M. Andrea Pisauro, Elsa Fouragnan, Chris Retzler et al.

Nature Communications • 2017

Current computational accounts posit that, in simple binary choices, humans accumulate evidence in favour of the different alternatives before committing to a decision. Neural correlates of this accumulating activity have been found during perceptual decisions in parietal and prefrontal cortex; however the source of such activity in value-based choices remains unknown. Here we use simultaneous EEG-fMRI and computational modelling to identify EEG signals reflecting an accumulation process and demonstrate that the within- and across-trial variability in these signals explains fMRI responses in posterior-medial frontal cortex. Consistent with its role in integrating the evidence prior to reaching a decision, this region also exhibits task-dependent coupling with the ventromedial prefrontal cortex and the striatum, brain areas known to encode the subjective value of the decision alternatives. These results further endorse the proposition of an evidence accumulation process during value-based decisions in humans and implicate the posterior-medial frontal cortex in this process.

Gousia Habib, Sparsh Sharma, Sara Ibrahim et al.

Future Internet • 2022

The real-world use cases of blockchain technology, such as faster cross-border payments, identity management, smart contracts, cryptocurrencies, and supply chain–blockchain technology are here to stay and have become the next innovation, just like the Internet. There have been attempts to formulate digital money, but they have not been successful due to security and trust issues. However, blockchain needs no central authority, and its operations are controlled by the people who use it. Furthermore, it cannot be altered or forged, resulting in massive market hype and demand. Blockchain has moved past cryptocurrency and discovered implementations in other real-life applications; this is where we can expect blockchain technology to be simplified and not remain a complex concept. Blockchain technology’s desirable characteristics are decentralization, integrity, immutability, verification, fault tolerance, anonymity, audibility, and transparency. We first conduct a thorough analysis of blockchain technology in this paper, paying particular attention to its evolution, applications and benefits, the specifics of cryptography in terms of public key cryptography, and the challenges of blockchain in distributed transaction ledgers, as well as the extensive list of blockchain applications in the financial transaction system. This paper presents a detailed review of blockchain technology, the critical challenges faced, and its applications in different fields. Blockchain in the transaction system is explained in detail with a summary of different cryptocurrencies. Some of the suggested solutions are given in the overall study of the paper.

Ana Villares, Céline Moreau, Chloé Bennati-Granier et al.

Scientific Reports • 2017

Lytic polysaccharide monooxygenases (LPMOs) are a class of powerful oxidative enzymes that breakdown recalcitrant polysaccharides such as cellulose. Here we investigate the action of LPMOs on cellulose fibers. After enzymatic treatment and dispersion, LPMO-treated fibers show intense fibrillation. Cellulose structure modifications visualized at different scales indicate that LPMO creates nicking points that trigger the disintegration of the cellulose fibrillar structure with rupture of chains and release of elementary nanofibrils. Investigation of LPMO action using solid-state NMR provides direct evidence of modification of accessible and inaccessible surfaces surrounding the crystalline core of the fibrils. The chains breakage likely induces modifications of the cellulose network and weakens fibers cohesion promoting their disruption. Besides the formation of new initiation sites for conventional cellulases, this work provides the first evidence of the direct oxidative action of LPMOs with the mechanical weakening of the cellulose ultrastructure. LPMOs can be viewed as promising biocatalysts for enzymatic modification or degradation of cellulose fibers.

Anjalie Field, Doron Kliger, Shuly Wintner et al.

Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing • 2018

Amidst growing concern over media manipulation, NLP attention has focused on overt strategies like censorship and "fake news". Here, we draw on two concepts from the political science literature to explore subtler strategies for government media manipulation: agenda-setting (selecting what topics to cover) and framing (deciding how topics are covered). We analyze 13 years (100K articles) of the Russian newspaper Izvestia and identify a strategy of distraction: articles mention the U.S. more frequently in the month directly following an economic downturn in Russia. We introduce embedding-based methods for cross-lingually projecting English frames to Russian, and discover that these articles emphasize U.S. moral failings and threats to the U.S. Our work offers new ways to identify subtle media manipulation strategies at the intersection of agenda-setting and framing.

