Search results for: multi-objective genetic algorithm

Authors: Abdolsalam Ghaderi

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In this paper, the single-depot capacitated location-routing problem under uncertainty is presented. The problem aims to find the optimal location of a single depot and the routing of vehicles to serve the customers when the parameters may change under different circumstances. This problem has many applications, especially in the area of supply chain management and distribution systems. To get closer to real-world situations, travel time of vehicles, the fixed cost of vehicles usage and customers’ demand are considered as a source of uncertainty. A combined approach including robust optimization and stochastic programming was presented to deal with the uncertainty in the problem at hand. For this purpose, a mixed integer programming model is developed and a heuristic algorithm based on Variable Neighborhood Search(VNS) is presented to solve the model. Finally, the computational results are presented and future research directions are discussed.

Keywords: location-routing problem, robust optimization, stochastic programming, variable neighborhood search

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Authors: Mayada Mazher, Ahmed Moustafa, Ahmed Abdellatif

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Background: Autophagy is a eukaryotic, highly conserved catabolic process implicated in many pathophysiologies such as wound healing. Autophagy-associated genes serve as a scaffolding platform for signal transduction of macrophage polarization during the inflammatory phase of wound healing and tissue repair process. In the current study, we report a model for the interplay between autophagy-associated genes and macrophages polarization associated genes. Methods: In silico analysis was performed on 249 autophagy-related genes retrieved from the public autophagy database and gene expression data retrieved from Gene Expression Omnibus (GEO); GSE81922 and GSE69607 microarray data macrophages polarization 199 DEGS. An integrated protein-protein interaction network was constructed for autophagy and macrophage gene sets. The gene sets were then used for GO terms pathway enrichment analysis. Common transcription factors for autophagy and macrophages' polarization were identified. Finally, microRNAs enriched in both autophagy and macrophages were predicated. Results: In silico prediction of common transcription factors in DEGs macrophages and autophagy gene sets revealed a new role for the transcription factors, HOMEZ, GABPA, ELK1 and REL, that commonly regulate macrophages associated genes: IL6,IL1M, IL1B, NOS1, SOC3 and autophagy-related genes: Atg12, Rictor, Rb1cc1, Gaparab1, Atg16l1. Conclusions: Autophagy and macrophages' polarization are interdependent cellular processes, and both autophagy-related proteins and macrophages' polarization related proteins coordinate in tissue remodelling via transcription factors and microRNAs regulatory network. The current work highlights a potential new role for transcription factors HOMEZ, GABPA, ELK1 and REL in wound healing.

Keywords: autophagy related proteins, integrated network analysis, macrophages polarization M1 and M2, tissue remodelling

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Authors: C. N. Vanitha, M. Usha

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In a real world scenario, the viability of the sensor networks has been proved by standardizing the technologies. Wireless sensor networks are vulnerable to both electronic and physical security breaches because of their deployment in remote, distributed, and inaccessible locations. The compromised sensor nodes send malicious data to the base station, and thus, the total network effectiveness will possibly be compromised. To detect and seclude the Route modifiers, a neural network based Pattern Learning predictor (PLP) is presented. This algorithm senses data at any node on present and previous patterns obtained from the en-route nodes. The eminence of any node is upgraded by their predicted and reported patterns. This paper propounds a solution not only to detect the route modifiers, but also to seclude the malevolent nodes from the network. The simulation result proves the effective performance of the network by the presented methodology in terms of energy level, routing and various network conditions.

Keywords: neural networks, pattern learning, security, wireless sensor networks

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Authors: Nazira B. Bekenova, Lydia A. Mukovozova, Andrej M. Grjibovski, Alma Z. Tokayeva, Yerbol M. Smail, Nurlan E. Aukenov

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Erysipelas is an infectious disease with socio-economic significance and prone to prolonged recurrent course (30%). Contribution of genetic factors, in particular the gene polymorphism of cytokines, can be essential in disease etiology and pathogenesis. Interleukin – 17 A are produced by T helpers of 17 type and plays a key role in development of local inflammation process. Local inflammatory process is a dominant in the clinic of erysipelas. Established that the skin and mucosas are primary areas of migration (homing) Th17-cell and their cytokines are stimulate the barrier function of the epithelium. We studied associations between gene polymorphism of IL-17A (C737T) rs 8193036 and IL-17A level in patients with erysipelas Kazakh population. Altogether, 90 cases with erysipelas and 90 healthy controls from an ethnic Kazakh population comprised the sample. Cases were identified at Clinical Infectious Diseases Hospital of Semey (Kazakhstan). The IL-17A (rs8193036) polymorphism was analyzed by a real time polymerase chain reaction. Plasma levels of IL-17 A were assessed by immuneenzyme analysis method using ‘Vector-Best’ test-system (Russia). Differences in levels of IL-17 A between CC, TT, CT groups were studied using Kruskal — Wallis test. Pairwise comparisons were performed using Mann-Whitney tests with Bonferroni correction (New significance level was set to 0.025). We found, that in patients with erysipelas with CC genotype the level of IL-17 A was higher (p= 0, 010) compared to the carriers of CT genotype. When compared the level of IL – 17 A between the patients with TT genotype and patients with CC genotype, also between the patients with CT genotype and patients with TT genotype statistically significant differences are not revealed (p = 0.374 and p = 0.043, respectively). Comparisons of IL-17 A plasma levels between the CC and CT genotypes, between the CC and TT genotypes, and between the TT and CT in healthy patients did not reveal significant differences (p = 0, 291). Therefore, we are determined the associations of gene polymorphism of IL-17 A (C737T) with its level in patients erysipelas carriers CC genotype.

