Miracle-dz/UniFAR-FTUs huggingface dataset:
Axial behaviour of HSS tubular sections strengthened by CFRP strips: an experimental investigation

Abstract The main objective of this investigation is to assess the feasibility of strengthening square hollow steel tubular sections subjected to compression and to develop or predict the suitable wrapping scheme of fibre reinforced polymer (FRP) to enhance the structural behaviour of it. For this study, compact mild steel tubes were used with the main variable being FRP characteristics. Carbon fibre has been considered and used as strips with several other parameters such as the number of layers, width and spacing of strips, the sectional area of strips, and wrapping scheme. Experiments were undertaken until column failure to fully understand the influence of FRP characteristics on the compressive behaviour of square hollow steel tubes including their failure modes, stress-strain behaviour, enhancement in load carrying capacity and effect of distribution of CFRP layers. The behaviour of externally bonded hollow steel tubular sections was compared with one another and also with the control specimen. From the test results, it was found that CFRP strengthening significantly increases the load carrying capacity and ductility of the hollow steel tubular members further.
SMDP-Based Downlink Packet Scheduling Scheme for Solar Energy Assisted Heterogeneous Networks

Renewable energy assisted heterogeneous networks (HetNets) can improve system capacity and reduce conventional energy consumption. In this paper, we propose a semi-Markov decision process (SMDP)-based downlink packet scheduling scheme for solar energy assisted HetNets, in which solar radiation is modeled as a continuous-time Markov chain (CTMC) and the arrivals of multi-class downlink packets are modeled as Poisson processes. We derive the state transition probabilities and the discounted state transition probabilities for the composite states of battery, solar radiation. To obtain an asymptotically optimal downlink packet scheduling policy, we solve the semi-Markov decision problem using the relative value iteration algorithm under average criterion and the value iteration algorithm under discounted criterion, respectively. The simulation results show that the average cost of the SMDP-based packet scheduling scheme is less than that of the heuristic packet scheduling scheme.
Energy Efficient Base Station Density Analysis for Heterogeneous Networks with eICIC

—Heterogeneous Networks (HetNets), where lowpower nodes are overlaid on the traditional macro base station (MBS), have been investigated as a promising paradigm to support the deluge of data traffic with higher spectral efficiency and energy efficiency. However, the density of BS and the intertier interference are the two important influence factors for the network performance implementation. As for the inter-tier interferences, time domain Enhanced Inter-cell Interference Coordination (eICIC) technique is adopted to mitigate it and improve the system capacity for HetNets in LTE-Advanced. In this, the reasonable configuration of the density of base stations is very essential for the network performance improvement, especially for the network energy optimization. Thus in this paper, we deduce the closed-form of network energy efficiency as a function of the density of BSs with eICIC technology based on stochastic geometry theory. Then the impacts of the BSs density on the network energy efficiency is further analyzed by simulation. Simulation results show that the theoretical derivation is verified well by Monte Carlo simulation and the reasonable BSs density configuration can improve the network energy efficiency effectively.
Mesh structure-independent modeling of patient-specific atrial fiber orientation

Abstract The fiber orientation in the atria has a significant contribution to the electrophysiologic behavior of the heart and to the genesis of arrhythmia. Atrial fiber orientation has a direct effect on excitation propagation, activation patterns and the P-wave. We present a rule-based algorithm that works robustly on different volumetric meshes composed of either isotropic hexahedra or arbitrary tetrahedra as well as on 3-dimensional triangular surface meshes in patient-specific geometric models. This method fosters the understanding of general proarrhythmic mechanisms and enhances patient-specific modeling approaches.
Resistance training-induced gains in muscle strength, body composition, and functional capacity are attenuated in elderly women with sarcopenic obesity