Daniel J. Egger, Jakub Mareček, Stefan Woerner

Quantum • 2021

There is an increasing interest in quantum algorithms for problems of integer programming and combinatorial optimization. Classical solvers for such problems employ relaxations, which replace binary variables with continuous ones, for instance in the form of higher-dimensional matrix-valued problems (semidefinite programming). Under the Unique Games Conjecture, these relaxations often provide the best performance ratios available classically in polynomial time. Here, we discuss how to warm-start quantum optimization with an initial state corresponding to the solution of a relaxation of a combinatorial optimization problem and how to analyze properties of the associated quantum algorithms. In particular, this allows the quantum algorithm to inherit the performance guarantees of the classical algorithm. We illustrate this in the context of portfolio optimization, where our results indicate that warm-starting the Quantum Approximate Optimization Algorithm (QAOA) is particularly beneficial at low depth. Likewise, Recursive QAOA for MAXCUT problems shows a systematic increase in the size of the obtained cut for fully connected graphs with random weights, when Goemans-Williamson randomized rounding is utilized in a warm start. It is straightforward to apply the same ideas to other randomized-rounding schemes and optimization problems.

Dale Mark N. Bristol, Jian-Ting Pung, Chin‐Tsan Wang

Bioresource Technology Reports • 2025

B. M. Arora, Pankaj Attri

Journal of Composites Science • 2020

Nanomaterials such as carbon nanotubes (CNTs) have been used as an excellent material for catalysis, separation, adsorption and disinfection processes. CNTs have grabbed the attention of the scientific community and they have the potential to adsorb most of the organic compounds from water. Unlike, reverse osmosis (RO), nanofiltration (NF) and ultrafiltration (UF) membranes aligned CNT membranes can act as high-flow desalination membranes. CNTs provide a relatively safer electrode solution for biosensors. The article is of the utmost importance for the scientists and technologists working in water purification technologies to eliminate the water crisis in the future. This review summarizes about the application of CNTs in water purification.

Shuai Wang, Zongyang Qiu, Yingnan Hou et al.

Cell Research • 2021

The current coronavirus disease 2019 (COVID-19) pandemic presents a global public health challenge. The viral pathogen responsible, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), binds to the host receptor ACE2 through its spike (S) glycoprotein, which mediates membrane fusion and viral entry. Although the role of ACE2 as a receptor for SARS-CoV-2 is clear, studies have shown that ACE2 expression is extremely low in various human tissues, especially in the respiratory tract. Thus, other host receptors and/or co-receptors that promote the entry of SARS-CoV-2 into cells of the respiratory system may exist. In this study, we found that the tyrosine-protein kinase receptor UFO (AXL) specifically interacts with the N-terminal domain of SARS-CoV-2 S. Using both a SARS-CoV-2 virus pseudotype and authentic SARS-CoV-2, we found that overexpression of AXL in HEK293T cells promotes SARS-CoV-2 entry as efficiently as overexpression of ACE2, while knocking out AXL significantly reduces SARS-CoV-2 infection in H1299 pulmonary cells and in human primary lung epithelial cells. Soluble human recombinant AXL blocks SARS-CoV-2 infection in cells expressing high levels of AXL. The AXL expression level is well correlated with SARS-CoV-2 S level in bronchoalveolar lavage fluid cells from COVID-19 patients. Taken together, our findings suggest that AXL is a novel candidate receptor for SARS-CoV-2 which may play an important role in promoting viral infection of the human respiratory system and indicate that it is a potential target for future clinical intervention strategies.

Nicolas Maring, Andreas Fyrillas, Mathias Pont et al.

