Search results for: prediction modelling

Authors: Linlong Mu, Maosong Huang

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Considering excavations in urban areas, the soil deformation induced by the excavations usually causes damage to the surrounding structures. Displacement control becomes a critical indicator of foundation design in order to protect the surrounding structures. Evaluation, the damage potential of the surrounding structures induced by the excavations, usually depends on the finite element method (FEM) because of the complexity of the excavation and the variety of the surrounding structures. Besides, evaluation the influence of the excavation on surrounding structures is a three-dimensional problem. And it is now well recognized that small strain behaviour of the soil influences the responses of the excavation significantly. Three-dimensional FEM considering small strain behaviour of the soil is a very complex method, which is hard for engineers to use. Thus, it is important to obtain a simplified method for engineers to predict the influence of the excavations on the surrounding structures. Based on large-scale finite element calculation with small-strain based soil model coupling with inverse analysis, an empirical method is proposed to calculate the three-dimensional soil movement induced by braced excavation. The empirical method is able to capture the small-strain behaviour of the soil. And it is suitable to be used in layered soil. Then the free-field soil movement is applied to the pile to calculate the responses of the pile in both vertical and horizontal directions. The asymmetric solutions for problems in layered elastic half-space are employed to solve the interactions between soil points. Both vertical and horizontal pile responses are solved through finite difference method based on elastic theory. Interactions among the nodes along a single pile, pile-pile interactions, pile-soil-pile interaction action and soil-soil interactions are counted to improve the calculation accuracy of the method. For passive piles, the shadow effects are also calculated in the method. Finally, the restrictions of the raft on the piles and the soils are summarized as: (1) the summations of the internal forces between the elements of the raft and the elements of the foundation, including piles and soil surface elements, is equal to 0; (2) the deformations of pile heads or of the soil surface elements are the same as the deformations of the corresponding elements of the raft. Validations are carried out by comparing the results from the proposed method with the results from the model tests, FEM and other existing literatures. From the comparisons, it can be seen that the results from the proposed method fit with the results from other methods very well. The method proposed herein is suitable to predict the responses of the pile-raft foundation induced by braced excavation in layered soil in both vertical and horizontal directions when the deformation is small. However, more data is needed to verify the method before it can be used in practice.

Keywords: excavation, pile-raft foundation, passive piles, deformation control, soil movement

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Authors: Calorine Twebaze, Jesca Balinga

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Field X is located on the Eastern shores of L. Albert, Uganda, on the rift flank where the gross sedimentary fill is typically less than 2,000m. The field was discovered in 2006 and encountered about 20.4m of net pay across three (3) stratigraphic intervals within the discovery well. The field covers an area of 3 km2, with the structural configuration comprising a 3-way dip-closed hanging wall anticline that seals against the basement to the southeast along the bounding fault. Field X had been mapped on reprocessed 3D seismic data, which was originally acquired in 2007 and reprocessed in 2013. The seismic data quality is good across the field, and reprocessing work reduced the uncertainty in the location of the bounding fault and enhanced the lateral continuity of reservoir reflectors. The current study was a re-evaluation of Field X to refine fault interpretation and understand the structural uncertainties associated with the field. The seismic data, and three (3) wells datasets were used during the study. The evaluation followed standard workflows using Petrel software and structural attribute analysis. The process spanned from seismic- -well tie, structural interpretation, and structural uncertainty analysis. Analysis of three (3) well ties generated for the 3 wells provided a geophysical interpretation that was consistent with geological picks. The generated time-depth curves showed a general increase in velocity with burial depth. However, separation in curve trends observed below 1100m was mainly attributed to minimal lateral variation in velocity between the wells. In addition to Attribute analysis, three velocity modeling approaches were evaluated, including the Time-Depth Curve, Vo+ kZ, and Average Velocity Method. The generated models were calibrated at well locations using well tops to obtain the best velocity model for Field X. The Time-depth method resulted in more reliable depth surfaces with good structural coherence between the TWT and depth maps with minimal error at well locations of 2 to 5m. Both the NNE-SSW rift border fault and minor faults in the existing interpretation were reevaluated. However, the new interpretation delineated an E-W trending fault in the northern part of the field that had not been interpreted before. The fault was interpreted at all stratigraphic levels and thus propagates from the basement to the surface and is an active fault today. It was also noted that the entire field is less faulted with more faults in the deeper part of the field. The major structural uncertainties defined included 1) The time horizons due to reduced data quality, especially in the deeper parts of the structure, an error equal to one-third of the reflection time thickness was assumed, 2) Check shot analysis showed varying velocities within the wells thus varying depth values for each well, and 3) Very few average velocity points due to limited wells produced a pessimistic average Velocity model.

Keywords: 3D seismic data interpretation, structural uncertainties, attribute analysis, velocity modelling approaches

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Authors: Debabrata Adhikari, Mikhail Matveev, Louise Brown, Andy Long, Jan Kočí

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A good understanding of mesoscale permeability of complex architectures in fibrous porous preforms is of particular interest in order to achieve efficient and cost-effective resin impregnation of liquid composite molding (LCM). Fabrics used in structural reinforcements are typically woven or stitched. However, 3D fabric reinforcement is of particular interest because of the versatility in the weaving pattern with the binder yarn and in-plain yarn arrangements to manufacture thick composite parts, overcome the limitation in delamination, improve toughness etc. To predict the permeability based on the available pore spaces between the inter yarn spaces, unit cell-based computational fluid dynamics models have been using the Stokes Darcy model. Typically, the preform consists of an arrangement of yarns with spacing in the order of mm, wherein each yarn consists of thousands of filaments with spacing in the order of μm. The fluid flow during infusion exchanges the mass between the intra and inter yarn channels, meaning there is no dead-end of flow between the mesopore in the inter yarn space and the micropore in the yarn. Several studies have employed the Brinkman equation to take into account the flow through dual-scale porosity reinforcement to estimate their permeability. Furthermore, to reduce the computational effort of dual scale flow, scale separation criteria based on the ratio between yarn permeability to the yarn spacing was also proposed to quantify the dual scale and negligible micro-scale flow regime for the prediction of mesoscale permeability. In the present work, the key parameter to identify the influence of intra yarn permeability on the mesoscale permeability has been investigated with the systematic study of weft and warp yarn spacing on the plane weave as well as the position of binder yarn and number of in-plane yarn layers on 3D weave fabric. The permeability tensor has been estimated using an OpenFOAM-based model for the various weave pattern with idealized geometry of yarn implemented using open-source software TexGen. Additionally, scale separation criterion has been established based on the various configuration of yarn permeability for the 3D fabric with both the isotropic and anisotropic yarn from Gebart’s model. It was observed that the variation of mesoscale permeability Kxx within 30% when the isotropic porous yarn is considered for a 3D fabric with binder yarn. Furthermore, the permeability model developed in this study will be used for multi-objective optimizations of the preform mesoscale geometry in terms of yarn spacing, binder pattern, and a number of layers with an aim to obtain improved permeability and reduced void content during the LCM process.

Keywords: permeability, 3D fabric, dual-scale flow, liquid composite molding

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Authors: Shaukat Ali Shahee, Usha Ananthakumar

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A data set exhibits class imbalance problem when one class has very few examples compared to the other class, and this is also referred to as between class imbalance. The traditional classifiers fail to classify the minority class examples correctly due to its bias towards the majority class. Apart from between-class imbalance, imbalance within classes where classes are composed of a different number of sub-clusters with these sub-clusters containing different number of examples also deteriorates the performance of the classifier. Previously, many methods have been proposed for handling imbalanced dataset problem. These methods can be classified into four categories: data preprocessing, algorithmic based, cost-based methods and ensemble of classifier. Data preprocessing techniques have shown great potential as they attempt to improve data distribution rather than the classifier. Data preprocessing technique handles class imbalance either by increasing the minority class examples or by decreasing the majority class examples. Decreasing the majority class examples lead to loss of information and also when minority class has an absolute rarity, removing the majority class examples is generally not recommended. Existing methods available for handling class imbalance do not address both between-class imbalance and within-class imbalance simultaneously. In this paper, we propose a method that handles between class imbalance and within class imbalance simultaneously for binary classification problem. Removing between class imbalance and within class imbalance simultaneously eliminates the biases of the classifier towards bigger sub-clusters by minimizing the error domination of bigger sub-clusters in total error. The proposed method uses model-based clustering to find the presence of sub-clusters or sub-concepts in the dataset. The number of examples oversampled among the sub-clusters is determined based on the complexity of sub-clusters. The method also takes into consideration the scatter of the data in the feature space and also adaptively copes up with unseen test data using Lowner-John ellipsoid for increasing the accuracy of the classifier. In this study, neural network is being used as this is one such classifier where the total error is minimized and removing the between-class imbalance and within class imbalance simultaneously help the classifier in giving equal weight to all the sub-clusters irrespective of the classes. The proposed method is validated on 9 publicly available data sets and compared with three existing oversampling techniques that rely on the spatial location of minority class examples in the euclidean feature space. The experimental results show the proposed method to be statistically significantly superior to other methods in terms of various accuracy measures. Thus the proposed method can serve as a good alternative to handle various problem domains like credit scoring, customer churn prediction, financial distress, etc., that typically involve imbalanced data sets.

