Search results for: low complexity

Authors: S. Oudjemia, F. Alim, S. Seddiki

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This paper shows the application of multifractal analysis for additional help in cancer diagnosis. The medical image processing is a very important discipline in which many existing methods are in search of solutions to real problems of medicine. In this work, we present results of multifractal analysis of brain MRI images. The purpose of this analysis was to separate between healthy and cancerous tissue of the brain. A nonlinear method based on multifractal detrending moving average (MFDMA) which is a generalization of the detrending fluctuations analysis (DFA) is used for the detection of abnormalities in these images. The proposed method could make separation of the two types of brain tissue with success. It is very important to note that the choice of this non-linear method is due to the complexity and irregularity of tumor tissue that linear and classical nonlinear methods seem difficult to characterize completely. In order to show the performance of this method, we compared its results with those of the conventional method box-counting.

Keywords: irregularity, nonlinearity, MRI brain images, multifractal analysis, brain tumor

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Authors: J. H. Lv, W. Z. Wang, S.W. Liu

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The major concern in the aviation industry is the flight safety. Although great effort has been put onto the development of material and system reliability, the failure cases of fatal accidents still occur nowadays. Due to the complexity of the aviation system, and the interaction among the failure components, the failure analysis of the related equipment is a little difficult. This study focuses on surveying the failure cases in aviation, which are extracted from failure analysis journals, including Engineering Failure Analysis and Case studies in Engineering Failure Analysis, in order to obtain the failure sensitive factors or failure sensitive parts. The analytical results show that, among the failure cases, fatigue failure is the largest in number of occurrence. The most failed components are the disk, blade, landing gear, bearing, and fastener. The frequently failed materials consist of steel, aluminum alloy, superalloy, and titanium alloy. Therefore, in order to assure the safety in aviation, more attention should be paid to the fatigue failures.

Keywords: aerospace, disk, failure analysis, fatigue

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Authors: Vittorio Caffi, Maria Pignataro, Antonio Cosimo Devito, Marco Pesenti

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This paper deals with the integration of advanced digital design and modelling tools and methodologies, known as Building Information Modelling, into the traditional Academy educational programs for building engineers and architects. Nowadays, the challenge the Academy has to face is to present the new tools and their features to the pupils, making sure they acquire the proper skills in order to leverage the potential they offer also for the other courses embedded in the educational curriculum. The syllabus here presented refers to the “Drawing for building engineering”, “2D and 3D laboratory” and “3D modelling” curricula of the MSc in Building Engineering of the Politecnico di Milano. Such topics, included since the first year in the MSc program, are fundamental to give the students the instruments to master the complexity of an architectural or building engineering project with digital tools, so as to represent it in its various forms.

Keywords: BIM, BIM curricula, computational design, digital modelling

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Authors: Sourita Ghosh, Falguni Pati, Suhanya Duraiswamy

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Spheroid, a simple three-dimensional cellular aggregate, allows us to simulate the in-vivo complexity of cellular signaling and interactions in greater detail than traditional 2D cell culture. It can be used as an in-vitro model for drug toxicity testing, tumor modeling and many other such applications specifically for cancer. Our work is focused on the development of an affordable, user-friendly, robust, reproducible, high throughput microfluidic device for water in oil droplet production, which can, in turn, be used for spheroids manufacturing. Here, we have investigated the droplet breakup between two non-Newtonian fluids, viz. silicone oil and decellularized liver matrix, which acts as our extra cellular matrix (ECM) for spheroids formation. We performed some biochemical assays to characterize the liver ECM, as well as rheological studies on our two fluids and observed a critical dependence of capillary number (Ca) on droplet breakup and homogeneous drop formation

Keywords: chip less, droplets, extracellular matrix, liver spheroid

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Authors: R. Nagarani

Abstract:

An intrinsic property of software in a real-world environment is its need to evolve, which is usually accompanied by the increase of software complexity and deterioration of software quality, making software maintenance a tough problem. Refactoring is regarded as an effective way to address this problem. Many refactoring approaches at the method and class level have been proposed. But the extent of research on software refactoring at the package level is less. This work presents a novel approach to refactor the package structures of object oriented software using genetic algorithm based community detection. It uses software networks to represent classes and their dependencies. It uses a constrained community detection algorithm to obtain the optimized community structures in software networks, which also correspond to the optimized package structures. It finally provides a list of classes as refactoring candidates by comparing the optimized package structures with the real package structures.

