Search results for: car sequencing and operator allocation

Authors: Wildan Syafitri

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The main purpose of the development is to embody public welfare. Its indication is shown by the increasing of the public prosperity in which it will be related to the consumption level as a consequence of the increasing of public income. One of the government’s efforts to increase public welfare is to create development equity in order to alleviate poor people. Poverty’s problem is not merely about the number and percentage of the poor people, but also it includes the gap and severity of poverty.the analysis method used is Benefit Incidence Analysis (BIA) that is an analysis method used to disclose the impact of government policy or individual access based on the income distribution in society. Further, the finding of the study revealed is that the highest number of the poor people in the village is those who are unemployed and have family members who are still in the Junior High School. The income distribution calculation shows a fairly good budget allocation applied with good mass ratio that is 0.31. In addition, the finding of this study also discloses that Indonesian Government policy to subsidize education cost for Elementary and Junior High School students has reached the right target. It is indicated by more benefits received by Elementary and Junior High School students who are poor and very poor than other income group.

Keywords: benefit incidence analysis, budget allocation, poverty, education

Procedia PDF Downloads 364

Authors: Huajuan Shi

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Background: The biopsy of the preimplantation embryo may increase the potential risk and concern of embryo viability. Clinically discarded spent embryo medium (SEM) has entered the view of researchers, sparking an interest in noninvasive embryo screening. However, one of the major restrictions is the extremelty low quantity of cf-RNA, which is difficult to efficiently and unbiased amplify cf-RNA using traditional methods. Hence, there is urgently need to an efficient and low bias amplification method which can comprehensively and accurately obtain cf-RNA information to truly reveal the state of SEM cf-RNA. Result: In this present study, we established an agarose PCR amplification system, and has significantly improved the amplification sensitivity and efficiency by ~90 fold and 9.29 %, respectively. We applied agarose to sequencing library preparation (named AG-seq) to quantify and characterize cf-RNA in SEM. The number of detected cf-RNAs (3533 vs 598) and coverage of 3' end were significantly increased, and the noise of low abundance gene detection was reduced. The increasing percentage 5' end adenine and alternative splicing (AS) events of short fragments (< 400 bp) were discovered by AG-seq. Further, the profiles and characterizations of cf-RNA in spent cleavage medium (SCM) and spent blastocyst medium (SBM) indicated that 4‐mer end motifs of cf-RNA fragments could remarkably differentiate different embryo development stages. Significance: This study established an efficient and low-cost SEM amplification and library preparation method. Not only that, we successfully described the characterizations of SEM cf-RNA of preimplantation embryo by using AG-seq, including abundance features fragment lengths. AG-seq facilitates the study of cf-RNA as a noninvasive embryo screening biomarker and opens up potential clinical utilities of trace samples.

Keywords: cell-free RNA, agarose, spent embryo medium, RNA sequencing, non-invasive detection

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Authors: Rakesh Dubey, Vaishali Bahl, Dalveer Kaur

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The spiralling urge for high rate data transmission over wireless mediums needs intelligent use of electromagnetic resources considering restrictions like power ingestion, spectrum competence, robustness against multipath propagation and implementation intricacy. Orthogonal frequency division multiplexing (OFDM) is a capable technique for next generation wireless communication systems. For such high rate data transfers there is requirement of proper allocation of resources like power and capacity amongst the sub channels. This paper illustrates various available methods of allocating power and the capacity requirement with the constraint of Shannon limit.

Keywords: Additive White Gaussian Noise, Multi-Carrier Modulation, Orthogonal Frequency Division Multiplexing (OFDM), Signal to Noise Ratio (SNR), Water Filling

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Authors: Hassen Hamouda, Mohamed Ouwais Kabaou, Med Salim Bouhlel

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The growth of wireless technology made opportunistic scheduling a widespread theme in recent research. Providing high system throughput without reducing fairness allocation is becoming a very challenging task. A suitable policy for resource allocation among users is of crucial importance. This study focuses on scheduling multiple streaming flows on the downlink of a WiMAX system based on orthogonal frequency division multiple access (OFDMA). In this paper, we take the first step in formulating and analyzing this problem scrupulously. As a result, we proposed a new scheduling scheme based on Round Robin (RR) Algorithm. Because of its non-opportunistic process, RR does not take in account radio conditions and consequently it affect both system throughput and multi-users diversity. Our contribution called MORRA (Modified Round Robin Opportunistic Algorithm) consists to propose a solution to this issue. MORRA not only exploits the concept of opportunistic scheduler but also takes into account other parameters in the allocation process. The first parameter is called courtesy coefficient (CC) and the second is called Buffer Occupancy (BO). Performance evaluation shows that this well-balanced scheme outperforms both RR and MaxSNR schedulers and demonstrate that choosing between system throughput and fairness is not required.

Keywords: OFDMA, opportunistic scheduling, fairness hierarchy, courtesy coefficient, buffer occupancy

Procedia PDF Downloads 266

Authors: Sagar Hedaoo, Fazle Baki, Ahmed Azab

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In supply chain management, network design for scalable manufacturing facilities is an emerging field of research. Facility location allocation assigns facilities to customers to optimize the overall cost of the supply chain. To further optimize the costs, capacities of these facilities can be changed in accordance with customer demands. A mathematical model is formulated to fully express the problem at hand and to solve small-to-mid range instances. A dedicated constraint has been developed to restrict emissions in line with the Kyoto protocol. This problem is NP-Hard; hence, a simulated annealing metaheuristic has been developed to solve larger instances. A case study on the USA-Canada cross border crossing is used.