Keywords: classification, imbalanced dataset, Lowner-John ellipsoid, model based clustering, oversampling

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Authors: Michela Quadrini

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Chord diagrams occur in mathematics, from the study of RNA to knot theory. They are widely used in theory of knots and links for studying the finite type invariants, whereas in molecular biology one important motivation to study chord diagrams is to deal with the problem of RNA structure prediction. An RNA molecule is a linear polymer, referred to as the backbone, that consists of four types of nucleotides. Each nucleotide is represented by a point, whereas each chord of the diagram stands for one interaction for Watson-Crick base pairs between two nonconsecutive nucleotides. A chord diagram is an oriented circle with a set of n pairs of distinct points, considered up to orientation preserving diffeomorphisms of the circle. A linear chord diagram (LCD) is a special kind of graph obtained cutting the oriented circle of a chord diagram. It consists of a line segment, called its backbone, to which are attached a number of chords with distinct endpoints. There is a natural fattening on any linear chord diagram; the backbone lies on the real axis, while all the chords are in the upper half-plane. Each linear chord diagram has a natural genus of its associated surface. To each chord diagram and linear chord diagram, it is possible to associate the intersection graph. It consists of a graph whose vertices correspond to the chords of the diagram, whereas the chord intersections are represented by a connection between the vertices. Such intersection graph carries a lot of information about the diagram. Our goal is to define an LCD equivalence class in terms of identity of intersection graphs, from which many chord diagram invariants depend. For studying these invariants, we introduce a new representation of Linear Chord Diagrams based on a set of appropriate topological operators that permits to model LCD in terms of the relations among chords. Such set is composed of: crossing, nesting, and concatenations. The crossing operator is able to generate the whole space of linear chord diagrams, and a multiple context free grammar able to uniquely generate each LDC starting from a linear chord diagram adding a chord for each production of the grammar is defined. In other words, it allows to associate a unique algebraic term to each linear chord diagram, while the remaining operators allow to rewrite the term throughout a set of appropriate rewriting rules. Such rules define an LCD equivalence class in terms of the identity of intersection graphs. Starting from a modelled RNA molecule and the linear chord, some authors proposed a topological classification and folding. Our LCD equivalence class could contribute to the RNA folding problem leading to the definition of an algorithm that calculates the free energy of the molecule more accurately respect to the existing ones. Such LCD equivalence class could be useful to obtain a more accurate estimate of link between the crossing number and the topological genus and to study the relation among other invariants.

Keywords: chord diagrams, linear chord diagram, equivalence class, topological language

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Authors: James Rigor Camacho, Wansu Lim

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Over the last few years, the development of new wearable and processing technologies has accelerated in order to harness physiological data such as electroencephalograms (EEGs) for EEG-based applications. EEG has been demonstrated to be a source of emotion recognition signals with the highest classification accuracy among physiological signals. However, when emotion recognition systems are used for real-time classification, the training unit is frequently left to run offline or in the cloud rather than working locally on the edge. That strategy has hampered research, and the full potential of using an edge AI device has yet to be realized. Edge AI devices are computers with high performance that can process complex algorithms. It is capable of collecting, processing, and storing data on its own. It can also analyze and apply complicated algorithms like localization, detection, and recognition on a real-time application, making it a powerful embedded device. The NVIDIA Jetson series, specifically the Jetson Nano device, was used in the implementation. The cEEGrid, which is integrated to the open-source brain computer-interface platform (OpenBCI), is used to collect EEG signals. An EEG-based real-time emotion recognition system on Edge AI is proposed in this paper. To perform graphical spectrogram categorization of EEG signals and to predict emotional states based on input data properties, machine learning-based classifiers were used. Until the emotional state was identified, the EEG signals were analyzed using the K-Nearest Neighbor (KNN) technique, which is a supervised learning system. In EEG signal processing, after each EEG signal has been received in real-time and translated from time to frequency domain, the Fast Fourier Transform (FFT) technique is utilized to observe the frequency bands in each EEG signal. To appropriately show the variance of each EEG frequency band, power density, standard deviation, and mean are calculated and employed. The next stage is to identify the features that have been chosen to predict emotion in EEG data using the K-Nearest Neighbors (KNN) technique. Arousal and valence datasets are used to train the parameters defined by the KNN technique.Because classification and recognition of specific classes, as well as emotion prediction, are conducted both online and locally on the edge, the KNN technique increased the performance of the emotion recognition system on the NVIDIA Jetson Nano. Finally, this implementation aims to bridge the research gap on cost-effective and efficient real-time emotion recognition using a resource constrained hardware device, like the NVIDIA Jetson Nano. On the cutting edge of AI, EEG-based emotion identification can be employed in applications that can rapidly expand the research and implementation industry's use.

Keywords: edge AI device, EEG, emotion recognition system, supervised learning algorithm, sensors

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Authors: Nader Mohamed

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The Late Cretaceous deposits are well developed through-out Egypt. This is due to a ‎transgression phase associated with the subsidence caused by the neo-Tethyan rift event that ‎took place across the northern margin of Africa, resulting in a period of dominantly marine ‎deposits in the Gulf of Suez. The Late Cretaceous Nezzazat Group represents the Cenomanian, ‎Turonian and clastic sediments of the Lower Senonian. The Nezzazat Group has been divided ‎into four formations namely, from base to top, the Raha Formation, the Abu Qada Formation, ‎the Wata Formation and the Matulla Formation. The Cenomanian Raha and the Lower Senonian ‎Matulla formations are the most important clastic sequence in the Nezzazat Group because they ‎provide the highest net reservoir thickness and the highest net/gross ratio. This study emphasis ‎on Matulla formation located in the eastern part of the Gulf of Suez. The three stratigraphic ‎surface sections (Wadi Sudr, Wadi Matulla and Gabal Nezzazat) which represent the exposed ‎Coniacian-Santonian sediments in Sinai are used for correlating Matulla sediments of Ras ‎Budran field. Cutting description, petrographic examination, log behaviors, biostratigraphy with ‎outcrops are used to identify the reservoir characteristics, lithology, facies environment logs and ‎subdivide the Matulla formation into three units. The lower unit is believed to be the main ‎reservoir where it consists mainly of sands with shale and sandy carbonates, while the other ‎units are mainly carbonate with some streaks of shale and sand. Reservoir modeling is an ‎effective technique that assists in reservoir management as decisions concerning development ‎and depletion of hydrocarbon reserves, So It was essential to model the Matulla reservoir as ‎accurately as possible in order to better evaluate, calculate the reserves and to determine the ‎most effective way of recovering as much of the petroleum economically as possible. All ‎available data on Matulla formation are used to build the reservoir structure model, lithofacies, ‎porosity, permeability and water saturation models which are the main parameters that describe ‎the reservoirs and provide information on effective evaluation of the need to develop the oil ‎potentiality of the reservoir. This study has shown the effectiveness of; 1) the integration of ‎geological data to evaluate and subdivide Matulla formation into three units. 2) Lithology and ‎facies environment interpretation which helped in defining the nature of deposition of Matulla ‎formation. 3) The 3D reservoir modeling technology as a tool for adequate understanding of the ‎spatial distribution of property and in addition evaluating the unlocked new reservoir areas of ‎Matulla formation which have to be drilled to investigate and exploit the un-drained oil. 4) This ‎study led to adding a new room of production and additional reserves to Ras Budran field. ‎

Keywords: geology, oil and gas, geoscience, sequence stratigraphy

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Authors: Hugo Lefeuvre, Jerôme Donnard, Michael Descostes, Sophie Billon, Samuel Duval, Tugdual Oger, Herve Toubon, Paul Sardini

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Spectroscopic autoradiography is a method of interest for geological sample analysis. Indeed, researchers may face different issues such as radioelement identification and quantification in the field of environmental studies. Imaging gaseous ionization detectors find their place in geosciences for conducting specific measurements of radioactivity to improve the monitoring of natural processes using naturally-occurring radioactive tracers, but also for the nuclear industry linked to the mining sector. In geological samples, the location and identification of the radioactive-bearing minerals at the thin-section scale remains a major challenge as the detection limit of the usual elementary microprobe techniques is far higher than the concentration of most of the natural radioactive decay products. The spatial distribution of each decay product in the case of uranium in a geomaterial is interesting for relating radionuclides concentration to the mineralogy. The present study aims to provide spectroscopic autoradiography analysis method for measuring the initial energy of alpha particles with a parallel ionization multiplier gaseous detector. The analysis method has been developed thanks to Geant4 modelling of the detector. The track of alpha particles recorded in the gas detector allow the simultaneous measurement of the initial point of emission and the reconstruction of the initial particle energy by a selection based on the linear energy distribution. This spectroscopic autoradiography method was successfully used to reproduce the alpha spectra from a 238U decay chain on a geological sample at the thin-section scale. The characteristics of this measurement are an energy spectrum resolution of 17.2% (FWHM) at 4647 keV and a spatial resolution of at least 50 µm. Even if the efficiency of energy spectrum reconstruction is low (4.4%) compared to the efficiency of a simple autoradiograph (50%), this novel measurement approach offers the opportunity to select areas on an autoradiograph to perform an energy spectrum analysis within that area. This opens up possibilities for the detailed analysis of heterogeneous geological samples containing natural alpha emitters such as uranium-238 and radium-226. This measurement will allow the study of the spatial distribution of uranium and its descendants in geo-materials by coupling scanning electron microscope characterizations. The direct application of this dual modality (energy-position) of analysis will be the subject of future developments. The measurement of the radioactive equilibrium state of heterogeneous geological structures, and the quantitative mapping of 226Ra radioactivity are now being actively studied.

