Search results for: Gaussian processes

Authors: Florian Mach, Eric Hund, Malte Stonis

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The dismantling of disused industrial facilities such as nuclear power plants or refineries is an enormous challenge for the planning and control of the logistic processes. Existing control models do not meet the requirements for a proper dismantling of industrial plants. Therefore, the paper presents an approach for the control of dismantling and post-processing processes (e.g. decontamination) in plant decommissioning. In contrast to existing approaches, the dismantling sequence and depth are selected depending on the capacity utilization of required post-processing processes by also considering individual characteristics of respective dismantling tasks (e.g. decontamination success rate, uncertainties regarding the process times). The results can be used in the dismantling of industrial plants (e.g. nuclear power plants) to reduce dismantling time and costs by avoiding bottlenecks such as capacity constraints.

Keywords: dismantling management, logistics planning and control models, nuclear power plant dismantling, reverse logistics

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Authors: Rui G. Serôdio, Carina Martins, Alexandra Serra, José A. Lima, Luísa Catita, Paula Lopes

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Building on social identity approach, we tested the impact of social categorization and comparison in the psychosocial process by which social entrepreneurs define their group identity. Specifically, we address how both differentiation and assimilation processes are set of in the context of constructing a novel, distinctive and socially salient – social entrepreneurs. As part of a larger research line, a quasi-experimental study with Social and Business Entrepreneurs, as well as “Lay People” provided evidence consistent with our predictions: (1) Social Entrepreneurs, in contrast with Lay People and Business Entrepreneurs, value more strongly social identity than personal identity, and the later is the only group that values Personal Differentiation; (2) unlike Entrepreneurs, Social Entrepreneurs display an ingroup bias across group evaluations; (3) Lay People, display a self-serving bias, although, overall, they allocate a more positive image to the target groups; (4) combining own vs. others evaluations across all groups, Social Entrepreneurs receive the more positive value. From the standpoint of social identity and self-categorization theories and their approach to group process, we discuss the processes of intergroup comparison and differentiation as core processes in the construction of a positive social identity. We illustrate it within the context of social entrepreneurship, a political and social “wave” that flows across Europe at this time.

Keywords: group processes, social entrepreneurship, social identity, business entrepreneurs

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Authors: Merve Begum Terzi, Orhan Arikan, Adnan Abaci, Mustafa Candemir

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Acute myocardial infarction is a major cause of death in the world. Therefore, its fast and reliable diagnosis is a major clinical need. ECG is the most important diagnostic methodology which is used to make decisions about the management of the cardiovascular diseases. In patients with acute myocardial ischemia, temporary chest pains together with changes in ST segment and T wave of ECG occur shortly before the start of myocardial infarction. In this study, a technique which detects changes in ST/T sections of ECG is developed for the early diagnosis of acute myocardial ischemia. For this purpose, a database of real ECG recordings that contains a set of records from 75 patients presenting symptoms of chest pain who underwent elective percutaneous coronary intervention (PCI) is constituted. 12-lead ECG’s of the patients were recorded before and during the PCI procedure. Two ECG epochs, which are the pre-inflation ECG which is acquired before any catheter insertion and the occlusion ECG which is acquired during balloon inflation, are analyzed for each patient. By using pre-inflation and occlusion recordings, ECG features that are critical in the detection of acute myocardial ischemia are identified and the most discriminative features for the detection of acute myocardial ischemia are extracted. A classification technique based on support vector machine (SVM) approach operating with linear and radial basis function (RBF) kernels to detect ischemic events by using ST-T derived joint features from non-ischemic and ischemic states of the patients is developed. The dataset is randomly divided into training and testing sets and the training set is used to optimize SVM hyperparameters by using grid-search method and 10fold cross-validation. SVMs are designed specifically for each patient by tuning the kernel parameters in order to obtain the optimal classification performance results. As a result of implementing the developed classification technique to real ECG recordings, it is shown that the proposed technique provides highly reliable detections of the anomalies in ECG signals. Furthermore, to develop a detection technique that can be used in the absence of ECG recording obtained during healthy stage, the detection of acute myocardial ischemia based on ECG recordings of the patients obtained during ischemia is also investigated. For this purpose, a Gaussian mixture model (GMM) is used to represent the joint pdf of the most discriminating ECG features of myocardial ischemia. Then, a Neyman-Pearson type of approach is developed to provide detection of outliers that would correspond to acute myocardial ischemia. Neyman – Pearson decision strategy is used by computing the average log likelihood values of ECG segments and comparing them with a range of different threshold values. For different discrimination threshold values and number of ECG segments, probability of detection and probability of false alarm values are computed, and the corresponding ROC curves are obtained. The results indicate that increasing number of ECG segments provide higher performance for GMM based classification. Moreover, the comparison between the performances of SVM and GMM based classification showed that SVM provides higher classification performance results over ECG recordings of considerable number of patients.

