Search results for: discrete fracture network

Authors: Nashrawan Naser Taha, Andrew M. Cox

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In a multi-cultural learning context, where ties are weak and dynamic, combining qualitative with quantitative research methods may be more effective. Such a combination may also allow us to answer different types of question, such as about people’s perception of the network. In this study the use of observation, interviews and photos were explored as ways of enhancing data from social network questionnaires. Integrating all of these methods was found to enhance the quality of data collected and its accuracy, also providing a richer story of the network dynamics and the factors that shaped these changes over time.

Keywords: mixed methods, social network analysis, multi-cultural learning, social network dynamics

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Authors: Mohsen Dourandish

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Iran has a huge pipeline network in every state of country which is the longest and vastest pipeline network after Russia and USA (360,000 Km high pressure pipelines and 250,000 Km distribution networks). Furthermore in recent years National Iranian Gas Company is planning to develop natural gas network to cover all cities and villages above 20 families, in a way that 97 percent of Iran population will be gas consumer by 2020. In this condition, network resiliency will be the first priority of NIGC and due to that several planning for increasing resiliency of gas network is under construction. The most important strategy of NIGC is converting tree form pattern network to loop gas networks and developing underground gas storage near main gas consuming centers. In this regard NIGC is planning for construction of over 3500 km high-pressure pipeline and also 10 TCM gas storage capacities in UGSs.

Keywords: Iranian gas network, peak shaving, resiliency, underground gas storage

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Authors: Abdelfatah Elsenosy, Mahmoud Ebrahim

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Background: Distal radius fractures are commonly encountered in orthopedic practice, especially in elderly patients. A number of clinical papers have supported the idea that anatomic restoration of the distal end of the radius is essential to gain superior results. Aim and objectives: The aim of the study is to systematically review the literature for the management of distal end radius in elderly persons (conservative treatment versus percutaneous wire fixation) as regards radiological and functional outcomes. Subjects and methods: Studies were identified from the Medline, Cochrane, EMBASE, and Google Scholar databases were searched until 2019 using combinations of the following search terms: distal radius fracture, conservative treatment, non-operative treatment, and nonsurgical treatment, surgical treatment, operative, elderly, and older. Reference lists of relevant studies were manually searched. Results: There was no statistical significance difference between CI and PKF groups’ frequency of complication in all of the selected studies. Based on the results, we recommend more analysis regarding every parameter of the radiographic and functional results and specific complications related to each fixation need to be accomplished, which requires more Randomized controlled trials (RCTs) with high quality. Conclusion: Surgical treatment seems to be more effective distal radius fracture compared with conservative treatment when the radiographic outcomes were analyzed, and no significant differences were detected in the functional outcomes and complication rate.

Keywords: radius, fracture, surgical, RCTs, conservative, radiographic, outcomes, orthopedic

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Authors: P. Panwalkar, K. Veravalli, M. Tofighi, A. Mofidi

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Introduction: Anterior femoral perforation or distal anterior nail position is a known complication of femoral nailing specifically in pertrochantric fractures fixed with cephalomedullary nail. This has been attributed to wrong entry point for the femoral nail, nail with large radius of curvature or malreduced fracture. Left alone anterior perforation of femur or abutment of nail on anterior femur will result in pain and risk stress riser at distal femur and periprosthetic fracture. There have been multiple techniques described to avert or correct this problem ranging from using different nail, entry point change, poller screw to deflect the nail position, use of shorter nail or use of curved guidewire or change of nail to ensure a nail with large radius of curvature Methods: We present this technique which we have used in order to centralise the femoral nail either when the nail has been put anteriorly or when the guide wire has been inserted too anteriorly prior to the insertion of the nail. This technique requires the use of femoral reduction spool from the nailing set. This technique was used by eight trainees of different level of experience under supervision. Results: This technique was easily reproducible without any learning curve without a need for opening of fracture site or change in the entry point with three different femoral nailing sets in twenty-five cases. The process took less than 10 minutes even when revising a malpositioned femoral nail. Conclusion: Our technique of using femoral reduction spool is easily reproducible and repeatable technique for avoidance of non-centralised femoral nail insertion and distal anterior perforation of femoral nail.

