Search results for: low complexity

Authors: Ali Nourollah, Mohsen Movahedinejad

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In this paper a new algorithm to generate random simple polygons from a given set of points in a two dimensional plane is designed. The proposed algorithm uses a genetic algorithm to generate polygons with few vertices. A new merge algorithm is presented which converts any two polygons into a simple polygon. This algorithm at first changes two polygons into a polygonal chain and then the polygonal chain is converted into a simple polygon. The process of converting a polygonal chain into a simple polygon is based on the removal of intersecting edges. The merge algorithm has the time complexity of O ((r+s) *l) where r and s are the size of merging polygons and l shows the number of intersecting edges removed from the polygonal chain. It will be shown that 1 < l < r+s. The experiments results show that the proposed algorithm has the ability to generate a great number of different simple polygons and has better performance in comparison to celebrated algorithms such as space partitioning and steady growth.

Keywords: Divide and conquer, genetic algorithm, merge polygons, Random simple polygon generation.

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Authors: Aniket Tiwari, Ameya London

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Web-based student attendance management system is required to assist the faculty and the lecturer for the time-consuming process. For this purpose, GSM/GPRS (Global System for Mobile Communication/General Packet Radio Service) based student’s attendance management system using RFID (Radio Frequency Identification) is a much convenient method to take the attendance. Student is provided with the RFID tags. When student comes near to the reader, it will sense the respective student and update attendance. The whole process is controlled using the microcontroller. The main advantage of this system is that it reduced the complexity comparison to student attendance system using RF technology. This system requires only one microcontroller for the operation, it is real time process. This paper reviews some of these monitoring systems and proposes a GPRS based student attendance system. The system can be easily accessed by the lecturers via the web and most importantly, the reports can be generated in real-time processing, thus, provides valuable information about the students’ commitments in attending the classes.

Keywords: RFID reader, RFID tags, student, attendance

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Authors: Mohammad Shams Esfand Abadi, Mohammad Ranjbar, Reza Ebrahimpour

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This work presents the distributed processing solution problem in a diffusion network based on the adapt then combine (ATC) and combine then adapt (CTA)selective partial update normalized least mean squares (SPU-NLMS) algorithms. Also, we extend this approach to dynamic selection affine projection algorithm (DS-APA) and ATC-DS-APA and CTA-DS-APA are established. The purpose of ATC-SPU-NLMS and CTA-SPU-NLMS algorithm is to reduce the computational complexity by updating the selected blocks of weight coefficients at every iteration. In CTA-DS-APA and ATC-DS-APA, the number of the input vectors is selected dynamically. Diffusion cooperation strategies have been shown to provide good performance based on these algorithms. The good performance of introduced algorithm is illustrated with various experimental results.

Keywords: selective partial update, affine projection, dynamic selection, diffusion, adaptive distributed networks

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Authors: William Anderson, Kyle Kobold, Oleg Yakimenko

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Green microgrids using mostly renewable energy (RE) for generation, are complex systems with inherent nonlinear dynamics. Among a variety of different optimization tools there are only a few ones that adequately consider this complexity. This paper evaluates applicability of two somewhat similar optimization tools tailored for standalone RE microgrids and also assesses a machine learning tool for performance prediction that can enhance the reliability of any chosen optimization tool. It shows that one of these microgrid optimization tools has certain advantages over another and presents a detailed routine of preparing input data to simulate RE microgrid behavior. The paper also shows how neural-network-based predictive modeling can be used to validate and forecast solar power generation based on weather time series data, which improves the overall quality of standalone RE microgrid analysis.

Keywords: microgrid, renewable energy, complex systems, optimization, predictive modeling, neural networks

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Authors: Nicholas Freer

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Rhythmic complexity in progressive metal is a developing area of analysis, particularly the interpretation of hyper-metric time spans as hierarchically significant rhythmic units of compositional organisation (Pieslak 2007, Charupakorn 2012, Capuzzo 2018, Calder 2018, Lucas 2018, Hannan 2020). This paper adds to this developing area by considering the relationships between the concepts of tactus, metric imposition, polymeter and rhythmic parallax in the Meshuggah composition “Do Not Look Down”. By considering an architectonic rhythmic framework within “Do Not Look Down” as the controlling organisation mechanism, an exploration of the interaction between distinct rhythmic layers and the composition’s formal segmentation and harmony (as riffs), reveals a pervasive structural misalignment between these elements. By exhibiting how Meshuggah’s manipulations of rhythmic complexities deliberately blur structural boundaries, creating misalignments in a flat approach to temporal partitioning (Nieto 2014), rhythmic characteristics of Meshuggah and the genre of Djent are exposed.

