Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of Convolutional Architectures for Offline Handwritten Digit Recognition

Deep learning implementations have resulted in significant performance improvements in several application domains and as such several network architectures have been developed to facilitate their methods. This paper presents a comparative study of two architectures among those which are implemented for handwriting recognition, Highway CNN and LeNet-5. The evaluation is performed on two separate machines for both CPU(Intel-i5 3250M) and a GPU(Nvidia GTX-1060). We compared them not only on the basis of their accuracy, but also their training time, recognition time and their memory requirements. Our experiments demonstrate the advantage of global training and feature mapping on the MNIST dataset.
Woven Light Emitting Display

Electrotextiles represent a huge potential in creating a new generation of flexible textile platforms for electronic systems and smart garments. In this research of electrotextile fabrication weaving method was used. Fully or partly replacing traditional (non-conductive) textile yarns with conductive textile yarns, it is possible to get woven conductive textile material or fabric circuit. In the paper weaving method is considered for electro textile design and process of flexible textile light-emitting display fabrication is described. Fabric with interwoven conductive yarns to which light-emitting diodes (LEDs) were attached was designed. This technology is not only limited to light illuminated displays but can also be used to build other smart textile constructions like sensors, conductive textile traces etc.
The design of molecular switches for biosensors

In nature, specialized proteins, specific receptors on or in target cells, recognize signaling molecules.1 They specifically bind the signaling molecule and then initiate a response in the target cell. Many receptors use the transduction mechanism utilizing ion channels. These so-called, ion-channel-linked receptors, are also known as transmitter-gated ion channels. The signaling is mediated by binding of a specific molecule, which that opens or closes the gate of the ion channel, thereby changing the ion permeability and electrical conductance of the channel.
Imbalanced phosphorus and nitrogen deposition in China's forests

Abstract. Acceleration of anthropogenic emissions in China has substantially increased nitrogen (N) deposition during the last 3 decades and may result in an imbalance of atmospheric N and phosphorus (P) inputs in terrestrial ecosystems. However, the status of P deposition in China is poorly understood. This study synthesized data on total P and total N concentrations in bulk precipitation and throughfall from published literature to assess the characteristics of P deposition, N deposition and N : P deposition ratio in China's forests. Our results show relatively high mean rates of bulk P deposition (0.38 kg P ha−1 yr−1) and total P deposition (0.69 kg P ha−1 yr−1), but they were accompanied by even more elevated N inputs via bulk deposition (16.5 kg N ha−1 yr−1) and total deposition (21.6 kg N ha−1 yr−1), resulting in high N : P ratios in bulk deposition (44.4) and total deposition (32.8). Based on the difference between total deposition and bulk deposition, canopy-captured dry P and N deposition was estimated to be 0.31 kg P ha−1 yr−1 and 5.1 kg N ha−1 yr−1, respectively. We found significantly higher P deposition and lower N : P ratios at sites nearby than those far from semiarid regions. The estimated bulk and total deposition of P and N both showed a significant power-law increase with decreasing distance to the nearest large cities either in the areas nearby or far from semiarid regions. Our results suggest an anthropogenic alternation of regional P and N cycling, which may shift large areas of China's forests towards human-induced P limitation especially in southern China.
Model-based SEM for dimensional metrology tasks in semiconductor and mask industry

The requirements on the use of scanning electron microscopy (SEM) as a measurement technique for the process control of dimensional parameters are most challenging in the production of masks and wafers in the semiconductor industry. Subtle details of SEM signal variations have to be thoroughly understood to be able to monitor and trace small process variations to changes in dimensional parameters of the features to be controlled. This paper reviews the fundamentals, special performance features and applications of existing SEM image contrast simulation packages based on Monte Carlo methods.
Accelerated Achilles tendon healing with interleukin-1 receptor antagonist protein in rabbits

e current study describes the use of interleukin-1 receptor antagonist protein (IL-1Ra) as a possible strategy for optimizing tendon healing and repair by presenting histologically visible changes in rabbit Achilles tendon tissue a er longitudinal tenotomy. e study was carried out on 26 Californian rabbits divided into two equal groups. One was the experimental IRAP (interleukin-1 receptor antagonist) group and the second one was the PBS (puri ed bu ered saline) group. e PBS group was the control group. A er longitudinal tenotomy in both groups of rabbits, in the IRAP group IL-1Ra was applied locally, whereas PBS was applied in the control group. IL-1 concentration in Achilles tendon tissue was measured with an ELISA IL-1 rabbit kit. Local application of IL-1Ra resulted in 2.5-fold lower IL- 1 tissue concentration and prevented cytokine cascade activation and its proin ammatory e ects. Consequently, it prevented chronic tendon in ammation and improved Achilles tendon healing in rabbits. Histological changes in tendon tissue samples were evaluated by Bonar scale score with statistically signi cant di erences between groups (P ≤ 0.01).
Removal of Endocrine Disruptors in Waste Waters by Means of Bioreactors