Keywords: femoral fracture, nailing, malposition, surgery

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Authors: Hong Li, Haiyang Yu, Shiqing Cheng, Nai Cao, Zhiliang Shi

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Unconventional resources have gradually become the main direction for oil and gas exploration and development. However, the productivity of gas wells, the level of water production, and the seepage law in tight fractured gas reservoirs are very different. These are the reasons why production prediction is so difficult. Firstly, a three-dimensional multi-scale fracture and multiphase mathematical model based on an embedded discrete fracture model (EDFM) is established. And the material balance method is used to calculate the water body multiple according to the production performance characteristics of water-producing gas well. This will help construct a 'virtual water body'. Based on these, this paper presents a numerical simulation process that can adapt to different production modes of gas wells. The research results show that fractures have a double-sided effect. The positive side is that it can increase the initial production capacity, but the negative side is that it can connect to the water body, which will lead to the gas production drop and the water production rise both rapidly, showing a 'scissor-like' characteristic. It is worth noting that fractures with different angles have different abilities to connect with the water body. The higher the angle of gas well development, the earlier the water maybe break through. When the reservoir is a single layer, there may be a stable production period without water before the fractures connect with the water body. Once connected, a 'scissors shape' will appear. If the reservoir has multiple layers, the gas and water will produce at the same time. The above gas-water relationship can be matched with the gas well production date of the Xujiahe gas reservoir in the Sichuan Basin. This method is used to predict the productivity of a well with hydraulic fractures in this gas reservoir, and the prediction results are in agreement with on-site production data by more than 90%. It shows that this research idea has great potential in the productivity prediction of water-producing gas wells. Early prediction results are of great significance to guide the design of development plans.

Keywords: EDFM, multiphase, multilayer, water body

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Authors: Chong Wang, Kellem M. Soares, Luis E. Kosteski

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The problem of toughening in brittle materials reinforced by fibers is complex, involving all the mechanical properties of fibers, matrix, the fiber/matrix interface, as well as the geometry of the fiber. An appropriate method applicable to the simulation and analysis of toughening is essential. In this work, we performed simulations and analysis of toughening in brittle matrix reinforced by randomly distributed fibers by means of the discrete elements method. At first, we put forward a mechanical model of the contribution of random fibers to the toughening of composite. Then with numerical programming, we investigated the stress, damage and bridging force in the composite material when a crack appeared in the brittle matrix. From the results obtained, we conclude that: (i) fibers with high strength and low elasticity modulus benefit toughening; (ii) fibers with relatively high elastic modulus compared to the matrix may result in considerable matrix damage (spalling effect); (iii) employment of high-strength synthetic fiber is a good option. The present work makes it possible to optimize the parameters in order to produce advanced ceramic with desired performance. We believe combination of the discrete element method (DEM) with the finite element method (FEM) can increase the versatility and efficiency of the software developed.

Keywords: bridging stress, discrete element method, fiber reinforced composites, toughening

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Authors: Taiki Baba, Tomoaki Hashimoto

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The random dither quantization method enables us to achieve much better performance than the simple uniform quantization method for the design of quantized control systems. Motivated by this fact, the stochastic model predictive control method in which a performance index is minimized subject to probabilistic constraints imposed on the state variables of systems has been proposed for linear feedback control systems with random dither quantization. In other words, a method for solving optimal control problems subject to probabilistic state constraints for linear discrete-time control systems with random dither quantization has been already established. To our best knowledge, however, the feasibility of such a kind of optimal control problems has not yet been studied. Our objective in this paper is to investigate the feasibility of stochastic model predictive control problems for linear discrete-time control systems with random dither quantization. To this end, we provide the results of numerical simulations that verify the feasibility of stochastic model predictive control problems for linear discrete-time control systems with random dither quantization.