Keywords: ECG classification, Gaussian mixture model, Neyman–Pearson approach, support vector machine

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Authors: K. Veluraja

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Glycan of glycolipids and glycoproteins is playing a significant role in living systems particularly in molecular recognition processes. Molecular recognition processes are attributed to their occurrence on the surface of the cell, sequential arrangement and type of sugar molecules present in the oligosaccharide structure and glyosidic linkage diversity (glycoinformatics) and conformational diversity (glycoconformatics). Molecular Dynamics Simulation study is a theoretical-cum-computational tool successfully utilized to establish glycoconformatics of glycan. The study on various oligosaccharides of glycan clearly indicates that oligosaccharides do exist in multiple conformational states and these conformational states arise due to the flexibility associated with a glycosidic torsional angle (φ,ψ) . As an example: a single disaccharide structure NeuNacα(2-3) Gal exists in three different conformational states due to the differences in the preferential value of glycosidic torsional angles (φ,ψ). Hence establishing three dimensional structural and conformational models for glycan (cartesian coordinates of every individual atoms of an oligosaccharide structure in a preferred conformation) is quite crucial to understand various molecular recognition processes such as glycan-toxin interaction and glycan-virus interaction. The gycoconformatics models obtained for various glycan through Molecular Dynamics Simulation stored in our 3DSDSCAR (3DSDSCAR.ORG) a public domain database and its utility value in understanding the molecular recognition processes and in drug design venture will be discussed.

Keywords: glycan, glycoconformatics, molecular dynamics simulation, oligosaccharide

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Authors: Tasneem Halawani, Yamen Khateeb

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With customers’ ever-increasing expectations for fast services provisioning for all their business needs, information technology (IT) organizations, as business partners, have to cope with this demanding environment and deliver their services in the most effective and efficient way. The purpose of this paper is to identify optimization and automation opportunities for the top requested IT services in a heterogenic digital environment and widely spread customer base. In collaboration with systems, processes, and subject matter experts (SMEs), the processes in scope were approached by analyzing four-year related historical data, identifying and surveying stakeholders, modeling the as-is processes, and studying systems integration/automation capabilities. This effort resulted in identifying several pain areas, including standardization, unnecessary customer and IT involvement, manual steps, systems integration, and performance measurement. These pain areas were addressed by standardizing the top five requested IT services, eliminating/automating 43 steps, and utilizing a single platform for end-to-end process execution. In conclusion, the optimization of IT service request processes in a heterogenic digital environment and widely spread customer base is challenging, yet achievable without compromising the service quality and customers’ added value. Further studies can focus on measuring the value of the eliminated/automated process steps to quantify the enhancement impact. Moreover, a similar approach can be utilized to optimize other IT service requests, with a focus on business criticality.

Keywords: automation, customer value, heterogenic, integration, IT services, optimization, processes

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Authors: Gyeongbeom Yi

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The purpose of this study is to introduce the analytic solution for determining the optimal capacity (lot-size) of a multiproduct, multistage production and inventory system to meet the finished product demand. Reasonable decision-making about the capacity of processes and storage units is an important subject for industry. The industrial solution for this subject is to use the classical economic lot sizing method, EOQ/EPQ (Economic Order Quantity/Economic Production Quantity) model, incorporated with practical experience. However, the unrealistic material flow assumption of the EOQ/EPQ model is not suitable for chemical plant design with highly interlinked processes and storage units. This study overcomes the limitation of the classical lot sizing method developed on the basis of the single product and single stage assumption. The superstructure of the plant considered consists of a network of serially and/or parallelly interlinked processes and storage units. The processes involve chemical reactions with multiple feedstock materials and multiple products as well as mixing, splitting or transportation of materials. The objective function for optimization is minimizing the total cost composed of setup and inventory holding costs as well as the capital costs of constructing processes and storage units. A novel production and inventory analysis method, PSW (Periodic Square Wave) model, is applied. The advantage of the PSW model comes from the fact that the model provides a set of simple analytic solutions in spite of a realistic description of the material flow between processes and storage units. The resulting simple analytic solution can greatly enhance the proper and quick investment decision for plant design and operation problem confronted in diverse economic situations.

Keywords: analytic solution, optimal design, process-storage network

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Authors: Wanchalerm Patanacharoenwong, Panaya Sudta, Prachya Bumrungkun

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The paper focuses on the development of a system that combines Internet of Things (IoT) technologies and deep learning algorithms for anomaly detection in residential Electric Vehicle (EV) charging in smart cities. With the increasing number of EVs, ensuring efficient and reliable charging systems has become crucial. The aim of this research is to develop an integrated IoT and deep learning system for detecting anomalies in residential EV charging and enhancing EV load profiling and event detection in smart cities. This approach utilizes IoT devices equipped with infrared cameras to collect thermal images and household EV charging profiles from the database of Thailand utility, subsequently transmitting this data to a cloud database for comprehensive analysis. The methodology includes the use of advanced deep learning techniques such as Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) algorithms. IoT devices equipped with infrared cameras are used to collect thermal images and EV charging profiles. The data is transmitted to a cloud database for comprehensive analysis. The researchers also utilize feature-based Gaussian mixture models for EV load profiling and event detection. Moreover, the research findings demonstrate the effectiveness of the developed system in detecting anomalies and critical profiles in EV charging behavior. The system provides timely alarms to users regarding potential issues and categorizes the severity of detected problems based on a health index for each charging device. The system also outperforms existing models in event detection accuracy. This research contributes to the field by showcasing the potential of integrating IoT and deep learning techniques in managing residential EV charging in smart cities. The system ensures operational safety and efficiency while also promoting sustainable energy management. The data is collected using IoT devices equipped with infrared cameras and is stored in a cloud database for analysis. The collected data is then analyzed using RNN, LSTM, and feature-based Gaussian mixture models. The approach includes both EV load profiling and event detection, utilizing a feature-based Gaussian mixture model. This comprehensive method aids in identifying unique power consumption patterns among EV owners and outperforms existing models in event detection accuracy. In summary, the research concludes that integrating IoT and deep learning techniques can effectively detect anomalies in residential EV charging and enhance EV load profiling and event detection accuracy. The developed system ensures operational safety and efficiency, contributing to sustainable energy management in smart cities.