The presence of Endocrine Disrupting Chemicals (EDCs) represents an area of concern in the environmental field. An EDC is defined as “an exogenous substance that causes adverse health effects in an intact organism, or its progeny, in consequence to the induced changes in endocrine functions” (EU Commission, 1996). A large number of chemical compounds have been recognized as EDCs. Among these, natural and synthetic steroid hormones, phytoestrogens, alkylphenols, phthalates, pesticides, surfactants and polychlorinated biphenyls (Soto et al., 1995, Jobling et al., 1995; Routledge & Sumpter, 1997). EDCs are not defined on the basis of their chemical nature, but by their biological effects. They exhibit agonistic or antagonistic properties depending on the kind of interaction with the receptors. As estrogenic receptors have similar structure between different animals, including humans, EDCs can affect the endocrine functions of many living species. The main mechanisms through which they interfere with the endocrine system are: i) the simulation of the activities of physiological hormones, thereby participating in the same reactions and causing the same effects; ii) the inactivation, with competitive action, of hormone receptors and, consequently, the neutralisation of their activity; iii) the interference with the synthesis, transport, metabolism and secretion of natural hormones, altering their physiological concentrations and therefore their corresponding endocrine functions. EDCs enter the environment from a variety of sources, such as effluent discharge pipes, agricultural runoff, landfills, atmospheric deposition and aerosols (Campbell et al., 2006). In particular aquatic ecosystems have been studied for the effect of wastewater treatment plant (WWTP) effluents, which are continuously discharged to the receiving water bodies (Jobling et al., 1998; Routledge et al., 1998; Tilton et al., 2002). Due to their incomplete removal during the waste treatment process, synthetic and natural estrogens are considered as the major responsible for the estrogenic activity associated with WWTP effluents (Gutendorf & Westendorf, 2001). So natural steroid hormones and the synthetic ethynylestradiol, alkylphenols, bisphenol A and phthalates are EDCs identified in sewage effluents (Desbrow
Modeling of Biotransformation of Crude Glycerol from Biodiesel Production by Using Dynamic Artificial Neural Network

Glycerol is a by-product from biodiesel industry. The fermentation of glycerol by different bacterial strains is a complex biotechnological process producing valuable products as organic acids, diols, and ethanol, which are used as reagents in chemical industry. In this study results obtained from biotransformation of glycerol by the help of bacterium from the genera of Pseudomonas, in particular Pseudomonas denitrificans 1625 to formation of 1,3-propanediol (1,3-PD) are represented for the first time. Mathematical modeling is the approach that can give an answer for the process performance in different scenarios. Having in mind the lack of information about the kinetics of this process we have proposed a dynamic Nonlinear AutoRegressive with eXogenous inputs – NARX Artificial Neural Network (ANN) for the time series modeling of the process. For that purpose real sets of time series of parallel batch experiments of the process at different initial concentration of the glycerol have been used. We have developed dynamic ANN model fed with the concentration values of the biomass as independent (exogenous) inputs and 5 and 7 time series interval of past values of the concentrations of the glycerol ATINER CONFERENCE PAPER SERIES No: IND2013-0821 6 and 1,3-propanediol fed with tapped delay line of 1 for prediction of their next values (glycerol and 1,3-propanediol). The developed model have been trained and validated with selected for the purpose data sets. The obtained after validation results have shown acceptable coincidence between measured and calculated data.
Development and Application in Clinical Routine of Computer Aided Detection ( CAD ) Algorithms for the Identification of Pulmonary Nodules

