Mojgan Fayyazi, Paramjotsingh Sardar, Sumit Infent Thomas et al.

Sustainability • 2023

Environmental emissions, global warming, and energy-related concerns have accelerated the advancements in conventional vehicles that primarily use internal combustion engines. Among the existing technologies, hydrogen fuel cell electric vehicles and fuel cell hybrid electric vehicles may have minimal contributions to greenhouse gas emissions and thus are the prime choices for environmental concerns. However, energy management in fuel cell electric vehicles and fuel cell hybrid electric vehicles is a major challenge. Appropriate control strategies should be used for effective energy management in these vehicles. On the other hand, there has been significant progress in artificial intelligence, machine learning, and designing data-driven intelligent controllers. These techniques have found much attention within the community, and state-of-the-art energy management technologies have been developed based on them. This manuscript reviews the application of machine learning and intelligent controllers for prediction, control, energy management, and vehicle to everything (V2X) in hydrogen fuel cell vehicles. The effectiveness of data-driven control and optimization systems are investigated to evolve, classify, and compare, and future trends and directions for sustainability are discussed.

Mostafa Ghasemi, Kimia Rostami, Hamed Farahani et al.

Sustainable Energy & Fuels • 2025

The dual challenge of clean energy generation and wastewater treatment has intensified interest in microbial fuel cells (MFCs) as sustainable, bio-electrochemical systems. In this study, four low-cost cathode catalysts based...

Ruoyang Song, Xinghua Liu, Zhongbao Wei et al.

IEEE Transactions on Transportation Electrification • 2024

The safety, life expectancy and operating cost of fuel cell hybrid electric vehicle (FCHEV) are highly dependent on the power allocation among the onboard power sources. Motivated by this, this article proposes a machine learning-based multi-physical-constrained energy management strategy to improve the driving economy, thermal safety, and durability of FCHEV. In particular, the fully-continues deep deterministic policy gradient (DDPG) algorithm is exploited to optimize the power distribution of FCHEV in a real-time fashion. Within the proposed framework, the thermal and aging behaviors of the hybrid power sources are scrutinized and optimized, for the first time, to enhance the safety and life performance of FCHEV. The proposed strategy is tested under typical road missions for validation. The unexpected temperature build-up of lithium-ion battery (LIB) and the degradation of hybrid system can be well suppressed to enhance the thermal safety and life performance. Moreover, comparative results suggest that the proposed strategy can optimize the hybrid sources split collaborated with the improvement of operating performance, economy performance and operating time.

D. Robledo, L. Roleda

Journal of Microbiology & Biology Education • 2024

ABSTRACT Bioelectricity is an interdisciplinary concept that encompasses the fields of chemistry, physics, and biology. It is the scientific study of membrane transport mechanisms that govern the formation and dissipation of ion gradients. Teaching and learning across disciplines, such as bioelectricity, are known among science teachers to be challenging and complex. One of the critical problems is that only a few teaching materials and learning resources specifically support interdisciplinary teaching, especially in science. This paper described the development of an improvised microbial fuel cell (iMFC) as an alternative activity that addresses scientific concepts of cellular respiration, reduction-oxidation reaction, and electricity generation in an interdisciplinary approach. In this activity, students designed, constructed, and tested their iMFCs. The learning gains of the students were measured using parallel pretest/post-test and analyzed using descriptive statistics and dependent t-tests. The perceptions of teachers and students on using the iMFC activity in teaching-learning bioelectricity were obtained from a survey questionnaire and interviews. Results revealed that the iMFC activity significantly improved students’ learning gains in bioelectricity, for the topics cellular respiration (t(239)=45.03; P < 0.01), reduction-oxidation reaction (t(239)=39.85; P < 0.01), and electricity (t(239)=31.1; P < 0.01), with computed normalized gains of 0.45, 0.50, and 0.39, respectively. Furthermore, seven subthemes emerged from the teachers’ and students’ perceptions, namely, knowledge acquisition, student engagement, academic emotions, affordability, student satisfaction, distractions, and cleanliness. Overall results indicated that the iMFC activity can be an effective teaching material for providing an authentic learning experience in a multidisciplinary topic like bioelectricity. Future investigations on the iMFC activity and its impact on other aspects of learning, such as students’ motivation, self-efficacy, and engagement, are recommended.

Peng Yin, Xiangfu Niu, Shuo-Bin Li et al.

Nature Communications • 2024

Carbon supported PtCo intermetallic alloys are known to be one of the most promising candidates as low-platinum oxygen reduction reaction electrocatalysts for proton-exchange-membrane fuel cells. Nevertheless, the intrinsic trade-off between particle size and ordering degree of PtCo makes it challenging to simultaneously achieve a high specific activity and a large active surface area. Here, by machine-learning-accelerated screenings from the immense configuration space, we are able to statistically quantify the impact of chemical ordering on thermodynamic stability. We find that introducing of Cu/Ni into PtCo can provide additional stabilization energy by inducing Co-Cu/Ni disorder, thus facilitating the ordering process and achieveing an improved tradeoff between specific activity and active surface area. Guided by the theoretical prediction, the small sized and highly ordered ternary Pt_2CoCu and Pt_2CoNi catalysts are experimentally prepared, showing a large electrochemically active surface area of ~90 m^2 g_Pt^‒1 and a high specific activity of ~3.5 mA cm^‒2. Platinum-based intermetallic alloys are promising candidates as low-platinum oxygen reduction reaction catalysts for proton exchange membrane fuel cells. Here, the authors develop small sized and highly ordered Pt_2CoCu and Pt_2CoNi catalysts for fuel cells by machine-learning accelerated computational screening.