Keywords: hypermeter, rhythmic parallax, meshuggah, temporal partitioning

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Authors: Zhengrong Wu, Haibo Yang

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In the context of escalating global climate change and environmental degradation, the complexity and frequency of natural disasters are continually increasing. Confronted with an abundance of information regarding natural disasters, traditional knowledge graph construction methods, which heavily rely on grammatical rules and prior knowledge, demonstrate suboptimal performance in processing complex, multi-source disaster information. This study, drawing upon past natural disaster reports, disaster-related literature in both English and Chinese, and data from various disaster monitoring stations, constructs question-answer templates based on large language models. Utilizing the P-Tune method, the ChatGLM2-6B model is fine-tuned, leading to the development of a disaster knowledge graph based on large language models. This serves as a knowledge database support for disaster emergency response.

Keywords: large language model, knowledge graph, disaster, deep learning

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Authors: R. K. Apalowo, D. Chronopoulos

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A Finite Element (FE) based scheme is presented for quantifying guided wave interaction with Localised Nonlinear Structural Damage (LNSD) within structures of arbitrary layering and geometric complexity. The through-thickness mode-shape of the structure is obtained through a wave and finite element method. This is applied in a time domain FE simulation in order to generate time harmonic excitation for a specific wave mode. Interaction of the wave with LNSD within the system is computed through an element activation and deactivation iteration. The scheme is validated against experimental measurements and a WFE-FE methodology for calculating wave interaction with damage. Case studies for guided wave interaction with crack and delamination are presented to verify the robustness of the proposed method in classifying and identifying damage.

Keywords: layered structures, nonlinear ultrasound, wave interaction with nonlinear damage, wave finite element, finite element

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Authors: Ibtissam Mharzi Alaoui, Camélia Sehaqui

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The success of a bank depends upon its effective risk management. With the growing complexity and diversity of financial products and services, as well as the accelerating pace of globalization over the past decade, risk management is becoming increasingly difficult. thus, all measurement and monitoring functions must be much more vigorous, relevant and adequate. The Shariah non-compliance risk is specific aspect of Islamic finance which ipso facto, deserves particular attention. It affects the validity of all Islamic financial contracts and it turns out to be likely to result in considerable losses on the overall Islamic financial institutions (IFIs). The purpose of this paper is to review the theoretical literature on Shariah non-compliance risk in order to give a clearer understanding of its sources, causes and consequences. Our intention through this work is to bring added value to the Islamic finance industry all over the world. The findings provide a useful reference work for the Islamic banks in structuring (or restructuring) of their own system of shariah risk management and internal control.

Keywords: Shariah non-compliance, risk management, financial products, Islamic finance.

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Authors: Galit Harel

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This article describes the long-term psychoanalytic psychotherapy of a young woman who had experienced trauma during her childhood. The details of the trauma were unknown, as all memory of the trauma had been repressed. Past trauma is analyzable through a prism of transference, dreaming and dreams, mental states, and thinking processes that offer an opportunity to explore and analyze the influence of both reality and fantasy on the patient. The presented case describes a therapeutic process that strives to discover hidden meanings through the unconscious system and illustrates the movement from unconscious to conscious during exploration of the patient’s personal trauma in treatment. The author discusses the importance of classical and contemporary psychoanalytic models of childhood sexual trauma through the discovery of manifest and latent content, unconscious fantasies, and actual events of trauma. It is suggested that the complexity of trauma is clarified by the tension between these models and by the inclusion of aspects of both of them for a complete understanding.