Lung Cancer is one of the main public health issues in developed countries, accounting for about 19% and 28% of cancer-related deaths in Europe (Ferlay et al., 2010) and the United States of America (Jemal et al., 2009), respectively, with a five-year survival rate of only 10-16% (Jemal et al., 2010). Computed Tomography (CT) has been shown to be the most sensitive imaging modality for the detection of small pulmonary nodules: low dose high resolution CT-based screening trials are regarded as a promising technique for detecting early-stage lung cancers (Team et al., 2011). The identification of early stage pathological Regions of Interests (ROIs) in low dose high resolution CT scans is a very difficult and time consuming task for radiologists, because of the large number (300/500) of noisy 2D slices to be analyzed. In order to support radiologists, researchers have started developing CAD algorithms to be applied to CT scans. Several studies (Das et al., 2006)(Brochu et al., 2007)(Matsumoto et al., 2008) reported an improvement in the sensitivity of radiologists when assisted by CAD systems (Awai et al., 2004). In addition, CAD systems act as detection rates equalizers between observers of different level of experience (Brown et al., 2005). Currently, the usage of CAD inside clinical diagnostic routine has not been a common and widespread practice yet. So far, the most common way to make CAD algorithms available in the clinical routine of health facilities is the deployment of standalone workstations, usually equipped with a vendor-dependent Graphic User Interface (GUI). This approach presents several drawbacks, such as the high fixed cost of the software licenses, hardware and the obsolescence of both. Furthermore, the computational power required by CAD algorithms is often very high (increasing with the complexity of the algorithms), often requiring powerful and expensive hardware. The emerging of Cloud Computing solutions, accessible via secure Web protocols, solves almost all the previous issues. Furthermore, a solution pointing toward cloud computing facilities provides the possibility of combining several CADs, with demonstrated benefits to the overall performance (van Ginneken et al., 2010). I started my PHD project inside the Magic5 group coordinated by the Turin Section of INFN. This group is aiming at: • Developing, validating and optimizing CAD algorithms for the automatic detection of pulmonary nodules in chest CT scans. • Spreading the usage of CAD inside clinical routine, making CAD algorithms available without requiring the users to install any kind of additional software/hardware. • Studying the impact of CAD algorithms on the performance of radiologists in clinical practice.
Effects of interannual environmental variability on the transport-retention dynamics in haddock Melanogrammus aeglefinus larvae on Georges Bank

Georges Bank is a region of high biological productivity characterized by a well- defined clockwise tidal rectified circulation gyre. Fluctuations in the year-class strength of haddock Melanogrammus aeglefinus on Georges Bank have been attributed to mortality during the highly vulnerable larval stages. While predation and starvation greatly impact on survival, advection to unfavorable regions may result in greatly reduced numbers of individuals. For successful self- recruitment to occur, individuals must remain within the shoal region of Georges Bank until settle- ment. An individual-based model (IBM) was utilized to estimate the retention of haddock eggs and larvae on Georges Bank annually for 1995 through 2009. The IBM was driven by the Finite-Volume Community Ocean Model (FVCOM) for the Gulf of Maine domain. Biological components of had- dock larvae were omitted to restrict analyses to the impact of circulation on passive transport. Inflow over the Scotian Shelf and tidal interaction patterns were identified as the major drivers of variability in interannual transport-retention dynamics. The simulations indicated that retention rates were highest in 2000 and 2003, and lowest in 1997. The above-average retention in 2000 and 2003 with anomalously large recruitment only in 2003 indicates that high retention appears to be necessary but not sufficient to explain large recruitment events of haddock on Georges Bank.
Online Behavioral Advertising: A Literature Review and Research Agenda

Advertisers are increasingly monitoring people's online behavior and using the information collected to show people individually targeted advertisements. This phenomenon is called online behavioral advertising (OBA). Although advertisers can benefit from OBA, the practice also raises concerns about privacy. Therefore, OBA has received much attention from advertisers, consumers, policymakers, and scholars. Despite this attention, there is neither a strong definition of OBA nor a clear accumulation of empirical findings. This article defines OBA and provides an overview of the empirical findings by developing a framework that identifies and integrates all factors that can explain consumer responses toward OBA. The framework suggests that the outcomes of OBA are dependent on advertiser-controlled factors (e.g., the level of personalization) and consumer-controlled factors (e.g., knowledge and perceptions about OBA and individual characteristics). The article also overviews the theoretical positioning of OBA by placing the theories that are used to explain consumers’ responses to OBA in our framework. Finally, we develop a research agenda and discuss implications for policymakers and advertisers.
CC BY of a Passively Compliant Inspired Robot Arm

This paper is on the design and analysis robot arm is actuated by Mckibben Artificial human biceps and triceps. The robot arm is kinematics. MATLAB is used for calculating the robot arm are also calculatedusing MATLAB. designed using Aluminum 6061 alloy and the required with the help of Pneumatic artificial
Cross-slot flow for extensional rheometry : From optimization to experimentation

In this work we propose and test an optimized cross-slot device able to generate a strong extensional flow comprising a large region of constant strain-rate along the centerline, a fundamental requirement for meaningful extensional rheometry measurements. In this type of opposed flow geometry there is a stagnation point at the center of symmetry (marked with an “S” in Figure 1), where the fluid velocity is zero but the local extension rate is finite [1]. A successful design would yield significant advantages over classical cross-slot devices, in which the imposed strain rate is well-defined only in the region very close to the stagnation point and decays rapidly along the exit channels. For the optimal shape design, we use the methodology proposed by Alves [2] which combines computational fluid dynamics (CFD) simulations, using a finite-volume viscoelastic code [3], with a CONDOR optimizer [4] and an automatic mesh generation procedure. The initial configuration (a conventional cross-slot with rounded corners) and the final optimized design are illustrated in Figure 1. The proposed microfluidic device consists of a cross-slot flow geometry with a nonconventional shape, which generates a homogeneous elongational flow with large regions of nearly constant extensional strain-rate. The optimized shape was found to be very insensitive to the imposed flow rate (and the corresponding dimensionless Deborah number), making it possible to obtain a nearly universal cross-slot capable of generating an ideal planar extensional flow for Newtonian and viscoelastic fluids at low Reynolds number. Figure 1: Conventional cross-slot flow geometry with rounded corners used as initial configurations (left handside) and optimized flow geometry obtained through optimal shape design (right hand-side). x y
The role of Amazonian anthropogenic soils in shifting cultivation: learning from farmers’ rationales

