Miracle-dz/UniFAR-FTUs huggingface dataset:
General Adaptive Neighborhood Image Processing for Biomedical Applications

In biomedical imaging, the image processing techniques using spatially invariant transformations, with fixed operational windows, give efficient and compact computing structures, with the conventional separation between data and operations. Nevertheless, these operators have several strong drawbacks, such as removing significant details, changing some meaningful parts of large objects, and creating artificial patterns. This kind of approaches is generally not sufficiently relevant for helping the biomedical professionals to perform accurate diagnosis and therapy by using image processing techniques. Alternative approaches addressing context-dependent processing have been proposed with the introduction of spatially-adaptive operators (Bouannaya & Schonfeld, 2008; Ciuc et al., 2000; Gordon & Rangayyan, 1984; Maragos & Vachier, 2009; Roerdink, 2009; Salembier, 1992), where the adaptive concept results from the spatial adjustment of the sliding operational window. A spatially-adaptive image processing approach implies that operators will no longer be spatially invariant, but must vary over the whole image with adaptive windows, taking locally into account the image context by involving the geometrical, morphological or radiometric aspects. Nevertheless, most of the adaptive approaches require a priori or extrinsic informations on the image for efficient processing and analysis. An original approach, called General Adaptive Neighborhood Image Processing (GANIP), has been introduced and applied in the past few years by Debayle & Pinoli (2006a;b); Pinoli & Debayle (2007). This approach allows the building of multiscale and spatially adaptive image processing transforms using context-dependent intrinsic operational windows. With the help of a specified analyzing criterion (such as luminance, contrast. . . ) and of the General Linear Image Processing (GLIP) (Oppenheim, 1967; Pinoli, 1997a), such transforms perform a more significant spatial and radiometric analysis. Indeed, they take intrinsically into account the local radiometric, morphological or geometrical characteristics of an image, and are consistent with the physical (transmitted or reflected light or electromagnetic radiation) and/or physiological (human visual perception) settings underlying the image formation processes. The proposed GAN-based transforms are very useful and outperforms several classical or modern techniques (Gonzalez & Woods, 2008) such as linear spatial transforms, frequency noise filtering, anisotropic diffusion, thresholding, region-based transforms used for image filtering and segmentation (Debayle & Pinoli, 2006b; 2009a; Pinoli & Debayle, 2007). This book chapter aims to first expose the fundamentals of the GANIP approach (Section 2) by introducing the GLIP frameworks, the General Adaptive Neighborhood (GAN) sets and two 21
Experimental and Thermoeconomic Analysis of Small-Scale Solar Organic Rankine Cycle (SORC) System

A small-scale solar organic Rankine cycle (ORC) is a promising renewable energy-driven power generation technology that can be used in the rural areas of developing countries. A prototype was developed and tested for its performance characteristics under a range of solar source temperatures. The solar ORC system power output was calculated based on the thermal and solar collector efficiency. The maximum solar power output was observed in April. The solar ORC unit power output ranged from 0.4 kW to 1.38 kW during the year. The highest power output was obtained when the expander inlet pressure was 13 bar and the solar source temperature was 120 °C. The area of the collector for the investigation was calculated based on the meteorological conditions of Busan City (South Korea). In the second part, economic and thermoeconomic analyses were carried out to determine the cost of energy per kWh from the solar ORC. The selling price of electricity generation was found to be $0.68/kWh and $0.39/kWh for the prototype and low cost solar ORC, respectively. The sensitivity analysis was carried out in order to find the influencing economic parameters for the change in NPV. Finally, the sustainability index was calculated to assess the sustainable development of the solar ORC system.
Image processing algorithms for retinal montage synthesis, mapping, and real-time location determination