Keywords: dreams, psychoanalytic psychotherapy, thinking processes, transference, trauma

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Authors: Alexander Guzman Urbina, Atsushi Aoyama

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The sustainability of traditional technologies employed in energy and chemical infrastructure brings a big challenge for our society. Making decisions related with safety of industrial infrastructure, the values of accidental risk are becoming relevant points for discussion. However, the challenge is the reliability of the models employed to get the risk data. Such models usually involve large number of variables and with large amounts of uncertainty. The most efficient techniques to overcome those problems are built using Artificial Intelligence (AI), and more specifically using hybrid systems such as Neuro-Fuzzy algorithms. Therefore, this paper aims to introduce a hybrid algorithm for risk assessment trained using near-miss accident data. As mentioned above the sustainability of traditional technologies related with energy and chemical infrastructure constitutes one of the major challenges that today’s societies and firms are facing. Besides that, the adaptation of those technologies to the effects of the climate change in sensible environments represents a critical concern for safety and risk management. Regarding this issue argue that social consequences of catastrophic risks are increasing rapidly, due mainly to the concentration of people and energy infrastructure in hazard-prone areas, aggravated by the lack of knowledge about the risks. Additional to the social consequences described above, and considering the industrial sector as critical infrastructure due to its large impact to the economy in case of a failure the relevance of industrial safety has become a critical issue for the current society. Then, regarding the safety concern, pipeline operators and regulators have been performing risk assessments in attempts to evaluate accurately probabilities of failure of the infrastructure, and consequences associated with those failures. However, estimating accidental risks in critical infrastructure involves a substantial effort and costs due to number of variables involved, complexity and lack of information. Therefore, this paper aims to introduce a well trained algorithm for risk assessment using deep learning, which could be capable to deal efficiently with the complexity and uncertainty. The advantage point of the deep learning using near-miss accidents data is that it could be employed in risk assessment as an efficient engineering tool to treat the uncertainty of the risk values in complex environments. The basic idea of using a Near-Miss Deep Learning Approach for Neuro-Fuzzy Risk Assessment in Pipelines is focused in the objective of improve the validity of the risk values learning from near-miss accidents and imitating the human expertise scoring risks and setting tolerance levels. In summary, the method of Deep Learning for Neuro-Fuzzy Risk Assessment involves a regression analysis called group method of data handling (GMDH), which consists in the determination of the optimal configuration of the risk assessment model and its parameters employing polynomial theory.

Keywords: deep learning, risk assessment, neuro fuzzy, pipelines

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Authors: Pegah Eshraghi, Zahra Sadat Zomorodian, Mohammad Tahsildoost

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Over the past decade, energy consumption in educational buildings has steadily increased. The purpose of this research is to provide a method to quickly predict the energy consumption of buildings using separate evaluation of zones and decomposing the building to eliminate the complexity of geometry at the early design stage. To produce this framework, machine learning algorithms such as Support vector regression (SVR) and Artificial neural network (ANN) are used to predict energy consumption and thermal comfort metrics in a school as a case. The database consists of more than 55000 samples in three climates of Iran. Cross-validation evaluation and unseen data have been used for validation. In a specific label, cooling energy, it can be said the accuracy of prediction is at least 84% and 89% in SVR and ANN, respectively. The results show that the SVR performed much better than the ANN.

Keywords: early stage of design, energy, thermal comfort, validation, machine learning

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Authors: Nor Asrina Binti Ramlee

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Voltage sag, voltage swell, high-frequency noise and voltage transients are kinds of disturbances in power quality. They are also known as power quality events. Equipment used in the industry nowadays has become more sensitive to these events with the increasing complexity of equipment. This leads to the importance of distributing clean power quality to the consumer. To provide better service, the best analysis on power quality is very vital. Thus, this paper presents the events detection focusing on voltage sag and swell. The method is developed by applying time domain signal analysis using wavelet transform approach in MATLAB. Four types of mother wavelet namely Haar, Dmey, Daubechies, and Symlet are used to detect the events. This project analyzed real interrupted signal obtained from 22 kV transmission line in Skudai, Johor Bahru, Malaysia. The signals will be decomposed through the wavelet mothers. The best mother is the one that is capable to detect the time location of the event accurately.