Keywords: alpha spectroscopy, digital autoradiography, mining activities, natural decay products

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Authors: Thor Alexis Sazon, Alexander Guzman-Urbina, Yasuhiro Fukushima

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This study outlines the method of how to develop a surrogate life cycle model based on fuzzy logic using three fuzzy inference methods: (1) the conventional Fuzzy Inference System (FIS), (2) the hybrid system of Data Analytics and Fuzzy Inference (DAFIS), which uses data clustering for defining the membership functions, and (3) the Adaptive-Neuro Fuzzy Inference System (ANFIS), a combination of fuzzy inference and artificial neural network. These methods were demonstrated with a case study where the Global Warming Potential (GWP) and the Levelized Cost of Energy (LCOE) of solar photovoltaic (PV) were estimated using Solar Irradiation, Module Efficiency, and Performance Ratio as inputs. The effects of using different fuzzy inference types, either Sugeno- or Mamdani-type, and of changing the number of input membership functions to the error between the calibration data and the model-generated outputs were also illustrated. The solution spaces of the three methods were consequently examined with a sensitivity analysis. ANFIS exhibited the lowest error while DAFIS gave slightly lower errors compared to FIS. Increasing the number of input membership functions helped with error reduction in some cases but, at times, resulted in the opposite. Sugeno-type models gave errors that are slightly lower than those of the Mamdani-type. While ANFIS is superior in terms of error minimization, it could generate solutions that are questionable, i.e. the negative GWP values of the Solar PV system when the inputs were all at the upper end of their range. This shows that the applicability of the ANFIS models highly depends on the range of cases at which it was calibrated. FIS and DAFIS generated more intuitive trends in the sensitivity runs. DAFIS demonstrated an optimal design point wherein increasing the input values does not improve the GWP and LCOE anymore. In the absence of data that could be used for calibration, conventional FIS presents a knowledge-based model that could be used for prediction. In the PV case study, conventional FIS generated errors that are just slightly higher than those of DAFIS. The inherent complexity of a Life Cycle study often hinders its widespread use in the industry and policy-making sectors. While the methodology does not guarantee a more accurate result compared to those generated by the Life Cycle Methodology, it does provide a relatively simpler way of generating knowledge- and data-based estimates that could be used during the initial design of a system.

Keywords: solar photovoltaic, fuzzy logic, inference system, artificial neural networks

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Authors: Ali Abdul Kadhim, Fue Lien

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Solid particle distribution on an impingement surface has been simulated utilizing a graphical processing unit (GPU). In-house computational fluid dynamics (CFD) code has been developed to investigate a 3D turbulent impinging jet using the lattice Boltzmann method (LBM) in conjunction with large eddy simulation (LES) and the multiple relaxation time (MRT) models. This paper proposed an improvement in the LBM-cellular automata (LBM-CA) probabilistic method. In the current model, the fluid flow utilizes the D3Q19 lattice, while the particle model employs the D3Q27 lattice. The particle numbers are defined at the same regular LBM nodes, and transport of particles from one node to its neighboring nodes are determined in accordance with the particle bulk density and velocity by considering all the external forces. The previous models distribute particles at each time step without considering the local velocity and the number of particles at each node. The present model overcomes the deficiencies of the previous LBM-CA models and, therefore, can better capture the dynamic interaction between particles and the surrounding turbulent flow field. Despite the increasing popularity of LBM-MRT-CA model in simulating complex multiphase fluid flows, this approach is still expensive in term of memory size and computational time required to perform 3D simulations. To improve the throughput of each simulation, a single GeForce GTX TITAN X GPU is used in the present work. The CUDA parallel programming platform and the CuRAND library are utilized to form an efficient LBM-CA algorithm. The methodology was first validated against a benchmark test case involving particle deposition on a square cylinder confined in a duct. The flow was unsteady and laminar at Re=200 (Re is the Reynolds number), and simulations were conducted for different Stokes numbers. The present LBM solutions agree well with other results available in the open literature. The GPU code was then used to simulate the particle transport and deposition in a turbulent impinging jet at Re=10,000. The simulations were conducted for L/D=2,4 and 6, where L is the nozzle-to-surface distance and D is the jet diameter. The effect of changing the Stokes number on the particle deposition profile was studied at different L/D ratios. For comparative studies, another in-house serial CPU code was also developed, coupling LBM with the classical Lagrangian particle dispersion model. Agreement between results obtained with LBM-CA and LBM-Lagrangian models and the experimental data is generally good. The present GPU approach achieves a speedup ratio of about 350 against the serial code running on a single CPU.

Keywords: CUDA, GPU parallel programming, LES, lattice Boltzmann method, MRT, multi-phase flow, probabilistic model

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Authors: Nahid Parween Anwar, Sadia Muzaffar Bhutta, Takbir Ali

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Mentoring is a structured activity which is designed to facilitate engagement between mentor and mentee to enhance mentee’s professional capability as an effective teacher. Both mentor and mentee are important elements of the ‘mentoring equation’ and play important roles in nourishing this dynamic, collaborative and reciprocal relationship. Cluster-Based Mentoring Programme (CBMP) provides an indigenous example of a project which focused on development of primary school teachers in selected clusters with a particular focus on their classroom practice. A study was designed to examine the efficacy of CBMP as part of Strengthening Teacher Education in Pakistan (STEP) project. This paper presents results of one of the components of this study. As part of the larger study, a cross-sectional survey was employed to explore nature and patterns of mentoring process from mentees’ perspectives in the selected districts of Sindh and Balochistan. This paper focuses on the results of the study related to the question: What are mentees’ perceptions of their mentors’ support for enhancing their classroom practice during mentoring process? Data were collected from mentees (n=1148) using a 5-point scale -‘Mentoring for Effective Primary Teaching’ (MEPT). MEPT focuses on seven factors of mentoring: personal attributes, pedagogical knowledge, modelling, feedback, system requirement, development and use of material, and gender equality. Data were analysed using SPSS 20. Mentees perceptions of mentoring practice of their mentors were summarized using mean and standard deviation. Results showed that mean scale scores on mentees’ perceptions of their mentors’ practices fell between 3.58 (system requirement) and 4.55 (personal attributes). Mentees’ perceives personal attribute of the mentor as the most significant factor (M=4.55) towards streamlining mentoring process by building good relationship between mentor and mentees. Furthermore, mentees have shared positive views about their mentors efforts towards promoting gender impartiality (M=4.54) during workshop and follow up visit. Contrary to this, mentees felt that more could have been done by their mentors in sharing knowledge about system requirement (e.g. school policies, national curriculum). Furthermore, some of the aspects in high scoring factors were highlighted by the mentees as areas for further improvement (e.g. assistance in timetabling, written feedback, encouragement to develop learning corners). Mentees’ perceptions of their mentors’ practices may assist in determining mentoring needs. The results may prove useful for the professional development programme for the mentors and mentees for specific mentoring programme in order to enhance practices in primary classrooms in Pakistan. Results would contribute into the body of much-needed knowledge from developing context.

