Search results for: Naturally-inspired algorithms and particle swarm optimization.

Authors: Niousha Bagheri Khulenjani, Mohammad Saniee Abadeh

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Learning from very big datasets is a significant problem for most present data mining and machine learning algorithms. MicroRNA (miRNA) is one of the important big genomic and non-coding datasets presenting the genome sequences. In this paper, a hybrid method for the classification of the miRNA data is proposed. Due to the variety of cancers and high number of genes, analyzing the miRNA dataset has been a challenging problem for researchers. The number of features corresponding to the number of samples is high and the data suffer from being imbalanced. The feature selection method has been used to select features having more ability to distinguish classes and eliminating obscures features. Afterward, a Convolutional Neural Network (CNN) classifier for classification of cancer types is utilized, which employs a Genetic Algorithm to highlight optimized hyper-parameters of CNN. In order to make the process of classification by CNN faster, Graphics Processing Unit (GPU) is recommended for calculating the mathematic equation in a parallel way. The proposed method is tested on a real-world dataset with 8,129 patients, 29 different types of tumors, and 1,046 miRNA biomarkers, taken from The Cancer Genome Atlas (TCGA) database.

Keywords: Cancer classification, feature selection, deep learning, genetic algorithm.

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Authors: Nurul-Akidah M. Yusak, Rahmah Mohamed, Noor Zuhaira Abd Aziz

Abstract:

The introduction of degradable plastic materials into agricultural sectors has represented a promising alternative to promote green agriculture and environmental friendly of modern farming practices. Major challenges of developing degradable agricultural films are to identify the most feasible types of degradation mechanisms, composition of degradable polymers and related processing techniques. The incorrect choice of degradable mechanisms to be applied during the degradation process will cause premature losses of mechanical performance and strength. In order to achieve controlled process of agricultural film degradation, the compositions of degradable agricultural film also important in order to stimulate degradation reaction at required interval of time and to achieve sustainability of the modern agricultural practices. A set of photodegradable polyethylene based agricultural film was developed and produced, following the selective optimization of processing parameters of the agricultural film manufacturing system. Example of agricultural films application for oil palm seedlings cultivation is presented.

Keywords: Photodegradable polyethylene, plasticulture, processing schemes.

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Authors: H. Mansor, S. B. Mohd-Noor, T. S. Gunawan, S. Khan, N. I. Othman, N. Tazali, R. B. Islam

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This paper provides a comparative study on the performances of standard PID and adaptive PID controllers tested on travel angle of a 3-Degree-of-Freedom (3-DOF) Quanser bench-top helicopter. Quanser, a well-known manufacturer of educational bench-top helicopter has developed Proportional Integration Derivative (PID) controller with Linear Quadratic Regulator (LQR) for all travel, pitch and yaw angle of the bench-top helicopter. The performance of the PID controller is relatively good; however, its performance could also be improved if the controller is combined with adaptive element. The objective of this research is to design adaptive PID controller and then compare the performances of the adaptive PID with the standard PID. The controller design and test is focused on travel angle control only. Adaptive method used in this project is self-tuning controller, which controller’s parameters are updated online. Two adaptive algorithms those are pole-placement and deadbeat have been chosen as the method to achieve optimal controller’s parameters. Performance comparisons have shown that the adaptive (deadbeat) PID controller has produced more desirable performance compared to standard PID and adaptive (poleplacement). The adaptive (deadbeat) PID controller attained very fast settling time (5 seconds) and very small percentage of overshoot (5% to 7.5%) for 10° to 30° step change of travel angle.

Keywords: Adaptive control, bench-top helicopter, deadbeat, pole-placement, self-tuning control.