R. Gurjar, M. Behera

Water and Environment Journal • 2023

Volatile fatty acid (VFA)‐rich leachate generated from acidogenesis of kitchen waste in a leach bed reactor (LBR) was utilized in an earthen microbial fuel cell (EMFC) to generate electricity. Effects of organic loading rate (OLR, 5–10 g VS/L·day) and pH (5–7) on LBR enumerated optimized parameters of OLR (10 g VS/L·day) and pH (5.74) to obtain total VFA (TVFA) of 7.7 ± 0.3 g/L in the leachate, with maximum contribution from acetic acid. Leachate obtained from the LBR was fed to the EMFC with varying OLR (2–7 kg COD/m3·day). The highest power density of 0.76 W/m3 (at OLR 7 kg COD/m3·day) was obtained with higher VFA content in the leachate. A neural network based on the Levenberg–Marquard function effectively predicted chemical oxygen demand and TVFA removal. This study establishes LBR as a techno‐economic method to obtain useful substrate for EMFC. Furthermore, the response modelling of EMFC demonstrates the potential of utilizing machine learning in biological treatment.

K. Onyelowe, A. Ebid, Rosa Belén Ramos Jiménez et al.

Scientific Reports • 2025

There is an initiative driven by the carbon-neutrality nature of biochar in recent times, where various countries across Europe and North America have introduced perks to encourage the production of biochar for construction purposes. This objective aligns with the zero greenhouse emission targets set by COP27 for 2050. This research work seeks to assess the effectiveness of biochar in soils with varying grain size distributions in enhancing the soil–water characteristic curve (SWCC). This work further explores the effect of different combinations of biochar content (0 to 15 mass %) on the bioelectricity generation from biochar-improved plant microbial fuel cells (BPMFC). Additionally, different machine learning models such as the “Gradient Boosting (GB)”, “CN2 Rule Induction (CN2)”, “Naive Bayes (NB)”, “Support vector machine (SVM), “Stochastic Gradient Descent (SGD)”, “K-Nearest Neighbors (KNN)”, “Tree Decision (Tree)”, “Random Forest (RF)”, and “Response Surface Methodology” (RSM), have been developed to predict SWCC based on soil suction, electric current, electrical potential, volumetric water content, temperature, and bulk density. The newly established model demonstrates a reasonable ability to predict SWCC and a cheaper technology in predicting the suction of unsaturated soils in relation to the studied bioelectric factors of the BPMFC. Overall, in this research paper, the GB, SVM and CN2 outclassed the other regression techniques in this order thereby proposing the cheapest technology with the highest performance index to predict the SWCC behavior of unsaturated soils in a BPMFC system.

M. K. Pasha, K. Munawar

International Journal of Education and Management Engineering • 2021

: Microbial Fuel Cell (MFC) is a bio-electrochemical device that generates electric current by using bacteria. MFCs are currently a topic of intense research and interest due to their ability to produce renewable energy along with added benefits such as wastewater treatment. Although the theoretical concepts and applicability of MFCs are great, their application, thus far has been limited due to the limits of power production. Current research aims to improve the efficiency as well as the upper limit of power production by MFCs. In parallel to current research, this study is designed with a similar aim to do a comprehensive data analysis on the topic of MFCs by using techniques of Artificial Intelligence. Therefore, we started this study by obtaining the relevant data through an extensive literature retrieval for developing Artificial Neural Network model. The data from the output layer was viewed by using VOSviewer software and was further subjected to analysis. The data collected through machine learning provided an insight about the optimal conditions of MFCs which would allow for maximum current production. It discusses two existing types of MFCs; namely mediator type and mediator free type of MFC. Anode respiring bacteria (ARB), also known as exoelectrogenes can be used as the mediator to transfer electrons by utilizing the substrate present at the anode. Our results suggest that different combinations of bacterium and biofilms can produce more electric current with improved stability. This study will provide an insight to improve the working capacity of MFCs. It is likely that MFCs will one day be used as a stand-alone power production method by optimizing the current production capacity. Moreover, these advancements will have a significant by utilizing MFCs for making chips and biosensors, and treating wastewater.

Adam Hess-Dunlop, Harshitha Kakani, Colleen Josephson

Proceedings of the 7th ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies • 2024

Soil microbial fuel cells (SMFCs) are an emerging technology which offer clean and renewable energy in environments where more traditional power sources, such as chemical batteries or solar, are not suitable. With further development, SMFCs show great promise for use in robust and affordable outdoor sensor networks, particularly for farmers. One of the greatest challenges in the development of this technology is understanding and predicting the fluctuations of SMFC energy generation, as the electro-generative process is not yet fully understood. Very little work currently exists attempting to model and predict the relationship between soil conditions and SMFC energy generation, and we are the first to use machine learning to do so. In this paper, we train Long Short Term Memory (LSTM) models to predict the future energy generation of SMFCs across timescales ranging from 3 minutes to 1 hour, with results ranging from 2.33% to 5.71% MAPE for median voltage prediction. For each timescale, we use quantile regression to obtain point estimates and to establish bounds on the uncertainty of these estimates. When comparing the median predicted vs. actual values for the total energy generated during the testing period, the magnitude of prediction errors ranged from 2.29% to 16.05%. To demonstrate the real-world utility of this research, we also simulate how the models could be used in an automated environment where SMFC-powered devices shut down and activate intermittently to preserve charge, with promising initial results. Our deep learning-based prediction and simulation framework would allow a fully automated SMFC-powered device to achieve a median 100+% increase in successful operations, compared to a naive model that schedules operations based on the average voltage generated in the past.

Farhad Shabani, Hemma Philamore, F. Matsuno

IEEE Access • 2021

The current methods of water quality monitoring tend to be costly, labor-intensive, and off-site. Also, they are not energetically sustainable and often require environmentally damaging power sources such as batteries. Microbial fuel cell (MFC) technology is a promising sustainable alternative to combat these issues due to its low cost, eco-friendly energy generation, and bio-sensing features. Extensive work has been done on using MFCs as bio-sensors or sources of power separately. However, little work has been done toward using MFCs for both applications at the same time. Additionally, previous studies using MFCs for water quality measurement have been mostly limited to laboratory conditions due to the biochemical complexity of the real-world. Another limitation of MFCs is how little power they can generate, requiring the MFC-based systems to have minimal power consumption. This work addresses these challenges and presents an energy-autonomous water quality sensing unit that uses a single MFC both as its sensory input and the sole source of power for computing the chemical oxygen demand (COD). In the proposed unit, geometric features of the voltage profile of the MFC (e.g., peak heights) are used as the inputs to a machine learning algorithm (support vector regression (SVR)). The electrical power generated by the MFC is used to drive a low-power microcontroller which logs the MFC voltage and runs the machine learning algorithm. Experimental evaluation showed that the device is capable of detecting the COD of natural pond water samples accurately (coefficient of determination $(R^{2})=0.94$ ). This work is the first demonstration of energy autonomy in an MFC-based sensing unit for measuring water quality and represents a step forward in the development of energy-autonomous sensors for environmental monitoring applications.

