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Journal articles on the topic 'Refined ABC Algorithm'

Author: Grafiati

Published: 3 June 2025

Last updated: 31 July 2025

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1

Lenin, K. "DECREASING ACTUAL POWER LOSS BY REFINED ABC ALGORITHM." International Journal of Research -GRANTHAALAYAH 5, no. 10 (2017): 63–71. http://dx.doi.org/10.29121/granthaalayah.v5.i10.2017.2269.

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Refined ABC algorithm (RABC) proposed in this paper to solve the optimal reactive power problem. An artificial bee colony (ABC) algorithm is one of copious swarm intelligence algorithms that employ the foraging behavior of honeybee colonies. To progress the convergence performance and search speed of finding the best solution RABC algorithm has been developed. The main objective in this problem is to minimize the real power loss and also to keep the variables within the specified limits. Proposed Refined ABC (RABC) algorithm has been tested in standard IEEE 118 & practical 191 bus test sys

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Dr.K.Lenin. "DECREASING ACTUAL POWER LOSS BY REFINED ABC ALGORITHM." International Journal of Research - Granthaalayah 5, no. 10 (2017): 63–71. https://doi.org/10.5281/zenodo.1039599.

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Refined ABC algorithm (RABC) proposed in this paper to solve the optimal reactive power problem. An artificial bee colony (ABC) algorithm is one of copious swarm intelligence algorithms that employ the foraging behavior of honeybee colonies. To progress the convergence performance and search speed of finding the best solution RABC algorithm has been developed. The main objective in this problem is to minimize the real power loss and also to keep the variables within the specified limits. Proposed Refined ABC (RABC) algorithm has been tested in standard IEEE 118 & practical 191 bus test sys

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Sun, Qiang, and Shupei Liu. "Locating abrupt disaster emergency logistics centres using improved artificial bee colony (IABC) algorithm." Science Progress 104, no. 2 (2021): 003685042110162. http://dx.doi.org/10.1177/00368504211016205.

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Emergency management is conceptualized as a complex, multi-objective optimization problem related to facility location. However, little research has been performed on the horizontal transportation of emergency logistics centres. This study makes contributions to the multi-objective locating abrupt disaster emergency logistics centres model with the smallest total cost and the largest customer satisfaction. The IABC algorithm is proposed in this paper to solve the multi-objective emergency logistics centres locating problem. IABC algorithm can effectively calculate the optimal location of abrup

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Ghaffar, Muhammad Arslan, Lei Peng, Muhammad Umer Aslam, Muhammad Adeel, and Salim Dassari. "Vehicle-UAV Integrated Routing Optimization Problem for Emergency Delivery of Medical Supplies." Electronics 13, no. 18 (2024): 3650. http://dx.doi.org/10.3390/electronics13183650.

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In recent years, the delivery of medical supplies has faced significant challenges due to natural disasters and recurrent public health emergencies. Addressing the need for improved logistics operations during such crises, this article presents an innovative approach, namely integrating vehicle and unmanned aerial vehicle (UAV) logistics to enhance the efficiency and resilience of medical supply chains. Our study introduces a dual-mode distribution framework which employs the density-based spatial clustering of applications with noise (DBSCAN) algorithm for efficiently clustering demand zones

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Saleh, Izzati, Nuradlin Borhan, and Wan Rahiman. "Smoothing RRT Path for Mobile Robot Navigation Using Bio-inspired Optimization Method." Pertanika Journal of Science and Technology 32, no. 5 (2024): 2327–42. http://dx.doi.org/10.47836/pjst.32.5.22.

