Search results for: SURF(Speed-Up Robust Features)

Authors: Nidal F. Shilbayeh, Belal AbuHaija, Zainab N. Al-Qudsy

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In this paper, a hybrid blind digital watermarking system using Discrete Wavelet Transform (DWT) and Contourlet Transform (CT) has been implemented and tested. The implemented combined digital watermarking system has been tested against five common types of image attacks. The performance evaluation shows improved results in terms of imperceptibility, robustness, and high tolerance against these attacks; accordingly, the system is very effective and applicable.

Keywords: discrete wavelet transform (DWT), contourlet transform (CT), digital image watermarking, copyright protection, geometric attack

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Authors: Puran Chandra Joshi

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The Himalaya is the newest mountain system on this earth. This highest as well as fragile mountain system is still rising up. The tectonic activities have been experienced by this entire area, so the geomorphology of the region is affected by it. As we know, geomorphology is the study of landforms and their processes on the earth surface. These landforms are very important for human beings and other creatures on this planet. Present paper traces out the geomorphological features and their significance along Dhauli Ganga river valley in the Himalaya. Study area falls in higher Himalaya, which has experienced glacial and fluvial processes. Dhauli Ganga river is a considerable tributary of river kali, which is the part of huge Gangetic system. Dhauli originates in the form of two tributaries from valley glaciers of the southern slopes of Kumaun-Tibbet water divide. The upper catchment of this river has been carved by the glacial activity. The area of investigation is a remote regionin, Kumaun Himalaya. The native people do seasonal migration due to harsh winters. In summers, they return back with their cattle. In this season, they also grow potatoes and pulses, especiallybeanson river terraces. This study is important for making policies in the entire area. Area has witnessed big landslide in the recent past. So, the present study becomes more important.

Keywords: himalaya, geomorphology, glacial, tectonics

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Authors: Tlotlollo Sidwell Hlalele, Shengzhi Du

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High voltage transmission is the most pivotal process in the electrical power industry. It requires a robust infrastructure that can last for decades without causing impairment in human life. Due to the so-called global warming, power transmission system has started to experience some challenges which could presumably escalate more in future. These challenges are earthquake resistance, transmission power losses, and high electromagnetic field. In this paper, research efforts aim to address these challenges are discussed. We focus in particular on the research in regenerative electric energy such as: wind, hydropower, biomass and sea-waves based on the energy storage and transmission possibility. We conclude by drawing attention to specific areas that we believe need more research.

Keywords: power transmission, regenerative energy, power quality, energy storage

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Authors: Sharon Li, Zhonghang Xia

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Post-database search is an essential phase in peptide identification using tandem mass spectrometry (MS/MS) to refine peptide-spectrum matches (PSMs) produced by database search engines. These engines frequently face difficulty differentiating between correct and incorrect peptide assignments. Despite advances in statistical and machine learning methods aimed at improving the accuracy of peptide identification, challenges remain in selecting critical features for these models. In this study, two machine learning models—a random forest tree and a support vector machine—were applied to three datasets to enhance PSMs. SHAP values were utilized to determine the significance of each feature within the models. The experimental results indicate that the random forest model consistently outperformed the SVM across all datasets. Further analysis of SHAP values revealed that the importance of features varies depending on the dataset, indicating that a feature's role in model predictions can differ significantly. This variability in feature selection can lead to substantial differences in model performance, with false discovery rate (FDR) differences exceeding 50% between different feature combinations. Through SHAP value analysis, the most effective feature combinations were identified, significantly enhancing model performance.

Keywords: peptide identification, SHAP value, feature selection, random forest tree, support vector machine

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Authors: Madjid Karimirad

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Floating solar was not very well-known in the renewable energy field a decade ago; however, there has been tremendous growth internationally with a Compound Annual Growth Rate (CAGR) of nearly 30% in recent years. To reach the goal of global net-zero emission by 2050, all renewable energy sources including solar should be used. Considering that 40% of the world’s population lives within 100 kilometres of the coasts, floating solar in coastal waters is an obvious energy solution. However, this requires more robust floating solar solutions. This paper tries to enlighten the fundamental requirements in the design of floating solar for offshore installations from the hydrodynamic and offshore engineering points of view. In this regard, a closer look at dynamic characteristics, stochastic behaviour and nonlinear phenomena appearing in this kind of structure is a major focus of the current article. Floating solar structures are alternative and very attractive green energy installations with (a) Less strain on land usage for densely populated areas; (b) Natural cooling effect with efficiency gain; and (c) Increased irradiance from the reflectivity of water. Also, floating solar in conjunction with the hydroelectric plants can optimise energy efficiency and improve system reliability. The co-locating of floating solar units with other types such as offshore wind, wave energy, tidal turbines as well as aquaculture (fish farming) can result in better ocean space usage and increase the synergies. Floating solar technology has seen considerable developments in installed capacities in the past decade. Development of design standards and codes of practice for floating solar technologies deployed on both inland water-bodies and offshore is required to ensure robust and reliable systems that do not have detrimental impacts on the hosting water body. Floating solar will account for 17% of all PV energy produced worldwide by 2030. To enhance the development, further research in this area is needed. This paper aims to discuss the main critical design aspects in light of the load and load effects that the floating solar platforms are subjected to. The key considerations in hydrodynamics, aerodynamics and simultaneous effects from the wind and wave load actions will be discussed. The link of dynamic nonlinear loading, limit states and design space considering the environmental conditions is set to enable a better understanding of the design requirements of fast-evolving floating solar technology.