Stilianos Louca

NAR Genomics and Bioinformatics • 2025

Abstract The relationship between gene content differences and microbial taxonomic divergence remains poorly understood, and algorithms for delineating novel microbial taxa above genus level based on multiple genome similarity metrics are lacking. Addressing these gaps is important for macroevolutionary theory, biodiversity assessments, and discovery of novel taxa in metagenomes. Here, I develop machine learning classifier models, based on multiple genome similarity metrics, to determine whether any two marine bacterial and archaeal (prokaryotic) metagenome-assembled genomes (MAGs) belong to the same taxon, from the genus up to the phylum levels. Metrics include average amino acid and nucleotide identities, and fractions of shared genes within various categories, applied to 14 390 previously published non-redundant MAGs. At all taxonomic levels, the balanced accuracy (average of the true-positive and true-negative rate) of classifiers exceeded 92%, suggesting that simple genome similarity metrics serve as good taxon differentiators. Predictor selection and sensitivity analyses revealed gene categories, e.g. those involved in metabolism of cofactors and vitamins, particularly correlated to taxon divergence. Predicted taxon delineations were further used to de novo enumerate marine prokaryotic taxa. Statistical analyses of those enumerations suggest that over half of extant marine prokaryotic phyla, classes, and orders have already been recovered by genome-resolved metagenomic surveys.

Research Square • 2023

Abstract The full text of this preprint has been withdrawn, as it was submitted in error. Therefore, the authors do not wish this work to be cited as a reference. Questions should be directed to the corresponding author.

A. Mohajer, M. S. Daliri, A. Mirzaei et al.

IEEE Transactions on Services Computing • 2023

Mobile Edge Computing (MEC) is a viable solution in response to the growing demand for broadband services in the new-generation heterogeneous systems. The dense deployment of small cell networks is a key feature of next-generation radio access networks aimed at providing the necessary capacity increase. Nonetheless, the problem of green networking and service computing will be of great importance in the downlink, because the uncontrolled installation of too many small cells may increase operational costs and emit more carbon dioxide. In addition, given the resource and computational limitation of the user layer, energy efficiency (EE) and fairness assurance are critical issues in MEC-based cellular systems. Considering the user fairness criteria, this paper proposes a dynamic optimization model which maximizes the total UL/DL EE along with satisfying the necessary QoS constraints. Based on the non-convex characteristics of the EE maximization problem, the mathematical model can be divided into two separate subproblems, i.e., computational carrier scheduling and resource allocation. So that, a subgradient method is applied for the computational resource allocation and also successive convex approximation (SCA) and dual decomposition methods are adopted to solve the max-min fairness problem. The simulation results exhibit considerable EE improvement for various traffic models in addition to guaranteeing the fairness requirements. It also proved that the proposed computational partitioning scheme managed to significantly improve the total throughput for mobile computing services.

Yue Yu, Xiao Tang, Jun Wu et al.

IEEE Transactions on Vehicular Technology • 2019

Multi-source data fusion to support vehicle on-road analysis is a promising service offered by mobile edge computing (MEC) for vehicles. With the fusion results delivered in near real-time, vehicle users (VUs) can peek around the corner, extend sensing range, reinforce and validate local observations. Consequently, there has emerged a new market between smart mobility service providers (SMSPs) and VUs in offering and purchasing multi-source data fusion results to support vehicle on-road analysis. Each SMSP and each VU compete with their peers to maximize their own profits. Also, VUs can receive multiple fusion results from several SMSPs and combine them to achieve a better inference. In this paper, we develop a multi-leader-follower game to model this complicated coupled problem. For the single-SMSP scenario, we analyze the properties of the leader-follower (L/F) Nash equilibrium and then reformulate the game as a mathematical program with equilibrium constraints (MPEC) to obtain the equilibrium. For the multi-SMSP scenario, the game is reinterpreted as an equilibrium problem with equilibrium constraints (EPEC), for which we analyze the local Nash equilibrium (LNE) with the assistance of variational inequality (VI) theory. Then, the block coordinate descent (BCD) method, which is low-complexity, is applied to solve the EPEC. Finally, numerical results are provided to validate the theoretical analysis and show that our proposed strategies maximize the utilities for both SMSPs and VUs.

C. Amitrano, G. Chirico, S. Pascale et al.

Sensors • 2020

Proximal sensors in controlled environment agriculture (CEA) are used to monitor plant growth, yield, and water consumption with non-destructive technologies. Rapid and continuous monitoring of environmental and crop parameters may be used to develop mathematical models to predict crop response to microclimatic changes. Here, we applied the energy cascade model (MEC) on green- and red-leaf butterhead lettuce (Lactuca sativa L. var. capitata). We tooled up the model to describe the changing leaf functional efficiency during the growing period. We validated the model on an independent dataset with two different vapor pressure deficit (VPD) levels, corresponding to nominal (low VPD) and off-nominal (high VPD) conditions. Under low VPD, the modified model accurately predicted the transpiration rate (RMSE = 0.10 Lm−2), edible biomass (RMSE = 6.87 g m−2), net-photosynthesis (rBIAS = 34%), and stomatal conductance (rBIAS = 39%). Under high VPD, the model overestimated photosynthesis and stomatal conductance (rBIAS = 76–68%). This inconsistency is likely due to the empirical nature of the original model, which was designed for nominal conditions. Here, applications of the modified model are discussed, and possible improvements are suggested based on plant morpho-physiological changes occurring in sub-optimal scenarios.

Xin Wang, Yuefeng Ji, Jiawei Zhang et al.