Keywords: erysipelas, interleukin – 17 A, Kazakh, polymorphism

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Authors: J. Al-Dmour, A. Sagahyroon, A. Al-Ali, S. Abusnana

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Radio Frequency Based Identification Systems have emerged as one of the possible valuable solutions that can be utilized in healthcare systems. Nowadays, RFID tags are available with built-in human vital signs sensors such as Body Temperature, Blood Pressure, Heart Rate, Blood Sugar level and Oxygen Saturation in Blood. This work proposes the design, implementation, and testing of an integrated mobile RFID-based health care system. The system consists of a wireless mobile vital signs data acquisition unit (RFID-DAQ) integrated with a fuzzy-logic–based software algorithm to monitor and assess patients conditions. The system is implemented and tested in ‘Rashid Center for Diabetes and Research’, Ajman, UAE. System testing results are compared with the Modified Early Warning System (MEWS) that is currently used in practice. We demonstrate that the proposed and implemented system exhibits an accuracy level that is comparable and sometimes better than the widely adopted MEWS system.

Keywords: healthcare, fuzzy logic, MEWS, RFID

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Authors: Rahmat Purwoko, Bambang Riyanto Trilaksono

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This paper gives comparison of INS/GNSS architecture namely Loosely Coupled and Tightly Coupled using Hardware in the Loop Simulation in Guided Missile RKX-200 rocket model. INS/GNSS Tightly Coupled architecture requires pseudo-range, pseudo-range rate, and position and velocity of each satellite in constellation from GPS (Global Positioning System) measurement. The Loosely Coupled architecture use estimated position and velocity from GNSS receiver. INS/GNSS architecture also requires angular rate and specific force measurement from IMU (Inertial Measurement Unit). Loosely Coupled arhitecture designed using 15 states Kalman Filter and Tightly Coupled designed using 17 states Kalman Filter. Integration algorithm calculation using ECEF frame. Navigation System implemented Zedboard All Programmable SoC.

Keywords: kalman filter, loosely coupled, navigation system, tightly coupled

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Authors: Wafa Al Jabri

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Sickle cell disease (SCD) is a genetic blood disorder that is characterized by a severe painful crisis. SCD among children requires long term dependencies and high caregiving demands that increase the overall family burdens. It is, therefore, essential to examine, support, and promote the well-being of families of children with SCD. Although there has been considerable progress in the international research on family quality of life (FQOL) in recent years; however, research in this field is relatively recent and diverse. Oman is a country in which family quality of life has definitely been under-researched. Therefore, the purpose of the study is to describe the FQOL in families of children with SCD in Oman. The study will also examine the relationships between child, mother, and family-related factors that may influence the overall FQOL. Theoretical Framework: The study is guided by the unified theory of family quality of life to help in understanding the concept of FQOL and the factors that shape it. Method:A convenience sample of 98 mothers of children with SCD will be recruited from the pediatric hematology clinic at Sultan Qaboos University Hospital in Oman to participate in this descriptive, cross sectional, correlational study. Data will be obtained using a self-administered questionnaire that includes child and mother socio-demographic data, questions about the number of visits and admissions to health care facilities for vaso- occlusive crises (VOCs), the Perceived Stress Scale-10, and the Beachcenter-FQOL scale. Anticipated Results: It is expected to find an association among frequency of VOCs, mother’s perceived stress level, and FQOL in families of children with SCD in Oman. Family type, socio-economic status, and number of SCD children in the family are also expected to influence the overall FQOL. Conclusion: The findings of the study might be pivotal in designing and implementing tailored family-based interventions to improve families’ wellbeing.

Keywords: family quality of life, sickle cell disaes, children, family well-being

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Authors: Xiuying Wang, Changliang Li, Bo Xu

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A fixed-length feature vector is required for many machine learning algorithms in NLP field. Word embeddings have been very successful at learning lexical information. However, they cannot capture the compositional meaning of sentences, which prevents them from a deeper understanding of language. In this paper, we introduce a novel hierarchical tree long short-term memory (HTLSTM) model that learns vector representations for sentences of arbitrary syntactic type and length. We propose to split one sentence into three hierarchies: short phrase, long phrase and full sentence level. The HTLSTM model gives our algorithm the potential to fully consider the hierarchical information and long-term dependencies of language. We design the experiments on both English and Chinese corpus to evaluate our model on sentiment analysis task. And the results show that our model outperforms several existing state of the art approaches significantly.

Keywords: deep learning, hierarchical tree long short-term memory, sentence representation, sentiment analysis

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Authors: Shih-Wen Hsiao, Chu-Hsuan Lee, Rong-Qi Chen

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When it comes to last, it is regarded as the critical foundation of shoe design and development. Not only the last relates to the comfort of shoes wearing but also it aids the production of shoe styling and manufacturing. In order to enhance the efficiency and application of last development, a computer aided methodology for customized last form designs is proposed in this study. The reverse engineering is mainly applied to the process of scanning for the last form. Then the minimum energy is used for the revision of surface continuity, the surface of the last is reconstructed with the feature curves of the scanned last. When the surface of a last is reconstructed, based on the foundation of the proposed last form reconstruction module, the weighted arithmetic mean method is applied to the calculation on the shape morphing which differs from the grading for the control mesh of last, and the algorithm of subdivision is used to create the surface of last mesh, thus the feet-fitting 3D last form of different sizes is generated from its original form feature with functions remained. Finally, the practicability of the proposed methodology is verified through later case studies.

