Miracle-dz/UniFAR-FTUs huggingface dataset:
A proposed model for measuring the brand equity in sports organizations

Today, the companies, usually, consider the presence of the intangible assets, specially the brand, in the strategic plans of the organizations. So much the scientific community of marketing, as the managerial world they deal that managing the brand and to know its value is a prior matter in altars to managing to differentiate the products and to reach a competitive advantage. In the world of the sport, in which increasingly the economic interests prevail over other interests of emotional type, it seems to be unavoidable to confront the study of the management of the brand equity of sports entities and clubs. They are organizations that try to compete on a market increasingly competitively and lucratively, and that for it must try to manage and to administer its resources efficiently. And one of these resources it constitutes the brand, the name of the sports entity. In this paper, after approaching the knowledge of the concept brand equity and its components, giving a particular vision of the importance that has the brand equity in sports organizations and proposing a model who helps the sports agent to understand and up to valuing the brand of the entity that it manages.
Coupled daily streamflow and water temperature modelling in large river basins

Realistic estimates of daily streamflow and water temperature are required for effective management of water resources (e.g. for electricity and drinking water production) and freshwater ecosystems. Although hydrological and process-based water temperature modelling approaches have been successfully applied to small catchments and short time periods, much less work has been done at large spatial and temporal scales. We present a physically based modelling framework for daily river discharge and water temperature simulations applicable to large river systems on a global scale. Model performance was tested globally at 1/2 × 1/2° spatial resolution and a daily time step for the period 1971–2000. We made specific evaluations on large river basins situated in different hydro-climatic zones and characterized by different anthropogenic impacts. Effects of anthropogenic heat discharges on simulated water temperatures were incorporated by using global gridded thermoelectric water use datasets and representing thermal discharges as point sources into the heat advection equation. This resulted in a significant increase in the quality of the water temperature simulations for thermally polluted basins (Rhine, Meuse, Danube and Mississippi). Due to large reservoirs in the Columbia which affect streamflow and thermal regimes, a reservoir routing model was used. This resulted in a significant improvement in the performance of the river discharge and water temperature modelling. Overall, realistic estimates were obtained at daily time step for both river discharge (median normalized BIAS = 0.3; normalized RMSE = 1.2; r = 0.76) and water temperature (median BIAS = −0.3 °C; RMSE = 2.8 °C; r = 0.91) for the entire validation period, with similar performance during warm, dry periods. Simulated water temperatures are sensitive to headwater temperature, depending on resolution and flow velocity. A high sensitivity of water temperature to river discharge (thermal capacity) was found during warm, dry conditions. The modelling approach has potential to be used for risk analyses and studying impacts of climate change and other anthropogenic effects (e.g. thermal pollution, dams and reservoir regulation) on large rivers.
An evaluation of real-time requirements for automatic sign language recognition using ANNs and HMMs - The LIBRAS use case

Sign languages are the natural way Deafs use to communicate with other people. They have their own formal semantic definitions and syntactic rules and are composed by a large set of gestures involving hands and head. Automatic recognition of sign languages (ARSL) tries to recognize the signs and translate them into a written language. ARSL is a challenging task as it involves background segmentation, hands and head posture modeling, recognition and tracking, temporal analysis and syntactic and semantic interpretation. Moreover, when real-time requirements are considered, this task becomes even more challenging. In this paper, we present a study of real time requirements of automatic sign language recognition of small sets of static and dynamic gestures of the Brazilian Sign Language (LIBRAS). For the task of static gesture recognition, we implemented a system that is able to work on small sub-sets of the alphabet - like A,E,I,O,U and B,C,F,L,V - reaching very high recognition rates. For the task of dynamic gesture recognition, we tested our system over a small set of LIBRAS words and collected the execution times. The aim was to gather knowledge regarding execution time of all the recognition processes (like segmentation, analysis and recognition itself) to evaluate the feasibility of building a real-time system to recognize small sets of both static and dynamic gestures. Our findings indicate that the bottleneck of our current architecture is the recognition phase.
Network Anomaly Detection Using RBF Neural Network with Hybrid QPSO