IEEE Access • 2019

WDM-PON-based mobile edge computing (MEC)-enabled fiber wireless access networks (MFWAN) have been identified as a promising technology for next-generation broadband access. Low-latency oriented network planning of the WDM-PON-based MFWAN would be required for low-latency access to newly emerging latency-sensitive applications in the fifth-generation (5G) era. However, this would require a low-latency oriented network design in the network planning phase and has thus become a crucial challenge. In this paper, we investigate low-latency oriented network planning of the WDM-PON-based MFWAN under physical and management constraints. To this end, we develop a mathematical model to minimize total transmission latency for all latency-sensitive services. Our model is composed of the propagation latency on the paths and the processing latency on the network equipment and is subject to constraints of maximal transmission distance, maximal PON power budget, bandwidth requests, and the fronthaul latency limit under some functional split options. Given the model’s complexity, we also propose a heuristic algorithm called latency-minimized integrated multi-associated positioning and routing algorithm (LMI-MAPRA). The simulation results show that the proposed algorithm outperforms the benchmark algorithm with more total transmission latency reductions in both sparse and dense networks. We also analyze the impact of key parameters on comparisons of different approaches in terms of low-latency optimal performances.

Mingyu Wang

IEEE Journal of Emerging and Selected Topics in Power Electronics • 2022

Electric machine emulator (EME) system is developed for power electronics test. The accuracy of emulator, especially the accuracy of motor model, is of vital importance in this system. The traditional lumped parameter permanent-magnet synchronous motor (PMSM) model ignores the nonlinearity. The finite-element analysis (FEA) model and the magnetic circuit analysis (MEC) are not suitable for EME real-time systems. In this article, we proposed an improved distributed parameter PMSM modeling method, which integrates the magnetic saturation, spatial harmonics, and cross coupling in a mathematical expression. The method reconstructs the dq-axis current versus flux linkage and electric angular, rederives the electromagnetic torque, and simplifies the realization schematic. It should be emphasized that the model can be applied on the real-time running system. With these improvements, the proposed model occupies much less RAM space compared with the lookup table method. The model can mimic the harmonics and hold the merits of high accuracy and high running speed. To validate the accuracy of the proposed method, the simulations and experiments on field-programmable gate arrays (FPGAs) are carried out. Sufficient comparison among three models is implemented. The results validate the accuracy of the proposed model.

Ashley F Stein-Merlob, Lihua Jin, Alan Garfinkel

Circulation • 2023

Introduction: Increased recognition of transthyretin cardiac amyloidosis (ATTR-CA) led to the development of treatments with different therapeutic targets. Optimal patient selection, treatment choice, timing and duration remain unclear. We evaluated the efficacy and timing of common and emerging ATTR-CA treatments with a mathematical model based on therapeutic mechanisms. Methods: We developed a system of ordinary differential equations describing transthyretin amyloid (ATTR) deposition and myocardial clearance by clusterin (Figure 1A,B). Published data and equilibrium analyses determined parameters for initial maximal ATTR density. Mechanisms of four therapies were modeled and simulated at varying levels of efficacy. Complete success of the mechanism defines 100% efficacy, i.e. full stabilization of tetramer. Expected clinical efficacy (ECE) is based on studies of clinical doses. Minimum effective clearance (MEC) is the theoretical efficacy required to reduce ATTR fibrils below the threshold for complete clearance. Results: All treatments reduced ATTR fibril density, correlating with efficacy (Fig 1C). At ECE, only NI006 demonstrated complete clearance of ATTR fibrils. With other current treatment strategies, amyloid density will be lowered but never cleared. The secondary mechanism of diflusinil did not change equilibrium behavior compared to tafamidis. MEC values and time to clearance at MEC were lowest for NI006 (14.7%; 65 days) and doxycycline (16.1%; 36 months). Tetramer stabilization required almost complete efficacy (99.3%) and about a decade (119 months) for tetramer clearance. Conclusion: This mathematical model predicts ATTR clearance by four distinct mechanisms of action. The novel anti-ATTR antibody NI006 is a promising treatment with predicted faster onset and higher effectiveness for clearance of ATTR fibrils from the myocardium. This model can evaluate future treatment strategies and therapeutic targets.

Bomin Mao, Jian Qiu, Nei Kato

IEEE Transactions on Vehicular Technology • 2024

With modern Electric Connected Vehicles (ECVs) becoming more intelligent and entertaining, the Multi-access Edge Computing (MEC) servers deployed near the Road Side Units (RSUs) have been expected to not only improve the computing performance, but also alleviate the ECVs' battery burden. However, the uneven spatial and temporal distribution of vehicle arrivals cause that the MEC servers in busy areas are overloaded, which results in degraded computation performance and limited energy alleviation for ECVs. In this paper, we consider the task distribution among nearby MEC servers and propose a novel partial offloading strategy where multiple blocks of the tasks are cooperatively processed by MEC servers in the vehicles' moving directions. After formulating the mathematical model to jointly optimize the computing latency and energy consumption of ECVs, a Deep Reinforcement Learning (DRL) based task partial offloading strategy is proposed and a Deep Q-Network (DQN) is adopted to make the offloading decision. Simulation results illustrate the significant alleviation of energy consumption and improvement of computation performance compared with conventional methods.

Dmitry Poluektov, A. Khakimov

Discrete and Continuous Models and Applied Computational Science • 2022

Online video services are among the most popular ways of content consumption. Video hosting servers have a very high load every day, which we propose to reduce by migrating the application with the video content in demand to the local Multi-access Edge Computing (MEC) server of the target. This makes it possible to improve the quality of services (QoS) provided to users by reducing the transmission delay. Therefore, an architecture has been proposed that allows, at times of increased demand for the same video content, to migrate the video service application to the edge servers of the network operator. To evaluate the performance of this approach, a mathematical model was developed in the form of a queuing system. The results of the numerical experiment make it possible to optimize the time of using local MEC servers to provide video content.