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Authors: Sallama Athab, Hala Bahjat, Zhang Yinghui

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Background detection is essential in video analyses; optimization is often needed in order to achieve real time calculation. Information gathered by dual cameras placed in the front and rear part of an Autonomous Vehicle (AV) is integrated for background detection. In this paper, real time calculation is achieved on the proposed technique by using Priority Regions (PR) and Parallel Processing together where each frame is divided into regions then and each region process is processed in parallel. PR division depends upon driver view limitations. A background detection system is built on the Temporal Difference (TD) and Gaussian Filtering (GF). Temporal Difference and Gaussian Filtering with multi threshold and sigma (weight) value are be based on PR characteristics. The experiment result is prepared on real scene. Comparison of the speed and accuracy with traditional background detection techniques, the effectiveness of PR and parallel processing are also discussed in this paper.

Keywords: Autonomous Vehicle, Background Detection, Dual Camera, Gaussian Filtering, Parallel Processing.

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Authors: Setareh Shekarsaraei, Marjan Moridi, Nasser L. Hadipour

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In this study, nuclear magnetic resonance spectroscopy and nuclear quadrupole resonance spectroscopy parameters of 14N (Nitrogen in imidazole ring) in N–H…O hydrogen bonding for Histidine hydrochloride monohydrate were calculated via density functional theory. We considered a five-molecule model system of Histidine hydrochloride monohydrate. Also we examined the trends of environmental effect on hydrogen bonds as well as cooperativity. The functional used in this research is M06-2X which is a good functional and the obtained results has shown good agreement with experimental data. This functional was applied to calculate the NMR and NQR parameters. Some correlations among NBO parameters, NMR and NQR parameters have been studied which have shown the existence of strong correlations among them. Furthermore, the geometry optimization has been performed using M062X/6-31++G(d,p) method. In addition, in order to study cooperativity and changes in structural parameters, along with increase in cluster size, natural bond orbitals have been employed.

Keywords: Hydrogen bonding, Density Functional Theory (DFT), Natural bond Orbitals (NBO), cooperativity effects.

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Authors: Mohamed K. Metwally, Sherif H. El-Gohary, Kyung Min Byun, Seung Moo Han, Soo Yeol Lee, Min Hyoung Cho, Gon Khang, Jinsung Cho, Tae-Seong Kim

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Photoacoustic imaging (PAI) is a non-invasive and non-ionizing imaging modality that combines the absorption contrast of light with ultrasound resolution. Laser is used to deposit optical energy into a target (i.e., optical fluence). Consequently, the target temperature rises, and then thermal expansion occurs that leads to generating a PA signal. In general, most image reconstruction algorithms for PAI assume uniform fluence within an imaging object. However, it is known that optical fluence distribution within the object is non-uniform. This could affect the reconstruction of PA images. In this study, we have investigated the influence of optical fluence distribution on PA back-propagation imaging using finite element method. The uniform fluence was simulated as a triangular waveform within the object of interest. The non-uniform fluence distribution was estimated by solving light propagation within a tissue model via Monte Carlo method. The results show that the PA signal in the case of non-uniform fluence is wider than the uniform case by 23%. The frequency spectrum of the PA signal due to the non-uniform fluence has missed some high frequency components in comparison to the uniform case. Consequently, the reconstructed image with the non-uniform fluence exhibits a strong smoothing effect.

Keywords: Finite Element Method, Fluence Distribution, Monte Carlo Method, Photoacoustic Imaging.

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Authors: Seongjun Kim, Sanghoon Shim, Jinwooung Kim, Jaehwan Jung, Sung-Ah Kim

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As the development of information and communication technology, the convergence of machine learning of the ICT area and design is attempted. In this way, it is possible to grasp the correlation between various design elements, which was difficult to grasp, and to reflect this in the design result. In architecture, there is an attempt to predict the performance, which is difficult to grasp in the past, by finding the correlation among multiple factors mainly through machine learning. In architectural design area, some attempts to predict the performance affected by various factors have been tried. With machine learning, it is possible to quickly predict performance. The aim of this study is to propose a model that predicts performance according to the block arrangement of apartment housing through machine learning and the design alternative which satisfies the performance such as the daylight hours in the most similar form to the alternative proposed by the designer. Through this study, a designer can proceed with the design considering various design alternatives and accurate performances quickly from the early design stage.

Keywords: Apartment housing, machine learning, multi-objective optimization, performance prediction.

