nils-herrmann/clean_scirepeval huggingface dataset:
Effectiveness of interventions to enhance healing of chronic ulcers of the foot in diabetes: a systematic review
Venue: Diabetes/metabolism research and reviews

The outcome of management of diabetic foot ulcers remains a challenge, and there remains continuing uncertainty concerning optimal approaches to management. It is for these reasons that in 2008 and 2012, the International Working Group of the Diabetic Foot (IWGDF) working group on wound healing published systematic reviews of the evidence to inform protocols for routine care and to highlight areas, which should be considered for further study. The same working group has now updated this review by considering papers on the interventions to improve the healing of chronic ulcers published between June 2010 and June 2014. Methodological quality of selected studies was independently assessed by two reviewers using Scottish Intercollegiate Guidelines Network criteria. Selected studies fell into the following ten categories: sharp debridement and wound bed preparation with larvae or hydrotherapy; wound bed preparation using antiseptics, applications and dressing products; resection of the chronic wound; oxygen and other gases, compression or negative pressure therapy; products designed to correct aspects of wound biochemistry and cell biology associated with impaired wound healing; application of cells, including platelets and stem cells; bioengineered skin and skin grafts; electrical, electromagnetic, lasers, shockwaves and ultrasound and other systemic therapies, which did not fit in the aforementioned categories. Heterogeneity of studies prevented pooled analysis of results. Of the 2161 papers identified, 30 were selected for grading following full text review. The present report is an update of the earlier IWGDF systematic reviews, and the conclusion is similar: that with the possible exception of negative pressure wound therapy in post operative wounds, there is little published evidence to justify the use of newer therapies. Analysis of the evidence continues to present difficulties in this field as controlled studies remain few and the majority continue to be of poor methodological quality.
Growth hormone co treatment within a GnRH agonist long protocol improves implantation and pregnancy rates in patients undergoing IVF ET
Venue: Archives of Gynecology and Obstetrics

ObjectiveThe efficacy of growth hormone (GH) co treatment within a GnRH agonist long regimen, in women with a normal ovarian response to controlled ovarian hyperstimulation (COH) for IVF was assessed.MethodsThis retrospective clinical trial was performed in a private assisted reproduction centre. The study involved 1114 patients who responded normally to high dose gonadotropin treatment. The study group of 556 patients was given in a daily subcutaneous injection of 4.5 IU of GH co treatment, starting from the initial day of gonadotropin treatment and lasting for 5 days. The control group of 558 patients received the same treatment protocol without the GH co treatment. The participants were further divided into two subgroups: age =35 years and age <35 years. The primary endpoint of the study was IVF ET outcomes.ResultsThe demographic characteristics did not significantly differ between the groups. The implantation rate (36.7 vs. 20.4 P 0.05) and clinical pregnancy rate (57.3 vs. 30.1 P 0.05) were significantly higher in the study group than in the control group. An analysis using a multivariate logistic regression model showed that GH was a significant factor for predicting pregnancy outcomes (OR 3.125, 95 CI 2.441 4.000) Furthermore, for the =35 year old group, the endometrial thickness was significantly greater (11.99 2.21 vs. 11.62 2.45, P 0.05) in the study group than in the control group; in contrast, for the <35 year old group, the high quality embryo rate was significantly higher (71.7 vs. 68.3 P 0.05) in the study group than in the control group.ConclusionOur study showed that co treatment with GH in a GnRH agonist long protocol in patients who responded normally while undergoing IVF ET could increase the implantation and pregnancy rates.
Parallel implementation and performance evaluation of facial recognition algorithms using open source technologies
Venue: 2016 International Conference on Electrical, Electronics, and Optimization Techniques (ICEEOT)

Critical to any automated surveillance system is the recognition component. Since the inception of Pattern Recognition Image processing, researchers across the globe continue to propose newer facial recognition algorithms. Each of these facial algorithms has its own pros and cons. A typical facial recognition algorithm consists of three steps namely: facial detection, feature extraction and recognition. In this paper we discuss serial and parallel implementation of four standard facial recognition algorithms namely, Principal Component Analysis (PCA) Linear Discriminant Analysis (LDA) Independent Component Analysis (ICA) and FISHER Face. Work presented in this paper consists of two sections. In section 1, we discuss serial and parallel implementation of the algorithms. Section 2, presents a comparative performance analysis of the above 4 algorithms. A major challenge in this effort is the ability to explore and exploit concurrency in existing facial recognition algorithms to improve performance. Based on the algorithms performance, we have made few suggestions regarding the suitability of each of these algorithms for different domains/applications. All algorithms are implemented using open source OPENCV and OPENMP libraries.
Multispectral Deep Neural Networks for Pedestrian Detection
Venue: BMVC