Keywords: cloud computing framework, recurrent neural networks, long short-term memory, Iot, EV charging, smart grids

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Authors: Karlijn Van Den Broek, Ian Walker, Christian Klöckner

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Campaigns aiming to induce energy-saving behaviour among householders use a wide range of approaches that address many different drivers thought to underpin this behaviour. However, little research has compared the relative importance of the different factors that influence energy behaviour, meaning campaigns are not informed about where best to focus resources. Therefore, this study applies the Comprehensive Action Determination Model (CADM) to compare the role of normative, intentional, habitual, and situational processes on energy-saving behaviour. An online survey on a sample of households (N = 247) measured the CADM variables and the data was analysed using structural equation modelling. Results showed that situational and habitual processes were best able to account for energy saving behaviour while normative and intentional processes had little predictive power. These findings suggest that policymakers should move away from motivating householders to save energy and should instead focus their efforts on changing energy habits and creating environments that facilitate energy saving behaviour. These findings add to the wider development in social and environmental psychology that emphasizes the importance of extra-personal variables such as the physical environment in shaping behaviour.

Keywords: energy consumption, behavioural modelling, environmental psychology theory, habits, values

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Authors: Chamindi Dilkushi Senaratne

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Communicating via a text or an SMS (Short Message Service) has become an integral part of our daily lives. With the increase in the use of mobile phones, text messaging has become a genre by itself worth researching and studying. It is undoubtedly a major phenomenon revealing language change. This paper attempts to describe the morphological processes of text language of urban bilinguals in Sri Lanka. It will be a typological study based on 500 English text messages collected from urban bilinguals residing in Colombo. The messages are selected by categorizing the deviant forms of language use apparent in text messages. These stylistic deviations are a deliberate skilled performance by the users of the language possessing an in-depth knowledge of linguistic systems to create new words and thereby convey their linguistic identity and individual and group solidarity via the message. The findings of the study solidifies arguments that the manipulation of language in text messages is both creative and appropriate. In addition, code mixing theories will be used to identify how existing morphological processes are adapted by bilingual users in Sri Lanka when texting. The study will reveal processes such as omission, initialism, insertion and alternation in addition to other identified linguistic features in text language. The corpus reveals the most common morphological processes used by Sri Lankan urban bilinguals when sending texts.

Keywords: bilingual, deviations, morphology, texts

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Authors: Partha Sarathi Mondal, Srabani Sanyal

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Different geomorphic agents produce a different landforms pattern. Similarly rivers also have a distinct and diverse landforms pattern. And even, within a river course different and distinct assemblage of landforms i.e. morphological variability are seen. These morphological variability are produced by different river processes. Channel and floodplain morphology helps to interpret river processes. Consequently morphological variability can be used as an important tool for assessing river processes, hydrological connectivity and river health, which will help us to draw inference about river processes and therefore, management of river health. The present study is documented on West Rapti river, a trans-boundary river flowing through Nepal and India, from its source to confluence with Ghaghra river in India. The river shows a significant morphological variability throughout its course. The present study tries to find out factors and processes responsible for the morphological variability of the river and in which way it can be applied in river management practices. For this purpose channel and floodplain morphology of West Rapti river was mapped as accurately as possible and then on the basis of process-form interactions, inferences are drawn to understand factors of morphological variability. The study shows that the valley setting of West Rapti river, in the Himalayan region, is confined and somewhere partly confined whereas, channel of the West Rapti river is single thread in most part of Himalayan region and braided in valley region. In the foothill region valley is unconfined and channel is braided, in middle part channel is meandering and valley is unconfined, whereas, channel is anthropogenically altered in the lower part of the course. Due to this the morphology of West Rapti river is highly diverse. These morphological variability are produced by different geomorphic processes. Therefore, for any river management it is essential to sustain these morphological variability so that the river could not cross the geomorphic threshold and environmental flow of the river along with the biodiversity of riparian region is maintained.

Keywords: channel morphology, environmental flow, floodplain morphology, geomorphic threshold

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Authors: Daniel Pinheiro Da Silva Junior, Aline Paes, Daniel De Oliveira, Christiano Lacerda Ghuerren, Marcio Duran

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The Courts of Accounts are institutions responsible for overseeing and point out irregularities of Public Administration expenses. They have a high demand for processes to be analyzed, whose decisions must be grounded on severity laws. Despite the existing large amount of processes, there are several cases reporting similar subjects. Thus, previous decisions on already analyzed processes can be a precedent for current processes that refer to similar topics. Identifying similar topics is an open, yet essential task for identifying similarities between several processes. Since the actual amount of topics is considerably large, it is tedious and error-prone to identify topics using a pure manual approach. This paper presents a tool based on Machine Learning and Natural Language Processing to assists in building a labeled dataset. The tool relies on Topic Modelling with Latent Dirichlet Allocation to find the topics underlying a document followed by Jensen Shannon distance metric to generate a probability of similarity between documents pairs. Furthermore, in a case study with a corpus of decisions of the Rio de Janeiro State Court of Accounts, it was noted that data pre-processing plays an essential role in modeling relevant topics. Also, the combination of topic modeling and a calculated distance metric over document represented among generated topics has been proved useful in helping to construct a labeled base of similar and non-similar document pairs.