Ning Luo

Computational Intelligence and Neuroscience • 2022

This paper analyzes the application of MEC multiserver heuristic joint task in resource allocation of the educational resource database. After constructing the scenario of educational resource database, a mathematical model is constructed from the dimensions of local execution strategy, unloading execution, and given educational resource allocation, in order to optimize the optimal allocation of educational resources through MEC. The results show that the DOOA scheme has good performance in terms of calculation cost and timeout rate. Compared with other benchmark schemes, the DQN-based unloading scheme has better performance, can effectively balance the load, and is better than the random unloading scheme and the SNR-based unloading scheme in terms of delay and calculation cost. The results show that the total hits of all category 1 users' content requests account for the proportion of the total content requests. The images have a small downward trend at the 15000 and 30000 time slots and then continue to rise. This shows that the proposed scheme can automatically adjust the caching strategy to adapt to the changes of content popularity, which proves that the agent can correctly perceive the changing trend of content popularity when the popularity of network content is unknown and improve the caching strategy accordingly to improve the cache hit rate. Therefore, the allocation of educational resources based on the MEC multiserver heuristic joint task is more reasonable and can achieve the optimal solution.

Zoubeir Mlika, S. Cherkaoui

IEEE Network • 2021

The interconnection of vehicles in the future fifth generation (5G) wireless ecosystem forms the so-called Internet of Vehicles (IoV). IoV offers new kinds of applications requiring delay-sensitive, compute-intensive, and bandwidth-hungry services. Mobile edge computing (MEC) and network slicing are two of the key enabler technologies in 5G networks that can be used to optimize the allocation of the network resources and guarantee the diverse requirements of IoV applications. As traditional model-based optimization techniques generally end up with NP-hard and strongly non-convex and nonlinear mathematical programming formulations, in this article, we introduce a model-free approach based on deep reinforcement learning (DRL) to solve the resource allocation problem in MEC-enabled IoV networks based on network slicing. Furthermore, the solution uses non-orthogonal multiple access (NOMA) to enable better exploitation of the scarce channel resources. The considered problem addresses jointly the channel and power allocation, the slice selection, and the vehicle selection (vehicle grouping). We model the problem as a single-agent Markov decision process. Then we solve it using DRL with the well-known deep Q learning (DQL) algorithm. We show that our approach is robust and effective under different network conditions compared to benchmark solutions.

Yu Cao, Chuang Liu, Junyue Yu

Energies • 2022

A brushless parallel hybrid excitation claw pole generator (HECPG) is proposed for electric vehicle (EV) application. Permanent magnet (PM) excitation method can reduce the volume of the machine and improve the power density and efficiency. Moreover, the voltage regulation can be ensured by field excitation. The flux path of the proposed HECPG is complex, and it will take a long time for 3D finite element analysis (FEA) to process it. To reduce simulation time, the mathematical model of the generator is given by a mesh-based 3D magnet equivalent circuit (MEC) network method considering radial and axial flux, magnetic saturation, and magnetic flux leakage. The performance of the generator is analyzed by FEA and prototype experiment. Finally, the results of 3D MEC, FEA, and experiment are compared. There is little difference between the three results, so 3D MEC can ensure the accuracy and significantly reduce the simulation time. The efficiency of the proposed HECPG is 90%, and the DC-Bus voltage can be modulated by changing the amplitude of field current.

Mohamad Afiq Mohd Asrul, M. F. Atan, Hafizah Abdul Halim Yun et al.

ASEAN Engineering Journal • 2024

The nonlinear phenomenon of the profile of substrate concentration and hydrogen production rate over 16 retention days in a 4 L double chamber of a microbial electrolysis cell (MEC) for bioelectrochemical production of hydrogen from sago wastewater validates the mathematical modeling results based on simplified microbial biofilm growth. The stoichiometric reaction and kinetics affect the substrate concentration curve behaviour, but the effects also include the bioelectrochemical balance for hydrogen production rate. The artificial neural network (ANN) predicts the experimental hydrogen production rate according to the input of pH of the catholyte at controlled applied potential of 0.8 V and current density of 0.632 A‧m-2. The convex method assists the model in finding the optimal input values that lead to the minimum mean square error (MSE) between modelling and experimental data. Evaluation of the COD removed efficiency, coulombic efficiency, and energy efficiency determines the process limit of the model MEC. At an optimum applied potential of 0.485 V, anode surface area of 0.098 m2, anodic chamber volume of 4 L, and initial substrate concentration of 2,500.99 mg‧L-1, the MEC model reached maximum steady-state percentage at 81.99% of COD removed efficiency, 69.01% of Coulombic efficiency, and 7.47% of energy efficiency.

Shi-Yu Zhang, Zhen-Yin Annie Chen, Chun-Cheng Lin

2025 International Conference on Artificial Intelligence in Information and Communication (ICAIIC) • 2025

With the rapid development of smart construction, the proliferation of sensors and smart devices on construction sites has introduced significant challenges in data processing and communication. Conventional cloud computing struggles to handle the real-time demands of construction data, and existing MEC deployment methods often neglect energy efficiency and the complexities of multi-story sites. This work proposes a mathematical model for 5G MEC deployment, addressing installation, connectivity, and energy consumption, and solves it using a hybrid algorithm combining simplified harmony search (SHS) and variable neighborhood search (VNS). By leveraging SHS for global exploration and VNS for efficient local optimization, the approach effectively tackles the NP-hard problem of 5G MEC server and base station placement. Experimental results on real-world construction scenarios validate its superiority in computational efficiency, energy savings, and cost reduction, establishing it as a viable solution for optimizing 5G MEC deployment in smart construction sites.

José Ramos Colín Robles, Ixbalank Torres Zúñiga, Glenda Cea¬-Barcia et al.

Memorias del Congreso Nacional de Control Automático • 2023

In this article it is presented an FPGA-based extremum seeking control that is used to maximize the hydrogen productivity rate in a microbial electrolysis cell (MEC) using the dilution rate as a control action. This extremum seeking control is based in the hydrogen productivity gradient and does not need a mathematical model. To achieve a positive energy balance, such optimization algorithm is implemented in an FPGA using a fixed point representation. By a closed loop simulation test and performance analysis of the FPGA-based extremum seeking control, it is demonstrated that FPGAs are the best implementation choice.

Ao Liu, Shaoshi Yang, Jing-Sheng Tan et al.