Objectives The purpose of this study was to compare the effects of resistance training (RT) on body composition, muscle strength, and functional capacity in elderly women with and without sarcopenic obesity (SO). Methods A total of 49 women (aged ≥60 years) were divided in two groups: without SO (non-SO, n=41) and with SO (n=8). Both groups performed a periodized RT program consisting of two weekly sessions for 16 weeks. All measures were assessed at baseline and postintervention, including anthropometry and body composition (dual-energy X-ray absorptiometry), muscle strength (one repetition maximum) for chest press and 45° leg press, and functional capacity (stand up, elbow flexion, timed “up and go”). Results After the intervention, only the non-SO group presented significant reductions in percentage body fat (−2.2%; P=0.006), waist circumference (−2.7%; P=0.01), waist-to-hip ratio (−2.3; P=0.02), and neck circumference (−1.8%; P=0.03) as compared with baseline. Muscle strength in the chest press and biceps curl increased in non-SO only (12.9% and 11.3%, respectively), while 45° leg press strength increased in non-SO (50.3%) and SO (40.5%) as compared with baseline. Performance in the chair stand up and timed “up and go” improved in non-SO only (21.4% and −8.4%, respectively), whereas elbow flexion performance increased in non-SO (23.8%) and SO (21.4%). Effect sizes for motor tests were of higher magnitude in the non-SO group, and in general, considered “moderate” compared to “trivial” in the SO group. Conclusion Results suggest that adaptations induced by 16 weeks of RT are attenuated in elderly woman with SO, compromising improvements in adiposity indices and gains in muscle strength and functional capacity.
How Do Grazers Achieve Their Distribution? A Continuum of Models from Random Diffusion to the Ideal Free Distribution Using Biased Random Walks

A conceptual model is described for generating distributions of grazing animals, according to their searching behavior, to investigate the mechanisms animals may use to achieve their distributions. The model simulates behaviors ranging from random diffusion, through taxis and cognitively aided navigation (i.e., using memory), to the optimization extreme of the Ideal Free Distribution. These behaviors are generated from simulation of biased diffusion that operates at multiple scales simultaneously, formalizing ideas of multiple‐scale foraging behavior. It uses probabilistic bias to represent decisions, allowing multiple search goals to be combined (e.g., foraging and social goals) and the representation of suboptimal behavior. By allowing bias to arise at multiple scales within the environment, each weighted relative to the others, the model can represent different scales of simultaneous decision‐making and scale‐dependent behavior. The model also allows different constraints to be applied to the animal's ability (e.g., applying food‐patch accessibility and information limits). Simulations show that foraging‐decision randomness and spatial scale of decision bias have potentially profound effects on both animal intake rate and the distribution of resources in the environment. Spatial variograms show that foraging strategies can differentially change the spatial pattern of resource abundance in the environment to one characteristic of the foraging strategy.
A Real-Time Edge-Preserving Denoising Filter

Even in todays world, where augmented reality glasses and 3d sensors become rapidly less expensive and widely more used, the most important sensor remains the 2d RGB camera. Every camera is an optical device and prone to sensor noise, especially in dark environments or environments with extreme high dynamic range. The here introduced filter removes a wide variation of noise, for example Gaussian noise and salt-and-pepper noise, but preserves edges. Due to the highly parallel structure of the method, the implementation on a GPU runs in real-time, allowing us to process standard images within tens of milliseconds. The filter is first tested on 2d image data and based on the Berkeley Image Dataset and Coco Dataset we outperform other standard methods. Afterwards, we show a generalization to arbitrary dimensions using noisy low level sensor data. As a result the filter can be used not only for image enhancement, but also for noise reduction on sensors like acceleremoters, gyroscopes, or GPS-trackers, which are widely used in robotic applications.
Impact of anti-charge sharing on the zero-frequency detective quantum efficiency of CdTe-based photon counting detector system: cascaded systems analysis and experimental validation