A novel hybrid algorithm based radial basis function (RBF) neural network is proposed for network anomaly detection in this paper. The quantum-behaved particle swarm optimization, which outperforms other optimization algorithm considerably on its simple architecture and fast convergence, has previously applied to solve optimum problem. However, the QPSO also has its own shortcomings. So, a hybrid algorithm in training RBF neural network was proposed. This new evolutionary algorithm, which is based on a hybrid of quantum-behaved particle swarm optimization (QPSO) and gradient descent algorithm (GD), is employed to train RBFNN. Experimental result on KDD99 intrusion detection datasets shows that this RBFNN using the novel hybrid algorithm has high detection rate while maintaining a low false positive rate.
Mode switch — Adaptive use of delay-sensitive or energy-aware communication in IEEE 802.15.4-based networks

We propose “mode switch”, an adaptive load-sensitive solution that supports both an energy-efficient operation mode for transmitting normal sensor data and an QoS-aware low-latency mode for high priority emergency messages. Typically, sensor networks are considered to operate efficiently w.r. t. energy consumption. A good example is the IEEE 802.15. 4 protocol, which has mainly been designed for this purpose. The standard IEEE 802.15. 4 beacon-enabled cluster-tree topology supports energy-efficient operation for low-rate data transmissions. However, in the case of detected alarms, high-priority emergency messages need to be transmitted with low-latency and guaranteed delivery. In this case, the cluster-tree topology appears to be inefficient and cannot provide the needed support. In this paper, we show how energy-efficiency and delay-sensitivity can be combined by developing an adaptive solution that completely switches between the fundamentally different operation modes beacon-enabled cluster-tree mode and non-beacon-enabled mesh mode. The key challenge is to provide a fast mode switch that also keeps track of necessary topology information. We present a protocol for this mode switch and demonstrate, based on extensive simulations, that the performance demands can be fulfilled with only reasonable overhead even for mid-size networks. Our simulation results clearly indicate that the system achieves the objected QoS and energy-efficiency with “mode switch” in real-time.
AIF inhibits tumor metastasis by protecting PTEN from oxidation

Apoptosis‐inducing factor (AIF) exerts dual roles on cell death and survival, but its substrates as a putative oxidoreductase and roles in tumorigenesis remain elusive. Here, we report that AIF physically interacts with and inhibits the oxidation of phosphatase and tensin homolog on chromosome ten (PTEN), a tumor suppressor susceptible for oxidation‐mediated inactivation. More intriguingly, we also identify PTEN as a mitochondrial protein and the ectopic expression of mitochondrial targeting sequence‐carrying PTEN almost completely inhibits Akt phosphorylation in PTEN‐deficient cells. AIF knockdown causes oxidation‐mediated inactivation of the lipid phosphatase activity of PTEN, with ensuing activation of Akt kinase, phosphorylation of the Akt substrate GSK‐3β, and activation of β‐catenin signaling in cancer cells. Through its effect on β‐catenin signaling, AIF inhibits epithelial–mesenchymal transition (EMT) and metastasis of cancer cells in vitro and in orthotopically implanted xenografts. Accordingly, the expression of AIF is correlated with the survival of human patients with cancers of multiple origins. These results identify PTEN as the substrate of AIF oxidoreductase and reveal a novel function for AIF in controlling tumor metastasis.
Bottom-up and top-down reasoning with convolutional latent-variable models

