Search results for: improved sparrow search algorithm

Authors: Alpesh Adeshara, Rajendrasinh Jadeja, Praghnesh Bhatt

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Implementation of advanced technologies requires sophisticated instruments that deal with the operation, control, restoration and protection of rapidly growing power system network under normal and abnormal conditions. Presently, the applications of Phasor Measurement Unit (PMU) are widely found in real time operation, monitoring, controlling and analysis of power system network as it eliminates the various limitations of Supervisory Control and Data Acquisition System (SCADA) conventionally used in power system. The use of PMU data is very rapidly increasing its importance for online and offline analysis. Wide Area Measurement System (WAMS) is developed as new technology by use of multiple PMUs in power system. The present paper proposes a model of MATLAB based PMU using Discrete Fourier Transform (DFT) algorithm and evaluation of its operation under different contingencies. In this paper, PMU based two bus system having WAMS network is presented as a case study.

Keywords: GPS global positioning system, PMU phasor measurement system, WAMS wide area monitoring system, DFT, PDC

Procedia PDF Downloads 496

Authors: Divya Agarwal, Pushpendra S. Bharti

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Autonomous mobile robots (AMR) are expected as smart tools for operations in every automation industry. Path planning and obstacle avoidance is the backbone of AMR as robots have to reach their goal location avoiding obstacles while traversing through optimized path defined according to some criteria such as distance, time or energy. Path planning can be classified into global and local path planning where environmental information is known and unknown/partially known, respectively. A number of sensors are used for data collection. A number of algorithms such as artificial potential field (APF), rapidly exploring random trees (RRT), bidirectional RRT, Fuzzy approach, Purepursuit, A* algorithm, vector field histogram (VFH) and modified local path planning algorithm, etc. have been used in the last three decades for path planning and obstacle avoidance for AMR. This paper makes an attempt to review some of the path planning and obstacle avoidance algorithms used in the field of AMR. The review includes comparative analysis of simulation and mathematical computations of path planning and obstacle avoidance algorithms using MATLAB 2018a. From the review, it could be concluded that different algorithms may complete the same task (i.e. with a different set of instructions) in less or more time, space, effort, etc.

Keywords: path planning, obstacle avoidance, autonomous mobile robots, algorithms

Procedia PDF Downloads 232

Authors: Kahlouche Amal, Touzene F. Zohra, Betatache Fatihaet Nouani Abdelouahab

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The development of the world production of the cheese these last decades, as well as agents' greater request cheap coagulants, accentuated the search for new surrogates of the rennet. What about the interest to explore the vegetable biodiversity, the source well cheap of many naturals metabolites that the scientists today praise it (thistle, latex of fig tree, Cardoon, seeds of melon). Indeed, a big interest is concerned the search for surrogates of vegetable origin. The objective of the study is to show the possibility of extracting a protease coagulant the milk from the cake of Sunflower, available raw material and the potential source of surrogates of rennet. so, the determination of the proteolytic activity of raw extracts, the purification, the elimination of the pigments of tint of the enzymatic preparations, a better knowledge of the coagulative properties through study of the effect of certain factors (temperature, pH, concentration in CaCl2) are so many factors which contribute to value milk particularly those produced by the small ruminants of the Algerian dairy exploitations. Otherwise, extracts coagulants of vegetable origin allowed today to value traditional, in addition, although the extract coagulants of vegetable origin made it possible today to develop traditional cheeses whose Iberian peninsula is the promoter, but the test of 'pressed paste not cooked' cheese manufacturing led to the semi-scale pilot; and that, by using the enzymatic extract of sunflower (Helianthus annus) which gave satisfactory results as well to the level of outputs as on the sensory level,which, statistically,did not give any significant difference between studied cheeses. These results confirm the possibility of use of this coagulase as a substitute of rennet commercial on an industrial scale.

Keywords: characterization, cheese, Rennet, sunflower

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Authors: Fahd Sabry Esmail, M. Badr Senousy, Mohamed Ragaie

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In recent years, there has been an explosion in the rate of using technology that help discovering the diseases. For example, DNA microarrays allow us for the first time to obtain a "global" view of the cell. It has great potential to provide accurate medical diagnosis, to help in finding the right treatment and cure for many diseases. Various classification algorithms can be applied on such micro-array datasets to devise methods that can predict the occurrence of Leukemia disease. In this study, we compared the classification accuracy and response time among eleven decision tree methods and six rule classifier methods using five performance criteria. The experiment results show that the performance of Random Tree is producing better result. Also it takes lowest time to build model in tree classifier. The classification rules algorithms such as nearest- neighbor-like algorithm (NNge) is the best algorithm due to the high accuracy and it takes lowest time to build model in classification.

Keywords: data mining, classification techniques, decision tree, classification rule, leukemia diseases, microarray data

Procedia PDF Downloads 320

Authors: R. Rajabpour, N. Talebbeydokhti, M. H. Ahmadi

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Carefully scheduling the operations of pumps can be resulted to significant energy savings. Schedules can be defined either implicit, in terms of other elements of the network such as tank levels, or explicit by specifying the time during which each pump is on/off. In this study, two new explicit representations based on time-controlled triggers were analyzed, where the maximum number of pump switches was established beforehand, and the schedule may contain fewer switches than the maximum. The optimal operation of pumping stations was determined using a Jumping Particle Swarm Optimization (JPSO) algorithm to achieve the minimum energy cost. The model integrates JPSO optimizer and EPANET hydraulic network solver. The optimal pump operation schedule of VanZyl water distribution system was determined using the proposed model and compared with those from Genetic and Ant Colony algorithms. The results indicate that the proposed model utilizing the JPSP algorithm outperformed the others and is a versatile management model for the operation of real-world water distribution system.