Inter-pixel communication and anti-charge sharing (ACS) technologies have been introduced to photon counting detector (PCD) systems to address the undesirable charge sharing problem. In addition to improving the energy resolution of PCD, ACS may also influence other aspects of PCD performance such as detector multiplicity (i.e. the number of pixels triggered by each interacted photon) and detective quantum efficiency (DQE). In this work, a theoretical model was developed to address how ACS impacts the multiplicity and zero-frequency DQE [DQE(0)] of PCD systems. The work focused on cadmium telluride (CdTe)-based PCD that often involves the generation and transport of K-fluorescence photons. Under the parallel cascaded systems analysis framework, the theory takes both photoelectric and scattering effects into account, and it also considers both the reabsorption and escape of photons. In a new theoretical treatment of ACS, it was considered as a modified version of the conventional single pixel (i.e. non-ACS) mode, but with reduced charge spreading distance and K-fluorescence travel distance. The proposed theoretical model does not require prior knowledge of the detailed ACS implementation method for each specific PCD, and its parameters can be experimentally determined using a radioisotope without invoking any Monte-Carlo simulation. After determining the model parameters, independent validation experiments were performed using a diagnostic x-ray tube and four different polychromatic beams (from 50 to 120 kVp). Both the theoretical and experimental results demonstrate that ACS increased the first and second moments of multiplicity for a majority of the x-ray energy and threshold levels tested, except when the threshold level was much lower than the x-ray energy level. However, ACS always improved DQE(0) at all energy and threshold levels tested.
Deep Sampling Networks

Deep convolutional neural networks achieve excellent image up-sampling performance. However, CNN-based methods tend to restore high-resolution results highly depending on traditional interpolations (e.g. bicubic). In this paper, we present a deep sampling network (DSN) for down-sampling and up-sampling without any cheap interpolation. First, the down-sampling subnetwork is trained without supervision, thereby preserving more information and producing better visual effects in the low-resolution image. Second, the up-sampling subnetwork learns a sub-pixel residual with dense connections to accelerate convergence and improve performance. DSN's down-sampling subnetwork can be used to generate photo-realistic low-resolution images and replace traditional down-sampling method in image processing. With the powerful down-sampling process, the co-training DSN set a new state-of-the-art performance for image super-resolution. Moreover, DSN is compatible with existing image codecs to improve image compression.
Workpiece Shape Deviations in Face Milling of Hybrid Structures

A currently common method to design high-performance workpieces is to combine two or more materials to one compound. This way, workpieces can be composed of the most qualified materials according to local loads.When machining high-performance workpiece compounds (e.g. cylinder crankcases), high quality requirements concerning the accuracy of dimension and shape as well as the surface roughness must be fulfilled. However, machining of workpiece compounds leads to unfavorable changes of the workpiece quality in comparison to machining of the single materials. Significant shape deviations occur when different materials are machined alternately in one cutting operation. This is due to unequal material properties, cutting characteristics, chip formation mechanisms as well as characteristic interactions between the single components.The focus of this research is on the process understanding as well as on the identification of measurable shape deviation indicators that describe the surface finish of hybrid structures. Here, the indicators material height deviation, transition deviation at the material joint, surface roughness deviation as well as surface defects (e.g. scratches) on the surface are presented. The overall aim of this research is to predict the surface finish resulting from face milling an aluminum/cast iron compound or a polyurethane/cast iron compound.
Robotic cloth manipulation for clothing assistance task using Dynamic Movement Primitives

The need of robotic clothing assistance in the field of assistive robotics is growing, as it is one of the most basic and essential assistance activities in daily life of elderly and disabled people. In this study we are investigating the applicability of using Dynamic Movement Primitives (DMP) as a task parameterization model for performing clothing assistance task. Robotic cloth manipulation task deals with putting a clothing article on both the arms. Robot trajectory varies significantly for various postures and also there can be various failure scenarios while doing cooperative manipulation with non-rigid and highly deformable clothing article. We have performed experiments on soft mannequin instead of human. Result shows that DMPs are able to generalize movement trajectory for modified posture.
Molecular, Genetic and Agronomic Approaches to Utilizing Pulses as Cover Crops and Green Manure into Cropping Systems

