Search results for: biologically inspired algorithm

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

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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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Authors: Andreas Johansson

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Conceptual understanding needs to be grounded on observation of physical phenomena, experiences or metaphors. Observation of physical phenomena using demonstration experiments has a long tradition within physics education and students need to develop mental models to relate the observations to concepts from scientific theories. This study investigates how live and video experiments involving an acoustic trap to visualize particle-field interaction, field properties and particle properties can help develop students' mental models and how they can be used differently to realize their potential as teaching tools. Initially, they were treated as analogs and the lesson designs were kept identical. With a design-based approach, the experimental and video designs, as well as best practices for a respective teaching tool, were then developed in iterations. Variation theory was used as a theoretical framework to analyze the planned respective realized pattern of variation and invariance in order to explain learning outcomes as measured by a pre-posttest consisting of conceptual multiple-choice questions inspired by the Force Concept Inventory and the Force and Motion Conceptual Evaluation. Interviews with students and teachers were used to inform the design of experiments and videos in each iteration. The lesson designs and the live and video experiments has been developed to help teachers improve student learning and make school physics more interesting by involving experimental setups that usually are out of reach and to bridge the gap between what happens in classrooms and in science research. As students’ conceptual knowledge also rises their interest in physics the aim is to increase their chances of pursuing careers within science, technology, engineering or mathematics.

Keywords: acoustic trap, design-based research, experiments, variation theory

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Authors: Ahmadreza Ebadati, Asaad Y. Shamseldin, Amin Ghadirian

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Seawall geometry plays a crucial role in mitigating wave impacts, though detailed exploration of its manipulation is limited. This study delves into the effects of varying cross-shore seawall geometry on the dynamics of wave impacts, with a particular focus on vertical seawalls. Inspired by foundational insights linking seawall shape to hydraulic efficiency, this investigation centres on how alterations in seawall geometry can influence wave energy dissipation and subsequent wave impacts. The study investigates the 2D interaction of regular waves with a period of 2.1s with a vertical seawall and berm featuring small-scale cross-shore protrusions and recesses. Utilising OpenFOAM® simulations and a k-ω SST turbulence model, this investigation compares results to a base case simulation, which is partially calibrated with experimental data from a flume study. The analysis evaluates various geometric modifications, specifically interchanged protrusions and recesses at different heights and orientations along the seawall. Findings suggest that specific configurations, such as interchanged protrusions and recesses, can mitigate initial impact forces, while certain arrangements may intensify subsequent impacts. Key insights include the identification of geometry configurations that can effectively reduce the force impulse of slamming waves on coastal structures and potentially decrease the frequency and cost of seawall maintenance. This research contributes to the field by advancing the understanding of how seawall geometry influences wave forces and by providing actionable insights for the design of more resilient seawall structures. Further exploration of seawall geometry variation is recommended, advocating additional case studies to optimise designs tailored to specific coastal environments.

Keywords: seawall geometry, wave impact loads, numerical simulation, coastal engineering, wave-structure interaction

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Authors: Nishantha Giguruwa

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This research investigates the transformative potential of smart container farming, building upon the successful cultivation of Japanese mushrooms at Sakai Farms in Aichi Prefecture, Japan, under the strategic collaboration with the Daikei Group. Inspired by this success, the study focuses on establishing an advanced urban strawberry farming laboratory with the aim of understanding strawberry farming technologies, fostering collaboration, and strategizing marketing approaches for both local and global markets. Positioned within the business framework of Sakai Farms and the Daikei Group, the study underscores the sustainability and forward-looking solutions offered by smart container farming in agriculture. The global significance of strawberries is emphasized, acknowledging their economic and cultural importance. The detailed examination of strawberry farming intricacies informs the technological framework developed for smart containers, implemented at Sakai Farms. Integral to this research is the incorporation of controlled bee pollination, a groundbreaking addition to the smart container farming model. The study anticipates future trends, outlining avenues for continuing exploration, stakeholder collaborations, policy considerations, and expansion strategies. Notably, the author expresses a strategic intent to approach the global market, leveraging the foreign student/faculty base at Ritsumeikan Asia Pacific University, where the author is affiliated. This unique approach aims to disseminate the research findings globally, contributing to the broader landscape of agricultural innovation. The integration of controlled bee pollination within this innovative framework not only enhances sustainability but also marks a significant stride in the evolution of urban agriculture, aligning with global agricultural trends.

Keywords: smart container farming, urban agriculture, strawberry farming technologies, controlled bee pollination, agricultural innovation

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Authors: Mohammed Al-Yamani, Yaser Miaji

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DHL Customer Solutions and Innovation Department (CSI) have been experiencing difficulties while comparing quotes for different customers in different years. Currently, the employees are processing data by opening several loaded Excel files where the quotes are and manually copying values to another Excel Workbook where the comparison is made. This project consists of developing a new and effective database for DHL CSI department so that information is stored altogether on the same catalog. That being said, we have been assigned to find an efficient algorithm that can deal with the different formats of the Excel Workbooks to copy and store the express customer rates for core products (DOX, WPX, IMP) for comparisons purposes.

