Hasil untuk "Systems engineering"

Ncamisile Nondumiso Maseko, Dirk Enke, Pius Adewale Owolawi et al.

Jianjun Chen, Liyuan Zheng, Huilai Zou et al.

Precise segmentation of individual teeth from digital three-dimensional (3D) tooth models is critical in computer-assisted orthodontic surgery. This study explores the application of Point Multi-Layer Perceptron (PointMLP) in processing 3D tooth models and introduces an innovative integration of the Graph Attentional Convolution (GAC) Layer with a graph attention mechanism. By incorporating the GAC Layer into PointMLP, the model can focus on key local regions in the 3D tooth model and dynamically adjust the attention applied to these areas. This enhanced attention mechanism allows the model to better capture subtle surface structures, facilitating the accurate extraction of valuable local features. Compared to other traditional segmentation algorithms, the proposed method shows improvements of 1.1, 2.04, 1.06, 2.2, and 1.8 percentage points in Overall Accuracy (OA), Sensitivity (SEN), Positive Predictive Value (PPV), and Intersection Over Union (IoU), respectively. At the same number of training epochs, our method outperforms both GAC and PointMLP in segmentation performance.

Abdulrahman M. Abdulghani, Azizol Abdullah, A. R. Rahiman et al.

Modern Software-Defined Wide Area Networks (SD-WANs) require adaptive controller placement addressing multi-objective optimization where latency minimization, load balancing, and fault tolerance must be simultaneously optimized. Traditional static approaches fail under dynamic network conditions with evolving traffic patterns and topology changes. This paper presents a novel hybrid framework integrating Gaussian Mixture Model (GMM) clustering with Multi-Agent Reinforcement Learning (MARL) for dynamic controller placement. The approach leverages probabilistic clustering for intelligent MARL initialization, reducing exploration requirements. Centralized Training with Decentralized Execution (CTDE) enables distributed optimization through cooperative agents. Experimental evaluation using real-world topologies demonstrates a noticeable reduction in the latency, improvement in network balance, and significant computational efficiency versus existing methods. Dynamic adaptation experiments confirm superior scalability during network changes. The hybrid architecture achieves linear scalability through problem decomposition while maintaining real-time responsiveness, establishing practical viability.

E. Shahri, P. Pedreiras, L. Almeida

The increasing prominence of concepts such as Smart Production and Industrial Internet of Things (IIoT) within the context of Industry 4.0 has introduced a new set of requirements for the engineering of industrial systems, including support for dynamic environments, timeliness guarantees, support for heterogeneity, interoperability and reliability. These requirements are further exacerbated at the network level by the notable rise in the number and variety of devices involved. To stay competitive in this ever-changing industrial landscape while boosting productivity, it is vital to meet those requirements, combining established protocols with emerging technologies. Software-Defined Networking (SDN) is the forefront traffic management paradigm that offers flexibility for complex industrial networks, enabling efficient resource allocation and dynamic reconfiguration. Message Queuing Telemetry Transport (MQTT) is a low-overhead protocol of the application layer that is gaining popularity in the scope of the IoT and IIoT. However, its Quality-of-Service (QoS) policies do not support timeliness requirements. This article presents a framework that seamlessly integrates SDN and MQTT, enhancing network management flexibility while satisfying real-time requirements found in industrial environments. It leverages the User Properties of MQTTv5 to allow specifying real-time requirements. MQTT traffic is intercepted by a Network Manager that extracts real-time information and instructs an SDN controller to deploy corresponding network reservations. MQTT traffic across multiple edge networks is propagated by selected brokers using multicasting. Extensive experiments validate the proposed approach, demonstrating its superiority over MQTT and Direct Multicast-MQTT (DM-MQTT) DM-MQTT in latency reduction. A response time analysis, validated experimentally, emphasizes robust performance across metrics.

Ahatesham Bhuiyan, Eftekhar Hossain, Mohammed Moshiul Hoque et al.