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This research addresses the challenges of using the Rapidly Exploring Random Tree (RRT) algorithm as a mobile robot path planner. While RRT is known for its flexibility and wide applicability, it has limitations, including careful tuning, susceptibility to local minima, and generating jagged paths. The main objective is to improve the smoothness of RRT-generated trajectories and reduce significant path curvature. A novel approach is proposed to achieve these, integrating the RRT path planner with a modified version of the Whale Optimization Algorithm (RRT-WOA). The modified WOA algorithm incor

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Tang, Yu, Min Xu, Jie Yue, and Shixiong Zheng. "Torsional Stiffness Correction of the Split-Type Triple-Box Steel Box Girder Based on Refined Simulation." Advances in Civil Engineering 2021 (June 28, 2021): 1–14. http://dx.doi.org/10.1155/2021/3667239.

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Split-type steel box girders are widely used in long-span bridges because of their good wind-resistance performance. In the design stage, a simple finite element model is usually established based on the beam element for wind-resistance design. However, since the irregular cross-beams and diaphragms in the split steel box girder cannot be virtually established, the stiffness of the girder will be underestimated. To improve the accuracy in simulating stiffness of the split-type triple-box steel box girder (STSBG) with the beam element model (BEM), a correction is made to the initial beam elemen

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Wu, Ji, Linqi Huang, Binquan He, et al. "Research on Multi-Objective Ore Blending Optimization Based on Non-Equilibrium Grade Polymetallic Mine of Shizhuyuan." Minerals 12, no. 11 (2022): 1358. http://dx.doi.org/10.3390/min12111358.

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The ore blending of non-equilibrium grade polymetallic ore is affected by various factors such as mineral resources, production technology, and economic efficiency. Aiming at the problems of Shizhuyuan Polymetallic Mine being diverse in nature, uneven in size, having many mining points, with large grade fluctuation, this paper proposes an optimization method for ore blending based on an artificial bee colony (ABC) algorithm. A multi-metal and multi-objective ore-blending optimization model is established with the minimum fluctuation of ore grade, mining transportation cost, and production capa

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Yuan, Chenyang, Chunlei Li, Hao Huang, Weifeng Bai, and Yunfei Xie. "Numerical Simulation Study on the Constitutive Model of Fully-Graded Concrete Based on Statistical Damage Theory." Buildings 13, no. 10 (2023): 2412. http://dx.doi.org/10.3390/buildings13102412.

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A statistical damage model (SDM) of fully-graded concrete was created using statistical damage theory, based on the mechanical properties of axial tension and axial compression of the material. The SDM considers two damage modes, fracture and yield, and explains the intrinsic connection between the mesoscopic damage evolution mechanism and the macroscopic nonlinear mechanical behavior of fully-graded concrete. The artificial bee colony (ABC) algorithm was used to obtain the optimal parameter combination through an intelligent search of parameters εa, εh, εb and H in the constitutive model by t

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Deng, Xiaoyi. "An Efficient Hybrid Artificial Bee Colony Algorithm for Customer Segmentation in Mobile E-commerce." Journal of Electronic Commerce in Organizations 11, no. 2 (2013): 53–63. http://dx.doi.org/10.4018/jeco.2013040105.

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Customer segmentation can enable company administrators to establish good customer relations and refine their marketing strategies to match customer expectations. To achieve optimal segmentation, a hybrid Artificial Bee Colony algorithm (ABC) is proposed to classify customers in mobile e-commerce environment, which is named KP-ABC. KP-ABC is based on three famous algorithms: the K-means, Particle Swarm Optimization (PSO), and ABC. The author first applied five clustering algorithms to a mobile customer segmentation problem using data collected from a well established chain restaurant which has

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Melveetil, Visakh Chandran. "An Optimal Multi-Modal Approach for Stock Market Price Forecasting with Fused Sentiment Analysis for Real Time Data." International Journal for Research in Applied Science and Engineering Technology 12, no. 9 (2024): 1605–22. http://dx.doi.org/10.22214/ijraset.2024.64417.