Keywords: power quality, voltage sag, voltage swell, wavelet transform

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Authors: Ronaldo Seroa Da Motta, Gabriela Santiago

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The main contribution of this study is the identification of the factors influencing the environmental licensing process of infrastructure projects in Brazil. These factors will be those that reflect the technical characteristics of the project, the corporate governance of the entrepreneur, and the institutional and regulatory governance of the environmental agency, including the number of interventions by non-licensing agencies. The model conditions these variables to the licensing processing time of 34 infrastructure projects. Our results indicated that the conditions would be more sensitive to the type of enterprise, complexity as in gas pipelines and hydroelectric plants in the most vulnerable biome with a greater value of the enterprise or the entrepreneur's assets, together with the number of employees of the licensing agency. The number of external interventions by other non-licensing institutions does not affect the licensing time. Such results challenge the current criticism that environmental licensing has been often pointed out as a barrier to speed up investments in infrastructure projects in Brazil due to the participation of civil society and other non-licensing institutions.

Keywords: environmental licensing, condionants, Brazil, timing process

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Authors: M. H. Korayem, Sh. Ameri, N. Yousefi Lademakhi

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To ensure the stability of closed-loop nonlinear model predictive control (NMPC) within a finite horizon, there is a need for appropriate design terminal ingredients, which can be a time-consuming and challenging effort. Otherwise, in order to ensure the stability of the control system, it is necessary to consider an infinite predictive horizon. Increasing the prediction horizon increases computational demand and slows down the implementation of the method. In this study, a new technique has been proposed to ensure system stability without terminal ingredients. This technique has been employed in the design of the NMPC algorithm, leading to a reduction in the computational complexity of designing terminal ingredients and computational burden. The studied system is a wheeled mobile robot (WMR) subjected to non-holonomic constraints. Simulation has been investigated for two problems: trajectory tracking and adjustment mode.

Keywords: wheeled mobile robot, nonlinear model predictive control, stability, without terminal ingredients

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Authors: Lilian Nantume Wampande

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Group-based strategies in the delivery of HIV care have opened up new avenues not only for meaningful participation for HIV positive people but also platforms for deconstruction and reconstruction of knowledge about living with the virus. Yet the contributions of such strategies among patients who live in high risk areas are still not explored. This case study research assessed the impact of HIV support networks on sexual health outcomes of HIV positive out-of-school adolescents residing in fishing islands of Kalangala in Uganda. The study population was out-of-school adolescents living with HIV and their sexual partners (n=269), members of their households (n=80) and their health service providers (n=15). Data were collected via structured interviews, observations and focus group discussions between August 2016 and March 2017. Data was then analyzed inductively to extract key themes related to the approaches and outcomes of the groups’ activities. The study findings indicate that support groups unite HIV positive adolescents in a bid for social renegotiation to achieve change but individual constraints surpass the groups’ intentions. Some adolescents for example reported increased fear which led to failure to cope, sexual violence, self-harm and denial of status as a result of the high expectations placed on them as members of the support groups. Further investigations around this phenomenon show that HIV networks play a monotonous role as information sources for HIV positive out-of-school adolescents which limit their creativity to seek information elsewhere. Results still indicate that HIV adolescent groups recognize the complexity of long-term treatment and stay in care leading to improved immunity for the majority yet; there is still scattered evidence about how effective they are among adolescents at different phases in the disease trajectory. Nevertheless, the primary focus of developing adolescent self-efficacy and coping skills significantly address a range of disclosure difficulties and supports autonomy. Moreover, the peer techniques utilized in addition to the almost homogeneous group characteristics accelerates positive confidence, hope and belongingness. Adolescent HIV-support groups therefore have the capacity to both improve and/or worsen sexual health outcomes for a young adolescent who is out-of-school. Communication interventions that seek to increase awareness about ‘self’ should therefore be emphasized more than just fostering collective action. Such interventions should be sensitive to context and gender. In addition, facilitative support supervision done by close and trusted health care providers, most preferably Village Health Teams (who are often community elected volunteers) would help to follow-up, mentor, encourage and advise this young adolescent in matters involving sexuality and health outcomes. HIV/AIDS prevention programs have extended their efforts beyond individual focus to those that foster collective action, but programs should rekindle interpersonal level strategies to address the complexity of individual behavior.