Image captioning, the process of generating natural language descriptions based on image content, has garnered attention in AI research for its implications in scene understanding and human-computer interaction. While much prior research has focused on caption generation for English, addressing low-resource languages like Bengali presents challenges, particularly in producing coherent captions linking visual objects with corresponding words. This paper proposes a context-aware attention mechanism over semantic attention to accurately diagnose objects for image captioning in Bengali. The proposed architecture consists of an encoder and a decoder block. We chose ResNet-50 over the other pre-trained models for encoding the image features due to its ability to solve the vanishing gradient problem and recognize complex object features. For decoding generated captions, a bidirectional Gated Recurrent Unit (GRU) architecture combined with an attention mechanism captures contextual dependencies in both directions, resulting in more accurate captions. The paper also highlights the challenge of transferring knowledge between domains, especially with culturally specific images. Evaluation of three Bengali benchmark datasets, namely BAN-Cap, BanglaLekhaImageCaption, and Bornon, demonstrates significant performance improvement in METEOR score over existing methods by approximately 30%, 18%, and 45%, respectively. The proposed context-aware, attention-based image captioning system significantly outperforms current state-of-the-art models in Bengali caption generation despite limitations in reference captions on certain datasets.

Naeem Abbas, Kegang Li, Yewuhalashet Fissha et al.

Abstract In this study, efforts were made to incorporate the influence of discontinuities and failure modes of rock into the classification of rock masses. The past tectonic activities may create microfractures in the rock body therefore the failure moods have been determined carefully under uniaxial compression. The results of the discontinuity analysis, conducted through kinematic study, highlighted the significant impact of wedge failure on the failure of the rock mass. In correlating the geological strength index with rock mass rating, it was observed that joint volume played a negative role, whereas compressive strength played a positive role. These correlations are particularly applicable for a certain rock type, as the compressive strength is inherently dependent on the type of rock. The analysis of failure modes under uniaxial compression reveals that the dissipation energy coefficient initially undergoes rapid increase before reaching its minimum value at the failure stage. The microstructures of the rock effect significantly the elastic and dissipation energy characteristics. Specifically, the axial splitting failure mode emerges as predominant. Given the area's past tectonic activity, these results emphasize the impact of microfractures within the rock body. Relating the failure criteria with the chemical composition of rock types reveals that rocks abundant in SiO2, such as gabbronorite, tend to exhibit brittle failure. Additionally, a dominance of Al2O3 over Fe2O3 suggests a predisposition towards brittle failure, while an increased ratio of CaO to MgO implies increased susceptibility to compression.

V. Ya. Tsvetkov

The development of society is accompanied by an increase in the complexity of management objects and management mechanisms. To counteract the growth of complexity, new management models and methods should be introduced. New methods include semasiological management which uses a model approach and induction principle. It borrows the ideas of semasiology from linguistics and forms management decisions on the basis of application of information management units. Despite the fact that this complicates the preliminary process of preparing for management, it also gives an advantage in the comparability of different management decisions and technologies. Semasiological management allows, when reconfiguring management, not to create management models anew, but to modernise them by replacing management information units or forming new combinations of these units. Semasiological management is related to onomasiological information modeling and requires its use. In addition, it can be used in automated management, smart management, and digital twin management. Semasiological management requires special organisation and specific training, such as a special management language. The research proposes a variant of semasiological management which is based on the application of the theory of information units.

Liu Z, Liang J, Hu H et al.