Keywords: floating solar, offshore renewable energy, wind and wave loading, design space

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Authors: Samabia Tehsin, Sumaira Kausar

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Due to the vast increase in the multimedia data in recent years, efficient and robust retrieval techniques are needed to retrieve and index images/ videos. Text embedded in the images can serve as the strong retrieval tool for images. This is the reason that text extraction is an area of research with increasing attention. English text extraction is the focus of many researchers but very less work has been done on other languages like Urdu. This paper is focusing on Urdu text extraction from video frames. This paper presents a text detection feature set, which has the ability to deal up with most of the problems connected with the text extraction process. To test the validity of the method, it is tested on Urdu news dataset, which gives promising results.

Keywords: caption text, content-based image retrieval, document analysis, text extraction

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Authors: Mohammad Reza Rahimi Khoygani, Reza Ghasemi

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In this paper, designing direct adaptive neural controller is applied for a class of a nonlinear pendulum dynamic system. The radial basis function (RBF) is used for the Neural network (NN). The adaptive neural controller is robust in presence of external and internal uncertainties. Both the effectiveness of the controller and robustness against disturbances are the merits of this paper. The promising performance of the proposed controllers investigates in simulation results.

Keywords: adaptive control, pendulum dynamical system, nonlinear control, adaptive neural controller, nonlinear dynamical, neural network, RBF, driven pendulum, position control

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Authors: Mohamed Abd Esselem Dems, Souhila Laib, Nadjia Latelli, Nadia Ouddai

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In this work, we have developed QSAR model for Relative Binding Affinity (RBA) of a large diverse set of estradiol among these derivatives, the organometallic derivatives. By dividing the dataset into a training set of 24 compounds and a test set of 6 compounds. The DFT method was used to calculate quantum chemical descriptors and physicochemical descriptors (MR and MLOGP) were performed using E-Dragon. All the validations indicated that the QSAR model built was robust and satisfactory (R2 = 90.12, Q2LOO = 86.61, RMSE = 0.272, F = 60.6473, Q2ext =86.07). We have therefore apply this model to predict the RBA, for two isomers β and α wherein Mn(CO)3 complex with the aromatic ring of estradiol, and the two isomers show little appreciation for the estrogenic receptor (RBAβ = 1.812 and RBAα = 1.741).

Keywords: DFT, estradiol, haptotropic rearrangement, QSAR, relative binding affinity

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Authors: Selva Georgena D., Videcoq Etienne, Caner Julien, Benselama Adel, Girault Manu

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A Two-Phase Thermosyphon Loop (TPTL) is a passive cooling system which does not require a pump to function. Therefore, TPTL is a simple and robust device and its physics is complex to describe because of the coupled phenomena: heat flux, nucleation, fluid dynamics and gravitational effects. Moreover, the dynamic behavior of TPTL shows some physical instabilities and the actual occurrence of such a behavior remains unknown. The aim of this study is to propose a thermal balance of the TPTL to better identify the fundamental reasons for the appearance of the instabilities.

Keywords: Two-phase flow, passive cooling system, thermal reliability, thermal experimental study, liquid-vapor phase change

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Authors: Xunan Huang, Caicai Zhang

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This paper investigated the code-mixing features in Mandarin-English bilingual children in Singapore. First, it examined whether the code-mixing rate was different in Mandarin Chinese and English contexts. Second, it explored the syntactic categories of code-mixing in Singapore bilingual children. Moreover, this study investigated whether morphological information was preserved when inserting syntactic components into the matrix language. Data are derived from the Singapore Bilingual Corpus, in which the recordings and transcriptions of sixty English-Mandarin 5-to-6-year-old children were preserved for analysis. Results indicated that the rate of code-mixing was asymmetrical in the two language contexts, with the rate being significantly higher in the Mandarin context than that in the English context. The asymmetry is related to language dominance in that children are more likely to code-mix when using their nondominant language. Concerning the syntactic categories of code-mixing words in the Singaporean bilingual children, we found that noun-mixing, verb-mixing, and adjective-mixing are the three most frequently used categories in code-mixing in the Mandarin context. This pattern mirrors the syntactic categories of code-mixing in the Cantonese context in Cantonese-English bilingual children, and the general trend observed in lexical borrowing. Third, our results also indicated that English vocabularies that carry morphological information are embedded in bare forms in the Mandarin context. These findings shed light upon how bilingual children take advantage of the two languages in mixed utterances in a bilingual environment.