Keywords: emission, mixed integer linear programming, metaheuristic, simulated annealing

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Authors: Mpho Mokoatle, Darlington Mapiye, James Mashiyane, Stephanie Muller, Gciniwe Dlamini

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Great advances in high-throughput sequencing technologies have resulted in availability of huge amounts of sequencing data in public and private repositories, enabling a holistic understanding of complex biological phenomena. Sequence data are used for a wide range of applications such as gene annotations, expression studies, personalized treatment and precision medicine. However, this rapid growth in sequence data poses a great challenge which calls for novel data processing and analytic methods, as well as huge computing resources. In this work, a machine and statistical learning approach for DNA sequence classification based on $k$-mer representation of sequence data is proposed. The approach is tested using whole genome sequences of Mycobacterium tuberculosis (MTB) isolates to (i) reduce the size of genomic sequence data, (ii) identify an optimum size of k-mers and utilize it to build classification models, (iii) predict the phenotype from whole genome sequence data of a given bacterial isolate, and (iv) demonstrate computing challenges associated with the analysis of whole genome sequence data in producing interpretable and explainable insights. The classification models were trained on 104 whole genome sequences of MTB isoloates. Cluster analysis showed that k-mers maybe used to discriminate phenotypes and the discrimination becomes more concise as the size of k-mers increase. The best performing classification model had a k-mer size of 10 (longest k-mer) an accuracy, recall, precision, specificity, and Matthews Correlation coeffient of 72.0%, 80.5%, 80.5%, 63.6%, and 0.4 respectively. This study provides a comprehensive approach for resampling whole genome sequencing data, objectively selecting a k-mer size, and performing classification for phenotype prediction. The analysis also highlights the importance of increasing the k-mer size to produce more biological explainable results, which brings to the fore the interplay that exists amongst accuracy, computing resources and explainability of classification results. However, the analysis provides a new way to elucidate genetic information from genomic data, and identify phenotype relationships which are important especially in explaining complex biological mechanisms.

Keywords: AWD-LSTM, bootstrapping, k-mers, next generation sequencing

Procedia PDF Downloads 137

Authors: Darlington Mapiye, Mpho Mokoatle, James Mashiyane, Stephanie Muller, Gciniwe Dlamini

Abstract:

Great advances in high-throughput sequencing technologies have resulted in availability of huge amounts of sequencing data in public and private repositories, enabling a holistic understanding of complex biological phenomena. Sequence data are used for a wide range of applications such as gene annotations, expression studies, personalized treatment and precision medicine. However, this rapid growth in sequence data poses a great challenge which calls for novel data processing and analytic methods, as well as huge computing resources. In this work, a machine and statistical learning approach for DNA sequence classification based on k-mer representation of sequence data is proposed. The approach is tested using whole genome sequences of Mycobacterium tuberculosis (MTB) isolates to (i) reduce the size of genomic sequence data, (ii) identify an optimum size of k-mers and utilize it to build classification models, (iii) predict the phenotype from whole genome sequence data of a given bacterial isolate, and (iv) demonstrate computing challenges associated with the analysis of whole genome sequence data in producing interpretable and explainable insights. The classification models were trained on 104 whole genome sequences of MTB isoloates. Cluster analysis showed that k-mers maybe used to discriminate phenotypes and the discrimination becomes more concise as the size of k-mers increase. The best performing classification model had a k-mer size of 10 (longest k-mer) an accuracy, recall, precision, specificity, and Matthews Correlation coeffient of 72.0 %, 80.5 %, 80.5 %, 63.6 %, and 0.4 respectively. This study provides a comprehensive approach for resampling whole genome sequencing data, objectively selecting a k-mer size, and performing classification for phenotype prediction. The analysis also highlights the importance of increasing the k-mer size to produce more biological explainable results, which brings to the fore the interplay that exists amongst accuracy, computing resources and explainability of classification results. However, the analysis provides a new way to elucidate genetic information from genomic data, and identify phenotype relationships which are important especially in explaining complex biological mechanisms

Keywords: AWD-LSTM, bootstrapping, k-mers, next generation sequencing

Procedia PDF Downloads 126

Authors: Muhammad Shafiq, Huynh Trung Luong

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In this paper, we proposed a commitment based revenue sharing contract for a supply chain comprising one manufacturer and one retailer facing highly uncertain demand of a short life span fashionable product. In our model, the retailer reserves a commitment level with the manufacturer prior to the selling season. In response, the manufacturer allocates and produces a specific quantity which is the maximum available quantity for the retailer. The retailer is motivated to commit more by offering higher revenue sharing percentage for reserved capacity than non-reserved capacity. Due to asymmetric information, it is found that the manufacturer can optimize quantity allocation decision while the commitment level decision of the retailer may not be optimal.