Keywords: DHL, solution design, ORACLE, EXCEL

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Authors: Kiana Zeighami, Morteza Ozlati Moghadam

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Designing a high accuracy and high precision motion controller is one of the important issues in today’s industry. There are effective solutions available in the industry but the real-time performance, smoothness and accuracy of the movement can be further improved. This paper discusses a complete solution to carry out the movement of three stepper motors in three dimensions. The objective is to provide a method to design a fully integrated System-on-Chip (SOC)-based motion controller to reduce the cost and complexity of production by incorporating Field Programmable Gate Array (FPGA) into the design. In the proposed method the FPGA receives its commands from a host computer via wireless internet communication and calculates the motion trajectory for three axes. A profile generator module is designed to realize the interpolation algorithm by translating the position data to the real-time pulses. This paper discusses an approach to implement the linear interpolation algorithm, since it is one of the fundamentals of robots’ movements and it is highly applicable in motion control industries. Along with full profile trajectory, the triangular drive is implemented to eliminate the existence of error at small distances. To integrate the parallelism and real-time performance of FPGA with the power of Central Processing Unit (CPU) in executing complex and sequential algorithms, the NIOS II soft-core processor was added into the design. This paper presents different operating modes such as absolute, relative positioning, reset and velocity modes to fulfill the user requirements. The proposed approach was evaluated by designing a custom-made FPGA board along with a mechanical structure. As a result, a precise and smooth movement of stepper motors was observed which proved the effectiveness of this approach.

Keywords: 3-axis linear interpolation, FPGA, motion controller, micro-stepping

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Authors: Dacheng Li, Bo Huang, Qinjin Han, Ming Li

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Remotely sensed imagery with the high spatial and temporal characteristics, which it is hard to acquire under the current land observation satellites, has been considered as a key factor for monitoring environmental changes over both global and local scales. On a basis of the limited high spatial-resolution observations, challenged studies called spatiotemporal fusion have been developed for generating high spatiotemporal images through employing other auxiliary low spatial-resolution data while with high-frequency observations. However, a majority of spatiotemporal fusion approaches yield to satisfactory assumption, empirical but unstable parameters, low accuracy or inefficient performance. Although the spatiotemporal fusion methodology via sparse representation theory has advantage in capturing reflectance changes, stability and execution efficiency (even more efficient when overcomplete dictionaries have been pre-trained), the retrieval of high-accuracy dictionary and its response to fusion results are still pending issues. In this paper, we employ additional image pairs (here each image-pair includes a Landsat Operational Land Imager and a Moderate Resolution Imaging Spectroradiometer acquisitions covering the partial area of Baotou, China) only into the coupled dictionary training process based on K-SVD (K-means Singular Value Decomposition) algorithm, and attempt to improve the fusion results of two existing sparse representation based fusion models (respectively utilizing one and two available image-pair). The results show that more eligible image pairs are probably related to a more accurate overcomplete dictionary, which generally indicates a better image representation, and is then contribute to an effective fusion performance in case that the added image-pair has similar seasonal aspects and image spatial structure features to the original image-pair. It is, therefore, reasonable to construct multi-dictionary training pattern for generating a series of high spatial resolution images based on limited acquisitions.

Keywords: spatiotemporal fusion, sparse representation, K-SVD algorithm, dictionary learning

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Authors: Reza Safdari, Goli Arji, Robab Abdolkhani Maryam zahmatkeshan

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Background and purpose: Cardiovascular diseases are among the most common diseases in all societies. The most important step in minimizing myocardial infarction and its complications is to minimize its risk factors. The amount of medical data is increasingly growing. Medical data mining has a great potential for transforming these data into information. Using data mining techniques to generate predictive models for identifying those at risk for reducing the effects of the disease is very helpful. The present study aimed to collect data related to risk factors of heart infarction from patients’ medical record and developed predicting models using data mining algorithm. Methods: The present work was an analytical study conducted on a database containing 350 records. Data were related to patients admitted to Shahid Rajaei specialized cardiovascular hospital, Iran, in 2011. Data were collected using a four-sectioned data collection form. Data analysis was performed using SPSS and Clementine version 12. Seven predictive algorithms and one algorithm-based model for predicting association rules were applied to the data. Accuracy, precision, sensitivity, specificity, as well as positive and negative predictive values were determined and the final model was obtained. Results: five parameters, including hypertension, DLP, tobacco smoking, diabetes, and A+ blood group, were the most critical risk factors of myocardial infarction. Among the models, the neural network model was found to have the highest sensitivity, indicating its ability to successfully diagnose the disease. Conclusion: Risk prediction models have great potentials in facilitating the management of a patient with a specific disease. Therefore, health interventions or change in their life style can be conducted based on these models for improving the health conditions of the individuals at risk.

Keywords: decision trees, neural network, myocardial infarction, Data Mining

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Authors: Husam Mohammed Saleh Alziyadi

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The drilling fluid has a significant impact on drilling efficiency. Drilling fluids have several functions which make them most important within the drilling process, such as lubricating and cooling the drill bit, removing cuttings from down of hole, preventing formation damage, suspending drill bit cuttings, , and also removing permeable formation as a result, the flow of fluid into the formation process is delayed. In the oil and gas sector, unconventional shale reserves have been a central player in meeting world energy demands. Oil-based drilling fluids (OBM) are generally favored for drilling shale plays due to negligible chemical interactions. Nevertheless, the industry has been inspired by strict environmental regulations to design water-based drilling fluids (WBM) capable of regulating shale-water interactions to boost their efficiency. However, traditional additives are too large to plug the micro-fractures and nanopores of the shale. Recently, nanotechnology in the oil and gas industries has shown a lot of promise, especially with drilling fluids based on nanoparticles. Nanotechnology has already made a huge contribution to technical developments in the energy sector. In the drilling industry, nanotechnology can make revolutionary changes. Nanotechnology creates nanomaterials with many attractive properties that can play an important role in improving the consistency of mud cake, reducing friction, preventing differential pipe sticking, preserving the stability of the borehole, protecting reservoirs, and improving the recovery of oil and gas. The selection of suitable nanomaterials should be based on the shale formation characteristics intended for drilling. The size, concentration, and stability of the NPs are three more important considerations. The effects of the environment are highly sensitive to these materials, such as changes in ionic strength, temperature, or pH, all of which occur under downhole conditions. This review paper focused on the previous research and recent development of environmentally friendly drilling fluids according to the regulatory environment and cost challenges.