Keywords: cluster-based mentoring programme, mentoring for effective primary teaching (MEPT), professional development, survey

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Authors: Ali Hashemifarzad, Jens Zum Hingst

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The structure of the world’s energy systems has changed significantly over the past years. One of the most important challenges in the 21st century in Germany (and also worldwide) is the energy transition. This transition aims to comply with the recent international climate agreements from the United Nations Climate Change Conference (COP21) to ensure sustainable energy supply with minimal use of fossil fuels. Germany aims for complete decarbonization of the energy sector by 2050 according to the federal climate protection plan. One of the stipulations of the Renewable Energy Sources Act 2017 for the expansion of energy production from renewable sources in Germany is that they cover at least 80% of the electricity requirement in 2050; The Gross end energy consumption is targeted for at least 60%. This means that by 2050, the energy supply system would have to be almost completely converted to renewable energy. An essential basis for the development of such a sustainable energy supply from 100% renewable energies is to predict the energy requirement by 2050. This study presents two scenarios for the final energy demand in Germany in 2050. In the first scenario, the targets for energy efficiency increase and demand reduction are set very ambitiously. To build a comparison basis, the second scenario provides results with less ambitious assumptions. For this purpose, first, the relevant framework conditions (following CUTEC 2016) were examined, such as the predicted population development and economic growth, which were in the past a significant driver for the increase in energy demand. Also, the potential for energy demand reduction and efficiency increase (on the demand side) was investigated. In particular, current and future technological developments in energy consumption sectors and possible options for energy substitution (namely the electrification rate in the transport sector and the building renovation rate) were included. Here, in addition to the traditional electricity sector, the areas of heat, and fuel-based consumptions in different sectors such as households, commercial, industrial and transport are taken into account, supporting the idea that for a 100% supply from renewable energies, the areas currently based on (fossil) fuels must be almost completely be electricity-based by 2050. The results show that in the very ambitious scenario a final energy demand of 1,362 TWh/a is required, which is composed of 818 TWh/a electricity, 229 TWh/a ambient heat for electric heat pumps and approx. 315 TWh/a non-electric energy (raw materials for non-electrifiable processes). In the less ambitious scenario, in which the targets are not fully achieved by 2050, the final energy demand will need a higher electricity part of almost 1,138 TWh/a (from the total: 1,682 TWh/a). It has also been estimated that 50% of the electricity revenue must be saved to compensate for fluctuations in the daily and annual flows. Due to conversion and storage losses (about 50%), this would mean that the electricity requirement for the very ambitious scenario would increase to 1,227 TWh / a.

Keywords: energy demand, energy transition, German Energiewende, 100% renewable energy production

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Authors: Harvey Sanchez-Restrepo, Jorge Louca

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Equity and education are major focus of government policies around the world due to its relevance for addressing the sustainable development goals launched by Unesco. In this research, we developed a primary analysis of a data set of more than one hundred educational and non-educational factors associated with learning, coming from a census-based large-scale assessment carried on in Ecuador for 1.038.328 students, their families, teachers, and school directors, throughout 2014-2018. Each participating student was assessed by a standardized computer-based test. Learning outcomes were calibrated through item response theory with two-parameters logistic model for getting raw scores that were re-scaled and synthetized by a learning index (LI). Our objective was to develop a network for modelling educational deprivation and analyze the structure of inequality gaps, as well as their relationship with socioeconomic status, school financing, and student's ethnicity. Results from the model show that 348 270 students did not develop the minimum skills (prevalence rate=0.215) and that Afro-Ecuadorian, Montuvios and Indigenous students exhibited the highest prevalence with 0.312, 0.278 and 0.226, respectively. Regarding the socioeconomic status of students (SES), modularity class shows clearly that the system is out of equilibrium: the first decile (the poorest) exhibits a prevalence rate of 0.386 while rate for decile ten (the richest) is 0.080, showing an intense negative relationship between learning and SES given by R= –0.58 (p < 0.001). Another interesting and unexpected result is the average-weighted degree (426.9) for both private and public schools attending Afro-Ecuadorian students, groups that got the highest PageRank (0.426) and pointing out that they suffer the highest educational deprivation due to discrimination, even belonging to the richest decile. The model also found the factors which explain deprivation through the highest PageRank and the greatest degree of connectivity for the first decile, they are: financial bonus for attending school, computer access, internet access, number of children, living with at least one parent, books access, read books, phone access, time for homework, teachers arriving late, paid work, positive expectations about schooling, and mother education. These results provide very accurate and clear knowledge about the variables affecting poorest students and the inequalities that it produces, from which it might be defined needs profiles, as well as actions on the factors in which it is possible to influence. Finally, these results confirm that network analysis is fundamental for educational policy, especially linking reliable microdata with social macro-parameters because it allows us to infer how gaps in educational achievements are driven by students’ context at the time of assigning resources.

Keywords: complex network, educational deprivation, evidence-based policy, large-scale assessments, policy informatics

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Authors: Sherly Shelton, Zhaohui Lin

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In recent decades, the inter-annual variability of summer precipitation over the India and Sri Lanka has intensified significantly with an increased frequency of both abnormally dry and wet summers. Therefore prediction of the inter-annual variability of summer precipitation is crucial and urgent for water management and local agriculture scheduling. However, none of the hypotheses put forward so far could understand the relationship to monsoon variability and related factors that affect to the South West Monsoon (SWM) variability in Sri Lanka. This study focused to identify the spatial and temporal variability of SWM rainfall events from June to September (JJAS) over Sri Lanka and associated trend. The monthly rainfall records covering 1980-2013 over the Sri Lanka are used for 19 stations to investigate long-term trends in SWM rainfall over Sri Lanka. The linear trends of atmospheric variables are calculated to understand the drivers behind the changers described based on the observed precipitation, sea surface temperature and atmospheric reanalysis products data for 34 years (1980–2013). Empirical orthogonal function (EOF) analysis was applied to understand the spatial and temporal behaviour of seasonal SWM rainfall variability and also investigate whether the trend pattern is the dominant mode that explains SWM rainfall variability. The spatial and stations based precipitation over the country showed statistically insignificant decreasing trends except few stations. The first two EOFs of seasonal (JJAS) mean of rainfall explained 52% and 23 % of the total variance and first PC showed positive loadings of the SWM rainfall for the whole landmass while strongest positive lording can be seen in western/ southwestern part of the Sri Lanka. There is a negative correlation (r ≤ -0.3) between SMRI and SST in the Arabian Sea and Central Indian Ocean which indicate that lower temperature in the Arabian Sea and Central Indian Ocean are associated with greater rainfall over the country. This study also shows that consistently warming throughout the Indian Ocean. The result shows that the perceptible water over the county is decreasing with the time which the influence to the reduction of precipitation over the area by weakening drawn draft. In addition, evaporation is getting weaker over the Arabian Sea, Bay of Bengal and Sri Lankan landmass which leads to reduction of moisture availability required for the SWM rainfall over Sri Lanka. At the same time, weakening of the SST gradients between Arabian Sea and Bay of Bengal can deteriorate the monsoon circulation, untimely which diminish SWM over Sri Lanka. The decreasing trends of moisture, moisture transport, zonal wind, moisture divergence with weakening evaporation over Arabian Sea, during the past decade having an aggravating influence on decreasing trends of monsoon rainfall over the Sri Lanka.

Keywords: Arabian Sea, moisture flux convergence, South West Monsoon, Sri Lanka, sea surface temperature

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Authors: Kaviyarasan D., Satish Kumar S. R.

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India being vast in area and population with great scope of international business, roadways and railways network connection within the country is expected to have a big growth. There are numerous rail-cum-road bridges constructed across many major rivers in India and few are getting very old. So there is more possibility of repairing or coming up with such new bridges in India. Analysis and design of such bridges are practiced through conventional procedure and end up with heavy and uneconomical sections. Such heavy class steel bridges when subjected to high seismic shaking has more chance to fail by stability because the members are too much rigid and stocky rather than being flexible to dissipate the energy. This work is the collective study of the researches done in the truss bridge and steel concrete composite truss bridges presenting the method of analysis, tools for numerical and analytical modeling which evaluates its seismic behaviour and collapse mechanisms. To ascertain the inelastic and nonlinear behaviour of the structure, generally at research level static pushover analysis is adopted. Though the static pushover analysis is now extensively used for the framed steel and concrete buildings to study its lateral action behaviour, those findings by pushover analysis done for the buildings cannot directly be used for the bridges as such, because the bridges have completely a different performance requirement, behaviour and typology as compared to that of the buildings. Long span steel bridges are mostly the truss bridges. Truss bridges being formed by many members and connections, the failure of the system does not happen suddenly with single event or failure of one member. Failure usually initiates from one member and progresses gradually to the next member and so on when subjected to further loading. This kind of progressive collapse of the truss bridge structure is dependent on many factors, in which the live load distribution and span to length ratio are most significant. The ultimate collapse is anyhow by the buckling of the compression members only. For regular bridges, single step pushover analysis gives results closer to that of the non-linear dynamic analysis. But for a complicated bridge like heavy class steel bridge or the skewed bridges or complicated dynamic behaviour bridges, nonlinear analysis capturing the progressive yielding and collapse pattern is mandatory. With the knowledge of the postelastic behaviour of the bridge and advancements in the computational facility, the current level of analysis and design of bridges has moved to state of ascertaining the performance levels of the bridges based on the damage caused by seismic shaking. This is because the buildings performance levels deals much with the life safety and collapse prevention levels, whereas the bridges mostly deal with the extent damages and how quick it can be repaired with or without disturbing the traffic after a strong earthquake event. The paper would compile the wide spectrum of modeling to analysis of the steel concrete composite truss bridges in general.