Multispectral pedestrian detection is essential for around the clock applications, e.g. surveillance and autonomous driving. We deeply analyze Faster R CNN for multispectral pedestrian detection task and then model it into a convolutional network (ConvNet) fusion problem. Further, we discover that ConvNet based pedestrian detectors trained by color or thermal images separately provide complementary information in discriminating human instances. Thus there is a large potential to improve pedestrian detection by using color and thermal images in DNNs simultaneously. We carefully design four ConvNet fusion architectures that integrate two branch ConvNets on different DNNs stages, all of which yield better performance compared with the baseline detector. Our experimental results on KAIST pedestrian benchmark show that the Halfway Fusion model that performs fusion on the middle level convolutional features outperforms the baseline method by 11% and yields a missing rate 3.5% lower than the other proposed architectures.
Robust control and actuator dynamics compensation for railway vehicles

A robust controller is designed for active steering of a high speed train bogie with solid axle wheel sets to reduce track irregularity effects on the vehicle's dynamics and improve stability and curving performance. A half car railway vehicle model with seven degrees of freedom equipped with practical accelerometers and angular velocity sensors is considered for the H control design. The controller is robust against the wheel/rail contact parameter variations. Field measurement data are used as the track irregularities in simulations. The control force is applied to the vehicle model via ball screw electromechanical actuators. To compensate the actuator dynamics, the time delay is identified online and is used in a second order polynomial extrapolation carried out to predict and modify the control command to the actuator. The performance of the proposed controller and actuator dynamics compensation technique are examined on a one car railway vehicle model with realistic structural parameters and nonlinear wheel and rail profiles. The results showed that for the case of nonlinear wheel and rail profiles significant improvements in the active control performance can be achieved using the proposed compensation technique.
Predisposing Characteristics of Adjacent Segment Disease After Lumbar Fusion
Venue: Spine

The aim of this study was to determine medical, radiographic, and surgical risk factors for the development of adjacent segment disease (ASD) after lumbar fusion.  ASD is a recognized outcome of spinal fusion that leads to increased costs and debilitating symptoms for patients. However, a comprehensive understanding of risk factors for the development of this surgical outcome does not exist.  The medical records of patients who received their first lumbar fusion for any indication were retrospectively examined for preoperative medical comorbidities and medications, as well as surgical approach and perioperative complications. A blinded reviewer assessed radiographs for each patient to examine sagittal alignment after fusion. Multivariable logistic regression was used to model the risk of developing ASD on the basis of one or more predictors.  A total of 137 patients fit the inclusion criteria; 9% required a follow up operation for degeneration at segments adjacent to the fusion. The ASD group had a mean follow up of 21.1 months prior to revision surgery and an overall follow up of 41.0 months. The average follow up in the control group was 14.0 months. Statistically significant independent predictors of developing ASD included antidepressant use [odds ratio (OR) 5.4] diagnosis of degenerative scoliosis (OR 34.2) fusion of L4 S1 (OR 56.5) having no decompressions adjacent to the fusion, and low sacral slope (OR 0.9) No patient who developed ASD received a decompression adjacent to the fusion such that an OR could not be generated for this independent predictor.  This study is the first to use a combination of medical, surgical, and postoperative sagittal balance as risk factors for the development of adjacent segment disease after lumbar fusion. The awareness of these risk factors may allow for better patient selection and surgical technique to decrease the probability of acquiring this adverse outcome.
Dense core model for cohesive subgraph discovery
Venue: Soc. Networks

Discovery of cohesive subgraphs is an important issue in social network analysis. As representative cohesive subgraphs, pseudo cliques have been developed by relaxing the perfection of cliques. By enumerating pseudo clique subgraphs, we can find some structures of interest such as a star like structure. However, a little more complicated structures such as a core/periphery structure is still hard to be found by them. Therefore, we propose a novel pseudo clique called r dense core and show the connection with the other pseudo cliques. Moreover, we show that a set of r dense core subgraphs gives an optimal solution in a graph partitioning problem. Several experiments on real life networks demonstrated the effectiveness for cohesive subgraph discovery.
Local Volumetric Hybrid Map Based Simultaneous Localization and Mapping With Moving Object Tracking
Venue: IEEE Transactions on Intelligent Transportation Systems