Keywords: nanotechnology, WBM, Drilling Fluid, nanofluids

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Authors: Deepak Kumar, Ravi Rajwanshi, Mohd. Aslam Yusuf, Nisha Kant Pandey, Preeti Singh, Mukesh Saxena, Neera Bhalla Sarin

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Global issues are leading to concerns over food security. These include climate change, urbanization, increase in population subsequently leading to greater energy and water demand. Futuristic approach for crop improvement involves gene pyramiding for agronomic traits that empower the plants to withstand multiple stresses. In an earlier study from the laboratory, the efficacy of overexpressing γ-tocopherol methyl transferase (γ-TMT) gene from the vitamin E biosynthetic pathway has been shown to result in six-fold increase of the most biologically active form, the α-tocopherol in Brassica juncea which resulted in alleviation of salt, heavy metal and osmoticum induced stress by the transgenic plants. The glyoxalase I (gly I) gene from the glyoxalase pathway has also been earlier shown by us to impart tolerance against multiple abioitc stresses by detoxification of the cytotoxic compound methylglyoxal in Brassica juncea. Recently, both the transgenes were pyramided in Brassica juncea lines through sexual crosses involving two stable Brassica juncea lines overexpressing γ-TMT and gly I genes respectively. The transgene integration was confirmed by PCR analysis and their mRNA expression was evident by RT-PCR analysis. Preliminary physiological investigations showed ~55% increased seed germination under 200 mM NaCl stress in the pyramided line and 81% higher seed germination under 200 mM mannitol stress as compared to the WT control plants. The pyramided lines also retained more chlorophyll content when the leaf discs were floated on NaCl (200, 400 and 600 mM) or mannitol (200, 400 and 600 mM) compared to the WT control plants. These plants had higher Relative Water Content and greater solute accumulation under stress compared to the parental plants having γ-TMT or the glyI gene respectively. The studies revealed the synergy of two components from different metabolic pathways in enhancing stress hardiness of the transgenic B. juncea plants. It was concluded that pyramiding of genes (γ-TMT and glyI) from diverse pathways can lead to enhanced tolerance to salt and mannitol stress (simulating drought conditions). This strategy can prove useful in enhancing the crop yields under various abiotic stresses.

Keywords: abiotic stress, brassica juncea, glyoxalase I, α-tocopherol

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Authors: Vitor M. Vilas Boas, Cleison D. Silva, Gustavo S. Mafra, Alexandre Trofino Neto

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Motor imagery (MI) based brain-computer interfaces (BCI) uses event-related (de)synchronization (ERS/ ERD), typically recorded using electroencephalography (EEG), to translate brain electrical activity into control commands. To mitigate undesirable artifacts and noise measurements on EEG signals, methods based on band-pass filters defined by a specific frequency band (i.e., 8 – 30Hz), such as the Infinity Impulse Response (IIR) filters, are typically used. Spatial techniques, such as Common Spatial Patterns (CSP), are also used to estimate the variations of the filtered signal and extract features that define the imagined motion. The CSP effectiveness depends on the subject's discriminative frequency, and approaches based on the decomposition of the band of interest into sub-bands with smaller frequency ranges (SBCSP) have been suggested to EEG signals classification. However, despite providing good results, the SBCSP approach generally increases the computational cost of the filtering step in IM-based BCI systems. This paper proposes the use of the Fast Fourier Transform (FFT) algorithm in the IM-based BCI filtering stage that implements SBCSP. The goal is to apply the FFT algorithm to reduce the computational cost of the processing step of these systems and to make them more efficient without compromising classification accuracy. The proposal is based on the representation of EEG signals in a matrix of coefficients resulting from the frequency decomposition performed by the FFT, which is then submitted to the SBCSP process. The structure of the SBCSP contemplates dividing the band of interest, initially defined between 0 and 40Hz, into a set of 33 sub-bands spanning specific frequency bands which are processed in parallel each by a CSP filter and an LDA classifier. A Bayesian meta-classifier is then used to represent the LDA outputs of each sub-band as scores and organize them into a single vector, and then used as a training vector of an SVM global classifier. Initially, the public EEG data set IIa of the BCI Competition IV is used to validate the approach. The first contribution of the proposed method is that, in addition to being more compact, because it has a 68% smaller dimension than the original signal, the resulting FFT matrix maintains the signal information relevant to class discrimination. In addition, the results showed an average reduction of 31.6% in the computational cost in relation to the application of filtering methods based on IIR filters, suggesting FFT efficiency when applied in the filtering step. Finally, the frequency decomposition approach improves the overall system classification rate significantly compared to the commonly used filtering, going from 73.7% using IIR to 84.2% using FFT. The accuracy improvement above 10% and the computational cost reduction denote the potential of FFT in EEG signal filtering applied to the context of IM-based BCI implementing SBCSP. Tests with other data sets are currently being performed to reinforce such conclusions.