Processes • 2023

Containers are used by an increasing number of Internet service providers to deploy their applications in multi-access edge computing (MEC) systems. Although container-based virtualization technologies significantly increase application availability, they may suffer expensive communication overhead and resource use imbalances. However, so far there has been a scarcity of studies to conquer these difficulties. In this paper, we design a workflow-based mathematical model for applications built upon interdependent multitasking composition, formulate a multi-objective combinatorial optimization problem composed of two subproblems—graph partitioning and multi-choice vector bin packing, and propose several joint task-containerization-and -container-placement methods to reduce communication overhead and balance multi-type computing resource utilization. The performance superiority of the proposed algorithms is demonstrated by comparison with the state-of-the-art task and container scheduling schemes.

C. Amitrano, G. Chirico, S. Pascale et al.

2019 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor) • 2019

A fundamental objective in precision horticulture is the development of mathematical explanatory models to be implemented in decision support systems for optimal crop management. Such models, designed to predict daily changes in cultivation variables for given boundary conditions, can be exploited to predict the direction and the magnitude of possible changes in crop variables, under both nominal and off-nominal conditions. In this way, control strategies, capable of counteract the negative effects of environmental disturbances on crops, can be promptly applied. In the present study, we evaluated the Modified Energy Cascade (MEC) model with data collected during experiments conducted in growth chambers with green and red-leaved lettuce, under controlled environmental conditions (lighting, temperature, irrigation) and two different vapor pressure deficits. Results showed that the model predictions were reliable compared with experimental measurements, especially for biomass production and photosynthesis. Thus, the MEC model can be a useful tool in decision support systems for crop management in controlled environment.

Alla Havrylova, O. Korol, S. Milevskyi

Cybersecurity: Education, Science, Technique • 2019

The subject of the research is a mathematical model of authentication of the transmitted message based on the McEliese scheme on shortened and elongated modified elliptic codes using the modified UMAC algorithm. The aim of this work is to develop such a scheme for the information exchange over Internet commverification and integrity of the transmitted information, taking into account the prevention of an increase in the costs of the actions taken. Tasks: analysis of existing ways to increase the resistance to hacking of transmitted messages over telecommunication networks; analysis of a message transfer scheme using blockchain technology; formalized description of a mathematical model for providing clear text authentication using a modified UMAC algorithm, as the formation of key data, a crypto-code construction (CCC) is used on the McEliese scheme on modified elliptic codes (MEС); development of data encryption and decryption algorithms using CCC based on McEliese on the MEC and UMAC algorithm. Аn approach was proposed to verify the authentication and verification of the information packet during transmission and reception via telecommunication channels, which allows using already known hashing methods to compare generated codegrams and transmitted messages for their correspondence, which increases the level of cryptographic stability of the transmitted data and the reliability of the received data. The developed schemes of algorithms for generating codеgrams and their decryption using the proposed approach make it possible to gradually demonstrate the implementation of procedures for generating codegrams and their hash codes using both shortening and lengthening the code. Further research should prove from a practical point of view the effectiveness of using this approach when transmitting a message regarding the preservation of its integrity and authenticity. Therefore, it is necessary to develop a test system that should implement the proposed approach, as well as evaluate the results obtained.unication channels, which would ensure the proper level of

T. Chou, Juei-Ping Liao

Mathematical Problems in Engineering • 2020

Industry 4.0 is a high degree of connection and integration of information and mechanical equipment. The goal of Industry 4.0 is to link equipment, production lines, factories, suppliers, products, and customers closely. With the continuous increase of smart manufacturing equipment, the responsibility of the purchasing department and purchasers will become heavier. Therefore, purchasers must understand these kinds of equipment and their functions in order to make a good decision. The objective of this paper is to establish a procurement decision support model. It presents corporate purchaser decision-making criteria as reference for the food processing machinery (FPM) manufacturing industry. Combining the concepts and methods of mean-end chain (MEC) and fuzzy analytic hierarchy process (FAHP), an MCDM model to select suitable machinery for the food processing industry was presented. Firstly, a hierarchical structure includes 2 factors, 7 criteria, and 25 subcriteria for FPM purchasers to select machines is constructed based on MEC analysis. Secondly, FAHP is used to solve the subjective weights of all criteria and subcriteria. Then, the key factors of FPM procurement were obtained. There are 50 FPM purchasers to be interviewed in first phase, and 50 AHP questionnaires were distributed to these purchasers in second phase. Fuzzy multicriteria decision analysis was carried out, and the top five most important attributes out of the 25 criteria were sorted out. They are switching cost, price, brand, professionalism of service personnel, and accessibility of after-sales services. This study uses actual industrial cases to verify the proposed method, and offers the practical purchasing decision support model based on the actual FPM purchaser and the manufacturer’s product manager. It will enhance the practicality of the research. In addition, the lack of the literature makes it difficult to establish an evaluation model. This study uses MEC and FAHP to obtain evaluation criteria and establish evaluation models, which will be the original contribution of the research.

Ebtehag A. E. Sakr, D. Khater, Z. M. Kheiralla et al.

Microbial Cell Factories • 2023

Background The application of exopolysaccharide-producing bacteria (EPS) in dual chamber microbial fuel cells (DCMFC) is critical which can minimize the chemical oxygen demand (COD) of molasses with bioelectricity production. Hence, our study aimed to evaluate the EPS production by the novel strain Bacillus piscis by using molasses waste. Therefore, statistical modeling was used to optimize the EPS production. Its structure was characterized by UV, FTIR, NMR, and monosaccharides compositions. Eventually, to highlight B. piscis' adaptability in energy applications, bioelectricity production by this organism was studied in the BCMFC fed by an optimized molasses medium. Results B. piscis OK324045 characterized by 16S rRNA is a potent EPS-forming organism and yielded a 6.42-fold increase upon supplementation of molasses (5%), MgSO_4 (0.05%), and inoculum size (4%). The novel exopolysaccharide produced by Bacillus sp. (EPS-BP5M) was confirmed by the structural analysis. The findings indicated that the MFC's maximum close circuit voltage (CCV) was 265 mV. The strain enhanced the performance of DCMFC achieving maximum power density (PD) of 31.98 mW m^−2, COD removal rate of 90.91%, and color removal of 27.68%. Furthermore, cyclic voltammetry (CV) revealed that anodic biofilms may directly transfer electrons to anodes without the use of external redox mediators. Additionally, CV measurements made at various sweep scan rates to evaluate the kinetic studies showed that the electron charge transfer was irreversible. The SEM images showed the biofilm growth distributed over the electrode’s surface. Conclusions This study offers a novel B. piscis strain for EPS-BP5M production, COD removal, decolorization, and electricity generation of the optimized molasses medium in MFCs. The biosynthesis of EPS-BP5M by a Bacillus piscis strain and its electrochemical activity has never been documented before. The approach adopted will provide significant benefits to sugar industries by generating bioelectricity using molasses as fuel and providing a viable way to improve molasses wastewater treatment.