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Abstract: This study presents an innovative fusion-based methodology that integrates real-time stock market technical indicators with news sentiment analysis from financial news feeds to enhance stock selection decisions. The proposed framework employs a Bidirectional Long Short-Term Memory (Bi-LSTM) model for forecasting stock prices and a Deep Neural Network (DNN) used in conjunction with transformer-based model for sentiment classification, both optimized through the incorporation of real-time datasets. To further refine feature selection, Artificial Bee Colony (ABC) and Firefly algorithms

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King, Martin S., Richard Rode, Isabelle Cohen-Codar, et al. "Predictive Genotypic Algorithm for Virologic Response to Lopinavir-Ritonavir in Protease Inhibitor-Experienced Patients." Antimicrobial Agents and Chemotherapy 51, no. 9 (2007): 3067–74. http://dx.doi.org/10.1128/aac.00388-07.

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ABSTRACT Several genotypic resistance algorithms have been proposed for quantitation of the degree of phenotypic resistance to the human immunodeficiency virus (HIV) protease inhibitor (PI) lopinavir (LPV), including the original LPV mutation score. In this study, we retrospectively evaluated 21 codons in HIV protease known to be associated with PI resistance in a large antiretroviral agent-experienced observational patient cohort, “Autorisation Temporaire d'Utilization” (ATU), to assess whether a more optimal algorithm could be derived by using virologic response data from patients treated wi

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Ngeljaratan, Luna, Mohamed A. Moustafa, Agung Sumarno, Agus Mudo Prasetyo, Dany Perwita Sari, and Maidina Maidina. "Improved Blob-Based Feature Detection and Refined Matching Algorithms for Seismic Structural Health Monitoring of Bridges Using a Vision-Based Sensor System." Infrastructures 9, no. 6 (2024): 97. http://dx.doi.org/10.3390/infrastructures9060097.

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The condition and hazard monitoring of bridges play important roles in ensuring their service continuity not only throughout their entire lifespan but also under extreme conditions such as those of earthquakes. Advanced structural health monitoring (SHM) systems using vision-based technology, such as surveillance, traffic, or drone cameras, may assist in preventing future impacts due to structural deficiency and are critical to the emergence of sustainable and smart transportation infrastructure. This study evaluates several feature detection and tracking algorithms and implements them in the

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Ye, Fang, Fei Che, and Lipeng Gao. "Multiobjective Cognitive Cooperative Jamming Decision-Making Method Based on Tabu Search-Artificial Bee Colony Algorithm." International Journal of Aerospace Engineering 2018 (December 9, 2018): 1–10. http://dx.doi.org/10.1155/2018/7490895.

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For the future information confrontation, a single jamming mode is not effective due to the complex electromagnetic environment. Selecting the appropriate jamming decision to coordinately allocate the jamming resources is the development direction of the electronic countermeasures. Most of the existing studies about jamming decision only pay attention to the jamming benefits, while ignoring the jamming cost. In addition, the conventional artificial bee colony algorithm takes too many iterations, and the improved ant colony (IAC) algorithm is easy to fall into the local optimal solution. Agains

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Liu, Lingyu, Lan Lin, Shen Sun, and Shuicai Wu. "Elucidating Multimodal Imaging Patterns in Accelerated Brain Aging: Heterogeneity through a Discriminant Analysis Approach Using the UK Biobank Dataset." Bioengineering 11, no. 2 (2024): 124. http://dx.doi.org/10.3390/bioengineering11020124.

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Accelerated brain aging (ABA) intricately links with age-associated neurodegenerative and neuropsychiatric diseases, emphasizing the critical need for a nuanced exploration of heterogeneous ABA patterns. This investigation leveraged data from the UK Biobank (UKB) for a comprehensive analysis, utilizing structural magnetic resonance imaging (sMRI), diffusion magnetic resonance imaging (dMRI), and resting-state functional magnetic resonance imaging (rsfMRI) from 31,621 participants. Pre-processing employed tools from the FMRIB Software Library (FSL, version 5.0.10), FreeSurfer, DTIFIT, and MELOD

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Lu, Kaiyin, Xinguang Zhang, Tianbo Zhai, and Mengjie Zhou. "Adaptive Sharding for UAV Networks: A Deep Reinforcement Learning Approach to Blockchain Optimization." Sensors 24, no. 22 (2024): 7279. http://dx.doi.org/10.3390/s24227279.