Keywords: community detection, complex network, genetic algorithm, package, refactoring

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Authors: Trisha Malhotra

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Sentiment analysis is a recent field of study that computationally assesses the emotional nature of a body of text. To assess its test-validity, sentiment analysis was carried out on the emotional corpus of text from a personal 15-day mood diary. Self-reported mood scores varied more or less accurately with daily mood evaluation score given by the software. On further assessment, it was found that while sentiment analysis was good at assessing ‘global’ mood, it was not able to ‘locally’ identify and differentially score synonyms of various emotional words. It is further critiqued for treating the intensity of an emotion as universal across cultures. Finally, the software is shown not to account for emotional complexity in sentences by treating emotions as strictly positive or negative. Hence, it is posited that a better output could be two (positive and negative) affect scores for the same body of text.

Keywords: analysis, data, diary, emotions, mood, sentiment

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Authors: B. Manikandan, A. Vijayaprabhu

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The conservative reversible gates are used to designed reversible sequential circuits. The sequential circuits are flip-flops and latches. The conservative logic gates are Feynman, Toffoli, and Fredkin. The design of two vectors testable sequential circuits based on conservative logic gates. All sequential circuit based on conservative logic gates can be tested for classical unidirectional stuck-at faults using only two test vectors. The two test vectors are all 1s, and all 0s. The designs of two vectors testable latches, master-slave flip-flops and double edge triggered (DET) flip-flops are presented. We also showed the application of the proposed approach toward 100% fault coverage for single missing/additional cell defect in the quantum- dot cellular automata (QCA) layout of the Fredkin gate. The conservative logic gates are in terms of complexity, speed, and area.

Keywords: DET, QCA, reversible logic gates, POS, SOP, latches, flip flops

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Authors: Chieh Lee

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Today, supply chain managers face extensive disruptions in raw material pricing, transportation block, and quality issue due to product complexity. While digitalization might help managers to mitigate the disruption risk and increase supply chain resilience by sharing information between sellers and buyers through the supply chain, entities are reluctant to build such a system. The main reason is it is not clear what information should be shared and who has access to the stored information. In this research, we propose a smart contract built by blockchain technology. This contract helps both buyer and seller to identify the type of information, the access to the information, and how to trace the information. This contract helps managers control their orders through the supply chain and address any disruption they see fit. Furthermore, with the same smart contract, the supplier can track the production process of an order and increase production efficiency by eliminating waste.

Keywords: blockchain, production process, smart contract, supply chain resilience

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Authors: Siti Rafeah Md Rafei, Karen Wang Yanping, Park Mi Kyoung

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Tissue samples are precious supply for molecular studies or disease identification diagnosed using molecular assays, namely real-time PCR (qPCR). It is critical to establish the most favorable nucleic acid extraction that gives the PCR-amplifiable genomic DNA. Furthermore, automated nucleic acid extraction is an appealing alternative to labor-intensive manual methods. Operational complexity, defined as the number of steps required to obtain an extracted sample, is one of the criteria in the comparison. Here we are comparing the One BioMed’s automated X8 platform with the commercially available manual-operated kits from QIAGEN Mini Kit and Roche. We extracted DNA from rat fresh-frozen tissue (from different type of organs) in the matrices. After tissue pre-treatment, it is added to the One BioMed’s X8 pre-filled cartridge, and the QIAGEN QIAmp column respectively. We found that the results after subjecting the eluates to the Real Time PCR using BIORAD CFX are comparable.

Keywords: DNA extraction, frozen tissue, PCR, qPCR, rat

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Authors: Kala Bharathan, R. Seshasayanan

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In this paper, a 1-bit Embedded Logic Full Adder (EFA) circuit in transistor level is proposed, which reduces logic complexity, gives low power and high speed. The design is further extended till 64 bits. To evaluate the performance of EFA, a 16, 32, 64-bit both Linear and Square root Carry Select Adder/Subtractor (CSLAS) Structure is also proposed. Realistic testing of proposed circuits is done on 8 X 8 Modified Booth multiplier and comparison in terms of power and delay is done. The EFA is implemented for different multiplier architectures for performance parameter comparison. Overall delay for CSLAS is reduced to 78% when compared to conventional one. The circuit implementations are done on TSMC 28nm CMOS technology using Cadence Virtuoso tool. The EFA has power savings of up to 14% when compared to the conventional adder. The present implementation was found to offer significant improvement in terms of power and speed in comparison to other full adder circuits.