Keywords: courts of accounts, data labelling, document similarity, topic modeling

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Authors: Ala Abdessemed, Mohamed Seddik Oussama Belahmadi, Nabil Charchar, Abdefettah Gherib, Bradai Fares, Boussadia Chouaib Nour El-Islem

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Algerian cities are confronted with large quantities of waste generated by the disposal of household and similar residues in technical landfills (CET), such as the one in the location of Batna. The interaction between waste components and incoming water generates leachates rich in organic matter and trace elements, which require treatment before discharge. The aim of this study was to propose an effective process for treating the leachates, which were subjected to an initial chemical treatment using the (H₂O₂/UV) system. Optimal treatment conditions were determined at [H₂O₂] of 0.3 M and pH of 8.6. Next, two hybrid biological treatment systems were applied: hybrid system I (H₂O₂/UV/bacteria) and hybrid system II (H₂O₂/UV/bacteria/microalgae). The three processes resulted in the following degradation rates, expressed in terms of total organic carbon (TOC) 27.4% for the (H₂O₂/UV) system; 58.1% for the hybrid system I (H₂O₂/UV/Bacteria); 67.86% for the hybrid system II (H₂O₂/UV/Bacteria/Microalgae). This study demonstrates that a hybrid approach combining advanced oxidation processes and biological treatments is a highly effective alternative to achieve satisfactory treatment.

Keywords: leachate, landfill, advanced oxidation processes, biological treatment, bacteria, microalgae, total organic carbon

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Authors: Hassan Noun, Christian Urban-Seelmann, Mohamed Abdelfattah, Guillaume Zeller, Rajesh G., Iryna Mozgova, Roland Lachmayer

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Several modules in automotive are usually modified and adapted for various project-specific applications. Due to a standardized safety concept, high reusability is accessible. A safety element out of context (SEooC) according to ISO 26262 can be a suitable approach. Based on the same safety concept and analysis, common modules can reach high usability. For developing according to a module out of context, an appropriate and detailed development approach is required. This paper shows how to derive these development processes for platform modules. Therefore, the detailed approach to the safety element out of context is derived. The aim is to create a detailed workflow for all phases of the development and integration of any kind of system modules. As an application example, an automotive project for an actuator control module is considered.

Keywords: functional safety, engineering processes, system engineering, electronic engineering

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Authors: Ibtissem Hosni, Lilia Bennaceur Farah, Saber Mohamed Naceur

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In order to assess the water availability in the soil, it is crucial to have information about soil distributed moisture content; this parameter helps to understand the effect of humidity on the exchange between soil, plant cover and atmosphere in addition to fully understanding the surface processes and the hydrological cycle. On the other hand, aerodynamic roughness length is a surface parameter that scales the vertical profile of the horizontal component of the wind speed and characterizes the surface ability to absorb the momentum of the airflow. In numerous applications of the surface hydrology and meteorology, aerodynamic roughness length is an important parameter for estimating momentum, heat and mass exchange between the soil surface and atmosphere. It is important on this side, to consider the atmosphere factors impact in general, and the natural erosion in particular, in the process of soil evolution and its characterization and prediction of its physical parameters. The study of the induced movements by the wind over soil vegetated surface, either spaced plants or plant cover, is motivated by significant research efforts in agronomy and biology. The known major problem in this side concerns crop damage by wind, which presents a booming field of research. Obviously, most models of soil surface require information about the aerodynamic roughness length and its temporal and spatial variability. We have used a bi-dimensional multi-scale (2D MLS) roughness description where the surface is considered as a superposition of a finite number of one-dimensional Gaussian processes each one having a spatial scale using the wavelet transform and the Mallat algorithm to describe natural surface roughness. We have introduced multi-layer aspect of the humidity of the soil surface, to take into account a volume component in the problem of backscattering radar signal. As humidity increases, the dielectric constant of the soil-water mixture increases and this change is detected by microwave sensors. Nevertheless, many existing models in the field of radar imagery, cannot be applied directly on areas covered with vegetation due to the vegetation backscattering. Thus, the radar response corresponds to the combined signature of the vegetation layer and the layer of soil surface. Therefore, the key issue of the numerical estimation of soil moisture is to separate the two contributions and calculate both scattering behaviors of the two layers by defining the scattering of the vegetation and the soil blow. This paper presents a synergistic methodology, and it is for estimating roughness and soil moisture from C-band radar measurements. The methodology adequately represents a microwave/optical model which has been used to calculate the scattering behavior of the aerodynamic vegetation-covered area by defining the scattering of the vegetation and the soil below.

