculin that mediates a variety of adhesive phenotypes in Saccharomyces cerevisiae. Flo11p is implicated in many developmental processes, including flocculation, formation of pseudohyphae, agar invasion, and formation of microbial mats and biofilms. However, Flo11p mediates different processes in different yeast strains. To investigate the mechanisms by which FLO11 determines these differences in colony morphology, flocculation, and invasion, we studied gene structure, function, and expression levels. Nonflocculent Saccharomyces cerevisiae 1278b cells exhibited significantly higher FLO11 mRNA expression, especially in the stationary phase, than highly flocculent S. cerevisiae var. diastaticus. The two strains varied in cell surface hydrophobicity, and Flo11p contributed significantly to surface hydrophobicity in S. cerevisiae var. diastaticus but not in strain 1278b. Sequencing of the FLO11 gene in S. cerevisiae var. diastaticus revealed strain-specific differences, including a 15-amino-acid insertion in the adhesion domain. Flo11p adhesion domains from strain 1278b and S. cerevisiae var. diastaticus were expressed and used to coat magnetic beads. The adhesion domain from each strain bound preferentially to homologous cells, and the preferences were independent of the cells in which the adhesion domains were produced. These results are consistent with the idea that strain-specific variations in the amino acid sequences in the adhesion domains cause different Flo11p flocculation activities. The results also imply that strain-specific differences in expression levels, posttranslational modifications, and allelic differences outside the adhesion domains have little effect on
Design, implementation and evaluation of automated surveillance systems

Automatic surveillance system is getting more and more sophisticated with the increasing calculation power that computers are reaching. The aim of this project is to take advantage of these tools and with the new classification and detection technology brought by neural networks, develop a surveillance application that can recognize certain behaviours (which are the detection of lost backpacks and suitcases, detection of abnormal crowd activity and heatmap of density occupation). To develop this program, python has been the selected programming language used, where YOLO and OpenCV form the spine of this project. After testing the code, it has been proved that due to the constrains of the detection for small objects, the project does not perform as it should for real development, but still it shows potential for the detection of lost backpacks in certain videos from the GBA dataset [1] and PETS2006 dataset [2]. The abnormal activity detection for crowds is made with a simple algorithm that seems to perform well, detecting the anomalies in all the testing dataset used, generated by the University of Minnesota [3]. Finally, the heatmap can display correctly the projection of people on the ground for five second, just as intended. The objective of this software is to be part of the core of what could be a future application with more modules that will be able to perform full automated surveillance tasks and gather useful information data, and these advances and future proposal will be explained in this memory.
A Multi-tier Cost Model for Effective User Scheduling in Fog Computing Networks

In this paper, we investigate a cost model and the resulted cost-minimization user scheduling problem in multi-tier fog computing networks. For a typical multi-tier fog computing network consisting of one fog control node (FCN), multiple fog access nodes (FANs) and user equipments (UEs), how to model the cost paid to FANs for motivating resources sharing, and how to effectively schedule UEs to minimize the overall cost for FCN, are still problems to be resolved. To solve these problems, a unified multi-tier cost model, including the service delay and a linear inverse demand dynamic payment scheme, is proposed, and a cost-minimization user scheduling problem is formulated. Further, the user scheduling problem is reformulated as a potential game and proven to possess a nash equilibrium (NE) solution. The corresponding distributed algorithm, namely Cost-Oriented User Scheduling (COUS) algorithm, is developed to achieve an NE of the user scheduling game. Analytical and simulation results show that the COUS can offer near-optimal performance in terms of the overall cost. Besides, the dynamic payment scheme can achieve a win-win outcome for both FANs and FCN, but an unfair workload distribution among FANs, compared with the fixed payment scheme.
Global Fault-Tolerant Sampled-Data Control for Large-Scale Switched Time-Delay Nonlinear Systems