Keywords: model predictive control, stochastic systems, probabilistic constraints, random dither quantization

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Authors: Motonobu Hattori

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In neural networks, when new patters are learned by a network, they radically interfere with previously stored patterns. This drawback is called catastrophic forgetting. We have already proposed a biologically inspired dual-network memory model which can much reduce this forgetting for static patterns. In this model, information is first stored in the hippocampal network, and thereafter, it is transferred to the neocortical network using pseudo patterns. Because, temporal sequence learning is more important than static pattern learning in the real world, in this study, we improve our conventional dual-network memory model so that it can deal with temporal sequences without catastrophic forgetting. The computer simulation results show the effectiveness of the proposed dual-network memory model.

Keywords: catastrophic forgetting, dual-network, temporal sequences, hippocampal

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Authors: Benzerara Mohammed, Redjel Bachir, Kebaili Bachir

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The cracking of the concrete is a more crucial problem with the development of the complex structures related to technological progress. The projections in the knowledge of the breaking process make it possible today for better prevention of the risk of the fracture. The breaking strength brutal of a quasi-fragile material like the concrete called Toughness, is measured by a breaking value of the factor of intensity of the constraints K1C for which the crack is propagated, it is an intrinsic property of material. Many studies reported in the literature treating of the concrete were carried out on specimens which are in fact inadequate compared to the intrinsic characteristic to identify. We started from this established fact, in order to compare the evolution of the parameter of toughness K1C measured by calling upon ordinary concrete specimens of three prismatics geometries different (10*10*84) cm³ and (5*20*120) cm³ &(12*20*120) cm³ containing from the side notches various depths simulating of the cracks was set up. The notches are carried out using triangular pyramidal plates into manufactured out of sheet coated placed at the centre of the specimens at the time of the casting, then withdrawn to leave the trace of a crack. The tests are carried out in 3 points bending test in mode 1 of fracture, by using the techniques of mechanical fracture. The evolution of the parameter of toughness K1C measured with the three geometries specimens gives almost the same results. They are acceptable and return in the beach of the results determined by various researchers (toughness of the ordinary concrete turns to the turn of the 1 MPa √m). These results inform us about the presence of an economy on the level of the geometrie specimen (5*20*120) cm³, therefore to use plates specimens later if one wants to master the toughness of this material complexes, astonishing but always essential that is the concrete.

Keywords: elementary representative volume, concrete, fissure, toughness

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Authors: Min Jindong, Wang Mingxia

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With the widespread use of artificial intelligence in life, computer vision, especially deep convolutional neural network models, has developed rapidly. With the increase of the complexity of the real visual target detection task and the improvement of the recognition accuracy, the target detection network model is also very large. The huge deep neural network model is not conducive to deployment on edge devices with limited resources, and the timeliness of network model inference is poor. In this paper, knowledge distillation is used to compress the huge and complex deep neural network model, and the knowledge contained in the complex network model is comprehensively transferred to another lightweight network model. Different from traditional knowledge distillation methods, we propose a novel knowledge distillation that incorporates multi-faceted features, called M-KD. In this paper, when training and optimizing the deep neural network model for target detection, the knowledge of the soft target output of the teacher network in knowledge distillation, the relationship between the layers of the teacher network and the feature attention map of the hidden layer of the teacher network are transferred to the student network as all knowledge. in the model. At the same time, we also introduce an intermediate transition layer, that is, an intermediate guidance layer, between the teacher network and the student network to make up for the huge difference between the teacher network and the student network. Finally, this paper adds an exploration module to the traditional knowledge distillation teacher-student network model. The student network model not only inherits the knowledge of the teacher network but also explores some new knowledge and characteristics. Comprehensive experiments in this paper using different distillation parameter configurations across multiple datasets and convolutional neural network models demonstrate that our proposed new network model achieves substantial improvements in speed and accuracy performance.