Keywords: brain-computer interfaces, fast Fourier transform algorithm, motor imagery, sub-band common spatial patterns

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Authors: Seyed Mehdi Ghezi, Hesam Hasanpoor

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This scientific article explores enhancement methods for ensemble learning with the aim of improving the performance of classifiers in the analysis and classification of brain signals. The research approach in this field consists of two main parts, each with its own strengths and weaknesses. The choice of approach depends on the specific research question and available resources. By combining these approaches and leveraging their respective strengths, researchers can enhance the accuracy and reliability of classification results, consequently advancing our understanding of the brain and its functions. The first approach focuses on utilizing machine learning methods to identify the best features among the vast array of features present in brain signals. The selection of features varies depending on the research objective, and different techniques have been employed for this purpose. For instance, the genetic algorithm has been used in some studies to identify the best features, while optimization methods have been utilized in others to identify the most influential features. Additionally, machine learning techniques have been applied to determine the influential electrodes in classification. Ensemble learning plays a crucial role in identifying the best features that contribute to learning, thereby improving the overall results. The second approach concentrates on designing and implementing methods for selecting the best classifier or utilizing meta-classifiers to enhance the final results in ensemble learning. In a different section of the research, a single classifier is used instead of multiple classifiers, employing different sets of features to improve the results. The article provides an in-depth examination of each technique, highlighting their advantages and limitations. By integrating these techniques, researchers can enhance the performance of classifiers in the analysis and classification of brain signals. This advancement in ensemble learning methodologies contributes to a better understanding of the brain and its functions, ultimately leading to improved accuracy and reliability in brain signal analysis and classification.

Keywords: ensemble learning, brain signals, classification, feature selection, machine learning, genetic algorithm, optimization methods, influential features, influential electrodes, meta-classifiers

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Authors: Seyed-Yaser Nabavi-Chashmi, Davood Asadi, Karim Ahmadi, Eren Demir

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The landing phase of a UAV is very critical as there are many uncertainties in this phase, which can easily entail a hard landing or even a crash. In this paper, the estimation of relative distance and velocity to the ground, as one of the most important processes during the landing phase, is studied. Using accurate measurement sensors as an alternative approach can be very expensive for sensors like LIDAR, or with a limited operational range, for sensors like ultrasonic sensors. Additionally, absolute positioning systems like GPS or IMU cannot provide distance to the ground independently. The focus of this paper is to determine whether we can measure the relative distance and velocity of UAV and ground in the landing phase using just low-resolution images taken by a monocular camera. The Lucas-Konda feature detection technique is employed to extract the most suitable feature in a series of images taken during the UAV landing. Two different approaches based on Extended Kalman Filters (EKF) have been proposed, and their performance in estimation of the relative distance and velocity are compared. The first approach uses the kinematics of the UAV as the process and the calculated optical flow as the measurement; On the other hand, the second approach uses the feature’s projection on the camera plane (pixel position) as the measurement while employing both the kinematics of the UAV and the dynamics of variation of projected point as the process to estimate both relative distance and relative velocity. To verify the results, a sequence of low-quality images taken by a camera that is moving on a specifically developed testbed has been used to compare the performance of the proposed algorithm. The case studies show that the quality of images results in considerable noise, which reduces the performance of the first approach. On the other hand, using the projected feature position is much less sensitive to the noise and estimates the distance and velocity with relatively high accuracy. This approach also can be used to predict the future projected feature position, which can drastically decrease the computational workload, as an important criterion for real-time applications.

Keywords: altitude estimation, drone, image processing, trajectory planning

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

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

Keywords: canny pruning, hand recognition, machine learning, skin tracking

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Authors: Ni Wenli, He Jing

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Repeat–Accumulate (RA) codes are subclass of LDPC codes with fast encoder structures. In this paper, we consider a nonbinary extension of binary LDPC codes over GF(q) and construct a non-binary RA code and a non-binary QC-LDPC code over GF(2^4), we construct non-binary RA codes with linear encoding method and non-binary QC-LDPC codes with algebraic constructions method. And the BER performance of RA and QC-LDPC codes over GF(q) are compared with BP decoding and by simulation over the Additive White Gaussian Noise (AWGN) channels.

Keywords: non-binary RA codes, QC-LDPC codes, performance comparison, BP algorithm

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Authors: Kevin R. Wilson, Roger Mantie

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Community arts-based learning and participatory education are pedagogical techniques that serve to be advantageous for students, curriculum development, and local communities. Using an interpretive approach to examine the significance of this arts-informed research in relation to the eight ‘design imperatives’ proposed as the new model for measuring quality in scholarship for Arizona State University as ‘A New American University’, the purpose of this study was to investigate personal, social, and cultural benefits resulting from student engagement in interdisciplinary community-based projects. Students from a graduate level music education class at the ASU Tempe campus (n=7) teamed with students from an undergraduate level community development class at the ASU Downtown Phoenix campus (n=14) to plan, facilitate, and evaluate seven community-based projects in several locations around the Phoenix-metro area. Data was collected using photo evidence, student reports, and evaluative measures designed by the students. The effectiveness of each project was measured in terms of their ability to meet the eight design imperatives to: 1) leverage place; 2) transform society; 3) value entrepreneurship; 4) conduct use-inspired research; 5) enable student success; 6) fuse intellectual disciplines; 7) be socially embedded; and 8) engage globally. Results indicated that this community arts-based project sufficiently captured the essence of each of these eight imperatives. Implications for how the nature of this interdisciplinary initiative allowed for the eight imperatives to manifest are provided, and project success is expounded upon in relation to utility of each imperative. Discussion is also given for how this type of service learning project formatted within the ‘New American University’ model for measuring quality in academia can be a beneficial pedagogical tool in higher education.

Keywords: community arts-based learning, participatory education, pedagogy, service learning

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Authors: M. K. Benhaoua, A. E. H. Benyamina, T. Djeradi, P. Boulet

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In this paper, we propose adaptive routing that considers the routing of communications in order to optimize the overall performance. The routing technique uses a newly proposed Algorithm to route communications between the tasks. The routing we propose of the communications leads to a better optimization of several performance metrics (time and energy consumption). Experimental results show that the proposed routing approach provides significant performance improvements when compared to those using static routing.

