Miracle-dz/UniFAR-FTUs huggingface dataset:
Lca as Environmental Assessment Tool in Waste to Energy and Contribution to Occupational Health and Safety

Life Cycle Assessment (LCA) is a set of tools and ideas in the field of Environmental Assessment and it is one of the most important management tools to understand product related environmental impacts. It covers the life span from the extraction of the raw materials to their disposal or reprocessing. The main developments over the past 40 years are shortly reviewed in a perspective that puts an emphasis on Waste to Energy issues. This aims to identify the advantages and limitations of LCA related to the models of production processes adopted as basis of environmental assessment in waste management. The importance of the concept of extended producer responsibility requires a risk assessment that takes into account a broader set of elements, including occupational health risks. The use of LCA as the sole tool to determine improvements to the process under investigation can be biased as the human factor is not always included in the system boundaries. Examples of inclusion of work environment issues in Life Cycle approaches as well as models that balance health, safety and environmental impact are presented.
A 2D-Model of Cell Sorting Induced by Propagation of Chemical Signals Along Spiral Waves

We study a model for cell sorting based in the presence of differential chemotactic sensitivities. The chemical waves which are responsible for the cell motion propagate along some spiral waves. We prove rigorously that cells with larger chemotactic sensitivity are trapped in a region close to the center of the spiral waves if these propagate along some archimedian spirals.
Stick-slip Oscillations in Oillwell Drilstrings: Distributed Parameter and Neutral Type Retarded Model Approaches

Abstract The drill pipe model is described by the wave equation with mixed boundary conditions in which a sliding velocity is considered at the top end. The proposal of an energy function for the distributed model allows to find a control law that ensures the energy dissipation during the drilling operation. The distributed parameter model is reduced through the d'Alembert transformation to a difference equation model. Torsional drillstring vibrations, also known as “stick-slip” oscillations appearing in oilwell drillstrings are a source of failures which reduce penetration rates and increase drilling operation costs. Some experience-based control strategies are evaluated in order to reduce stick-slip oscillations. The use of the angular velocity at the drillstring upper part, the torque on the bit and the weight on the bit is shown to have a key effect in the reduction of drillstring torsional vibrations.
Chapter 2 Bacterial Community Structures of Antarctic Soils

Antarctica’s ice-free environments span diverse habitats, ranging from well developed and nutrient rich soils in the coastal areas, to poorly developed and oligotrophic soils in the continent’s deserts and high elevation sites. Though most terrestrial environments in Antarctica are typified by harsh environmental conditions, many soils are home to abundant and diverse bacterial communities. These communities are locally adapted, varying both between and within different regions of the continent, and typically reflecting the local physicochemical and biological characteristics of the soils. Environmental conditions are changing rapidly in many areas, due to increased human activity on the continent and the impacts of climate change. This chapter reviews characteristics of bacterial communities in soils across Antarctica in relation to their environment, and discusses the potential responses of bacterial communities to contemporary environmental change. Continued and coordinated efforts to understand bacterial community structure and function in Antarctic soils will be necessary to monitor and predict ecological responses in these changing environments, and to shape management practices that will ensure the protection and preservation of biodiversity in Antarctica’s terrestrial ecosystems.
Fast Computation of Sepλ via Interpolation-based Globality Certificates

Given two square matrices $A$ and $B$, we propose a new approach for computing the smallest value $\varepsilon \geq 0$ such that $A+E$ and $A+F$ share an eigenvalue, where $\|E\|=\|F\|=\varepsilon$. In 2006, Gu and Overton proposed the first algorithm for computing this quantity, called $\mathrm{sep}_\lambda(A,B)$ ("sep-lambda"), using ideas inspired from an earlier algorithm of Gu for computing the distance to uncontrollability. However, the algorithm of Gu and Overton is extremely expensive, which limits it to the tiniest of problems, and until now, no other algorithms have been known. Our new algorithm can be orders of magnitude faster and can scale up to problems where $A$ and $B$ are of moderate size. Moreover, our method consists of many "embarrassingly parallel" computations, and so it can be further accelerated on multi-core hardware.
Large deviation principles for first-order scalar conservation laws with stochastic forcing

In this paper, we established the Freidlin-Wentzell type large deviation principles for first-order scalar conservation laws perturbed by small multiplicative noise. Due to the lack of the viscous terms in the stochastic equations, the kinetic solution to the Cauchy problem for these first-order conservation laws is studied. Then, based on the well-posedness of the kinetic solutions, we show that the large deviations holds by utilising the weak convergence approach.
Fast Glare Detection in Document Images

