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Authors: Kaveh Mollazade, Hojat Ahmadi, Mahmoud Omid, Reza Alimardani

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Recently, the issue of machine condition monitoring and fault diagnosis as a part of maintenance system became global due to the potential advantages to be gained from reduced maintenance costs, improved productivity and increased machine availability. The aim of this work is to investigate the effectiveness of a new fault diagnosis method based on power spectral density (PSD) of vibration signals in combination with decision trees and fuzzy inference system (FIS). To this end, a series of studies was conducted on an external gear hydraulic pump. After a test under normal condition, a number of different machine defect conditions were introduced for three working levels of pump speed (1000, 1500, and 2000 rpm), corresponding to (i) Journal-bearing with inner face wear (BIFW), (ii) Gear with tooth face wear (GTFW), and (iii) Journal-bearing with inner face wear plus Gear with tooth face wear (B&GW). The features of PSD values of vibration signal were extracted using descriptive statistical parameters. J48 algorithm is used as a feature selection procedure to select pertinent features from data set. The output of J48 algorithm was employed to produce the crisp if-then rule and membership function sets. The structure of FIS classifier was then defined based on the crisp sets. In order to evaluate the proposed PSD-J48-FIS model, the data sets obtained from vibration signals of the pump were used. Results showed that the total classification accuracy for 1000, 1500, and 2000 rpm conditions were 96.42%, 100%, and 96.42% respectively. The results indicate that the combined PSD-J48-FIS model has the potential for fault diagnosis of hydraulic pumps.

Keywords: Power Spectral Density, Machine ConditionMonitoring, Hydraulic Pump, Fuzzy Logic.

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Authors: Saule Mussabekova

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Recent advances in genetics have allowed increasing acutely the capacities of the formation of reliable evidence in conducting forensic examinations. Thus, traces of biological origin are important sources of information about a crime. Currently, around the world, sexual offenses have increased, and among them are those in which the criminals use various detergents to remove traces of their crime. A feature of modern synthetic detergents is the presence of biological additives - enzymes. Enzymes purposefully destroy stains of biological origin. To study the nature and extent of the impact of modern washing powders on saliva stains on the physical evidence, specially prepared test specimens of different types of tissues to which saliva was applied have been examined. Materials and Methods: Washing machines of famous manufacturers of household appliances have been used with different production characteristics and advertised brands of washing powder for test washing. Over 3,500 experimental samples were tested. After washing, the traces of saliva were identified using modern research methods of forensic medicine. Results: The influence was tested and the dependence of the use of different washing programs, types of washing machines and washing powders in the process of establishing saliva trace and identify of the stains on the physical evidence while washing was revealed. The results of experimental and practical expert studies have shown that in most cases it is not possible to draw the conclusions in the identification of saliva traces on physical evidence after washing. This is a consequence of the effect of biological additives and other additional factors on traces of saliva during washing. Conclusions: On the basis of the results of the study, the feasibility of saliva traces of the stains on physical evidence after washing is established. The use of modern molecular genetic methods makes it possible to partially solve the problems arising in the study of unlaundered evidence. Additional study of physical evidence after washing facilitates detection and investigation of sexual offenses against women and children.

Keywords: Saliva research, modern synthetic detergents, laundry detergents, forensic medicine.

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Authors: Rashima Mahajan, Dipali Bansal, Shweta Singh

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Real time non-invasive Brain Computer Interfaces have a significant progressive role in restoring or maintaining a quality life for medically challenged people. This manuscript provides a comprehensive review of emerging research in the field of cognitive/affective computing in context of human neural responses. The perspectives of different emotion assessment modalities like face expressions, speech, text, gestures, and human physiological responses have also been discussed. Focus has been paid to explore the ability of EEG (Electroencephalogram) signals to portray thoughts, feelings, and unspoken words. An automated workflow-based protocol to design an EEG-based real time Brain Computer Interface system for analysis and classification of human emotions elicited by external audio/visual stimuli has been proposed. The front end hardware includes a cost effective and portable Emotiv EEG Neuroheadset unit, a personal computer and a set of external stimulators. Primary signal analysis and processing of real time acquired EEG shall be performed using MATLAB based advanced brain mapping toolbox EEGLab/BCILab. This shall be followed by the development of MATLAB based self-defined algorithm to capture and characterize temporal and spectral variations in EEG under emotional stimulations. The extracted hybrid feature set shall be used to classify emotional states using artificial intelligence tools like Artificial Neural Network. The final system would result in an inexpensive, portable and more intuitive Brain Computer Interface in real time scenario to control prosthetic devices by translating different brain states into operative control signals.

Keywords: Brain Computer Interface (BCI), Electroencephalogram (EEG), EEGLab, BCILab, Emotiv, Emotions, Interval features, Spectral features, Artificial Neural Network, Control applications.

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Authors: Lynette Sheridan Burns, Benjamin J. Matthews

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The context of post-industrial journalism is one in which the material circumstances of mechanical publication have been displaced by digital technologies, increasing the distance between the orthodoxy of the newsroom and the culture of journalistic writing. Content is, with growing frequency, created for delivery via the internet, publication on web-based ‘platforms’ and consumption on screen media. In this environment, the question is not ‘who is a journalist?’ but ‘what is journalism?’ today. The changes bring into sharp relief new distinctions between journalistic work and journalistic labor, providing a key insight into the current transition between the industrial journalism of the 20^th century, and the post-industrial journalism of the present. In the 20^th century, the work of journalists and journalistic labor went hand-in-hand as most journalists were employees of news organizations, whilst in the 21^st century evidence of a decoupling of ‘acts of journalism’ (work) and journalistic employment (labor) is beginning to appear. This 'decoupling' of the work and labor that underpins journalism practice is far reaching in its implications, not least for institutional structures. Under these conditions we are witnessing the emergence of expanded ‘entrepreneurial’ journalism, based on smaller, more independent and agile - if less stable - enterprise constructs that are a feature of creative industries. Entrepreneurial journalism is realized in a range of organizational forms from social enterprise, through to profit driven start-ups and hybrids of the two. In all instances, however, the primary motif of the organization is an ideological definition of journalism. An example is the Scoop Foundation for Public Interest Journalism in New Zealand, which owns and operates Scoop Publishing Limited, a not for profit company and social enterprise that publishes an independent news site that claims to have over 500,000 monthly users. Our paper demonstrates that this journalistic work meets the ideological definition of journalism; conducted within the creative industries using an innovative organizational structure that offers a new, viable post-industrial future for journalism.

Keywords: Creative industries, digital communication, journalism, post-industrial.