Keywords: aerodynamic, bi-dimensional, vegetation, synergistic

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Authors: Shogo Kato, Daisuke Okamoto, Satoko Tsuru, Yoshinori Iizuka, Ryoko Shimono

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Healthcare safety has been perceived important. It is essential to prevent troubles in healthcare processes for healthcare safety. Trouble prevention is based on trouble prediction using accumulated knowledge on processes, troubles, and countermeasures. However, information on troubles has not been accumulated in hospitals in the appropriate structure, and it has not been utilized effectively to prevent troubles. In the previous study, though a detailed knowledge acquisition process for trouble prediction was proposed, the knowledgebase for countermeasures was not involved. In this paper, we aim to propose the structure of the knowledgebase for countermeasures in the knowledge acquisition process for trouble prediction in healthcare process. We first design the structure of countermeasures and propose the knowledge representation form on countermeasures. Then, we evaluate the validity of the proposal, by applying it into an actual hospital.

Keywords: trouble prevention, knowledge structure, structured knowledge, reusable knowledge

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Authors: Jiajia Pan, Hung Tao Shen

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Freeze and thaw occurs seasonally in river banks in northern countries. Little is known on how the riverbank soil temperature responds to air temperature changes and how freeze and thaw develops in a river bank seasonally. This study presents a two-dimensional heat conduction model for numerical investigations of seasonal freeze and thaw processes in an idealized river bank. The model uses the finite difference method and it is convenient for applications. The model is validated with an analytical solution and a field case with soil temperature distributions. It is then applied to the idealized river bank in terms of partially and fully saturated conditions with or without ice cover influence. Simulated results illustrate the response processes of the river bank to seasonal air temperature variations. It promotes the understanding of freeze and thaw processes in river banks and prepares for further investigation of frost and thaw impacts on riverbank stability.

Keywords: freeze and thaw, riverbanks, 2D model, heat conduction

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Authors: Masood Ahmed

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There is a plethora of literature (including judicial and extra-judicial comments) concerning the virtues of alternative dispute resolution processes within the English civil justice system. Lord Woolf in his Access to Justice Report ushered in a new pro-ADR philosophy and this was reinforced by Sir Rupert Jackson in his review of civil litigation costs. More recently, Briggs LJ, in his review of the Chancery Court, reiterated the significant role played by ADR and the need for better integration of ADR processes within the Chancery Court. His Lordship also noted that ADR which had failed to produce a settlement (i.e. a failed-ADR) continued to play a significant role in contributing to a ‘substantial narrowing of the issues or increased focus on the key issues’ which were ‘capable of assisting both the parties and the court in the economical determination of the dispute at trial.’ With the assistance of empirical data, this paper investigates the nature of failed-ADR and, in particular, assesses the effectiveness of failed-ADR processes as a tool in: (a) narrowing the legal and/or factual issues which may assist the courts in more effective and efficient case management of the dispute; (b) assisting the parties in the future settlement of the matter. This paper will also measure the effectiveness of failed-ADR by considering the views and experiences of legal practitioners who have engaged in failed-ADR.

Keywords: English civil justice system, alternative dispute resolution processes, civil court process, empirical data from legal profession regarding failed ADR

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Authors: Juan M. Rodriguez-Diaz

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The precision of parameter estimators in the Gaussian linear model is traditionally accounted by the variance-covariance matrix of the asymptotic distribution. However, this measure can underestimate the true variance, specially for small samples. Traditionally, optimal design theory pays attention to this variance through its relationship with the model's information matrix. For this reason it seems convenient, at least in some cases, adapt the optimality criteria in order to get the best designs for the actual variance structure, otherwise the loss in efficiency of the designs obtained with the traditional approach may be very important.

Keywords: correlated observations, information matrix, optimality criteria, variance-covariance matrix

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Authors: Elena Puica

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This paper aims to investigate the impact of Supply Chain Management (SCM) on the environment by applying a Machine Learning model while pointing out the efficiency of the technology used. The Machine Learning model was used to derive the efficiency and optimization of technology used in SCM and the environmental impact of SCM processes. The model applied is a predictive classification model and was trained firstly to determine which stage of the SCM has more outputs and secondly to demonstrate the efficiency of using advanced technology in SCM instead of recuring to traditional SCM. The outputs are the emissions generated in the environment, the consumption from different steps in the life cycle, the resulting pollutants/wastes emitted, and all the releases to air, land, and water. This manuscript presents an innovative approach to applying advanced technology in SCM and simultaneously studies the efficiency of technology and the SCM's impact on the environment. Identifying the conceptual relationships between SCM practices and their impact on the environment is a new contribution to the research. The authors can take a forward step in developing recent studies in SCM and its effects on the environment by applying technology.