Nature Photonics • 2024

Abstract Quantum computing aims at exploiting quantum phenomena to efficiently perform computations that are unfeasible even for the most powerful classical supercomputers. Among the promising technological approaches, photonic quantum computing offers the advantages of low decoherence, information processing with modest cryogenic requirements, and native integration with classical and quantum networks. So far, quantum computing demonstrations with light have implemented specific tasks with specialized hardware, notably Gaussian boson sampling, which permits the quantum computational advantage to be realized. Here we report a cloud-accessible versatile quantum computing prototype based on single photons. The device comprises a high-efficiency quantum-dot single-photon source feeding a universal linear optical network on a reconfigurable chip for which hardware errors are compensated by a machine-learned transpilation process. Our full software stack allows remote control of the device to perform computations via logic gates or direct photonic operations. For gate-based computation, we benchmark one-, two- and three-qubit gates with state-of-the art fidelities of 99.6 ± 0.1%, 93.8 ± 0.6% and 86 ± 1.2%, respectively. We also implement a variational quantum eigensolver, which we use to calculate the energy levels of the hydrogen molecule with chemical accuracy. For photon native computation, we implement a classifier algorithm using a three-photon-based quantum neural network and report a six-photon boson sampling demonstration on a universal reconfigurable integrated circuit. Finally, we report on a heralded three-photon entanglement generation, a key milestone toward measurement-based quantum computing.

Xian Zeng, Peng Zhang, Weidong He et al.

Nucleic Acids Research • 2017

There has been renewed interests in the exploration of natural products (NPs) for drug discovery, and continuous investigations of the therapeutic claims and mechanisms of traditional and herbal medicines. In-silico methods have been employed for facilitating these studies. These studies and the optimization of in-silico algorithms for NP applications can be facilitated by the quantitative activity and species source data of the NPs. A number of databases collectively provide the structural and other information of ∼470 000 NPs, including qualitative activity information for many NPs, but only ∼4000 NPs are with the experimental activity values. There is a need for the activity and species source data of more NPs. We therefore developed a new database, NPASS (Natural Product Activity and Species Source) to complement other databases by providing the experimental activity values and species sources of 35 032 NPs from 25 041 species targeting 5863 targets (2946 proteins, 1352 microbial species and 1227 cell-lines). NPASS contains 446 552 quantitative activity records (e.g. IC50, Ki, EC50, GI50 or MIC mainly in units of nM) of 222 092 NP-target pairs and 288 002 NP-species pairs. NPASS, http://bidd2.nus.edu.sg/NPASS/, is freely accessible with its contents searchable by keywords, physicochemical property range, structural similarity, species and target search facilities.

Pengcheng Han, Chao Su, Yanfang Zhang et al.

Nature Communications • 2021

Multiple SARS-CoV-2 variants of concern (VOCs) have been emerging and some have been linked to an increase in case numbers globally. However, there is yet a lack of understanding of the molecular basis for the interactions between the human ACE2 (hACE2) receptor and these VOCs. Here we examined several VOCs including Alpha, Beta, and Gamma, and demonstrate that five variants receptor-binding domain (RBD) increased binding affinity for hACE2, and four variants pseudoviruses increased entry into susceptible cells. Crystal structures of hACE2-RBD complexes help identify the key residues facilitating changes in hACE2 binding affinity. Additionally, soluble hACE2 protein efficiently prevent most of the variants pseudoviruses. Our findings provide important molecular information and may help the development of novel therapeutic and prophylactic agents targeting these emerging mutants.

Nasir Saeed, Abdulkadir Çelik, Tareq Y. Al-Naffouri et al.

Ad Hoc Networks • 2019

Guangjing Huang, Yuting Dai, Chao Yang et al.

Journal of Aerospace Engineering • 2021

The purpose of this study was to investigate the energy-harvesting performance of a piezoelectric free-flying aircraft model under discrete gust. A fluid-structure-electric coupled simulation framework was established by loose coupling the computational fluid dynamics (CFD) solver and the electromechanical finite-element model. The field velocity method was used to introduce vertical gust velocities to the CFD computation. The pitch and plunge rigid degrees of freedom (DOF) were considered, together with elastic DOF in the electromechanical finite-element model by means of multibody dynamics. The output energy density and mean power were used to evaluate the harvesting performance. The effects of external load resistance, free-flow velocity, and gust frequency, especially the rigid DOFs were studied, respectively. A prototype of the piezoelectric free-flying aircraft model was fabricated. The output voltage was tested at different flow velocities and different sinusoidal gust frequencies with a given external load resistance of 10.3 MΩ in the wind tunnel test. The macrofiber composite (MFC) received an optimal voltage of 107 V and an optimal mean power of 0.188 mW at the flow velocity of 22 m/s and a gust frequency of 3.5 Hz, around the first bending mode. Moreover, the electromechanical finite-element model and the coupled simulation framework were verified by the ground vibration test and the wind tunnel test, respectively. Results indicated that the rigid pitch and plunge DOF may decrease the harvested energy to only one-third of the energy of a wall-mounted structure. The present work provides an effective theoretical and experimental basis for further studying the energy harvesting and vibration control of free-flying aircraft.