Keywords: adolescent, HIV, support groups, Uganda

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Authors: Colette Malyack, Pius Egbelu

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Business-to-Customer (B2C) delivery options have improved to meet increased demand in recent years. The change in end users has forced logistics networks to focus on customer service and sentiment that would have previously been the priority of the company or organization of origin. This has led to increased pressure on logistics companies to extend traditional B2B networks into a B2C solution while accommodating additional costs, roadblocks, and customer sentiment; the result has been the creation of the omnichannel delivery network encompassing a number of traditional and modern methods of package delivery. In this paper the many solutions within the omnichannel delivery network are defined and discussed. It can be seen through this analysis that the omnichannel delivery network can be applied to reduce the complexity of package delivery and provide customers with more options. Applied correctly the result is a reduction in cost to the logistics company over time, even with an initial increase in cost to obtain the technology.

Keywords: network planning, last mile delivery, omnichannel delivery network, omnichannel logistics

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Authors: Daniel Martinez, Tim de Leeuw, Stefan Haefliger

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The challenges of managing a large and geographically dispersed R&D organization have been further increasing during the past years, concentrating on the leverage of a geo-graphically dispersed body of knowledge in an efficient and effective manner. In order to reduce complexity and improve performance, firms introduce product modularity as one key element for global R&D network teams to develop their products and projects in collaboration. However, empirical studies on the effects of product modularity on innovation performance are really scant. Furthermore, some researchers have suggested that product modularity promotes innovation performance, while others argue that it inhibits innovation performance. This research fills this gap by investigating the impact of product modularity on various dimensions of innovation performance, i.e. effectiveness and efficiency. By constructing the theoretical framework, this study suggests that that there is an inverted U-shaped relationship between product modularity and innovation performance. Moreover, this research work suggests that the optimum of innovation performance efficiency will be at a higher level than innovation performance effectiveness at a given product modularity level.

Keywords: modularity, innovation performance, networks, R&D, collaboration

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Authors: Jyoti Singh, Ahmed Stifi, Sascha Gentes

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The process of construction significantly contributes to high level of risks, complexity and uncertainties leading to cost and time overrun, customer dissatisfaction etc. lean construction is important as it is a comprehensive system of tools and concepts focusing on moving closer to customer satisfaction by understanding the process, identifying the waste and eliminating it. The proposed work includes identification of knowledge areas from lean perspective, lean tools/concepts used in lean construction and establishing a relationship matrix between knowledge areas and lean tools/concepts, thus developing and building up a lean construction body of knowledge (LCBOK), i.e. a guide to lean construction, aiming to provide guidelines to manage individual projects and also helping construction industry to minimise waste and maximize value to the customer. In this study, we identified 8 knowledge areas and 62 lean tools/concepts from lean perspective and also one tool can help to manage two or more knowledge areas.

Keywords: knowledge areas, lean body matrix, lean construction, lean tools

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Authors: Mohammed Saif Al-Saidi, John P. T. Mo

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Modern engineering assets are complex and very high in value. They are expected to function for years to come, with ability to handle the change in technology and ageing modification. The aging of an engineering asset and continues increase of vendors and contractors numbers forces the asset operation management (or Owner) to design an asset system which can capture these changes. Furthermore, an accurate performance measurement and risk evaluation processes are highly needed. Therefore, this paper explores the nature of the asset management system performance evaluation for an engineering asset based on the System Support Engineering (SSE) principles. The research work explores the asset support system from a range of perspectives, interviewing managers from across a refinery organisation. The factors contributing to complexity of an asset management system are described in context which clusters them into several key areas. It is proposed that SSE framework may then be used as a tool for analysis and management of asset. The paper will conclude with discussion of potential application of the framework and opportunities for future research.