Zhigang Liu,1,* Jiahui Liang,1,* Hangzhan Hu,1,* Mengli Wu,1,* Jingjing Ma,1 Ziwei Ma,1 Jianing Ji,1 Hengyi Chen,2 Xiaoquan Li,1 Zhizeng Wang,1,2 Yang Luo2,3 1Joint National Laboratory for Antibody Drug Engineering, Clinical Laboratory of the First Affiliated Hospital, School of Medicine, Henan University, Kaifeng, 475004, People’s Republic of China; 2Center of Smart Laboratory and Molecular Medicine, Jiangjin Hospital, School of Medicine, Chongqing University, Chongqing, 400044, People’s Republic of China; 3College of Life Science and Laboratory Medicine, Kunming Medical University, Kunming, 650500, People’s Republic of China*These authors contributed equally to this workCorrespondence: Zhizeng Wang, Joint National Laboratory for Antibody Drug Engineering, Clinical Laboratory of the First Affiliated Hospital, School of Medicine, Henan University, Kaifeng, 475004, People’s Republic of China, Tel +86 15093628687, Email wzhzeng@126.com Yang Luo, Center of Smart Laboratory and Molecular Medicine, Jiangjin Hospital, School of Medicine, Chongqing University, Chongqing, 400044, People’s Republic of China, Tel +86 13594088001, Email luoy@cqu.edu.cnIntroduction: Neutralizing antibodies (NAbs) are essential for preventing reinfection with SARS-CoV-2 and the recurrence of COVID-19; nonetheless, the formation of NAbs following vaccination and infection remains enigmatic due to the lack of a practical and effective NAb assay in routine laboratory settings. In this study, we developed a convenient lateral flow assay for the rapid and precise measurement of serum NAb levels within 20 minutes.Methods: Receptor-binding domain-fragment crystallizable (RBD-Fc) and angiotensin-converting enzyme 2-histidine tag (ACE2-His) were expressed by the eukaryotic expression systems of Spodoptera frugiperda clone 9 and human embryonic kidney 293T, respectively. Then, colloidal gold was synthesized and conjugated with ACE2. After optimizing various operating parameters, an NAb lateral flow assay was constructed. Subsequently, its detection limit, specificity, and stability were systematically evaluated, and clinical samples were analyzed to validate its clinical feasibility.Results: RBD-Fc and ACE2-His were obtained with 94.01% and 90.05% purity, respectively. The synthesized colloidal gold had a uniform distribution with an average diameter of 24.15 ± 2.56 nm. With a detection limit of 2 μg/mL, the proposed assay demonstrated a sensitivity of 97.80% and a specificity of 100% in 684 uninfected clinical samples. By evaluating 356 specimens from infected individuals, we observed that the overall concordance rate between the proposed assay and conventional enzyme-linked immunosorbent assay was 95.22%, and we noticed that 16.57% (59/356) of individuals still did not produce NAbs after infection (both by ELISA and the proposed assay). All the above tests by this assay can obtain results within 20 minutes by the naked eye without any additional instruments or equipment.Conclusion: The proposed assay can expediently and reliably detect anti-SARS-CoV-2 NAbs after infection, and the results provide valuable data to facilitate effective prevention and control of SARS-CoV-2.Clinical trial registration: Serum and blood samples were used under approval from the Biomedical Research Ethics Subcommittee of Henan University, and the clinical trial registration number was HUSOM-2022-052. We confirm that this study complies with the Declaration of Helsinki.Keywords: clinical detection, colloidal gold, neutralizing antibody, point-of-care test, SARS-CoV-2

Yunfeng Jiang, Shu Huang, Jie Sheng et al.

In order to investigate the hydrogen permeation behavior of 316L stainless steel during the microstructural evolution induced by laser peening (LP), an electrochemical hydrogen charging system for initial hydrogen charging of LPed and non-LPed specimen was developed. Afterward, the microhardness, residual stress, and microstructures of the samples were determined and analyzed. Finally, electrochemical hydrogen permeation experiments were undertaken to verify LP's influence on hydrogen permeation parameters of 316L. The results showed that LP reduced the hydrogen-induced hardening rate of the alloy and additionally invoked high magnitude compressive residual stress on its surface. At the layer close to the face of the specimen, the grain refinement rate was as high as 56.18%, which was accompanied by the appearance of high-density dislocations. Compared with the non-LPed sample, the hydrogen permeation time increased significantly, and the saturation current density in steady state hydrogen permeation also decreased gradually.

Karen Anderson, Brandi M. Shabaga, Serge Wich et al.

Summary This journal (Drone Systems and Applications; DSA) conducted a targeted “horizon scan” during 2022 within our team of editors and associate editors. We asked—Which research areas currently under-represented in Drone Systems and Applications would you like to see more heavily represented in the future? The process highlighted five areas of interest and potential growth: Drones in the geosciences Aquatic drones Ground drones Drones within calibration/validation experiments Drones and computer vision Over the past two years (2020–22), the journal has published over 50 papers with a strong leaning towards aerial drones for ecology and also with an engineering focus. DSA is keen to receive new submissions addressing the five highlighted areas, which lie firmly within the aims and scope of the journal. Further to the horizon scan, we propose two special collections for the coming year—one addressing drone applications (drones in geoscience applications) and a second addressing drone systems (aquatic drone systems). We would like to hear from scientists and practitioners in these fields as both contributors and (or) collection editors.

Muhammad Irfan, Zohaib Mushtaq, Nabeel Ahmed Khan et al.