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Authors: I.Daut, N. Gomesh, M. Irwanto, Y. M. Irwan

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Public awareness towards green energy are on the rise and this can be prove by many product being manufactured or prerequired to be made as energy saving devices mainly to save consumer from spending more on utility billing. These schemes are popular nowadays and many homemade appliances are turned into energy saving gadget which attracts the attention of consumers. Knowing the public demands and pattern towards purchasing home appliances thus the idea of “energy saving suction hood (ESSH)" is proposed. The ESSH can be used in many places that require smoke ventilation or even to reduce the room temperature as many conventional suction hoods (CSH) do, but this device works automatically by the usage of sensors that detects the smoke/temperature and automatically spins the exhaust fan. As it turns, the mechanical rotation rotates the AC generator which is coupled together with the fan and then charges the battery. The innovation of this product is, it does not rely on the utility supply as it is also hook up with a solar panel which also charges the battery, Secondly, it generates energy as the exhaust fan mechanically rotates. Thirdly, an energy loop back feature is introduced to this system which will supply for the ventilator fan. Another major innovation is towards interfacing this device with an in house production of generator. This generator is produced by proper design on stator as well as rotor to reduce the losses. A comparison is made between the ESSH and the CSH and result shows that the ESSH saves 172.8kWh/year of utility supply which is used by CSH. This amount of energy can save RM 3.14 from monthly utility bill and a total of RM 37.67 per year. In fact this product can generate 175 Watt of power from generator(75W) and solar panel(100W) that can be used either to supply other household appliances and/or to loop back to supply the fans motor. The innovation of this system is essential for future production of other equipment by using the loopback power method and turning most equipment into a standalone system.

Keywords: Energy saving suction hood (ESSH), conventional suction hoods (CSH), energy, and power

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Authors: Hritwik Ghosh, Irfan Sadiq Rahat, Sachi Nandan Mohanty, J. V. R. Ravindra, Abdus Sobur

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In the rapidly evolving landscape of medical diagnostics, the early detection and accurate classification of skin cancer remain paramount for effective treatment outcomes. This research delves into the transformative potential of artificial intelligence (AI), specifically deep learning (DL), as a tool for discerning and categorizing various skin conditions. Utilizing a diverse dataset of 3,000 images, representing nine distinct skin conditions, we confront the inherent challenge of class imbalance. This imbalance, where conditions like melanomas are over-represented, is addressed by incorporating class weights during the model training phase, ensuring an equitable representation of all conditions in the learning process. Our approach presents a hybrid model, amalgamating the strengths of two renowned convolutional neural networks (CNNs), VGG16 and ResNet50. These networks, pre-trained on the ImageNet dataset, are adept at extracting intricate features from images. By synergizing these models, our research aims to capture a holistic set of features, thereby bolstering classification performance. Preliminary findings underscore the hybrid model's superiority over individual models, showcasing its prowess in feature extraction and classification. Moreover, the research emphasizes the significance of rigorous data pre-processing, including image resizing, color normalization, and segmentation, in ensuring data quality and model reliability. In essence, this study illuminates the promising role of AI and DL in revolutionizing skin cancer diagnostics, offering insights into its potential applications in broader medical domains.

Keywords: Artificial intelligence, machine learning, deep learning, skin cancer, dermatology, convolutional neural networks, image classification, computer vision, healthcare technology, cancer detection, medical imaging.

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Authors: Kaushik Sathupadi, Sandesh Achar

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Pervasive computing is a growing research field that aims to acknowledge human physical life routines (HPLR) based on body-worn sensors such as MEMS (Micro-Electro-Mechanical Systems) sensors-based technologies. The use of these technologies for human activity recognition is progressively increasing. On the other hand, personalizing human life routines using numerous machine-learning techniques has always been an intriguing topic. In contrast, various methods have demonstrated the ability to recognize basic movement patterns. However, it still needs to be improved to anticipate the dynamics of human living patterns. This study presents state-of-the-art techniques for recognizing static and dynamic patterns and forecasting those challenging activities from multi-fused sensors. Furthermore, numerous MEMS signals are extracted from one self-annotated IM-WSHA dataset and two benchmarked datasets. First, raw data were processed with z-normalization and denoiser methods. Then, we adopted statistical, local binary pattern, auto-regressive model, and intrinsic time scale decomposition major features for feature extraction from different domains. Next, the acquired features are optimized using maximum relevance and minimum redundancy (mRMR). Finally, the artificial neural network is applied to analyze the whole system's performance. As a result, we attained a 90.27% recognition rate for the self-annotated dataset, while the HARTH and KU-HAR achieved 83% on nine living activities and 90.94% on 18 static and dynamic routines. Thus, the proposed HPLR system outperformed other state-of-the-art systems when evaluated with other methods in the literature.

Keywords: Artificial intelligence, machine learning, gait analysis, local binary pattern, statistical features, micro-electro-mechanical systems, maximum relevance and minimum redundancy.

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Authors: Hoai Nam Huynh, Edouard Rivière-Lorphèvre, Olivier Verlinden

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Manufacturing technologies are becoming continuously more diversified over the years. The increasing use of robots for various applications such as assembling, painting, welding has also affected the field of machining. Machining robots can deal with larger workspaces than conventional machine-tools at a lower cost and thus represent a very promising alternative for machining applications. Furthermore, their inherent structure ensures them a great flexibility of motion to reach any location on the workpiece with the desired orientation. Nevertheless, machining robots suffer from a lack of stiffness at their joints restricting their use to applications involving low cutting forces especially finishing operations. Vibratory instabilities may also happen while machining and deteriorate the precision leading to scrap parts. Some researchers are therefore concerned with the identification of optimal parameters in robotic machining. This paper continues the development of a virtual robotic machining simulator in order to find optimized cutting parameters in terms of depth of cut or feed per tooth for example. The simulation environment combines an in-house milling routine (DyStaMill) achieving the computation of cutting forces and material removal with an in-house multibody library (EasyDyn) which is used to build a dynamic model of a 3-DOF planar robot with flexible links. The position of the robot end-effector submitted to milling forces is controlled through an inverse kinematics scheme while controlling the position of its joints separately. Each joint is actuated through a servomotor for which the transfer function has been computed in order to tune the corresponding controller. The output results feature the evolution of the cutting forces when the robot structure is deformable or not and the tracking errors of the end-effector. Illustrations of the resulting machined surfaces are also presented. The consideration of the links flexibility has highlighted an increase of the cutting forces magnitude. This proof of concept will aim to enrich the database of results in robotic machining for potential improvements in production.

Keywords: Control, machining, multibody, robotic, simulation.

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Authors: N. N. Mosola, S. J. Molete, L. S. Masoebe, M. Letsae

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Hand gesture recognition is a technique used to locate, detect, and recognize a hand gesture. Detection and recognition are concepts of Artificial Intelligence (AI). AI concepts are applicable in Human Computer Interaction (HCI), Expert systems (ES), etc. Hand gesture recognition can be used in sign language interpretation. Sign language is a visual communication tool. This tool is used mostly by deaf societies and those with speech disorder. Communication barriers exist when societies with speech disorder interact with others. This research aims to build a hand recognition system for Lesotho’s Sesotho and English language interpretation. The system will help to bridge the communication problems encountered by the mentioned societies. The system has various processing modules. The modules consist of a hand detection engine, image processing engine, feature extraction, and sign recognition. Detection is a process of identifying an object. The proposed system uses Canny pruning Haar and Haarcascade detection algorithms. Canny pruning implements the Canny edge detection. This is an optimal image processing algorithm. It is used to detect edges of an object. The system employs a skin detection algorithm. The skin detection performs background subtraction, computes the convex hull, and the centroid to assist in the detection process. Recognition is a process of gesture classification. Template matching classifies each hand gesture in real-time. The system was tested using various experiments. The results obtained show that time, distance, and light are factors that affect the rate of detection and ultimately recognition. Detection rate is directly proportional to the distance of the hand from the camera. Different lighting conditions were considered. The more the light intensity, the faster the detection rate. Based on the results obtained from this research, the applied methodologies are efficient and provide a plausible solution towards a light-weight, inexpensive system which can be used for sign language interpretation.