Keywords: 3D last design, customization, reverse engineering, weighted morphing, shape blending

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Authors: Misrak Assefa

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The Amharic language is the second most widely spoken Semitic language in the world. There are several new overloaded on the web. Searching some useful documents from the web on a specific topic, which is written in the Amharic language, is a challenging task. Hence, document categorization is required for managing and filtering important information. In the classification of Amharic text news, there is still a gap in the domain of information that needs to be launch. This study attempts to design an automatic Amharic news classification using a supervised learning mechanism on four un-touch classes. To achieve this research, 4,182 news articles were used. Naive Bayes (NB) and Decision tree (j48) algorithms were used to classify the given Amharic dataset. In this paper, k-fold cross-validation is used to estimate the accuracy of the classifier. As a result, it shows those algorithms can be applicable in Amharic news categorization. The best average accuracy result is achieved by j48 decision tree and naïve Bayes is 95.2345 %, and 94.6245 % respectively using three categories. This research indicated that a typical decision tree algorithm is more applicable to Amharic news categorization.

Keywords: text categorization, supervised machine learning, naive Bayes, decision tree

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Authors: Antonio Boggia, Luisa Paolotti, Gianluca Massei, Lucia Rocchi, Elaine Pace, Maria Attard

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The aim of this paper is to present a methodology and a computer model for sustainability assessment based on the integration of Multi-criteria Decision Analysis (MCDA) with a Geographic Information System (GIS). It presents the result of a study for the implementation of a model for measuring sustainability to address the policy actions for the improvement of sustainability at territory level. The aim is to rank areas in order to understand the specific technical and/or financial support that is required to develop sustainable growth. Assessing sustainable development is a multidimensional problem: economic, social and environmental aspects have to be taken into account at the same time. The tool for a multidimensional representation is a proper set of indicators. The set of indicators must be integrated into a model, that is an assessment methodology, to be used for measuring sustainability. The model, developed by the Environmental Laboratory of the University of Perugia, is called GeoUmbriaSUIT. It is a calculation procedure developed as a plugin working in the open-source GIS software QuantumGIS. The multi-criteria method used within GeoUmbriaSUIT is the algorithm TOPSIS (Technique for Order Preference by Similarity to Ideal Design), which defines a ranking based on the distance from the worst point and the closeness to an ideal point, for each of the criteria used. For the sustainability assessment procedure, GeoUmbriaSUIT uses a geographic vector file where the graphic data represent the study area and the single evaluation units within it (the alternatives, e.g. the regions of a country, or the municipalities of a region), while the alphanumeric data (attribute table), describe the environmental, economic and social aspects related to the evaluation units by means of a set of indicators (criteria). The use of the algorithm available in the plugin allows to treat individually the indicators representing the three dimensions of sustainability, and to compute three different indices: environmental index, economic index and social index. The graphic output of the model allows for an integrated assessment of the three dimensions, avoiding aggregation. The presence of separate indices and graphic output make GeoUmbriaSUIT a readable and transparent tool, since it doesn’t produce an aggregate index of sustainability as final result of the calculations, which is often cryptic and difficult to interpret. In addition, it is possible to develop a “back analysis”, able to explain the positions obtained by the alternatives in the ranking, based on the criteria used. The case study presented is an assessment of the level of sustainability in the six regions of Malta, an island state in the middle of the Mediterranean Sea and the southernmost member of the European Union. The results show that the integration of MCDA-GIS is an adequate approach for sustainability assessment. In particular, the implemented model is able to provide easy to understand results. This is a very important condition for a sound decision support tool, since most of the time decision makers are not experts and need understandable output. In addition, the evaluation path is traceable and transparent.

Keywords: GIS, multi-criteria analysis, sustainability assessment, sustainable development

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Authors: Weaam Aldeeban, Majd Aljamali, Lama A. Youssef

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Background & Objective: Warfarin is considered a problematic drug due to its narrow therapeutic window and wide inter-individual response variations, which are attributed to demographic, environmental, and genetic factors, particularly single nucleotide polymorphism (SNPs) in the genes encoding VKORC1 and CYP2C9 involved in warfarin's mechanism of action and metabolism, respectively. CYP2C9*2rs1799853 and CYP2C9*3rs1057910 alleles are linked to reduced enzyme activity, as carriers of either or both alleles are classified as moderate or slow metabolizers, and therefore exhibit higher sensitivity of warfarin compared with wild type (CYP2C9*1*1). Our study aimed to assess the frequency of *1, *2, and *3 alleles in the CYP2C9 gene in a cohort of Syrian patients receiving a maintenance dose of warfarin for different indications, the impact of genotypes on warfarin dosing, and the frequency of adverse effects (i.e., bleedings). Subjects & Methods: This retrospective cohort study encompassed 94 patients treated with warfarin. Patients’ genotypes were identified by sequencing the polymerase chain reaction (PCR) specific products of the gene encoding CYP2C9, and the effects on warfarin therapeutic outcomes were investigated. Results: Sequencing revealed that 43.6% of the study population has the *2 and/or *3 SNPs. The mean weekly maintenance dose of warfarin was 37.42 ± 15.5 mg for patients with the wild-type allele (CYP2C9*1*1), whereas patients with one or both variants (*2 and/or *3) demanded a significantly lower dose (28.59 ±11.58 mg) of warfarin, (P= 0.015). A higher percentage (40.7%) of patients with allele *2 and/or *3 experienced hemorrhagic accidents compared with only 17.9% of patients with the wild type *1*1, (P = 0.04). Conclusions: Our study proves an association between *2 and *3 genotypes and higher sensitivity to warfarin and a tendency to bleed, which necessitates lowering the dose. These findings emphasize the significance of CYP2C9 genotyping prior to commencing warfarin therapy in order to achieve optimal and faster dose control and to ensure effectiveness and safety.