Keywords: machine-learning model in SCM, SCM processes, SCM and the environmental impact, technology in SCM

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Authors: Etim, Victoria Enefiok, Dada, Oluseyi Akintunde, Bassey Okon

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Phonological disorder is a serious and disturbing issue to many parents and teachers. Efforts towards resolving the problem have been undermined by other specific disabilities which were hidden to many regular and special education teachers. It is against this background that this study was motivated to provide data on the prevalence of phonological disorders in children with specific language impairment (CWSLI) as the first step towards critical intervention. The study was a survey of 15 CWSLI from St. Louise Inclusive schools, Ikot Ekpene in Akwa Ibom State of Nigeria. Phonological Processes Diagnostic Scale (PPDS) with 17 short sentences, which cut across the five phonological processes that were examined, were validated by experts in test measurement, phonology and special education. The respondents were made to read the sentences with emphasis on the targeted sounds. Their utterances were recorded and analyzed in the language laboratory using Praat Software. Data were also collected through friendly interactions at different times from the clients. The theory of generative phonology was adopted for the descriptive analysis of the phonological processes. Data collected were analyzed using simple percentage and composite bar chart for better understanding of the result. The study found out that CWSLI exhibited the five phonological processes under investigation. It was revealed that 66.7%, 80%, 73.3%, 80%, and 86.7% of the respondents have severe deficit in fricative stopping, velar fronting, liquid gliding, final consonant deletion and cluster reduction, respectively. It was therefore recommended that a nationwide survey should be carried out to have national statistics of CWSLI with phonological deficits and develop intervention strategies for effective therapy to remediate the disorder.

Keywords: language disorders, phonology, phonological processes, specific language impairment

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Authors: Ben Soltane Cheima, Ittansa Yonas Kelbesa

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Speaker Identification (SI) is the task of establishing identity of an individual based on his/her voice characteristics. The SI task is typically achieved by two-stage signal processing: training and testing. The training process calculates speaker specific feature parameters from the speech and generates speaker models accordingly. In the testing phase, speech samples from unknown speakers are compared with the models and classified. Even though performance of speaker identification systems has improved due to recent advances in speech processing techniques, there is still need of improvement. In this paper, a Closed-Set Tex-Independent Speaker Identification System (CISI) based on a Multiple Classifier System (MCS) is proposed, using Mel Frequency Cepstrum Coefficient (MFCC) as feature extraction and suitable combination of vector quantization (VQ) and Gaussian Mixture Model (GMM) together with Expectation Maximization algorithm (EM) for speaker modeling. The use of Voice Activity Detector (VAD) with a hybrid approach based on Short Time Energy (STE) and Statistical Modeling of Background Noise in the pre-processing step of the feature extraction yields a better and more robust automatic speaker identification system. Also investigation of Linde-Buzo-Gray (LBG) clustering algorithm for initialization of GMM, for estimating the underlying parameters, in the EM step improved the convergence rate and systems performance. It also uses relative index as confidence measures in case of contradiction in identification process by GMM and VQ as well. Simulation results carried out on voxforge.org speech database using MATLAB highlight the efficacy of the proposed method compared to earlier work.

Keywords: feature extraction, speaker modeling, feature matching, Mel frequency cepstrum coefficient (MFCC), Gaussian mixture model (GMM), vector quantization (VQ), Linde-Buzo-Gray (LBG), expectation maximization (EM), pre-processing, voice activity detection (VAD), short time energy (STE), background noise statistical modeling, closed-set tex-independent speaker identification system (CISI)

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Authors: Mladen Milicevic

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In this paper, subjective film sound will be addressed as it gets represented in narrative cinema. First, 'meta-diegetic' sound will be briefly explained followed by transition to “oneiric” sound. The representation of oneiric sound refers to a situation where film characters are experiencing some sort of an altered state of consciousness. Looking at an antlered state of consciousness in terms of human brain processes will point out to the cinematic ways of expression, which 'mimic' those processes. Using several examples for different films will illustrate these points.

Keywords: oneiric, ASC, film, sound

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Authors: Marie Manikis

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This paper argues that the scholarship to date on victims in the criminal process has mainly adopted a private conception of victims –as bearers of individual interests, rights, and remedies– rather than a conception of the victim as an actor with public functions and interests, who has historically and continuously taken on an active role in the common law tradition. This conception enables a greater understanding of the various developments around victim participation in common law criminal justice systems and provides a useful analytical tool to understand the different roles of victims in England and Wales and the United States. Indeed, the main focus on individual rights and the conception of the victim as a private entity undermines the distinctive and increasing role victims play in the wider criminal justice process as agents of accountability through administrative-based processes within and outside courts, including private prosecutions, internal review processes within prosecutorial agencies, judicial review, and ombudsmen processes.

Keywords: victims, participation, criminal justice, accountability

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Authors: Joseph C. Chen, Venkata Karthik Jakka

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The main objective of this research is to optimize tensile strength and dimensional accuracy in injection molding processes using Taguchi Parameter Design. An L16 orthogonal array (OA) is used in Taguchi experimental design with five control factors at four levels each and with non-controllable factor vibration. A total of 32 experiments were designed to obtain the optimal parameter setting for the process. The optimal parameters identified for the shrinkage are shot volume, 1.7 cubic inch (A4); mold term temperature, 130 ºF (B1); hold pressure, 3200 Psi (C4); injection speed, 0.61 inch3/sec (D2); and hold time of 14 seconds (E2). The optimal parameters identified for the tensile strength are shot volume, 1.7 cubic inch (A4); mold temperature, 160 ºF (B4); hold pressure, 3100 Psi (C3); injection speed, 0.69 inch3/sec (D4); and hold time of 14 seconds (E2). The Taguchi-based optimization framework was systematically and successfully implemented to obtain an adjusted optimal setting in this research. The mean shrinkage of the confirmation runs is 0.0031%, and the tensile strength value was found to be 3148.1 psi. Both outcomes are far better results from the baseline, and defects have been further reduced in injection molding processes.