Keywords: object detection, knowledge distillation, convolutional network, model compression

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Authors: Gunasekaran Raja, Ramkumar Jayaraman, Rajakumar Arul, Kottilingam Kottursamy

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Broadband Wireless Network (BWN) is the promising technology nowadays due to the increased number of smartphones. Buffering scheme using network coding considers the reliability and proper degree distribution in Worldwide interoperability for Microwave Access (WiMAX) multi-hop network. Using network coding, a secure way of transmission is performed which helps in improving throughput and reduces the packet loss in the multicast network. At the outset, improved network coding is proposed in multicast wireless mesh network. Considering the problem of performance overhead, degree distribution makes a decision while performing buffer in the encoding / decoding process. Consequently, BuS (Buffer Scheme) based on network coding is proposed in the multi-hop network. Here the encoding process introduces buffer for temporary storage to transmit packets with proper degree distribution. The simulation results depend on the number of packets received in the encoding/decoding with proper degree distribution using buffering scheme.

Keywords: encoding and decoding, buffer, network coding, degree distribution, broadband wireless networks, multicast

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Authors: Iliya Musa Adamu

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This paper seeks to develop an expert system that would troubleshoot computer network and evaluate the network system performance so as to reduce the workload on technicians and increase the efficiency and effectiveness of solutions proffered to computer network problems. The platform of the system was developed using ASP.NET, whereas the codes are implemented in Visual Basic and integrated with SQL Server 2005. The knowledge base was represented using production rule, whereas the searching method that was used in developing the network troubleshooting expert system is the forward-chaining-rule-based-system. This software tool offers the advantage of providing an immediate solution to most computer network problems encountered by computer users.

Keywords: expert system, forward chaining rule based system, network, troubleshooting

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Authors: Madushani Rodrigo, Banuka Athuraliya

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In today's world of medical diagnosis and prediction, machine learning stands out as a strong tool, transforming old ways of caring for health. This study analyzes the use of machine learning in the specialized domain of sports medicine, with a focus on the timely and accurate detection of bone fractures in cricket athletes. Failure to identify bone fractures in real time can result in malunion or non-union conditions. To ensure proper treatment and enhance the bone healing process, accurately identifying fracture locations and types is necessary. When interpreting X-ray images, it relies on the expertise and experience of medical professionals in the identification process. Sometimes, radiographic images are of low quality, leading to potential issues. Therefore, it is necessary to have a proper approach to accurately localize and classify fractures in real time. The research has revealed that the optimal approach needs to address the stated problem and employ appropriate radiographic image processing techniques and object detection algorithms. These algorithms should effectively localize and accurately classify all types of fractures with high precision and in a timely manner. In order to overcome the challenges of misidentifying fractures, a distinct model for fracture localization and classification has been implemented. The research also incorporates radiographic image enhancement and preprocessing techniques to overcome the limitations posed by low-quality images. A classification ensemble model has been implemented using ResNet18 and VGG16. In parallel, a fracture segmentation model has been implemented using the enhanced U-Net architecture. Combining the results of these two implemented models, the FracXpert system can accurately localize exact fracture locations along with fracture types from the available 12 different types of fracture patterns, which include avulsion, comminuted, compressed, dislocation, greenstick, hairline, impacted, intraarticular, longitudinal, oblique, pathological, and spiral. This system will generate a confidence score level indicating the degree of confidence in the predicted result. Using ResNet18 and VGG16 architectures, the implemented fracture segmentation model, based on the U-Net architecture, achieved a high accuracy level of 99.94%, demonstrating its precision in identifying fracture locations. Simultaneously, the classification ensemble model achieved an accuracy of 81.0%, showcasing its ability to categorize various fracture patterns, which is instrumental in the fracture treatment process. In conclusion, FracXpert has become a promising ML application in sports medicine, demonstrating its potential to revolutionize fracture detection processes. By leveraging the power of ML algorithms, this study contributes to the advancement of diagnostic capabilities in cricket athlete healthcare, ensuring timely and accurate identification of bone fractures for the best treatment outcomes.

Keywords: multiclass classification, object detection, ResNet18, U-Net, VGG16

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Authors: Zhaojunfeng

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Driven by the national policy of "East Data and Western Calculation", the computing first network will attract a new wave of development. As the foundation of the development of the computing first network, the computing force bearer network has become the key direction of technology research and development in the industry. This article will analyze typical computing force application scenarios and bearing requirements and sort out the SLA indicators of computing force applications. On this basis, this article carries out research and discussion on the key technologies of computing force bearer network in a slice packet network, and finally, gives evolution policy for SPN computing force bearer network to support the development of SPN computing force bearer network technology and network deployment.

