Search results for: hyperspectral image classification using tree search algorithm

Authors: Hannah K. M. Tang, Muhammad Ateeq, Mark J. Lake, Badr Abdullah, Frederic A. Bezombes

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There is little research applying virtual reality (VR) to the treatment of musculoskeletal injury in athletes. This is despite their prevalence, and the implications for physical and psychological health. Nevertheless, developments of wireless VR headsets better facilitate dynamic movement in VR environments (VREs), and more research is expected in this emerging field. This systematic review identified publications that used VR interventions for the analysis or treatment of lower-limb musculoskeletal injury of athletes. It established a search protocol, and through narrative discussion, identified existing trends. Database searches encompassed four term sets: 1) VR systems; 2) musculoskeletal injuries; 3) sporting population; 4) movement outcome analysis. Overall, a total of 126 publications were identified through database searching, and twelve were included in the final analysis and discussion. Many of the studies were pilot and proof of concept work. Seven of the twelve publications were observational studies. However, this may provide preliminary data from which clinical trials will branch. If specified, the focus of the literature was very narrow, with very similar population demographics and injuries. The trends in the literature findings emphasised the role of VR and attentional focus, the strategic manipulation of movement outcomes, and the transfer of skill to the real-world. Causal inferences may have been undermined by flaws, as most studies were limited by the practicality of conducting a two-factor clinical-VR-based study. In conclusion, by assessing the exploratory studies, and combining this with the use of numerous developments, techniques, and tools, a novel application could be established to utilise VR with dynamic movement, for the effective treatment of specific musculoskeletal injuries of athletes.

Keywords: athletes, lower-limb musculoskeletal injury, rehabilitation, return-to-sport, virtual reality

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Authors: Hussaini Doko Ibrahim, Hamilton Cyprian Chinwenyi, Henrietta Nkem Ude

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In this paper, efforts were made to examine and compare the algorithmic iterative solutions of the conjugate gradient method as against other methods such as Gauss-Seidel and Jacobi approaches for solving systems of linear equations of the form Ax=b, where A is a real n×n symmetric and positive definite matrix. We performed algorithmic iterative steps and obtained analytical solutions of a typical 3×3 symmetric and positive definite matrix using the three methods described in this paper (Gauss-Seidel, Jacobi, and conjugate gradient methods), respectively. From the results obtained, we discovered that the conjugate gradient method converges faster to exact solutions in fewer iterative steps than the two other methods, which took many iterations, much time, and kept tending to the exact solutions.

Keywords: conjugate gradient, linear equations, symmetric and positive definite matrix, gauss-seidel, Jacobi, algorithm

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Authors: S. Nickolas, Shobha K.

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The treatment of incomplete data is an important step in the data pre-processing. Missing values creates a noisy environment in all applications and it is an unavoidable problem in big data management and analysis. Numerous techniques likes discarding rows with missing values, mean imputation, expectation maximization, neural networks with evolutionary algorithms or optimized techniques and hot deck imputation have been introduced by researchers for handling missing data. Among these, imputation techniques plays a positive role in filling missing values when it is necessary to use all records in the data and not to discard records with missing values. In this paper we propose a novel artificial neural network based clustering algorithm, Adaptive Resonance Theory-2(ART2) for imputation of missing values in mixed attribute data sets. The process of ART2 can recognize learned models fast and be adapted to new objects rapidly. It carries out model-based clustering by using competitive learning and self-steady mechanism in dynamic environment without supervision. The proposed approach not only imputes the missing values but also provides information about handling the outliers.

Keywords: ART2, data imputation, clustering, missing data, neural network, pre-processing

Procedia PDF Downloads 277

Authors: Ghofrane Rehaiem, Hamza Gharsellaoui, Samir Benahmed

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In this study, Artificial Neural Networks (ANNs) were used for modeling the parameters that allow the real-time scheduling of embedded systems under resources constraints designed for real-time applications running. The objective of this work is to implement a neural networks based approach for real-time scheduling of embedded systems in order to handle real-time constraints in execution scenarios. In our proposed approach, many techniques have been proposed for both the planning of tasks and reducing energy consumption. In fact, a combination of Dynamic Voltage Scaling (DVS) and time feedback can be used to scale the frequency dynamically adjusting the operating voltage. Indeed, we present in this paper a hybrid contribution that handles the real-time scheduling of embedded systems, low power consumption depending on the combination of DVS and Neural Feedback Scheduling (NFS) with the energy Priority Earlier Deadline First (PEDF) algorithm. Experimental results illustrate the efficiency of our original proposed approach.

Keywords: optimization, neural networks, real-time scheduling, low-power consumption

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Authors: A. Nuchitprasittichai, N. Lerdritsirikoon, T. Khamsing

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Simulation modeling can be used to solve real world problems. It provides an understanding of a complex system. To develop a simplified model of process simulation, a suitable experimental design is required to be able to capture surface characteristics. This paper presents the experimental design and algorithm used to model the process simulation for optimization problem. The CO₂ liquefaction based on external refrigeration with two refrigeration circuits was used as a simulation case study. Latin Hypercube Sampling (LHS) was purposed to combine with existing Central Composite Design (CCD) samples to improve the performance of CCD in generating the second order model of the system. The second order model was then used as the objective function of the optimization problem. The results showed that adding LHS samples to CCD samples can help capture surface curvature characteristics. Suitable number of LHS sample points should be considered in order to get an accurate nonlinear model with minimum number of simulation experiments.