In this paper, the global sampled-data control problem has been considered for large-scale switched time-delay nonlinear systems subject to actuator failures. A reduced-order observer is developed to estimate unmeasured states, and a decentralized sampled-data controller is designed with an allowable sampling period. It can be shown that all states of the corresponding closed-loop system are globally uniformly ultimately bounded under arbitrary switchings through the choice of the suitable Lyapunov– Krasoviskii functional. Finally, the validity of the proposed control scheme is verified using an example.
Ni addition induced modification of structural, magnetic properties and antistructural modeling of Zn1-xNixFe2O4 (x = 0.0 - 1.0) nanoferrites

Abstract Nano-crystalline Zn1-xNixFe2O4 (x = 0.0 - 1.0) nano-ferrites were synthesized by using sol–gel auto-combustion method. X-ray diffraction (XRD), magnetic measurements were used to monitor the effect of Ni addition on structural, magnetic properties. XRD patterns confirm the formation of single phase nanocrystalline cubic spinel structure. For sample with x = 1.0, a small fraction of Fe2O3 was also detected. Calculated parameters revealed that Ni2+ addition changecrystallite sizes (Ds) and (DW-H) respectively in the range between 30.29 – 39.13 nm, and 37.52 – 64.57 nm. Observed changes in the cell volume (V), specific surface area (S), X-ray density (ρXRD), are attributed to the difference in ionic radii of Fe3+, Ni2+, Zn2+ ions. The Ni2+ ions migrate predominantly to octahedral sites (thus pushing ferric ions on A sites) leading to increased inversion parameter and decrease of oxygen parameter (u) from 0.3836 to 0.3796. Exact matching of aexp, ath suggests that the estimated cation distribution is in good agreement with the real distribution and with magnetic data obtained from VSM. Hc varies between 66.49 Oe– 185.20 Oe, while, migration of cations leads to changes in Ms. In order to explain the nature of the surface defects the antistructural model is used in the current study.
Learning deep representations for robotics applications

In this thesis, two hierarchical learning representations are explored in computer vision tasks. First, a novel graph theoretic method for statistical shape analysis, called Compositional Hierarchy of Parts (CHOP), was proposed. The method utilises line-based features as its building blocks for the representation of shapes. A deep, multi-layer vocabulary is learned by recursively compressing this initial representation. The key contribution of this work is to formulate layerwise learning as a frequent sub-graph discovery problem, solved using the Minimum Description Length (MDL) principle. The experiments show that CHOP employs part shareability and data compression features, and yields state-of- the-art shape retrieval performance on 3 benchmark datasets. In the second part of the thesis, a hybrid generative-evaluative method was used to solve the dexterous grasping problem. This approach combines a learned dexterous grasp generation model with two novel evaluative models based on Convolutional Neural Networks (CNNs). The data- efficient generative method learns from a human demonstrator. The evaluative models are trained in simulation, using the grasps proposed by the generative approach and the depth images of the objects from a single view. On a real grasp dataset of 49 scenes with previously unseen objects, the proposed hybrid architecture outperforms the purely generative method, with a grasp success rate of 77.7% to 57.1%. The thesis concludes by comparing the two families of deep architectures, compositional hierarchies and DNNs, providing insights on their strengths and weaknesses.
Carbon nanotube alignment and sorting: Attempting a sulfur moiety as anchoring component

Abstract Previously, the Schipper group established a method for simultaneously sorting and aligning single walled carbon nanotubes using an alignment relay technique (ART) with molecule 1. Here, further synthetic investigations were pursued to explore another anchoring group and expand functionalization onto different surfaces, in particular for gold substrates. A thiophosphonate moiety (2) is attempted as a substitute for the phosphonate ester on 1 as sulfur has been shown to efficiently chemisorb onto gold surfaces and could make an interesting substrate pursuit for better carbon nanotube alignment. For 2 – it has potential to be obtained through the use of Lawesson’s reagent, although currently the synthesis here failed to demonstrate isolation of the compound. Therefore, additional screening for different intermediate sulfur aligning molecules was also conducted, as to improve the density along side molecule 1 for ART applications. Outcomes are compared with the results on gold surfaces yielding from 1 to establish if more or fewer nanotubes are present on the surface in a horizontally aligned manner with respect to one another. Orientation is characterized via atomic force microscopy and chiralities of the nanotubes are observed via Raman spectroscopy. GRAPHICAL ABSTRACT