Machine learning (ML) based bearing fault detection is an emerging application of Artificial Intelligence (AI) that has proven its utility in effectively classifying various faults for timely measures. There are myriad studies dedicated to the effective classification of bearing faults under different conditions and experimental settings. In this study, we proposed a weighted voting ensemble (WVE) of three low-computation custom-designed convolutional neural networks (CNNs) to classify bearing faults at 48 KHz. Some of the recent studies have exploited 1-d time-series signals and time-frequency based 2-d transformations for bearing fault classification. However, 1-d signals lack contextual information and higher-dimensional interpretations whereas time-frequency based transformations provide a more appropriate, visually perceivable and explainable representation of the time and frequency changes. Therefore in this study, a scalogram based representation of the signals is leveraged for classification using the CNN. Furthermore, the class imbalance is a significant challenge that affects the modelling behavior and possibly create biases. This study provides a novel density and distance hybrid over-sampling approach namely Density-Aware SMOTE(DA-SMOTE) built upon the SMOTE methodology for a more refined representation of synthetic samples within the minority class distribution. The experimentation procedures were carried out before and after the oversampling and it was observed that the balanced dataset acquired much better accuracy then the imbalanced dataset. This is evident by the fact that the highest validation accuracy for the proposed ensemble method (WVCNN) reached at 0-HP and 1-HP reached 99.28% and 99.13% while for the over-sampled dataset the accuracy soared to 99.71% and 99.87% for 0 and 1-HP respectively. The performance was evaluated for other metrics apart from the accuracy to assess the model’s performance in terms of chance occurrences and the class wise performance.

Zhihao FANG, Zhengquan LI, Mingwei ZHANG

In order to better implement the water environmental management policies, water quality evaluation is the basic step, that is to reasonably divide it into specific water quality category according to multiple water quality parameters in a certain water area.Aimed at this problem, an improved Naive Bayes classification method was proposed, which endowed different attributes with different weights, weakened the assumption of Naive Bayes conditional independence, and made the classification result closer to the actual category.Firstly, referred to the data released by the national surface water quality automatic monitoring station, 500 water quality data were selected as samples, and an evaluation system with four indicators was established, including dissolved oxygen, permanganate index, ammonia nitrogen and total phosphorus.And then, the improved Naive Bayes classification method was used to learn and evaluate the samples, and its classification performance by the five fold cross validation method was verified.The results show that the accuracy, precision, recall and F1 value of the improved Naive Bayes classification method reach 96.0%, 95.9%, 93.8% and 94.8% respectively, with higher performance index of water quality data classification compared with other Naive Bayes classification method, which can provide some reference for the problem of water quality data classification encountered in actual engineering.

Noor Hellyda Hermawati, Susy Rosyida

PT. Telkom Akses Pontianak memiliki sistem informasi Inventory yang selama ini digunakan, selama melakukan penelitian ditemukanlah beberarapa temuan, yaitu seperti informasi terkait ketersedian material, sistem yang kurang efektif terkait data pengeluaran barang yang berdampak pada laporan periodik perusahaan, dan kurangnya optimalisasi Sumber Daya Manusia yang ada. sehingga dengan permsalahan yang ada menjadi dasar untuk melakukan audit sistem informasi yang digunakan. Audit mengacu pada framework COBIT 5 dengan menggunakan Domain MEA ditemukanlah hasil dari tingkat kapabilitas masing-masing sub domain MEA itu sendiri dan juga Gap Analisisnya. Dengan nilai kapabilitas dari subdomain MEA 01 senilai 3,83, Subdomain MEA 02 senilai 3,60, dan Subdomain MEA 03 senilai 3,69, dengan nilai rata-rata yaitu 3,70 dengan keterangan Predictable Process yang berarti objek yang diteliti sudah mencapai proses yang ditetapkan berjalan dalam suatu batas yang ditentukan untuk mencapai tujuan prosesnya. Serta dengan perhitungan Gap Analisis yaitu pada subdomain MEA 01 senilai 1,2, Subdomain MEA 02 senilai 1,4, dan Subdomain MEA 03 senilai 1,3, dengan nilai rata-rata yaitu 1,3 yang berarti perusahaan masih perlu meningkatkan terkait sistem informasi Inventory yang digunakan agar dapat memperoleh hasil yang optimal bagi seluruh pemangku kepentingan.