Keywords: JPSO, operation, optimization, water distribution system

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Authors: Ovidiu Domşa, Nicolae Bold

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Using Information and Communication Technologies (ICT) notions in education and three basic processes of education (teaching, learning and assessment) can bring benefits to the pupils and the professional development of teachers. In this matter, we refer to these notions as concepts taken from the informatics area and apply them to the domain of education. These notions refer to genetic algorithms and arborescent structures, used in the specific process of assessment or evaluation. This paper uses these kinds of notions to generate subtrees from a main tree of tests related between them by their degree of difficulty. These subtrees must contain the highest number of connections between the nodes and the lowest number of missing edges (which are subtrees of the main tree) and, in the particular case of the non-existence of a subtree with no missing edges, the subtrees which have the lowest (minimal) number of missing edges between the nodes, where a node is a test and an edge is a direct connection between two tests which differs by one degree of difficulty. The subtrees are represented as sequences. The tests are the same (a number coding a test represents that test in every sequence) and they are reused for each sequence of tests.

Keywords: chromosome, genetic algorithm, subtree, test

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Authors: Akhilesh Kumar, Sathyanarayan G., Nirmala S.

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An unusual approach is developed to determine the orbit of satellites/space objects. The determination of orbits is considered a boundary value problem and has been solved using the finite difference method (FDM). Only positions of the satellites/space objects are known at two end times taken as boundary conditions. The technique of finite difference has been used to calculate the orbit between end times. In this approach, the governing equation is defined as the satellite's equation of motion with a perturbed acceleration. Using the finite difference method, the governing equations and boundary conditions are discretized. The resulting system of algebraic equations is solved using Tri Diagonal Matrix Algorithm (TDMA) until convergence is achieved. This methodology test and evaluation has been done using all GPS satellite orbits from National Geospatial-Intelligence Agency (NGA) precise product for Doy 125, 2023. Towards this, two hours of twelve sets have been taken into consideration. Only positions at the end times of each twelve sets are considered boundary conditions. This algorithm is applied to all GPS satellites. Results achieved using FDM compared with the results of NGA precise orbits. The maximum RSS error for the position is 0.48 [m] and the velocity is 0.43 [mm/sec]. Also, the present algorithm is applied on the IRNSS satellites for Doy 220, 2023. The maximum RSS error for the position is 0.49 [m], and for velocity is 0.28 [mm/sec]. Next, a simulation has been done for a Highly Elliptical orbit for DOY 63, 2023, for the duration of 6 hours. The RSS of difference in position is 0.92 [m] and velocity is 1.58 [mm/sec] for the orbital speed of more than 5km/sec. Whereas the RSS of difference in position is 0.13 [m] and velocity is 0.12 [mm/sec] for the orbital speed less than 5km/sec. Results show that the newly created method is reliable and accurate. Further applications of the developed methodology include missile and spacecraft targeting, orbit design (mission planning), space rendezvous and interception, space debris correlation, and navigation solutions.

Keywords: finite difference method, grid generation, NavIC system, orbit perturbation

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Authors: Sally Kafelghazal

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Introduction: Arab Americans, who include immigrants, refugees, or U.S. born persons of Middle Eastern or North African descent, may experience significant difficulties during acculturation to Western society. Influential stressors include relocation, loss of social support, language barriers, and economic factors, all of which can impact mental health. There is limited research investigating the effects of acculturation on the mental health of the Arab American population. Objectives: The purpose of this study is to identify ways in which acculturation impacts the mental health of Arab Americans, specifically the development of depression and anxiety. Method: A literature search was conducted using PubMed and PsycArticles (ProQuest), utilizing the following search terms: “Arab Americans,” “Arabs,” “mental health,” “depression,” “anxiety,” “acculturation.” Thirty-nine articles were identified and of those, nine specifically investigated the relationship between acculturation and mental health in Arab Americans. Three of the nine focused exclusively on depression. Results: Several risk factors were identified that contribute to poor mental health associated with acculturation, which include immigrant or refugee status, facing discrimination, and religious ideology. Protective factors include greater levels of acculturation, being U.S. born, and greater heritage identity. Greater mental health disorders were identified in Arab Americans compared to normative samples, perhaps particularly depression; none of the articles specifically addressed anxiety. Conclusion: The current research findings support the potential association between the process of acculturation and greater levels of mental health disorders in Arab Americans. However, the diversity of the Arab American population makes it difficult to draw consistent conclusions. Further research needs to be conducted in order to assess which subgroups in the Arab American population are at highest risk for developing new or exacerbating existing mental health disorders in order to devise more effective interventions.