Keywords: asset management, performance, evaluation, modern engineering, System Support Engineering (SSE)

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Authors: Yozna Gurung

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This paper presents preliminary findings from an on-going study on the use of Internal State Terms (IST) in the production of narratives of Hindi-English bilinguals in an attempt to identify children at risk for Specific Language Impairment. Narratives were examined for macrostructure (story structure and story complexity) and internal state terms or mental state terms (IST/MST). 31 students generated stories based on six pictures that were matched for content and story structure in L1 (Hindi) and L2 (English) using a wordless picture narrative. From 30 sample population, 2 students are at risk of Specific Language Impairment, according to this study i.e 6.45%. They showed least development in story grammar as well as IST in both their languages.

Keywords: internal state terms, macrostructure, specific language impairment, wordless picture narrative

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Authors: Natina Yaduma, Allan Williams, Sangwon Park, Andrew Lockwood

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This paper analyses flexible working, and the employment of migrants, as determinants of productivity in hotels. Controlling for the institutional environment, by focussing on a single firm, it analyses data on actual hours worked and outputs, on a weekly basis, over an 8 year period. The unusually disaggregated data allows the paper to examine not only inter-establishment, but also intra-establishment (departmental) variations in productivity, and to compare financial versus physical measures. The findings emphasise the complexity of productivity findings, sometimes contrasting evidence for establishments versus departments, and the positive but scale and measure-specific contributions of both the employment of migrants and flexible working, especially the utilisation of zero hours contracts.

Keywords: labour productivity, physical productivity, financial productivity, numerical flexibility, functional flexibility, migrant employment, cero-contract employment

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Authors: Shyam Parhi

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Airports GIS program collects Airports data, validate and verify it, and stores it in specific database. Airports GIS allows authorized users to submit changes to airport data. The verified data is used to develop several engineering applications. One of these applications is electronic Airport Layout Plan (eALP) whose primary aim is to move from paper to digital form of ALP. The first phase of development of eALP was completed recently and it was tested for a few pilot program airports across different regions. We conducted gap analysis and noticed that a lot of development work is needed to fine tune at least six mandatory sheets of eALP. It is important to note that significant amount of programming is needed to move from out-of-box ArcGIS to a much customized ArcGIS which will be discussed. The ArcGIS viewer capability to display essential features like runway or taxiway or the perpendicular distance between them will be discussed. An enterprise level workflow which incorporates coordination process among different lines of business will be highlighted.

Keywords: geospatial data, geology, geographic information systems, aviation

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Authors: Muhammad Yousaf Sadiq

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The complexity arises in defining the construction quality due to its perception, based on inherent market conditions and their requirements, the diversified stakeholders itself and their desired output. An quantitative survey based approach was adopted in this constructive study. A questionnaire-based survey was conducted for the assessment of construction Quality perception and expectations in the context of quality improvement technique. The survey feedback of professionals of the leading construction organizations/companies of Pakistan construction industry were analyzed. The financial capacity, organizational structure, and construction experience of the construction firms formed basis for their selection. The quality perception was found to be project-scope-oriented and considered as an excess cost for a construction project. Any quality improvement technique was expected to maximize the profit for the employer, by improving the productivity in a construction project. The study is beneficial for the construction professionals to assess the prevailing construction quality perception and the expectations from implementation of any quality improvement technique in construction projects.

Keywords: construction quality, expectation, improvement, perception

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Authors: Bruno Diniz Fecchio

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Operations have been demanding increasingly challenging operational targets for the dewatering process, requiring lower humidity for concentrates. Chemical dewatering agents are able to improve solid/liquid separation processes, allowing operations to deal with increased complexity caused by either mineralogical changes or seasonal events that present operations with challenging moisture requirements for transportation and downstream steps. These chemicals reduce water retention by reducing the capillary pressure of the mineral and contributing to improved water drainage. This current study addresses the reagent effects on pile dewatering for Bauxite. Such chemicals were able to decrease the moisture of granulated Bauxite (particle size of 5 – 50 mm). The results of the laboratory scale tests and industrial trials presented the obtention of up to 11% relative moisture reduction, which reinforced the strong interaction between dewatering agents and the particle surface of granulated Bauxite. The evaluated dewatering agents, however, did not present any negative impact on these operations.