Zhonghao Wan, Yuqing Sun, Daniel C.W. Tsang et al.

Chemical Engineering Journal • 2020

Benjamin Wilfong, Henry A. Le Berre, Anand Radhakrishnan et al.

Computer Physics Communications • 2026

Eric B. Emmons, Benjamin J. De Corte, Young-Cho Kim et al.

Journal of Neuroscience • 2017

Although frontostriatal circuits are critical for the temporal control of action, how time is encoded in frontostriatal circuits is unknown. We recorded from frontal and striatal neurons while rats engaged in interval timing, an elementary cognitive function that engages both areas. We report four main results. First, "ramping" activity, a monotonic change in neuronal firing rate across time, is observed throughout frontostriatal ensembles. Second, frontostriatal activity scales across multiple intervals. Third, striatal ramping neurons are correlated with activity of the medial frontal cortex. Finally, interval timing and striatal ramping activity are disrupted when the medial frontal cortex is inactivated. Our results support the view that striatal neurons integrate medial frontal activity and are consistent with drift-diffusion models of interval timing. This principle elucidates temporal processing in frontostriatal circuits and provides insight into how the medial frontal cortex exerts top-down control of cognitive processing in the striatum. SIGNIFICANCE STATEMENT The ability to guide actions in time is essential to mammalian behavior from rodents to humans. The prefrontal cortex and striatum are critically involved in temporal processing and share extensive neuronal connections, yet it remains unclear how these structures represent time. We studied these two brain areas in rodents performing interval-timing tasks and found that time-dependent "ramping" activity, a monotonic increase or decrease in neuronal activity, was a key temporal signal. Furthermore, we found that striatal ramping activity was correlated with and dependent upon medial frontal activity. These results provide insight into information-processing principles in frontostriatal circuits.

Xin Li, Yan Wang, Di Wu et al.

Chemistry of Materials • 2016

Na energy storage technology is strategically attractive for large scale applications such as grid energy storage. We show in this paper that there is a clear relation between the Jahn–Teller activity of a transition metal ion at the end of charge and the mobility of Na in a cathode material. This is particularly important as mobility at the end of charge limits the capacity of current materials. Hence, by using this classical piece of physics in the battery world, it is possible to create higher capacity Na-cathode materials. Even more exciting is that the ideal element to impart this effect on cathodes is Fe, which is the least expensive of the transition metal oxides and can therefore enable low cost cathode materials.

Cliff C. Kerr, Robyn M. Stuart, Dina Mistry et al.

PLoS Computational Biology • 2021

The COVID-19 pandemic has created an urgent need for models that can project epidemic trends, explore intervention scenarios, and estimate resource needs. Here we describe the methodology of Covasim (COVID-19 Agent-based Simulator), an open-source model developed to help address these questions. Covasim includes country-specific demographic information on age structure and population size; realistic transmission networks in different social layers, including households, schools, workplaces, long-term care facilities, and communities; age-specific disease outcomes; and intrahost viral dynamics, including viral-load-based transmissibility. Covasim also supports an extensive set of interventions, including non-pharmaceutical interventions, such as physical distancing and protective equipment; pharmaceutical interventions, including vaccination; and testing interventions, such as symptomatic and asymptomatic testing, isolation, contact tracing, and quarantine. These interventions can incorporate the effects of delays, loss-to-follow-up, micro-targeting, and other factors. Implemented in pure Python, Covasim has been designed with equal emphasis on performance, ease of use, and flexibility: realistic and highly customized scenarios can be run on a standard laptop in under a minute. In collaboration with local health agencies and policymakers, Covasim has already been applied to examine epidemic dynamics and inform policy decisions in more than a dozen countries in Africa, Asia-Pacific, Europe, and North America.