Although laser retinal surgery is the best available treatment for choroidal neovascularization, the current procedure has a low success rate (50%). Challenges, such as motion-compensated beam steering, ensuring complete coverage and minimizing incidental photodamage, can be overcome with improved instrumentation. This paper presents core image processing algorithms for (1) rapid identification of branching and crossover points of the retinal vasculature; (2) automatic montaging of video retinal angiograms; (3) real-time location determination and tracking using a combination of feature-tagged point-matching and dynamic-pixel templates. These algorithms tradeoff conflicting needs for accuracy, robustness to image variations (due to movements and the difficulty of providing steady illumination) and noise, and operational speed in the context of available hardware. The algorithm for locating vasculature landmarks performed robustly at a speed of 16-30 video image frames/s depending upon the field on a Silicon Graphics workstation. The montaging algorithm performed at a speed of 1.6-4 s for merging 5-12 frames. The tracking algorithm was validated by manually locating six landmark points on an image sequence with 180 frames, demonstrating a mean-squared error of 1.35 pixels. It successfully detected and rejected instances when the image dimmed, faded, lost contrast, or lost focus.
Voltage Tracking of a DC-DC Flyback Converter Using Neural Network Control

This paper proposes a neural network control scheme of a DC-DC Flyback converter that will step up a 12V DC and applied it on brushless DC motor with 12 and 24V dc. In this technique, a back propagation learning algorithm is derived. The controller is designed to track the output voltage of the DC-DC converter and to improve performance of the Flyback converter during transient operations. Furthermore, to investigate the effectiveness of the proposed controller, some operations such as starting-up and reference voltage variations are verified. The numerical simulation results show that the proposed controller has a better performance compare to the conventional PI-Controller method.
The coronal Ne/O abundance of alpha Centauri

Recent improvements in the modeling of solar convection and line formation led to downward revisions of the solar photospheric abundances of the lighter elements, which in turn led to changes in the radiative opacity of the solar interior and hence to conflicts with the solar convection zone depth as inferred from helioseismic oscillation frequencies. An increase of the solar Ne/O abundance to values as observed for nearby stars has been proposed as a solution. Because of the absence of strong neon lines in the optical, neon abundances are difficult to measure and the correct solar and stellar Ne/O abundances are currently hotly debated. Based on X-ray spectra obtained with XMM-Newton, we determine a reference value of Ne/O for the inactive, solar-like star alpha Cen (primarily alpha Cen B, which is the dominant component in X-rays), with three independent, line-based methods, using differential emission measure reconstruction and an emission measure-independent method. Our results indicate a value of approx. 0.28 for Ne/O in alpha Cen, approximately twice the value measured for the Sun, but still below the average value obtained for other stars. The low Ne/O abundance of the Sun is peculiar when compared to alpha Cen and other stars; our results emphasize the necessity to obtain more and accurate Ne/O abundance measurements of low activity stars.
The entropy metric in diversity of Multiobjective Evolutionary Algorithms

Quality assessment of Multiobjective Optimization Evolutionary Algorithms (MOEA) has been a major concern in scientific field during the last few years. The entropy metric is introduced and highlighted in computing the diversity of MOEA. Existing performance metrics are briefly reviewed. The advantages of entropy approach over other diversity metrics are classified here. Besides, the definition of the entropy metric and the approach of measuring diversity based on entropy are presented, in which section a detailed explanation about how to choose appropriate parameters in entropy measurement and programming techniques are given. Above all, some significant conclusions arised from the further study of this entropy metric are summarized, which may have a meaningful impact on the high-efficient comparisons of MOEA performance on a quantitative basis. Last but not the least, it will help researchers to determine whether or not a population of solutions has reached maturity, in order to end the optimization process evaluation process.
Serum Vascular Endothelial Growth Factor (VEGF-C) as a Diagnostic and Prognostic Marker in Patients with Ovarian Cancer