Keywords: supply chain coordination, revenue sharing contract, commitment based revenue sharing, quantity allocation

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Authors: Zhou Luchen, Wu Yubing, Burra Venkata Durga Kumar

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As the scale of the network becomes larger and more complex than before, the density of user devices is also increasing. The development of Unmanned Aerial Vehicle (UAV) networks is able to collect and transform data in an efficient way by using software-defined networks (SDN) technology. This paper proposed a three-layer distributed and dynamic cluster architecture to manage UAVs by using an AI-based resource allocation calculation algorithm to address the overloading network problem. Through separating services of each UAV, the UAV hierarchical cluster system performs the main function of reducing the network load and transferring user requests, with three sub-tasks including data collection, communication channel organization, and data relaying. In this cluster, a head node and a vice head node UAV are selected considering the Central Processing Unit (CPU), operational (RAM), and permanent (ROM) memory of devices, battery charge, and capacity. The vice head node acts as a backup that stores all the data in the head node. The k-means clustering algorithm is used in order to detect high load regions and form the UAV layered clusters. The whole process of detecting high load areas, forming and selecting UAV clusters, and moving the selected UAV cluster to that area is proposed as offloading traffic algorithm.

Keywords: k-means, resource allocation, SDN, UAV network, unmanned aerial vehicles

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Authors: Swarkar Sharma, Ekta Rai, Ankit Mahajan, Parvinder Kumar, Manoj K Dhar, Sushil Razdan, Kumarasamy Thangaraj, Carol Wise, Shiro Ikegawa M.D., K.K. Pandita M.D.

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Rare disorders are poorly understood hence, remain uncharacterized or patients are misdiagnosed and get poor medical attention. A rare mysterious skeletal disorder that remained unidentified for decades and rendered many people physically challenged and disabled for life has been reported in an isolated remote village ‘Arai’ of Poonch district of Jammu and Kashmir. This village is located deep in mountains and the population residing in the region is highly consanguineous. In our survey of the region, 70 affected people were reported, showing similar phenotype, in the village with a population of approximately 5000 individuals. We were able to collect samples from two multi generational extended families from the village. Through Whole Exome sequencing (WES), we identified a rare variation NM_003880.3:c.156C>A NP_003871.1:p.Cys52Ter, which results in introduction of premature stop codon in WISP3 gene. We found this variation perfectly segregating with the disease in one of the family. However, this variation was absent in other family. Interestingly, a novel splice site mutation at position c.643+1G>A of WISP3 gene, perfectly segregating with the disease was observed in the second family. Thus, exploiting WES and putting different evidences together (familial histories and genetic data, clinical features, radiological and biochemical tests and findings), the disease has finally been diagnosed as a very rare recessive hereditary skeletal disease “Progressive Pseudorheumatoid Arthropathy of Childhood” (PPAC) also known as “Spondyloepiphyseal Dysplasia Tarda with Progressive Arthropathy” (SEDT-PA). This genetic characterization and identification of the disease causing mutations will aid in genetic counseling, critically required to curb this rare disorder and to prevent its appearance in future generations in the population. Further, understanding of the role of WISP3 gene the biological pathways should help in developing treatment for the disorder.

Keywords: whole exome sequencing, Next Generation Sequencing, rare disorders

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Authors: Ayman K. El Essawy, Amal M. Hosny, Hala M. Abu Shady

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The rapid detection of TB and drug resistance, both optimizes treatment and improves outcomes. In the current study, respiratory specimens were collected from 155 patients. Conventional susceptibility testing and MIC determination were performed for rifampicin (RIF) and isoniazid (INH). Genotype MTBDRplus assay, which is a molecular genetic assay based on the DNA-STRIP technology and specific gene sequencing with primers for rpoB, KatG, and mab-inhA genes were used to detect mutations associated with resistance to rifampicin and isoniazid. In comparison to other categories, most of rifampicin resistant (61.5%) and isoniazid resistant isolates (47.1%) were from patients relapsed in treatment. The genotypic profile (using Genotype MTBDRplus assay) of multi-drug resistant (MDR) isolates showed missing of katG wild type 1 (WT1) band and appearance of mutation band katG MUT2. For isoniazid mono-resistant isolates, 80% showed katG MUT1, 20% showed katG MUT1, and inhA MUT1, 20% showed only inhA MUT1. Accordingly, 100% of isoniazid resistant strains were detected by this assay. Out of 17 resistant strains, 16 had mutation bands for katG distinguished high resistance to isoniazid. The assay could clearly detect rifampicin resistance among 66.7% of MDR isolates that showed mutation band rpoB MUT3 while 33.3% of them were considered as unknown. One mono-resistant rifampicin isolate did not show rifampicin mutation bands by Genotype MTBDRplus assay, but it showed an unexpected mutation in Codon 531 of rpoB by DNA sequence analysis. Rifampicin resistance in this strain could be associated with a mutation in codon 531 of rpoB (based on molecular sequencing), and Genotype MTBDRplus assay could not detect the associated mutation. If the results of Genotype MTBDRplus assay and sequencing were combined, this strain shows hetero-resistance pattern. Gene sequencing of eight selected isolates, previously tested by Genotype MTBDRplus assay, could detect resistance mutations mainly in codon 315 (katG gene), position -15 in inhA promotes gene for isoniazid resistance and codon 531 (rpoB gene) for rifampicin resistance. Genotyping techniques allow distinguishing between recurrent cases of reinfection or reactivation and supports epidemiological studies.