Glare is a phenomenon that occurs when the scene has a reflection of a light source or has one in it. This luminescence can hide useful information from the image, making text recognition virtually impossible. In this paper, we propose an approach to detect glare in images taken by users via mobile devices. Our method divides the document into blocks and collects luminance features from the original image and black-white strokes histograms of the binarized image. Finally, glare is detected using a convolutional neural network on the aforementioned histograms and luminance features. The network consists of several feature extraction blocks, one for each type of input, and the detection block, which calculates the resulting glare heatmap based on the output of the extraction part. The proposed solution detects glare with high recall and f-score.
Selective Brain Damage: Measuring the Disparate Impact of Model Pruning

Neural network pruning techniques have demonstrated it is possible to remove the majority of weights in a network with surprisingly little degradation to test set accuracy. However, this measure of performance conceals significant differences in how different classes and images are impacted by pruning. We find that certain examples, which we term pruning identified exemplars (PIEs), and classes are systematically more impacted by the introduction of sparsity. Removing PIE images from the test-set greatly improves top-1 accuracy for both pruned and non-pruned models. These hard-to-generalize-to images tend to be mislabelled, of lower image quality, depict multiple objects or require fine-grained classification. These findings shed light on previously unknown trade-offs, and suggest that a high degree of caution should be exercised before pruning is used in sensitive domains.
Enhancing Programmability, Portability, and Performance with Rich Cross-Layer Abstractions

Programmability, performance portability, and resource efficiency have emerged as critical challenges in harnessing complex and diverse architectures today to obtain high performance and energy efficiency. While there is abundant research, and thus significant improvements, at different levels of the stack that address these very challenges, in this thesis, we observe that we are fundamentally limited by the interfaces and abstractions between the application and the underlying system/hardware--specifically, the hardware-software interface. The existing narrow interfaces pose two critical challenges. First, significant effort and expertise are required to write high-performance code to harness the full potential of today's diverse and sophisticated hardware. Second, as a hardware/system designer, architecting faster and more efficient systems is challenging as the vast majority of the program's semantic content gets lost in translation with today's hardware-software interface. Moving towards the future, these challenges in programmability and efficiency will be even more intractable as we architect increasingly heterogeneous and sophisticated systems. This thesis makes the case for rich low-overhead cross-layer abstractions as a highly effective means to address the above challenges. These abstractions are designed to communicate higher-level program information from the application to the underlying system and hardware in a highly efficient manner, requiring only minor additions to the existing interfaces. In doing so, they enable a rich space of hardware-software cooperative mechanisms to optimize for performance. We propose 4 different approaches to designing richer abstractions between the application, system software, and hardware architecture in different contexts to significantly improve programmability, portability, and performance in CPUs and GPUs.
Blockchain-based System Evaluation: The Effectiveness of Blockchain on E-Procurements

Electronic systems tend to simplify the tedious traditional scheme and basically focuses on the platform design and process organization. The integrity of the output of an automated system is not left behind but the possibility of internal manipulation is still high. This paper presents the current issues in company procurements and the solution in the form of blockchain technology. Several individuals and professionals were asked to evaluate a blockchain-based procurement system in comparison to the current electronic (e-procurement) system. A blockchain-based system has the capability to hold transactional data with complete decentralization and eliminate the growing number of fraud cases in companies and organizations. This paper mainly focuses on the effectiveness of a blockchain-based system in company procurements.
Imaging systematics and clustering of DESI main targets

We evaluate the impact of imaging systematics on the clustering of luminous red galaxies (LRG), emission-line galaxies (ELG), and quasars (QSO) targeted for the upcoming Dark Energy Spectroscopic Instrument (DESI) survey. Using Data Release 7 of the DECam Legacy Survey, we study the effects of astrophysical foregrounds, stellar contamination, differences between north galactic cap and south galactic cap measurements, and variations in imaging depth, stellar density, galactic extinction, seeing, airmass, sky brightness, and exposure time before presenting survey masks and weights to mitigate these effects. With our sanitized samples in hand, we conduct a preliminary analysis of the clustering amplitude and evolution of the DESI main targets. From measurements of the angular correlation functions, we determine power law fits $r_0 = 7.78 \pm 0.26\, h^{-1}$Mpc, γ = 1.98 ± 0.02 for LRGs and $r_0 = 5.45 \pm 0.1\, h^{-1}$Mpc, γ = 1.54 ± 0.01 for ELGs. Additionally, from the angular power spectra, we measure the linear biases and model the scale-dependent biases in the weakly non-linear regime. Both sets of clustering measurements show good agreement with survey requirements for LRGs and ELGs, attesting that these samples will enable DESI to achieve precise cosmological constraints. We also present clustering as a function of magnitude, use cross-correlations with external spectroscopy to infer dN/dz and measure clustering as a function of luminosity, and probe higher order clustering statistics through counts-in-cells moments.
On the time-evolution of resonant triads in rotational capillary-gravity water waves