Keywords: bauxite, dewatering agents, pile dewatering, moisture reduction

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Authors: J. Yang, Y. Ma, X. Zhang, S. Li, Y. Zhang

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The traditional k-means algorithm has been widely used as a simple and efficient clustering method. However, the algorithm often converges to local minima for the reason that it is sensitive to the initial cluster centers. In this paper, an algorithm for selecting initial cluster centers on the basis of minimum spanning tree (MST) is presented. The set of vertices in MST with same degree are regarded as a whole which is used to find the skeleton data points. Furthermore, a distance measure between the skeleton data points with consideration of degree and Euclidean distance is presented. Finally, MST-based initialization method for the k-means algorithm is presented, and the corresponding time complexity is analyzed as well. The presented algorithm is tested on five data sets from the UCI Machine Learning Repository. The experimental results illustrate the effectiveness of the presented algorithm compared to three existing initialization methods.

Keywords: degree, initial cluster center, k-means, minimum spanning tree

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Authors: Vikrant Gupta, Amrit Goswami

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The fixed income market forms the basis of the modern financial market. All other assets in financial markets derive their value from the bond market. Owing to its over-the-counter nature, corporate bonds have relatively less data publicly available and thus is researched upon far less compared to Equities. Bond price prediction is a complex financial time series forecasting problem and is considered very crucial in the domain of finance. The bond prices are highly volatile and full of noise which makes it very difficult for traditional statistical time-series models to capture the complexity in series patterns which leads to inefficient forecasts. To overcome the inefficiencies of statistical models, various machine learning techniques were initially used in the literature for more accurate forecasting of time-series. However, simple machine learning methods such as linear regression, support vectors, random forests fail to provide efficient results when tested on highly complex sequences such as stock prices and bond prices. hence to capture these intricate sequence patterns, various deep learning-based methodologies have been discussed in the literature. In this study, a recurrent neural network-based deep learning model using long short term networks for prediction of corporate bond prices has been discussed. Long Short Term networks (LSTM) have been widely used in the literature for various sequence learning tasks in various domains such as machine translation, speech recognition, etc. In recent years, various studies have discussed the effectiveness of LSTMs in forecasting complex time-series sequences and have shown promising results when compared to other methodologies. LSTMs are a special kind of recurrent neural networks which are capable of learning long term dependencies due to its memory function which traditional neural networks fail to capture. In this study, a simple LSTM, Stacked LSTM and a Masked LSTM based model has been discussed with respect to varying input sequences (three days, seven days and 14 days). In order to facilitate faster learning and to gradually decompose the complexity of bond price sequence, an Empirical Mode Decomposition (EMD) has been used, which has resulted in accuracy improvement of the standalone LSTM model. With a variety of Technical Indicators and EMD decomposed time series, Masked LSTM outperformed the other two counterparts in terms of prediction accuracy. To benchmark the proposed model, the results have been compared with traditional time series models (ARIMA), shallow neural networks and above discussed three different LSTM models. In summary, our results show that the use of LSTM models provide more accurate results and should be explored more within the asset management industry.

Keywords: bond prices, long short-term memory, time series forecasting, empirical mode decomposition

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Authors: L. Bikulciene, E. Venskaityte, G. Jarusevicius

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Non-stationary and nonlinear signals generated by living complex systems defy traditional mechanistic approaches, which are based on homeostasis. Previous our studies have shown that the evaluation of the interactions of physiological signals by using special analysis methods is suitable for observation of physiological processes. It is demonstrated the possibility of using deep physiological model, based interpretation of the changes of the human body’s functional states combined with an application of the analytical method based on matrix theory for the physiological signals analysis, which was applied on high risk cardiac patients. It is shown that evaluation of cardiac signals interactions show peculiar for each individual functional changes at the onset of hemodynamic restoration procedure. Therefore we suggest that the alterations of functional state of the body, after patients overcome surgery can be complemented by the data received from the suggested approach of the evaluation of functional variables interactions.