Keywords: warfarin, CYP2C9, polymorphisms, Syrian, hemorrhage

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Authors: Dan Kanmegne

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Africa will become the most populated continent by the end of the century, with around 4 billion inhabitants. Food security and climate changes will become continental issues since agricultural practices depend on climate but also contribute to global emissions and land degradation. Agroforestry has been identified as a cost-efficient and reliable strategy to address these two issues. It is defined as the integrated management of trees and crops/animals in the same land unit. Agroforestry provides benefits in terms of goods (fruits, medicine, wood, etc.) and services (windbreaks, fertility, etc.), and is acknowledged to have a great potential for carbon sequestration; therefore it can be integrated into reduction mechanisms of carbon emissions. Particularly in sub-Saharan Africa, the constraint stands in the lack of information about both areas under agroforestry and the characterization (composition, structure, and management) of each agroforestry system at the country level. This study describes and quantifies “what is where?”, earliest to the quantification of carbon stock in different systems. Remote sensing (RS) is the most efficient approach to map such a dynamic technology as agroforestry since it gives relatively adequate and consistent information over a large area at nearly no cost. RS data fulfill the good practice guidelines of the Intergovernmental Panel On Climate Change (IPCC) that is to be used in carbon estimation. Satellite data are getting more and more accessible, and the archives are growing exponentially. To retrieve useful information to support decision-making out of this large amount of data, satellite data needs to be organized so to ensure fast processing, quick accessibility, and ease of use. A new solution is a data cube, which can be understood as a multi-dimensional stack (space, time, data type) of spatially aligned pixels and used for efficient access and analysis. A data cube for Burkina Faso has been set up from the cooperation project between the international service provider WASCAL and Germany, which provides an accessible exploitation architecture of multi-temporal satellite data. The aim of this study is to map and characterize agroforestry systems using the Burkina Faso earth observation data cube. The approach in its initial stage is based on an unsupervised image classification of a normalized difference vegetation index (NDVI) time series from 2010 to 2018, to stratify the country based on the vegetation. Fifteen strata were identified, and four samples per location were randomly assigned to define the sampling units. For safety reasons, the northern part will not be part of the fieldwork. A total of 52 locations will be visited by the end of the dry season in February-March 2020. The field campaigns will consist of identifying and describing different agroforestry systems and qualitative interviews. A multi-temporal supervised image classification will be done with a random forest algorithm, and the field data will be used for both training the algorithm and accuracy assessment. The expected outputs are (i) map(s) of agroforestry dynamics, (ii) characteristics of different systems (main species, management, area, etc.); (iii) assessment report of Burkina Faso data cube.

Keywords: agroforestry systems, Burkina Faso, earth observation data cube, multi-temporal image classification

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Authors: Mayank Gupta, Siba Prasad Samal, Vasu Kakkirala

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The internet world and its priorities have changed considerably in the last decade. Browsing on smart phones has increased manifold and is set to explode much more. Users spent considerable time browsing different websites, that gives a great deal of insight into user’s preferences. Instead of plain information classifying different aspects of browsing like Bookmarks, History, and Download Manager into useful categories would improve and enhance the user’s experience. Most of the classification solutions are server side that involves maintaining server and other heavy resources. It has security constraints and maybe misses on contextual data during classification. On device, classification solves many such problems, but the challenge is to achieve accuracy on classification with resource constraints. This on device classification can be much more useful in personalization, reducing dependency on cloud connectivity and better privacy/security. This approach provides more relevant results as compared to current standalone solutions because it uses content rendered by browser which is customized by the content provider based on user’s profile. This paper proposes a Naive Bayes based lightweight classification engine targeted for a resource constraint devices. Our solution integrates with Web Browser that in turn triggers classification algorithm. Whenever a user browses a webpage, this solution extracts DOM Tree data from the browser’s rendering engine. This DOM data is a dynamic, contextual and secure data that can’t be replicated. This proposal extracts different features of the webpage that runs on an algorithm to classify into multiple categories. Naive Bayes based engine is chosen in this solution for its inherent advantages in using limited resources compared to other classification algorithms like Support Vector Machine, Neural Networks, etc. Naive Bayes classification requires small memory footprint and less computation suitable for smartphone environment. This solution has a feature to partition the model into multiple chunks that in turn will facilitate less usage of memory instead of loading a complete model. Classification of the webpages done through integrated engine is faster, more relevant and energy efficient than other standalone on device solution. This classification engine has been tested on Samsung Z3 Tizen hardware. The Engine is integrated into Tizen Browser that uses Chromium Rendering Engine. For this solution, extensive dataset is sourced from dmoztools.net and cleaned. This cleaned dataset has 227.5K webpages which are divided into 8 generic categories ('education', 'games', 'health', 'entertainment', 'news', 'shopping', 'sports', 'travel'). Our browser integrated solution has resulted in 15% less memory usage (due to partition method) and 24% less power consumption in comparison with standalone solution. This solution considered 70% of the dataset for training the data model and the rest 30% dataset for testing. An average accuracy of ~96.3% is achieved across the above mentioned 8 categories. This engine can be further extended for suggesting Dynamic tags and using the classification for differential uses cases to enhance browsing experience.