Keywords: M. tuberculosis, rpoB, KatG, inhA, genotype MTBDRplus

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Authors: Violina R. Angelova, Venelina T. Popova, Radka V. Ivanova, Givko T. Ivanov, Krasimir I. Ivanov

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A comparative research on the impact of compost on uptake and allocation of nutrients and heavy metals and quality of Oriental tobacco Krumovgrad 90 has been carried out. The experiment was performed on an agricultural field contaminated by the lead zinc smelter near the town of Kardzali, Bulgaria, after closing the lead production. The compost treatments had significant effects on the uptake and allocation of plant nutrients and heavy metals. The incorporation of compost leads to decrease in the amount of heavy metals present in the tobacco leaves, with Cd, Pb and Zn having values of 36%, 12% and 6%, respectively. Application of the compost leads to increased content of potassium, calcium and magnesium in the leaves of tobacco, and therefore, may favorably affect the burning properties of tobacco. The incorporation of compost in the soil has a negative impact on the quality and typicality of the oriental tobacco variety of Krumovgrad 90. The incorporation of compost leads to an increase in the size of the tobacco plant leaves, the leaves become darker in colour, less fleshy and undergo a change in form, becoming (much) broader in the second, third and fourth stalk position. This is accompanied by a decrease in the quality of the tobacco. The incorporation of compost also results in an increase in the mineral substances (pure ash), total nicotine and nitrogen, and a reduction in the amount of reducing sugars, which causes the quality of the tobacco leaves to deteriorate (particularly in the third and fourth harvests).

Keywords: chemical composition, compost, heavy metals, oriental tobacco, quality

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Authors: Lata Kumari Dhanesh Tiwary, Pradeep Kumar Mishra

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The effluent coming from various industries such as textile, carpet, food, pharmaceutical and many other industries is big challenge due to its recalcitrant and xenobiotiocs in nature. Recently, biodegradation of dye wastewater through biological means was widely used due to eco-friendly and cost effective with the higher percentage of removal of dye from wastewater. The present study deals with the biodegradation and decolourization of Direct Red 23 dye using indigenously isolated bacterial consortium. The bacterial consortium was isolated from soil sample from dye contaminated site near a cluster of Carpet industries of Bhadohi, Uttar Pradesh, India. The bacterial strain formed consortia were identified and characterized by morphological, biochemical and 16S rRNA gene sequence analysis. The bacterial strain mainly Staphylococcus saprophyticus strain BHUSS X3 (KJ439576), Microbacterium sp. BHUMSp X4 (KJ740222) and Staphylococcus saprophyticus strain BHUSS X5 (KJ439576) were used as consortia for further studies of dye decolorization. Experimental investigations were made in a Sequencing Air- lift bioreactor using the synthetic solution of Direct Red 23 dye by optimizing various parameters for efficient degradation of dye. The effect of several operating parameters such as flow rate, pH, temperature, initial dye concentration and inoculums size on removal of dye was investigated. The efficiency of isolated bacterial consortia from dye contaminated area in Sequencing Air- lift Bioreactor with different concentration of dye between 100-1200 mg/l at different hydraulic rate (HRTs) 26h and 10h. The maximum percentage of dye decolourization 98% was achieved when operated at HRT of 26h. The percentage of decolourization of dye was confirmed by using UV-Vis spectrophotometer and HPLC.

Keywords: carpet industry, bacterial consortia, sequencing air-lift bioreactor

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Authors: Abdel-Aziz M. Mohamed

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This paper presents a state-of-the-art survey of the operations research models developed for internal audit planning. Two alternative approaches have been followed in the literature for audit planning: (1) identifying the optimal audit frequency; and (2) determining the optimal audit resource allocation. The first approach identifies the elapsed time between two successive audits, which can be presented as the optimal number of audits in a given planning horizon, or the optimal number of transactions after which an audit should be performed. It also includes the optimal audit schedule. The second approach determines the optimal allocation of audit frequency among all auditable units in the firm. In our review, we discuss both the deterministic and probabilistic models developed for audit planning. In addition, game theory models are reviewed to find the optimal auditing strategy based on the interactions between the auditors and the clients.

Keywords: operations research applications, audit frequency, audit-staff scheduling, audit planning

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Authors: Ebrahim Ghanbari, Mohammad Reza Nematollahi

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Integrating deterministic and probabilistic safety assessment (IDPSA) is one of the most commonly used issues in the field of safety analysis of power plant accident. It has also been recognized today that the role of human error in creating these accidents is not less than systemic errors, so the human interference and system errors in fault and event sequences are necessary. The integration of these analytical topics will be reflected in the frequency of core damage and also the study of the use of water resources in an accident such as the loss of all electrical power of the plant. In this regard, the SBO accident was simulated for the pressurized water reactor in the deterministic analysis issue, and by analyzing the operator's behavior in controlling the accident, the results of the combination of deterministic and probabilistic assessment were identified. The results showed that the best performance of the plant operator would reduce the risk of an accident by 10%, as well as a decrease of 6.82 liters/second of the water sources of the plant.