We investigate an effect of the resonant interaction in the case of one-directional propagation of capillary-gravity surface waves arising as the free surface of a rotational water flow. Specifically, we assume constant vorticity in the body of the fluid which physically corresponds to an underlying current with a linear horizontal velocity profile. We consider the interaction of three distinct modes, and we obtain the dynamic equations for a resonant triad. Setting the constant vorticity equal to zero, we recover the well known integrable three-wave system.We investigate an effect of the resonant interaction in the case of one-directional propagation of capillary-gravity surface waves arising as the free surface of a rotational water flow. Specifically, we assume constant vorticity in the body of the fluid which physically corresponds to an underlying current with a linear horizontal velocity profile. We consider the interaction of three distinct modes, and we obtain the dynamic equations for a resonant triad. Setting the constant vorticity equal to zero, we recover the well known integrable three-wave system.
haracteristics of innovating firms in Tunisia : The essential role of xternal knowledge sources

This article is dedicated to the analysis of the first innovation survey of the Tunisian firms. Starting from basic mechanisms of innovation processes and existing results on other developing countries, we test a set of assumptions on the characteristics of innovating firms in a developing country like Tunisia. The analysis of product and process innovations shows the essential role played by external technical knowledge sources: firms must be able to benefit at least from one such a source in order to attain a significant innovation propensity. We also show that the profile of Tunisian firms can be contrasted with other developing countries. The main contrast concerns the limited role of internal R&D and the insignificant 300
Superparamagnetic iron oxide nanoparticles conjugated with doxorubicin for targeting breast cancer

Development of the next generations of cancer therapy modalities is currently a crucial requirement of oncology. Advances in nanotechnology are bringing us closer to the development of dual and multifunctional nanoparticles that are challenging the traditional distinction between diagnostic and treatment agents. The chase of innovative, multifunctional, more efficient, and safer treatments is a major challenge in preclinical nanoparticle-mediated thermotherapeutic research for breast cancer. Here, we report that iron oxide nanoparticles have the dual capacity to act as both magnetic and drug delivery agents. The aim of this work was to investigate the in vitro effect of the loading of doxorubicin (DOX) on negatively charged polycarboxylic iron-oxide nanoparticles (SPIONs) and Rhodamine B functionalized SPIONs on breast carcinoma cell lines. For proper analysis and understanding of cell behavior after administration of DOX-SPIONs compared with free DOX, a complex set of in vitro tests, including production of MTT assay, cell cycle determination, and cellular uptake, were utilized. In summary, we have developed a magnetic nanoparticle-based drug delivery system that sequentially delivers the cytotoxic drug doxorubicin to breast cancer cells (MCF-7 and MDA-MB-231). The drug-coated nanoparticles, DOX-NPs, were assembled stepwise, with doxorubicin adsorbed to bare iron oxide nanoparticles first, by electrostatic reaction and allowed for the complexation of doxorubicin. In contrast, they were internalized to a much greater extent in MCF-7 and MDA-MB-231 cells and were cytotoxic due to the synergistic action of the two drugs and the effects of hyperthermia. The drug-coated particles were able to inhibit growth and proliferation of breast cancer cells in vitro, indicating that the system has potential to act as an antimetastatic chemothermotherapeutic agent.
The Identity of Cultural Arts: Thai Massage at Wat Pho to Communicate the English Materials for Tourism

The main study of this work was to communicate the identity of cultural arts: Thai massage at Wat Pho the to communicate English materials for tourism. There were two purposes the following items: 1) The identity of cultural arts: Thai massage at Wat Pho is located at Phra Nakhon district in Bangkok, Thailand. It is on Rattanakosin Island which is established directly south of the Palace. This temple is one of the six temples in Thailand as the highest level of the first-class royal temples. The temple was also the earliest center for public education in Thailand and originated a school of Thai medicine. It is known as the first traditional Thai massage. 2) The communication of the identity of cultural Arts: Thai massage at Wat Pho to communicate the English materials for tourism which the author found and designed to present in the English materials can be summed up Thai massage.
Coalition Formation Game for Delay Reduction in Instantly Decodable Network Coding Assisted D2D Communications