VEGF-C is regarded as one of the most efficient factors in regulating lymphangiogenesis. The aim of this study was to better understand the role of VEGF-C in the progression of ovarian cancer and to assess its diagnostic and prognostic significance. A total of 109 patients with ovarian cancer, 76 patients with benign ovarian diseases, and 50 healthy controls were recruited in this study. Serum levels of VEGF-C were determined by ELISA method. The results showed that serum levels of VEGF-C were significantly higher in the patients with ovarian cancer than those with benign ovarian diseases and healthy controls (P<0.01). Serum level of VEGF-C was correlated with FIGO stage, lymph node metastasis, tumor resectability, and survival of the patients (P<0.05). The areas of receiver operating curves of VEGF-C were higher than those of CA125 in different screening groups. Analysis using the Kaplan-meier method indicated that patients with high VEGF-C had significantly shorter overall survival time than those with low VEGF-C (P<0.0001). In a multivariate analysis along with clinical prognostic parameters, serum VEGF-C was identified as an independent adverse prognostic variable for overall survival. These results indicated that serum VEGF-C may be a clinically useful indicator for diagnostic and prognostic evaluation in ovarian cancer patients.
A rare haplotype of the vitamin D receptor gene is protective against diabetic nephropathy.

BACKGROUND Vitamin D and its analogues are reported to have renoprotective effects in chronic kidney disease including diabetic nephropathy (DN). Vitamin D(3) is converted to 1,25(OH)D(3) by CYP2R1 and CYP27B1. The biological action of 1,25(OH)D(3) is mediated via its receptor. VDR, CYP27B1 or CYP2R1 gene variants could modify the biological activity of vitamin D(3). We have conducted the first case-control association study to determine the relationship between polymorphisms in VDR, CYP27B1 and CYP2R1 genes, and the risk of DN in individuals with type 1 diabetes. METHODS Eight VDR single-nucleotide polymorphisms (SNPs) rs10735810 FokI C>T, rs1544410 BsmI G>A, rs7975232 ApaI G>T, rs731236 TaqI T>C, rs4303288 G>T, rs11168275 C>T, rs12721366 G>A and rs2544043 G>C were investigated with CYP27B1 rs4646536 T>C and CYP2R1 rs10741657 G>A. Genotyping was performed using pyrosequencing, Taqman, Sequenom or direct sequencing technologies in 1329 type 1 diabetics (655 nephropaths, 674 non-nephropaths). RESULTS No significant differences were observed in genotype or allele frequencies between case and control groups for VDR, CYP27B1 or CYP2R1 SNPs, either before or after stratification by recruitment centre or when restricted to patients with end-stage renal disease. A previously identified haplotype block from rs1544410 to rs731236 was confirmed at the 3'-end of VDR. Comparison of haplotype frequencies identified the rare AGT haplotype as significantly protective against DN, 3.1% cases versus 5.8% controls; chi(2) = 11.05, Pc = 0.009 by the permutation test. CONCLUSIONS Our study has identified a rare VDR haplotype that is protective against DN in patients with type 1 diabetes. Replication in a large, independent cohort is required to confirm this finding.
Multi Level DC Voltage Reinjection for Especial Applications using STATCOM

A new concept, the multi level voltage/current reinjection is described in this paper. A voltage source converter is proposed, in which the multistep output voltage waveform is achieved by the reinjection of different DC voltage levels via the common neutral point of the converter bridges an 84-pulse voltage source converter (VSC), assembled by combining one twelve-pulse VSC, in conjunction with an asymmetric single phase seven-level converter plus an injection transformer. With this arrangement, the VSC output's total harmonic distortion in voltages is reduced, allowing it to be used in especial applications or as the basement of flexible A.C. transmission systems (FACTS) devices. The proposed strategy allows savings in the number of employed switches. Simulations results are provided to show the proposal appropriateness.
Cost Optimization Strategy for Iterative Integration of Multi-critical Functions in IMA and TTEthernet Architecture

Throughout the life cycle of an aircraft, its avionic architecture can be updated or re-configured leading to what we call the iterative integration problem. In particular, new avionics functions of mixed criticality must be added to computation modules. This can cause a modification of the scheduling parameters, inducing a re-certification of modules, whose cost depends on the criticality level of the modified functions. We define a new approach to help the system designer in producing a proper modified scheduling at a minimal cost while satisfying some real-time constraints. The emphasis is put on the iterative process and we focus on Integrated Modular Avionics (IMA) architecture supported by a time-triggered network. First, we give a general formalization of the problem and provide some results on the real-time constraints considered. In particular, we show how they can directly be taken into account in the variable domains. Then, our approach consists in formalizing the problem as a Binary Integer Problem. Thus, using an off-the-shelf solver, an optimal scheduling minimizing the cost can be automatically determined.
Behavioral meaningful opioidergic stimulation activates kappa receptor gene expression