Keywords: IDPSA, human error, SBO, risk

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Authors: Çağrı Özgün Kibiroğlu, Zeynep Turgut

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This paper considers a hub location problem where the network service area partitioned into predetermined zones (represented by node clusters is given) and potential hub nodes capacity levels are determined a priori as a selection criteria of hub to investigate congestion effect on network. The objective is to design hub network by determining all required hub locations in the node clusters and also allocate non-hub nodes to hubs such that the total cost including transportation cost, opening cost of hubs and penalty cost for exceed of capacity level at hubs is minimized. A mixed integer linear programming model is developed introducing additional constraints to the traditional model of capacitated multiple allocation hub location problem and empirically tested.

Keywords: hub location problem, p-hub median problem, clustering, congestion

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Authors: Risalatul Latifah, Bunawas Bunawas, Lailatul Muqmiroh, Anggraini D. Sensusiati

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Along with the increasing frequency of the use of CT-Scan for radiodiagnostics purposes, it is necessary to study radiation protection. This study examined aspects of radiation protection of workers. This study tried using thermoluminescent dosimeter (TLD) for evaluating radiation shielding and estimating safe time for workers during CT Scan examination. Six TLDs were placed on door, wall, and window inside and outside of the CT Scan room for 1 month. By using TLD monitoring, it could be seen how much radiation was exposed in the operator room. The results showed the effective dose at door, window, and wall was respectively 0.04 mSv, 0.05 mSv, and 0.04 mSv. With these values, it could be evaluated the effectiveness of radiation shielding on doors, glass and walls were respectively 90.6%, 95.5%, and 92.2%. By applying the dose constraint and the estimation of the accumulated dose for one month, radiation workers were still safe to perform the irradiation for 180 patients.

Keywords: CT scan room, TLD, radiation worker, dose constraint

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Authors: Po-Jung Huang, Chi-Ching Lee, Bertrand Chin-Ming Tan, Yuan-Ming Yeh, Julie Lichieh Chu, Tin-Wen Chen, Cheng-Yang Lee, Ruei-Chi Gan, Hsuan Liu, Petrus Tang

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Whole-exome sequencing focuses on the protein coding regions of disease/cancer associated genes based on a priori knowledge is the most cost-effective method to study the association between genetic alterations and disease. Recent advances in high throughput sequencing technologies and proteomic techniques has provided an opportunity to integrate genomics and proteomics, allowing readily detectable mutated peptides corresponding to mutated genes. Since sequence database search is the most widely used method for protein identification using Mass spectrometry (MS)-based proteomics technology, a mutant proteome database is required to better approximate the real protein pool to improve disease-associated mutated protein identification. Large-scale whole exome/genome sequencing studies were launched by National Cancer Institute (NCI), Broad Institute, and The Cancer Genome Atlas (TCGA), which provide not only a comprehensive report on the analysis of coding variants in diverse samples cell lines but a invaluable resource for extensive research community. No existing database is available for the collection of mutant protein sequences related to the identified variants in these studies. CMPD is designed to address this issue, serving as a bridge between genomic data and proteomic studies and focusing on protein sequence-altering variations originated from both germline and cancer-associated somatic variations.

Keywords: TCGA, cancer, mutant, proteome

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Authors: Claude Takenga, Rolf-Dietrich Berndt, Erling Si, Markus Diem, Guohui Qiao, Melanie Gau, Michael Brandstoetter, Martin Kampel, Michael Dworzak

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In patients with acute lymphoblastic leukaemia (ALL), treatment response is increasingly evaluated with minimal residual disease (MRD) analyses. Flow Cytometry (FCM) is a fast and sensitive method to detect MRD. However, the interpretation of these multi-parametric data requires intensive operator training and experience. This paper presents a pipeline-software, as a ready-to-use FCM-based MRD-assessment tool for the daily clinical practice for patients with ALL. The new tool increases accuracy in assessment of FCM-MRD in samples which are difficult to analyse by conventional operator-based gating since computer-aided analysis potentially has a superior resolution due to utilization of the whole multi-parametric FCM-data space at once instead of step-wise, two-dimensional plot-based visualization. The system developed as a telemedical network reduces the work-load and lab-costs, staff-time needed for training, continuous quality control, operator-based data interpretation. It allows dissemination of automated FCM-MRD analysis to medical centres which have no established expertise for the benefit of an even larger community of diseased children worldwide. We established a telemedical network system for analysis and clinical follow-up and treatment monitoring of Leukaemia. The system is scalable and adapted to link several centres and laboratories worldwide.

Keywords: data security, flow cytometry, leukaemia, telematics platform, telemedicine

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Authors: Fatima Z. Cherhabil, Mammar Sedrati, Sonia-Sabrina Bendib‎

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In order to support the continued growth, critical latency of ‎IoT ‎applications, and ‎various obstacles of traditional data centers, ‎mobile edge ‎computing (MEC) has ‎emerged as a promising solution that extends cloud data-processing and decision-making to edge devices. ‎By adopting a MEC structure, IoT applications could be executed ‎locally, on ‎an edge server, different fog nodes, or distant cloud ‎data centers. However, we are ‎often ‎faced with wanting to optimize conflicting criteria such as ‎minimizing energy ‎consumption of limited local capabilities (in terms of CPU, RAM, storage, bandwidth) of mobile edge ‎devices and trying to ‎keep ‎high performance (reducing ‎response time, increasing throughput and service availability) ‎at the same ‎time‎. Achieving one goal may affect the other, making task offloading (TO), ‎resource allocation (RA), and service placement (SP) complex ‎processes. ‎It is a nontrivial multi-objective optimization ‎problem ‎to study the trade-off between conflicting criteria. ‎The paper provides a survey on different TO, SP, and RA recent multi-‎objective optimization (MOO) approaches used in edge computing environments, particularly artificial intelligent (AI) ones, to satisfy various objectives, constraints, and dynamic conditions related to IoT applications‎.