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As unmanned aerial vehicle (UAV) technology expands into diverse applications, the demand for enhanced performance intensifies. Blockchain sharding technology offers promising avenues for improving data processing capabilities and security in drone networks. However, the inherent mobility of UAVs and their dynamic operational environment pose significant challenges to conventional sharding techniques, often resulting in communication latencies and data synchronization delays that compromise efficiency. This study presents a novel blockchain-based adaptive sharding framework specifically design

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16

Pan, Jie, Jingwei Huang, Yunli Wang, Gengdong Cheng, and Yong Zeng. "A self-learning finite element extraction system based on reinforcement learning." Artificial Intelligence for Engineering Design, Analysis and Manufacturing 35, no. 2 (2021): 180–208. http://dx.doi.org/10.1017/s089006042100007x.

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AbstractAutomatic generation of high-quality meshes is a base of CAD/CAE systems. The element extraction is a major mesh generation method for its capabilities to generate high-quality meshes around the domain boundary and to control local mesh densities. However, its widespread applications have been inhibited by the difficulties in generating satisfactory meshes in the interior of a domain or even in generating a complete mesh. The element extraction method's primary challenge is to define element extraction rules for achieving high-quality meshes in both the boundary and the interior of a g

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17

HAN, WENHU, and HAOJUN QIN. "A VELOCITY SELF-LEARNING ALGORITHM FOR TIME-OPTIMAL TRAJECTORY PLANNING ALONG FULLY SPECIFIED PATH – Part II." REVUE ROUMAINE DES SCIENCES TECHNIQUES — SÉRIE ÉLECTROTECHNIQUE ET ÉNERGÉTIQUE 70, no. 2 (2025): 241–46. https://doi.org/10.59277/rrst-ee.2025.2.15.

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In response to the problem of uncertainty in the system dynamics model during time-optimal trajectory planning for industrial handling robots, a novel online, self-learning, model-free time-optimal trajectory planning method is proposed. First, offline kinematic constraints and the Hermite interpolation algorithm are used to obtain the optimal spline velocity curve under kinematic constraints. Then, online trajectory data of the robot's operation is collected, and the trajectory generation method using a self-learning strategy is employed to iteratively refine the trajectory iteratively, resul

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NAN, WENHU, and HAOJUN QIN. "A VELOCITY SELF-LEARNING ALGORITHM FOR TIME-OPTIMAL TRAJECTORY PLANNING ALONG the FULLY SPECIFIED PATH – PART 1." REVUE ROUMAINE DES SCIENCES TECHNIQUES — SÉRIE ÉLECTROTECHNIQUE ET ÉNERGÉTIQUE 70, no. 1 (2025): 109–14. https://doi.org/10.59277/rrst-ee.2025.1.19.

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In response to the problem of uncertainty in the system dynamics model during the time-optimal trajectory planning for the industrial handling robots, a novel online self-learning model-free time-optimal trajectory planning method is proposed. First, offline kinematic constraints and the Hermite interpolation algorithm are used to obtain the optimal spline velocity curve under kinematic constraints. Then, online trajectory data of the robot's operation is collected, and the trajectory generation method using a self-learning strategy is employed to refine the trajectory iteratively, resulting i

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19

Kong, Minseok, and Jungmin So. "Empirical Analysis of Automated Stock Trading Using Deep Reinforcement Learning." Applied Sciences 13, no. 1 (2023): 633. http://dx.doi.org/10.3390/app13010633.