Knop Krzysztof

The article presents the result of multidimensional analysis of ‘Behaton’ type paving stones’ nonconformities for improving the production process by improving the quality of the final product. Statistical tools, including SPC tools and quality tools, both basic and new, were used to analyse nonconformities in the spatial-temporal system, i.e. according to the type of nonconformity and according to the examined months. The purpose of using the data analysis tools was to thoroughly analyse the cases of nonconformities of the tested product, obtain information on the structure of these nonconformities in the various terms, and information on the stability and predictability of the numerical structure of nonconformity over time. Potential causes influencing a large percentage of paving stone defects were identified, factors and variables influencing the most frequently occurring nonconformities were determined, and improvement actions were proposed. As a result of the multidimensional and multifaceted analyses of paving stone nonconformities, it was shown that in the structure of nonconformity there were cases that were unusual in terms of the number of occurrences, and the lack of stability in the number of nonconformities in terms of the examined months was proven. Three critical nonconformities of the tested product were identified: side surface defects, vertical edge defects, and scratches and cracks. It was determined that the most important factor causing a large percentage of nonconformity was the time of shaking and vibrating the concrete, which was significantly related to the technical condition of the machines, and the most important reason for a large percentage of paving stone nonconformity was the lack of efficient maintenance. Machine, method, and man turned out to be the most important categories of problem factors and specific remedial actions were proposed. A multidimensional look at the structure of paving stone nonconformity as well as the factor and causes causing them has brought a lot of valuable information for the management staff of the analysed company, thanks to which it is possible to improve the production process and improve the quality of the final product.

Fuli Zhou, Yandong He, Felix T. S. Chan et al.

With the increasing demand of individual customption and awareness of cost reduction in express delivery organizations, the Chinese express industry faced with serious challenges especially under the background of government’s strict restrictions on environment and transportation. Therefore, a new service mode called joint distruction (JD) is being tried by the logistics industry, which is expected to address the challenges on online shopping. However, the insufficient understanding of JD adoption factors and their complicated interactions blocks the effectively implementation of the joint distribution. This study aims at identifying potential factors for JD adoption and promoting an effective joint distribution by discovering the interactive relationships among addressed factors. Firstly, potential ingredients for the adoption and implementation of JD are summarized from the literature and industrial interviews. Then, 23 variables are selected and classified into as objectives, drivers, barriers and affected operations. The Interpretive Structural Modeling (ISM) approach is then employed to analyze the crucial factors and the mutual influences amongst 23 variables. Finally, a case study is performed to construct the hierarchical structure of factors toward joint distribution adoption using the proposed ISM-modeling steps. The perplex hierarchical co-relationships are also identified by categorizing the driving variables and dependent variables. Results can assist express enterprises to promote the novel joint distribution mode and acheive higher efficiency of logistics operation by better understanding on crucial factors of JD adoption and implementation.

Calan M. Farley, Calan M. Farley, Patricia Kaynaroglu et al.

Disaster search dogs traverse diverse and unstable surfaces found in collapsed buildings. It is unknown if the physical conditioning on a treadmill involves the same muscle groups that are involved in rubble search. This 14-week prospective cohort study was conducted to investigate changes within the thermal gradients of specific dog muscles following treadmill compared to rubble search. Nine dogs, ranging in age from 6 months to 4 years, were randomly assigned to one of two groups. Each week the two groups would participate in either 20 min of treadmill or rubble searches. Prior to exercise, the dogs were weighed and then kenneled in a temperature-controlled study room for 20 min at 21°C. Pre-exercise thermal images were then captured of the standing dog from the dorsal, left and right lateral, and caudal perspectives, and of the sitting dog from the rostral perspective. Following a 10-min warm-up period of stretches, dogs proceeded to either treadmill or search. Upon completion, dogs were kenneled in the study room for 20 min prior to post-exercise thermal images. Images were sectioned into 22 muscle regions, the pre-exercise images were subtracted from the post-exercise images to determine the temperature difference (ΔT) for that dog, on that day, for that activity. Thermography measures radiant energy, temperature, and converts this information into an image. This study looked at ΔT within a region pre and post-exercise. The study failed to find a statistically significant difference in the ΔT within each muscle group between treadmill and search activities. There was a decrease in ΔT within all muscle regions over the of the study except for the right cranial shoulder, right caudal shoulder, and right hamstring for the treadmill activity only. The decrease was significant in the pelvis, left longissimus, right cranial shoulder for the search activity, left oblique, left caudal shoulder, and left quadricep muscular regions. These findings suggest that ΔT in muscle groups are similar between treadmill exercise and rubble search. Regardless of the exercise type, 14 weeks of structured Search and Rescue training and treadmill exercise resulted in less ΔT associated with a structured weekly exercise.