Keywords: arab americans, arabs, mental health, anxiety, depression, acculturation

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Authors: Julien Romieux, Fabio Verdicchio

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Wireless Sensor Networks (WSNs), which sense environmental data with battery-powered nodes, require multi-hop communication. This power-demanding task adds an extra workload that is unfairly distributed across the network. As a result, nodes run out of battery at different times: this requires an impractical individual node maintenance scheme. Therefore we investigate a new Cooperative Sensing approach that extends the WSN operational life and allows a more practical network maintenance scheme (where all nodes deplete their batteries almost at the same time). We propose a novel cooperative algorithm that derives a piecewise representation of the sensed signal while controlling approximation accuracy. Simulations show that our algorithm increases WSN operational life and spreads communication workload evenly. Results convey a counterintuitive conclusion: distributing workload fairly amongst nodes may not decrease the network power consumption and yet extend the WSN operational life. This is achieved as our cooperative approach decreases the workload of the most burdened cluster in the network.

Keywords: cooperative signal processing, signal representation and approximation, power management, wireless sensor networks

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Authors: Long Cheng, Tong He, Iraj Mantegh, Wen-Fang Xie

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Path planning is essential for UAVs (Unmanned Aerial Vehicle) with autonomous navigation in unknown environments. In this paper, an adaptive probabilistic sampling algorithm is proposed for the GPS-denied environment, which can be utilized for autonomous navigation system of multiple UAVs in a dynamically-changing structured environment. This method can be used for Unmanned Aircraft Systems Traffic Management (UTM) solutions and in autonomous urban aerial mobility, where a number of platforms are expected to share the airspace. A path network is initially built off line based on available environment map, and on-board sensors systems on the flying UAVs are used for continuous situational awareness and to inform the changes in the path network. Simulation results based on MATLAB and Gazebo in different scenarios and algorithms performance measurement show the high efficiency and accuracy of the proposed technique in unknown environments.

Keywords: path planning, adaptive probabilistic sampling, obstacle avoidance, multiple unmanned aerial vehicles, unknown environments

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Authors: Je-Seok Shin, Wook-Won Kim, Jin-O Kim

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The offshore wind farm needs to be designed carefully considering economics and reliability aspects. There are many decision-making problems for designing entire offshore wind farm, this paper focuses on an inner grid layout which means the connection between wind turbines as well as between wind turbines and an offshore substation. A methodology proposed in this paper determines the connections and the cable type for each connection section using K-clustering, minimum spanning tree and cable selection algorithms. And then, a cost evaluation is performed in terms of investment, power loss and reliability. Through the cost evaluation, an optimal layout of inner grid is determined so as to have the lowest total cost. In order to demonstrate the validity of the methodology, the case study is conducted on 240MW offshore wind farm, and the results show that it is helpful to design optimally offshore wind farm.

Keywords: offshore wind farm, optimal layout, k-clustering algorithm, minimum spanning algorithm, cable type selection, power loss cost, reliability cost

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Authors: Tingwei Shu, Dong Zhou, Chengjun Guo

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Semantic communication is an emerging form of communication that realize intelligent communication by extracting semantic information of data at the source and transmitting it, and recovering the data at the receiving end. It can effectively solve the problem of data transmission under the situation of large data volume, low SNR and restricted bandwidth. With the development of Deep Learning, semantic communication further matures and is gradually applied in the fields of the Internet of Things, Uumanned Air Vehicle cluster communication, remote sensing scenarios, etc. We propose an improved semantic communication system for the situation where the data volume is huge and the spectrum resources are limited during the transmission of remote sensing images. At the transmitting, we need to extract the semantic information of remote sensing images, but there are some problems. The traditional semantic communication system based on Convolutional Neural Network cannot take into account the global semantic information and local semantic information of the image, which results in less-than-ideal image recovery at the receiving end. Therefore, we adopt the improved vision-Transformer-based structure as the semantic encoder instead of the mainstream one using CNN to extract the image semantic features. In this paper, we first perform pre-processing operations on remote sensing images to improve the resolution of the images in order to obtain images with more semantic information. We use wavelet transform to decompose the image into high-frequency and low-frequency components, perform bilinear interpolation on the high-frequency components and bicubic interpolation on the low-frequency components, and finally perform wavelet inverse transform to obtain the preprocessed image. We adopt the improved Vision-Transformer structure as the semantic coder to extract and transmit the semantic information of remote sensing images. The Vision-Transformer structure can better train the huge data volume and extract better image semantic features, and adopt the multi-layer self-attention mechanism to better capture the correlation between semantic features and reduce redundant features. Secondly, to improve the coding efficiency, we reduce the quadratic complexity of the self-attentive mechanism itself to linear so as to improve the image data processing speed of the model. We conducted experimental simulations on the RSOD dataset and compared the designed system with a semantic communication system based on CNN and image coding methods such as BGP and JPEG to verify that the method can effectively alleviate the problem of excessive data volume and improve the performance of image data communication.