Cover crops constitute one of the most promising agronomic practices towards a more sustainable agriculture. Their beneficial effects on main crops, soil and environment are many and various, while risks and disadvantages may also appear. Several legumes show a high potential but further research is required in order to suggest the optimal legume cover crops for each case in terms of their productivity and ability to suppress weeds. The additional cost associated with cover crops should also be addressed and in this context the use of grain legumes such as cowpea, faba bean and pea could be of high interest. Some of the aspects of these grain legumes as far as their use as cover crops, their genetic diversity and their breeding using conventional and molecular approaches are discussed in the present review. The specific species seem to have a high potential for use as cover crops, especially if their noticeable genetic diversity is exploited and their breeding focuses on several desirable traits.
Automatic localization of the left ventricle in cardiac MRI images using deep learning

Automatic localization of the left ventricle (LV) in cardiac MRI images is an essential step for automatic segmentation, functional analysis, and content based retrieval of cardiac images. In this paper, we introduce a new approach based on deep Convolutional Neural Network (CNN) to localize the LV in cardiac MRI in short axis views. A six-layer CNN with different kernel sizes was employed for feature extraction, followed by Softmax fully connected layer for classification. The pyramids of scales analysis was introduced in order to take account of the different sizes of the heart. A publically-available database of 33 patients was used for learning and testing. The proposed method was able it localize the LV with 98.66%, 83.91% and 99.07% for accuracy, sensitivity and specificity respectively.
The Effect of Processed Total Motile Sperm Counts and Twenty Four Hour Sperm Survival on the Efficacy of Intrauterine Insemination in Male Infertility

Objective: To compare the effectiveness of IUI, based on Pre-treatment Semen analysis results, in treating male factor infertility.Design: A retrospective cohort design of 1,768 infertile couples undergoing 5,219 IUI cycles, who had pretreatment advanced semen analysis were evaluated. An advanced semen analysis consists of a basic semen analysis and processed total motile sperm counts through a density gradient sperm prep and recording 24 hours sperm survival of these sperm culture media in an incubator. Logistic regression analysis was used to assess the significance of prognostic factors in sperm parameters to predict the pregnancy rates with IUI.Results: No basic semen analysis parameter accurately predicted IUI success. Clinical pregnancy rate for first cycle of IUI was 15.6% when >10 × 106 processed total motile sperm was available and 13.7% in all cycles. This group contained 1264/1768 (71.5%) of couples in the study. The pregnancy rate in the first cycle was 18.2% if their 24 hour survival was >70% and 15.9% in all cycles and 1008/1264 (57%) of couples were in this group. No pregnancy was achieved for processed total motile sperm counts <5 × 106 (168/1768) or 9.5% of couples and 3.6% for those with 24 hour survivals <30% in first cycle and 2.0% for all cycles for 601/1768 (34%) of the couples. Strong positive correlations between processed total motile sperm counts and PR (r=0.83; p<0.001) and between 24 hours survival and PR (r=0.79; p<0.001) were seen by linear regression analysis. High correlations were also noted between processed total motile sperm counts (r=0.71; p<0.001) and 24 hours survivals (r=0.76; p<001) in the advanced semen analysis and those in the IUI samples.Conclusion: Both processed total motile sperm counts and 24 hour survival are useful predictors of whether a couple should be treated with levels below threshold levels have a very poor prognosis with IUIs.
Complete transformation of ZnO and CuO nanoparticles in culture medium and lymphocyte cells during toxicity testing