Keywords: cardiac diseases, complex systems theory, ECG analysis, matrix analysis

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Authors: Georgiana Ciobotaru

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The vocabulary that foreign students assimilate once they start studying the Romanian language must allow them to develop the linguistic competence of oral and written expression, but also the intercultural one, necessary for their integration into the new socio-cultural environment. Therefore, the familiarization courses with Romanian as a foreign language aim at fundamental language acquisitions in order to obtain the expected level of Romanian language. They also relate differently to the new culture and the new language they come in contact with, having a distinct way of expressing themselves. Foreign students want to continue their university and postgraduate studies at specialized faculties in the country; therefore, they need both a general language for their integration into society and for interaction with others, Romanians or students from countries other than their own, but also from a specialized language that facilitates didactic communication and professional development. The complexity of the vocabulary must thus cover the daily communication needs, but also the subsequent evolution of each one. This paper aims to illustrate the most important semantic fields that students must assimilate in order to crystallize a linguistic identity in the new context of their personal and professional development and to help them cope with the culture shock.

Keywords: integration, intercultural, language, linguistic, vocabulary

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Authors: T. Sanches, K. Bousson

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As part of the development of a 4D autopilot system for unmanned aerial vehicles (UAVs), i.e. a time-dependent robust trajectory generation and control algorithm, this work addresses the problem of optimal path control based on the flight sensors data output that may be unreliable due to noise on data acquisition and/or transmission under certain circumstances. Although several filtering methods, such as the Kalman-Bucy filter or the Linear Quadratic Gaussian/Loop Transfer Recover Control (LQG/LTR), are available, the utter complexity of the control system, together with the robustness and reliability required of such a system on a UAV for airworthiness certifiable autonomous flight, required the development of a proper robust filter for a nonlinear system, as a way of further mitigate errors propagation to the control system and improve its ,performance. As such, a nonlinear algorithm based upon the LQG/LTR, is validated through computational simulation testing, is proposed on this paper.

Keywords: autonomous flight, LQG/LTR, nonlinear state estimator, robust flight control

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Authors: Egemen Sert, Leila Hedayatifar, Rachel A. Rigg, Amir Akhavan, Olha Buchel, Dominic Elias Saadi, Aabir Abubaker Kar, Alfredo J. Morales, Yaneer Bar-Yam

Abstract:

The complexity of providing timely and cost-effective distribution of finished goods from industrial facilities to customers makes effective operational coordination difficult, yet effectiveness is crucial for maintaining customer service levels and sustaining a business. Logistics planning becomes increasingly complex with growing numbers of customers, varied geographical locations, the uncertainty of future orders, and sometimes extreme competitive pressure to reduce inventory costs. Linear optimization methods become cumbersome or intractable due to a large number of variables and nonlinear dependencies involved. Here we develop a complex systems approach to optimizing logistics networks based upon dimensional reduction methods and apply our approach to a case study of a manufacturing company. In order to characterize the complexity in customer behavior, we define a “customer space” in which individual customer behavior is described by only the two most relevant dimensions: the distance to production facilities over current transportation routes and the customer's demand frequency. These dimensions provide essential insight into the domain of effective strategies for customers; direct and indirect strategies. In the direct strategy, goods are sent to the customer directly from a production facility using box or bulk trucks. In the indirect strategy, in advance of an order by the customer, goods are shipped to an external warehouse near a customer using trains and then "last-mile" shipped by trucks when orders are placed. Each strategy applies to an area of the customer space with an indeterminate boundary between them. Specific company policies determine the location of the boundary generally. We then identify the optimal delivery strategy for each customer by constructing a detailed model of costs of transportation and temporary storage in a set of specified external warehouses. Customer spaces help give an aggregate view of customer behaviors and characteristics. They allow policymakers to compare customers and develop strategies based on the aggregate behavior of the system as a whole. In addition to optimization over existing facilities, using customer logistics and the k-means algorithm, we propose additional warehouse locations. We apply these methods to a medium-sized American manufacturing company with a particular logistics network, consisting of multiple production facilities, external warehouses, and customers along with three types of shipment methods (box truck, bulk truck and train). For the case study, our method forecasts 10.5% savings on yearly transportation costs and an additional 4.6% savings with three new warehouses.

Keywords: logistics network optimization, direct and indirect strategies, K-means algorithm, dimensional reduction

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