Keywords: central composite design, CO2 liquefaction, latin hypercube sampling, simulation-based optimization

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Authors: Yu-Jen Chang, Yun Chen, Hei-Lam Wong

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The Economic Lot Scheduling Problem (ELSP) is a valuable mathematical model that can support decision-makers to make scheduling decisions. The basic period approach is effective for solving the ELSP. The assumption for applying the basic period approach is that a product must use its maximum production rate to be produced. However, a product can lower its production rate to reduce the average total cost when a facility has extra idle time. The past researches discussed how a product adjusts its production rate under the common cycle approach. To the best of our knowledge, no studies have addressed how a product lowers its production rate under the basic period approach. This research is the first paper to discuss this topic. The research develops a simple fixed rate approach that adjusts the production rate of a product under the basic period approach to solve the ELSP. Our numerical example shows our approach can find a better solution than the traditional basic period approach. Our mathematical model that applies the fixed rate approach under the basic period approach can serve as a reference for other related researches.

Keywords: economic lot, basic period, genetic algorithm, fixed rate

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Authors: Sanghamitra Roy

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In recent times, India has experienced the phenomenal rise in the number of registered vehicles and vehicular trips, particularly intra-city trips in most of its urban areas. The increase in vehicle ownership and use have increased parking demand immensely and accommodating the same is now a matter of big concern. Most cities do not have adequate off-street parking facilities thus forcing people to park on the streets. This has resulted in decreased carrying capacity, decreased traffic speed, increased congestion, and increased environmental problems. While integrated multi-modal transportation system is the answer to such problems, parking issues will continue to exist. In Kolkata, only 6.4% land is devoted for roads. The consequences of this huge crunch in road spaces coupled with increased parking demand are severe particularly in the CBD and major commercial areas, making the role of off-street parking facilities in Kolkata even more critical. To meaningfully address parking issues, it is important to identify the factors that influence parking demand so that it can be assessed and comprehensive parking policies and plans for the city can be formulated. This paper aims at identifying the factors that contribute towards parking demand for offices in Kolkata and their degree of correlation with parking demand. The study is limited to home-to-work trips located within Kolkata Municipal Corporation (KMC) where parking related issues are most pronounced. The data for the study is collected through personal interviews, questionnaires and direct observations from offices across the wards of KMC. SPSS is used for classification of the data and analyses of the same. The findings of this study will help in re-assessment of the parking requirements specified in The Kolkata Municipal Corporation Building Rules as a step towards alleviating parking related issues in the city.

Keywords: building rules, office spaces, parking demand, urbanization

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Authors: Hsiang-Ru Lin

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Cholesterol plays an essential role in the regulation of the progression of numerous important diseases including atherosclerosis and Alzheimer disease so the generation of suitable cholesterol-lowering reagents is urgent to develop. Liver X receptor (LXR) is a ligand-activated transcription factor whose natural ligands are cholesterols, oxysterols and glucose. Once being activated, LXR can transactivate the transcription action of various genes including CYP7A1, ABCA1, and SREBP1c, involved in the lipid metabolism, glucose metabolism and inflammatory pathway. Essentially, the upregulation of ABCA1 facilitates cholesterol efflux from the cells and attenuates the production of beta-amyloid (ABeta) 42 in brain so LXR is a promising target to develop the cholesterol-lowering reagents and preventative treatment of Alzheimer disease. Engelhardia roxburghiana is a deciduous tree growing in India, China, and Taiwan. However, its chemical composition is only reported to exhibit antitubercular and anti-inflammatory effects. In this study, four compounds, engelheptanoxides A, C, engelhardiol A, and B isolated from the root of Engelhardia roxburghiana were evaluated for their agonistic activity against LXR by the transient transfection reporter assays in the HepG2 cells. Furthermore, their interactive modes with LXR ligand binding pocket were generated by molecular modeling programs. By using the cell-based biological assays, engelheptanoxides A, C, engelhardiol A, and B showing no cytotoxic effect against the proliferation of HepG2 cells, exerted obvious LXR agonistic effects with similar activity as T0901317, a novel synthetic LXR agonist. Further modeling studies including docking and SAR (structure-activity relationship) showed that these compounds can locate in LXR ligand binding pocket in the similar manner as T0901317. Thus, LXR is one of nuclear receptors targeted by pharmaceutical industry for developing treatments of Alzheimer and atherosclerosis diseases. Importantly, the cell-based assays, together with molecular modeling studies suggesting a plausible binding mode, demonstrate that engelheptanoxides A, C, engelhardiol A, and B function as LXR agonists. This is the first report to demonstrate that the extract of Engelhardia roxburghiana contains LXR agonists. As such, these active components of Engelhardia roxburghiana or subsequent analogs may show important therapeutic effects through selective modulation of the LXR pathway.