Convolutional neural nets (CNNs) have demonstrated remarkable performance in recent history. Such approaches tend to work in a "unidirectional" bottom-up feed-forward fashion. However, biological evidence suggests that feedback plays a crucial role, particularly for detailed spatial understanding tasks. This work introduces "bidirectional" architectures that also reason with top-down feedback: neural units are influenced by both lower and higher-level units. We do so by treating units as latent variables in a global energy function. We call our models convolutional latent-variable models (CLVMs). From a theoretical perspective, CLVMs unify several approaches for recognition, including CNNs, generative deep models (e.g., Boltzmann machines), and discriminative latent-variable models (e.g., DPMs). From a practical perspective, CLVMs are particularly well-suited for multi-task learning. We describe a single architecture that simultaneously achieves state-of-the-art accuracy for tasks spanning both high-level recognition (part detection/localization) and low-level grouping (pixel segmentation). Bidirectional reasoning is particularly helpful for detailed low-level tasks, since they can take advantage of top-down feedback. Our architectures are quite efficient, capable of processing an image in milliseconds. We present results on benchmark datasets with both part/keypoint labels and segmentation masks (such as PASCAL and LFW) that demonstrate a significant improvement over prior art, in both speed and accuracy.
Stationary foreground detection for video-surveillance based on foreground and motion history images

Stationary foreground detection is a common stage in many video-surveillance applications. In this paper, we propose an approach for stationary foreground detection in video based on the spatio-temporal variation of foreground and motion data. Foreground data are obtained by Background Subtraction to detect regions of interest. Motion data allows to filter out the moving regions and it is estimated using median filters over sliding windows. Spatiotemporal patterns of both data are computed through history images and the final detection is obtained using a two-threshold scheme that considers motion activity. Partial visibility of stationary foreground for short-time intervals is handled to increase robustness. The results over challenging video-surveillance sequences show an improvement of the proposed approach against the related work.
Distinct impacts of syndesmophyte formation on male and female patients with ankylosing spondylitis

Aim: Ankylosing spondylitis (AS), a chronic inflammatory autoimmune disease, mainly affects the axial skeleton, leading to sacroiliitis and rigidity of the spine. Both spinal rigidity and syndesmophyte development can affect bone formation and resorption. In addition, inflammatory cytokines and cell adhesion molecules are correlated with bone metabolism. The aim of this study was to investigate the effects of gender difference and syndesmophyte formation on cytokines, adhesion molecules and bone metabolism markers in AS patients.
The Ecology of Microbial Communities Associated with Macrocystis pyrifera

Kelp forests are characterized by high biodiversity and productivity, and the cycling of kelp-produced carbon is a vital process in this ecosystem. Although bacteria are assumed to play a major role in kelp forest carbon cycling, knowledge of the composition and diversity of these bacterial communities is lacking. Bacterial communities on the surface of Macrocystis pyrifera and adjacent seawater were sampled at the Hopkins Marine Station in Monterey Bay, CA, and further studied using 454-tag pyrosequencing of 16S RNA genes. Our results suggest that M. pyrifera-dominated kelp forests harbor distinct microbial communities that vary temporally. The distribution of sequence tags assigned to Gammaproteobacteria, Alphaproteobacteria and Bacteriodetes differed between the surface of the kelp and the surrounding water. Several abundant Rhodobacteraceae, uncultivated Gammaproteobacteria and Bacteriodetes-associated tags displayed considerable temporal variation, often with similar trends in the seawater and the surface of the kelp. Bacterial community structure and membership correlated with the kelp surface serving as host, and varied over time. Several kelp-specific taxa were highly similar to other bacteria known to either prevent the colonization of eukaryotic larvae or exhibit antibacterial activities. Some of these kelp-specific bacterial associations might play an important role for M. pyrifera. This study provides the first assessment of the diversity and phylogenetic profile of the bacterial communities associated with M. pyrifera.
The Prospect of Market-Driven Improvements in Animal Welfare: Lessons from the Case of Grass Milk in Denmark