Keywords: semantic communication, transformer, wavelet transform, data processing

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Authors: Roza Afarin, Saeed Mozaffari

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The aim of this paper is image encryption using Genetic Algorithm (GA). The proposed encryption method consists of two phases. In modification phase, pixels locations are altered to reduce correlation among adjacent pixels. Then, pixels values are changed in the diffusion phase to encrypt the input image. Both phases are performed by GA with binary chromosomes. For modification phase, these binary patterns are generated by Local Binary Pattern (LBP) operator while for diffusion phase binary chromosomes are obtained by Bit Plane Slicing (BPS). Initial population in GA includes rows and columns of the input image. Instead of subjective selection of parents from this initial population, a random generator with predefined key is utilized. It is necessary to decrypt the coded image and reconstruct the initial input image. Fitness function is defined as average of transition from 0 to 1 in LBP image and histogram uniformity in modification and diffusion phases, respectively. Randomness of the encrypted image is measured by entropy, correlation coefficients and histogram analysis. Experimental results show that the proposed method is fast enough and can be used effectively for image encryption.

Keywords: correlation coefficients, genetic algorithm, image encryption, image entropy

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Authors: Enver Kucukkulahli, Pakize Erdogmus, Kemal Polat

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The 2D image segmentation is a significant process in finding a suitable region in medical images such as MRI, PET, CT etc. In this study, we have focused on 2D MRI images for image segmentation process. We have designed a GUI (graphical user interface) written in MATLABTM for 2D MRI images. In this program, there are two different interfaces including data pre-processing and image clustering or segmentation. In the data pre-processing section, there are median filter, average filter, unsharp mask filter, Wiener filter, and custom filter (a filter that is designed by user in MATLAB). As for the image clustering, there are seven different image segmentations for 2D MR images. These image segmentation algorithms are as follows: PSO (particle swarm optimization), GA (genetic algorithm), Lloyds algorithm, k-means, the combination of Lloyds and k-means, mean shift clustering, and finally BBO (Biogeography Based Optimization). To find the suitable cluster number in 2D MRI, we have designed the histogram based cluster estimation method and then applied to these numbers to image segmentation algorithms to cluster an image automatically. Also, we have selected the best hybrid method for each 2D MR images thanks to this GUI software.

Keywords: image segmentation, clustering, GUI, 2D MRI

Procedia PDF Downloads 377

Authors: Daniela V. Bavaresco, Tamy Colonetti, Antonio Jose Grande, Francesc Colom, Joao Quevedo, Samira S. Valvassori, Maria Ines da Rosa

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Objective: Performed a systematic review and meta-analysis to evaluated the potential effect of the cyclo-oxygenases (Cox)-2 inhibitor Celecoxib adjunct treatment in Bipolar Disorder (BD), through of randomized controlled trials. Method: A search of the electronic databases was proceeded, on MEDLINE, EMBASE, Scopus, Cochrane Central Register of Controlled Trials (CENTRAL), Biomed Central, Web of Science, IBECS, LILACS, PsycINFO (American Psychological Association), Congress Abstracts, and Grey literature (Google Scholar and the British Library) for studies published from January 1990 to February 2018. A search strategy was developed using the terms: 'Bipolar disorder' or 'Bipolar mania' or 'Bipolar depression' or 'Bipolar mixed' or 'Bipolar euthymic' and 'Celecoxib' or 'Cyclooxygenase-2 inhibitors' or 'Cox-2 inhibitors' as text words and Medical Subject Headings (i.e., MeSH and EMTREE) and searched. The therapeutic effects of adjunctive treatment with Celecoxib were analyzed, it was possible to carry out a meta-analysis of three studies included in the systematic review. The meta-analysis was performed including the final results of the Young Mania Rating Scale (YMRS) at the end of randomized controlled trials (RCT). Results: Three primary studies were included in the systematic review, with a total of 121 patients. The meta-analysis had significant effect in the YMRS scores from patients with BD who used Celecoxib adjuvant treatment in comparison to placebo. The weighted mean difference was 5.54 (95%CI=3.26-7.82); p < 0.001; I2 =0%). Conclusion: The systematic review suggests that adjuvant treatment with Celecoxib improves the response of major treatments in patients with BD when compared with adjuvant placebo treatment.

Keywords: bipolar disorder, Cox-2 inhibitors, Celecoxib, systematic review, meta-analysis

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Authors: Güzi̇n Ti̇rkeş, Özge Teki̇n, Kerem Kurtuluş, Y. Yekta Yurtseven, Murat Baran

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A scanning electron microscope (SEM) is a type of electron microscope mainly used in nanoscience and nanotechnology areas. Automatic image recognition and classification are among the general areas of application concerning SEM. In line with these usages, the present paper proposes a deep learning algorithm that classifies SEM images into nine categories by means of an online application to simplify the process. The NFFA-EUROPE - 100% SEM data set, containing approximately 21,000 images, was used to train and test the algorithm at 80% and 20%, respectively. Validation was carried out using a separate data set obtained from the Middle East Technical University (METU) in Turkey. To increase the accuracy in the results, the Inception ResNet-V2 model was used in view of the Fine-Tuning approach. By using a confusion matrix, it was observed that the coated-surface category has a negative effect on the accuracy of the results since it contains other categories in the data set, thereby confusing the model when detecting category-specific patterns. For this reason, the coated-surface category was removed from the train data set, hence increasing accuracy by up to 96.5%.