Keywords: Liver X receptor (LXR), Engelhardia roxburghiana, CYP7A1, ABCA1, SREBP1c, HepG2 cells

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Authors: Thewodros K. Geberemariam

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The rapid physical expansion of urbanization coupled with aging infrastructure presents a unique decision and management challenges for many big city municipalities. Cities must therefore upgrade and maintain the existing aging urban drainage infrastructure systems to keep up with the demands. Given the overall contribution of assets to municipal revenue and the importance of infrastructure to the success of a livable city, many municipalities are currently looking for a robust and smart urban drainage infrastructure asset management solution that combines management, financial, engineering and technical practices. This robust decision-making shall rely on sound, complete, current and relevant data that enables asset valuation, impairment testing, lifecycle modeling, and forecasting across the multiple asset portfolios. On this paper, predictive computational intelligence (CI) and multi-class machine learning (ML) coupled with online, offline, and historical record data that are collected from an array of multi-parameter sensors are used for the extraction of different operational and non-conforming patterns hidden in structured and unstructured data to determine and produce actionable insight on the current and future states of the network. This paper aims to improve the strategic decision-making process by identifying all possible alternatives; evaluate the risk of each alternative, and choose the alternative most likely to attain the required goal in a cost-effective manner using historical and near real-time urban drainage infrastructure data for urban drainage infrastructures assets that have previously not benefited from computational intelligence and machine learning advancements.

Keywords: computational intelligence, machine learning, urban drainage infrastructure, machine learning, classification, prediction, asset management space

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Authors: Siddharth Sarma, Ayush Majumdar, Nidhi Sabu, Mufaddal Jiruwaala, Shilpa Paygude

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Digital Elevation Models (DEMs) are digital representations of the Earth’s topography, which include information about the elevation, slope, aspect, and other terrain attributes. DEMs play a crucial role in various applications, including terrain analysis, urban planning, and environmental modeling. In this paper, TerraEnhance is proposed, a distinct approach for high-resolution DEM generation using Generative Adversarial Networks (GANs) combined with Real-ESRGANs. By learning from a dataset of low-resolution DEMs, the GANs are trained to upscale the data by 10 times, resulting in significantly enhanced DEMs with improved resolution and finer details. The integration of Real-ESRGANs further enhances visual quality, leading to more accurate representations of the terrain. A post-processing layer is introduced, employing high-pass filtering to refine the generated DEMs, preserving important details while reducing noise and artifacts. The results demonstrate that TerraEnhance outperforms existing methods, producing high-fidelity DEMs with intricate terrain features and exceptional accuracy. These advancements make TerraEnhance suitable for various applications, such as terrain analysis and precise environmental modeling.

Keywords: DEM, ESRGAN, image upscaling, super resolution, computer vision

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Authors: Xi Vivien Wu, Emily Neo Kim Ang, Yi Jung Tung, Wenru Wang

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Background and Objective: The increase in life expectancy and emphasis on self-reliance for the older adults are global phenomena. As such, lifelong learning in the community is considered a viable means of promoting successful and active aging. This systematic review aims to examine various lifelong learning programs for community-dwelling older adults and to synthesize the contents and outcomes of these lifelong learning programs. Methods: A systematic search was conducted in July to December 2016. Two reviewers were engaged in the process to ensure creditability of the selection process. Narrative description and analysis were applied with the support of a tabulation of key data including study design, interventions, and outcomes. Results: Eleven articles, which consisted of five randomized controlled trials and six quasi-experimental studies, were included in this review. Interventions included e-health literacy programs with the aid of computers and the Internet (n=4), computer and Internet training (n=3), physical fitness programs (n=2), music program (n=1), and intergenerational program (n=1). All studies used objective measurement tools to evaluate the outcomes of the study. Conclusion: The systematic review indicated lifelong learning programs resulted in positive outcomes in terms of physical health, mental health, social behavior, social support, self-efficacy and confidence in computer usage, and increased e-health literacy efficacy. However, the lifelong learning programs face challenges such as funding shortages, program cuts, and increasing costs. A comprehensive lifelong learning program could be developed to enhance the well-being of the older adults at a more holistic level. Empirical research can be done to explore the effectiveness of this comprehensive lifelong learning program.

Keywords: community-dwelling older adults, e-health literacy program, lifelong learning program, the wellbeing of the older adults

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Authors: Oluwaseun K. Oyebode, Josiah A. Adeyemo

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Hydrological modelling plays a crucial role in the planning and management of water resources, most especially in water stressed regions where the need to effectively manage the available water resources is of critical importance. However, due to the complex, nonlinear and dynamic behaviour of hydro-climatic interactions, achieving reliable modelling of water resource systems and accurate projection of hydrological parameters are extremely challenging. Although a significant number of modelling techniques (process-based and data-driven) have been developed and adopted in that regard, the field of hydrological modelling is still considered as one that has sluggishly progressed over the past decades. This is majorly as a result of the identification of some degree of uncertainty in the methodologies and results of techniques adopted. In recent times, evolutionary computation (EC) techniques have been developed and introduced in response to the search for efficient and reliable means of providing accurate solutions to hydrological related problems. This paper presents a comprehensive review of the underlying principles, methodological needs and applications of a promising evolutionary computation modelling technique – genetic programming (GP). It examines the specific characteristics of the technique which makes it suitable to solving hydrological modelling problems. It discusses the opportunities inherent in the application of GP in water related-studies such as rainfall estimation, rainfall-runoff modelling, streamflow forecasting, sediment transport modelling, water quality modelling and groundwater modelling among others. Furthermore, the means by which such opportunities could be harnessed in the near future are discussed. In all, a case for total embracement of GP and its variants in hydrological modelling studies is made so as to put in place strategies that would translate into achieving meaningful progress as it relates to modelling of water resource systems, and also positively influence decision-making by relevant stakeholders.