Simple Summary Increased consumption of animal welfare-friendly products is suggested as one way of addressing public worries about the welfare of farm animals. However, the factors that drive and limit markets for animal welfare-friendly products are poorly understood. Based on an analysis of market for grass milk in Denmark, we conclude that successful cases of market-driven improvements in animal welfare require the joint presence of a number of positive drivers as well as low consumption barriers. Abstract Citizens in many European countries urge that the welfare of farm animals should be improved. Policy-makers propose that this could, at least to some extent, be achieved through increased consumption of animal products produced under labeling schemes guaranteeing higher standards of animal welfare. Yet considerable uncertainties exist about the ability of the market to promote animal welfare. So far the consumption of most welfare-friendly products has been limited, and the impact of driving and limiting factors is poorly understood. Reviewing market studies, we identify the factors that have shaped the relatively successful market for grass milk in Denmark. We conclude that the positive drivers such as an appealing animal welfare attribute and animal welfare being bundled with other qualities are essentially the same as those operating in connection with less successful animal welfare-friendly products. It is therefore to be expected that other animal welfare-friendly food products marketed via “natural behaviors” in the farm animals will catch the interest of consumers. However, grass milk consumption has been supported by proper labeling, ready availability and low price premiums as well as multifaceted public support. This suggests that successful cases require the joint presence of a number of positive drivers as well as low consumption barriers.
A multicarrier CDMA system with adaptive subchannel allocation for forward links

Multicarrier transmission schemes have been introduced into code-division multiple access (CDMA) systems to gain advantages for high data rate transmission. One of the methods is to transmit identical narrowband direct-sequence (DS) waveforms in parallel over a number of subchannels using frequency diversity. In this paper, we propose a multicarrier CDMA system with an adaptive subchannel allocation method for forward links. In the proposed system, instead of identical DS waveforms being transmitted over a number of subchannels in parallel, each user's DS waveform is transmitted over the user's favourite subchannel which has the largest fading amplitude among all the subchannels. We analyze the performance characteristics of the system when orthogonal and random signature sequences are used. The proposed system is shown to have performance gain over the conventional multicarrier CDMA system. We also investigate how the performance is influenced when the signal is not perfectly allocated into the best subchannel.
Working around BGP: An Incremental Approach to Improving Security and Accuracy in Interdomain Routing

BGP is essential to the operation of the Internet, but is vulnerable to both accidental failures and malicious attacks. We propose a new protocol that works in concert with BGP, which Autonomous Systems will use to help detect and mitigate accidentally or maliciously introduced faulty routing information. The protocol differs from previous efforts at securing BGP in that it is receiver-driven, meaning that there is a mechanism for recipients of BGP UPDATE messages to corroborate the information they receive and to provide feedback. We argue that our new protocol can be adopted incrementally, and we show that there is incentive for network operators to do so. We also describe our prototype implementation.
SFB 35 - Transmembrane Transporters in Health and Disease

s Plenary Lectures 10 15 Abstracts Session 1 16 18s Session 1 16 18 Abstracts Session 2 19 21s Session 2 19 21 Abstracts Session 3 22 24s Session 3 22 24 Abstracts Session 4 25 27s Session 4 25 27 Abstracts Session 5 28 31s Session 5 28 31 Abstracts Session 6 32 34s Session 6 32 34 Abstracts Session 7 35 37s Session 7 35 37 Abstracts Session 9 38 41s Session 9 38 41 Poster Presentations 42 89
Effect of Microscopic Structure on High-Cycle Fatigue Behavior in Nano-Components

In order to investigate the effect of microscopic structure on fatigue behavior of nanoscale components, a resonant fatigue experiment is conducted using a nanocomponents specimen where the test section is composed of a single crystalline Si substrate, a 200 nm thickness Cu polycrystalline film and a SiN amorphous layer. In the specimen, only the Cu portion plastically deforms because the yield stress is lower than those of other materials. The shape and the crystalline orientation of each grain on the surface of Cu portion are specified by means of EBSD. Although crystallographic slip bands with a width of a few tens of nanometers appear only in a grain of Cu portion, the grain is different from that expected by the Schmid factor. A FEM analysis, which takes into account the deformation anisotropy of grains, reveals that shear stress to generate slip bands is concentrated on the grain owing to the deformation constraint by neighboring crystals and components.
Does Obesity Really Make the Femur Stronger? BMD, Geometry, and Fracture Incidence in the Women's Health Initiative‐Observational Study