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There are several automated stock trading programs using reinforcement learning, one of which is an ensemble strategy. The main idea of the ensemble strategy is to train DRL agents and make an ensemble with three different actor–critic algorithms: Advantage Actor–Critic (A2C), Deep Deterministic Policy Gradient (DDPG), and Proximal Policy Optimization (PPO). This novel idea was the concept mainly used in this paper. However, we did not stop there, but we refined the automated stock trading in two areas. First, we made another DRL-based ensemble and employed it as a new trading agent. We named

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Pintaudi, Basilio, Alessandro Nani, Ilaria Gironi, and Federico Bertuzzi. "Advanced technology for type 1 diabetes care in a deaf-mute patient." BMJ Case Reports 15, no. 1 (2022): e245336. http://dx.doi.org/10.1136/bcr-2021-245336.

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A 48-year-old deaf-mute man attending our outpatient clinic for long-term severely decompensated type 1 diabetes (mean glycated haemoglobin over 100 mmol/mol) has proved to be the best candidate for the beneficial implant of a next-generation closed loop insulin pump featuring a new refined auto-correction algorithm. The patient had already worn a stand-alone real-time continuous glucose monitoring (rt-CGM) device for 2 years, but his haemoglobin A1c (HbA1c) remained out of target with ample glucose variability. Then, we decided to use a novel advanced hybrid closed-loop insulin pump (the Mini

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21

Cohen, Inbar, Mali Salmon-Divon, Sapir Peled, Irit Avivi, Yael C. Cohen, and Efrat Luttwak. "Using Machine Learning Methods to Explore the Role of Non-Coding RNAs in DLBCL." Blood 138, Supplement 1 (2021): 4324. http://dx.doi.org/10.1182/blood-2021-152315.

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Abstract Diffuse large B-cell lymphoma (DLBCL) is a complex and aggressive malignancy. The standard-of-care chemo-immunotherapeutic regimen, which consists of R-CHOP (rituximab, cyclophosphamide, doxorubicin, vincristine, and prednisone), leads to a complete response in most patients. Unfortunately, 30-40% of patients are either refractory to the current treatment regimen or experience disease relapse after complete response, and thus, these patients exhibit a dismal prognosis. Gene expression profiling has delineated two distinct molecular subtypes of DLBCL, the germinal center B-cell-like (G

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Bonavita, M., V. D’Orazi, D. Mesa, et al. "Orbiting a binary." Astronomy & Astrophysics 608 (December 2017): A106. http://dx.doi.org/10.1051/0004-6361/201731003.

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Aims. In this paper we present the results of the SPHERE observation of the HD 284149 system, aimed at a more detailed characterisation of both the primary and its brown dwarf companion. Methods. We observed HD 284149 in the near-infrared with SPHERE, using the imaging mode (IRDIS+IFS) and the long-slit spectroscopy mode (IRDIS-LSS). The data were reduced using the dedicated SPHERE pipeline, and algorithms such as PCA and TLOCI were applied to reduce the speckle pattern. Results. The IFS images revealed a previously unknown low-mass (~0.16 M⊙) stellar companion (HD 294149 B) at ~0.1′′, compati

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Flümann, Ruth, Pascal Nieper, Tim Lohmann, et al. "A Novel Autochthonous Mouse Model Serves As a Preclinical Evaluation Platform and Explores Dual BTK and BCL2 Inhibition for Activated B Cell-like Diffuse Large B Cell Lymphoma." Blood 138, Supplement 1 (2021): 712. http://dx.doi.org/10.1182/blood-2021-146871.