Keywords: chromium, lightweight engine, mobile computing, Naive Bayes, Tizen, web browser, webpage classification

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Authors: Sidi Mohamed Mesli, Mohamed Habchi, Arslane Boudghene Stambouli, Rafik Benallal

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The BaMnMF7 (M=Fe,V, transition metal fluoride glass, assuming isomorphous replacement) have been structurally studied through the simultaneous simulation of their neutron diffraction patterns by reverse Monte Carlo (RMC) and by the Hybrid Reverse Monte Carlo (HRMC) analysis. This last is applied to remedy the problem of the artificial satellite peaks that appear in the partial pair distribution functions (PDFs) by the RMC simulation. The HRMC simulation is an extension of the RMC algorithm, which introduces an energy penalty term (potential) in acceptance criteria. The idea of this work is to apply the Buckingham potential at the title glass by ignoring the van der Waals terms, in order to make a fit of the partial pair distribution functions and give the most possible realistic features. When displaying the partial PDFs, we suggest that the Buckingham potential is useful to describe average correlations especially in similar interactions.

Keywords: fluoride glasses, RMC simulation, hybrid RMC simulation, Buckingham potential, partial pair distribution functions

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Authors: Jinsiang Shaw, Pik-Hoe Chen

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This paper aims to integrate human recognition, motion recognition, and object tracking technologies without requiring a pre-training database model for motion recognition or the unknown object itself. Furthermore, it can simultaneously track multiple users and multiple objects. Unlike other existing human motion recognition methods, our approach employs a rule-based condition method to determine if a user hand is approaching or departing an object. It uses a background subtraction method to separate the human and object from the background, and employs behavior features to effectively interpret human object-grabbing actions. With an object’s histogram characteristics, we are able to isolate and track it using back projection. Hence, a moving object trajectory can be recorded and the object itself can be located. This particular technique can be used in a camera surveillance system in a shopping area to perform real-time intelligent surveillance, thus preventing theft. Experimental results verify the validity of the developed surveillance algorithm with an accuracy of 83% for shoplifting detection.

Keywords: Automatic Tracking, Back Projection, Motion Recognition, Shoplifting

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Authors: Jara Kaye S. Villanueva, M. Rosario Concepcion O. Ang

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For the past few years, Philippines has depended most of its energy source on oil, coal, and fossil fuel. According to the Department of Energy (DOE), the dominance of coal in the energy mix will continue until the year 2020. The expanding energy needs in the country have led to increasing efforts to promote and develop renewable energy. This research is a part of the government initiative in preparation for renewable energy development and expansion in the country. The Philippine Renewable Energy Resource Mapping from Light Detection and Ranging (LiDAR) Surveys is a three-year government project which aims to assess and quantify the renewable energy potential of the country and to put them into usable maps. This study focuses on the site suitability analysis of the four renewable energy sources – biomass (coconut, corn, rice, and sugarcane), hydro, solar, and wind energy. The site assessment is a key component in determining and assessing the most suitable locations for the construction of renewable energy power plants. This method maximizes the use of both the technical methods in resource assessment, as well as taking into account the environmental, social, and accessibility aspect in identifying potential sites by utilizing and integrating two different methods: the Multi-Criteria Decision Analysis (MCDA) method and Geographic Information System (GIS) tools. For the MCDA, Analytical Hierarchy Processing (AHP) is employed to determine the parameters needed for the suitability analysis. To structure these site suitability parameters, various experts from different fields were consulted – scientists, policy makers, environmentalists, and industrialists. The need to have a well-represented group of people to consult with is relevant to avoid bias in the output parameter of hierarchy levels and weight matrices. AHP pairwise matrix computation is utilized to derive weights per level out of the expert’s gathered feedback. Whereas from the threshold values derived from related literature, international studies, and government laws, the output values were then consulted with energy specialists from the DOE. Geospatial analysis using GIS tools translate this decision support outputs into visual maps. Particularly, this study uses Euclidean distance to compute for the distance values of each parameter, Fuzzy Membership algorithm which normalizes the output from the Euclidean Distance, and the Weighted Overlay tool for the aggregation of the layers. Using the Natural Breaks algorithm, the suitability ratings of each of the map are classified into 5 discrete categories of suitability index: (1) not suitable (2) least suitable, (3) suitable, (4) moderately suitable, and (5) highly suitable. In this method, the classes are grouped based on the best groups similar values wherein each subdivision are set from the rest based on the big difference in boundary values. Results show that in the entire Philippine area of responsibility, biomass has the highest suitability rating with rice as the most suitable at 75.76% suitability percentage, whereas wind has the least suitability percentage with score 10.28%. Solar and Hydro fall in the middle of the two, with suitability values 28.77% and 21.27%.

Keywords: site suitability, biomass energy, hydro energy, solar energy, wind energy, GIS

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Authors: Zhi Liu, Hui Jiang, Jin Li, Kunhao Chen, Langfang Zhang

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Fast acquisition of the seismic information and accurate assessment of the earthquake disaster is the key problem for emergency rescue after a destructive earthquake. In order to meet the requirements of the earthquake emergency response and rescue for the cities and counties, a quasi real-time comprehensive evaluation system for earthquake disaster is developed. Based on monitoring data of Micro-Electro-Mechanical Systems (MEMS) strong motion network, structure database of a county area and the real-time disaster information by the mobile terminal after an earthquake, fragility analysis method and dynamic correction algorithm are synthetically obtained in the developed system. Real-time evaluation of the seismic disaster in the county region is finally realized to provide scientific basis for seismic emergency command, rescue and assistant decision.