Keywords: convolutional neural networks, deep learning, image classification, scanning electron microscope

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Authors: Nima Amani, Ali Salehi Dastjerdi, Fatemeh Ahmadi, Ardalan Sabamehr

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The cost overrun of the construction projects has always been one of the main problems of the construction companies caused by the risky nature of the construction projects. Therefore, today, the application of risk management is inevitable. Although in theory, the issue of risk management is divided into the opportunities and threats management, in practice, most of the projects have been focused on the threats management. However, considering the opportunities management and applying the opportunities-response strategies can lead to the improved profitability of the construction projects of the companies. In this paper, a new technique is developed to identify the opportunities in the construction projects using an improved protocol and propose the appropriate opportunities-response strategies to the construction companies to provide them with higher profitability. To evaluate the effectiveness of the protocol for selecting the most appropriate strategies in ‎response to the opportunities and threats, two projects from a construction company in Iran were ‎studied. Both projects selected were in mid-range in terms of size and similar in terms of time, ‎run time and costs. Finally, the output indicates that using the proposed opportunities-response strategies show that the company's profitability in the future can be increased approximately for similar projects.

Keywords: opportunities management, risk-response strategy, opportunity-response strategy, productivity, risk management

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Authors: Sadek Younes, Fali Leyla, Rikioui Tayeb, Zizouni Khaled

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Stabilization of clay with lime as bearing stratum is an alternative to replacement of original soil. By adding lime to clay soil, the soil workability is improved due to the combination of calcium ions to the clay minerals, which means, modified soil properties. The paper investigates the effect of hydrated lime on the behaviour of lime treated, arid zones clay (Abadla Clay). A number of mechanical and hydraulic tests were performed to identify the effect of lime dosage and compaction water content on the compressibility, permeability, and shear strength parameters of the soil. Test results show that the soil parameters can be improved through additives such as lime. Overall, the addition percentages of 6% and 9% lime give the best desired results. Also, results revealed that the compressibility behavior of lime-treated soil strongly affected by lime content. The results are presented in terms of modern interpretation of the behaviour of treated soils, in comparison with the parameters of the untreated soil.

Keywords: arid zones, compressibility, lime, soil behaviour, soil stabilization, unsaturated soil

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Authors: Farahnaz Karami

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Routing is an important, challenging task in mobile ad hoc networks due to node mobility, lack of central control, unstable links, and limited resources. An ant colony has been found to be an attractive technique for routing in Mobile Ad Hoc Networks (MANETs). However, existing swarm intelligence based routing protocols find an optimal path by considering only one or two route selection metrics without considering correlations among such parameters making them unsuitable lonely for routing real time applications. Fuzzy logic combines multiple route selection parameters containing uncertain information or imprecise data in nature, but does not have multipath routing property naturally in order to provide load balancing. The objective of this paper is to design a routing algorithm using fuzzy logic and ant colony that can solve some of routing problems in mobile ad hoc networks, such as nodes energy consumption optimization to increase network lifetime, link failures rate reduction to increase packet delivery reliability and providing load balancing to optimize available bandwidth. In proposed algorithm, the path information will be given to fuzzy inference system by ants. Based on the available path information and considering the parameters required for quality of service (QoS), the fuzzy cost of each path is calculated and the optimal paths will be selected. NS2.35 simulation tools are used for simulation and the results are compared and evaluated with the newest QoS based algorithms in MANETs according to packet delivery ratio, end-to-end delay and routing overhead ratio criterions. The simulation results show significant improvement in the performance of these networks in terms of decreasing end-to-end delay, and routing overhead ratio, and also increasing packet delivery ratio.

Keywords: mobile ad hoc networks, routing, quality of service, ant colony, fuzzy logic

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Authors: Suman Dutta, Manish Agrawal, C. S. Jog

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Accurate computation of hydrodynamic forces on floating structures and their deformation finds application in the ocean and naval engineering and wave energy harvesting. This manuscript presents a monolithic, finite element strategy for fluid-structure interaction involving hyper-elastic solids partly submerged in an incompressible fluid. A velocity-based Arbitrary Lagrangian-Eulerian (ALE) formulation has been used for the fluid and a displacement-based Lagrangian approach has been used for the solid. The flexibility of the ALE technique permits us to treat the free surface of the fluid as a Lagrangian entity. At the interface, the continuity of displacement, velocity and traction are enforced using the mortar method. In the mortar method, the constraints are enforced in a weak sense using the Lagrange multiplier method. In the literature, the mortar method has been shown to be robust in solving various contact mechanics problems. The time-stepping strategy used in this work reduces to the generalized trapezoidal rule in the Eulerian setting. In the Lagrangian limit, in the absence of external load, the algorithm conserves the linear and angular momentum and the total energy of the system. The use of monolithic coupling with an energy-conserving time-stepping strategy gives an unconditionally stable algorithm and allows the user to take large time steps. All the governing equations and boundary conditions have been mapped to the reference configuration. The use of the exact tangent stiffness matrix ensures that the algorithm converges quadratically within each time step. The robustness and good performance of the proposed method are demonstrated by solving benchmark problems from the literature.