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Abstract Diffuse large B cell lymphoma (DLBCL) is the most common lymphoid malignancy in adults. Both biologically and clinically, DLBCL represents a highly heterogeneous disease. DLBCL has been subdivided into germinal center B cell (GCB)-like and activated B cell (ABC)-like DLBCL, on the basis of gene expression profiling, which separates DLBCL according to the presumed cell of origin (COO). This COO-based classifier distinguishes sub-entities displaying distinct biological features, pathogenesis and clinical response to frontline therapy. In addition to this classic transcriptome-based stra

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24

Zhi, Yang Lim, Norhafiz Azis, Ahmad Hafiz Mohd Hashim, Mohd Amran Mohd Radzi, Nor Mohd Haziq Norsahperi, and Azrul Mohd Ariffin. "Acoustic Emission Partial Discharge Localization in Oil Based on Artificial Bee Colony." Pertanika Journal of Science and Technology 33, no. 1 (2025). https://doi.org/10.47836/pjst.33.1.11.

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This study explores the application of an artificial bee colony (ABC) to locate partial discharge (PD) in a test tank based on acoustic emission (AE) approach. Data from a previous AE PD experimental study, which includes the coordinates of 3 AE sensors and the time difference of arrival (TDOA), were used to construct the nonlinear localization equations. It is known that localization algorithms are among the factors that can affect PD localization accuracy, and the ongoing research in this area underscores the need for further advancements in this topic. Therefore, the ABC was proposed to est

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Dong, Yiqun, Jinyi Ma, Can Wang, and Jianliang Ai. "Knowledge-Driven Accurate Opponent Trajectory Prediction for Gun-Dominated Autonomous Air Combat." Journal of Aerospace Information Systems, March 9, 2023, 1–13. http://dx.doi.org/10.2514/1.i011214.

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The autonomous air combat (AAC) technique has been a lasting topic for decades. Accurate opponent trajectory prediction provides a fundamental basis for decision making in AAC. In this work, we propose a knowledge-driven scheme for the opponent trajectory prediction problem in one-versus-one gun-dominated within-visual-range (WVR) air combat. A dedicated air combat engagement database is first constructed via skilled human pilots flying WVR air combat. Two baseline algorithms following rule-based and learning-based paradigms are developed and optimized. The knowledge-driven scheme begins with

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Laval, Guillaume, Etienne Patin, Lluis Quintana‐Murci, and Gaspard Kerner. "Deep estimation of the intensity and timing of natural selection from ancient genomes." Molecular Ecology Resources, August 31, 2024. http://dx.doi.org/10.1111/1755-0998.14015.

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AbstractLeveraging past allele frequencies has proven to be key for identifying the impact of natural selection across time. However, this approach suffers from imprecise estimations of the intensity (s) and timing (T) of selection, particularly when ancient samples are scarce in specific epochs. Here, we aimed to bypass the computation of allele frequencies across arbitrarily defined past epochs and refine the estimations of selection parameters by implementing convolutional neural networks (CNNs) algorithms that directly use ancient genotypes sampled across time. Using computer simulations,

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27

Ji, Fanrong, Yunquan Nan, Aifang Wei, Peiyan Fan, Zhaoyuan Luo, and Xiaoqing Song. "Residential Building Duration Prediction Based on Mean Clustering and Neural Network." Advances in Civil Engineering 2024, no. 1 (2024). http://dx.doi.org/10.1155/2024/2444698.

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The duration of a residential building project will directly influence its successful implementation; hence, it is essential to estimate a reasonable timeframe. In this study, a genetic algorithm (GA) was employed to optimize and refine the weights and thresholds of a back propagation (BP) neural network, thereby creating a GA‐BP neural network model. A dataset comprising 111 instances of residential building durations was gathered, segmented into 90 training sets and 21 test sets. The model was validated and assessed through root mean square error (RMSE), correlation coefficient (R), and aver

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28

Bengel, Lars, Matthias Thimm, and Tjitze Rienstra. "Learning argumentation frameworks from labelings." Argument & Computation, July 18, 2023, 1–39. http://dx.doi.org/10.3233/aac-220018.