Keywords: mobile edge computing, multi-objective optimization, artificial ‎intelligence ‎approaches, task offloading, resource allocation, ‎ service placement

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Authors: Ravi Patel, Krishna K. Krishnan

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In global supply chains, appropriate and sustainable suppliers play a vital role in supply chain development and feasibility. In a larger organization with huge number of suppliers, it is necessary to divide suppliers based on their past history of quality and delivery of each product category. Since performance of any organization widely depends on their suppliers, well evaluated selection criteria and decision-making models lead to improved supplier assessment and development. In this paper, SCOR® performance evaluation approach and ISO standards are used to determine selection criteria for better utilization of supplier assessment by using hybrid model of Analytic Hierchchy Problem (AHP) and Fuzzy Techniques for Order Preference by Similarity to Ideal Solution (FTOPSIS). AHP is used to determine the global weightage of criteria which helps TOPSIS to get supplier score by using triangular fuzzy set theory. Both qualitative and quantitative criteria are taken into consideration for the proposed model. In addition, a multi-product and multi-time period model is selected for order allocation. The optimization model integrates multi-objective integer linear programming (MOILP) for order allocation and a hybrid approach for supplier selection. The proposed MOILP model optimizes order allocation based on manufacturing process and product tolerances as per manufacturer’s requirement for quality product. The integrated model and solution approach are tested to find optimized solutions for different scenario. The detailed analysis shows the superiority of proposed model over other solutions which considered individual decision making models.

Keywords: AHP, fuzzy set theory, multi-criteria decision making, multi-objective integer linear programming, TOPSIS

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Authors: Kuang-Chi Pan, Shu-Jen Tuan

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Scolothrips takahashii Priesner (Thysanoptera: Thripidae) is a common predatory thrips which feeds on spider mites; it is considered an important natural enemy and a potential biological control agent against spider mites. In order to evaluate the efficacy of S. takahashii against tetranychid mites, life table and functional response study were conducted at 25±1°C, with Tetranychus urticae Priesner as prey. The intrinsic rate of increase (r), finite rate of increase (λ), net reproduction rate (R₀), mean generation time (T) were 0.1674 d⁻¹, 1.1822d⁻¹, 62.26 offspring/individual, and 24.68d. The net consumption rate (C₀) was 846.15, mean daily consumption rate was 51.92 eggs for females and 19.28 eggs for males. S. takahashii exhibited type III functional response when offered T. urticae deutonymphs. Based on the random predator equation, the estimated maximum attack rate (a) and handling time (Th) were 0.1376h⁻¹ and 0.7883h. In addition, a life table experiment was conducted to evaluate the offspring sex allocation and population dynamic of Tetranychus ludeni Zacher under group-rearing conditions with different sex ratios. All bisexual groups produced offspring with similar sex allocation patterns, which started with the majority of females, then transited during the middle of the oviposition period and turned male-biased at the end of the oviposition period.

Keywords: Scolothrips takahashii, Tetranychus urticae, Tetranychus ludeni, two-sex life table, functional response, sex allocation

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Authors: Rouhallah Bagheri, Morteza Mahmoudi, Hadi Moheb-Alizadeh

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In this paper, we develop a supplier selection and order allocation multi-objective model in stochastic environment in which purchasing cost, percentage of delivered items with delay and percentage of rejected items provided by each supplier are supposed to be stochastic parameters following any arbitrary probability distribution. To do so, we use dependent chance programming (DCP) that maximizes probability of the event that total purchasing cost, total delivered items with delay and total rejected items are less than or equal to pre-determined values given by decision maker. After transforming the above mentioned stochastic multi-objective programming problem into a stochastic single objective problem using minimum deviation method, we apply a genetic algorithm to get the later single objective problem solved. The employed genetic algorithm performs a simulation process in order to calculate the stochastic objective function as its fitness function. At the end, we explore the impact of stochastic parameters on the given solution via a sensitivity analysis exploiting coefficient of variation. The results show that as stochastic parameters have greater coefficients of variation, the value of objective function in the stochastic single objective programming problem is worsened.