Keywords: injection molding processes, taguchi parameter design, tensile strength, high-density polyethylene(HDPE)

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Authors: Farnoosh Faal

Abstract:

The rapid growth of social media has brought significant changes to the field of urban e-planning. This study aims to review the existing literature on the impact of social media on urban e-planning processes. The study begins with a discussion of the evolution of social media and its role in urban e-planning. The review covers research on the use of social media for public engagement, citizen participation, stakeholder communication, decision-making, and monitoring and evaluation of urban e-planning initiatives. The findings suggest that social media has the potential to enhance public participation and improve decision-making in urban e-planning processes. Social media platforms such as Facebook, Twitter, and Instagram can provide a platform for citizens to engage with planners and policymakers, express their opinions, and provide feedback on planning proposals. Social media can also facilitate the collection and analysis of data, including real-time data, to inform urban e-planning decision-making. However, the literature also highlights some challenges associated with the use of social media in urban e-planning. These challenges include issues related to the representativeness of social media users, the quality of information obtained from social media, the potential for bias and manipulation of social media content, and the need for effective data management and analysis. The study concludes with recommendations for future research on the use of social media in urban e-planning. The recommendations include the need for further research on the impact of social media on equity and social justice in planning processes, the need for more research on effective strategies for engaging underrepresented groups, and the development of guidelines for the use of social media in urban e-planning processes. Overall, the study suggests that social media has the potential to transform urban e-planning processes but that careful consideration of the opportunities and challenges associated with its use is essential for effective and ethical planning practice.

Keywords: social media, Urban e-planning, public participation, citizen engagement

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Authors: Suyeb Ahmed Khan, Mahmood Mian

Abstract:

Simulation plays an important role during all phases of the design and engineering of communications systems, from early stages of conceptual design through the various stages of implementation, testing, and fielding of the system. In the present paper, a simulation model has been constructed for the WCDMA system in order to evaluate the performance. This model describes multiusers effects and calculation of BER (Bit Error Rate) in 3G mobile systems using Simulink MATLAB 7.1. Gaussian Approximation defines the multi-user effect on system performance. BER has been analyzed with comparison between transmitting data and receiving data.

Keywords: WCDMA, simulations, BER, MATLAB

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Authors: Mazyar Yosofi, Olivier Kerbrat, Pascal Mognol

Abstract:

Life cycle assessment of additive manufacturing processes has evolved significantly since these past years. A lot of existing studies mainly focused on energy consumption. Nowadays, new methodologies of life cycle inventory acquisition came through the literature and help manufacturers to take into account all the input and output flows during the manufacturing step of the life cycle of products. Indeed, the environmental analysis of the phenomena that occur during the manufacturing step of additive manufacturing processes is going to be well known. Now it becomes possible to count and measure accurately all the inventory data during the manufacturing step. Optimization of the environmental performances of processes can now be considered. Environmental performance improvement can be made by varying process parameters. However, a lot of these parameters (such as manufacturing speed, the power of the energy source, quantity of support materials) affect directly the mechanical properties, surface finish and the dimensional accuracy of a functional part. This study aims to improve the environmental performance of an additive manufacturing process without deterioration of the part quality. For that purpose, the authors have developed a generic method that has been applied on multiple parts made by additive manufacturing processes. First, a complete analysis of the process parameters is made in order to identify which parameters affect only the environmental performances of the process. Then, multiple parts are manufactured by varying the identified parameters. The aim of the second step is to find the optimum value of the parameters that decrease significantly the environmental impact of the process and keep the part quality as desired. Finally, a comparison between the part made by initials parameters and changed parameters is made. In this study, the major finding claims by authors is to reduce the environmental impact of an additive manufacturing process while respecting the three quality criterion of parts, mechanical properties, dimensional accuracy and surface roughness. Now that additive manufacturing processes can be seen as mature from a technical point of view, environmental improvement of these processes can be considered while respecting the part properties. The first part of this study presents the methodology applied to multiple academic parts. Then, the validity of the methodology is demonstrated on functional parts.

Keywords: additive manufacturing, environmental impact, environmental improvement, mechanical properties

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Authors: Dávid Kovács, Bálint Csanády, Dániel Boros, Iván Ivkovic, Lóránt Nagy, Dalma Tóth-Lakits, László Márkus, András Lukács

Abstract:

The modeling practice of financial instruments has seen significant change over the last decade due to the recognition of time-dependent and stochastically changing correlations among the market prices or the prices and market characteristics. To represent this phenomenon, the Stochastic Correlation Process (SCP) has come to the fore in the joint modeling of prices, offering a more nuanced description of their interdependence. This approach has allowed for the attainment of realistic tail dependencies, highlighting that prices tend to synchronize more during intense or volatile trading periods, resulting in stronger correlations. Evidence in statistical literature suggests that, similarly to the volatility, the SCP of certain stock prices follows rough paths, which can be described using fractional differential equations. However, estimating parameters for these equations often involves complex and computation-intensive algorithms, creating a necessity for alternative solutions. In this regard, the Fractional Ornstein-Uhlenbeck (fOU) process from the family of fractional processes offers a promising path. We can effectively describe the rough SCP by utilizing certain transformations of the fOU. We employed neural networks to understand the behavior of these processes. We had to develop a fast algorithm to generate a valid and suitably large sample from the appropriate process to train the network. With an extensive training set, the neural network can estimate the process parameters accurately and efficiently. Although the initial focus was the fOU, the resulting model displayed broader applicability, thus paving the way for further investigation of other processes in the realm of financial mathematics. The utility of SCP extends beyond its immediate application. It also serves as a springboard for a deeper exploration of fractional processes and for extending existing models that use ordinary Wiener processes to fractional scenarios. In essence, deploying both SCP and fractional processes in financial models provides new, more accurate ways to depict market dynamics.