Keywords: bridge engineering, performance based design of steel truss bridge, seismic design of composite bridge, steel-concrete composite bridge

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Authors: Seham A. S. Mohamed, Sarah R. Baker, Christopher Deery, Mario V. Vettore

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Background: The link between oral health conditions and school performance and attendance remain unclear among Middle Eastern children. The association has been studied extensively in the Western region; however, several concerns have been raised regarding the reliability and validity of measures, low quality of studies, inadequate inclusion of potential confounders, and the lack of a conceptual framework. These limitations have meant that, to date, there has been no detailed understanding of the association or of the key social, clinical, behavioural and parental factors which may impact the association. Aim: To examine the association between oral health conditions and children’s school performance and attendance at Grade 2 in Muharraq city in the Kingdom of Bahrain using Heilmann et al.’s (2015) life course framework for oral health. Objectives: To (1) describe the prevalence of oral health conditions among 7-8 years old schoolchildren in the city of Muharraq; (2) analyse the social, biological, behavioural, and parental pathways that link early and current life exposures with children’s current oral health status; (3) examine the association between oral health conditions and school performance and attendance among schoolchildren; (4) explore the early and current life course social, biological, behavioural and parental factors associated with children’s school outcomes. Design: A time-ordered-cross-sectional study was conducted with 466 schoolchildren aged 7-8 years and their parents from Muharraq city in KoB. Data were collected through parents’ self-administered questionnaires, children’s face-face interviews, and dental clinical examinations. Outcome variables, including school performance and school attendance data, were obtained from the parents and school records. The data were analysed using structural equation modelling (SEM). Results: Dental caries, the consequence of dental caries (PUFA/pufa), and enamel developmental defects (EDD) prevalence were 93.4%, 25.7%, and 17.2%, respectively. The findings from the SEM showed that children born in families with high SES were less likely to suffer from dentine dental caries (β= -0.248) and more likely to earn high school performance (β= 0.136) at 7-8 years of age in Muharraq. From the current life course of children, the dental plaque was associated significantly and directly with enamel caries (β= 0.094), dentine caries (β= 0.364), treated teeth (filled or extracted because of dental caries) (β= 0.121), and indirectly associated with dental pain (β= 0.057). Further, dentine dental caries was associated significantly and directly with low school performance (β= -0.155). At the same time, the dental plaque was indirectly associated with low school performance via dental caries (β = −0.044). Conversely, treated teeth were associated directly with high school performance (β= 0.100). Notably, none of the OHCs, biological, SES, behavioural, or parental conditions was related to school attendance in children. Conclusion: The life course approach was adequate to examine the role of OHCs on children’s school performance and attendance. Birth and current (7-8-year-olds) social factors were significant predictors of poor OH and poor school performance.

Keywords: dental caries, life course, Bahrain, school outcomes

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Authors: Angela T. H. Kwan, Wenjun Liang, Jack Wellington, Mohammad Mofatteh, Thanh N. Nguyen, Pingzhong Fu, Juanmei Chen, Zile Yan, Weijuan Wu, Yongting Zhou, Shuiquan Yang, Sijie Zhou, Yimin Chen

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Background: Endovascular Therapy (EVT)—in the form of mechanical thrombectomy—following intravenous thrombolysis is the standard gold treatment for patients with acute ischemic stroke (AIS) due to large vessel occlusion (LVO). It is well established that an ASPECTS ≥ 7 is associated with an increased likelihood of positive post-EVT outcomes, as compared to an ASPECTS < 7. There is also prognostic utility in coupling posterior circulation ASPECTS (pc-ASPECTS) with magnetic resonance imaging for evaluating the post-EVT functional outcome. However, the value of pc-ASPECTS applied to CT must be explored further to determine its usefulness in predicting functional outcomes following EVT. Objective: In this study, we aimed to determine whether pc-ASPECTS on CT can predict post-EVT functional outcomes among patients with AIS due to LVO. Methods: A total of 247 consecutive patients aged 18 and over receiving EVT for LVO-related AIS were recruited into a prospective database. The data were retrospectively analyzed between March 2019 to February 2022 from two comprehensive tertiary care stroke centers: Foshan Sanshui District People’s Hospital and First People's Hospital of Foshan in China. Patient parameters included EVT within 24hrs of symptom onset, premorbid modified Rankin Scale (mRS) ≤ 2, presence of distal and terminal cerebral blood vessel occlusion, and subsequent 24–72-hour post-stroke onset CT scan. Univariate comparisons were performed using the Fisher exact test or χ2 test for categorical variables and the Mann–Whitney U test for continuous variables. A p-value of ≤ 0.05 was statistically significant. Results: A total of 247 patients met the inclusion criteria; however, 3 were excluded due to the absence of post-CTs and 8 for pre-EVT ASPECTS < 7. Overall, 236 individuals were examined: 196 anterior circulation ischemic strokes and 40 posterior strokes of basilar artery occlusion. We found that both baseline post- and pc-ASPECTS ≥ 7 serve as strong positive markers of favorable outcomes at 90 days post-EVT. Moreover, lower rates of inpatient mortality/hospice discharge, 90-day mortality, and 90-day poor outcome were observed. Moreover, patients in the post-ASPECTS ≥ 7 anterior circulation group had shorter door-to-recanalization time (DRT), puncture-to-recanalization time (PRT), and last known normal-to-puncture-time (LKNPT). Conclusion: Patients of anterior and posterior circulation ischemic strokes with baseline post- and pc-ASPECTS ≥ 7 may benefit from EVT.

Keywords: endovascular therapy, thrombectomy, large vessel occlusion, cerebral ischemic stroke, ASPECTS

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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: David Edgar

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To understand how the brain’s subconscious and conscious functions, we must conquer the physics of Unity, which leads to duality’s algorithm. Where the subconscious (bottom-up) and conscious (top-down) processes function together to produce and consume intelligence, we use terms like ‘time is relative,’ but we really do understand the meaning. In the brain, there are different processes and, therefore, different observers. These different processes experience time at different rates. A sensory system such as the eyes cycles measurement around 33 milliseconds, the conscious process of the frontal lobe cycles at 300 milliseconds, and the subconscious process of the thalamus cycle at 5 milliseconds. Three different observers experience time differently. To bridge observers, the thalamus, which is the fastest of the processes, maintains a synchronous state and entangles the different components of the brain’s physical process. The entanglements form a synchronous cohesion between the brain components allowing them to share the same state and execute in the same measurement cycle. The thalamus uses the shared state to control the firing sequence of the brain’s linear subconscious process. Sharing state also allows the brain to cheat on the amount of sensory data that must be exchanged between components. Only unpredictable motion is transferred through the synchronous state because predictable motion already exists in the shared framework. The brain’s synchronous subconscious process is entirely based on energy conservation, where prediction regulates energy usage. So, the eyes every 33 milliseconds dump their sensory data into the thalamus every day. The thalamus is going to perform a motion measurement to identify the unpredictable motion in the sensory data. Here is the trick. The thalamus conducts its measurement based on the original observation time of the sensory system (33 ms), not its own process time (5 ms). This creates a data payload of synchronous motion that preserves the original sensory observation. Basically, a frozen moment in time (Flat 4D). The single moment in time can then be processed through the single state maintained by the synchronous process. Other processes, such as consciousness (300 ms), can interface with the synchronous state to generate awareness of that moment. Now, synchronous data traveling through a separate faster synchronous process creates a theoretical time tunnel where observation time is tunneled through the synchronous process and is reproduced on the other side in the original time-relativity. The synchronous process eliminates time dilation by simply removing itself from the equation so that its own process time does not alter the experience. To the original observer, the measurement appears to be instantaneous, but in the thalamus, a linear subconscious process generating sensory perception and thought production is being executed. It is all just occurring in the time available because other observation times are slower than thalamic measurement time. For life to exist in the physical universe requires a linear measurement process, it just hides by operating at a faster time relativity. What’s interesting is time dilation is not the problem; it’s the solution. Einstein said there was no universal time.