Keywords: computational modelling, evolutionary algorithms, genetic programming, hydrological modelling

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Authors: Xinge Liao, Francesc Xavier Roige Ventura, Dolores Sanchez Aguilera

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With the development of the Internet, the study of tourism behavior has rapidly expanded from the traditional physical market to the online market. Data on the Internet is characterized by dynamic changes, and new data appear all the time. In recent years the generation of a large volume of data was characterized, such as forums, blogs, and other sources, which have expanded over time and space, together they constitute large-scale Internet data, known as Big Data. This data of technological origin that derives from the use of devices and the activity of multiple users is becoming a source of great importance for the study of geography and the behavior of tourists. The study will focus on cultural heritage tourist practices in the context of Big Data. The research will focus on exploring the characteristics and behavior of Chinese tourists in relation to the cultural heritage of Catalonia. Geographical information, target image, perceptions in user-generated content will be studied through data analysis from Weibo -the largest social networks of blogs in China. Through the analysis of the behavior of heritage tourists in the Big Data environment, this study will understand the practices (activities, motivations, perceptions) of cultural tourists and then understand the needs and preferences of tourists in order to better guide the sustainable development of tourism in heritage sites.

Keywords: Barcelona, Big Data, Catalonia, cultural heritage, Chinese tourism market, tourists’ behavior

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Authors: Mitsuhiro Okayasu

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To better understand the lattice characteristics of lead zirconate titanate (PZT) ceramics, the lattice orientations and domain-switching characteristics have been directly examined during loading and unloading using various experimental techniques. Upon loading, the PZT ceramics are fractured linear and nonlinearly during the compressive loading process. The strain characteristics of the PZT ceramic were directly affected by both the lattice and domain switching strain. Due to the piezoelectric ceramic, electrical activity of lightning-like behavior occurs in the PZT ceramics, which attributed to the severe domain-switching leading to weak piezoelectric property. The characteristics of domain-switching and reverse switching are detected during the loading and unloading processes. The amount of domain-switching depends on the grain, due to different stress levels. In addition, two patterns of 90˚ domain-switching systems are characterized, namely (i) 90˚ turn about the tetragonal c-axis and (ii) 90˚ rotation of the tetragonal a-axis. In this case, PZT ceramic was loaded by the thermal stress at 80°C. Extent of domain switching is related to the direction of c-axis of the tetragonal structure, e.g., that axis, orientated close to the loading direction, makes severe domain switching. It is considered that there is 90˚ domain switching, but in actual, the angle of domain switching is less than 90˚, e.g., 85.4° ~ 90.0°. In situ TEM observation of the domain switching characteristics of PZT ceramic has been conducted with increasing the sample temperature from 25°C to 300°C, and the domain switching like behavior is directly observed from the lattice image, where the severe domain switching occurs less than 100°C.

Keywords: PZT, lead zirconate titanate, piezoelectric ceramic, domain switching, material property

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Authors: Mounia El Hafyani, Khalid El Himdi

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Precipitation is one of the key variables in water resource planning. The importance of modeling wet and dry durations is a crucial pointer in engineering hydrology. The objective of this study is to model and analyze the distribution of wet and dry durations. For this purpose, the daily rainfall data from 1967 to 2017 of the Moroccan city of Kenitra’s station are used. Three models are implemented for the distribution of wet and dry durations, namely the first-order Markov chain, the second-order Markov chain, and the truncated negative binomial law. The adherence of the data to the proposed models is evaluated using Chi-square and Kolmogorov-Smirnov tests. The Akaike information criterion is applied to assess the most effective model distribution. We go further and study the law of the number of wet and dry days among k consecutive days. The calculation of this law is done through an algorithm that we have implemented based on conditional laws. We complete our work by comparing the observed moments of the numbers of wet/dry days among k consecutive days to the calculated moment of the three estimated models. The study shows the effectiveness of our approach in modeling wet and dry durations of daily precipitation.

Keywords: Markov chain, rainfall, truncated negative binomial law, wet and dry durations

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Authors: Semih Erdogan

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In this paper, the normal and shear stress distributions in Vlasov beams are determined by two-dimensional triangular finite element formulations. The proposed formulations take into account the warping effects along the beam axis. The shape of the considered beam sections may be arbitrary and varied throughout its length. The stiffness matrices and force vectors are derived for transversal forces, uniform torsion, and nonuniform torsion. The proposed finite element algorithm is validated by comparing the analytical solutions, structural engineering books, and related articles. The numerical examples include beams with different cross-section types such as solid, thick-walled, closed-thin-walled, and open-thin-walled sections. Materials defined in the examples are homogeneous, isotropic, and linearly elastic. Through these examples, the study demonstrates the capability of the proposed method to address a wide range of practical engineering scenarios.