Keywords: dependent chance programming, genetic algorithm, minimum deviation method, order allocation, supplier selection

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Authors: L. Lamola, E. Manolas, A. Krause

Abstract:

Background: The incidence of childhood cancer incidence is increasing gradually in low-middle income countries, such as South Africa. Globally, there is an extensive range of familial- and hereditary-cancer syndromes, where underlying germline variants increase the likelihood of developing cancer in childhood. Next-Generation Sequencing (NGS) technologies have been key in determining the occurrence and genetic contribution of germline variants to paediatric cancer development. We aimed to design and evaluate a candidate gene panel specific to inherited cancer-predisposing genes to provide a comprehensive insight into the contribution of germline variants to childhood cancer. Methods: 32 paediatric patients (aged 0-18 years) diagnosed with a malignant tumour were recruited, and biological samples were obtained. After quality control, DNA was sequenced using an ion Ampliseq 50 candidate gene panel design and Ion Torrent S5 technologies. Sequencing variants were called using Ion Torrent Suite software and were subsequently annotated using Ion Reporter and Ensembl's VEP. High priority variants were manually analysed using tools such as MutationTaster, SIFT-INDEL and VarSome. Putative identified candidates were validated via Sanger Sequencing. Results: The patients studied had a variety of cancers, the most common being nephroblastoma (13), followed by osteosarcoma (4) and astrocytoma (3). We identified 10 pathogenic / likely pathogenic variants in 10 patients, most of which were novel. Conclusions: According to the literature, we expected ~10% of our patient population to harbour pathogenic or likely pathogenic germline variants, however, we reported about 3 times (~30%) more than we expected. Majority of the identified variants are novel; this may be because this is the first study of its kind in an understudied South African population.

Keywords: Africa, genetics, germline-variants, paediatric-cancer

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Authors: Ahmed I. Alutabi, Naghmeh Dezhabad, Sudhakar Ganti

Abstract:

Data centers have been growing in size and dema nd continuously in the last two decades. Planning for the deployment of resources has been shallow and always resorted to over-provisioning. Data center operators try to maximize the availability of their services by allocating multiple of the needed resources. One resource that has been wasted, with little thought, has been energy. In recent years, programmable resource allocation has paved the way to allow for more efficient and robust data centers. In this work, we examine the predictability of resource usage in a data center environment. We use a number of models that cover a wide spectrum of machine learning categories. Then we establish a framework to guarantee the client service level agreement (SLA). Our results show that using prediction can cut energy loss by up to 55%.

Keywords: machine learning, artificial intelligence, prediction, data center, resource allocation, green computing

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Authors: Wedad Albalawi

Abstract:

The mathematical inequalities have been the core of mathematical study and used in almost all branches of mathematics as well in various areas of science and engineering. The inequalities by Hardy, Littlewood and Polya were the first significant composition of several science. This work presents fundamental ideas, results and techniques, and it has had much influence on research in various branches of analysis. Since 1934, various inequalities have been produced and studied in the literature. Furthermore, some inequalities have been formulated by some operators; in 1989, weighted Hardy inequalities have been obtained for integration operators. Then, they obtained weighted estimates for Steklov operators that were used in the solution of the Cauchy problem for the wave equation. They were improved upon in 2011 to include the boundedness of integral operators from the weighted Sobolev space to the weighted Lebesgue space. Some inequalities have been demonstrated and improved using the Hardy–Steklov operator. Recently, a lot of integral inequalities have been improved by differential operators. Hardy inequality has been one of the tools that is used to consider integrity solutions of differential equations. Then, dynamic inequalities of Hardy and Coposon have been extended and improved by various integral operators. These inequalities would be interesting to apply in different fields of mathematics (functional spaces, partial differential equations, mathematical modeling). Some inequalities have been appeared involving Copson and Hardy inequalities on time scales to obtain new special version of them. A time scale is an arbitrary nonempty closed subset of the real numbers. Then, the dynamic inequalities on time scales have received a lot of attention in the literature and has become a major field in pure and applied mathematics. There are many applications of dynamic equations on time scales to quantum mechanics, electrical engineering, neural networks, heat transfer, combinatorics, and population dynamics. This study focuses on Hardy and Coposon inequalities, using Steklov operator on time scale in double integrals to obtain special cases of time-scale inequalities of Hardy and Copson on high dimensions. The advantage of this study is that it uses the one-dimensional classical Hardy inequality to obtain higher dimensional on time scale versions that will be applied in the solution of the Cauchy problem for the wave equation. In addition, the obtained inequalities have various applications involving discontinuous domains such as bug populations, phytoremediation of metals, wound healing, maximization problems. The proof can be done by introducing restriction on the operator in several cases. The concepts in time scale version such as time scales calculus will be used that allows to unify and extend many problems from the theories of differential and of difference equations. In addition, using chain rule, and some properties of multiple integrals on time scales, some theorems of Fubini and the inequality of H¨older.