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Authors: Asma Rabaoui, Zied Lachiri, Noureddine Ellouze

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Discrimination between different classes of environmental sounds is the goal of our work. The use of a sound recognition system can offer concrete potentialities for surveillance and security applications. The first paper contribution to this research field is represented by a thorough investigation of the applicability of state-of-the-art audio features in the domain of environmental sound recognition. Additionally, a set of novel features obtained by combining the basic parameters is introduced. The quality of the features investigated is evaluated by a HMM-based classifier to which a great interest was done. In fact, we propose to use a Multi-Style training system based on HMMs: one recognizer is trained on a database including different levels of background noises and is used as a universal recognizer for every environment. In order to enhance the system robustness by reducing the environmental variability, we explore different adaptation algorithms including Maximum Likelihood Linear Regression (MLLR), Maximum A Posteriori (MAP) and the MAP/MLLR algorithm that combines MAP and MLLR. Experimental evaluation shows that a rather good recognition rate can be reached, even under important noise degradation conditions when the system is fed by the convenient set of features.

Keywords: Sounds recognition, HMM classifier, Multi-style training, Environmental Adaptation, Feature combinations.

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Authors: C. A. Akaash Emmanuel Raj, V. R. Sanal Kumar

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In this paper using smart materials we have proposed a specially manufactured variable volume spin tub for loading clothes for negating the vibration to a certain extent for getting better operating performance. Additionally, we have recommended a variable spinning speed rotor for handling varieties of garments for an efficient washing, aiming for increasing the life span of both the garments and the machine. As a part of the conflicting dynamic constraints and demands of the customer friendly design optimization of a lucrative and cosmetic washing machine we have proposed a drier and a desalination system capable to supply desirable heat and a pleasing fragrance to the garments. We thus concluded that while incorporating variable volume and variable spinning speed tub integrated with a drier and desalination system, the washing machine could meet the varieties of domestic requirements of the customers cost-effectively.

Keywords: Customer friendly washing machine, drier design, quick cloth cleaning, variable tub volume washing machine, variable spinning speed washing machine.

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Authors: Sharif E. Guseynov, Jekaterina V. Aleksejeva, Janis S. Rimshans

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A filtering problem of almost incompressible liquid chemical compound in the porous inhomogeneous 3D domain is studied. In this work general approaches to the solution of twodimensional filtering problems in ananisotropic, inhomogeneous and multilayered medium are developed, and on the basis of the obtained results mathematical models are constructed (according to Ollendorff method) for studying the certain engineering and technical problem of filtering the almost incompressible liquid chemical compound in the porous inhomogeneous 3D domain. For some of the formulated mathematical problems with additional requirements for the structure of the porous inhomogeneous medium, namely, its isotropy, spatial periodicity of its permeability coefficient, solution algorithms are proposed. Continuation of the current work titled ”On one mathematical model for filtration of weakly compressible chemical compound in the porous heterogeneous 3D medium. Part II: Determination of the reference directions of anisotropy and permeabilities on these directions” will be prepared in the shortest terms by the authors.

Keywords: Porous media, filtering, permeability, elliptic PDE.

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Authors: Ajay Mandal, Achinta Bera

Abstract:

Nanoemulsions are a class of emulsions with a droplet size in the range of 50–500 nm and have attracted a great deal of attention in recent years because it is unique characteristics. The physicochemical properties of nanoemulsion suggests that it can be successfully used to recover the residual oil which is trapped in the fine pore of reservoir rock by capillary forces after primary and secondary recovery. Oil-in-water nanoemulsion which can be formed by high-energy emulsification techniques using specific surfactants can reduce oil-water interfacial tension (IFT) by 3-4 orders of magnitude. The present work is aimed on characterization of oil-inwater nanoemulsion in terms of its phase behavior, morphological studies; interfacial energy; ability to reduce the interfacial tension and understanding the mechanisms of mobilization and displacement of entrapped oil blobs by lowering interfacial tension both at the macroscopic and microscopic level. In order to investigate the efficiency of oil-water nanoemulsion in enhanced oil recovery (EOR), experiments were performed to characterize the emulsion in terms of their physicochemical properties and size distribution of the dispersed oil droplet in water phase. Synthetic mineral oil and a series of surfactants were used to prepare oil-in-water emulsions. Characterization of emulsion shows that it follows pseudo-plastic behaviour and drop size of dispersed oil phase follows lognormal distribution. Flooding experiments were also carried out in a sandpack system to evaluate the effectiveness of the nanoemulsion as displacing fluid for enhanced oil recovery. Substantial additional recoveries (more than 25% of original oil in place) over conventional water flooding were obtained in the present investigation.