Keywords: ALE, floating body, fluid-structure interaction, monolithic, mortar method

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Authors: Himanshu Shekhar Maharana, S. K .Dash

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Economic Load Dispatch (ELD) proves to be a vital optimization process in electric power system for allocating generation amongst various units to compute the cost of generation, the cost of emission involving global warming gases like sulphur dioxide, nitrous oxide and carbon monoxide etc. In this dissertation, we emphasize ramp rate constriction factor based particle swarm optimization (RRCPSO) for analyzing various performance objectives, namely cost of generation, cost of emission, and a dual objective function involving both these objectives through the experimental simulated results. A 6-unit 30 bus IEEE test case system has been utilized for simulating the results involving improved weight factor advanced ramp rate limit constraints for optimizing total cost of generation and emission. This method increases the tendency of particles to venture into the solution space to ameliorate their convergence rates. Earlier works through dispersed PSO (DPSO) and constriction factor based PSO (CPSO) give rise to comparatively higher computational time and less good optimal solution at par with current dissertation. This paper deals with ramp rate and constriction factor based well defined ramp rate PSO to compute various objectives namely cost, emission and total objective etc. and compares the result with DPSO and weight improved PSO (WIPSO) techniques illustrating lesser computational time and better optimal solution. 

Keywords: economic load dispatch (ELD), constriction factor based particle swarm optimization (CPSO), dispersed particle swarm optimization (DPSO), weight improved particle swarm optimization (WIPSO), ramp rate and constriction factor based particle swarm optimization (RRCPSO)

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Authors: Anda-veronica Gherman, Dimitrios Varthalitis

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Neurosurgery, like many surgical specialties, undoubtedly exhibits a significant gender gap, particularly in leadership positions. While increasing women representation in neurosurgery is important, it is crucial to increase their presence in leadership positions. Across the globe and Europe there are concerning trends of only 4% of all neurosurgical departments being chaired by women. This study aims to explore the situation regarding gender disparities in leadership in the United Kingdom and to identify possible contributing factors as well as discussing future strategies to bridge this gap. Methods: A literature review was conducted utilising PubMed as main database with search keywords including ‘female neurosurgeon’, ‘women neurosurgeon’, ‘gender disparity’, ‘leadership’ and ‘UK’. Additionally, a manual search of all neurosurgical departments in the UK was performed to identify the current female department leads and training director leads. Results: The literature search identified a paucity of literature addressing specifically leadership in female neurosurgeons within the UK, with very few published papers specifically on this topic. Despite more than half of medical students in the UK being female, only a small proportion pursue a surgical career, with neurosurgery being one of the least represented specialties. Only 27% of trainee neurosurgeons are female, and numbers are even lower at a consultant level, where women represent just 8%.Findings from published studies indicated that only 6.6% of leadership positions in neurosurgery are occupied by women in the UK. Furthermore, our manual searches across UK neurosurgical departments revealed that around 5% of department lead positions are currently held by women. While this figure is slightly higher than the European average of 4%, it remains lower compared to figures of 10% in other North-West European countries. The situation is slightly more positive looking at the training directors, with 15% being female. Discussion: The findings of this study highlight a significant gender disparity in leadership positions within neurosurgery in the UK, which may have important implications, perpetuating the lack of diversity on the decision-making process, limiting the career advancement opportunities of women and depriving the neurosurgical field from the voices, opinions and talents of women. With women representing half of the population, there is an undeniable need for more female leaders at the policy-making level. There are many barriers that can contribute to these numbers, including bias, stereotypes, lack of mentorship and work-like balance. A few solutions to overcome these barriers can be training programs addressing bias and impostor syndrome, leadership workshops tailored for female needs, better workplace policies, increased in formal mentorship and increasing the visibility of women in neurosurgery leadership positions through media, speaking opportunities, conferences, awards etc. And lastly, more research efforts should focus on the leadership and mentorship of women in neurosurgery, with an increased number of published papers discussing these issues.

Keywords: female neurosurgeons, female leadership, female mentorship, gender disparities

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Authors: Mehrnaz Mostafavi

Abstract:

The assessment and categorization of incidental lung nodules present a considerable challenge in healthcare, often necessitating resource-intensive multiple computed tomography (CT) scans for growth confirmation. This research addresses this issue by introducing a distinct computational approach leveraging radiomics and deep-learning methods. However, understanding local services is essential before implementing these advancements. With diverse tracking methods in place, there is a need for efficient and accurate identification approaches, especially in the context of managing lung nodules alongside pre-existing cancer scenarios. This study explores the integration of text-based algorithms in medical data curation, indicating their efficacy in conjunction with machine learning and deep-learning models for identifying lung nodules. Combining medical images with text data has demonstrated superior data retrieval compared to using each modality independently. While deep learning and text analysis show potential in detecting previously missed nodules, challenges persist, such as increased false positives. The presented research introduces a Structured-Query-Language (SQL) algorithm designed for identifying pulmonary nodules in a tertiary cancer center, externally validated at another hospital. Leveraging natural language processing (NLP) and machine learning, the algorithm categorizes lung nodule reports based on sentence features, aiming to facilitate research and assess clinical pathways. The hypothesis posits that the algorithm can accurately identify lung nodule CT scans and predict concerning nodule features using machine-learning classifiers. Through a retrospective observational study spanning a decade, CT scan reports were collected, and an algorithm was developed to extract and classify data. Results underscore the complexity of lung nodule cohorts in cancer centers, emphasizing the importance of careful evaluation before assuming a metastatic origin. The SQL and NLP algorithms demonstrated high accuracy in identifying lung nodule sentences, indicating potential for local service evaluation and research dataset creation. Machine-learning models exhibited strong accuracy in predicting concerning changes in lung nodule scan reports. While limitations include variability in disease group attribution, the potential for correlation rather than causality in clinical findings, and the need for further external validation, the algorithm's accuracy and potential to support clinical decision-making and healthcare automation represent a significant stride in lung nodule management and research.