Abanaoub Efraim, Mai Saeed, Mariam Ahmed Elbaz et al.

Microbial Cell Factories • 2023

Malachite Green (MG) dye of the triphenylmethane group is a toxic compound used in the aquaculture industry as an antifungal agent, however, it can accumulate in fish and pose toxicity. The present work aims to remove MG in Microbial Fuel Cell (MFC) as a sustainable and eco-friendly solution. Out of six samples, the highest malachite green degradation was obtained by a sample obtained from Robiki tannery site in agar plates in 24 h at 37 °C. Robiki sample was used to inoculate the anodic chamber in Microbial Fuel cell, the resulting average electricity production was 195.76 mV for two weeks. The decolorization average was almost 88%. The predominant bacteria responsible for MG decolorization and electricity production were identified using 16S rRNA as Shewanella chilikensis strain MG22 (Accession no. OP795826) and formed a heavy biofilm on the anode. At the end of the decolorization process, MG was added again for re-use of water. The results showed efficiency for re-use 3 times. To ensure the sterility of treated water for re-use, both UV and filter sterilization were used, the latter proved more efficient. The obtained results are promising, MFC can be used as recirculating aquaculture system (RAS). The same aquaculture water can be treated multiple times which provides a sustainable solution for water conservation. Graphical Abstract

Juan Diego Mejía, Cindy Stephany Rojas, L. A. Franco et al.

Advances in Bioscience and Biotechnology • 2013

This study presents the simulation of a MFC with Pseudomonas aeruginosa based on a metabolic flux analysis (MFA) which arises as a linear programming model that served as input for the fuel cell model. The linear model was implemented in Xpress MP? and the coupling model in Comsol Multiphysics?. The in silico model predicted maximum potentials of 0.135 V which were consistent with those obtained in the experimental cell. Afterwards, an optimization platform based on multiobjective optimization approach was implemented aimed to determine genes that increase the average cell power. cysA, cysP and rpoS mutants which were predicted to increase the power of the cell, were experimentally tested with an air cathode fuel cell finding an augment up to 35-fold in the average power density for the rpoS mutant. Power densities were obtained through experimentation in the range of [0.13 - 5] mW/m2. Electron shuttle rise was qualitative corroborated trough cyclic voltammetry tests, which allowed to visualize the augment of the peaks for rpoS mutation.

Witchayut Niyom

• 2015

In this study, three identical two compartment single chamber air-breathing microbial fuel cells (MFC) were used to treat sulfate-rich wastewater simultaneously with electricity generation at the COD:SO42- ratio of 1, 3, and 6 in MFC1, MFC3, and MFC6, respectively. COD, sulfate, and sulfide removal, electricity generation, and mechanisms in MFCs were investigated. The MFCs were continuously operated at a hydraulic retention time of 24 hr in the first compartment. Glucose equivalent to 3,000 mgCOD/L and sulfate concentrations of 3,000, 1,000, and 500 mgSO42-/L were fed into MFC1, MFC3, and MFC6, corresponding to the COD:SO42- ratio of 1, 3, and 6, respectively. For the first compartments, COD removal efficiencies were 56.06 ± 10.67, 62.49 ± 11.21, and 63.22 ± 11.57% in MFC1, MFC3, and MFC6, respectively. Sulfate removal was 1,209 ± 455, 964 ± 93, and 492 ± 44 mgSO42-/L in MFC1, MFC3, and MFC6, respectively, whereas dissolved sulfide concentrations of 400 ± 69, 265 ± 59, and 119 ± 32 mgS2-/L were observed in MFC1, MFC3, and MFC6, respectively. From the microbial community analysis with 16S rRNA gene amplicon sequencing (MiSeq, Illumina), Tolumunas spp. were predominant species in all of the MFCs. These microorganisms were the fermenters that can ferment glucose into VFAs and acetate, which can be further consumed by sulfate-reducing bacteria (SRB, Desulfovibrio spp.) and methanogens (Methanoregulaceae and Methanosaetaceae). SRB (17.32% of total sequences) were the most abundant in MFC1 whereas methanogens (4.13% of total sequences) were the highest in MFC6. For the second compartment of MFCs, the COD removal efficiencies were 0.15 ± 9.83, 7.98 ± 10.23, and 9.98 ± 16.50% in MFC1, MFC3, and MFC6, respectively, whereas sulfate removal was negligible. Sulfide removal was 49.51 ± 57.74, 24.08 ± 13.74, and 15.69 ± 21.30 mgS2-/L in MFC1, MFC3, and MFC6, respectively. The maximum power generation of 9.33, 1.79, and 1.41 mW/m2 were achieved on the first day of operation. Then, both OCV and voltage across the electrodes decreased over time, resulting from sulfur accumulation on the anode electrodes as suggested by the results from scanning electron microscopy equipped with energy-dispersive X-ray (SEM/EDX). In addition, nonexo-electrogenic microorganisms on the anode electrodes could also increase the voltage losses in the systems. The main mechanism of electrical generation in all MFCs was abiotic sulfide oxidation. Klebsiella, Tolumonas, and Methanoseata were the predominant microorganisms on the anode electrodes, which were not exo-electrogenic microorganisms.

Fabian Kubannek, Jonathan Block, Balakrishnan Munirathinam et al.