Keywords: Vlasov beams, warping function, nonuniform torsion, finite element method, normal and shear stresses, cross-section properties

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Authors: Wambugu Naftaly, Ruisheng Wang, Zhijun Wang

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Models based on convolutional neural networks (CNNs), in conjunction with Transformer, have excelled in semantic segmentation, a fundamental task for intelligent Earth observation using remote sensing (RS) imagery. Nonetheless, tokenization in the Transformer model undermines object structures and neglects inner-patch local information, whereas CNNs are unable to simulate global semantics due to limitations inherent in their convolutional local properties. The integration of the two methodologies facilitates effective global-local feature aggregation and interactions, potentially enhancing segmentation results. Inspired by the merits of CNNs and Transformers, we introduce a layer-level feature aggregation network (LLFA-Net) to address semantic segmentation of fine-resolution remote sensing (FRRS) images for land cover classification. The simple yet efficient system employs a transposed unit that hierarchically utilizes dense high-level semantics and sufficient spatial information from various encoder layers through a layer-level feature aggregation module (LLFAM) and models global contexts using structured Transformer blocks. Furthermore, the decoder aggregates resultant features to generate rich semantic representation. Extensive experiments on two public land cover datasets demonstrate that our proposed framework exhibits competitive performance relative to the most recent frameworks in semantic segmentation.

Keywords: land cover mapping, semantic segmentation, remote sensing, vision transformer networks, deep learning

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Authors: Badr M. Alshammari, T. Guesmi

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In recent years, the combined economic and emission power dispatch is one of the main problems of electrical power system. It aims to schedule the power generation of generators in order to minimize cost production and emission of harmful gases caused by fossil-fueled thermal units such as CO, CO₂, NO_x, and SO₂. To solve this complicated multi-objective problem, an improved version of the particle swarm optimization technique that includes non-dominated sorting concept has been proposed. Valve point loading effects and system losses have been considered. The three-unit and ten-unit benchmark systems have been used to show the effectiveness of the suggested optimization technique for solving this kind of nonconvex problem. The simulation results have been compared with those obtained using genetic algorithm based method. Comparison results show that the proposed approach can provide a higher quality solution with better performance.

Keywords: power dispatch, valve point loading effects, multiobjective optimization, Pareto solutions

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Authors: Chi-Ching Lee, Po-Jung Huang, Kuo-Yang Huang, Petrus Tang

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Background: Recent advances in high-throughput research technologies such as new-generation sequencing and multi-dimensional liquid chromatography makes it possible to dissect the complete transcriptome and proteome in a single run for the first time. However, it is almost impossible for many laboratories to handle and analysis these “BIG” data without the support from a bioinformatics team. We aimed to provide a web-based analysis platform for users with only limited knowledge on bio-computing to study the functional genomics and proteomics. Method: We use NCI-60 as an example dataset to demonstrate the power of the web-based analysis platform and data delivering system: C-eXpress takes a simple text file that contain the standard NCBI gene or protein ID and expression levels (rpkm or fold) as input file to generate a distribution map of gene/protein expression levels in a heatmap diagram organized by color gradients. The diagram is hyper-linked to a dynamic html table that allows the users to filter the datasets based on various gene features. A dynamic summary chart is generated automatically after each filtering process. Results: We implemented an integrated database that contain pre-defined annotations such as gene/protein properties (ID, name, length, MW, pI); pathways based on KEGG and GO biological process; subcellular localization based on GO cellular component; functional classification based on GO molecular function, kinase, peptidase and transporter. Multiple ways of sorting of column and rows is also provided for comparative analysis and visualization of multiple samples.

Keywords: cancer, visualization, database, functional annotation

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Authors: Marie Alaghband, Niloofar Yousefi, Ivan Garibay

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Facial expressions are important parts of both gesture and sign language recognition systems. Despite the recent advances in both fields, annotated facial expression datasets in the context of sign language are still scarce resources. In this manuscript, we introduce an annotated sequenced facial expression dataset in the context of sign language, comprising over 3000 facial images extracted from the daily news and weather forecast of the public tv-station PHOENIX. Unlike the majority of currently existing facial expression datasets, FePh provides sequenced semi-blurry facial images with different head poses, orientations, and movements. In addition, in the majority of images, identities are mouthing the words, which makes the data more challenging. To annotate this dataset we consider primary, secondary, and tertiary dyads of seven basic emotions of "sad", "surprise", "fear", "angry", "neutral", "disgust", and "happy". We also considered the "None" class if the image’s facial expression could not be described by any of the aforementioned emotions. Although we provide FePh as a facial expression dataset of signers in sign language, it has a wider application in gesture recognition and Human Computer Interaction (HCI) systems.