Keywords: Nanoemulsion, Characterization, Enhanced Oil Recovery, Particle Size Distribution

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Authors: Isaac O. Asante, Yushi Jiang, Hailin Tao

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Livestreaming marketing, the new electronic commerce element, has become an optional marketing channel following the COVID-19 pandemic, and many sellers are leveraging the features presented by livestreaming to increase sales. This study was conducted to measure real-time observable interactions between consumers and sellers. Based on the affordance theory, this study conceptualized constructs representing the interactive features and examined how they drive consumers’ purchase willingness during livestreaming sessions using 1238 datasets from Amazon Live, following the manual observation of transaction records. Using structural equation modeling, the ordinary least square regression suggests that live viewers, new followers, live chats, and likes positively affect purchase willingness. The Sobel and Monte Carlo tests show that new followers, live chats, and likes significantly mediate the relationship between live viewers and purchase willingness. The study presents a way of measuring interactions in livestreaming commerce and proposes a way to manually gather data on consumer behaviors in livestreaming platforms when the application programming interface (API) of such platforms does not support data mining algorithms.

Keywords: Livestreaming marketing, live chats, live viewers, likes, new followers, purchase willingness.

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Authors: Ghazi Al Sukkar, Yazid Khattabi, Shifen Zhong

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Most of the recent wireless LANs, broadband access networks, and digital broadcasting use Orthogonal Frequency Division Multiplexing techniques. In addition, the increasing demand of Data and Internet makes fiber optics an important technology, as fiber optics has many characteristics that make it the best solution for transferring huge frames of Data from a point to another. Radio over fiber is the place where high quality RF is converted to optical signals over single mode fiber. Optimum values for the bias level and the switching voltage for Mach-Zehnder modulator are important for the performance of radio over fiber links. In this paper, we propose a method to optimize the two parameters simultaneously; the bias and the switching voltage point of the external modulator of a radio over fiber system considering RF gain. Simulation results show the optimum gain value under these two parameters.

Keywords: OFDM, Mach Zehnder Bias Voltage, switching voltage, radio-over-fiber, RF gain.

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Authors: Sanjoy Mukherjee

Abstract:

Discussions on concepts of Single Axis Tracker (SAT) are becoming more and more apt for developing countries like India not just as an advancement in racking technology but due to the utmost necessity of reaching at the lowest Levelized Cost of Energy (LCOE) targets. With this increasing competition and significant fall in feed-in tariffs of solar PV projects, developers are under constant pressure to secure investment for their projects and eventually earn profits from them. Moreover, being the second largest populated country, India suffers from scarcity of land because of higher average population density. So, to mitigate the risk of this dual edged sword with reducing trend of unit (kWh) cost at one side and utilization of land on the other, tracking evolved as the call of the hour. Therefore, the prime objectives of this paper are not only to showcase how STT proves to be an effective mechanism to get more gain in Global Incidence in collector plane (G_inc) with respect to traditional mounting systems but also to introduce Seasonally Tilted Tracker (STT) technology as a possible option for high latitude locations.

Keywords: Tracking system, grid-connected PV systems, cost reduction.