In this paper, we present a novel framework for solving two environmental perception tasks concurrently simultaneous localization and mapping, and moving object tracking by using a Velodyne laser scanner. To extract proper input data for these tasks from the sensor, several sensor data preprocessing algorithms are first addressed. For the simultaneous localization and mapping problem, we propose a local volumetric hybrid map based approach using Rao Blackwellized particle filters. We represent the static environments with the hybrid map consisting of feature and 3 D grid maps. This framework basically allows us to utilize the traditional approaches using a single map. In addition, we derive a new sampling formula by combining a feature measurement likelihood to the traditional grid map based approach, and this significantly improves the accuracy and efficiency of the algorithm. The proposed moving object tracking algorithm is achieved based on the geometric and multiple motion models. We introduce a robust extraction and parameterization method of the geometric shape based on predefined contour models. Then, the geometric shape is inferred with a multiple base point method. We establish three motion models, which are utilized for tracking in an adaptive way by using the well known interacting multiple model algorithm. The algorithms proposed are evaluated using the data sets collected from our test vehicle in the complex urban scenarios. The experimental results show that our approach works well even in real outdoor environments and outperforms traditional approaches.
Pixel Intensity Based Cumulative Features for Moving Object Tracking (MOT) in Darkness

Moving Object Tracking (MOT) is one of the frequent used tasks in computer vision systems and on the same time a challenging issue particularly in darkness. Vehicle tracking involves finding new position of vehicles in consecutive frames. This paper presents MOT algorithm that is developed for advanced driver safety applications like automatic high beam control, forward collision warning. Accordingly, the proposed approach targets vehicle tracking in the dark environment. Hence, a camera is mounted on the host vehicle to capture video frames of the traffic ahead. The scope involves tracking of both oncoming and preceding vehicles. The vehicles are tracked in consecutive frames using grayscale information and robust structure features. The features representation of the vehicle region is based on cumulative pixel intensity information. The implementation for feature extraction is optimized by using a dynamic programming approach to meet the constraints of a real time application. Simulation results thus obtained are promising in state of the art.
Moving object tracking using Laplacian DCT based perceptual hash
Venue: 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET)

Moving object tracking is one of the basic and hot research domains in the computer vision area. This work presents a novel and effective method to track moving objects under a static background. Proposed method first executes the pre processing tasks to remove noise from video frames. Then, with the help of rectangular window, we select the target object region in the first video frame (reference frame) Next, it applies the Laplacian operator on the selected target objects for sharpening and edge detection. The algorithm then applies the DCT and selects the few high energy coefficients. Subsequently, it computes the perceptual hash of the selected target objects with the help of mean of all the AC values of the block. Using perceptual hash of a target object, we find the similar object in subsequent frames of the video. The proposed method is correct for tracking the moving target object with varying object size and significant amount of noise. This work has been formulated, implemented and tested on real indoor outdoor video sequences and the results are found to be adequate as it proved from the performance evaluation.
Translation of Rhetoric in Arabic Preposition in the Text of Al Qur an

Preposition was a word that completes the meaning of the verb and noun in the sentence structure of Arabic. The use of prepositions in Arabic was truly significant in understanding the Arabic text, especially holy texts. The question was why prepositions were used in sentences such, whereas the other joints could also give the same meaning? Article presented an analysis of syntax semantics of prepositions fy Fi and `l~cAla in al Quran al Karim across science of Arabic rhetoric (Balagha) Data were restricted to partial phrase fy lDll and `l~ lhd~ ever discussed by Ibn Athir. There are 29 places in the Quran that mention fy lDll and 6 points for `l~ lhd~ Critical analysis based on the discipline of linguistics is expected to expose the use of prepositions in Arabic from the precision and beauty of the Arabic language itself. Summary of the study suggest that research on elements of the rhetoric says that other reasonable tasks highlighted in subsequent studies.
Scan Line Based Road Marking Extraction from Mobile LiDAR Point Clouds+
Venue: Sensors

Mobile Mapping Technology (MMT) is one of the most important 3D spatial data acquisition technologies. The state of the art mobile mapping systems, equipped with laser scanners and named Mobile LiDAR Scanning (MLS) systems, have been widely used in a variety of areas, especially in road mapping and road inventory. With the commercialization of Advanced Driving Assistance Systems (ADASs) and self driving technology, there will be a great demand for lane level detailed 3D maps, and MLS is the most promising technology to generate such lane level detailed 3D maps. Road markings and road edges are necessary information in creating such lane level detailed 3D maps. This paper proposes a scan line based method to extract road markings from mobile LiDAR point clouds in three steps: (1) preprocessing; (2) road points extraction; (3) road markings extraction and refinement. In preprocessing step, the isolated LiDAR points in the air are removed from the LiDAR point clouds and the point clouds are organized into scan lines. In the road points extraction step, seed road points are first extracted by Height Difference (HD) between trajectory data and road surface, then full road points are extracted from the point clouds by moving least squares line fitting. In the road markings extraction and refinement step, the intensity values of road points in a scan line are first smoothed by a dynamic window median filter to suppress intensity noises, then road markings are extracted by Edge Detection and Edge Constraint (EDEC) method, and the Fake Road Marking Points (FRMPs) are eliminated from the detected road markings by segment and dimensionality feature based refinement. The performance of the proposed method is evaluated by three data samples and the experiment results indicate that road points are well extracted from MLS data and road markings are well extracted from road points by the applied method. A quantitative study shows that the proposed method achieves an average completeness, correctness, and F measure of 0.96, 0.93, and 0.94, respectively. The time complexity analysis shows that the scan line based road markings extraction method proposed in this paper provides a promising alternative for offline road markings extraction from MLS data.
POLE LIKE OBJECT EXTRACTION FROM MOBILE LIDAR DATA