Keywords: bilingual children, code-mixing, English, Mandarin Chinese

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Authors: Kent Salomonsson, Xuefang Zhao, Sara Kallin

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Human soft tissue is loaded and deformed by any activity, an effect known as a stress-strain relationship, and is often described by a load and tissue elongation curve. Several advances have been made in the fields of biology and mechanics of soft human tissue. However, there is limited information available on in vivo tissue mechanical characteristics and behavior. Confident mechanical properties of human soft tissue cannot be extrapolated from e.g. animal testing. Thus, there is need for non invasive methods to analyze mechanical characteristics of soft human tissue. In the present study, the internal mechanical conditions of the lower limb, which is subject to an external load, is studied by use of the finite element method. A detailed finite element model of the lower limb is made possible by use of MRI scans. Skin, fat, bones, fascia and muscles are represented separately and the material properties for them are obtained from literature. Previous studies have been shown to address macroscopic deformation features, e.g. indentation depth, to a large extent. However, the detail in which the internal anatomical features have been modeled does not reveal the critical internal strains that may induce hypoxia and/or eventual tissue damage. The results of the present study reveals that lumped material models, i.e. averaging of the material properties for the different constituents, does not capture regions of critical strains in contrast to more detailed models.

Keywords: FEM, tissue, indentation, properties

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Authors: Meysam Saidi

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While the specific externalities and required regulatory measures in relation to Islamic banking are fairly uncertain, the business is growing across the world. Unofficial data indicate that the Islamic Finance market is growing with annual rate of 15% and it has reached 1.3 $ trillion size. This trend is associated with inherent systematic connection of Islamic financial institutions to other entities and different sectors of economies. Islamic banking has been subject of market development policies in major economies, most notably the UK. This trend highlights the need for identification of distinct risk features of Islamic banking and crafting customized regulatory measures. So far there has not been a significant systemic crisis in this market which can be attributed to its distinct nature. However, the significant growth and spread of its products worldwide necessitate an in depth study of its nature for customized congruent regulatory measures. In the post financial crisis era some market analysis and reports suggested that the Islamic banks fairly weathered the crisis. As far as heavily blamed conventional financial products such as subprime mortgage backed securities and speculative credit default swaps were concerned the immunity claim can be considered true, as Islamic financial institutions were not directly exposed to such products. Nevertheless, similar to the experience of the conventional banking industry, it can be only a matter of time for Islamic banks to face failures that can be specific to the nature of their business. Using the experience of conventional banking regulations and identifying those peculiarities of Islamic banking that need customized regulatory approach can aid to prevent major failures. Frank Knight has stated that “We perceive the world before we react to it, and we react not to what we perceive, but always to what we infer”. The debate over congruent Islamic banking regulations might not be an exception to Frank Knight’s statement but I will try to base my discussion on concrete evidences. This paper first analyzes both theoretical and actual features of Islamic banking in order to ascertain to its peculiarities in terms of market stability and other externalities. Next, the paper discusses distinct features of Islamic financial transactions and banking which might require customized regulatory measures. Finally, the paper explores how a more transparent path for the Islamic banking regulations can be drawn.

Keywords: Islamic banking, regulation, risks, capital requirements, customer protection, financial stability

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Authors: Muhammad Haseeb

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It is crucial to have a balanced approach to food's energy and nutritional content in a world with limited resources. The preservation of the environment is vital, and both the agrifood-making and food service sectors will be requested to use fewer resources to produce a wider range of existing foods and develop imaginative foods that are physiologically appropriate for a better sense of good health, have long shelf lives and are conveniently transportable. Delivering healthy diets that satisfy consumer expectations from robust and sustainable agrifood systems is necessary in a world that is changing and where natural resources are running out. Across the whole food supply chain, an integrated multi-sectoral approach is needed to alleviate global food and nutrition insecurity.

Keywords: health, food, nutrition, supply chain

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Authors: Sahraoui Halima, Dahani Ameur, Tigrine Abedelkader

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DigiMesh technology represents a pioneering advancement in wireless networking, offering cost-effective and energy-efficient capabilities. Its inherent simplicity and adaptability facilitate the seamless transfer of data between network nodes, extending the range and ensuring robust connectivity through autonomous self-healing mechanisms. In light of these advantages, this study introduces a medical platform harnessed with DigiMesh wireless network technology characterized by low power consumption, immunity to interference, and user-friendly operation. The primary application of this platform is the real-time, long-distance monitoring of Electrocardiogram (ECG) signals, with the added capacity for simultaneous monitoring of ECG signals from multiple patients. The experimental setup comprises key components such as Raspberry Pi, E-Health Sensor Shield, and Xbee DigiMesh modules. The platform is composed of multiple ECG acquisition devices labeled as Sensor Node 1 and Sensor Node 2, with a Raspberry Pi serving as the central hub (Sink Node). Two communication approaches are proposed: Single-hop and multi-hop. In the Single-hop approach, ECG signals are directly transmitted from a sensor node to the sink node through the XBee3 DigiMesh RF Module, establishing peer-to-peer connections. This approach was tested in the first experiment to assess the feasibility of deploying wireless sensor networks (WSN). In the multi-hop approach, two sensor nodes communicate with the server (Sink Node) in a star configuration. This setup was tested in the second experiment. The primary objective of this research is to evaluate the performance of both Single-hop and multi-hop approaches in diverse scenarios, including open areas and obstructed environments. Experimental results indicate the DigiMesh network's effectiveness in Single-hop mode, with reliable communication over distances of approximately 300 meters in open areas. In the multi-hop configuration, the network demonstrated robust performance across approximately three floors, even in the presence of obstacles, without the need for additional router devices. This study offers valuable insights into the capabilities of DigiMesh wireless technology for real-time ECG monitoring in healthcare applications, demonstrating its potential for use in diverse medical scenarios.