Keywords: fractional Ornstein-Uhlenbeck process, fractional stochastic processes, Heston model, neural networks, stochastic correlation, stochastic differential equations, stochastic volatility

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Authors: Xiaoyu Shen, Fang Fang, Chujun Qiu

Abstract:

Herewith we present a Fourier method for credit risk quantification and allocation in the factor-copula model framework. The key insight is that, compared to directly computing the cumulative distribution function of the portfolio loss via Monte Carlo simulation, it is, in fact, more efficient to calculate the transformation of the distribution function in the Fourier domain instead and inverting back to the real domain can be done in just one step and semi-analytically, thanks to the popular COS method (with some adjustments). We also show that the Euler risk allocation problem can be solved in the same way since it can be transformed into the problem of evaluating a conditional cumulative distribution function. Once the conditional or unconditional cumulative distribution function is known, one can easily calculate various risk metrics. The proposed method not only fills the niche in literature, to the best of our knowledge, of accurate numerical methods for risk allocation but may also serve as a much faster alternative to the Monte Carlo simulation method for risk quantification in general. It can cope with various factor-copula model choices, which we demonstrate via examples of a two-factor Gaussian copula and a two-factor Gaussian-t hybrid copula. The fast error convergence is proved mathematically and then verified by numerical experiments, in which Value-at-Risk, Expected Shortfall, and conditional Expected Shortfall are taken as examples of commonly used risk metrics. The calculation speed and accuracy are tested to be significantly superior to the MC simulation for real-sized portfolios. The computational complexity is, by design, primarily driven by the number of factors instead of the number of obligors, as in the case of Monte Carlo simulation. The limitation of this method lies in the "curse of dimension" that is intrinsic to multi-dimensional numerical integration, which, however, can be relaxed with the help of dimension reduction techniques and/or parallel computing, as we will demonstrate in a separate paper. The potential application of this method has a wide range: from credit derivatives pricing to economic capital calculation of the banking book, default risk charge and incremental risk charge computation of the trading book, and even to other risk types than credit risk.

Keywords: credit portfolio, risk allocation, factor copula model, the COS method, Fourier method

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Authors: Rutej R. Mehta, Michael A. Chappell

Abstract:

Introduction: Arterial spin labelling (ASL) is an increasingly popular perfusion MRI technique, in which arterial blood water is magnetically labelled in the neck before flowing into the brain, providing a non-invasive measure of cerebral blood flow (CBF). The accuracy of ASL CBF measurements, however, is hampered by dispersion effects; the distortion of the ASL labelled bolus during its transit through the vasculature. In spite of this, the current recommended implementation of ASL – the white paper (Alsop et al., MRM, 73.1 (2015): 102-116) – does not account for dispersion, which leads to the introduction of errors in CBF. Given that the transport time from the labelling region to the tissue – the arterial transit time (ATT) – depends on the region of the brain and the condition of the patient, it is likely that these errors will also vary with the ATT. In this study, various dispersion models are assessed in comparison with the white paper (WP) formula for CBF quantification, enabling the errors introduced by the WP to be quantified. Additionally, this study examines the relationship between the errors associated with the WP and the ATT – and how this is influenced by dispersion. Methods: Data were simulated using the standard model for pseudo-continuous ASL, along with various dispersion models, and then quantified using the formula in the WP. The ATT was varied from 0.5s-1.3s, and the errors associated with noise artefacts were computed in order to define the concept of significant error. The instantaneous slope of the error was also computed as an indicator of the sensitivity of the error with fluctuations in ATT. Finally, a regression analysis was performed to obtain the mean error against ATT. Results: An error of 20.9% was found to be comparable to that introduced by typical measurement noise. The WP formula was shown to introduce errors exceeding 20.9% for ATTs beyond 1.25s even when dispersion effects were ignored. Using a Gaussian dispersion model, a mean error of 16% was introduced by using the WP, and a dispersion threshold of σ=0.6 was determined, beyond which the error was found to increase considerably with ATT. The mean error ranged from 44.5% to 73.5% when other physiologically plausible dispersion models were implemented, and the instantaneous slope varied from 35 to 75 as dispersion levels were varied. Conclusion: It has been shown that the WP quantification formula holds only within an ATT window of 0.5 to 1.25s, and that this window gets narrower as dispersion occurs. Provided that the dispersion levels fall below the threshold evaluated in this study, however, the WP can measure CBF with reasonable accuracy if dispersion is correctly modelled by the Gaussian model. However, substantial errors were observed with other common models for dispersion with dispersion levels similar to those that have been observed in literature.

Keywords: arterial spin labelling, dispersion, MRI, perfusion

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