Keywords: component-computing force bearing, bearing requirements of computing force application, dual-SLA indicators for computing force applications, SRv6, evolution strategies

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Authors: W. Oudad, H. Fikirini, K. Boulenouar

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Material fracture by opening (mode I) is not alone responsible for fracture propagation. Many industrial examples show the presence of mode II and mixed mode I + II. In the present work the three-dimensional and non-linear finite element method is used to estimate the performance of the bonded composite repair of metallic aircraft structures by analyzing the plastic zone size ahead of repaired cracks under mixed mode loading. The computations are made according to Von Mises and Tresca criteria. The extension of the plastic zone which takes place at the tip of a crack strictly depends on many variables, such as the yield stress of the material, the loading conditions, the crack size and the thickness of the cracked component, The obtained results show that the presence of the composite patch reduces considerably the size of the plastic zone ahead of the crack. The effects of the composite orientation layup (adhesive properties) and the patch thickness on the plastic zone size ahead of repaired cracks were analyzed.

Keywords: crack, elastic-plastic, J integral, patch, plastic zone

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Authors: Abdelhadi Lotfi, Abdelkader Benyettou

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In this work, a training algorithm for probabilistic neural networks (PNN) is presented. The algorithm addresses one of the major drawbacks of PNN, which is the size of the hidden layer in the network. By using a cross-validation training algorithm, the number of hidden neurons is shrunk to a smaller number consisting of the most representative samples of the training set. This is done without affecting the overall architecture of the network. Performance of the network is compared against performance of standard PNN for different databases from the UCI database repository. Results show an important gain in network size and performance.

Keywords: classification, probabilistic neural networks, network optimization, pattern recognition

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Authors: Mohamed Raouf Benmakrelouf, Wafa Karouche, Joseph Rynkiewicz

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In this paper, we propose an alternative method to construct a Bayesian Network (BN). This method relies on a convolutional neural network (CNN classifier), which determinates the edges of the network skeleton. We train a CNN on a normalized empirical probability density distribution (NEPDF) for predicting causal interactions and relationships. We have to find the optimal Bayesian network structure for causal inference. Indeed, we are undertaking a search for pair-wise causality, depending on considered causal assumptions. In order to avoid unreasonable causal structure, we consider a blacklist and a whitelist of causality senses. We tested the method on real data to assess the influence of education on the voting intention for the extreme right-wing party. We show that, with this method, we get a safer causal structure of variables (Bayesian Network) and make to identify a variable that satisfies the backdoor criterion.

Keywords: Bayesian network, structure learning, optimal search, convolutional neural network, causal inference

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Authors: Samba Raju, Chiluveru, Manoj Tripathy

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Intelligibility is an essential characteristic of a speech signal, which is used to help in the understanding of information in speech signal. Background noise in the environment can deteriorate the intelligibility of a recorded speech. In this paper, we presented a simple variance subtracted - variable level discrete wavelet transform, which improve the intelligibility of speech. The proposed algorithm does not require an explicit estimation of noise, i.e., prior knowledge of the noise; hence, it is easy to implement, and it reduces the computational burden. The proposed algorithm decides a separate decomposition level for each frame based on signal dominant and dominant noise criteria. The performance of the proposed algorithm is evaluated with speech intelligibility measure (STOI), and results obtained are compared with Universal Discrete Wavelet Transform (DWT) thresholding and Minimum Mean Square Error (MMSE) methods. The experimental results revealed that the proposed scheme outperformed competing methods

Keywords: discrete wavelet transform, speech intelligibility, STOI, standard deviation

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Authors: Christopher Godwin Udomboso, Angela Unna Chukwu, Isaac Kwame Dontwi

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In selecting a Statistical Neural Network model, the Network Information Criterion (NIC) has been observed to be sample biased, because it does not account for sample sizes. The selection of a model from a set of fitted candidate models requires objective data-driven criteria. In this paper, we derived and investigated the Adjusted Network Information Criterion (ANIC), based on Kullback’s symmetric divergence, which has been designed to be an asymptotically unbiased estimator of the expected Kullback-Leibler information of a fitted model. The analyses show that on a general note, the ANIC improves model selection in more sample sizes than does the NIC.