Keywords: DigiMesh protocol, ECG signal, real-time monitoring, medical platform

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Authors: Diana Ruth Caga-Anan

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Classroom management is a critical skill but the styles are constantly evolving. It is constantly under pressure particularly in the college education level due to diversity in student profiles, modes of delivery, and marketization of higher education. This study sought to analyze the extent of implementation of classroom management practices (CMPs) of the college instructors of the Hotel, Restaurant, and Institution Management of a premier university in the Philippines. It was also determined if their length of teaching affects their classroom management style. A questionnaire with sixteen 'evidenced-based' CMPs grouped into five critical features of classroom management, adopted from the literature search of Simonsen et al. (2008), was administered to 4 instructor-respondents and to their 88 students. Weighted mean scores of each of the CMPs revealed that there were differences between the instructors’ self-scores and their students’ ratings on their implementation of CMPs. The critical feature of classroom management 'actively engage students in observable ways' got the highest mean score, corresponding to 'always' from the instructors’ self-rating and 'frequently' from their students’ ratings. However, 'use a continuum of strategies to respond to inappropriate behaviors' got the lowest scores from both the instructors and their students corresponding only to 'occasionally'. Analysis of variance showed that the only CMP affected by the length of teaching is the practice of 'prompting students to respond'. Based on the findings, some recommendations for the instructors to improve on the critical feature where they scored low are discussed and suggestions are included for future research.

Keywords: classroom management, CMPs, critical features, evidence-based classroom management practices

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Authors: Andreas D. Jansson

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The availability of inexpensive, yet competent hardware allows for increased level of automation and self-optimization in the context of Industry 4.0. However, such agents require high quality information about their surroundings along with a robust strategy for collision avoidance, as they may cause expensive damage to equipment or other agents otherwise. Manually defining a strategy to cover all possibilities is both time-consuming and counter-productive given the capabilities of modern hardware. This paper explores the idea of a model-free self-optimizing obstacle avoidance strategy for multiple autonomous agents in a simulated dynamic environment using the Q-learning algorithm.

Keywords: autonomous vehicles, industry 4.0, multi-agent system, obstacle avoidance, Q-learning, simulation

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Authors: Walid Moudani

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The healthcare environment is generally perceived as being information rich yet knowledge poor. However, there is a lack of effective analysis tools to discover hidden relationships and trends in data. In fact, valuable knowledge can be discovered from application of data mining techniques in healthcare system. In this study, a proficient methodology for the extraction of significant patterns from the coronary heart disease warehouses for heart attack prediction, which unfortunately continues to be a leading cause of mortality in the whole world, has been presented. For this purpose, we propose to enumerate dynamically the optimal subsets of the reduced features of high interest by using rough sets technique associated to dynamic programming. Therefore, we propose to validate the classification using Random Forest (RF) decision tree to identify the risky heart disease cases. This work is based on a large amount of data collected from several clinical institutions based on the medical profile of patient. Moreover, the experts’ knowledge in this field has been taken into consideration in order to define the disease, its risk factors, and to establish significant knowledge relationships among the medical factors. A computer-aided system is developed for this purpose based on a population of 525 adults. The performance of the proposed model is analyzed and evaluated based on set of benchmark techniques applied in this classification problem.

Keywords: multi-classifier decisions tree, features reduction, dynamic programming, rough sets

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Authors: Narendra Akhadkar, Philippe Delcambre

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In power protection and control products, assembly process variations due to the individual parts manufactured from single or multi-cavity tooling is a major problem. The dimensional and geometrical variations on the individual parts, in the form of manufacturing tolerances and assembly tolerances, are sources of clearance in the kinematic joints, polarization effect in the joints, and tolerance stack-up. All these variations adversely affect the quality of product, functionality, cost, and time-to-market. Variation simulation analysis may be used in the early product design stage to predict such uncertainties. Usually, variations exist in both manufacturing processes and materials. In the tolerance analysis, the effect of the dimensional and geometrical variations of the individual parts on the functional characteristics (conditions) of the final assembled products are studied. A functional characteristic of the product may be affected by a set of interrelated dimensions (functional parameters) that usually form a geometrical closure in a 3D chain. In power protection and control products, the prerequisite is: when a fault occurs in the electrical network, the product must respond quickly to react and break the circuit to clear the fault. Usually, the response time is in milliseconds. Any failure in clearing the fault may result in severe damage to the equipment or network, and human safety is at stake. In this article, we have investigated two important functional characteristics that are associated with the robust performance of the product. It is demonstrated that the experimental data obtained at the Schneider Electric Laboratory prove the very good prediction capabilities of the variation simulation performed using CETOL (tolerance analysis software) in an industrial context. Especially, this study allows design engineers to better understand the critical parts in the product that needs to be manufactured with good, capable tolerances. On the contrary, some parts are not critical for the functional characteristics (conditions) of the product and may lead to some reduction of the manufacturing cost, ensuring robust performance. The capable tolerancing is one of the most important aspects in product and manufacturing process design. In the case of miniature circuit breaker (MCB), the product's quality and its robustness are mainly impacted by two aspects: (1) allocation of design tolerances between the components of a mechanical assembly and (2) manufacturing tolerances in the intermediate machining steps of component fabrication.