Keywords: multi-processor systems-on-chip (mpsocs), network-on-chip (noc), heterogeneous architectures, adaptive routin

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Authors: N. Reichbach, A. Kuperman

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The paper describes a design of a monitoring system for super capacitor packs in propulsion systems, allowing determining the instantaneous energy capacity under power loading. The system contains real-time recursive-least-squares identification mechanism, estimating the values of pack capacitance and equivalent series resistance. These values are required for accurate calculation of the state-of-energy.

Keywords: real-time monitoring, RLS identification algorithm, state-of-energy, super capacitor

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Authors: Luminita Marin, Dalila Belei, Carmen Dumea

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Aggregation induced emission (AIE) is an intriguing optical phenomenon recently evidenced by Tang and his co-workers, for which aggregation works constructively in the improving of light emission. The AIE challenging phenomenon is quite opposite to the notorious aggregation caused quenching (ACQ) of light emission in the condensed phase, and comes in line with requirements of photonic and optoelectronic devices which need solid state emissive substrates. This paper reports a series of ten new aggregation induced emission (AIE) low molecular weight compounds based on triazole and pyridine-N-oxide heterocyclic units bonded by short flexible chains, obtained by a „click” chemistry reaction. The compounds present extremely weak luminescence in solution but strong light emission in solid state. To distinguish the influence of the crystallinity degree on the emission efficiency, the photophysical properties were explored by UV-vis and photoluminescence spectroscopy in solution, water suspension, amorphous and crystalline films. On the other hand, the compound morphology of the up mentioned states was monitored by dynamic light scattering, scanning electron microscopy, atomic force microscopy and polarized light microscopy methods. To further understand the structural design – photophysical properties relationship, single crystal X-ray diffraction on some understudy compounds was performed too. The UV-vis absorption spectra of the triazole water suspensions indicated a typical behaviour for nanoparticle formation, while the photoluminescence spectra revealed an emission intensity enhancement up to 921-fold higher of the crystalline films compared to solutions, clearly indicating an AIE behaviour. The compounds have the tendency to aggregate forming nano- and micro- crystals in shape of rose-like and fibres. The crystals integrity is kept due to the strong lateral intermolecular forces, while the absence of face-to-face forces explains the enhanced luminescence in crystalline state, in which the intramolecular rotations are restricted. The studied flexible triazoles draw attention to a new structural design in which small biologically friendly luminophore units are linked together by small flexible chains. This design enlarges the variety of the AIE luminogens to the flexible molecules, guiding further efforts in development of new AIE structures for appropriate applications, the biological ones being especially envisaged.

Keywords: aggregation induced emission, pyridine-N-oxide, triazole

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Authors: Elmabruk Laias, Abdalla M. Hanashi, Mohammed Alnas

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Worldwide Interoperability for Microwave Access (WiMAX) became one of the most challenging issues, since it was responsible for distributing available resources of the network among all users this leaded to the demand of constructing and designing high efficient scheduling algorithms in order to improve the network utilization, to increase the network throughput, and to minimize the end-to-end delay. In this study, the proposed algorithm focuses on an efficient mechanism to serve non-real time traffic in congested networks by considering channel status.

Keywords: WiMAX, Quality of Services (QoS), OPNE, Diff-Serv (DS).

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Authors: Gaweł Magdalena, Lipkowska Anna, Olbert Magdalena, Frąckiewicz Ewelina, Librowski Tadeusz, Nowak Gabriel, Pilc Andrzej

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Heme oxygenase-1 (HO-1), also known as heat shock protein 32 (HSP32), has been shown to be implicated in cytoprotection in various organs. Its activation plays a significant role in acute and chronic inflammation, protecting cells from oxidative injury and apoptosis. This inducible isoform of HO catalyzes the first and rate-limiting step in heme degradation to produce equimolar quantities of biologically active products: carbon monoxide (CO), free iron and biliverdin. CO has been reported to possess anti-apoptotic properties. Moreover, it inhibits the production of proinflammatory cytokines and stimulates the synthesis of the anti-inflammatory interleukin-10 (IL-10), as well as promotes vasodilatation at sites of inflammation. The second product of catalytic HO-1 activity, free cytotoxic iron, is promptly sequestered into the iron storage protein ferritin, which lowers the pro-oxidant state of the cell. The third product, biliverdin, is subsequently converted by biliverdin reductase into the bile pigment bilirubin, the most potent endogenous antioxidant among the constituents of human serum, which modulates immune effector functions and suppresses inflammatory response. Furthermore, being one of the so-called stress proteins, HO-1 adaptively responds to different stressors, such as reactive oxygen species (ROS), inflammatory cytokines and heavy metals and thus protects cells against such conditions as ischemia, hemorrhagic shock, heat shock or hypoxia. It is suggested that pharmacologic modulation of HO-1 may represent an effective strategy for prevention of stress and drug-induced gastrointestinal toxicity. HO-1 is constitutively expressed in normal gastric, intestinal and colonic mucosa and up-regulated during inflammation. It has been proven that HO-1 up-regulated by hemin, heme and cobalt-protoporphyrin ameliorates experimental colitis. In addition, the up-regulation of HO-1 partially explains the mechanism of action of 5-aminosalicylic acid (5-ASA), which is used clinically as an anti-colitis agent. In 2009 Ueda et al. has reported for the first time that mucosal protection by Polaprezinc, a chelate compound of zinc and L-carnosine used as an anti-ulcer drug in Japan, is also attributed to induction of HO-1 in the stomach. Since then, inducers of HO-1 are desired subject of research, as they may constitute therapeutically effective anti-ulcer drugs.