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Authors: M. M. Pawade, S. S. Banwait

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Electrical Discharge Machine (EDM) is especially used for the manufacturing of 3-D complex geometry and hard material parts that are extremely difficult-to-machine by conventional machining processes. In this paper authors review the research work carried out in the development of die-sinking EDM within the past decades for the improvement of machining characteristics such as Material Removal Rate, Surface Roughness and Tool Wear Ratio. In this review various techniques reported by EDM researchers for improving the machining characteristics have been categorized as process parameters optimization, multi spark technique, powder mixed EDM, servo control system and pulse discriminating. At the end, flexible machine controller is suggested for Die Sinking EDM to enhance the machining characteristics and to achieve high-level automation. Thus, die sinking EDM can be integrated with Computer Integrated Manufacturing environment as a need of agile manufacturing systems.

Keywords: Electrical Discharge Machine, Flexible Machine Controller, Material Removal Rate, Tool Wear Ratio.

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Authors: Mohamed A. Mahfouz, M. A. Ismail

Abstract:

Biclustering is a very useful data mining technique for identifying patterns where different genes are co-related based on a subset of conditions in gene expression analysis. Association rules mining is an efficient approach to achieve biclustering as in BIMODULE algorithm but it is sensitive to the value given to its input parameters and the discretization procedure used in the preprocessing step, also when noise is present, classical association rules miners discover multiple small fragments of the true bicluster, but miss the true bicluster itself. This paper formally presents a generalized noise tolerant bicluster model, termed as μBicluster. An iterative algorithm termed as BIDENS based on the proposed model is introduced that can discover a set of k possibly overlapping biclusters simultaneously. Our model uses a more flexible method to partition the dimensions to preserve meaningful and significant biclusters. The proposed algorithm allows discovering biclusters that hard to be discovered by BIMODULE. Experimental study on yeast, human gene expression data and several artificial datasets shows that our algorithm offers substantial improvements over several previously proposed biclustering algorithms.

Keywords: Machine learning, biclustering, bi-dimensional clustering, gene expression analysis, data mining.

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Authors: T. Aydin, M. F. Alaeddinoglu

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People, throughout the history, have made estimates and inferences about the future by using their past experiences. Developing information technologies and the improvements in the database management systems make it possible to extract useful information from knowledge in hand for the strategic decisions. Therefore, different methods have been developed. Data mining by association rules learning is one of such methods. Apriori algorithm, one of the well-known association rules learning algorithms, is not commonly used in spatio-temporal data sets. However, it is possible to embed time and space features into the data sets and make Apriori algorithm a suitable data mining technique for learning spatiotemporal association rules. Lake Van, the largest lake of Turkey, is a closed basin. This feature causes the volume of the lake to increase or decrease as a result of change in water amount it holds. In this study, evaporation, humidity, lake altitude, amount of rainfall and temperature parameters recorded in Lake Van region throughout the years are used by the Apriori algorithm and a spatio-temporal data mining application is developed to identify overflows and newlyformed soil regions (underflows) occurring in the coastal parts of Lake Van. Identifying possible reasons of overflows and underflows may be used to alert the experts to take precautions and make the necessary investments.

Keywords: Apriori algorithm, association rules, data mining, spatio-temporal data.

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Authors: Gülfem I. Alptekin, S. Emre Alptekin

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The next generation wireless systems, especially the cognitive radio networks aim at utilizing network resources more efficiently. They share a wide range of available spectrum in an opportunistic manner. In this paper, we propose a quality management model for short-term sub-lease of unutilized spectrum bands to different service providers. We built our model on competitive secondary market architecture. To establish the necessary conditions for convergent behavior, we utilize techniques from game theory. Our proposed model is based on potential game approach that is suitable for systems with dynamic decision making. The Nash equilibrium point tells the spectrum holders the ideal price values where profit is maximized at the highest level of customer satisfaction. Our numerical results show that the price decisions of the network providers depend on the price and QoS of their own bands as well as the prices and QoS levels of their opponents- bands.

Keywords: cognitive radio networks, game theory, nextgeneration wireless networks, spectrum management.