Keywords: Canny pruning, hand recognition, machine learning, skin tracking.

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Authors: Hesham Abdel-Khalek, Sherif M. Hafez, Abdel-Hamid M. el-Lakany, Yasser Abuel-Magd

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Investment in a constructed facility represents a cost in the short term that returns benefits only over the long term use of the facility. Thus, the costs occur earlier than the benefits, and the owners of facilities must obtain the capital resources to finance the costs of construction. A project cannot proceed without an adequate financing, and the cost of providing an adequate financing can be quite large. For these reasons, the attention to the project finance is an important aspect of project management. Finance is also a concern to the other organizations involved in a project such as the general contractor and material suppliers. Unless an owner immediately and completely covers the costs incurred by each participant, these organizations face financing problems of their own. At a more general level, the project finance is the only one aspect of the general problem of corporate finance. If numerous projects are considered and financed together, then the net cash flow requirements constitute the corporate financing problem for capital investment. Whether project finance is performed at the project or at the corporate level does not alter the basic financing problem .In this paper, we will first consider facility financing from the owner's perspective, with due consideration for its interaction with other organizations involved in a project. Later, we discuss the problems of construction financing which are crucial to the profitability and solvency of construction contractors. The objective of this paper is to present the steps utilized to determine the best combination of minimum project financing. The proposed model considers financing; schedule and maximum net area .The proposed model is called Project Financing and Schedule Integration using Genetic Algorithms "PFSIGA". This model intended to determine more steps (maximum net area) for any project with a subproject. An illustrative example will demonstrate the feature of this technique. The model verification and testing are put into consideration.

Keywords: Project Management, Large-scale ConstructionProjects, Cash flow, Interest, Investment, Loan, Optimization, Scheduling, Financing and Genetic Algorithms.

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Authors: Radul Shishkov, Petar Penchev

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The digital era has revolutionized architectural practices, with Building Information Modeling (BIM) emerging as a pivotal tool for architects, engineers, and construction professionals. However, the transition from traditional methods to BIM-centric approaches poses significant challenges, particularly in the context of existing structures. This research presents a technical approach to bridge this gap through the development of algorithms that facilitate the automated transformation of 3D point cloud data into detailed BIM models. The core of this research lies in the application of algorithmic modeling and computational design methods to interpret and reconstruct point cloud data — a collection of data points in space, typically produced by 3D scanners — into comprehensive BIM models. This process involves complex stages of data cleaning, feature extraction, and geometric reconstruction, which are traditionally time-consuming and prone to human error. By automating these stages, our approach significantly enhances the efficiency and accuracy of creating BIM models for existing buildings. The proposed algorithms are designed to identify key architectural elements within point clouds, such as walls, windows, doors, and other structural components, and to translate these elements into their corresponding BIM representations. This includes the integration of parametric modeling techniques to ensure that the generated BIM models are not only geometrically accurate but also embedded with essential architectural and structural information. This research contributes significantly to the field of architectural technology by providing a scalable and efficient solution for the integration of existing structures into the BIM framework. It paves the way for more seamless and integrated workflows in renovation and heritage conservation projects, where the accuracy of existing conditions plays a critical role. The implications of this study extend beyond architectural practices, offering potential benefits in urban planning, facility management, and historical preservation.

Keywords: Algorithmic modeling, Building Information Modeling, point cloud, reconstruction.

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Authors: Davide D’Aria, Paolo Falcone, Luigi Maggi, Aldo Cero, Giovanni Amoroso

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The paper presents a methodology for real-time structural health monitoring and geophysical applications. The key elements of the system are a high performance MIMO RADAR sensor, an optical camera and a dedicated set of software algorithms encompassing interferometry, tomography and photogrammetry. The MIMO Radar sensor proposed in this work, provides an extremely high sensitivity to displacements making the system able to react to tiny deformations (up to tens of microns) with a time scale which spans from milliseconds to hours. The MIMO feature of the system makes the system capable of providing a set of two-dimensional images of the observed scene, each mapped on the azimuth-range directions with noticeably resolution in both the dimensions and with an outstanding repetition rate. The back-scattered energy, which is distributed in the 3D space, is projected on a 2D plane, where each pixel has as coordinates the Line-Of-Sight distance and the cross-range azimuthal angle. At the same time, the high performing processing unit allows to sense the observed scene with remarkable refresh periods (up to milliseconds), thus opening the way for combined static and dynamic structural health monitoring. Thanks to the smart TX/RX antenna array layout, the MIMO data can be processed through a tomographic approach to reconstruct the three-dimensional map of the observed scene. This 3D point cloud is then accurately mapped on a 2D digital optical image through photogrammetric techniques, allowing for easy and straightforward interpretations of the measurements. Once the three-dimensional image is reconstructed, a 'repeat-pass' interferometric approach is exploited to provide the user of the system with high frequency three-dimensional motion/vibration estimation of each point of the reconstructed image. At this stage, the methodology leverages consolidated atmospheric correction algorithms to provide reliable displacement and vibration measurements.

Keywords: Interferometry, MIMO RADAR, SAR, tomography.

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Authors: S. Behnam Malekzadeh, I. Kerr, T. Kaempffer, T. Harper, A Watson

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The Site C Hydroelectric dam is currently being constructed in north-eastern British Columbia on sub-horizontal sedimentary strata that dip approximately 15 meters from one bank of the Peace River to the other. More than 615 pressure sensors (Vibrating Wire Piezometers) have been installed on bedding planes (BPs) since construction began, with over 80 more planned before project completion. These pressure measurements are essential to monitor the stability of the rock foundation during and after construction and for dam safety purposes. BPs are identified by their clay gouge infilling, which varies in thickness from less than 1 to 20 mm and can be challenging to identify as the core drilling process often disturbs or washes away the gouge material. Without the use of depth predictions from nearby boreholes, stratigraphic markers, and downhole geophysical data, it is difficult to confidently identify BP targets for the sensors. In this paper, a Case-Based Reasoning (CBR) method was used to develop an empirical model called the Bedding Plane Elevation Prediction (BPEP) to help geologists and geotechnical engineers to predict geological features and BPs at new locations in a fast and accurate manner. To develop CBR, a database was developed based on 64 pressure sensors already installed on key bedding planes BP25, BP28, and BP31 on the Right Bank, including BP elevations and coordinates. 13 (20%) of the most recent cases were selected to validate and evaluate the accuracy of the developed model, while the similarity was defined as the distance between previous cases and recent cases to predict the depth of significant BPs. The average difference between actual BP elevations and predicted elevations for above BPs was ± 55 cm, while the actual results showed that 69% of predicted elevations were within ± 79 cm of actual BP elevations while 100% of predicted elevations for new cases were within ± 99 cm range. Eventually, the actual results will be used to develop the database and improve BPEP to perform as a learning machine to predict more accurate BP elevations for future sensor installations.

Keywords: Case-Based Reasoning, CBR, geological feature, geology, piezometer, pressure sensor, core logging, dam construction.