Keywords: annotated facial expression dataset, gesture recognition, sequenced facial expression dataset, sign language recognition

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Authors: C. M. Williams, R. English, P. King, I. M. Brown

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Introduction: Pseudoangiomatous Stromal Hyperplasia (PASH) is a benign fibrous proliferation of breast stroma affecting predominantly premenopausal women with no significant increased risk of breast cancer. Informal recommendations for management have continued to evolve over recent years from surgical excision to observation, although there are no specific national guidelines. This study assesses the safety of a non-surgical approach to PASH management by review of cases at a single centre. Methods: Retrospective case series review (January 2011 – August 2016) was conducted on consecutive PASH cases. Diagnostic classification (clinical, radiological and histological), management outcomes, and breast cancer incidence were recorded. Results: 43 patients were followed up for median of 25 months (3-64) with 75% symptomatic at presentation. 12% of cases (n=5) had a radiological score (BIRADS MMG or US) ≥ 4 of which 3 were confirmed malignant. One further malignancy was detected and proven radiologically occult and contralateral. No patients were diagnosed with a malignancy during follow-up. Treatment evolved from 67% surgical in 2011 to 33% in 2016. Conclusions: The management of PASH has transitioned in line with other published experience. The preliminary findings suggest this appears safe with no evidence of missed malignancies; however, longer follow up is required to confirm long-term safety. Recommendations: PASH with suspicious radiological findings ( ≥ U4/R4) warrants multidisciplinary discussion for excision. In the absence of histological or radiological suspicion of malignancy, PASH can be safely managed without surgery.

Keywords: benign breast disease, conservative management, malignancy, pseudoangiomatous stromal hyperplasia, surgical excision

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Authors: Cavidan Yakupoglu, Kurt Rohloff

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In this study, we aim to provide a novel parameter selection model for the BFVrns scheme, which is one of the prominent FHE schemes. Parameter selection in lattice-based FHE schemes is a practical challenges for experts or non-experts. Towards a solution to this problem, we introduce a hybrid principles-based approach that combines theoretical with experimental analyses. To begin, we use regression analysis to examine the parameters on the performance and security. The fact that the FHE parameters induce different behaviors on performance, security and Ciphertext Expansion Factor (CEF) that makes the process of parameter selection more challenging. To address this issue, We use a multi-objective optimization algorithm to select the optimum parameter set for performance, CEF and security at the same time. As a result of this optimization, we get an improved parameter set for better performance at a given security level by ensuring correctness and security against lattice attacks by providing at least 128-bit security. Our result enables average ~ 5x smaller CEF and mostly better performance in comparison to the parameter sets given in [1]. This approach can be considered a semiautomated parameter selection. These studies are conducted using the PALISADE homomorphic encryption library, which is a well-known HE library. The abstract goes here.

Keywords: lattice cryptography, fully homomorphic encryption, parameter selection, LWE, RLWE

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Authors: Mohammed Alenezy

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The function of cardiac motion can be used as an indicator of the heart abnormality by evaluating longitudinal, circumferential, and Radial Strain of the left ventricle. In this paper, the Radial Strain and SR is studied using tagged MRI (tMRI) data during the cardiac cycle on the mid-ventricle level of the left ventricle. Materials and methods: The short-axis view of the left ventricle of five healthy human (three males and two females) and four healthy male rats were imaged using tagged magnetic resonance imaging (tMRI) technique covering the whole cardiac cycle on the mid-ventricle level. Images were processed using Image J software to calculate the left ventricle wall Radial Strain and radial SR. The left ventricle Radial Strain and radial SR were calculated at the mid-ventricular level during the cardiac cycle. The peak Radial Strain for the human and rat heart was 40.7±1.44, and 46.8±0.68 respectively, and it occurs at 40% of the cardiac cycle for both human and rat heart. The peak diastolic and systolic radial SR for human heart was -1.78 s-1 ± 0.02 s-1 and 1.10±0.08 s-1 respectively, while for rat heart it was -5.16± 0.23s-1 and 4.25±0.02 s-1 respectively. Conclusion: This results show the ability of the tMRI data to characterize the cardiac motion during the cardiac cycle including diastolic and systolic phases which can be used as an indicator of the cardiac dysfunction by estimating the left ventricle Radial Strain and radial SR at different locations of the cardiac tissue. This study approves the validity of the tagged MRI data to describe accurately the cardiac radial motion.

Keywords: left ventricle, radial strain, tagged MRI, cardiac cycle

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Authors: Bryan Christian Baybay, M. Francesca Ronario

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This study is intended to answer the question “What are the communication styles of selected Filipino gays in breaking their silence on their sexual orientation to their parents?” In this regard, six cases of Filipino gay disclosures were examined through in-depth interviews. The participants were selected through purposive sampling and snowball technique. The theories, Rhetorical Sensitivity of Roderick Hart and Communicator Style of Robert Norton were used to analyze the gathered data and to give support to the communication attitudes, message processing, message rendering and communication styles exhibited in each disclosure. As secondary data and validation, parents and experts in the field of communication, sociology, and psychology were also interviewed and consulted. The study found that Filipino gays vary in the communication styles they use during the disclosure with their parents. All communication styles: impression-leaving, contentious, open, dramatic, dominant, precise, relaxed, friendly, animated, and communicator image were observed by the gays depending on their motivation, relationship and thoughts contemplated. These results lend ideas for future researchers to look into the communication patterns and/or styles of lesbians, bisexuals, transgenders and queers or expand researches on the same subject and the utilization of Social Judgment and Relational Dialectics theories in determining and analyzing LGBTQ communication.