Object detection and recognition from LiDAR (Light Detection And Ranging) data has been a research topic in the fields of photogrammetry and computer vision. Unlike point clouds collected in well controlled indoor environments, point clouds in urban environments are more complex due to complexity of the real world. For example, trees sometimes close to signs or buildings, which will cause occlusions in the point clouds. Current object detection or reconstruction algorithms will have problems when recognizing objects with severe occlusions caused by trees etc. In this paper, a robust vegetation removal method and a DBSCAN based pole like object detection method are proposed. Based on observation that major difference between vegetation and other rigid objects is their penetrability with respect to LiDAR, we introduce a local roughness measure to differentiate rigid objects from non rigid ones (vegetation in this paper) First, a local sphere with a small radius is generated for each input point. Three principal components of the local sphere are then calculated, and a plane is determined. The roughness is obtained through calculating the standard deviation of distances from all inside points to the plane by a weighted summation of the normalized distances. The further the point to the plane, the smaller the weight is. Finally, a graph cuts based method is introduced to classify the input point sets into two groups. The data term is defined by the normalized roughness of the current point, and the smoothness term is defined by the normalized distance between the point and its nearest neighbour point. In terms of pole like object detection, first, a uniformed 2D grid is generated through projecting all the points to the XY plane. The seed points of the pole like objects are obtained by determining the x and y coordinates by the centres of the highest density cells of the grid and the z coordinate by the mean height of the point sets of each object. Finally, a DBSCAN based method is introduced to obtain the rest points of each pole like object. Experimental results show that the proposed vegetation removal method achieves state of the art results from both mobile LiDAR and airborne LiDAR data. The proposed pole like object detection approach turns out to be very efficient.
Multi variable optimization of diesel engine fuelled with biodiesel using grey Taguchi method

The current work is aimed at the correlated multiple criteria optimization of various parameters of a turbocharged, direct injection, compression ignition engine alternatively fuelled with neat biodiesel and its 20 blend with commercial diesel (B20) using grey Taguchi method (GTM) The GTM converts multiple objectives of a problem into a single objective function by an optimization technique. The process environment consisting of three input variables such as type of fuel, engine speed, and load was considered in this study. An orthogonal array was used for the design of experiments on the basis of L9 (33) The optimal parameters were determined by the grey relational grade based on GTM. The consequent optimal combination of input parameters was used to maximize the output parameters including engine torque, brake power, heat release, and injection pressure with the possible diminution of brake specific fuel consumption of the engine. During the study, it was found that B20 as a fuel, 1,800 rpm as a speed, and 100 as a load offer an optimal parametric combination at which the desired output results are achieved. Moreover, analysis of variance approach based on statistical software of Minitab 16 was used to investigate the comparative impacts of input variables on the output responses. It was known that load is the predominant factor with an influence of 92.42 on the output parameters. Finally, a confirmatory test was performed to validate the results using artificial neural network in MATLAB.
LPV MRAC Method for Aircraft with Structural Damage
Venue: 2016 35th Chinese Control Conference (CCC)

Abrupt structural damage poses significant challenges to the flight safety and flight quality of aircraft. In this paper, a direct model reference adaptive control (MRAC) method based on a linear parameter varying (LPV) model is proposed to recover the control performance and flight quality of damaged aircraft. The design of the controller is based on a polytopic LPV model and the higher order singular value decomposition (HOSVD) model reduction method to reduce the computational cost of identifying the damaged aircraft model. The proposed controller also extends a previous MRAC method which assumes the input matrix is unchanged for different damage cases by identifying an uncertain input parameter online. The developed LPV MRAC method is validated by simulation on NASA's generic transport model (GTM) with left wing tip loss damage and shown to be capable of compensating the damage effects and restoring the aircraft's control performance shortly after the damage. The flight quality of the closed loop damaged aircraft is also evaluated by the C star criterion and shown to be within Class I under the proposed controller.