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Authors: S. Apprich, F. Wulle, A. Lechler, A. Pott, A. Verl

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Large-scale machine tools for the manufacturing of large work pieces, e.g. blades, casings or gears for wind turbines, feature pose-dependent dynamic behavior. Small structural damping coefficients lead to long decay times for structural vibrations that have negative impacts on the production process. Typically, these vibrations are handled by increasing the stiffness of the structure by adding mass. This is counterproductive to the needs of sustainable manufacturing as it leads to higher resource consumption both in material and in energy. Recent research activities have led to higher resource efficiency by radical mass reduction that is based on controlintegrated active vibration avoidance and damping methods. These control methods depend on information describing the dynamic behavior of the controlled machine tools in order to tune the avoidance or reduction method parameters according to the current state of the machine. This paper presents the appearance, consequences and challenges of the pose-dependent dynamic behavior of lightweight large-scale machine tool structures in production. It starts with the theoretical introduction of the challenges of lightweight machine tool structures resulting from reduced stiffness. The statement of the pose-dependent dynamic behavior is corroborated by the results of the experimental modal analysis of a lightweight test structure. Afterwards, the consequences of the pose-dependent dynamic behavior of lightweight machine tool structures for the use of active control and vibration reduction methods are explained. Based on the state of the art of pose-dependent dynamic machine tool models and the modal investigation of an FE-model of the lightweight test structure, the criteria for a pose-dependent model for use in vibration reduction are derived. The description of the approach for a general posedependent model of the dynamic behavior of large lightweight machine tools that provides the necessary input to the aforementioned vibration avoidance and reduction methods to properly tackle machine vibrations is the outlook of the paper.

Keywords: Dynamic behavior, lightweight, machine tool, pose-dependency.

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Authors: Lily Ingsrisawang, Supawadee Ingsriswang, Saisuda Somchit, Prasert Aungsuratana, Warawut Khantiyanan

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This paper presents the methodology from machine learning approaches for short-term rain forecasting system. Decision Tree, Artificial Neural Network (ANN), and Support Vector Machine (SVM) were applied to develop classification and prediction models for rainfall forecasts. The goals of this presentation are to demonstrate (1) how feature selection can be used to identify the relationships between rainfall occurrences and other weather conditions and (2) what models can be developed and deployed for predicting the accurate rainfall estimates to support the decisions to launch the cloud seeding operations in the northeastern part of Thailand. Datasets collected during 2004-2006 from the Chalermprakiat Royal Rain Making Research Center at Hua Hin, Prachuap Khiri khan, the Chalermprakiat Royal Rain Making Research Center at Pimai, Nakhon Ratchasima and Thai Meteorological Department (TMD). A total of 179 records with 57 features was merged and matched by unique date. There are three main parts in this work. Firstly, a decision tree induction algorithm (C4.5) was used to classify the rain status into either rain or no-rain. The overall accuracy of classification tree achieves 94.41% with the five-fold cross validation. The C4.5 algorithm was also used to classify the rain amount into three classes as no-rain (0-0.1 mm.), few-rain (0.1- 10 mm.), and moderate-rain (>10 mm.) and the overall accuracy of classification tree achieves 62.57%. Secondly, an ANN was applied to predict the rainfall amount and the root mean square error (RMSE) were used to measure the training and testing errors of the ANN. It is found that the ANN yields a lower RMSE at 0.171 for daily rainfall estimates, when compared to next-day and next-2-day estimation. Thirdly, the ANN and SVM techniques were also used to classify the rain amount into three classes as no-rain, few-rain, and moderate-rain as above. The results achieved in 68.15% and 69.10% of overall accuracy of same-day prediction for the ANN and SVM models, respectively. The obtained results illustrated the comparison of the predictive power of different methods for rainfall estimation.

Keywords: Machine learning, decision tree, artificial neural network, support vector machine, root mean square error.

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Authors: Thomas Bryan, Veton Kepuska, Ivica Kostanic

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Speaker recognition is performed in high Additive White Gaussian Noise (AWGN) environments using principals of Computational Auditory Scene Analysis (CASA). CASA methods often classify sounds from images in the time-frequency (T-F) plane using spectrograms or cochleargrams as the image. In this paper atomic decomposition implemented by matching pursuit performs a transform from time series speech signals to the T-F plane. The atomic decomposition creates a sparsely populated T-F vector in “weight space” where each populated T-F position contains an amplitude weight. The weight space vector along with the atomic dictionary represents a denoised, compressed version of the original signal. The arraignment or of the atomic indices in the T-F vector are used for classification. Unsupervised feature learning implemented by a sparse autoencoder learns a single dictionary of basis features from a collection of envelope samples from all speakers. The approach is demonstrated using pairs of speakers from the TIMIT data set. Pairs of speakers are selected randomly from a single district. Each speak has 10 sentences. Two are used for training and 8 for testing. Atomic index probabilities are created for each training sentence and also for each test sentence. Classification is performed by finding the lowest Euclidean distance between then probabilities from the training sentences and the test sentences. Training is done at a 30dB Signal-to-Noise Ratio (SNR). Testing is performed at SNR’s of 0 dB, 5 dB, 10 dB and 30dB. The algorithm has a baseline classification accuracy of ~93% averaged over 10 pairs of speakers from the TIMIT data set. The baseline accuracy is attributable to short sequences of training and test data as well as the overall simplicity of the classification algorithm. The accuracy is not affected by AWGN and produces ~93% accuracy at 0dB SNR.

Keywords: Time-frequency plane, atomic decomposition, envelope sampling, Gabor atoms, matching pursuit, sparse dictionary learning, sparse autoencoder.

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Authors: Mohamed A. Saleem

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Using mostly seismic data, this study intends to show some examples of igneous intrusions found in some areas of the Sirt Basin and explore the period of their emplacement as well as the interrelationships between these sills. The study area is located in the south of the Zallah Trough, south-west Sirt basin, Libya. It is precisely between the longitudes 18.35ᵒ E and 19.35ᵒ E, and the latitudes 27.8ᵒ N and 28.0ᵒ N. Based on a variety of criteria that are usually used as marks on the igneous intrusions, 12 igneous intrusions (Sills), have been detected and analysed using 3D seismic data. One or more of the following were used as identification criteria: the high amplitude reflectors paired with abrupt reflector terminations, vertical offsets, or what is described as a dike-like connection, the violation, the saucer form, and the roughness. Because of their laying between the hosting layers, the majority of these intrusions are classified as sills. Another distinguishing feature is the intersection geometry link between some of these sills. Every single sill has given a name just to distinguish the sills from each other such as S-1, S-2, and … S-12. To avoid the repetition of description, the common characteristics and some statistics of these sills are shown in summary tables, while the specific characters that are not common and have been noticed for each sill are shown individually. The sills, S-1, S-2, and S-3, are approximately parallel to one other, with the shape of these sills being governed by the syncline structure of their host layers. The faults that dominated the strata (pre-upper Cretaceous strata) have a significant impact on the sills; they caused their discontinuity, while the upper layers have a shape of anticlines. S-1 and S-10 are the group's deepest and highest sills, respectively, with S-1 seated near the basement's top and S-10 extending into the sequence of the upper cretaceous. The dramatic escalation of sill S-4 can be seen in North-South profiles. The majority of the interpreted sills are influenced and impacted by a large number of normal faults that strike in various directions and propagate vertically from the surface to the basement's top. This indicates that the sediment sequences were existed before the sill’s intrusion, deposited, and that the younger faults occurred more recently. The pre-upper cretaceous unit is the current geological depth for the Sills S-1, S-2 … S-9, while Sills S-10, S-11, and S-12 are hosted by the Cretaceous unit. Over the sills S-1, S-2, and S-3, which are the deepest sills, the pre-upper cretaceous surface has a slightly forced folding, these forced folding is also noticed above the right and left tips of sill S-8 and S-6, respectively, while the absence of these marks on the above sequences of layers supports the idea that the aforementioned sills were emplaced during the early upper cretaceous period.