Keywords: neural synchronization, natural intelligence, 99.95% IoT data transmission savings, artificial subconscious intelligence (ASI)

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Authors: Olena Kavats, Dmitry Khramov, Kateryna Sergieieva, Vladimir Vasyliev, Iurii Kavats

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Geoinformation technologies of space agromonitoring are a means of operative decision making support in the tasks of managing the agricultural sector of the economy. Existing technologies use satellite images in the optical range of electromagnetic spectrum. Time series of optical images often contain gaps due to the presence of clouds and haze. A geoinformation technology is created. It allows to fill gaps in time series of optical images (Sentinel-2, Landsat-8, PROBA-V, MODIS) with radar survey data (Sentinel-1) and use information about agrometeorological conditions of the growing season for individual monitoring years. The technology allows to perform crop classification and mapping for spring-summer (winter and spring crops) and autumn-winter (winter crops) periods of vegetation, monitoring the dynamics of crop state seasonal changes, crop yield forecasting. Crop classification is based on supervised classification algorithms, takes into account the peculiarities of crop growth at different vegetation stages (dates of sowing, emergence, active vegetation, and harvesting) and agriculture land state characteristics (row spacing, seedling density, etc.). A catalog of samples of the main agricultural crops (Ukraine) is created and crop spectral signatures are calculated with the preliminary removal of row spacing, cloud cover, and cloud shadows in order to construct time series of crop growth characteristics. The obtained data is used in grain crop growth tracking and in timely detection of growth trends deviations from reference samples of a given crop for a selected date. Statistical models of crop yield forecast are created in the forms of linear and nonlinear interconnections between crop yield indicators and crop state characteristics (temperature, precipitation, vegetation indices, etc.). Predicted values of grain crop yield are evaluated with an accuracy up to 95%. The developed technology was used for agricultural areas monitoring in a number of Great Britain and Ukraine regions using EOS Crop Monitoring Platform (https://crop-monitoring.eos.com). The obtained results allow to conclude that joint use of Sentinel-1 and Sentinel-2 images improve separation of winter crops (rapeseed, wheat, barley) in the early stages of vegetation (October-December). It allows to separate successfully the soybean, corn, and sunflower sowing areas that are quite similar in their spectral characteristics.

Keywords: geoinformation technology, crop classification, crop yield prediction, agricultural monitoring, EOS Crop Monitoring Platform

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Authors: Lina E. Homman, Alexis C. Edwards, Seung Bin Cho, Danielle M. Dick, Kenneth S. Kendler

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Research supports an association between alcohol problems and internalising symptoms, but the understanding of how the two phenotypes relate to each other is poor. It has been hypothesized that the relationship between the phenotypes is causal; however investigations in regards to direction are inconsistent. Clarity of the relationship between the two phenotypes may be provided by investigating the phenotypes developmental inter-relationships longitudinally. The objective of the study was to investigate a) changes in alcohol problems and internalising symptoms in college students across time and b) the direction of effect of growth between alcohol problems and internalising symptoms from late adolescent to emerging adulthood c) possible gender differences. The present study adds to the knowledge of comorbidity of alcohol problems and internalising symptoms by examining a longitudinal sample of college students and by examining the simultaneous development of the symptoms. A sample of college students is of particular interest as symptoms of both phenotypes often have their onset around this age. A longitudinal sample of college students from a large, urban, public university in the United States was used. Data was collected over a time period of 2 years at 3 time points. Latent growth models were applied to examine growth trajectories. Parallel process growth models were used to assess whether initial level and rate of change of one symptom affected the initial level and rate of change of the second symptom. Possible effects of gender and ethnicity were investigated. Alcohol problems significantly increased over time, whereas internalizing symptoms remained relatively stable. The two phenotypes were significantly correlated in each wave, correlations were stronger among males. Initial level of alcohol problems was significantly positively correlated with initial level of internalising symptoms. Rate of change of alcohol problems positively predicted rate of change of internalising symptoms for females but not for males. Rate of change of internalising symptoms did not predict rate of change of alcohol problems for either gender. Participants of Black and Asian ethnicities indicated significantly lower levels of alcohol problems and a lower increase of internalising symptoms across time, compared to White participants. Participants of Black ethnicity also reported significantly lower levels of internalising symptoms compared to White participants. The present findings provide additional support for a positive relationship between alcohol problems and internalising symptoms in youth. Our findings indicated that both internalising symptoms and alcohol problems increased throughout the sample and that the phenotypes were correlated. The findings mainly implied a bi-directional relationship between the phenotypes in terms of significant associations between initial levels as well as rate of change. No direction of causality was indicated in males but significant results were found in females where alcohol problems acted as the main driver for the comorbidity of alcohol problems and internalising symptoms; alcohol may have more detrimental effects in females than in males. Importantly, our study examined a population-based longitudinal sample of college students, revealing that the observed relationships are not limited to individuals with clinically diagnosed mental health or substance use problems.

Keywords: alcohol, comorbidity, internalising symptoms, longitudinal modelling

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Authors: Aleksandra Zieminska-Stolarska, Ireneusz Zbicinski

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Introduction: The objective of the present work was to develop and validate a 3D CFD numerical model for simulating flow through 17 kilometers long dam reservoir of a complex bathymetry. In contrast to flowing waters, dam reservoirs were not emphasized in the early years of water quality modeling, as this issue has never been the major focus of urban development. Starting in the 1970s, however, it was recognized that natural and man-made lakes are equal, if not more important than estuaries and rivers from a recreational standpoint. The Sulejow Reservoir (Central Poland) was selected as the study area as representative of many lowland dam reservoirs and due availability of a large database of the ecological, hydrological and morphological parameters of the lake. Method: 3D, 2-phase and 1-phase CFD models were analysed to determine hydrodynamics in the Sulejow Reservoir. Development of 3D, 2-phase CFD model of flow requires a construction of mesh with millions of elements and overcome serious convergence problems. As 1-phase CFD model of flow in relation to 2-phase CFD model excludes from the simulations the dynamics of waves only, which should not change significantly water flow pattern for the case of lowland, dam reservoirs. In 1-phase CFD model, the phases (water-air) are separated by a plate which allows calculations of one phase (water) flow only. As the wind affects velocity of flow, to take into account the effect of the wind on hydrodynamics in 1-phase CFD model, the plate must move with speed and direction equal to the speed and direction of the upper water layer. To determine the velocity at which the plate will move on the water surface and interacts with the underlying layers of water and apply this value in 1-phase CFD model, the 2D, 2-phase model was elaborated. Result: Model was verified on the basis of the extensive flow measurements (StreamPro ADCP, USA). Excellent agreement (an average error less than 10%) between computed and measured velocity profiles was found. As a result of work, the following main conclusions can be presented: •The results indicate that the flow field in the Sulejow Reservoir is transient in nature, with swirl flows in the lower part of the lake. Recirculating zones, with the size of even half kilometer, may increase water retention time in this region •The results of simulations confirm the pronounced effect of the wind on the development of the water circulation zones in the reservoir which might affect the accumulation of nutrients in the epilimnion layer and result e.g. in the algae bloom. Conclusion: The resulting model is accurate and the methodology develop in the frame of this work can be applied to all types of storage reservoir configurations, characteristics, and hydrodynamics conditions. Large recirculating zones in the lake which increase water retention time and might affect the accumulation of nutrients were detected. Accurate CFD model of hydrodynamics in large water body could help in the development of forecast of water quality, especially in terms of eutrophication and water management of the big water bodies.

Keywords: CFD, mathematical modelling, dam reservoirs, hydrodynamics

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Authors: Fanny Desbiolles, Laure Malleret, Isabelle Laffont-Schwob, Christophe Tiliacos, Anne Piram, Mohamed Sarakha, Pascal Wong-Wah-Chung

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Pharmaceuticals in the environment have become a very hot topic in the recent years. This interest is related to the large amounts dispensed and to their release in urine or faeces from treated patients, resulting in their ubiquitous presence in water resources and wastewater treatment plants (WWTP) effluents. Thereby, many studies focused on the prediction of pharmaceuticals’ behaviour, to assess their fate and impacts in the environment. Carbamazepine is a widely consumed psychotropic pharmaceutical, thus being one of the most commonly detected drugs in the environment. This organic pollutant was proved to be persistent, especially with respect to its non-biodegradability, rendering it recalcitrant to usual biological treatment processes. Consequently, carbamazepine is very little removed in WWTP with a maximum abatement rate of 5 % and is then often released in natural surface waters. To better assess the environmental fate of carbamazepine in aqueous media, its photochemical transformation was undertaken in four natural waters (two French rivers, the Berre salt lagoon, Mediterranean Sea water) representative of coastal and inland water types. Kinetic experiments were performed in the presence of light using simulated solar irradiation (Xe lamp 300W). Formation of short-lifetime species was highlighted using chemical trap and laser flash photolysis (nanosecond). Identification of transformation by-products was assessed by LC-QToF-MS analyses. Carbamazepine degradation was observed after a four-day exposure and an abatement of 20% maximum was measured yielding to the formation of many by-products. Moreover, the formation of hydroxyl radicals (•OH) was evidenced in waters using terephthalic acid as a probe, considering the photochemical instability of its specific hydroxylated derivative. Correlations were implemented using carbamazepine degradation rate, estimated hydroxyl radical formation and chemical contents of waters. In addition, laser flash photolysis studies confirmed •OH formation and allowed to evidence other reactive species, such as chloride (Cl2•-)/bromine (Br2•-) and carbonate (CO3•-) radicals in natural waters. Radicals mainly originate from dissolved phase and their occurrence and abundance depend on the type of water. Rate constants between reactive species and carbamazepine were determined by laser flash photolysis and competitive reactions experiments. Moreover, LC-QToF-MS analyses of by-products help us to propose mechanistic pathways. The results will bring insights to the fate of carbamazepine in various water types and could help to evaluate more precisely potential ecotoxicological effects.