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We consider the problem of learning argumentation frameworks from a given set of labelings such that every input is a σ-labeling of these argumentation frameworks. Our new algorithm takes labelings and computes attack constraints for each argument that represent the restrictions on argumentation frameworks that are consistent with the input labelings. Having constraints on the level of arguments allows for a very effective parallelization of all computations. An important element of this approach is maintaining a representation of all argumentation frameworks that satisfy the input labelings i

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29

von Känel, Rafael, та Benjamin Matschke. "Solving 𝑆-unit, Mordell, Thue, Thue–Mahler and Generalized Ramanujan–Nagell Equations via the Shimura–Taniyama Conjecture". Memoirs of the American Mathematical Society 286, № 1419 (2023). http://dx.doi.org/10.1090/memo/1419.

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In the first part we construct algorithms (over Q \mathbb {Q} ) which we apply to solve S S -unit, Mordell, cubic Thue, cubic Thue–Mahler and generalized Ramanujan–Nagell equations. As a byproduct we obtain alternative practical approaches for various classical Diophantine problems, including the fundamental problem of finding all elliptic curves over Q \mathbb {Q} with good reduction outside a given finite set of rational primes. The first type of our algorithms uses modular symbols, and the second type combines explicit height bounds with efficient sieves. In particular we construct a refine

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Ibrahim, Mahmoud, Ebtesam M. Ba‐Essa, Jason Baker, et al. "Cardio‐renal‐metabolic disease in primary care setting." Diabetes/Metabolism Research and Reviews, December 19, 2023. http://dx.doi.org/10.1002/dmrr.3755.

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AbstractIn the primary care setting providers have more tools available than ever before to impact positively obesity, diabetes, and their complications, such as renal and cardiac diseases. It is important to recognise what is available for treatment taking into account diabetes heterogeneity. For those who develop type 2 diabetes (T2DM), effective treatments are available that for the first time have shown a benefit in reducing mortality and macrovascular complications, in addition to the well‐established benefits of glucose control in reducing microvascular complications. Some of the newer m

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Sherr, Jennifer L., Simon Bergford, Robin L. Gal, et al. "Exploring Factors That Influence Postexercise Glycemia in Youth With Type 1 Diabetes in the Real World: The Type 1 Diabetes Exercise Initiative Pediatric Study (T1DEXIP)." Diabetes Care, February 27, 2024. http://dx.doi.org/10.2337/dc23-2212.

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OBJECTIVE To explore 24-h postexercise glycemia and hypoglycemia risk, data from the Type 1 Diabetes Exercise Initiative Pediatric (T1DEXIP) study were analyzed to examine factors that may influence glycemia. RESEARCH DESIGN AND METHODS This was a real-world observational study with participant self-reported physical activity, food intake, and insulin dosing (multiple daily injection users). Heart rate, continuous glucose data, and available pump data were collected. RESULTS A total of 251 adolescents (42% females), with a mean ± SD age of 14 ± 2 years, and hemoglobin A1c (HbA1c) of 7.1 ± 1.3%

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Maryam, Jehangir1 and Syed Farhan Ahmad. "STRUCTURAL STUDIES OF ASPARTIC ENDOPEPTIDASE pep2 FROM NEOSARTORYA FISHERICA USING HOMOLGY MODELING TECHNIQUE." March 29, 2013. https://doi.org/10.5281/zenodo.1406897.

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International Journal on Bioinformatics & Biosciences (IJBB) Vol.3, No.1, March 2013 DOI : 10.5121/ijbb.2013.3102 7 STRUCTURAL STUDIES OF ASPARTIC ENDOPEPTIDASE pep2 FROM NEOSARTORYA FISHERICA USING HOMOLGY MODELING TECHNIQUES Maryam Jehangir1 and Syed Farhan Ahmad2 1Department of Bio-informatics, Shaheed Benazir Bhutto Women University, Canal Road university town, Peshawar maryam.bioinfo@yahoo.com 2Department of Biotechnology & Bioinformatics, Islamic International University, Islamabad zaid_khan68@yahoo.com ABSTRACT Aspartic endo peptidase pep2 protein of neosartorya fisherica belong

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