Keywords: heme oxygenase-1, gastric lesions, gastroprotection, Polaprezinc

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Authors: Ju-Hong Lee, Ching-Wei Liao

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Owing to close antenna spacing between antenna sensors within a compact space, a part of data in one antenna sensor would outflow to other antenna sensors when the antenna sensors in an antenna array operate simultaneously. This phenomenon is called mutual coupling effect (MCE). It has been shown that the performance of antenna array systems can be degraded when the antenna sensors are in close proximity. Especially, in a systems equipped with massive antenna sensors, the degradation of beamforming performance due to the MCE is significantly inevitable. Moreover, it has been shown that even a small angle error between the true direction angle of the desired signal and the steering angle deteriorates the effectiveness of an array beamforming system. However, the true direction vector of the desired signal may not be exactly known in some applications, e.g., the application in land mobile-cellular wireless systems. Therefore, it is worth developing robust techniques to deal with the problem due to the MCE and steering angle error for array beamforming systems. In this paper, we present an efficient technique for performing adaptive beamforming with robust capabilities against the MCE and the steering angle error. Only the data vector received by an antenna array is required by the proposed technique. By using the received array data vector, a correlation matrix is constructed to replace the original correlation matrix associated with the received array data vector. Then, the mutual coupling matrix due to the MCE on the antenna array is estimated through a recursive algorithm. An appropriate estimate of the direction angle of the desired signal can also be obtained during the recursive process. Based on the estimated mutual coupling matrix, the estimated direction angle, and the reconstructed correlation matrix, the proposed technique can effectively cure the performance degradation due to steering angle error and MCE. The novelty of the proposed technique is that the implementation procedure is very simple and the resulting adaptive beamforming performance is satisfactory. Simulation results show that the proposed technique provides much better beamforming performance without requiring complicated complexity as compared with the existing robust techniques.

Keywords: adaptive beamforming, mutual coupling effect, recursive algorithm, steering angle error

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Authors: Mahtab Makaremi Masouleh, Günter Wozniak

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This work is part of a major research project concerning the design of a light temporary installable textile flood control structure. The motivation for this work is the great need of applying light structures for the protection of coastal areas from detrimental effects of rapid water runoff. The prime objective of the study is the numerical analysis of the interaction among free surface water flow and slender shaped pliable structures, playing a key role in safety performance of the intended system. First, the behavior of down scale membrane is examined under hydrostatic pressure by the Abaqus explicit solver, which is part of the finite element based commercially available SIMULIA software. Then the procedure to achieve a stable and convergent solution for strongly coupled media including fluids and structures is explained. A partitioned strategy is imposed to make both structures and fluids be discretized and solved with appropriate formulations and solvers. In this regard, finite element method is again selected to analyze the structural domain. Moreover, computational fluid dynamics algorithms are introduced for solutions in flow domains by means of a commercial package of Star CCM+. Likewise, SIMULIA co-simulation engine and an implicit coupling algorithm, which are available communication tools in commercial package of the Star CCM+, enable powerful transmission of data between two applied codes. This approach is discussed for two different cases and compared with available experimental records. In one case, the down scale membrane interacts with open channel flow, where the flow velocity increases with time. The second case illustrates, how the full scale flexible flood barrier behaves when a massive flotsam is accelerated towards it.

Keywords: finite element formulation, finite volume algorithm, fluid-structure interaction, light pliable structure, VOF multiphase model

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Authors: Deniz Guven, Wil Ward, Jinming Duan, Li Bai

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Over the past two decades or so, Optical Coherence Tomography (OCT) has been used to diagnose retina and optic nerve diseases. The retinal nerve fibre layer, for example, is a powerful diagnostic marker for detecting and staging glaucoma. With the advances in optical imaging hardware, the adoption of OCT is now commonplace in clinics. More and more OCT images are being generated, and for these OCT images to have clinical applicability, accurate automated OCT image segmentation software is needed. Oct image segmentation is still an active research area, as OCT images are inherently noisy, with the multiplicative speckling noise. Simple edge detection algorithms are unsuitable for detecting retinal layer boundaries in OCT images. Intensity fluctuation, motion artefact, and the presence of blood vessels also decrease further OCT image quality. In this paper, we introduce a new method for segmenting three-dimensional (3D) OCT images. This involves the use of a Kalman filter, which is commonly used in computer vision for object tracking. The Kalman filter is applied to the 3D OCT image volume to track the retinal layer boundaries through the slices within the volume and thus segmenting the 3D image. Specifically, after some pre-processing of the OCT images, points on the retinal layer boundaries in the first image are identified, and curve fitting is applied to them such that the layer boundaries can be represented by the coefficients of the curve equations. These coefficients then form the state space for the Kalman Filter. The filter then produces an optimal estimate of the current state of the system by updating its previous state using the measurements available in the form of a feedback control loop. The results show that the algorithm can be used to segment the retinal layers in OCT images. One of the limitations of the current algorithm is that the curve representation of the retinal layer boundary does not work well when the layer boundary is split into two, e.g., at the optic nerve, the layer boundary split into two. This maybe resolved by using a different approach to representing the boundaries, such as b-splines or level sets. The use of a Kalman filter shows promise to developing accurate and effective 3D OCT segmentation methods.