Keywords: geometrical variation, product robustness, tolerance analysis, variation simulation

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Authors: Philipp Sommer, Amgad Agoub

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The construction sector significantly impacts global CO₂ emissions, particularly through the energy usage of residential buildings. To address this, various governments, including Germany's, are focusing on reducing emissions via sustainable refurbishment initiatives. This study examines the application of machine learning (ML) to estimate energy demands dynamically in residential buildings and enhance the potential for large-scale sustainable refurbishment. A major challenge in Germany is the lack of extensive publicly labeled datasets for energy performance, as energy performance certificates, which provide critical data on building-specific energy requirements and consumption, are not available for all buildings or require on-site inspections. Conversely, England and other countries in the European Union (EU) have rich public datasets, providing a viable alternative for analysis. This research adapts insights from these English datasets to the German context by developing a comprehensive data schema and calibration dataset capable of predicting building energy demand effectively. The study proposes a minimal feature set, determined through feature importance analysis, to optimize the ML model. Findings indicate that ML significantly improves the scalability and accuracy of energy demand forecasts, supporting more effective emissions reduction strategies in the construction industry. Integrating energy performance certificates into municipal heat planning in Germany highlights the transformative impact of data-driven approaches on environmental sustainability. The goal is to identify and utilize key features from open data sources that significantly influence energy demand, creating an efficient forecasting model. Using Extreme Gradient Boosting (XGB) and data from energy performance certificates, effective features such as building type, year of construction, living space, insulation level, and building materials were incorporated. These were supplemented by data derived from descriptions of roofs, walls, windows, and floors, integrated into three datasets. The emphasis was on features accessible via remote sensing, which, along with other correlated characteristics, greatly improved the model's accuracy. The model was further validated using SHapley Additive exPlanations (SHAP) values and aggregated feature importance, which quantified the effects of individual features on the predictions. The refined model using remote sensing data showed a coefficient of determination (R²) of 0.64 and a mean absolute error (MAE) of 4.12, indicating predictions based on efficiency class 1-100 (G-A) may deviate by 4.12 points. This R² increased to 0.84 with the inclusion of more samples, with wall type emerging as the most predictive feature. After optimizing and incorporating related features like estimated primary energy consumption, the R² score for the training and test set reached 0.94, demonstrating good generalization. The study concludes that ML models significantly improve prediction accuracy over traditional methods, illustrating the potential of ML in enhancing energy efficiency analysis and planning. This supports better decision-making for energy optimization and highlights the benefits of developing and refining data schemas using open data to bolster sustainability in the building sector. The study underscores the importance of supporting open data initiatives to collect similar features and support the creation of comparable models in Germany, enhancing the outlook for environmental sustainability.

Keywords: machine learning, remote sensing, residential building, energy performance certificates, data-driven, heat planning

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Authors: Hanwei Gao, Louis Jezequel, Eric Cabrol, Bernard Vitry

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The chassis system is composed of complex elements that take up all the loads from the tire-ground contact area and thus it plays an important role in numerous specifications such as durability, comfort, crash, etc. During the development of new vehicle projects in Renault, durability validation is always the main focus while deployment of comfort comes later in the project. Therefore, sometimes design choices have to be reconsidered because of the natural incompatibility between these two specifications. Besides, robustness is also an important point of concern as it is related to manufacturing costs as well as the performance after the ageing of components like shock absorbers. In this paper an approach is proposed aiming to realize a multi-objective optimization between chassis endurance and comfort while taking the random factors into consideration. The adaptive-sparse polynomial chaos expansion method (PCE) with Chebyshev polynomial series has been applied to predict responses’ uncertainty intervals of a system according to its uncertain-but-bounded parameters. The approach can be divided into three steps. First an initial design of experiments is realized to build the response surfaces which represent statistically a black-box system. Secondly within several iterations an optimum set is proposed and validated which will form a Pareto front. At the same time the robustness of each response, served as additional objectives, is calculated from the pre-defined parameter intervals and the response surfaces obtained in the first step. Finally an inverse strategy is carried out to determine the parameters’ tolerance combination with a maximally acceptable degradation of the responses in terms of manufacturing costs. A quarter car model has been tested as an example by applying the road excitations from the actual road measurements for both endurance and comfort calculations. One indicator based on the Basquin’s law is defined to compare the global chassis durability of different parameter settings. Another indicator related to comfort is obtained from the vertical acceleration of the sprung mass. An optimum set with best robustness has been finally obtained and the reference tests prove a good robustness prediction of Chebyshev PCE method. This example demonstrates the effectiveness and reliability of the approach, in particular its ability to save computational costs for a complex system.