Keywords: Sirt Basin, Zallah Trough, igneous intrusions, seismic data.

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Authors: Hoa Pham

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The literature on language teaching and second language acquisition has been largely driven by monolingual ideology with a common assumption that a second language (L2) is best taught and learned in the L2 only. The current study challenges this assumption by reporting learners' positive perceptions of tertiary level teachers' code switching practices in Vietnam. The findings of this study contribute to our understanding of code switching practices in language classrooms from a learners' perspective. Data were collected from student participants who were working towards a Bachelor degree in English within the English for Business Communication stream through the use of focus group interviews. The literature has documented that this method of interviewing has a number of distinct advantages over individual student interviews. For instance, group interactions generated by focus groups create a more natural environment than that of an individual interview because they include a range of communicative processes in which each individual may influence or be influenced by others - as they are in their real life. The process of interaction provides the opportunity to obtain the meanings and answers to a problem that are "socially constructed rather than individually created" leading to the capture of real-life data. The distinct feature of group interaction offered by this technique makes it a powerful means of obtaining deeper and richer data than those from individual interviews. The data generated through this study were analysed using a constant comparative approach. Overall, the students expressed positive views of this practice indicating that it is a useful teaching strategy. Teacher code switching was seen as a learning resource and a source supporting language output. This practice was perceived to promote student comprehension and to aid the learning of content and target language knowledge. This practice was also believed to scaffold the students' language production in different contexts. However, the students indicated their preference for teacher code switching to be constrained, as extensive use was believed to negatively impact on their L2 learning and trigger cognitive reliance on the L1 for L2 learning. The students also perceived that when the L1 was used to a great extent, their ability to develop as autonomous learners was negatively impacted. This study found that teacher code switching was supported in certain contexts by learners, thus suggesting that there is a need for the widespread assumption about the monolingual teaching approach to be re-considered.

Keywords: Code switching, L1 use, L2 teaching, Learners’ perception.

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Authors: G. Candel, D. Naccache

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t-SNE is an embedding method that the data science community has widely used. It helps two main tasks: to display results by coloring items according to the item class or feature value; and for forensic, giving a first overview of the dataset distribution. Two interesting characteristics of t-SNE are the structure preservation property and the answer to the crowding problem, where all neighbors in high dimensional space cannot be represented correctly in low dimensional space. t-SNE preserves the local neighborhood, and similar items are nicely spaced by adjusting to the local density. These two characteristics produce a meaningful representation, where the cluster area is proportional to its size in number, and relationships between clusters are materialized by closeness on the embedding. This algorithm is non-parametric. The transformation from a high to low dimensional space is described but not learned. Two initializations of the algorithm would lead to two different embedding. In a forensic approach, analysts would like to compare two or more datasets using their embedding. A naive approach would be to embed all datasets together. However, this process is costly as the complexity of t-SNE is quadratic, and would be infeasible for too many datasets. Another approach would be to learn a parametric model over an embedding built with a subset of data. While this approach is highly scalable, points could be mapped at the same exact position, making them indistinguishable. This type of model would be unable to adapt to new outliers nor concept drift. This paper presents a methodology to reuse an embedding to create a new one, where cluster positions are preserved. The optimization process minimizes two costs, one relative to the embedding shape and the second relative to the support embedding’ match. The embedding with the support process can be repeated more than once, with the newly obtained embedding. The successive embedding can be used to study the impact of one variable over the dataset distribution or monitor changes over time. This method has the same complexity as t-SNE per embedding, and memory requirements are only doubled. For a dataset of n elements sorted and split into k subsets, the total embedding complexity would be reduced from O(n2) to O(n2/k), and the memory requirement from n2 to 2(n/k)2 which enables computation on recent laptops. The method showed promising results on a real-world dataset, allowing to observe the birth, evolution and death of clusters. The proposed approach facilitates identifying significant trends and changes, which empowers the monitoring high dimensional datasets’ dynamics.

Keywords: Concept drift, data visualization, dimension reduction, embedding, monitoring, reusability, t-SNE, unsupervised learning.

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Authors: A. Schuchter, M. Promegger

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The outbreak of the COVID-19 pandemic has shown us that online education is so much more than just a cool feature for teachers – it is an essential part of modern teaching. In online math teaching, it is common to use tools to share screens, compute and calculate mathematical examples, while the students can watch the process. On the other hand, flipped classroom models are on the rise, with their focus on how students can gather knowledge by watching videos and on the teacher’s use of technological tools for information transfer. This paper proposes a co-educational teaching approach for coding and engineering subjects with the help of drone-building to spark interest in technology and create a platform for knowledge transfer. The project combines aspects from mathematics (matrices, vectors, shaders, trigonometry), physics (force, pressure and rotation) and coding (computational thinking, block-based programming, JavaScript and Python) and makes use of collaborative-shared 3D Modeling with clara.io, where students create mathematics knowhow. The instructor follows a problem-based learning approach and encourages their students to find solutions in their own time and in their own way, which will help them develop new skills intuitively and boost logically structured thinking. The collaborative aspect of working in groups will help the students develop communication skills as well as structural and computational thinking. Students are not just listeners as in traditional classroom settings, but play an active part in creating content together by compiling a Handbook of Knowledge (called “open book”) with examples and solutions. Before students start calculating, they have to write down all their ideas and working steps in full sentences so other students can easily follow their train of thought. Therefore, students will learn to formulate goals, solve problems, and create a ready-to use product with the help of “reverse engineering”, cross-referencing and creative thinking. The work on drones gives the students the opportunity to create a real-life application with a practical purpose, while going through all stages of product development.

Keywords: Flipped classroom, co-creational education, coding, making, drones, co-education, ARCS-model, problem-based learning.

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Authors: Siddhartha Bhattacharyya, Paramartha Dutta, Ujjwal Maulik, Prashanta Kumar Nandi

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A multilayer self organizing neural neural network (MLSONN) architecture for binary object extraction, guided by a beta activation function and characterized by backpropagation of errors estimated from the linear indices of fuzziness of the network output states, is discussed. Since the MLSONN architecture is designed to operate in a single point fixed/uniform thresholding scenario, it does not take into cognizance the heterogeneity of image information in the extraction process. The performance of the MLSONN architecture with representative values of the threshold parameters of the beta activation function employed is also studied. A three layer bidirectional self organizing neural network (BDSONN) architecture comprising fully connected neurons, for the extraction of objects from a noisy background and capable of incorporating the underlying image context heterogeneity through variable and adaptive thresholding, is proposed in this article. The input layer of the network architecture represents the fuzzy membership information of the image scene to be extracted. The second layer (the intermediate layer) and the final layer (the output layer) of the network architecture deal with the self supervised object extraction task by bi-directional propagation of the network states. Each layer except the output layer is connected to the next layer following a neighborhood based topology. The output layer neurons are in turn, connected to the intermediate layer following similar topology, thus forming a counter-propagating architecture with the intermediate layer. The novelty of the proposed architecture is that the assignment/updating of the inter-layer connection weights are done using the relative fuzzy membership values at the constituent neurons in the different network layers. Another interesting feature of the network lies in the fact that the processing capabilities of the intermediate and the output layer neurons are guided by a beta activation function, which uses image context sensitive adaptive thresholding arising out of the fuzzy cardinality estimates of the different network neighborhood fuzzy subsets, rather than resorting to fixed and single point thresholding. An application of the proposed architecture for object extraction is demonstrated using a synthetic and a real life image. The extraction efficiency of the proposed network architecture is evaluated by a proposed system transfer index characteristic of the network.