Keywords: optical coherence tomography, image segmentation, Kalman filter, object tracking

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Authors: David X. Dong, Qingming Zhang, Meng Lu

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Nitrites enter waterways as runoff from croplands and are discharged from many industrial sites. Excessive nitrite inputs to water bodies lead to eutrophication. On-site rapid detection of nitrite is of increasing interest for managing fertilizer application and monitoring water source quality. Existing methods for detecting nitrites use spectrophotometry, ion chromatography, electrochemical sensors, ion-selective electrodes, chemiluminescence, and colorimetric methods. However, these methods either suffer from high cost or provide low measurement accuracy due to their poor selectivity to nitrites. Therefore, it is desired to develop an accurate and economical method to monitor nitrites in environments. We report a low-cost optical sensor, in conjunction with a machine learning (ML) approach to enable high-accuracy detection of nitrites in water sources. The sensor works under the principle of measuring molecular absorptions of nitrites at three narrowband wavelengths (295 nm, 310 nm, and 357 nm) in the ultraviolet (UV) region. These wavelengths are chosen because they have relatively high sensitivity to nitrites; low-cost light-emitting devices (LEDs) and photodetectors are also available at these wavelengths. A regression model is built, trained, and utilized to minimize cross-sensitivities of these wavelengths to the same analyte, thus achieving precise and reliable measurements with various interference ions. The measured absorbance data is input to the trained model that can provide nitrite concentration prediction for the sample. The sensor is built with i) a miniature quartz cuvette as the test cell that contains a liquid sample under test, ii) three low-cost UV LEDs placed on one side of the cell as light sources, with each LED providing a narrowband light, and iii) a photodetector with a built-in amplifier and an analog-to-digital converter placed on the other side of the test cell to measure the power of transmitted light. This simple optical design allows measuring the absorbance data of the sample at the three wavelengths. To train the regression model, absorbances of nitrite ions and their combination with various interference ions are first obtained at the three UV wavelengths using a conventional spectrophotometer. Then, the spectrophotometric data are inputs to different regression algorithm models for training and evaluating high-accuracy nitrite concentration prediction. Our experimental results show that the proposed approach enables instantaneous nitrite detection within several seconds. The sensor hardware costs about one hundred dollars, which is much cheaper than a commercial spectrophotometer. The ML algorithm helps to reduce the average relative errors to below 3.5% over a concentration range from 0.1 ppm to 100 ppm of nitrites. The sensor has been validated to measure nitrites at three sites in Ames, Iowa, USA. This work demonstrates an economical and effective approach to the rapid, reagent-free determination of nitrites with high accuracy. The integration of the low-cost optical sensor and ML data processing can find a wide range of applications in environmental monitoring and management.

Keywords: optical sensor, regression model, nitrites, water quality

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Authors: Mariam Dianat Sabet Gilani, Lars M. Blank, Birgitta E. Ebert

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Plant-derived lupane-type, pentacyclic triterpenoids are biologically active compounds that are highly interesting for applications in medical, pharmaceutical, and cosmetic industries. Due to the low abundance of these valuable compounds in their natural sources, and the environmentally harmful downstream process, alternative production methods, such as microbial cell factories, are investigated. Engineered Saccharomyces cerevisiae strains, harboring the heterologous genes for betulinic acid synthesis, can produce up to 2 g L-1 triterpenoids, showing high potential for large-scale production of triterpenoids. One limitation of the microbial synthesis is the intracellular product accumulation. It not only makes cell disruption a necessary step in the downstream processing but also limits productivity and product yield per cell. To overcome these restrictions, the aim of this study is to develop an in-situ extraction method, which extracts triterpenoids into a second organic phase. Such a continuous or sequential product removal from the biomass keeps the cells in an active state and enables extended production time or biomass recycling. After screening of twelve different solvents, selected based on product solubility, biocompatibility, as well as environmental and health impact, isopropyl myristate (IPM) was chosen as a suitable solvent for in-situ product removal from S. cerevisiae. Impedance-based single-cell analysis and off-gas measurement of carbon dioxide emission showed that cell viability and physiology were not affected by the presence of IPM. Initial experiments demonstrated that after the addition of 20 vol % IPM to cultures in the stationary phase, 40 % of the total produced triterpenoids were extracted from the cells into the organic phase. In future experiments, the application of IPM in a repeated batch process will be tested, where IPM is added at the end of each batch run to remove triterpenoids from the cells, allowing the same biocatalysts to be used in several sequential batch steps. Due to its high biocompatibility, the amount of IPM added to the culture can also be increased to more than 20 vol % to extract more than 40 % triterpenoids in the organic phase, allowing the cells to produce more triterpenoids. This highlights the potential for the development of a continuous large-scale process, which allows biocatalysts to produce intracellular products continuously without the necessity of cell disruption and without limitation of the cell capacity.

Keywords: betulinic acid, biocompatible solvent, in-situ extraction, isopropyl myristate, process development, secondary metabolites, triterpenoids, yeast

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Authors: Kristina Rosengren, Petra Brannefors, Eric Carlstrom

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Background: Implementation and use of person-centered care (PCC) is increasing worldwide, and why the current study intends to increase knowledge regarding how PCC is used in different European countries. Purpose: To describe the extent of person-centred care in 23 European countries in relation to use specific countries healthcare system (Beveridge, Bismarck, Mixed/OOP). Methods: The study was conducted by literature review inspired by Spice, both scientific empirical studies (Cinahl, Medline, Scopus) as well as grey literature (Google) were used. Totally 1194 documents were included divided into Cinahl n=139, Medline n=245, Scopus n=493 and Google n=317. Data were analysed with descriptive (percentage, range) regarding content and scope of PCC/country according to content and scope of PCC in each country, grouped into the healthcare system (Beveridge, Bismarck, Mixed/OOP) and geographic placement. Results: PCC were most common in UK (England, Scotland, Wales, North Ireland), n=481, 40.3%, Sweden (n=231, 19.3%), The Netherlands (n=80, 6.7%), Ireland (n=79, 6.6%) and Norway (n=61, 5.1%); and less common in Poland (0.6%), Hungary (0.5%), Greece (0.4%), Latvia (0.4%) and Serbia (0%). Beveridge healthcare system (12/23=0.5217, 52.2%) show 85 percent of documents with advantage of scientific literature valued 2.92 (n=999, 0.55-4.07), compare to advantage of grey literature in Bismarck (10/23=0.4347, 43.5%) with 15 percentage valued 2.35 (n=190, 0-3.27) followed by Mixed/OOP (1/23=4%) with 0.4 valued 2.25. Conclusions: Seven out of 10 countries with Beveridge health system used PCC compare to less-used PCC in countries with the Bismarck system. Research, as well as national regulations regarding PCC, are important tools to motivate the advantage of PCC in clinical practice. Moreover, implementation of PCC needs a systematic approach, from national (politicians), regional (guideline) and local (specific healthcare settings) levels visualized by decision-making as law, mission, policies, and routines in clinical practice to establish a well-integrated phenomenon in Europe.