Keywords: time scales, inequality of hardy, inequality of coposon, steklov operator

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Authors: Jin Liang, James H. Liu, Ti-Jun Xiao

Abstract:

It is known that there exist many physical phenomena where abrupt or impulsive changes occur either in the system dynamics, for example, ad-hoc network, or in the input forces containing impacts, for example, the bombardment of space antenna by micrometeorites. There are many other examples such as ultra high-speed optical signals over communication networks, the collision of particles, inventory control, government decisions, interest changes, changes in stock price, etc. These are impulsive phenomena. Hence, as a combination of the traditional initial value problems and the short-term perturbations whose duration can be negligible in comparison with the duration of the process, the systems with impulsive conditions (i.e., impulsive systems) are more realistic models for describing the impulsive phenomenon. Such a situation is also suitable for the delay systems, which include some of the past states of the system. So far, there have been a lot of research results in the study of impulsive systems with delay both in finite and infinite dimensional spaces. In this paper, we investigate the periodicity of solutions to the nonautonomous impulsive evolution equations with infinite delay in Banach spaces, where the coefficient operators (possibly unbounded) in the linear part depend on the time, which are impulsive systems in infinite dimensional spaces and come from the optimal control theory. It was indicated that the study of periodic solutions for these impulsive evolution equations with infinite delay was challenging because the fixed point theorems requiring some compactness conditions are not applicable to them due to the impulsive condition and the infinite delay. We are happy to report that after detailed analysis, we are able to combine the techniques developed in our previous papers, and some new ideas in this paper, to attack these impulsive evolution equations and derive periodic solutions. More specifically, by virtue of the related transition operator family (evolution family), we present a Poincaré operator given by the nonautonomous impulsive evolution system with infinite delay, and then show that the operator is a condensing operator with respect to Kuratowski's measure of non-compactness in a phase space by using an Amann's lemma. Finally, we derive periodic solutions from bounded solutions in view of the Sadovskii fixed point theorem. We also present a relationship between the boundedness and the periodicity of the solutions of the nonautonomous impulsive evolution system. The new results obtained here extend some earlier results in this area for evolution equations without impulsive conditions or without infinite delay.

Keywords: impulsive, nonautonomous evolution equation, optimal control, periodic solution

Procedia PDF Downloads 224

Authors: Han-Qin Zheng, Yi-Fan Chiang-Hsieh, Chia-Hung Chien, Wen-Chi Chang

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As the most important non-vascular plants, algae have many research applications, including high species diversity, biofuel sources, adsorption of heavy metals and, following processing, health supplements. With the increasing availability of next-generation sequencing (NGS) data for algae genomes and transcriptomes, an integrated resource for retrieving gene expression data and metabolic pathway is essential for functional analysis and systems biology in algae. However, gene expression profiles and biological pathways are displayed separately in current resources, and making it impossible to search current databases directly to identify the cellular response mechanisms. Therefore, this work develops a novel AlgaePath database to retrieve gene expression profiles efficiently under various conditions in numerous metabolic pathways. AlgaePath, a web-based database, integrates gene information, biological pathways, and next-generation sequencing (NGS) datasets in Chlamydomonasreinhardtii and Neodesmus sp. UTEX 2219-4. Users can identify gene expression profiles and pathway information by using five query pages (i.e. Gene Search, Pathway Search, Differentially Expressed Genes (DEGs) Search, Gene Group Analysis, and Co-Expression Analysis). The gene expression data of 45 and 4 samples can be obtained directly on pathway maps in C. reinhardtii and Neodesmus sp. UTEX 2219-4, respectively. Genes that are differentially expressed between two conditions can be identified in Folds Search. Furthermore, the Gene Group Analysis of AlgaePath includes pathway enrichment analysis, and can easily compare the gene expression profiles of functionally related genes in a map. Finally, Co-Expression Analysis provides co-expressed transcripts of a target gene. The analysis results provide a valuable reference for designing further experiments and elucidating critical mechanisms from high-throughput data. More than an effective interface to clarify the transcript response mechanisms in different metabolic pathways under various conditions, AlgaePath is also a data mining system to identify critical mechanisms based on high-throughput sequencing.

Keywords: next-generation sequencing (NGS), algae, transcriptome, metabolic pathway, co-expression

Procedia PDF Downloads 382

Authors: Pulak Swain, A. K. Ojha

Abstract:

Portfolio optimization is based on dealing with the problems of efficient asset allocation. Risk and Expected return are two conflicting criteria in such problems, where the investor prefers the return to be high and the risk to be low. Using multi-objective approach we can solve those type of problems. However the information which we have for the input parameters are generally ambiguous and the input values can fluctuate around some nominal values. We can not ignore the uncertainty in input values, as they can affect the asset allocation drastically. So we use Robust Optimization approach to the problems where the input parameters comes under box uncertainty. In this paper, we solve the multi criteria robust problem with the help of  E- constraint method.

Keywords: portfolio optimization, multi-objective optimization, ϵ - constraint method, box uncertainty, robust optimization

Procedia PDF Downloads 117

Authors: Andressa dos Santos Nicolau, João P. da S.C Algusto, Claudio Márcio do N. A. Pereira, Roberto Schirru

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In case of abnormal situations, the nuclear power plant (NPP) operators must follow written procedures to check the condition of the plant and to classify the type of emergency. In this paper, we proposed a Real Time Expert System in order to improve operator’s performance in case of transient or accident with reactor shutdown. The expert system’s knowledge is based on the sequence of events (SoE) of known accident and two emergency procedures of the Brazilian Pressurized Water Reactor (PWR) NPP and uses two kinds of knowledge representation: rule and logic trees. The results show that the system was able to classify the response of the automatic protection systems, as well as to evaluate the conditions of the plant, diagnosing the type of occurrence, recovery procedure to be followed, indicating the shutdown root cause, and classifying the emergency level.

Keywords: emergence procedure, expert system, operator support, PWR nuclear power plant

Procedia PDF Downloads 308