Keywords: chassis durability, Chebyshev polynomials, multi-objective optimization, polynomial chaos expansion, ride comfort, robust design

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Authors: Omar A. Abuobidalla, Mingyuan Chen, Satyaveer S. Chauhan

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Railroad transportation of hazardous materials freights is important to the North America economics that supports the national’s supply chain. This paper introduces various extensions of the dynamic hazardous materials trip plan problems. The problem captures most of the operational features of a real-world railroad transportations systems that dynamically initiates a set of blocks and assigns each shipment to a single block path or multiple block paths. The dynamic hazardous materials trip plan policies have distinguishing features that are integrating the blocking plan, and the block activation decisions. We also present a non-linear mixed integer programming formulation for each variant and present managerial insights based on a hypothetical railroad network. The computation results reveal that the dynamic car scheduling policies are not only able to take advantage of the capacity of the network but also capable of diminishing the population, and environment risks by rerouting the active blocks along the least risky train services without sacrificing the cost advantage of the railroad. The empirical results of this research illustrate that the issue of integrating the blocking plan, and the train makeup of the hazardous materials freights must receive closer attentions.

Keywords: dynamic car scheduling, planning and scheduling hazardous materials freights, airborne hazardous materials, gaussian plume model, integrated blocking and routing plans, box model

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Authors: Sultan Mahmud, Qamar Zaman, Travis Esau, Young Chang

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Strawberry powdery mildew (PM) is a serious disease that has a significant impact on strawberry production. Field scouting is still a major way to find PM disease, which is not only labor intensive but also almost impossible to monitor disease severity. To reduce the loss caused by PM disease and achieve faster automatic detection of the disease, this paper proposes an approach for detection of the disease, based on image texture and classified with support vector machines (SVMs) and k-nearest neighbors (kNNs). The methodology of the proposed study is based on image processing which is composed of five main steps including image acquisition, pre-processing, segmentation, features extraction and classification. Two strawberry fields were used in this study. Images of healthy leaves and leaves infected with PM (Sphaerotheca macularis) disease under artificial cloud lighting condition. Colour thresholding was utilized to segment all images before textural analysis. Colour co-occurrence matrix (CCM) was introduced for extraction of textural features. Forty textural features, related to a physiological parameter of leaves were extracted from CCM of National television system committee (NTSC) luminance, hue, saturation and intensity (HSI) images. The normalized feature data were utilized for training and validation, respectively, using developed classifiers. The classifiers have experimented with internal, external and cross-validations. The best classifier was selected based on their performance and accuracy. Experimental results suggested that SVMs classifier showed 98.33%, 85.33%, 87.33%, 93.33% and 95.0% of accuracy on internal, external-I, external-II, 4-fold cross and 5-fold cross-validation, respectively. Whereas, kNNs results represented 90.0%, 72.00%, 74.66%, 89.33% and 90.3% of classification accuracy, respectively. The outcome of this study demonstrated that SVMs classified PM disease with a highest overall accuracy of 91.86% and 1.1211 seconds of processing time. Therefore, overall results concluded that the proposed study can significantly support an accurate and automatic identification and recognition of strawberry PM disease with SVMs classifier.

Keywords: powdery mildew, image processing, textural analysis, color co-occurrence matrix, support vector machines, k-nearest neighbors

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Authors: A. Hameed, N. Upreti, P. Srivastava

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Main objective the present study is to investigate microscopic, sub-microscopic, clay mineralogical and geochemical characteristics of three calcic soil profiles from the western Thar Desert for the last 30 ka paleoclimatic information. Thin-sections of the soils show weakly to moderately developed pedofeatures dominated by powdery to well-indurated pedogenic calcium carbonate. Sub-microscopy of the representative calcretes show extensive growth of fibrous palygorskite in pore spaces of micritic and sparitic nodules. XRD of the total clay ( < 2 µm) and fine clay ( < 0.2 µm) fractions of the soils show dominance of smectite, palygorskite, chlorite, mica, kaolinite and small amounts of quartz and feldspar. Formation of the palygorskite is attributed to pedogenic processes associated with Bw, Bss and Bwk horizons during drier conditions over the last 30 ka. Formation of palygorskite was mainly favoured by strongly evaporating percolating water and precipitation of secondary calcite, high pH (9-10), high Mg, Si and low Al activities during pedogenesis. Age estimate and distribution of calcretes, palygorskite, and illuvial features indicate fluctuating monsoonal strength during MIS3-MIS1 stages. The pedogenic features in calcic soils of western Thar suggest relatively arid conditions during MIS3-MIS2 transition and LGM time that changed to relatively wetter conditions during post LGM time and again returned to dry conditions at ~4 ka in MIS1.