Abstract Here, we present evidence on complete transformation of ZnO and CuO nanoparticles, which are among the most heavily studied metal oxide particles, during 24 h in vitro toxicological testing with human T-lymphocytes. Synchrotron radiation-based X-ray absorption near edge structure (XANES) spectroscopy results revealed that Zn speciation profiles of 30 nm and 80 nm ZnO nanoparticles, and ZnSO4- exposed cells were almost identical with the prevailing species being Zn-cysteine. This suggests that ZnO nanoparticles are rapidly transformed during a standard in vitro toxicological assay, and are sequestered intracellularly, analogously to soluble Zn. Complete transformation of ZnO in the test conditions was further supported by almost identical Zn spectra in medium to which ZnO nanoparticles or ZnSO4 was added. Likewise, Cu XANES spectra for CuO and CuSO4-exposed cells and cell culture media were similar. These results together with our observation on similar toxicological profiles of ZnO and soluble Zn, and CuO and soluble Cu, underline the importance of dissolution and subsequent transformation of ZnO and CuO nanoparticles during toxicological testing and provide evidence that the nano-specific effect of ZnO and CuO nanoparticles is negligible in this system. We strongly suggest to account for this aspect when interpreting the toxicological results of ZnO and CuO nanoparticles.
Direct spondylolisthesis identification and measurement in MR/CT using detectors trained by articulated parameterized spine model

The identification of spondylolysis and spondylolisthesis is important in spinal diagnosis, rehabilitation, and surgery planning. Accurate and automatic detection of spinal portion with spondylolisthesis problem will significantly reduce the manual work of physician and provide a more robust evaluation for the spine condition. Most existing automatic identification methods adopted the indirect approach which used vertebrae locations to measure the spondylolisthesis. However, these methods relied heavily on automatic vertebra detection which often suffered from the pool spatial accuracy and the lack of validated pathological training samples. In this study, we present a novel spondylolisthesis detection method which can directly locate the irregular spine portion and output the corresponding grading. The detection is done by a set of learning-based detectors which are discriminatively trained by synthesized spondylolisthesis image samples. To provide sufficient pathological training samples, we used a parameterized spine model to synthesize different types of spondylolysis images from real MR/CT scans. The parameterized model can automatically locate the vertebrae in spine images and estimate their pose orientations, and can inversely alter the vertebrae locations and poses by changing the corresponding parameters. Various training samples can then be generated from only a few spine MR/CT images. The preliminary results suggest great potential for the fast and efficient spondylolisthesis identification and measurement in both MR and CT spine images.
Building hybrid machine translation systems by using an EBMT preprocessor to create partialtranslations

This paper presents a hybrid machine translation framework based on a preprocessor that translates fragments of the input text by using example-based machine translation techniques. The preprocessor resembles a translation memory with named-entity and chunk generalization, and generates a high quality partial translation that is then completed by the main translation engine, which can be either rule-based (RBMT) or statistical (SMT). Results are reported for both RBMT and SMT hybridization as well as the preprocessor on its own, showing the effectiveness of our approach.
A Video Dataset for an Efficient Camcording Attack Evaluation

Any video watermarking scheme dedicated to copyright protection should be robust against several attacks and especially against malicious and dangerous attacks such as camcording. Indeed, this attack has become a real problem for cinematographic production companies. However, until now the researchers don’t evaluate the robustness of their video watermarking approaches against this attack or they consider it as a combination of some usual attacks. To resolve this problem, several studies proposed camcording simulators which encourage and help researchers in video watermarking domain to include the camcording in the robustness evaluation. In this paper, a dataset of camcorder videos dedicated to an efficient robustness evaluation of watermarking schemes is proposed which can help researches on camcording simulators’ creation. In this dataset, videos are captured in realistic scenarios in the cinema and are recorded using five capture devices and from four positions. In more, the proposed dataset contains marked versions of the proposed videos using three different video watermarking techniques. This allows researchers comparing their approaches with these techniques. Experimental results show that the robustness evaluation based on the proposed dataset is more efficient than simulators based evaluation thanks to the diversity of the used capturing devices and the real conditions of