Keywords: carbamazepine, kinetic and mechanistic approaches, natural waters, photodegradation

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Authors: Liubov Lutsyshyn, Yaroslava Zhukova

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Over the past twenty years, there has been a drastic change in the mode of nutrition in many countries which has been reflected in the development of new products, production techniques, and has also led to the expansion of sales markets for food products. Studies have shown that solution of the food safety problems is almost impossible without the active and systematic activity of organizations directly involved in the production, storage and sale of food products, as well as without management of end-to-end traceability and exchange of information. The aim of this research is development of the integrated system of the quality management and safety assurance based on the principles of HACCP, traceability and system approach with creation of an algorithm for the identification and monitoring of parameters of technological process of manufacture of cooked sausage products. Methodology of implementation of the integrated system based on the principles of HACCP, traceability and system approach during the manufacturing of cooked sausage products for effective provision for the defined properties of the finished product has been developed. As a result of the research evaluation technique and criteria of performance of the implementation and operation of the system of the quality management and safety assurance based on the principles of HACCP have been developed and substantiated. In the paper regularities of influence of the application of HACCP principles, traceability and system approach on parameters of quality and safety of the finished product have been revealed. In the study regularities in identification of critical control points have been determined. The algorithm of functioning of the integrated system of the quality management and safety assurance has also been described and key requirements for the development of software allowing the prediction of properties of finished product, as well as the timely correction of the technological process and traceability of manufacturing flows have been defined. Based on the obtained results typical scheme of the integrated system of the quality management and safety assurance based on HACCP principles with the elements of end-to-end traceability and system approach for manufacture of cooked sausage products has been developed. As a result of the studies quantitative criteria for evaluation of performance of the system of the quality management and safety assurance have been developed. A set of guidance documents for the implementation and evaluation of the integrated system based on the HACCP principles in meat processing plants have also been developed. On the basis of the research the effectiveness of application of continuous monitoring of the manufacturing process during the control on the identified critical control points have been revealed. The optimal number of critical control points in relation to the manufacture of cooked sausage products has been substantiated. The main results of the research have been appraised during 2013-2014 under the conditions of seven enterprises of the meat processing industry and have been implemented at JSC «Kyiv meat processing plant».

Keywords: cooked sausage products, HACCP, quality management, safety assurance

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Authors: Rajkumar Ghosh

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Earthquakes are natural disasters that pose a significant risk to human life and infrastructure. Effective earthquake mitigation measures require a thorough understanding of the dynamics of seismic occurrences, including thrust movement. Traditionally, estimating thrust movement has relied on typical techniques that may not capture the full complexity of these events. Therefore, investigating alternative approaches, such as incorporating out-of-sequence thrust movement data, could enhance earthquake mitigation strategies. This review aims to provide an overview of the applications of out-of-sequence thrust movement in earthquake mitigation. By examining existing research and studies, the objective is to understand how precise estimation of thrust movement can contribute to improving structural design, analyzing infrastructure risk, and developing early warning systems. The study demonstrates how to estimate out-of-sequence thrust movement using multiple data sources, including GPS measurements, satellite imagery, and seismic recordings. By analyzing and synthesizing these diverse datasets, researchers can gain a more comprehensive understanding of thrust movement dynamics during seismic occurrences. The review identifies potential advantages of incorporating out-of-sequence data in earthquake mitigation techniques. These include improving the efficiency of structural design, enhancing infrastructure risk analysis, and developing more accurate early warning systems. By considering out-of-sequence thrust movement estimates, researchers and policymakers can make informed decisions to mitigate the impact of earthquakes. This study contributes to the field of seismic monitoring and earthquake risk assessment by highlighting the benefits of incorporating out-of-sequence thrust movement data. By broadening the scope of analysis beyond traditional techniques, researchers can enhance their knowledge of earthquake dynamics and improve the effectiveness of mitigation measures. The study collects data from various sources, including GPS measurements, satellite imagery, and seismic recordings. These datasets are then analyzed using appropriate statistical and computational techniques to estimate out-of-sequence thrust movement. The review integrates findings from multiple studies to provide a comprehensive assessment of the topic. The study concludes that incorporating out-of-sequence thrust movement data can significantly enhance earthquake mitigation measures. By utilizing diverse data sources, researchers and policymakers can gain a more comprehensive understanding of seismic dynamics and make informed decisions. However, challenges exist, such as data quality difficulties, modelling uncertainties, and computational complications. To address these obstacles and improve the accuracy of estimates, further research and advancements in methodology are recommended. Overall, this review serves as a valuable resource for researchers, engineers, and policymakers involved in earthquake mitigation, as it encourages the development of innovative strategies based on a better understanding of thrust movement dynamics.

Keywords: earthquake, out-of-sequence thrust, disaster, human life

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Authors: Lorena Mihelac, Janez Povh

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In 2017, we conducted a research study using data consisting of 160 musical excerpts from different musical styles, to analyze the impact of entropy of the harmony on the acceptability of music. In measuring the entropy of harmony, we were interested in unigrams (individual chords in the harmonic progression) and bigrams (the connection of two adjacent chords). In this study, it has been found that 53 musical excerpts out from 160 were evaluated by participants as very complex, although the entropy of the harmonic progression (unigrams and bigrams) was calculated as low. We have explained this by particularities of chord progression, which impact the listener's feeling of complexity and acceptability. We have evaluated the same data twice with new participants in 2018 and with the same participants for the third time in 2019. These three evaluations have shown that the same 53 musical excerpts, found to be difficult and complex in the study conducted in 2017, are exhibiting a high feeling of complexity again. It was proposed that the content of these musical excerpts, defined as “irregular,” is not meeting the listener's expectancy and the basic perceptual principles, creating a higher feeling of difficulty and complexity. As the “irregularities” in these 53 musical excerpts seem to be perceived by the participants without being aware of it, affecting the pleasantness and the feeling of complexity, they have been defined as “subliminal irregularities” and the 53 musical excerpts as “irregular.” In our recent study (2019) of the same data (used in previous research works), we have proposed a new measure of the complexity of harmony, “regularity,” based on the irregularities in the harmonic progression and other plausible particularities in the musical structure found in previous studies. We have in this study also proposed a list of 10 different particularities for which we were assuming that they are impacting the participant’s perception of complexity in harmony. These ten particularities have been tested in this paper, by extending the analysis in our 53 irregular musical excerpts from harmony to melody. In the examining of melody, we have used the computational model “Information Dynamics of Music” (IDyOM) and two information-theoretic measures: entropy - the uncertainty of the prediction before the next event is heard, and information content - the unexpectedness of an event in a sequence. In order to describe the features of melody in these musical examples, we have used four different viewpoints: pitch, interval, duration, scale degree. The results have shown that the texture of melody (e.g., multiple voices, homorhythmic structure) and structure of melody (e.g., huge interval leaps, syncopated rhythm, implied harmony in compound melodies) in these musical excerpts are impacting the participant’s perception of complexity. High information content values were found in compound melodies in which implied harmonies seem to have suggested additional harmonies, affecting the participant’s perception of the chord progression in harmony by creating a sense of an ambiguous musical structure.