M.A. Cheloui, N. Baharlou-Houreh, M. R. Aligoodarz

International Journal of Hydrogen Energy • 2025

Arash Javanmard, Fathiah Mohamed Zuki, Wan Mohd Ashri Wan Daud et al.

Process Safety and Environmental Protection • 2025

Tzu‐Hsuan Lan, Wei‐Mon Yan, Thangavel Sangeetha et al.

Sensors and Materials • 2017

Microbial fuel cells (MFCs) are bio-electrochemical transducers that produce electrical energy by the decomposition of organic matter with the aid of microorganisms. Through the transfer of electrons, energy can be delivered to an electrode surface, and understanding the electron transport mechanisms has become very crucial. Although significant research has been carried out in this field, relatively few reports have been based on numerical simulations. Therefore, this study was initiated to design a computational model through numerical simulation and apply it to the betterment of MFCs. Three important biochemical mechanisms of MFCs such as direct electron transport, transport through electron shuttles, and transport through nanowires were considered for simulation. The results showed that the function of the thickness of the active biofilm ( j max ) was obtained at a substrate concentration of 1.1 M with a current of 0.16 mA. The direct electron transport mechanism was reported to produce the maximum current density of 15.14 mA/m 2 . The direct transport also used a higher concentration of substrate to generate power than the nanowire transport and electron shuttle processes. These findings provide useful information on the enhancement of the performance of MFCs and especially on the application of numerical simulations for their scale-up process.

Mohamed Taha Mhiri, Walid Larbi, Mnaouar Chouchane et al.

Composite Structures • 2025

Md Saifuddin Ahmed Atique, Cai Xia Yang

Volume 3: Advanced Materials: Design, Processing, Characterization and Applications; Advances in Aerospace Technology • 2022

Abstract Energy harvesting from vibration sources was a very promising field of research throughout the last few decades among the engineers and scientist as considering the necessity of renewable/green energy for the welfare of mankind. Unused vibration energy exists in the surrounding or machineries was always tried to be utilized. Since then, by using piezoelectric transduction, researchers started to harvest the vibration energy. However, after the invention of piezo ceramics Macro Fiber Composites (MFC) by NASA, the research in this field augmented a lot due to its high efficiency to convert mechanical strain or vibration to useful electrical power and vice versa. Apart from energy harvesting researcher concentrated to utilize this harvested energy for daily life and hence application of this harvested energy for structural health monitoring inaugurated. Recent study showed that, the vibration energy harvested from the vehicles or aerospace (UAV) structure is good enough to power its onboard structural health monitoring unit though for feeding this power to any other onboard electrical system is still challenging due to low power generation along with its random production. Moreover, Macro Fiber Composites (MFC) can be used as an actuator to change the shape of aircraft wing to enhance aerodynamic performance and hence, application of MFC for wing morphing design has become popular throughout these years. The purpose of this research work is to depict the recent progress & development that took place in the field of energy harvesting & wing morphing research using macro fiber composites and combining the existing knowledge continue the work further, the future of this harvested energy, new design concept & upcoming challenges along with its possible solution. This work investigates the different configuration of macro fiber composites (MFC) for piezoelectric energy harvesting and its contribution for wing morphing design with enhanced aerodynamics. For the first part of this work, uniform MFC configuration was modeled and built up based on the Euler-Bernoulli beam theory. When the governing differential equations of the systems were derived, by applying the harmonic base excitation, coupled vibration response and the voltage response were obtained. The prediction of the mathematical model was at first verified by unimorph MFC with a brass substrate obtained from the state of art and then validation was justified by MFC unimorph along with three different substrate materials (copper, zinc alloy & galvanized steel) and thickness for the first time in this type of research. Computational & analytical solution revealed that, among these three substrates and for same thickness, maximum peak power at resonance excitation was obtained for the copper substrate. For the second part of the study (i) computational analysis was performed and the output was compared with the real time data obtained from the wind tunnel experiment and the conclusion stood that, with the increment of the incoming flow velocity, the power output from the MFC increases with a thin airfoil made of copper substrates and two MFC on its upper surface (ii) wing morphing design was performed for a NACA 0012 airfoil for the first time where macro fiber composite actuators were used to change the top and bottom surfaces of the airfoil with a view to recording the enhanced aerodynamics performance the designed morphing wing. CFD simulation results were compared with the wind tunnel testing data from the state of art for NACA 0014 for all identical parameters. The enhanced aerodynamics performance observed for designed wing morphing can be used for future concepts like maneuvering of the aircraft without the help of ailerons or for the purpose of active flow control over the aircraft wing.