Keywords: palygorskite, clay minerals, Thar, aridisol, late quaternary

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Authors: Kambiz Teimour Najad

Abstract:

The basic methodology of GPR involves the use of a transmitting antenna to send electromagnetic waves into the subsurface, which then bounce back to the surface and are detected by a receiving antenna. The transmitter and receiver antennas are typically placed on the ground surface and moved across the area of interest to create a profile of the subsurface. The GPR system consists of a control unit that powers the antennas and records the data, as well as a display unit that shows the results of the survey. The control unit sends a pulse of electromagnetic energy into the ground, which propagates through the soil or rock until it encounters a change in material or structure. When the electromagnetic wave encounters a buried object or structure, some of the energy is reflected back to the surface and detected by the receiving antenna. The GPR data is then processed using specialized software that analyzes the amplitude and travel time of the reflected waves. By interpreting the data, GPR can provide information on the depth, location, and nature of subsurface features and structures. GPR has several advantages over other geophysical survey methods, including its ability to provide high-resolution images of the subsurface and its non-invasive nature, which minimizes disruption to the site. However, the effectiveness of GPR depends on several factors, including the type of soil or rock, the depth of the features being investigated, and the frequency of the electromagnetic waves used. In environmental hazard assessments, GPR can be used to detect buried structures, such as underground storage tanks, pipelines, or utilities, which may pose a risk of contamination to the surrounding soil or groundwater. GPR can also be used to assess soil stability by identifying areas of subsurface voids or sinkholes, which can lead to the collapse of the surface. Additionally, GPR can be used to map the extent and movement of groundwater contamination, which is critical in designing effective remediation strategies. the methodology of GPR in environmental hazard assessments involves the use of electromagnetic waves to create high of the subsurface, which are then analyzed to provide information on the depth, location, and nature of subsurface features and structures. This information is critical in identifying and mitigating environmental hazards, and the non-invasive nature of GPR makes it a valuable tool in this field.

Keywords: GPR, hazard, landslide, rock fall, contamination

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Authors: Selda Uzal Seyfi

Abstract:

Goat milk is a quite important in human nutrition. In order to meet the demand to the goat and sheep milk occurring in the recent years, an increase is seen in the demand to housing projects, which will enable animals to be sheltered in the suitable environments. This study was carried out in between 2012 and 2013, in order to identify the existing cases of sheep and goat housings in the province Konya and their possibilities to be developed. In the study, in the province Konya, 25 pieces of sheep and goat farms and 46 pieces of sheep and goat housings (14 sheep housings, 3 goat housings, and 29 housings, in which both sheep and goat are bred ) that are present in the farm were investigated as material. In the study, examining the general features of the farms that are present in the region and structural features of housings that are present in the farms, it is studied whether or not they are suitable for animal breeding. As a result of the study, the barns were evaluated as insufficient in terms of barn design, although 48% of they were built after 2000. In 63% of housings examined, stocking density of resting area was below the value of 1 m2/animal and in 59% of the housings, stocking density of courtyard area was below the 2 m2/animal. Feeding length, in 57% of housings has a value of 0.30 m and below. In the region, it will be possible to obtain the desired productivity level by building new barn designs, developed in accordance with the animal behaviors and welfare. Carrying out the necessary works is an important issue in terms of country and regional economy.

Keywords: barn design, goat housing, sheep housing, structural analysis

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Authors: C. Manjula, Lilly Florence

Abstract:

Software technology is developing rapidly which leads to the growth of various industries. Now-a-days, software-based applications have been adopted widely for business purposes. For any software industry, development of reliable software is becoming a challenging task because a faulty software module may be harmful for the growth of industry and business. Hence there is a need to develop techniques which can be used for early prediction of software defects. Due to complexities in manual prediction, automated software defect prediction techniques have been introduced. These techniques are based on the pattern learning from the previous software versions and finding the defects in the current version. These techniques have attracted researchers due to their significant impact on industrial growth by identifying the bugs in software. Based on this, several researches have been carried out but achieving desirable defect prediction performance is still a challenging task. To address this issue, here we present a machine learning based hybrid technique for software defect prediction. First of all, Genetic Algorithm (GA) is presented where an improved fitness function is used for better optimization of features in data sets. Later, these features are processed through Decision Tree (DT) classification model. Finally, an experimental study is presented where results from the proposed GA-DT based hybrid approach is compared with those from the DT classification technique. The results show that the proposed hybrid approach achieves better classification accuracy.