Keywords: Beta activation function, fuzzy cardinality, multilayer self organizing neural network, object extraction,

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Authors: M. Garcia-Alsina, J. Cobarsí

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The role of controlled vocabularies in information retrieval is broadly recognized as a relevant feature. Besides, there is a standing demand that editors and databases should consider the effective introduction of controlled vocabularies in their procedures to index scientific literature. That is especially important because information retrieval is pointed out as a significant point to drive systematic literature review. Hence, a first question emerges: Are the controlled vocabularies at this moment considered? On the other hand, subject searching in the catalogs is complex mainly due to the dichotomy between keywords from authors versus keywords based on controlled vocabularies. Finally, there is some demand to unify the terminology related to health to make easier the medical history exploitation and research. Considering these features, this paper focuses on controlled vocabularies related to the health field and their role for storing, classifying, and retrieving relevant literature. The objective is knowing which role plays the controlled vocabularies related to the health field to index and retrieve research literature in data bases such as Web of Science (WoS) and Scopus. So, this exploratory research is grounded over two research questions: 1) Which are the terms considered in specific controlled vocabularies of the health field; and 2) How papers are indexed in relevant databases to be easily retrieved, considering keywords vs specific health’ controlled vocabularies? This research takes as fieldwork the controlled vocabularies related to health and the scientific interest for 1918 flu pandemic, also known equivocally as ‘Spanish flu’. This interest has been fostered by the emergence in the early 21st of epidemics of pneumonic diseases caused by virus. Searches about and with controlled vocabularies on WoS and Scopus databases are conducted. First results of this work in progress are surprising. There are different controlled vocabularies for the health field, into which the terms collected and preferred related to ‘1918 pandemic’ are identified. To summarize, ‘Spanish influenza epidemic’ or ‘Spanish flu’ are collected as not preferred terms. The preferred terms are: ‘influenza’ or ‘influenza pandemic, 1918-1919’. Although the controlled vocabularies are clear in their election, most of the literature about ‘1918 pandemic’ is retrievable either by ‘Spanish’ or by ‘1918’ disjunct, and the dominant word to retrieve literature is ‘Spanish’ rather than ‘1918’. This is surprising considering the existence of suitable controlled vocabularies related to health topics, and the modern guidelines of World Health Organization concerning naming of diseases that point out to other preferred terms. A first conclusion is the failure of using controlled vocabularies for a field such as health, and in consequence for WoS and Scopus. This research opens further research questions about which is the role that controlled vocabularies play in the instructions to authors that journals deliver to documents’ authors.

Keywords: Controlled vocabularies, indexing, 1918 influenza, information retrieval, keywords, 1918 pandemic, scientific databases.

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Authors: Orkide Donma, Mustafa M. Donma

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Association between insulin resistance (IR) and hematological parameters has long been a matter of interest. Within this context, body mass index (BMI), red blood cells, white blood cells and platelets were involved in this discussion. Parameters related to platelets associated with IR may be useful indicators for the identification of IR. Platelet indices such as mean platelet volume (MPV), platelet distribution width (PDW) and plateletcrit (PCT) are being questioned for their possible association with IR. The aim of this study was to investigate the association between platelet (PLT) count as well as PLT indices and the surrogate indices used to determine IR in morbid obese (MO) children. A total of 167 children participated in the study. Three groups were constituted. The number of cases was 34, 97 and 36 children in the normal BMI, MO and metabolic syndrome (MetS) groups, respectively. Sex- and age-dependent BMI-based percentile tables prepared by World Health Organization were used for the definition of morbid obesity. MetS criteria were determined. BMI values, homeostatic model assessment for IR (HOMA-IR), alanine transaminase-to-aspartate transaminase ratio (ALT/AST) and diagnostic obesity notation model assessment laboratory (DONMA-lab) index values were computed. PLT count and indices were analyzed using automated hematology analyzer. Data were collected for statistical analysis using SPSS for Windows. Arithmetic mean and standard deviation were calculated. Mean values of PLT-related parameters in both control and study groups were compared by one-way ANOVA followed by Tukey post hoc tests to determine whether a significant difference exists among the groups. The correlation analyses between PLT as well as IR indices were performed. Statistically significant difference was accepted as p-value < 0.05. Increased values were detected for PLT (p < 0.01) and PCT (p > 0.05) in MO group compared to those observed in children with N-BMI. Significant increases for PLT (p < 0.01) and PCT (p < 0.05) were observed in MetS group in comparison with the values obtained in children with N-BMI (p < 0.01). Significantly lower MPV and PDW values were obtained in MO group compared to the control group (p < 0.01). HOMA-IR (p < 0.05), DONMA-lab index (p < 0.001) and ALT/AST (p < 0.001) values in MO and MetS groups were significantly increased compared to the N-BMI group. On the other hand, DONMA-lab index values also differed between MO and MetS groups (p < 0.001). In the MO group, PLT was negatively correlated with MPV and PDW values. These correlations were not observed in the N-BMI group. None of the IR indices exhibited a correlation with PLT and PLT indices in the N-BMI group. HOMA-IR showed significant correlations both with PLT and PCT in the MO group. All of the three IR indices were well-correlated with each other in all groups. These findings point out the missing link between IR and PLT activation. In conclusion, PLT and PCT may be related to IR in addition to their identities as hemostasis markers during morbid obesity. Our findings have suggested that DONMA-lab index appears as the best surrogate marker for IR due to its discriminative feature between morbid obesity and MetS.

Keywords: Children, insulin resistance, metabolic syndrome, plateletcrit, platelet indices.

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Authors: Roberto Sabatini, Celia Bartel, Anish Kaharkar, Tesheen Shaid, Subramanian Ramasamy

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Two multisensor system architectures for navigation and guidance of small Unmanned Aircraft (UA) are presented and compared. The main objective of our research is to design a compact, light and relatively inexpensive system capable of providing the required navigation performance in all phases of flight of small UA, with a special focus on precision approach and landing, where Vision Based Navigation (VBN) techniques can be fully exploited in a multisensor integrated architecture. Various existing techniques for VBN are compared and the Appearance-Based Navigation (ABN) approach is selected for implementation. Feature extraction and optical flow techniques are employed to estimate flight parameters such as roll angle, pitch angle, deviation from the runway centreline and body rates. Additionally, we address the possible synergies of VBN, Global Navigation Satellite System (GNSS) and MEMS-IMU (Micro-Electromechanical System Inertial Measurement Unit) sensors, and the use of Aircraft Dynamics Model (ADM) to provide additional information suitable to compensate for the shortcomings of VBN and MEMS-IMU sensors in high-dynamics attitude determination tasks. An Extended Kalman Filter (EKF) is developed to fuse the information provided by the different sensors and to provide estimates of position, velocity and attitude of the UA platform in real-time. The key mathematical models describing the two architectures i.e., VBN-IMU-GNSS (VIG) system and VIGADM (VIGA) system are introduced. The first architecture uses VBN and GNSS to augment the MEMS-IMU. The second mode also includes the ADM to provide augmentation of the attitude channel. Simulation of these two modes is carried out and the performances of the two schemes are compared in a small UA integration scheme (i.e., AEROSONDE UA platform) exploring a representative cross-section of this UA operational flight envelope, including high dynamics manoeuvres and CAT-I to CAT-III precision approach tasks. Simulation of the first system architecture (i.e., VIG system) shows that the integrated system can reach position, velocity and attitude accuracies compatible with the Required Navigation Performance (RNP) requirements. Simulation of the VIGA system also shows promising results since the achieved attitude accuracy is higher using the VBN-IMU-ADM than using VBN-IMU only. A comparison of VIG and VIGA system is also performed and it shows that the position and attitude accuracy of the proposed VIG and VIGA systems are both compatible with the RNP specified in the various UA flight phases, including precision approach down to CAT-II.