Keywords: statistical neural network, network information criterion, adjusted network, information criterion, transfer function

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Authors: Mohammad Hossein Ahmadi, Vahid Haghighatdoost

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Spatial databases have been an active area of research over years. In this paper, we study how to answer the General Time-Dependent Sequenced Route queries. Given the origin and destination of a user over a time-dependent road network graph, an ordered list of categories of interests and a departure time interval, our goal is to find the minimum travel time path along with the best departure time that minimizes the total travel time from the source location to the given destination passing through a sequence of points of interests belonging to each of the specified categories of interest. The challenge of this problem is the added complexity to the optimal sequenced route queries, where we assume that first the road network is time dependent, and secondly the user defines a departure time interval instead of one single departure time instance. For processing general time-dependent sequenced route queries, we propose two solutions as Discrete-Time and Continuous-Time Sequenced Route approaches, finding approximate and exact solutions, respectively. Our proposed approaches traverse the road network based on A*-search paradigm equipped with an efficient heuristic function, for shrinking the search space. Extensive experiments are conducted to verify the efficiency of our proposed approaches.

Keywords: trip planning, time dependent, sequenced route query, road networks

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Authors: Ahmed. S. Alasmari, M. S. Soliman, Magdy M. El-Rayes

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This study focuses on the effect of the addition of magnesium (Mg) and silver (Ag) on the mechanical properties of aluminum based alloys. The alloying elements will be added at different levels using the factorial design of experiments of 2²; the two factors are Mg and Ag at two levels of concentration. The superior mechanical properties of the produced Al-Cu-Mg-Ag alloys after aging will be resulted from a unique type of precipitation named as Ω-phase. The formed precipitate enhanced the tensile strength and thermal stability. This paper further investigated the microstructure and mechanical properties of as cast Al–Cu–Mg–Ag alloys after being complete homogenized treatment at 520 °C for 8 hours followed by isothermally age hardening process at 190 °C for different periods of time. The homogenization at 520 °C for 8 hours was selected based on homogenization study at various temperatures and times. The alloys’ microstructures were studied by using optical microscopy (OM). In addition to that, the fracture surface investigation was performed using a scanning electronic microscope (SEM). Studying the microstructure of aged Al-Cu-Mg-Ag alloys reveal that the grains are equiaxed with an average grain size of about 50 µm. A detailed fractography study for fractured surface of the aged alloys exhibited a mixed fracture whereby the random fracture suggested crack propagation along the grain boundaries while the dimples indicated that the fracture was ductile. The present result has shown that alloy 5 has the highest hardness values and the best mechanical behaviors.

Keywords: precipitation hardening, aluminum alloys, aging, design of experiments, analysis of variance, heat treatments

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Authors: Smail Tigani, Mohamed Ouzzif

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This work presents a discrete quantitative state prediction algorithm with intelligent behavior making it able to self-improve some performance aspects. The specificity of this algorithm is the capacity of self-rectification of the prediction strategy before the final decision. The auto-rectification mechanism is based on two parallel mathematical models. In one hand, the algorithm predicts the next state based on event transition matrix updated after each observation. In the other hand, the algorithm extracts its residues trend with a linear regression representing historical residues data-points in order to rectify the first decision if needs. For a normal distribution, the interactivity between the two models allows the algorithm to self-optimize its performance and then make better prediction. Designed key performance indicator, computed during a Monte Carlo simulation, shows the advantages of the proposed approach compared with traditional one.