Huiji Gao, Jiliang Tang, Huan Liu

Proceedings of the International AAAI Conference on Web and Social Media • 2021

Location-based social networks (LBSNs) have become a popular form of social media in recent years. They provide location related services that allow users to "check-in'' at geographical locations and share such experiences with their friends. Millions of "check-in'' records in LBSNs contain rich information of social and geographical context and provide a unique opportunity for researchers to study user's social behavior from a spatial-temporal aspect, which in turn enables a variety of services including place advertisement, traffic forecasting, and disaster relief. In this paper, we propose a social-historical model to explore user's check-in behavior on LBSNs. Our model integrates the social and historical effects and assesses the role of social correlation in user's check-in behavior. In particular, our model captures the property of user's check-in history in forms of power-law distribution and short-term effect, and helps in explaining user's check-in behavior. The experimental results on a real world LBSN demonstrate that our approach properly models user's check-ins and shows how social and historical ties can help location prediction.

Pierre‐Yves Le Traon, Antonio Reppucci, Enrique Álvarez Fanjul et al.

Frontiers in Marine Science • 2019

The Copernicus Marine Environment Monitoring Service (CMEMS) provides regular and systematic reference information on the physical and biogeochemical ocean and sea-ice state for the global ocean and the European regional seas. CMEMS serves a wide range of users (more than 15,000 users are now registered to the service) and applications. Observations are a fundamental pillar of the CMEMS value-added chain that goes from observation to information and users. Observations are used by CMEMS Thematic Assembly Centres (TACs) to derive high-level data products and by CMEMS Monitoring and Forecasting Centres (MFCs) to validate and constrain their global and regional ocean analysis and forecasting systems. This paper presents an overview of CMEMS, its evolution, and how the value of in situ and satellite observations is increased through the generation of high-level products ready to be used by downstream applications and services. The complementary nature of satellite and in situ observations is highlighted. Long-term perspectives for the development of CMEMS are described and implications for the evolution of the in situ and satellite observing systems are outlined. Results from Observing System Evaluations (OSEs) and Observing System Simulation Experiments (OSSEs) illustrate the high dependencies of CMEMS systems on observations. Finally future CMEMS requirements for both satellite and in situ observations are detailed.

Zafer Ortatepe, Ahmet Karaarslan

International Journal of Electrical Power & Energy Systems • 2020

Rubén Tolosana, Rubén Vera-Rodríguez, Julián Fiérrez et al.

Biblos-e Archivo (Universidad Autónoma de Madrid) • 2020

Kenan Hatipoglu, Mohammed M. Olama

2021 IEEE Power & Energy Society General Meeting (PESGM) • 2021

This paper proposes a new distributed model-free control (MFC) strategy for dynamic voltage control to diminish distribution systems' voltage violations. The objective is to maintain all critical load bus voltages within the acceptable ANSI Range A (+/- 5% of nominal). The distributed MFC strategy, which only requires local voltage measurements from designated load buses, controls online the reactive power generation of available synchronous generator (SG)-based and photovoltaic (PV)-based distributed generators (DGs). The distributed MFC strategy is computationally efficient and does not require modelling of the different system components and disturbances. Time-domain dynamic simulations are conducted for the 21-bus test distribution system fed by multiple DGs to verify the performance of the proposed MFC strategy, and the results are compared against the conventional model-based microgrid voltage stabilizer (MGVS) control strategy. The simulation results show that the distributed MFC strategy provides minimal voltage violations and achieves the dynamic voltage stability of the system under diverse disturbances.