Keywords: Global Navigation Satellite System (GNSS), Lowcost Navigation Sensors, MEMS Inertial Measurement Unit (IMU), Unmanned Aerial Vehicle, Vision Based Navigation.

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Authors: U. K. Sahin, N. Erdumlu, C. Saricam, I. Gocek, M. H. Arslan, H. Acikgoz-Tufan, B. Kalav

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Finishing is an important part of fabric processing with which a wide range of features are imparted to greige or colored fabrics for various end-uses. Especially, by the addition or impartation of nano-scaled particles to the fabric structure composite fabrics, a kind of composite materials can be acquired. Composite materials, generally shortened as composites or in other words composition materials, are engineered or naturally occurring materials made from two or more component materials with significantly different physical, mechanical or chemical characteristics remaining separate and distinctive at the macroscopic or microscopic scale within the end product structure. Therefore, the technique finishing which is one of the fundamental methods to be applied on fabrics for obtainment of composite fabrics with many functionalities was used in the current study with the same purpose. However, regardless of the finishing materials applied, the efficient life of finished product on offering desired feature is low, since the durability of finishes on the material is limited. Any increase in durability of these finishes on textiles would enhance the life of use for textiles, which will result in happier users. Therefore, in this study, since higher durability was desired for the finishing materials fixed on the fabrics, nano-scaled hollow structured cyclodextrins were chemically imparted by grafting to the structure of conventional cotton fabrics by the help of finishing technique in order to be fixed permanently. By this way, a processed and functionalized base fabric having potential to be treated in the subsequent processes with many different finishing agents and nanomaterials could be obtained. Henceforth, this fabric can be used as a multi-functional fabric due to the encapturing ability of cyclodextrins to molecules/particles via physical/chemical means. In this study, scoured and rinsed woven bleached plain weave 100% cotton fabrics were utilized because textiles made of cotton are the most demanded textile products in the textile market by the textile consumers in daily life. Cotton fabric samples were immersed in treating baths containing β-cyclodextrin and 1,2,3,4-butanetetracarboxylic acid and to reduce the curing temperature the catalyst sodium hypophosphite monohydrate was used. All impregnated fabric samples were pre-dried. The reaction of grafting was performed in dry state. The treated and cured fabric samples were rinsed with warm distilled water and dried. The samples were dried for 4 h and weighed before and after finishing and rinsing. Stability and durability of β-cyclodextrins on fabric surface against external factors such as washing as well as strength of functionalized fabric in terms of tensile and tear strength were tested. Presence and homogeneity of distribution of β-cyclodextrins on fabric surface were characterized.

Keywords: Cotton fabric, cyclodextrin, improved durability, multifunctional composite textile.

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Authors: Mohammad H. Taheri, David J. Brown, Nasser Sherkat

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Engagement is one of the most important factors in determining successful outcomes and deep learning in students. Existing approaches to detect student engagement involve periodic human observations that are subject to inter-rater reliability. Our solution uses real-time multimodal multisensor data labeled by objective performance outcomes to infer the engagement of students. The study involves four students with a combined diagnosis of cerebral palsy and a learning disability who took part in a 3-month trial over 59 sessions. Multimodal multisensor data were collected while they participated in a continuous performance test. Eye gaze, electroencephalogram, body pose, and interaction data were used to create a model of student engagement through objective labeling from the continuous performance test outcomes. In order to achieve this, a type of continuous performance test is introduced, the Seek-X type. Nine features were extracted including high-level handpicked compound features. Using leave-one-out cross-validation, a series of different machine learning approaches were evaluated. Overall, the random forest classification approach achieved the best classification results. Using random forest, 93.3% classification for engagement and 42.9% accuracy for disengagement were achieved. We compared these results to outcomes from different models: AdaBoost, decision tree, k-Nearest Neighbor, naïve Bayes, neural network, and support vector machine. We showed that using a multisensor approach achieved higher accuracy than using features from any reduced set of sensors. We found that using high-level handpicked features can improve the classification accuracy in every sensor mode. Our approach is robust to both sensor fallout and occlusions. The single most important sensor feature to the classification of engagement and distraction was shown to be eye gaze. It has been shown that we can accurately predict the level of engagement of students with learning disabilities in a real-time approach that is not subject to inter-rater reliability, human observation or reliant on a single mode of sensor input. This will help teachers design interventions for a heterogeneous group of students, where teachers cannot possibly attend to each of their individual needs. Our approach can be used to identify those with the greatest learning challenges so that all students are supported to reach their full potential.

Keywords: Affective computing in education, affect detection, continuous performance test, engagement, flow, HCI, interaction, learning disabilities, machine learning, multimodal, multisensor, physiological sensors, Signal Detection Theory, student engagement.

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Authors: W. S. Mohamed, A. A. Hammam

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Sandy soils under arid and semi-arid conditions are characterized by poor physical and biochemical properties such as low water retention, rapid organic matter decomposition, low nutrients use efficiency, and limited crop productivity. Addition of organic amendments is crucial to develop soil properties and consequently enhance nutrients use efficiency and lessen organic carbon decomposition. Two years field experiments were developed to investigate the feasibility of using poultry manure and its derived biochar integrated with different levels of N fertilizer as a soil amendment for newly reclaimed sandy soils in Western Desert of El-Minia Governorate, Egypt. Results of this research revealed that poultry manure and its derived biochar addition induced pronounced effects on soil moisture content at saturation point, field capacity (FC) and consequently available water. Data showed that application of poultry manure (PM) or PM-derived biochar (PMB) in combination with inorganic N levels had caused significant changes on a range of the investigated sandy soil biochemical properties including pH, EC, mineral N, dissolved organic carbon (DOC), dissolved organic N (DON) and quotient DOC/DON. Overall, the impact of PMB on soil physical properties was detected to be superior than the impact of PM, regardless the inorganic N levels. In addition, the obtained results showed that PM and PM application had the capacity to stimulate vigorous growth, nutritional status, production levels of wheat and sorghum, and to increase soil organic matter content and N uptake and recovery compared to control. By contrast, comparing between PM and PMB at different levels of inorganic N, the obtained results showed higher relative increases in both grain and straw yields of wheat in plots treated with PM than in those treated with PMB. The interesting feature of this research is that the biochar derived from PM increased treated sandy soil organic carbon (SOC) 1.75 times more than soil treated with PM itself at the end of cropping seasons albeit double-applied amount of PM. This was attributed to the higher carbon stability of biochar treated sandy soils increasing soil persistence for carbon decomposition in comparison with PM labile carbon. It could be concluded that organic manures applied to sandy soils under arid and semi-arid conditions are subjected to high decomposition and mineralization rates through crop seasons. Biochar derived from organic wastes considers as a source of stable carbon and could be very hopeful choice for substituting easily decomposable organic manures under arid conditions. Therefore, sustainable agriculture and productivity in newly reclaimed sandy soils desire one high rate addition of biochar derived from organic manures instead of frequent addition of such organic amendments.