We evaluated farmers' rationales to understand their decision making in relation to the use of fertile anthropogenic soils, i.e., Amazonian dark earths (ADE), and for dealing with changes in shifting cultivation in Central Amazonia. We analyzed qualitative information from 196 interviews with farmers in 21 riverine villages along the Madeira River. In order to decide about crop management options to attain their livelihood objectives, farmers rely on an integrated and dynamic understanding of their biophysical and social environment. Farmers associate fallow development with higher crop yields and lower weed pressure, but ADE is always associated with high yields and high weeding requirements. Amazonian dark earths are also seen as an opportunity to grow different crops and/or grow crops in more intensified management systems. However, farmers often maintain simultaneously intensive swiddens on ADE and extensive swiddens on nonanthropogenic soils. Farmers acknowledge numerous changes in their socioeconomic environment that affect their shifting cultivation systems, particularly their growing interaction with market economies and the incorporation of modern agricultural practices. Farmers considered that shifting cultivation systems on ADE tend to be more prone to changes leading to intensification, and we identified cases, e.g., swiddens used for watermelon cultivation, in which market demand led to overintensification and resulted in ADE degradation. This shows that increasing intensification can be a potential threat to ADE and can undermine the importance of these soils for agricultural production, for the conservation of agrobiodiversity, and for local livelihoods. Given that farmers have an integrated knowledge of their context and respond to socioeconomic and agro-ecological changes in their environment, we argue that understanding farmers' knowledge and rationales is crucial to identify sustainable pathways for the future of ADE and of smallholder agriculture in Amazonia.
AGRONOMY & SOILS The Impact of Variable, Cold Minimum Temperatures on Boll Retention, Boll Growth, and Yield Recovery of Cotton

A previous field experiment reported that cotton grown in the tropical dry season, where minimum temperatures during flowering can be cold (< 10°C), yielded a greater proportion of cotton from bolls pollinated when minima were warmer. The retention and growth of bolls was postulated to be reduced by cold night temperatures. However, biotic stresses (e.g., pests) might have confounded this response. Reported herein is a pot experiment where biotic stresses were effectively removed. Over two seasons, (i) ambient and (ii) ambient plus 5 to 6°C (range 1024°C), night thermal conditions were imposed from 1 wk prior to first flower to 2 wk after last effective flower. Day temperatures were the same. Average ambient minimum temperature for the treatment period was 12.6°C (range 5.9-21.1 °C) and 10.2°C (range 2.6-21.0°C) in 2003 and 2004. Flowers were damaged by low ambient minimum temperatures occurring near anthesis, which led to shedding or lower seed number per boll which reduced boll size. The latter could be due to poor pollination and competition for assimilates. Shedding was correlated (p < 0.01) with minimum temperature at anthesis with < 40% survival when minima were < 6°C. Seed cotton yield was the same. It was concluded that the yield recovery from variable, low minimum temperatures during flowering was (1) similar to where damage to fruit was biotic with compensation occurring on later flowering fruiting sites provided temperatures were warm; and (2) unlikely to be impacted by damage to photosynthetic apparatus due to the minimum temperatures observed here.
Microwave Power Measurements: Standards and Transfer Techniques

In this chapter, precision power measurement, which is probably the most important area in RF and microwave metrology, will be discussed. Firstly, the background of RF and microwave power measurements and standards will be introduced. Secondly, the working principle of primary power standard (i.e., microcalorimeter) will be descri‐ bed, followed by the discussions of direct comparison transfer technique. Finally, there will be some discussions about the performance evaluation and uncertainty estima‐ tion for microwave power measurements.
A Search-Classify Framework for Cluttered Scene Understanding

We present a search-classify framework which interleaves segmentation and classification in an iterative manner. Using a robust classifier we traverse the scene and gradually propagate classification information. We reinforce classification by a template fitting step which yields a scene reconstruction. We deform-to-fit templates to classified objects to resolve classification ambiguities. The resulting reconstruction is an approximation which captures the general scene arrangement. We demonstrate the effectiveness of the framework for cluttered indoor scenes.