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Authors: Tunjo Peric, Zoran Babic

Abstract:

The authors of this work indicate by means of a concrete example that it is possible to apply efficaciously the method of multiple criteria programming in dealing with the problem of determining the optimal production plan for a certain period of time. The work presents: (1) the selection of optimization criteria, (2) the setting of the problem of determining an optimal production plan, (3) the setting of the model of multiple criteria programming in finding a solution to a given problem, (4) the revised surrogate trade-off method, (5) generalized multicriteria model for solving production planning problem and problem of choosing technological variants in the metal manufacturing industry. In the final part of this work the authors reflect on the application of the method of multiple criteria programming while determining the optimal production plan in manufacturing enterprises.

Keywords: multi-criteria programming, production planning, technological variant, Surrogate Worth Trade-off Method.

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Authors: F.C. Amadi, G. C. Enyi, G. G. Nasr

Abstract:

Hydraulic fracturing is one of the most important stimulation techniques available to the petroleum engineer to extract hydrocarbons in tight gas sandstones. It allows more oil and gas production in tight reservoirs as compared to conventional means. The main aim of the study is to optimize the hydraulic fracturing as technique and for this purpose three multi-zones layer formation is considered and fractured contemporaneously. The three zones are named as Zone1 (upper zone), Zone2 (middle zone) and Zone3 (lower zone) respectively and they all occur in shale rock. Simulation was performed with Mfrac integrated software which gives a variety of 3D fracture options. This simulation process yielded an average fracture efficiency of 93.8%for the three respective zones and an increase of the average permeability of the rock system. An average fracture length of 909 ft with net height (propped height) of 210 ft (average) was achieved. Optimum fracturing results was also achieved with maximum fracture width of 0.379 inches at an injection rate of 13.01 bpm with 17995 Mscf of gas production.

Keywords: Hydraulic fracturing, Mfrac, Optimisation, Tight reservoir.

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Authors: Jyoti Chaturvedi Gursaran

Abstract:

Nature conducts its action in a very private manner. To reveal these actions classical science has done a great effort. But classical science can experiment only with the things that can be seen with eyes. Beyond the scope of classical science quantum science works very well. It is based on some postulates like qubit, superposition of two states, entanglement, measurement and evolution of states that are briefly described in the present paper. One of the applications of quantum computing i.e. implementation of a novel quantum evolutionary algorithm(QEA) to automate the time tabling problem of Dayalbagh Educational Institute (Deemed University) is also presented in this paper. Making a good timetable is a scheduling problem. It is NP-hard, multi-constrained, complex and a combinatorial optimization problem. The solution of this problem cannot be obtained in polynomial time. The QEA uses genetic operators on the Q-bit as well as updating operator of quantum gate which is introduced as a variation operator to converge toward better solutions.

Keywords: Quantum computing, qubit, superposition, entanglement, measurement of states, evolution of states, Scheduling problem, hard and soft constraints, evolutionary algorithm, quantum evolutionary algorithm.

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Authors: Silvio R. R. Sanches, Bianca C. Barbosa, Beatriz R. Brum, Cléber G.Corrêa

Abstract:

In order to evaluate the quality of a segmentation algorithm, the researchers use subjective or objective metrics. Although subjective metrics are more accurate than objective ones, objective metrics do not require user feedback to test an algorithm. Objective metrics require subjective experiments only during their development. Subjective experiments typically display to users some videos (generated from frames with segmentation errors) that simulate the environment of an application domain. This user feedback is crucial information for metric definition. In the subjective experiments applied to develop some state-of-the-art metrics used to test segmentation algorithms, the videos displayed during the experiments did not contain audio. Audio is an essential component in applications such as videoconference and augmented reality. If the audio influences the user’s perception, using only videos without audio in subjective experiments can compromise the efficiency of an objective metric generated using data from these experiments. This work aims to identify if the audio influences the user’s perception of segmentation quality in background substitution applications with audio. The proposed approach used a subjective method based on formal video quality assessment methods. The results showed that audio influences the quality of segmentation perceived by a user.

Keywords: Background substitution, influence of audio, segmentation evaluation, segmentation quality.