Keywords: communication attitudes, communication styles, Filipino gays, self-disclosure, sexual orientation

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Authors: Mehrdad Shahrbanozadeh, Gholam Abbas Barani, Saeed Shojaee

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In this study, a finite element (Seep3D model) and an artificial neural network (ANN) model were developed to simulate flow through dam foundation. Seep3D model is capable of simulating three-dimensional flow through a heterogeneous and anisotropic, saturated and unsaturated porous media. Flow through the Shahid Abbaspour dam foundation has been used as a case study. The FEM with 24960 triangular elements and 28707 nodes applied to model flow through foundation of this dam. The FEM being made denser in the neighborhood of the curtain screen. The ANN model developed for Shahid Abbaspour dam is a feedforward four layer network employing the sigmoid function as an activator and the back-propagation algorithm for the network learning. The water level elevations of the upstream and downstream of the dam have been used as input variables and the piezometric heads as the target outputs in the ANN model. The two models are calibrated and verified using the Shahid Abbaspour’s dam piezometric data. Results of the models were compared with those measured by the piezometers which are in good agreement. The model results also revealed that the ANN model performed as good as and in some cases better than the FEM.

Keywords: seepage, dam foundation, finite element method, neural network, seep 3D model

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Authors: Lyamine Guezouli, Kamel Barka, Zineb Seghir

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Wireless sensor and actor networks (WSANs) present a research challenge for different practice areas. Researchers are trying to optimize the use of such networks through their research work. This optimization is done on certain criteria, such as improving energy efficiency, exploiting node heterogeneity, self-adaptability and self-configuration. In this article, we present our proposal for BIFSA (Biologically-Inspired Framework for Wireless Sensor and Actor networks). Indeed, BIFSA is a middleware that addresses the key issues of wireless sensor and actor networks. BIFSA consists of two types of agents: sensor agents (SA) that operate at the sensor level to collect and transport data to actors and actor agents (AA) that operate at the actor level to transport data to base stations. Once the sensor agent arrives at the actor, it becomes an actor agent, which can exploit the resources of the actors and vice versa. BIFSA allows agents to evolve their genetic structures and adapt to the current network conditions. The simulation results show that BIFSA allows the agents to make better use of all the resources available in each type of node, which improves the performance of the network.

Keywords: wireless sensor and actor networks, self-management, genetic algorithm, agent.

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Authors: Flur R. Ismagilov, Irek Kh. Khayrullin, Vyacheslav E. Vavilov, Ruslan D. Karimov, Anton S. Gorbunov, Danis R. Farrakhov

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The paper presents another structural scheme of high-temperature starter-generator with external rotor to be installed on High Pressure Shaft (HPS) of aircraft engines (AE) to implement More Electrical Engine concept. The basic materials to make this starter-generator (SG) were selected and justified. Multi-criteria optimization of the developed structural scheme was performed using a genetic algorithm and Pareto method. The optimum (in Pareto terms) active length and thickness of permanent magnets of SG were selected as a result of the optimization. Using the dimensions obtained, allowed to reduce the weight of the designed SG by 10 kg relative to a base option at constant thermal loads. Multidisciplinary computer simulation was performed on the basis of the optimum geometric dimensions, which proved performance efficiency of the design. We further plan to make a full-scale sample of SG of HPS and publish the results of its experimental research.

Keywords: high-temperature starter-generator, more electrical engine, multi-criteria optimization, permanent magnet

Procedia PDF Downloads 375

Authors: M Jeffri Arlinandes Chandra, Puwaningdyah Murti Wahyuni, Dewi Mutiara M Jeffri Arlinandes Chandra, Puwaningdyah Murti Wahyuni, Dewi Mutiara

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Indonesia is a state of the law in accordance with article 1 paragraph 3 of the Constitution of the Republic of Indonesia (1945 Constitution), namely, 'the State of Indonesia is a state of law'. The consequences of the rule of law are making the law as the main commanding officer or making the law as a basis for carrying out an action taken by the state. The types of regulations and procedures for the formation of legislation in Indonesia are contained in Law Number 12 of 2011 concerning the Formation of Legislation. Various attempts were made to make quality regulations both in the formal hierarchy and material hierarchy such as synchronization and harmonization in the formation of laws and regulations so that there is no conflict between equal and hierarchical laws, but the fact is that there are still many conflicting regulations found between one another. This can be seen clearly in the many laws and regulations that were sued to judicial institutions such as the Constitutional Court (MK) and the Supreme Court (MA). Therefore, it is necessary to have a formulation regarding the governance of the formation of laws and regulations so as to minimize the occurrence of lawsuits to the court so that positive law can be realized which can be used today and for the future (ius constituendum). The research method that will be used in this research is a combination of normative research (library research) supported by empirical data from field research so that it can formulate concepts and answer the challenges being faced. First, the structuring of laws and regulations in Indonesia must start from the inventory of laws and regulations, whether they can be classified based on the type of legislation, what are they set about, the year of manufacture, etc. so that they can be clearly traced to the regulations relating to the formation of laws and regulations. Second, the search and revocation/revocation of laws and regulations that do not exist in the state registration system. Third, the periodic evaluation system is carried out at every level of the hierarchy of laws and regulations. These steps will form an ideal model of laws and regulations in Indonesia both in terms of content and material so that the instructions can be codified and clearly inventoried so that they can be accessed by the wider community as a concrete manifestation of the principle that all people know the law (presumptio iures de iure).