Keywords: Biochar, dissolved organic carbon, N-uptake, poultry, sandy soil.

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Authors: M. Invigorito, G. Elia, M. Panelli

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Hybrid propulsion combines beneficial properties of both solid and liquid rockets, such as multiple restarts, throttability as well as simplicity and reduced costs. A nitrous oxide (N₂O)/paraffin-based hybrid rocket engine demonstrator is currently under development at the Italian Aerospace Research Center (CIRA) within the national research program HYPROB, funded by the Italian Ministry of Research. Nitrous oxide belongs to the class of self-pressurizing propellants that exhibit a high vapor pressure at standard ambient temperature. This peculiar feature makes those fluids very attractive for space rocket applications because it avoids the use of complex pressurization systems, leading to great benefits in terms of weight savings and reliability. To avoid feed-system-coupled instabilities, the phase change is required to occur through the injectors. In this regard, the oxidizer is stored in liquid condition while target chamber pressures are designed to lie below vapor pressure. The consequent cavitation and flash vaporization constitute a remarkably complex phenomenology that arises great modelling challenges. Thus, it is clear that the design of the injection system is fundamental for the full exploitation of hybrid rocket engine throttability. The Analytical Hierarchy Process has been used to select the injection architecture as best compromise among different design criteria such as functionality, technology innovation and cost. The impossibility to use engineering simplified relations for the dimensioning of the injectors led to the needs of applying a numerical approach based on OpenFOAM^®. The numerical tool has been validated with selected experimental data from literature. Quantitative, as well as qualitative comparisons are performed in terms of mass flow rate and pressure drop across the injector for several operating conditions. The results show satisfactory agreement with the experimental data. Modeling assumptions, together with their impact on numerical predictions are discussed in the paper. Once assessed the reliability of the numerical tool, the injection plate has been designed and sized to guarantee the required amount of oxidizer in the combustion chamber and therefore to assure high combustion efficiency. To this purpose, the plate has been designed with multiple injectors whose number and diameter have been selected in order to reach the requested mass flow rate for the two operating conditions of maximum and minimum thrust. The overall design has been finally verified through three-dimensional computations in cavitating non-reacting conditions and it has been verified that the proposed design solution is able to guarantee the requested values of mass flow rates.

Keywords: Hybrid rocket, injection system design, OpenFOAM®, cavitation

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Authors: A.H. Sajedipoor, N. Hedayat, A.Rohani, Z.Yazdi

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Sediment formation and its transport along the river course is considered as important hydraulic consideration in river engineering. Their impact on the morphology of rivers on one hand and important considerations of which in the design and construction of the hydraulic structures on the other has attracted the attention of experts in arid and semi-arid regions. Under certain conditions where the momentum energy of the flow stream reaches a specific rate, the sediment materials start to be transported with the flow. This can usually be analyzed in two different categories of suspended and bed load materials. Sedimentation phenomenon along the waterways and the conveyance of vast volume of materials into the canal networks can potentially influence water abstraction in the intake structures. This can pose a serious threat to operational sustainability and water delivery performance in the canal networks. The situation is serious where ineffective watershed management (poor vegetation cover in the water basin) is the underlying cause of soil erosion which feeds the materials into the waterways that intern would necessitate comprehensive study. The present paper aims to present an analytical investigation of the sediment process in the waterways on one hand and estimation of the sediment load transport into the lined canals using the SHARC software on the other. For this reason, the paper focuses on the comparative analysis of the hydraulic behaviors of the Sabilli main canal that feeds the pumping station with that of the Western canal in the Greater Dezful region to identify effective factors in sedimentation and ways of mitigating their impact on water abstraction in the canal systems. The method involved use of observational data available in the Dezful Dastmashoon hydrometric station along a 6 km waterway of the Sabilli main canal using the SHARC software to estimate the suspended load concentration and bed load materials. Results showed the transport of a significant volume of sediment loads from the waterways into the canal system which is assumed to have arisen from the absence of stilling basin on one hand and the gravity flow on the other has caused serious challenges. This is contrary to what occurs in the Sabilli canal, where the design feature which incorporates a settling basin just before the pumping station is the major cause of reduced sediment load transport into the canal system.Results showed that modification of the present design features by constructing a settling basin just upstream of the western intake structure can considerably reduce the entry of sediment materials into the canal system. Not only this can result in the sustainability of the hydraulic structures but can also improve operational performance of water conveyance and distribution system, all of which are the pre-requisite to secure reliable and equitable water delivery regime for the command area.

Keywords: Sedimentation, main canal, Sabilli, western canal, dez diversion weir.

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Authors: Volker Wannack

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Regional and global strategies focusing on hydrogen (H2) and blockchain technologies are fueling remarkable advancements. These strategies underpin the revolutionary 'Blockchain Based Hydrogen Market (BBH2)' project, with the primary objective of creating a Blockchain Minimum Viable Product (B-MVP) tailored to the hydrogen market. The B-MVP harnesses blockchain's capabilities, establishing a unified platform for secure, automated transactions via smart contracts. This innovation promises to reshape hydrogen logistics, trade, and transactions. The B-MVP carries transformative potential across diverse sectors, benefiting renewable energy producers, surplus energy-based hydrogen manufacturers, grid operators, and consumers. By implementing standardized, automated, tamper-proof processes, it bolsters cost-efficiency and enables transparent, traceable transactions. Its core mission is to verify the integrity of 'green' hydrogen, tracing its journey from renewable producers to end-users. This emphasis on transparency fosters economic, ecological, and social sustainability within a secure, transparent market. A standout feature of the B-MVP is its cross-border adaptability, obviating the need for nation-specific data storage, and broadening its global reach. This adaptability also spurs long-term job creation by establishing a dedicated blockchain operating firm. By attracting skilled labor and offering training, the B-MVP fortifies the hydrogen sector's workforce. Furthermore, it catalyzes innovative business models, luring more companies and startups, contributing to sustained job growth. For example, data analysis can tailor tariffs to offer demand-centric network capacities to producers and operators, providing tamper-proof pricing options to redistributors and end-customers. Beyond technological and economic progress, the B-MVP amplifies the prominence of national and international standards efforts. The region implementing the B-MVP becomes recognized as a pioneer in climate-friendly, sustainable, and forward-thinking practices, generating interest and attention beyond its geographic boundaries. Additionally, it fosters knowledge transfer between academia and industry, promoting scientific advancements, aligning with innovation management, and nurturing an innovation culture in the hydrogen sector. Through blockchain-hydrogen integration, the B-MVP champions comprehensive innovation, contributing to a sustainable future in the hydrogen industry. Implementation involves evaluating blockchain tech, developing smart contracts, and ensuring interoperability with existing systems. Scalability testing and data format development further validate the B-MVP's potential. BBH2 secures funding under the 'Technology Offensive Hydrogen,' a part of the Federal Ministry of Economics and Climate Protection's 7th Energy Research Program.

Keywords: Hydrogen, blockchain, sustainability, structural change.

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