Keywords: embedded logic, full adder, pdp, xor gate

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Authors: Sudhina H. K, Ravi M. Yadahalli, N. M. Shetti

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Reconfigurable antennas represent a recent innovation in antenna design that changes from classical fixed-form, Fixed function antennas to modifiable structures that can be adapted to fit the requirements of a time varying system. The ability to control the operating band of an antenna system can have many useful applications. Systems that operate in an acquire-and-track configuration would see a benefit from active bandwidth control. In such systems a wide band search mode is first employed to find a desired signal, Then a narrow band track mode is used to follow only that signal. Utilizing active antenna bandwidth control, A single antenna would function for both the wide band and narrow band configurations providing the rejection of unwanted signals with the antenna hardware. This ability to move a portion of the RF filtering out of the receiver and onto the antenna itself will also aid in reducing the complexity of the often expensive RF processing subsystems.

Keywords: designing methods, mems, stack, reconfigurable elements

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Authors: Musaab Riyadh, Norwati Mustapha, Dina Riyadh

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Trajectory similarity can be defined as the cost of transforming one trajectory into another based on certain similarity method. It is the core of numerous mining tasks such as clustering, classification, and indexing. Various approaches have been suggested to measure similarity based on the geometric and dynamic properties of trajectory, the overlapping between trajectory segments, and the confined area between entire trajectories. In this article, an evaluation of these approaches has been done based on computational cost, usage memory, accuracy, and the amount of data which is needed in advance to determine its suitability to stream mining applications. The evaluation results show that the stream mining applications support similarity methods which have low computational cost and memory, single scan on data, and free of mathematical complexity due to the high-speed generation of data.

Keywords: global distance measure, local distance measure, semantic trajectory, spatial dimension, stream data mining

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Authors: Flora Xu, Marta Fernandez Olmos

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This article is mainly about critical assessment and comprehensive understanding of the business strategy in the post COVID-19 scenario. This study aims to elucidate how companies are responding to the unique challenges posed by the pandemic and how these measures are shaping the future of the business environment. The pandemic has exposed the fragility and flexibility of the global supply chain, and procurement and production strategies should be reconsidered. It should increase the diversity of suppliers and the flexibility of the supply chain, and some companies are considering transferring their survival to the local market. This can increase local employment and reduce international transportation disruptions and customs issues. By shortening the distance between production and market, companies can respond more quickly to changes in demand and unforeseen events. The demand for remote work and online solutions will increase the adoption of digital technology and accelerate the digital transformation of many organizations. Marketing and communication strategies need to adapt to a constantly changing environment. The business resilience strategy was emphasized as a key component of the response to the COVID-19. The company is seeking to strengthen its risk management capabilities and develop a business continuity plan to cope with future unexpected disruptions. The pandemic has reconfigured human resource practices and changed the way companies manage their employees. Remote work has become the norm, and companies focus on managing workers' health and well-being, as well as flexible work policies to ensure operations and support for employees during crises. This change in human resources practice has a lasting impact on how companies apply talent and labor management in the post COVID-19 world. The pandemic has prompted a significant review of business strategies as companies adapt to constantly changing environments and seek to ensure their sustainability and profitability in times of crisis. This strategic reassessment has led to product diversification, exploring international markets and adapting to the changing market. Companies have responded to the unprecedented challenges brought by the COVID-19. The COVID-19 has promoted innovation effort in key areas and focused on the responsibility in today's business strategy for sustainability and the importance of corporate society. The important challenge of formulating and implementing business strategies in uncertain times. These challenges include making quick and agile decisions in turbulent environments, risk management, and adaptability to constantly changing market conditions. The COVID-19 highlights the importance of strategic planning and informed decision-making - making in a business environment characterized by uncertainty and complexity. In short, the pandemic has reconfigured the way companies handle business strategies and emphasized the necessity of preparing for future challenges in a business world marked by uncertainty and complexity.

Keywords: business strategy, crisis, digitalization, uncertainty

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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

Abstract:

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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