Keywords: legislation, review, evaluation, reconstruction

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Authors: Edward R. Moad

Abstract:

Al-Ghazali’s Tahāfat al Falāsifa is widely construed as an attack on philosophy in favor of theological fideism. Consequently, he has been blamed for ‘death of philosophy’ in the Muslim world. ‘Falsifa’ however is not philosophy itself, but rather a range of philosophical doctrines mainly influenced by or inherited form Greek thought. In these terms, this work represents a defense of philosophy against what we could call ‘falsifical’ fideism. In the introduction, Ghazali describes his target audience as, not the falasifa, but a group of pretenders engaged in taqlid to a misconceived understanding of falasifa, including the belief that they were capable of demonstrative certainty in the field of metaphysics. He promises to use falsifa standards of logic (with which he independently agrees), to show that that the falasifa failed to demonstratively prove many of their positions. Whether or not he succeeds in that, the exercise of subjecting alleged proofs to critical scrutiny is quintessentially philosophical, while uncritical adherence to a doctrine, in the name of its being ‘philosophical’, is decidedly unphilosophical. If we are to blame the intellectual decline of the Muslim world on someone’s ‘bad’ way of thinking, rather than more material historical circumstances (which is already a mistake), then blame more appropriately rests with modernist Muslim thinkers who, under the influence of orientalism (and like Ghazali’s philosophical pretenders) mistook taqlid to the falasifa as philosophy itself. The discussion of the Tahāfut takes place in the context of an epistemic (and related social) hierarchy envisioned by the falasifa, corresponding to the faculties of the sense, the ‘estimative imagination’ (wahm), and the pure intellect, along with the respective forms of discourse – rhetoric, dialectic, and demonstration – appropriate to each category of that order. Al-Farabi in his Book of Letters describes a relation between dialectic and demonstration on the one hand, and theology and philosophy on the other. The latter two are distinguished by method rather than subject matter. Theology is that which proceeds dialectically, while philosophy is (or aims to be?) demonstrative. Yet, Al-Farabi tells us, dialectic precedes philosophy like ‘nourishment for the tree precedes its fruit.’ That is, dialectic is part of the process, by which we interrogate common and imaginative notions in the pursuit of clearly understood first principles that we can then deploy in the demonstrative argument. Philosophy is, therefore, something we aspire to through, and from a discursive condition of, dialectic. This stands in apparent contrast to the understanding of Ibn Sina, for whom one arrives at the knowledge of first principles through contact with the Active Intellect. It also stands in contrast to that of Ibn Rushd, who seems to think our knowledge of first principles can only come through reading Aristotle. In conclusion, based on Al-Farabi’s framework, Ghazali’s Tahafut is a truly an exercise in philosophy, and an effort to keep the door open for true philosophy in the Muslim mind, against the threat of a kind of developing theology going by the name of falsifa.

Keywords: philosophy, incoherence, theology, Tahafut

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Authors: Soheila Sadeghi

Abstract:

In the dynamic landscape of project management, scope changes are an inevitable reality that can significantly impact project performance. These changes, whether initiated by stakeholders, external factors, or internal project dynamics, can lead to cost overruns and schedule delays. Accurately predicting the consequences of these changes is crucial for effective project control and informed decision-making. This study aims to develop predictive models to estimate the impact of scope changes on project cost and schedule using machine learning techniques. The research utilizes a comprehensive dataset containing detailed information on project tasks, including the Work Breakdown Structure (WBS), task type, productivity rate, estimated cost, actual cost, duration, task dependencies, scope change magnitude, and scope change timing. Multiple machine learning models are developed and evaluated to predict the impact of scope changes on project cost and schedule. These models include Linear Regression, Decision Tree, Ridge Regression, Random Forest, Gradient Boosting, and XGBoost. The dataset is split into training and testing sets, and the models are trained using the preprocessed data. Cross-validation techniques are employed to assess the robustness and generalization ability of the models. The performance of the models is evaluated using metrics such as Mean Squared Error (MSE) and R-squared. Residual plots are generated to assess the goodness of fit and identify any patterns or outliers. Hyperparameter tuning is performed to optimize the XGBoost model and improve its predictive accuracy. The feature importance analysis reveals the relative significance of different project attributes in predicting the impact on cost and schedule. Key factors such as productivity rate, scope change magnitude, task dependencies, estimated cost, actual cost, duration, and specific WBS elements are identified as influential predictors. The study highlights the importance of considering both cost and schedule implications when managing scope changes. The developed predictive models provide project managers with a data-driven tool to proactively assess the potential impact of scope changes on project cost and schedule. By leveraging these insights, project managers can make informed decisions, optimize resource allocation, and develop effective mitigation strategies. The findings of this research contribute to improved project planning, risk management, and overall project success.

Keywords: cost impact, machine learning, predictive modeling, schedule impact, scope changes

Procedia PDF Downloads 48