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Authors: Ali Pour Yazdanpanah, Farideh Foroozandeh Shahraki, Emma Regentova

Abstract:

The reconstruction from sparse-view projections is one of important problems in computed tomography (CT) limited by the availability or feasibility of obtaining of a large number of projections. Traditionally, convex regularizers have been exploited to improve the reconstruction quality in sparse-view CT, and the convex constraint in those problems leads to an easy optimization process. However, convex regularizers often result in a biased approximation and inaccurate reconstruction in CT problems. Here, we present a nonconvex, Lipschitz continuous and non-smooth regularization model. The CT reconstruction is formulated as a nonconvex constrained L1 − L2 minimization problem and solved through a difference of convex algorithm and alternating direction of multiplier method which generates a better result than L0 or L1 regularizers in the CT reconstruction. We compare our method with previously reported high performance methods which use convex regularizers such as TV, wavelet, curvelet, and curvelet+TV (CTV) on the test phantom images. The results show that there are benefits in using the nonconvex regularizer in the sparse-view CT reconstruction.

Keywords: Computed tomography, sparse-view reconstruction, L1 −L2 minimization, non-convex, difference of convex functions.

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Authors: L. K. Davis

Abstract:

The effects of the evolution force are observable in nature at all structural levels ranging from small molecular systems to conversely enormous biospheric systems. However, the evolution force and work associated with formation of biological structures has yet to be described mathematically or theoretically. In addressing the conundrum, we consider evolution from a unique perspective and in doing so we introduce the “Fundamental Theory of the Evolution Force: FTEF”. We utilized synthetic evolution artificial intelligence (SYN-AI) to identify genomic building blocks and to engineer 14-3-3 ζ docking proteins by transforming gene sequences into time-based DNA codes derived from protein hierarchical structural levels. The aforementioned served as templates for random DNA hybridizations and genetic assembly. The application of hierarchical DNA codes allowed us to fast forward evolution, while dampening the effect of point mutations. Natural selection was performed at each hierarchical structural level and mutations screened using Blosum 80 mutation frequency-based algorithms. Notably, SYN-AI engineered a set of three architecturally conserved docking proteins that retained motion and vibrational dynamics of native Bos taurus 14-3-3 ζ.

Keywords: 14-3-3 docking genes, synthetic protein design, time based DNA codes, writing DNA code from scratch.

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Authors: Zakari Yaou, Lirong Cui

Abstract:

In a product development process, understanding the functional behavior of the system, the role of components in achieving functions and failure modes if components/subsystem fails its required function will help develop appropriate design validation and verification program for reliability assessment. The integration of these three issues will help design and reliability engineers in identifying weak spots in design and planning future actions and testing program. This case study demonstrate the advantage of unascertained theory described in the subjective cognition uncertainty, and then applies blind number (BN) theory in describing the uncertainty of the mechanical system failure process and the same time used the same theory in bringing out another mechanical reliability system model. The practical calculations shows the BN Model embodied the characters of simply, small account of calculation but betterforecasting capability, which had the value of macroscopic discussion to some extent.

Keywords: Mechanical structure Design, time-dependent stochastic process, unascertained information, blind number theory.

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Authors: Abdul Rauf, Muhammad Irfan, Muhammad Nadeem, Ishtiaq Ahmed, Hafiz Muhammad Nasir Iqbal

Abstract:

Rhizopus oligosporus was used in the present study for the production of protease enzyme under SSF. Sunflower meal was used as by-product of oil industry incorporated with organic salts was employed for the production of protease enzyme. The main purpose of the present was to study different parameters of protease productivity, its yields and to optimize basal fermentation conditions. The optimal conditions found for protease production using sunflower meal as a substrate in the present study were inoculum size (1%), substrate (Sunflower meal), substrate concentration (20 g), pH (3), cultivation period (72 h), incubation temperature (35oC), substrate to diluent-s ratio (1:2) and tween 81 (1 mL). The maximum production of protease in the presence of cheaper substrate at low concentration and stability at acidic pH, these characteristics make the strain and its enzymes useful in different industry.