Engineering in Life Sciences • 2022

Abstract In the present study, it is shown that the concentration dependency of undefined mixed culture anodic biofilms does not follow a single kinetic curve, such as the Nernst‐Monod curve. The biofilms adapt to concentration changes, which inevitably have to be applied to record kinetic curves, resulting in strong shifts of the kinetic parameters. The substrate concentration in a continuously operated bioelectrochemical system was changed rapidly via acetate pulses to record Nernst‐Monod curves which are not influenced by biofilm adaptation processes. The values of the maximum current density j max and apparent half‐saturation rate constant K s increased from 0.5 to 1 mA cm −2 and from 0.5 to 1.6 mmol L −1 , respectively, within approximately 5 h. Double pulse experiments with a starvation phase between the two acetate pulses showed that j max and K s decrease reversibly through an adaptation process when no acetate is available. Pseudo‐capacitive charge values estimated from non‐turnover cyclic voltammograms (CV) led to the hypothesis that biofilm adaptation and the observed shift of the Nernst‐Monod curves occurred due to changes in the concentration of active redox proteins in the biofilm. It is argued that concentration‐related parameters of kinetic models for electroactive biofilms are only valid for the operating points where they have been determined and should always be reported with those conditions.

K. Phayungphan, N. Rakmak, A. Promraksa

Water Practice and Technology • 2020

Abstract Anaerobic digestion is a highly complex process, particularly in co-digestion between poorly-defined, complex co-substrates like distillery wastewater, molasses, and crude glycerine. Thus, in this article, the authors tackled the problems by using Monod two-substrate with an intermediate (M2SI) model to represent accumulated biomethane evolution (ABE) obtained from the co-substrates, including easily degradable, slowly degradable substrates and intermediate. The M2SI model predictions were compared with the traditional Monod model's simulation results to clarify an outstanding of the present model in the aspect of modeling and control. Different behaviors of ABE curves from batch experiments were used to calibrate the M2SI model prediction with sensitivity analysis of the model parameters. It was found that the M2SI model gives a correct trend to describe the co-digestion process with multiple substrates and complex microbial activities with satisfactory fitting accuracy. At the same time, simple Monod kinetics have a good fit for dilute pure distillery wastewater, but the estimated microbial growth kinetics were counterintuitive. Therefore, the M2SI Model has a broader range of applications for co-digestion dealing with the complexity of multiple microbial activities to consume inherently complex or artificial co-substrates.

Alain Garnier, Bruno Gaillet

Biotechnology and Bioengineering • 2015

ABSTRACT Not so many fermentation mathematical models allow analytical solutions of batch process dynamics. The most widely used is the combination of the logistic microbial growth kinetics with Luedeking–Piret bioproduct synthesis relation. However, the logistic equation is principally based on formalistic similarities and only fits a limited range of fermentation types. In this article, we have developed an analytical solution for the combination of Monod growth kinetics with Luedeking–Piret relation, which can be identified by linear regression and used to simulate batch fermentation evolution. Two classical examples are used to show the quality of fit and the simplicity of the method proposed. A solution for the combination of Haldane substrate‐limited growth model combined with Luedeking–Piret relation is also provided. These models could prove useful for the analysis of fermentation data in industry as well as academia. Biotechnol. Bioeng. 2015;112: 2468–2474. © 2015 Wiley Periodicals, Inc.

Hermann Eberl

Contemporary Mathematics • 2024

Monod kinetics is an important nonlinearity that appears in mathematical modelling of microbial systems, but (under different names) also in many other applications in Mathematical Biology and Process Engineering. Although seemingly innocuous, for some extreme parameter values (notably very small half saturation concentrations and large decay rates), sophisticated high order solvers for ordinary differential equations have been known to fail. We explore this breakdown situation and suggest a simple, low order, easy to implement method that is inspired by so-called Nonstandard Finite Difference or Mickens schemes. We find that these can be a viable alternative to modern initial value problem solvers, in the problematic cases of extreme parameter values.

Sami G. A. Flimban, Taeyoung Kim, Iqbal Mohammad Ibrahim Ismail et al.

Preprints.org • 2018

Fossil fuels and carbon origin resources are affecting our environment. Therefore, alternative energy sources have to be established to co-produce energy along with fossil fuels and carbon origin resources until it is the right time to replace them. Microbial Fuel Cell (MFC) is a promising technology in the field of energy production. Compared to the conventional power sources it is more efficient and not controlled by the Carnot cycle. Its high efficiencies, low noise, and less pollutant output could make it revolutionize in the power generation industry with a shift from centrally located generating stations and long-distance transmission lines to dispersed power generation at load sites. In this review, several characteristics of the MFC technology will be highlighted. First, a brief history of abiotic to biological fuel cells and subsequently, microbial fuel cells is presented. Second, the focus is then shifted to elements responsible for the making MFC working with efficiency. Setup of the MFC system for every element and their assembly is then introduced, followed by an explanation of the working machinery principle. Finally, microbial fuel cell designs and types of main configurations used are presented along with scalability of the technology for the proper application.

Federico Maggi, Fiona H. M. Tang, William J. Riley

International Journal of Chemical Kinetics • 2018

ABSTRACT Accurate prediction of the temperature response of the velocity v of a biochemical reaction has wide applications in cell biology, reaction design, and biomass yield enhancement. Here, we introduce a simple but comprehensive mechanistic approach that uses thermodynamics and biochemical kinetics to describe and link the reaction rate and Michaelis–Menten constants ( k T and T ) with the biomass yield and mortality rate ( T and δ T ) as explicit functions of . The temperature control is exerted by catabolic enthalpy at low temperatures and catabolic entropy at high temperatures, whereas changes in cell and enzyme–substrate heat capacity shift the anabolic electron use efficiency e A and the maximum reaction velocity v max . We show that cells have optimal growth when the catabolic (differential) free energy of activation decreases the cell free energy harvest required to duplicate their internal structures as long as electrons for anabolism are available. With the described approach, we accurately predicted observed glucose fermentation and ammonium nitrification dynamics across a wide temperature range with a minimal number of thermodynamics parameters, and we highlight how kinetic parameters are linked to each other using first principles.