Chin‐Tsan Wang, Tzu‐Hsuan Lan, Aristotle T. Ubando et al.

International Journal of Energy Research • 2021

Microbial fuel cells (MFCs) are a promising technology that uses microorganisms to simultaneously generate bioelectricity while treating wastewater. To further improve the performance of the MFC, it is essential to understand and evaluate the electron transfer mechanism. However, redesigning the electron transfer mechanism of MFCs through an experimental approach is costly and time-consuming. Hence, in this study, a numerical modeling approach is implemented through the Nernst-Monod kinetic equation, which is validated by experimental results. A nanowire conductive transferring pathway is considered between the microorganisms and anode electrodes of a batch-type MFC. Moreover, two types of bacteria are utilized such as the Shewanella oneidensis MR-1 and Shewanella putrefacient with substrate concentrations of 0.5 M sodium lactate. The results have shown that the limiting current density of the MFC from the computational model is 1514 mA m−2. On the other hand, the current density from the experimental approach for Shewanella oneidensis MR-1 is 497 mA m−2 while for Shewanella putrefacient is 140 mA m−2. The anode activation loss of 491 Ω is lower than the cathode activation loss of 643 Ω, which indicates the relative influence of the cathode activation loss on the bioelectricity generation of the MFC. In addition, the results revealed that the nanowire electron transfer mechanism in the anode biofilm was less affected by the concentration losses. This then indicates that the physical mechanism of the nanowire electron transfer can be effectively used to investigate the batch-type MFCs. In turn, the results of this study will contribute to the development of an improved MFC.

Jiahuan Lu, Rui Xiong, Jinpeng Tian et al.

Nature Communications • 2023

State of health is a critical state which evaluates the degradation level of batteries. However, it cannot be measured directly but requires estimation. While accurate state of health estimation has progressed markedly, the time- and resource-consuming degradation experiments to generate target battery labels hinder the development of state of health estimation methods. In this article, we design a deep-learning framework to enable the estimation of battery state of health in the absence of target battery labels. This framework integrates a swarm of deep neural networks equipped with domain adaptation to produce accurate estimation. We employ 65 commercial batteries from 5 different manufacturers to generate 71,588 samples for cross-validation. The validation results indicate that the proposed framework can ensure absolute errors of less than 3% for 89.4% of samples (less than 5% for 98.9% of samples), with a maximum absolute error of less than 8.87% in the absence of target labels. This work emphasizes the power of deep learning in precluding degradation experiments and highlights the promise of rapid development of battery management algorithms for new-generation batteries using only previous experimental data.

Zaoxing Liu, Antonis Manousis, Gregory Vorsanger et al.

Proceedings of the 2016 ACM SIGCOMM Conference • 2016

Network management requires accurate estimates of metrics for traffic engineering (e.g., heavy hitters), anomaly detection (e.g., entropy of source addresses), and security (e.g., DDoS detection). Obtaining accurate estimates given router CPU and memory constraints is a challenging problem. Existing approaches fall in one of two undesirable extremes: (1) low fidelity general-purpose approaches such as sampling, or (2) high fidelity but complex algorithms customized to specific application-level metrics. Ideally, a solution should be both general (i.e., supports many applications) and provide accuracy comparable to custom algorithms. This paper presents UnivMon, a framework for flow monitoring which leverages recent theoretical advances and demonstrates that it is possible to achieve both generality and high accuracy. UnivMon uses an application-agnostic data plane monitoring primitive; different (and possibly unforeseen) estimation algorithms run in the control plane, and use the statistics from the data plane to compute application-level metrics. We present a proof-of-concept implementation of UnivMon using P4 and develop simple coordination techniques to provide a ``one-big-switch'' abstraction for network-wide monitoring. We evaluate the effectiveness of UnivMon using a range of trace-driven evaluations and show that it offers comparable (and sometimes better) accuracy relative to custom sketching solutions.

Qiong Wu, Hongmei Ge, Qiang Fan et al.