The periaqueductal gray (PAG) has been reported to be a location for opioid regulation of pain and a potential site for behavioral selection in females. Opioid-mediated behavioral and physiological responses differ according to the activity of opioid receptor subtypes. The present study investigated the effects of the peripheral injection of the kappa-opioid receptor agonist U69593 into the dorsal subcutaneous region of animals on maternal behavior and on Oprk1 gene activity in the PAG of female rats. Female Wistar rats weighing 200-250 g at the beginning of the study were randomly divided into 2 groups for maternal behavior and gene expression experiments. On day 5, pups were removed at 7:00 am and placed in another home cage that was distant from their mother. Thirty minutes after removing the pups, the dams were treated with U69593 (0.15 mg/kg, sc) or 0.9% saline (up to 1 mL/kg) and after 30 min were evaluated in the maternal behavior test. Latencies in seconds for pup retrieval, grouping, crouching, and full maternal behavior were scored. The results showed that U69593 administration inhibited maternal behavior (P < 0.05) because a lower percentage of U69593 group dams showed retrieval of first pup, retrieving all pups, grouping, crouching and displaying full maternal behavior compared to the saline group. Opioid gene expression was evaluated using real-time reverse-transcription polymerase chain reaction (RT-PCR). A single injection of U69593 increased Oprk1 PAG expression in both virgin (P < 0.05) and lactating female rats (P < 0.01), with no significant effect on Oprm1 or Oprd1 gene activity. Thus, the expression of kappa-opioid receptors in the PAG may be modulated by single opioid receptor stimulation and behavioral meaningful opioidergic transmission in the adult female might occur simultaneously to specific changes in gene expression of kappa-opioid receptor subtype. This is yet another alert for the complex role of the opioid system in female reproduction.
Towards an Improved Characterization of Fractal Network Traffic

Network traffic exhibits fractal characteristics such as self-similarity and long-range dependence. Several estimators of the fractal parameters have been developed, but few consider the possibility of tracking the time evolution of those parameters. Some fractal-aware network-related algorithms such as effective bandwidth estimation, control admission or traffic prediction could improve their performance if an accurate description of the time evolution of traffic fractality were developed. The Discrete Wavelet Transform (DWT) is a powerful tool capable of performing a multiresolution analysis of a time series. The Abry-Veitch estimator (based on a graphic tool called LogScale Diagram) is an accurate and efficient DWT-based estimator of the fractal parameters, but it lacks adaptability to possible changes in the aforementioned parameters. In order to detect the changes, we combine the DWT and its non-orthogonal version, the Stationary Wavelet Transform (SWT) with the Schwarz Information Criterion (SIC), a variance change points detection algorithm founded on information theory concepts. Both DWT-SIC and SWT-SIC algorithms, together with a first implementation of a “real-time” estimator, were described in previous works. This paper describes the last refinements added to our algorithms: 1) an automatic clustering procedure built on the Hough Transform; 2) a combined DWT/SWT algorithm exploiting the different resolutions of both transforms across time scales. Furthermore, it discusses the role of parameters such as the minimum segment size and the significance level of the SIC algorithm. We also present updated results deriving from the analysis of synthetic and real traffic traces, which show non-negligible variance of the fractal parameters.
Mediation practices for learning in MOOC courses to promote open innovation