Keywords: discrete state, Markov Chains, linear regression, auto-adaptive systems, decision making, Monte Carlo Simulation

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Authors: Eleni Tsangouri, Xander Hillewaere, David Garoz Gómez, Dimitrios Aggelis, Filip Du Prez, Danny Van Hemelrijck

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Tapered Double Cantilever Beam (TDCB) is the most commonly used test set-up to evaluate the self-healing feature of thermoset polymers autonomously activated in the presence of crack. TDCB is a modification of the established fracture mechanics set-up of Double Cantilever Beam and is designed to provide constant strain energy release rate with crack length under stable load evolution (mode-I). In this study, the damage of virgin and autonomously healed TDCB polymer samples is evaluated considering the load-crack opening diagram, the strain maps provided by Digital Image Correlation technique and the fractography maps given by optical microscopy. It is shown that the pre-crack introduced prior to testing (razor blade tapping), the loading rate and the length of the side groove are the features that dominate the crack propagation and lead to inconstant fracture energy release rate.

Keywords: polymers, autonomous healing, fracture, tapered double cantilever beam

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Authors: Jamal Alasiri, Naif Hakeem, Saoud Almaslmani

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Background: Patients with humeral shaft fractures have two different treatment options. Surgical therapy has lesser risks of non-union, mal-union, and re-intervention than non-surgical therapy. These positive clinical outcomes of the surgical approach make it a preferable treatment option despite the risks of radial nerve palsy and additional surgery-related risk. We aimed to evaluate patients’ outcomes and return to work after surgical vs. non-surgical management of shaft humeral fracture. Methods: We used databases, including PubMed, Medline, and Cochrane Register of Controlled Trials, from 2010 to January 2022 to search for potential randomised controlled trials (RCTs) and cohort studies comparing the patients’ related outcome measures and return to work between surgical and non-surgical management of humerus fracture. Results: After carefully evaluating 1352 articles, we included three RCTs (232 patients) and one cohort study (39 patients). The surgical intervention used plate/nail fixation, while the non-surgical intervention used a splint or brace procedure to manage shaft humeral fracture. The pooled DASH effects of all three RCTs at six (M.D: -7.5 [-13.20, -1.89], P: 0.009) I2:44%) and 12 months (M.D: -1.32 [-3.82, 1.17], p:0.29, I2: 0%) were higher in patients treated surgically than in non-surgical procedures. The pooled constant Murley score at six (M.D: 7.945[2.77,13.10], P: 0.003) I2: 0%) and 12 months (M.D: 1.78 [-1.52, 5.09], P: 0.29, I2: 0%) were higher in patients who received non-surgical than surgical therapy. However, pooled analysis for patients returning to work for both groups remained inconclusive. Conclusion: Altogether, we found no significant evidence supporting the clinical benefits of surgical over non-surgical therapy. Thus, the non-surgical approach remains the preferred therapeutic choice for managing shaft humeral fractures due to its lesser side effects.

Keywords: shaft humeral fracture, surgical treatment, Patient-related outcomes, return to work, DASH

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Authors: Navaneeth Krishnan, Chandan N. Bhagwat, Aparajit P. Utpat

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Cloud Computing provides us a means to upload data and use applications over the internet. As the number of devices connecting to the cloud grows, there is undue pressure on the cloud infrastructure. Fog computing or Network Based Computing or Edge Computing allows to move a part of the processing in the cloud to the network devices present along the node to the cloud. Therefore the nodes connected to the cloud have a better response time. This paper proposes a method of moving the computation from the cloud to the network by introducing an android like appstore on the networking devices.

Keywords: cloud computing, fog computing, network devices, appstore

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Authors: M. Kollar, A. Zieba

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In this paper, we present a method for a time synchronization between the two eNodeBs (eNBs) in E-UTRAN (Evolved Universal Terrestrial Radio Access) network. The two eNBs are cooperating in so-called inter eNB CA (Carrier Aggregation) case and connected via asymmetrical IP network. We solve the problem by using broadcasting signals generated in E-UTRAN as synchronization signals. The results show that the time synchronization with the proposed method is possible with the error significantly less than 1 ms which is sufficient considering the time transmission interval is 1 ms in E-UTRAN. This makes this method (with low complexity) more suitable than Network Time Protocol (NTP) in the mobile applications with generated broadcasting signals where time synchronization in asymmetrical network is required.