Keywords: decision tree, genetic algorithm, machine learning, software defect prediction

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Authors: Rongbo Shen, Jianhua Yao, Kezhou Yan, Kuan Tian, Cheng Jiang, Ke Zhou

Abstract:

This study proposes a generative adversarial networks (GAN) framework to perform synthetic sampling in feature space, i.e., feature augmentation, to address the class imbalance problem in medical image analysis. A feature extraction network is first trained to convert images into feature space. Then the GAN framework incorporates adversarial learning to train a feature generator for the minority class through playing a minimax game with a discriminator. The feature generator then generates features for minority class from arbitrary latent distributions to balance the data between the majority class and the minority class. Additionally, a data cleaning technique, i.e., Tomek link, is employed to clean up undesirable conflicting features introduced from the feature augmentation and thus establish well-defined class clusters for the training. The experiment section evaluates the proposed method on two medical image analysis tasks, i.e., mass classification on mammogram and cancer metastasis classification on histopathological images. Experimental results suggest that the proposed method obtains superior or comparable performance over the state-of-the-art counterparts. Compared to all counterparts, our proposed method improves more than 1.5 percentage of accuracy.

Keywords: class imbalance, synthetic sampling, feature augmentation, generative adversarial networks, data cleaning

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Authors: Reshaa Alruwaili

Abstract:

The primary aim of this study was to examine the hypothesis presented by Self Determination theory which suggests that autonomy impacts positively the expression of love. Other hypotheses were also examined which suggest that other variables explain the expression of love, including: dyadic adjustment (dyadic consensus, dyadic satisfaction and dyadic cohesion), couple satisfaction, age, gender, the length of marriage, number of children and attachment styles. The participants were Saudi couples, which provided the opportunity to consider the influence of Saudi culture on the expression of love. A questionnaire was employed to obtain measures of all the relevant variables, including a measure of expression of love that was built from 27 items, constituting verbal, physical and caring features, and a measure of autonomy based on three features: authorship, interest-taking and susceptibility. Data were collected from both members of 34 Saudi couples. Descriptive analysis of both expression of love and autonomy was conducted. Correlation and regression were used to assess the relationships between expression of love and autonomy and other variables. Results indicated that Saudi couples who most often express their love tend to be more than somewhat autonomous. Not much difference was found between husbands and wives in expressing love, although wives were slightly more autonomous than husbands. Expression of love was enhanced by the autonomy of the participants to a greater extent when dyadic satisfaction was controlled, since the latter was negatively correlated with autonomy and had no effect on the expression of love. Basic psychological needs, dyadic consensus and dismissive-avoidant attachment improve the expression of love, while it is decreased by the number of children.

Keywords: autonomy, determination theory, expression of love, dyadic adjustment

Procedia PDF Downloads 233

Authors: L. Bendifallah, S. Doumandji, K. Louadi, S. Iserbyt, F. Acheuk

Abstract:

Due to their ecological key position and diversity, plant-bee relationships constitute excellent models to understand the processes of food specialisation. The purpose of this study is to define and identify the most important species of bees foraging broadbean flowers, we estimated morphological, phonological and behavioural features. We discuss the results by considering the food specialisation level of the visitor. In the studied populations (Algiers, Algeria), visiting bees belong to four different genus: Apis, Andrena, Eucera and Xylocopa. Eucera is foraging broad beans flowers during months of April, May. The genus Andrena and Xylocopa were found on weeds after the flowering period of beans. The two species have not a preferred type of vegetation compared to Eucera. The main pollinators were generalist bees such as Apis mellifera L. and Xylocopa pubescens Spinola (Apidae), and specialist bees such Eucera numida Lep. (Apidae). The results show that no one of the studied species, neither the specialist, nor the generalist ones, share adaptative morphological or behavioural features that may improve foraging on Vicia faba. However, there is a narrow synchronisation between the daily and yearly phenologies of Eucera numida and those of V. faba. This could be an adaptation of the specialist bee to its host plant. Thus, the food specialisation of Eucera numida, as for most specialist bees, would be more linked to its adapted phenology than to an adapted morphology.

Keywords: Vicia faba, bees, pollinators, Algeria

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Authors: Aleksandr S. Ermolenko

Abstract:

Allometry is an important factor of morphological integration, contributing to the organization of the phenotype and its variability. The allometric change in the shape of the hand is particularly important in primate evolution, as the hand has important taxonomic features. Some of these features are known to parts with the shape, especially the ratio of the lengths of the index and ring fingers (2d: 4d ratio). The hand is a fairly well-studied system in the context of the evolutionary development of complex morphological structures since it consists of various departments (basipodium, metapodium, acropodium) that form a single structure –autopodium. In the present study, we examined the allometric variability of acropodium. We tested the null hypothesis that there would be no difference in allometric variation between the two components. Geometric morphometry based on a procrustation of 16 two-dimensional (2D) landmarks was analyzed using multivariate shape-by-size regressions in samples from 100 people (50 men and 50 women). The results obtained show that men have significantly greater allometric variability for the ring finger (variability in the transverse axis prevails), while women have significantly greater allometric variability for the index finger (variability in the longitudinal axis prevails). The influence of the middle finger on the shape of the hand is typical for both men and women. The influence of the little finger on the shape of the hand, regardless of gender, was not revealed. The results of this study support the hypothesis that allometry contributes to the organization of variation in the human hand.

Keywords: human hand, size and shape, 2d:4d ratio, geometric morphometry

Procedia PDF Downloads 158