Heavier individuals have higher hip BMD and more robust femur geometry, but it is unclear whether values vary in proportion with body weight in obesity. We studied the variation of hip BMD and geometry across categories of body mass index (BMI) in a subset of postmenopausal non‐Hispanic whites (NHWs) from the Women's Health Initiative Observational Cohort (WHI‐OS). The implications on fracture incidence were studied among NHWs in the entire WHI‐OS. Baseline DXA scans of hip and total body from 4642 NHW women were divided into BMI (kg/m2) categories: underweight (<18.5), healthy weight (18.5–24.9), overweight (25–29.9), and mild (30–34.9), moderate (35–39.9), and extreme obesity (>40). Femur BMD and indices of bone axial (cross‐sectional area [CSA]) and bending strength (section modulus [SM]) were extracted from DXA scans using the hip structure analysis (HSA) method and compared among BMI categories after adjustment for height, age, hormone use, diabetes, activity level, femur neck‐shaft angle, and neck length. The association between BMI and incident fracture was studied in 78,013 NHWs from the entire WHI‐OS over 8.5 ± 2.6 (SD) yr of follow‐up. Fracture incidence (cases/1000 person‐years) was compared among BMI categories for hip alone, central body (hip, pelvis, spine, ribs, and shoulder girdle), upper extremity (humerus and distal), and lower extremity (femur shaft and distal but not hip). Femur BMD, CSA, and SM were larger in women with higher BMI, but values scaled in proportion to lean and not to fat or total body mass. Women with highest BMI reported more falls in the 12 mo before enrollment, more prevalent fractures, and had lower measures of physical activity and function. Incidence of hip fractures and all central body fractures declined with BMI. Lower extremity fractures distal to the hip trended upward, and upper extremity incidence was independent of BMI. BMD, CSA, and SM vary in proportion to total body lean mass, supporting the view that bones adapt to prevalent muscle loads. Because lean mass is a progressively smaller fraction of total mass in obesity, femur BMD, CSA, and SM decline relative to body weight in higher BMI categories. Traumatic forces increase with body weight, but fracture rates at the hip and central body were less frequent with increasing BMI, possibly because of greater soft tissue padding. There was no evident protective effect in fracture rates at less padded distal extremity sites. Upper extremity fractures showed no variation with BMI, and lower extremity fracture rates were higher only in the overweight (BMI = 25–29.9 kg/m2).
Sex Differences in Colorectal Cancer Survival: Population-Based Analysis of 164,996 Colorectal Cancer Patients in Germany

Risk of colorectal cancer (CRC) is considerably higher in men compared to women; however, there is inconclusive evidence of sex differences in CRC prognosis. We aimed to assess and explain sex differences in 5-year relative survival using standard and model-based period analysis among 164,996 patients diagnosed with CRC from 1997 to 2006 and reported to 11 German cancer registries covering a population of 33 million inhabitants. Age-adjusted 5-year relative survival was higher in women (64.5% vs. 61.9%, P<0.0001). A substantial survival advantage of women was confirmed in multivariate analysis after adjusting for CRC stage and subsite in subjects under 65 years of age (relative excess risk, RER 0.86, 95% CI 0.82–0.90), but not in older subjects (RER 1.01, 95% CI 0.98–1.04); this pattern was similar in the 1st and in the 2nd to 5th year after diagnosis. The survival advantage of women varied by CRC stage and age and was most pronounced for localized disease (RERs 0.59–0.88 in various age subgroups) and in patients under 45 years of age (RERs 0.59, 0.72 and 0.76 in patients with localized, regional or advanced disease, respectively). On the contrary, sex differences in survival did not vary by location of CRC. In conclusion, our large population-based study confirmed a survival advantage of female compared to male CRC patients, most notably in young and middle aged patients and patients with localized disease. The effect of sex hormones, either endogenous or through hormonal replacement therapy, might be the most plausible explanation for the observed patterns.