Keywords: quasi real-time, earthquake disaster data collection, MEMS accelerometer, dynamic correction, comprehensive evaluation

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Authors: Yahia Massinissa, Yahia Mouloud

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Cystic fibrosis (CF), the most common lethal genetic disease in the Europe population, is caused by mutations in the transmembrane conductance regulator gene (CFTR). It affects most organs including an epithelial tissue, base of hydroelectrolytic transepithelial transport, notably that aerial ways, the pancreas, the biliary ways, the intestine, sweat glands and the genital tractus. The gene whose anomalies are responsible of the cystic fibrosis codes for a protein Cl channel named CFTR (cystic fibrosis transmembrane conductance regulator) that exercises multiple functions in the cell, one of the most important in control of sodium and chlorine through epithelia. The deficient function translates itself notably by an abnormal production of viscous secretion that obstructs the execrator channels of this target organ: one observes then a dilatation, an inflammation and an atrophy of these organs. It also translates itself by an increase of the concentration in sodium and in chloride in sweat, to the basis of the sweat test. In order to do a phenotypical and genotypical diagnosis at a part of the Algerian population, our survey has been carried on 16 patients with evocative symptoms of the cystic fibrosis at that the clinical context has been confirmed by a sweat test. However, anomalies of the CFTR gene have been determined by electrophoresis in gel of polyacrylamide of the PCR products (polymerase chain reaction), after enzymatic digestion, then visualized to the ultraviolet (UV) after action of the ethidium bromide. All mutations detected at the time of our survey have already been identified at patients attained by this pathology in other populations of the world. However, the important number of found mutation with regard to the one of the studied patients testifies that the origin of this big clinical variability that characterizes the illness in the consequences of an enormous diversity of molecular defects of the CFTR gene.

Keywords: cystic fibrosis, CFTR gene, polymorphism, algerian population, sweat test, genotypical diagnosis

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Authors: A. Abbou, V. Makis, N. Salari

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We study the condition-based maintenance (CBM) problem of a system subject to stochastic deterioration. The system is composed of three units (or modules): (i) Module 1 deterioration follows a Markov process with two operational states and one failure state. The operational states are partially observable through periodic condition monitoring. (ii) Module 2 deterioration follows a Gamma process with a known failure threshold. The deterioration level of this module is fully observable through periodic inspections. (iii) Only the operating age information is available of Module 3. The lifetime of this module has a general distribution. A CBM policy prescribes when to initiate a maintenance intervention and which modules to repair during intervention. Our objective is to determine the optimal CBM policy minimizing the long-run expected average cost of operating the system. This is achieved by formulating a Markov decision process (MDP) and developing the value iteration algorithm for solving the MDP. We provide numerical examples illustrating the cost-effectiveness of the optimal CBM policy through a comparison with heuristic policies commonly found in the literature.

Keywords: reliability, maintenance optimization, Markov decision process, heuristics

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Authors: Kefaya Qaddoum

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Most of greenhouse growers desire a determined amount of yields in order to accurately meet market requirements. The purpose of this paper is to model a simple but often satisfactory supervised classification method. The original naive Bayes have a serious weakness, which is producing redundant predictors. In this paper, utilized regularization technique was used to obtain a computationally efficient classifier based on naive Bayes. The suggested construction, utilized L1-penalty, is capable of clearing redundant predictors, where a modification of the LARS algorithm is devised to solve this problem, making this method applicable to a wide range of data. In the experimental section, a study conducted to examine the effect of redundant and irrelevant predictors, and test the method on WSG data set for tomato yields, where there are many more predictors than data, and the urge need to predict weekly yield is the goal of this approach. Finally, the modified approach is compared with several naive Bayes variants and other classification algorithms (SVM and kNN), and is shown to be fairly good.

Keywords: tomato yield prediction, naive Bayes, redundancy, WSG

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Authors: H. Mousavi, M. Sharifi, H. Pourvaziri

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Redundancy Allocation Problem (RAP) is a well-known mathematical problem for modeling series-parallel systems. It is a combinatorial optimization problem which focuses on determining an optimal assignment of components in a system design. In this paper, to be more practical, we have considered the problem of redundancy allocation of series system with interval valued reliability of components. Therefore, during the search process, the reliabilities of the components are considered as a stochastic variable with a lower and upper bounds. In order to optimize the problem, we proposed a simulated annealing based on cloud theory (CBSAA). Also, the Monte Carlo simulation (MCS) is embedded to the CBSAA to handle the random variable components’ reliability. This novel approach has been investigated by numerical examples and the experimental results have shown that the CBSAA combining MCS is an efficient tool to solve the RAP of systems with interval-valued component reliabilities.