Vangelis Marinakis

European buildings are producing a massive amount of data from a wide spectrum of energy-related sources, such as smart meters’ data, sensors and other Internet of things devices, creating new research challenges. In this context, the aim of this paper is to present a high-level data-driven architecture for buildings data exchange, management and real-time processing. This multi-disciplinary big data environment enables the integration of cross-domain data, combined with emerging artificial intelligence algorithms and distributed ledgers technology. Semantically enhanced, interlinked and multilingual repositories of heterogeneous types of data are coupled with a set of visualization, querying and exploration tools, suitable application programming interfaces (APIs) for data exchange, as well as a suite of configurable and ready-to-use analytical components that implement a series of advanced machine learning and deep learning algorithms. The results from the pilot application of the proposed framework are presented and discussed. The data-driven architecture enables reliable and effective policymaking, as well as supports the creation and exploitation of innovative energy efficiency services through the utilization of a wide variety of data, for the effective operation of buildings.

Pavol Rafajdus, Valeria Hrabovcova, Pavel Lehocky et al.

In this paper the effect of saturation on torque production of a reluctance synchronous motor (RSM), which was originally built as an induction motor (IM), is investigated. The rotor was replaced with new one, designed as synchronous reluctance cageless rotor with barriers, the shape and number of which were optimized to maximize the reluctance ratio. The torque measurement was done while the RSM was fed by frequency converter controlled by a microcontroller with closed loop field oriented control strategy to find out how saturation effects the developed torque at various values of the currents and speeds. It is shown how the load angle at which the maximum torque was achieved is changed. It was found out that the load angle was shifted to higher values depending on the speed of operation.

Edward WŁODARCZYK, Bartosz FIKUS

The authors investigated radial vibrations of a metal thick-walled spherical reservoir forced by an internal surge-pressure. The reservoir is located in a compressible elastic medium. In this paper, the medium’s compressibility is represented by the Poisson’s ratio ν. Analytical closed-form formulae determining the dynamic state of mechanical parameters in the reservoir wall have been derived. These formulae were obtained for the surge pressure p(t) = p0 = const. From analysis of these formulae it follows that the Poisson’s ratio ν, substantially influences variations of the parameters of reservoir wall in space and time. All parameters intensively decrease in space along with an increase of the Lagrangian coordinate r. On the contrary, these parameters oscillate versus time around their static values. These oscillations decay in the course of time. We can mark out two ranges of parameter ν values in which vibrations of the parameters are “damped” (there is no energy loss due to internal friction, energy is transferred from reservoir to further layers of the medium) at a different rate. Thus, Poisson’s ratio in the range below about 0.4 causes intensive decay of parameter oscillations and reduces reservoir dynamics to static state in no time. On the other hand, in the range 0.4 < ν < 0.5, the “damping” of parameter vibrations of the reservoir wall is very low. In the limiting case when ν = 0.5 (incompressible medium) “damping” vanishes and the parameters harmonically oscillate around their static values. In the range 0.4 < ν < 0.5, insignificant increase of Poisson’s ratio causes a considerable increase of the parameter vibration amplitude and decrease of vibration “damping”.

Amit Ghosh, Huimin Zhao, Nathan D Price

Biofuels derived from lignocellulosic biomass offer promising alternative renewable energy sources for transportation fuels. Significant effort has been made to engineer Saccharomyces cerevisiae to efficiently ferment pentose sugars such as D-xylose and L-arabinose into biofuels such as ethanol through heterologous expression of the fungal D-xylose and L-arabinose pathways. However, one of the major bottlenecks in these fungal pathways is that the cofactors are not balanced, which contributes to inefficient utilization of pentose sugars. We utilized a genome-scale model of S. cerevisiae to predict the maximal achievable growth rate for cofactor balanced and imbalanced D-xylose and L-arabinose utilization pathways. Dynamic flux balance analysis (DFBA) was used to simulate batch fermentation of glucose, D-xylose, and L-arabinose. The dynamic models and experimental results are in good agreement for the wild type and for the engineered D-xylose utilization pathway. Cofactor balancing the engineered D-xylose and L-arabinose utilization pathways simulated an increase in ethanol batch production of 24.7% while simultaneously reducing the predicted substrate utilization time by 70%. Furthermore, the effects of cofactor balancing the engineered pentose utilization pathways were evaluated throughout the genome-scale metabolic network. This work not only provides new insights to the global network effects of cofactor balancing but also provides useful guidelines for engineering a recombinant yeast strain with cofactor balanced engineered pathways that efficiently co-utilizes pentose and hexose sugars for biofuels production. Experimental switching of cofactor usage in enzymes has been demonstrated, but is a time-consuming effort. Therefore, systems biology models that can predict the likely outcome of such strain engineering efforts are highly useful for motivating which efforts are likely to be worth the significant time investment.