Consider a wireless broadcast device-to-device (D2D) network wherein users' devices are interested in receiving some popular files. Each user's device possesses part of the content which is acquired in previous transmissions and cooperates with others to recover the missing packets by exchanging Instantly Decodable Network Coding (IDNC) packets. Recently, a distributed solution, relying on a non-cooperative game-theoretic formulation, has been proposed to reduce the communication time for fully connected D2D networks, i.e., single-hop D2D networks. In this paper, we develop a distributed game-theoretical solution to reduce the communication time for a more realistic scenario of a decentralized and partially connected, i.e., multi-hop, IDNC-enabled D2D network. The problem is modeled as a coalition game with cooperative-players wherein the payoff function is derived so that decreasing individual payoff results in the desired cooperative behavior. Given the intractability of the formulation, the coalition game is relaxed to a coalition formation game (CFG) involving the formation of disjoint coalitions. A distributed algorithm relying on merge-and-split rules is designed for solving the relaxed problem. The effectiveness of the proposed solution is validated through extensive numerical comparisons against existing methods in the literature.
Detecting Bots and Assessing Their Impact in Social Networks

Bots Impact Opinions in Social Networks: Let’s Measure How Much There is a serious threat posed by bots that try to manipulate opinions in social networks. In “Assessing the Impact of Bots on Social Networks,” Nicolas Guenon des Mesnards, David Scott Hunter, Zakaria el Hjouiji, and Tauhid Zaman present a new set of operational capabilities to detect these bots and measure their impact. They developed an algorithm based on the Ising model from statistical physics to find coordinating gangs of bots in social networks. They then created an algorithm based on opinion dynamics models to quantify the impact that bots have on opinions in a social network. They applied their algorithms to a variety of real social network data sets. They found that, for topics such as Brexit, the bots had little impact, whereas for topics such as the U.S. presidential debate and the Gilets Jaunes protests in France, the bots had a significant impact.
BOUNDS ON MULTI-ASSET DERIVATIVES VIA NEURAL NETWORKS

Using neural networks, we compute bounds on the prices of multi-asset derivatives given information on prices of related payoffs. As a main example, we focus on European basket options and include information on the prices of other similar options, such as spread options and/or basket options on subindices. We show that, in most cases, adding further constraints gives rise to bounds that are considerably tighter. Our approach follows the literature on constrained optimal transport and, in particular, builds on the work of Eckstein & Kupper (2018) [Computation of optimal transport and related hedging problems via penalization and neural networks, Appl. Math. Optimiz. 1–29].
The Deep Learning Revolution and Its Implications for Computer Architecture and Chip Design

The past decade has seen a remarkable series of advances in machine learning, and in particular deep learning approaches based on artificial neural networks, to improve our abilities to build more accurate systems across a broad range of areas, including computer vision, speech recognition, language translation, and natural language understanding tasks. This paper is a companion paper to a keynote talk at the 2020 International Solid-State Circuits Conference (ISSCC) discussing some of the advances in machine learning, and their implications on the kinds of computational devices we need to build, especially in the post-Moore's Law-era. It also discusses some of the ways that machine learning may also be able to help with some aspects of the circuit design process. Finally, it provides a sketch of at least one interesting direction towards much larger-scale multi-task models that are sparsely activated and employ much more dynamic, example- and task-based routing than the machine learning models of today.
15TH EUROPEAN TURBULENCE CONFERENCE, 25-28 AUGUST, 2015, DELFT, THE NETHERLANDS MECHANICS OF DENSE SUSPENSIONS IN TURBULENT CHANNEL FLOWS

Dense suspensions are usually investigated in the laminar limit where inertial effects are insignificant. When the flow rate is high enough, i.e. at high Reynolds number, the flow may become turbulent and the interaction between solid and liquid phase modifies the turbulence we know in single-phase fluids. In the present work, we study turbulent channel flows laden with finite-size particles at high volume fraction by means of Direct Numerical Simulations. A direct-forcing Immersed Boundary Method has been adopted to couple liquid and solid phases. We will show that the turbulence is attenuated in dense cases, even though the overall drag is increased because of the particle contribution to the total stress.
Comparison Study of Frequencies and Electronic Properties by Quantum Mechanical Calculation between Cyclacene and Carbon nanotube.

Semi empirical quantum mechanical calculations were carried out on the most stable structure for calculating the vibration frequencies and IR absorption intensities for normal coordinates of the cyclacene named mono (C56H28) and Carbon nanotube named tetra (C140H28)- ring layer of (7,7) armchair single wall carbon nanotube (SWCNTs) at their equilibrium geometries which were all found to have D7d symmetry point group. Assignment of the modes of vibration (3N-6) was done depending on the pictures of their modes by applying (Gaussian 03) program. Comparison of the vibration frequencies which are active in IR spectra of the two molecules. , the results showed that C-H stretching vibration frequencies value increased with increased of length nano tube (rings layer SWCNT). The results include the relation for axial bonds, which are the vertical C-C bonds (annular bonds) in the rings and for circumferential bonds which are the outer ring bonds. Also include the assignment of puckering, breathing and clock-anticlockwise bending vibrations. They allow a comparative view of the charge density at the carbon atom .