Keywords: redundancy allocation problem, simulated annealing, cloud theory, monte carlo simulation

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Authors: Sharique Ahmed, Khushtar Anwar Salman

Abstract:

Alpha-1-antitrypsin (AAT) is a major plasma serine protease inhibitor (SERPIN). Hereditary AAT deficiency is one of the common diseases in some part of the world. AAT is mainly produced in the liver and functions to protect the lung against proteolytic damage (e.g., from neutrophil elastase) acting as the major inhibitor for neutrophil elastase. α (1)-Antitrypsin (AAT) deficiency is an under recognized genetic condition that affects approximately 1 in 2,000 to 1 in 5,000 individuals and predisposes to liver disease and early-onset emphysema. Not only does α-1-antitrypsin deficiency lead to disabling syndrome of pulmonary emphysema, there are other disorders too which include ANCA (antineutrophilic cytoplasmic antibody) positive Wegener's granulomatosis, diffuse bronchiectasis, necrotizing panniculitis in α-1-antitrypsin phenotype (S), idiopathic pulmonary fibrosis and steroid dependent asthma. Augmentation therapy with alpha-1 antitrypsin (AAT) from human plasma has been available for specific treatment of emphysema due to AAT deficiency. Apart from this several observations have also suggested a role for endogenous suppressors of HIV-1, alpha-1 antitrypsin (AAT) has been identified to be one of those. In view of its varied important role in humans, serum from a mammalian source was chosen for the isolation and purification. Studies were performed on the homogeneous fraction. This study suggests that the buffalo serum α-1-antritrypsin has characteristics close to ovine, dog, horse and more importantly to human α-1-antritrypsin in terms of its hydrodynamic properties such as molecular weight, carbohydrate content, etc. The similarities in the hydrodynamic properties of buffalo serum α-1-antitrypsin with other sources of mammalian α-1-antitrypsin mean that it can be further studied and be a potential source for "augmentation therapy", as well as a source of AAT replacement therapy to raise serum levels above the protective threshold. Other parameters like the amino acid sequence, the effect of denaturants, and the thermolability or thermostability of the inhibitor will be the interesting basis of future studies on buffalo serum alpha-1 antitrypsin (AAT).

Keywords: α-1-antitrypsin, augmentation therapy , hydrodynamic properties, serine protease inhibitor

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Authors: Riznaldi Akbar

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In this study, we compared the performance of the Artificial Neural Network (ANN) model with back-propagation algorithm in correctly predicting in-sample and out-of-sample external debt crisis in Indonesia. We found that exchange rate, foreign reserves, and exports are the major determinants to experiencing external debt crisis. The ANN in-sample performance provides relatively superior results. The ANN model is able to classify correctly crisis of 89.12 per cent with reasonably low false alarms of 7.01 per cent. In out-of-sample, the prediction performance fairly deteriorates compared to their in-sample performances. It could be explained as the ANN model tends to over-fit the data in the in-sample, but it could not fit the out-of-sample very well. The 10-fold cross-validation has been used to improve the out-of-sample prediction accuracy. The results also offer policy implications. The out-of-sample performance could be very sensitive to the size of the samples, as it could yield a higher total misclassification error and lower prediction accuracy. The ANN model could be used to identify past crisis episodes with some accuracy, but predicting crisis outside the estimation sample is much more challenging because of the presence of uncertainty.

Keywords: debt crisis, external debt, artificial neural network, ANN

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Authors: Aswandi Anas Husin, Cut Rizlani Kholibrina

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Harvesting camphor resin has been carried out since the beginning of civilization on the west coast of Sumatra. Oil or crystals that containing borneol are harvested from the camphor tree (Dryobalanops aromatica). Non-timber forest products are utilized for the manufacture of fragrances, antiseptics, anti-inflammatory, analgesic as well as effective for the treatment of blocked arteries. Based on exploration on the west coast of Sumatra, these endemic tree species were found remaining growing in groups on small spots in the lowlands to the hills. Some populations are found at an altitude of 700 meters above sea level in Kadabuhan, Jongkong and Sultan Daulat in Subulussalam district, Singkohor and Lake Paris in Aceh Singkil district, and Sirandorung and Manduamas in the north of Barus, Central Tapanuli district. These multi-purpose tree species was also identified as being able to adapt to the Singkil Peat Swamp. The decline in tree population has a direct impact on reducing their productivity. Conventionally, the crystals are harvested by cutting and splitting the stem into wooden blocks. In this way about 1.5-2.5 kg of crystals are obtained with various qualities. Camphor retrieval can also be done by making a notch on a standing tree trunk and collecting liquid resin (ombil) that is removed from the injured resin channel. Twigs and leaves also contain borneol. The aromatic content in this section opens opportunities for the supply of borneol through the distillation process. Vegetative propagation technology is needed to overcome the limitations of available seeds. This breeding strategy for vulnerable species starts with gathering genetic material from various provenances which are then used to support the provision of basic populations, breeding populations, multiplication populations and production populations for extensive development of camphor tree plantations

Keywords: camphor, conservation, natural borneol, productivity, vulnerable species

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Authors: Zewdneh Zana Zate

Abstract:

The study focuses on the evolutionary and ecological genomics of both wild and cultivated Coffea arabica L. at its center of origin, Ethiopia, aiming to uncover how this vital species may withstand future climate changes. Utilizing bioclimatic models, we project the future distribution of Arabica under varied climate scenarios for 2050 and 2080, identifying potential conservation zones and immediate risk areas. Through whole-genome resequencing of accessions from Ethiopian gene banks, this research assesses genetic diversity and divergence between wild and cultivated populations. It explores relationships, demographic histories, and potential hybridization events among Coffea arabica accessions to better understand the species' origins and its connection to parental species. This genomic analysis also seeks to detect signs of natural or artificial selection across populations. Integrating these genomic discoveries with ecological data, the study evaluates the current and future ecological and genomic vulnerabilities of wild Coffea arabica, emphasizing necessary adaptations for survival. We have identified key genomic regions linked to environmental stress tolerance, which could be crucial for breeding more resilient Arabica varieties. Additionally, our ecological modeling predicted a contraction of suitable habitats, urging immediate conservation actions in identified key areas. This research not only elucidates the evolutionary history and adaptive strategies of Arabica but also informs conservation priorities and breeding strategies to enhance resilience to climate change. By synthesizing genomic and ecological insights, we provide a robust framework for developing effective management strategies aimed at sustaining Coffea arabica, a species of profound global importance, in its native habitat under evolving climatic conditions.