Keywords: Acidic protease, Rhizopus oligosporus, Mediaoptimization, Solid state Fermentation

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Authors: M. Tahani, G. Karimi

Abstract:

In this paper after reviewing some previous studies, in order to optimize the above knee prosthesis, beside the inertial properties a new controlling parameter is informed. This controlling parameter makes the prosthesis able to act as a multi behavior system when the amputee is opposing to different environments. This active prosthesis with the new controlling parameter can simplify the control of prosthesis and reduce the rate of energy consumption in comparison to recently presented similar prosthesis “Agonistantagonist active knee prosthesis". In this paper three models are generated, a passive, an active, and an optimized active prosthesis. Second order Taylor series is the numerical method in solution of the models equations and the optimization procedure is genetic algorithm. Modeling the prosthesis which comprises this new controlling parameter (SEP) during the swing phase represents acceptable results in comparison to natural behavior of shank. Reported results in this paper represent 3.3 degrees as the maximum deviation of models shank angle from the natural pattern. The natural gait pattern belongs to walking at the speed of 81 m/min.

Keywords: Above knee prosthesis, active controlling parameter, ballistic motion, swing phase.

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Authors: Ahmed K. Hasan, A. A. Zaidan, Anas Majeed, B. B. Zaidan, Rosli Salleh, Omar Zakaria, Ali Zuheir

Abstract:

Wireless mesh networks based on IEEE 802.11 technology are a scalable and efficient solution for next generation wireless networking to provide wide-area wideband internet access to a significant number of users. The deployment of these wireless mesh networks may be within different authorities and without any planning, they are potentially overlapped partially or completely in the same service area. The aim of the proposed model is design a new model to Enhancement Throughput of Unplanned Wireless Mesh Networks Deployment Using Partitioning Hierarchical Cluster (PHC), the unplanned deployment of WMNs are determinates there performance. We use throughput optimization approach to model the unplanned WMNs deployment problem based on partitioning hierarchical cluster (PHC) based architecture, in this paper the researcher used bridge node by allowing interworking traffic between these WMNs as solution for performance degradation.

Keywords: Wireless Mesh Networks, 802.11s Internetworking, partitioning Hierarchical Cluste.

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Authors: R. Talebi, A. Abdel-Dayem, J. Johnson

Abstract:

Three-dimensional reconstruction of small objects has been one of the most challenging problems over the last decade. Computer graphics researchers and photography professionals have been working on improving 3D reconstruction algorithms to fit the high demands of various real life applications. Medical sciences, animation industry, virtual reality, pattern recognition, tourism industry, and reverse engineering are common fields where 3D reconstruction of objects plays a vital role. Both lack of accuracy and high computational cost are the major challenges facing successful 3D reconstruction. Fringe projection has emerged as a promising 3D reconstruction direction that combines low computational cost to both high precision and high resolution. It employs digital projection, structured light systems and phase analysis on fringed pictures. Research studies have shown that the system has acceptable performance, and moreover it is insensitive to ambient light. This paper presents an overview of fringe projection approaches. It also presents an experimental study and implementation of a simple fringe projection system. We tested our system using two objects with different materials and levels of details. Experimental results have shown that, while our system is simple, it produces acceptable results.

Keywords: Digital fringe projection, 3D reconstruction, phase unwrapping, phase shifting.

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Authors: Nitin Singh, Meng Ling, Talha Ahmed, Tianxia Zhao, Reinier van de Pol

Abstract:

We propose the use of Reinforcement Learning (RL) as a viable alternative for optimizing supply chain management, particularly in scenarios with stochasticity in product demands. RL’s adaptability to changing conditions and its demonstrated success in diverse fields of sequential decision-making make it a promising candidate for addressing supply chain problems. We investigate the impact of demand fluctuations in a multi-product supply chain system and develop RL agents with learned generalizable policies. We provide experimentation details for training RL agents and a statistical analysis of the results. We study generalization ability of RL agents for different demand uncertainty scenarios and observe superior performance compared to the agents trained with fixed demand curves. The proposed methodology has the potential to lead to cost reduction and increased profit for companies dealing with frequent inventory movement between supply and demand nodes.

Keywords: Inventory Management, Reinforcement Learning, Supply Chain Optimization, Uncertainty.

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