Keywords: IP scheduled throughput, E-UTRAN, Evolved Universal Terrestrial Radio Access Network, NTP, Network Time Protocol, assymetric network, delay

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Authors: Kolesnik Nadezda A.

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The research aims to elaborate inter-organizational network relationships management model to maximize value co-creation. We propose a network management framework that requires evaluation of network partners with respect to their position and role in network; and elaboration of appropriate relationship development strategy with partners in network. Empirical research and approval is based on the case study method, including structured in-depth interviews with the companies from b2b market.

Keywords: inter-organizational networks, value co-creation, model, B2B market

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Authors: Tarek Duzan

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Fractures are a fundamental property of most reservoirs. Despite their abundance, they remain difficult to detect and quantify. The most effective characterization of fractured reservoirs is accomplished by integrating geological, geophysical, and engineering data. Detection of fractures and defines their relative contribution is crucial in the early stages of exploration and later in the production of any field. Because fractures could completely change our thoughts, efforts, and planning to produce a specific field properly. From the structural point of view, all reservoirs are fractured to some point of extent. North Gialo field is thought to be a naturally fractured reservoir to some extent. Historically, natural fractured reservoirs are more complicated in terms of their exploration and production efforts, and most geologists tend to deny the presence of fractures as an effective variable. Our aim in this paper is to determine the degree of fracturing, and consequently, our evaluation and planning can be done properly and efficiently from day one. The challenging part in this field is that there is no enough data and straightforward well testing that can let us completely comfortable with the idea of fracturing; however, we cannot ignore the fractures completely. Logging images, available well testing, and limited core studies are our tools in this stage to evaluate, model, and predict possible fracture effects in this reservoir. The aims of this study are both fundamental and practical—to improve the prediction and diagnosis of natural-fracture attributes in N. Gialo hydrocarbon reservoirs and accurately simulate their influence on production. Moreover, the production of this field comes from 2-phase plan; a self depletion of oil and then gas injection period for pressure maintenance and increasing ultimate recovery factor. Therefore, well understanding of fracturing network is essential before proceeding with the targeted plan. New analytical methods will lead to more realistic characterization of fractured and faulted reservoir rocks. These methods will produce data that can enhance well test and seismic interpretations, and that can readily be used in reservoir simulators.

Keywords: natural fracture, sandstone reservoir, geological, geophysical, and engineering data

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Authors: A. Bere, H. G. Sithuba, K. Kyei, C. Sigauke

Abstract:

We employ a discrete logit survival model to investigate the risk factors for early alcohol intake among students at two tertiary institutions in Thohoyandou, South Africa. Data were collected from a sample of 744 students using a self-administered questionnaire. Significant covariates were arrived at through a regularization algorithm implemented using the glmmLasso package. The tuning parameter was determined using a five-fold cross-validation algorithm. The baseline hazard was modelled as a smooth function of time through the use of spline functions. The results show that the hazard of initial alcohol intake peaks at the age of about 16 years and that at any given time, being of a male gender, prior use of other drugs, having drinking peers, having experienced negative life events and physical abuse are associated with a higher risk of alcohol intake debut.

Keywords: cross-validation, discrete hazard model, LASSO, smooth baseline hazard

Procedia PDF Downloads 161

Authors: Sourour Ramzi, Claudia Sarrico

Abstract:

Little has been done on network DEA in education, and nobody has attempted to model the whole education supply chain using network DEA. As such the contribution of the present paper is to propose a model for measuring the efficiency of education supply chains using network DEA. First, we use a general survey of data envelopment analysis (DEA) to establish the emergent themes for research in DEA, and focus on the theme of Network DEA. Second, we use a survey on two-stage DEA models, and Network DEA to write a state of the art on Network DEA, particularly applied to supply chain management. Third, we use a survey on DEA applications to establish the most influential papers on DEA education applications, in order to establish the state of the art on applications of DEA in education, in general, and applications of DEA to education using network DEA, in particular. Finally, we propose a model for measuring the performance of education supply chains of different education systems (countries or states within a country, for instance). We then use this model on some empirical data.

Keywords: supply chain, education, data envelopment analysis, network DEA

Procedia PDF Downloads 346