With the increase of the training offered in online environments over the last decade, one of the key elements in these training events is mediation as an enabler best results approval and learning achievements. In this context the role of mediation is key, especially when there is no teacher or tutor figure that is in contact with the participants. The aim of this research, that is derived from a doctoral program, is to analyze the practices of mediation in massive open online courses (MOOC) in the areas of energy sustainability and propose a mediation model that considers the open innovation as a key element. In this way answers the research question regarding the relationship between the practices of teaching and learning technological mediation with MOOC courses. The research is the result of cooperation between government institutions in Mexico such as CONACYT and SENER with the Technological Institute of Monterrey, the latter by offering 10 massive courses on the subject of energy sustainability, whose course name in spanish is: "Generación de energías y limpias y energías convencionales" [Generation of clean energy and conventional energy], and its participantes constitute the research population, who, through various instruments of qualitative and quantitative, will be consulted regarding mediation and its relationship with learning. The results of this study allow thus to answer the research question, in addition to the generation of proposals in the field of mediation that promote open innovation. It is expected that the results represent a contribution in the sense of determining the relationship mediation - outcomes of learning in MOOC courses.
Brain 4 Cars : Sensory-Fusion Recurrent Neural Models for Driver Activity Anticipation

Advanced Driver Assistance Systems (ADAS) have made driving safer over the last decade. They prepare vehicles for unsafe road conditions and alert drivers if they perform a dangerous maneuver. However, many accidents are unavoidable because by the time drivers are alerted, it is already too late. Anticipating maneuvers beforehand can alert drivers before they perform the maneuver and also give ADAS more time to avoid or prepare for the danger. In this work we anticipate driving maneuvers several seconds before they occur [2]. For this purpose we build a vehicular-sensory platform with multiple cameras, tactile sensors, GPS, and a computing device. Our sensors capture the driving context from both inside and outside of the car. We introduce a sensoryfusion Recurrent Neural Network (RNN) architecture which jointly learns to anticipate and fuse information from multiple sensory streams. Our architecture use Long-Short Term Memory (LSTM) units to capture long temporal dependencies. We evaluate our approach on a diverse data set with 1180 miles of natural free-way and city driving and show that we can anticipate maneuvers 3.5 seconds before they occur with over 80% F1-score in real-time.1
Bounded and continuous control

The purpose of this note is to clarify the relation between bounded and continuous control as it occurs in various proofs of the Novikov conjecture [2, 3]. In [2] the bounded categories C of [8] are used to prove the algebraic K-theory Novikov conjecture for certain groups. In [3] the continuously controlled categories B of [1] are used to prove the algebraic Kand Ltheory Novikov conjectures for a larger class of groups. Since continuous control is finer than bounded control, there should be a forgetful functor from continuously controlled algebra to bounded algebra. This is only true, however, after delooping the categories. A functor from bounded algebra to continuously controlled algebra does not always exist, but if the metric space can be given an ideal boundary satisfying conditions of the type that “bounded is small at infinity”, one gets a functor from bounded control to continuous control at infinity. To make this precise let us recall some definitions. We shall use the language of algebraic Kor L-theory, but it is relatively straightforward to modify these considerations to topological K-theory or A-theory, using [5, 4]. Let A be a small additive category, M a proper metric space. 1. Definition. The bounded category C(M ;A) has objects A = {Ax}x∈M , a collection of objects from A indexed by points of M , satisfying {x|Ax 6= 0} is locally finite in M . A morphism φ : A → B is a collection of morphisms φy : Ax → By so that there exists k = k(φ) so φy = 0 if dM(x, y) > k. Composition is defined as matrix multiplication. Given a subspace N ⊂ M , we denote the full subcategory with objects A so that {x|Ax 6= 0} is contained in a bounded neighborhood of N by C(M ;A)N . It is easy to see that C(M ;A) is C(M,A)N -filtered in the sense of Karoubi [6]. We denote the quotient category by C(M,A) , the category of germs away from N . C(M ;A) thus has the same objects as C(M,A), but morphisms are identified if they agree except in a bounded neighborhood of N . Next we recall the definition of continuous control. Let (X, Y ) be a pair of Hausdorff spaces, Y closed in X, and contained in X − Y , A a small additive category. 2. Definition. The continuously controlled category B(X, Y ;A) has objects A = {Ax}x∈X−Y satisfying {x|Ax 6= 0} is locally finite in X − Y . A morphism φ : A → B is a collection of morphisms φy : Ax → By satisfying that for every z ∈ Y it is continuously