Keywords: lung cancer diagnosis, structured-query-language (SQL), natural language processing (NLP), machine learning, CT scans

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Authors: Amir Khaleghipour, Amirhossein Moghanian, Elhamalsadat Ghaffari

Abstract:

Modified bioactive glass has been considered as a promising multifunctional candidate in bone repair and regeneration due to its attractive properties. The present study mainly aims to evaluate how the individual substitution of lithium (L-BG) and magnesium (M-BG) for calcium can affect the in vitro bioactivity of sol-gel derived substituted 58S bioactive glass (BG); and to present one composition in both of the 60SiO₂–(36-x)CaO–4P₂O₅–(x)Li₂O and 60SiO₂–(36-x)CaO–4P₂O₅–(x)MgO quaternary systems (where x= 0, 5, 10 mol.%) with improved biocompatibility, enhanced alkaline phosphatase (ALP) activity, and the most efficient antibacterial activity against methicillin-resistant Staphylococcus aureus bacteria. To address these aims, and study the effect of CaO/Li₂O and CaO/MgO substitution up to 10 mol % in 58S-BGs, the samples were characterized by X-ray diffraction, Fourier transform infrared spectroscopy, inductively coupled plasma atomic emission spectrometry and scanning electron microscopy after immersion in simulated body fluid up to 14 days. Results indicated that substitution of either CaO/ Li₂O and CaO/ MgO had a retarding effect on in vitro hydroxyapatite (HA) formation due to the lower supersaturation degree for nucleation of HA compared with 58s-BG. Meanwhile, magnesium had a more pronounced effect. The 3-(4, 5dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) and alkaline phosphatase (ALP) assays showed that both substitutions of CaO/ Li₂O and CaO/ MgO up to 5mol % in 58s-BGs led to increased biocompatibility and stimulated proliferation of the pre-osteoblast MC3T3 cells with respect to the control. On the other hand, substitution of either Li or Mg for Ca in the 58s BG composition resulted in improved bactericidal efficiency against MRSA bacteria. Taken together, sample 58s-BG with 5 mol % CaO/Li₂O substitution (BG-5L) was considered as a multifunctional biomaterial in bone repair/regeneration with improved biocompatibility, enhanced ALP activity as well enhanced antibacterial efficiency against methicillin-resistant Staphylococcus aureus (MRSA) bacteria among all of the synthesized L-BGs and M-BGs.

Keywords: alkaline, alkaline earth, bioactivity, biomedical applications, sol-gel processes

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Authors: Li Qingjian, Li Ke, He Chun, Huang Yong

Abstract:

In this paper, the method of combining the Pohl Seidman's deep belief network with the self-organizing neural network is proposed to classify the target. This method is mainly aimed at the high nonlinearity of the hyperspectral image, the high sample dimension and the difficulty in designing the classifier. The main feature of original data is extracted by deep belief network. In the process of extracting features, adding known labels samples to fine tune the network, enriching the main characteristics. Then, the extracted feature vectors are classified into the self-organizing neural network. This method can effectively reduce the dimensions of data in the spectrum dimension in the preservation of large amounts of raw data information, to solve the traditional clustering and the long training time when labeled samples less deep learning algorithm for training problems, improve the classification accuracy and robustness. Through the data simulation, the results show that the proposed network structure can get a higher classification precision in the case of a small number of known label samples.

Keywords: DBN, SOM, pattern classification, hyperspectral, data compression

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Authors: Soumi Banerjee

Abstract:

In this research paper we will go deep into understanding Benedict Anderson’s definition of the nation as an imagined community and we will analyze why and how national identities were created through long and complex processes, and how there can exist strong emotional bonds between people within an imagined community, given the fact that these people have never known each other personally, but will still feel some form of imagined unity. Such identity construction on the part of an individual or within societies are always in some sense in a state of flux as imagined communities are ever changing, which provides us with the ontological foundation for reaching on this paper. This sort of identity crisis among individuals living in the Western world, who are in search for psychological comfort and security, illustrates a possible need for spatially dislocated, ontologically insecure and vulnerable individuals to have a secure identity. To create such an identity there has to be something to build upon, which could be achieved through what may be termed as ‘homesteading’. This could in short, and in my interpretation of Kinnvall and Nesbitt’s concept, be described as a search for security that involves a search for ‘home’, where home acts as a secure place, which one can build an identity around. The next half of the paper will then look into how populism and identity have played an increasingly important role in the political elections in the so-called western democracies of the world, using the U.S. as an example. Notions of ‘us and them’, the people and the elites will be looked into and analyzed through a social constructivist theoretical lens. Here we will analyze how such narratives about identity and the nation state affects people, their personality development and identity in different ways by studying the U.S. President Donald Trump’s speeches and analyze if and how he used different identity creating narratives for gaining political and popular support. The reason to choose narrative analysis as a method in this research paper is to use the narratives as a device to understand how the perceived notions of 'us and them' can initiate huge identity crisis with a community or a nation-state. This is a relevant subject as results and developments such as rising populist rightwing movements are being felt in a number of European states, with the so-called Brexit vote in the U.K. and the election of Donald Trump as president are two of the prime examples. This paper will then attempt to argue that these mechanisms are strengthened and gaining significance in situations when humans in an economic, social or ontologically vulnerable position, imagined or otherwise, in a general and broad meaning perceive themselves to be under pressure, and a sense of insecurity is rising. These insecurities and sense of being under threat have been on the rise in many of the Western states that are otherwise usually perceived to be some of the safest, democratically stable and prosperous states in the world, which makes it of interest to study what has changed, and help provide some part of the explanation as to how creating a ‘them’ in the discourse of national identity can cause massive security crisis.