Keywords: entropy and information content, harmony, subliminal (ir)regularity, IDyOM

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Authors: Nadine Maier, Martin Mensinger, Enea Tallushi

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In general, today, two types of launching bearings are used in the construction of large steel and steel concrete composite bridges. These are sliding rockers and systems with hydraulic bearings. The advantages and disadvantages of the respective systems are under discussion. During incremental launching, the center of the webs of the superstructure is not perfectly in line with the center of the launching bearings due to unavoidable tolerances, which may have an influence on the buckling behavior of the web plates. These imperfections are not considered in the current design against plate buckling, according to DIN EN 1993-1-5. It is therefore investigated whether the design rules have to take into account any eccentricities which occur during incremental launching and also if this depends on the respective launching bearing. Therefore, at the Technical University Munich, large-scale buckling tests were carried out on longitudinally stiffened plates under biaxial stresses with the two different types of launching bearings and eccentric load introduction. Based on the experimental results, a numerical model was validated. Currently, we are evaluating different parameters for both types of launching bearings, such as load introduction length, load eccentricity, the distance between longitudinal stiffeners, the position of the rotation point of the spherical bearing, which are used within the hydraulic bearings, web, and flange thickness and imperfections. The imperfection depends on the geometry of the buckling field and whether local or global buckling occurs. This and also the size of the meshing is taken into account in the numerical calculations of the parametric study. As a geometric imperfection, the scaled first buckling mode is applied. A bilinear material curve is used so that a GMNIA analysis is performed to determine the load capacity. Stresses and displacements are evaluated in different directions, and specific stress ratios are determined at the critical points of the plate at the time of the converging load step. To evaluate the load introduction of the transverse load, the transverse stress concentration is plotted on a defined longitudinal section on the web. In the same way, the rotation of the flange is evaluated in order to show the influence of the different degrees of freedom of the launching bearings under eccentric load introduction and to be able to make an assessment for the case, which is relevant in practice. The input and the output are automatized and depend on the given parameters. Thus we are able to adapt our model to different geometric dimensions and load conditions. The programming is done with the help of APDL and a Python code. This allows us to evaluate and compare more parameters faster. Input and output errors are also avoided. It is, therefore, possible to evaluate a large spectrum of parameters in a short time, which allows a practical evaluation of different parameters for buckling behavior. This paper presents the results of the tests as well as the validation and parameterization of the numerical model and shows the first influences on the buckling behavior under eccentric and multi-axial load introduction.

Keywords: buckling behavior, eccentric load introduction, incremental launching, large scale buckling tests, multi axial stress states, parametric numerical modelling

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Authors: Miguel Contreras, David Long, Will Bachman

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Background: Microscopy plays a central role in cell and developmental biology. In particular, fluorescence microscopy can be used to visualize specific cellular components and subsequently quantify their morphology through development of virtual-cell models for study of effects of mechanical forces on cells. However, there are challenges with these imaging experiments, which can make it difficult to quantify cell morphology: inconsistent results, time-consuming and potentially costly protocols, and limitation on number of labels due to spectral overlap. To address these challenges, the objective of this project is to develop an automatic computational machine learning pipeline to predict cellular components morphology for virtual-cell generation based on fluorescence cell membrane confocal z-stacks. Methods: Registered confocal z-stacks of nuclei and cell membrane of endothelial cells, consisting of 20 images each, were obtained from fluorescence confocal microscopy and normalized through software pipeline for each image to have a mean pixel intensity value of 0.5. An open source machine learning algorithm, originally developed to predict fluorescence labels on unlabeled transmitted light microscopy cell images, was trained using this set of normalized z-stacks on a single CPU machine. Through transfer learning, the algorithm used knowledge acquired from its previous training sessions to learn the new task. Once trained, the algorithm was used to predict morphology of nuclei using normalized cell membrane fluorescence images as input. Predictions were compared to the ground truth fluorescence nuclei images. Results: After one week of training, using one cell membrane z-stack (20 images) and corresponding nuclei label, results showed qualitatively good predictions on training set. The algorithm was able to accurately predict nuclei locations as well as shape when fed only fluorescence membrane images. Similar training sessions with improved membrane image quality, including clear lining and shape of the membrane, clearly showing the boundaries of each cell, proportionally improved nuclei predictions, reducing errors relative to ground truth. Discussion: These results show the potential of pre-trained machine learning algorithms to predict cell morphology using relatively small amounts of data and training time, eliminating the need of using multiple labels in immunofluorescence experiments. With further training, the algorithm is expected to predict different labels (e.g., focal-adhesion sites, cytoskeleton), which can be added to the automatic machine learning pipeline for direct input into Principal Component Analysis (PCA) for generation of virtual-cell mechanical models.

Keywords: cell morphology prediction, computational machine learning, fluorescence microscopy, virtual-cell models

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Authors: Yuan Yue, Wen-Wei Wang

Abstract:

The objective of this research is to comprehensively evaluate the impact of alkaline environments on the durability of Glass Fiber Reinforced Polymer (GFRP) reinforcements in concrete structures and further explore their potential value within the construction industry. Specifically, we investigate the effects of two widely used high-performance concrete (HPC) materials on the durability of GFRP bars when embedded within them under varying temperature conditions. A total of 279 GFRP bar specimens were manufactured for microcosmic and mechanical performance tests. Among them, 270 specimens were used to test the residual tensile strength after 120 days of immersion, while 9 specimens were utilized for microscopic testing to analyze degradation damage. SEM techniques were employed to examine the microstructure of GFRP and cover concrete. Unidirectional tensile strength experiments were conducted to determine the remaining tensile strength after corrosion. The experimental variables consisted of four types of concrete (engineering cementitious composite (ECC), ultra-high-performance concrete (UHPC), and two types of ordinary concrete with different compressive strengths) as well as three acceleration temperatures (20, 40, and 60℃). The experimental results demonstrate that high-performance concrete (HPC) offers superior protection for GFRP bars compared to ordinary concrete. Two types of HPC enhance durability through different mechanisms: one by reducing the pH of the concrete pore fluid and the other by decreasing permeability. For instance, ECC improves embedded GFRP's durability by lowering the pH of the pore fluid. After 120 days of immersion at 60°C under accelerated conditions, ECC (pH=11.5) retained 68.99% of its strength, while PC1 (pH=13.5) retained 54.88%. On the other hand, UHPC enhances FRP steel's durability by increasing porosity and compactness in its protective layer to reinforce FRP reinforcement's longevity. Due to fillers present in UHPC, it typically exhibits lower porosity, higher densities, and greater resistance to permeation compared to PC2 with similar pore fluid pH levels, resulting in varying degrees of durability for GFRP bars embedded in UHPC and PC2 after 120 days of immersion at a temperature of 60°C - with residual strengths being 66.32% and 60.89%, respectively. Furthermore, SEM analysis revealed no noticeable evidence indicating fiber deterioration in any examined specimens, thus suggesting that uneven stress distribution resulting from interface segregation and matrix damage emerges as a primary causative factor for tensile strength reduction in GFRP rather than fiber corrosion. Moreover, long-term prediction models were utilized to calculate residual strength values over time for reinforcement embedded in HPC under high temperature and high humidity conditions - demonstrating that approximately 75% of its initial strength was retained by reinforcement embedded in HPC after 100 years of service.

Keywords: GFRP bars, HPC, degeneration, durability, residual tensile strength.

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Authors: Henry Lau, Dilupa Nakandala, Li Zhao

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In today’s business, it is well-recognized that risk is an important factor that needs to be taken into consideration before a decision is made. Studies indicate that both the number of risks faced by organizations and their potential consequences are growing. Supply chain risk management has become one of the major concerns for practitioners and researchers. Supply chain leaders and scholars are now focusing on the importance of managing supply chain risk. In order to meet the challenge of managing and mitigating supply chain risk (SCR), we must first identify the different dimensions of SCR and assess its relevant probability and severity. SCR has been classified in many different ways, and there are no consistently accepted dimensions of SCRs and several different classifications are reported in the literature. Basically, supply chain risks can be classified into two dimensions namely disruption risk and operational risk. Disruption risks are those caused by events such as bankruptcy, natural disasters and terrorist attack. Operational risks are related to supply and demand coordination and uncertainty, such as uncertain demand and uncertain supply. Disruption risks are rare but severe and hard to manage, while operational risk can be reduced through effective SCM activities. Other SCRs include supply risk, process risk, demand risk and technology risk. In fact, the disorganized classification of SCR has created confusion for SCR scholars. Moreover, practitioners need to identify and assess SCR. As such, it is important to have an overarching framework tying all these SCR dimensions together for two reasons. First, it helps researchers use these terms for communication of ideas based on the same concept. Second, a shared understanding of the SCR dimensions will support the researchers to focus on the more important research objective: operationalization of SCR, which is very important for assessing SCR. In general, fresh food supply chain is subject to certain level of risks, such as supply risk (low quality, delivery failure, hot weather etc.) and demand risk (season food imbalance, new competitors). Effective strategies to mitigate fresh food supply chain risk are required to enhance operations. Before implementing effective mitigation strategies, we need to identify the risk sources and evaluate the risk level. However, assessing the supply chain risk is not an easy matter, and existing research mainly use qualitative method, such as risk assessment matrix. To address the relevant issues, this paper aims to analyze the risk factor of the fresh food supply chain using an approach comprising both fuzzy logic and hierarchical holographic modeling techniques. This novel approach is able to take advantage the benefits of both of these well-known techniques and at the same time offset their drawbacks in certain aspects. In order to develop this integrated approach, substantial research work is needed to effectively combine these two techniques in a seamless way, To validate the proposed integrated approach, a case study in a fresh food supply chain company was conducted to verify the feasibility of its functionality in a real environment.

Keywords: fresh food supply chain, fuzzy logic, hierarchical holographic modelling, operationalization, supply chain risk

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