Keywords: coffea arabica, climate change adaptation, conservation strategies, genomic resilience

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Authors: Yuan-Lin Chen

Abstract:

This paper presents an approaching forward collision probability index (AFCPI) for alerting and assisting driver in keeping safety distance to avoid the forward collision accident in highway driving. The time to collision (TTC) and time headway (TH) are used to evaluate the TTC forward collision probability index (TFCPI) and the TH forward collision probability index (HFCPI), respectively. The Mamdani fuzzy inference algorithm is presented combining TFCPI and HFCPI to calculate the approaching collision probability index of the vehicle. The AFCPI is easier to understand for the driver who did not even have any professional knowledge in vehicle professional field. At the same time, the driver’s behavior is taken into account for suiting each driver. For the approaching index, the value 0 is indicating the 0% probability of forward collision, and the values 0.5 and 1 are indicating the 50% and 100% probabilities of forward collision, respectively. The AFCPI is useful and easy-to-understand for alerting driver to avoid the forward collision accidents when driving in highway.

Keywords: approaching index, forward collision probability, time to collision, time headway

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Authors: Kamal, Dinesh Kumar, J. P. Narayan, Komal Rani

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This paper presents the effects of shape of basement topography on the characteristics of the basin-generated surface (BGS) waves and associated average spectral amplification (ASA) in the 3D basins having circular surface area. Seismic responses were computed using a recently developed 3D fourth-order spatial accurate time-domain finite-difference (FD) algorithm based on parsimonious staggered-grid approximation of 3D viscoelastic wave equations. An increase of amplitude amplification and ASA towards the centre of different considered basins was obtained. Further, it may be concluded that ASA in basin very much depends on the impedance contrast, exposure area of basement to the incident wave front, edge-slope, focusing of the BGS-waves and sediment-damping. There is an urgent need of incorporation of a map of differential ground motion (DGM) caused by the BGS-waves as one of the output maps of the seismic microzonation.

Keywords: 3D viscoelastic simulation, basin-generated surface waves, maximum displacement, average spectral amplification

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Authors: Preethy Mary Donald, Renjith George

Abstract:

Oral tori are localized non-neoplastic protuberances of maxilla and mandible. Torus palatinus (TP) is found on the midline of the roof of mouth existing as single growth or in clusters. Torus mandibularis(TM) is located on the lingual aspect of the mandible commonly between canine and premolar region. Etiology of their presence was not clear and was found to be multifactorial. Their variations in relation to age, gender, ethnicity and also the characteristics of TP and TM have become the interest of multiple studies. The objectives of this study were to determine the prevalence of torus palatinus (TP) and torus mandibularis (TM) among patients who have visited outpatient department, Faculty of Dentistry, Melaka Manipal Medical College. 108 patients were examined for the presence of oral tori at the outpatient department, Faculty of Dentistry, Melaka-Manipal Medical College. Factors such as age, gender, ethnicity of the patients and size, shape, location of the oral tori were studied. For TP, Malays (62.96%) have been found to have the highest prevalence than Chinese (43.3%) and Indians (35.71%). For TM, Chinese (7.46%) had predominated compared to Malays (7.41%) and Indians (0%). There is no significant association between occurrence of TP and TM with age, gender and ethnicity. For Torus palatinus, the most common size was Grade 1(1-3mm), most common location was molar region, and the most common shape was spindle. For Torus mandibularis, the most frequent location was canine premolar region and exists in unilateral single or bilateral single fashion. The overall prevalence rates were 47.2% for TP and 6.48% for TM. However, there is no significant association between occurrence of TP and TM with age, gender and ethnicity. The results showed variations in clinical characteristics and support the findings that occurrence of tori is a dynamic phenomenon which is multifactorial owing to the environmental factors such as stress from occlusion and dietary habits. It could be due to the genetic make-up of the individual.

Keywords: torus palatinus, torus mandibularis, age, gender

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Authors: Armenuhi Ghazaryan, Qnarik Poghosyan, Tadevos Markosyan

Abstract:

We have considered the high-order harmonic generation in-plane graphene quantum dots of hexagonal shape by the independent quasiparticle approximation-tight binding model. We have investigated how such a nonlinear effect is affected by a strong optical wave field, quantum dot typical band gap and lateral size, and dephasing processes. The equation of motion for the density matrix is solved by performing the time integration with the eight-order Runge-Kutta algorithm. If the optical wave frequency is much less than the quantum dot intrinsic band gap, the main aspects of multiphoton high harmonic emission in quantum dots are revealed. In such a case, the dependence of the cutoff photon energy on the strength of the optical pump wave is almost linear. But when the wave frequency is comparable to the bandgap of the quantum dot, the cutoff photon energy shows saturation behavior with an increase in the wave field strength.

Keywords: strong wave field, multiphoton, bandgap, wave field strength, nanostructure

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