Keywords: identity crisis, migration, ontological security(in), nation-states

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Authors: Ying Zhao, Xingyan Bin

Abstract:

Semi-supervised clustering algorithms have been shown effective to improve clustering process with even limited supervision. However, semi-supervised hierarchical clustering remains challenging due to the complexities of expressing constraints for agglomerative clustering algorithms. This paper proposes novel semi-supervised agglomerative clustering algorithms to build a hierarchy based on a known reference tree. We prove that by enforcing distance constraints defined by a reference tree during the process of hierarchical clustering, the resultant tree is guaranteed to be consistent with the reference tree. We also propose a framework that allows the hierarchical tree generation be aware of levels of levels of the agglomerative tree under creation, so that metric weights can be learned and adopted at each level in a recursive fashion. The experimental evaluation shows that the additional cost of our contraint-based semi-supervised hierarchical clustering algorithm (HAC) is negligible, and our combined semi-supervised HAC algorithm outperforms the state-of-the-art algorithms on real-world datasets. The experiments also show that our proposed methods can improve clustering performance even with a small number of unevenly distributed labeled data.

Keywords: semi-supervised clustering, hierarchical agglomerative clustering, reference trees, distance constraints

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Authors: B. Paygozar, S.A. Dizaji, A.C. Kandemir

Abstract:

In this study, the effects of the modified adhesive including different concentrations of Nano-silica are surveyed on the bonding strength of the adhesive scarf joints. The nanoparticles are added in two different concentrations, to an epoxy-based two-component structural adhesive, Araldite 2011, to survey the influences of the nanoparticle weight percentage on the failure load of the joints compared to that of the joints manufactured by the neat adhesive. The effects of being exposure to a moist ambience on the joint strength are also investigated for the joints produced of both neat and modified adhesives. For this purpose, an ageing process was carried out on the joints of both neat and improved kinds with variable immersion periods (20, 40 and 60 days). All the specimens were tested under a quasi-static tensile loading of 2 mm/min speed so as to find the quantities of the failure loads. Outcomes indicate that the failure loads of the joints with modified adhesives are measurably higher than that of the joint with neat adhesive, even while being put for a while under a moist condition. Another result points out that humidity lessens the bonding strength of all the joints of both types as the exposure time increases, which can be attributed to the change in the failure mode.

Keywords: bonding strength, humidity, nano-silica, scarf joint

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Authors: Jalal Abdulkareem Sultan, Abdulhakeem Luqman Hasan

Abstract:

With the development of health care systems competition, hospitals face more and more pressures. Meanwhile, resource allocation has a vital effect on achieving competitive advantages in hospitals. Selecting the appropriate number of beds is one of the most important sections in hospital management. However, in real situation, bed allocation selection is a multiple objective problem about different items with vagueness and randomness of the data. It is very complex. Hence, research about bed allocation problem is relatively scarce under considering multiple departments, nursing hours, and stochastic information about arrival and service of patients. In this paper, we develop a fuzzy multiobjective bed allocation model for overcoming uncertainty and multiple departments. Fuzzy objectives and weights are simultaneously applied to help the managers to select the suitable beds about different departments. The proposed model is solved by using Artificial Bee Colony (ABC), which is a very effective algorithm. The paper describes an application of the model, dealing with a public hospital in Iraq. The results related that fuzzy multi-objective model was presented suitable framework for bed allocation and optimum use.

Keywords: bed allocation problem, fuzzy logic, artificial bee colony, multi-objective optimization

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Authors: G. Rajeshwari, V. D. M. Jabez Daniel

Abstract:

Radio Frequency Identification systems are used for automated identification in various applications such as automobiles, health care and security. It is also called as the automated data collection technology. RFID readers are placed in any area to scan large number of tags to cover a wide distance. The placement of the RFID elements may result in several types of collisions. A major challenge in RFID system is collision avoidance. In the previous works the collision was avoided by using algorithms such as ALOHA and tree algorithm. This work proposes collision reduction and increased throughput through reading enhancement method with tree algorithm. The reading enhancement is done by improving interrogation procedure and increasing the data handling capacity of RFID reader with parallel processing. The work is simulated using Xilinx ISE 14.5 verilog language. By implementing this in the RFID system, we can able to achieve high throughput and avoid collision in the reader at a same instant of time. The overall system efficiency will be increased by implementing this.

Keywords: antenna, anti-collision protocols, data management system, reader, reading enhancement, tag

Procedia PDF Downloads 306