Keywords: Beveridge, Bismarck, Europe, evidence-based, literature review, person-centered care

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Authors: Cristian Păuna

Abstract:

In the first decades of the 21st century, in the electronic trading environment, algorithmic capital investments became the primary tool to make a profit by speculations in financial markets. A significant number of traders, private or institutional investors are participating in the capital markets every day using automated algorithms. The autonomous trading software is today a considerable part in the business intelligence system of any modern financial activity. The trading decisions and orders are made automatically by computers using different mathematical models. This paper will present one of these models called Price Prediction Line. A mathematical algorithm will be revealed to build a reliable trend line, which is the base for limit conditions and automated investment signals, the core for a computerized investment system. The paper will guide how to apply these tools to generate entry and exit investment signals, limit conditions to build a mathematical filter for the investment opportunities, and the methodology to integrate all of these in automated investment software. The paper will also present trading results obtained for the leading German financial market index with the presented methods to analyze and to compare different automated investment algorithms. It was found that a specific mathematical algorithm can be optimized and integrated into an automated trading system with good and sustained results for the leading German Market. Investment results will be compared in order to qualify the presented model. In conclusion, a 1:6.12 risk was obtained to reward ratio applying the trigonometric method to the DAX Deutscher Aktienindex on 24 months investment. These results are superior to those obtained with other similar models as this paper reveal. The general idea sustained by this paper is that the Price Prediction Line model presented is a reliable capital investment methodology that can be successfully applied to build an automated investment system with excellent results.

Keywords: algorithmic trading, automated trading systems, high-frequency trading, DAX Deutscher Aktienindex

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Authors: Akhila Vasudev, Himanshu Kaushik, Tadikonda Venkata Bharat

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A large number of mine tailings are produced every year as part of the extraction process of phosphates, gold, copper, and other materials. Mine tailings are high in water content and have very slow dewatering behavior. The efficient design of tailings dam and economical disposal of these slurries requires the knowledge of tailings consolidation behavior. The large-strain consolidation theory closely predicts the self-weight consolidation of these slurries as the theory considers the conservation of mass and momentum conservation and considers the hydraulic conductivity as a function of void ratio. Classical laboratory techniques, such as settling column test, seepage consolidation test, etc., are expensive and time-consuming for the estimation of hydraulic conductivity variation with void ratio. Inverse estimation of the constitutive relationships from the measured settlement versus time curves is explored. In this work, inverse analysis based on metaheuristics techniques will be explored for predicting the hydraulic conductivity parameters for mine tailings from the base excess pore water pressure dissipation curve and the initial conditions of the mine tailings. The proposed inverse model uses particle swarm optimization (PSO) algorithm, which is based on the social behavior of animals searching for food sources. The finite-difference numerical solution of the forward analytical model is integrated with the PSO algorithm to solve the inverse problem. The method is tested on synthetic data of base excess pore pressure dissipation curves generated using the finite difference method. The effectiveness of the method is verified using base excess pore pressure dissipation curve obtained from a settling column experiment and further ensured through comparison with available predicted hydraulic conductivity parameters.

Keywords: base excess pore pressure, hydraulic conductivity, large strain consolidation, mine tailings

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Authors: María Dolores Caro, Eugenio M. Fedriani, Ángel F. Tenorio

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When lorries transport dangerous goods, there exist some legal stipulations in the European Union for assuring the security of the rest of road users as well as of those goods being transported. At this respect, lorry drivers cannot park in usual parking areas, because they must use parking areas with special conditions, including permanent supervision of security personnel. Moreover, drivers are compelled to satisfy additional regulations about resting and driving times, which involve in the practical possibility of reaching the suitable parking areas under these time parameters. The “European Agreement concerning the International Carriage of Dangerous Goods by Road” (ADR) is the basic regulation on transportation of dangerous goods imposed under the recommendations of the United Nations Economic Commission for Europe. Indeed, nowadays there are no enough parking areas adapted for dangerous goods and no complete study have suggested the best locations to build new areas or to adapt others already existing to provide the areas being necessary so that lorry drivers can follow all the regulations. The goal of this paper is to show how many additional parking areas should be built in the Iberian Peninsula to allow that lorry drivers may park in such areas under their restrictions in resting and driving time. To do so, we have modeled the problem via graph theory and we have applied a new efficient algorithm which determines an optimal solution for the problem of locating new parking areas to complement those already existing in the ADR for the Iberian Peninsula. The solution can be considered minimal since the number of additional parking areas returned by the algorithm is minimal in quantity. Obviously, graph theory is a natural way to model and solve the problem here proposed because we have considered as nodes: the already-existing parking areas, the loading-and-unloading locations and the bifurcations of roads; while each edge between two nodes represents the existence of a road between both nodes (the distance between nodes is the edge's weight). Except for bifurcations, all the nodes correspond to parking areas already existing and, hence, the problem corresponds to determining the additional nodes in the graph such that there are less up to 100 km between two nodes representing parking areas. (maximal distance allowed by the European regulations).

Keywords: dangerous goods, parking areas, Iberian peninsula, graph-based modeling

Procedia PDF Downloads 580