Wireless Communications and Mobile Computing • 2020

Various emerging vehicular applications such as autonomous driving and safety early warning are used to improve the traffic safety and ensure passenger comfort. The completion of these applications necessitates significant computational resources to perform enormous latency-sensitive/nonlatency-sensitive and computation-intensive tasks. It is hard for vehicles to satisfy the computation requirements of these applications due to the limit computational capability of the on-board computer. To solve the problem, many works have proposed some efficient task offloading schemes in computing paradigms such as mobile fog computing (MFC) for the vehicular network. In the MFC, vehicles adopt the IEEE 802.11p protocol to transmit tasks. According to the IEEE 802.11p, tasks can be divided into high priority and low priority according to the delay requirements. However, no existing task offloading work takes into account the different priorities of tasks transmitted by different access categories (ACs) of IEEE 802.11p. In this paper, we propose an efficient task offloading strategy to maximize the long-term expected system reward in terms of reducing the executing time of tasks. Specifically, we jointly consider the impact of priorities of tasks transmitted by different ACs, mobility of vehicles, and the arrival/departure of computing tasks, and then transform the offloading problem into a semi-Markov decision process (SMDP) model. Afterwards, we adopt the relative value iterative algorithm to solve the SMDP model to find the optimal task offloading strategy. Finally, we evaluate the performance of the proposed scheme by extensive experiments. Numerical results indicate that the proposed offloading strategy performs well compared to the greedy algorithm.

Yash Khandelwal, Abhinandan Routray, Rajeev Kumar Singh et al.

IET Power Electronics • 2020

This study presents a hybrid multilevel inverter (HMLI), capable of generating 19 voltage levels using a single DC voltage source. The peak inverse voltage of all the switches is restrained within the input DC source voltage in the proposed HMLI, which requires only three capacitors and 15 switches to achieve 19 voltage levels. In the proposed HMLI, one of the capacitors is self‐balanced, while the other two capacitors are balanced using a modified finite control set model predictive control (MFCS‐MPC). The proposed MFCS‐MPC reduces the computation time considerably without affecting the performance of the system. The proposed HMLI is compared with some other HMLI topologies to show its merits in terms of active and passive components. Finally, the steady‐state and dynamic performance of the proposed topology and its control algorithm is validated through simulation and experimentation for a 1 kW prototype.

Anyang Tong, Chao Tang, Wenjian Wang

Expert Systems with Applications • 2024

Mo Tao, Tianyi Gao, Xianling Li et al.

Frontiers in Control Engineering • 2021

This paper presents a data-driven predictive controller based on the broad learning algorithm without any prior knowledge of the system model. The predictive controller is realized by regressing the predictive model using online process data and the incremental broad learning algorithm. The proposed model predictive control (MPC) approach requires less online computational load compared to other neural network based MPC approaches. More importantly, the precision of the predictive model is enhanced with reduced computational load by operating an appropriate approximation of the predictive model. The approximation is proved to have no influence on the convergence of the predictive control algorithm. Compared with the partial form dynamic linearization aided model free control (PFDL-MFC), the control performance of the proposed predictive controller is illustrated through the continuous stirred tank heater (CSTH) benchmark.

Cristóbal González, Alejandro Angulo, Fernando Mancilla–David

IEEE Transactions on Power Electronics • 2025

This paper addresses the main performance issues of multistep–finite–control–set model predictive control (MFCS–MPC) that make it impractical in medium–voltage high–power grid–connected converters. Current MFCS–MPC techniques synthesize nonperiodic steady–state voltages that produce harmonic–rich continuous and unpredictable Fourier spectra with variable switching frequency. This article leverages advances in optimized pulse patterns and MFCS–MPC to meet grid codes while operating at a low, fixed switching frequency and rendering a fast dynamic response. To keep the strategy viable in real–time, enhancements to the sphere decoder algorithm that solves the optimization problem were made, and the computationally efficient Frank–Wolfe algorithm was used to find a new center for the sphere during transients. The control scheme is assessed through simulation and hardware–in–the–loop experiments, demonstrating its real–time viability.

Hossam Eddine Glida, Chelihi Abdelghani, Chouki Sentouh

Proceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering • 2024

This paper presents a novel model-free control approach, Flower Pollination Algorithm-based Model-Free Control (FPA-MFC), for trajectory tracking of mini-drone quadrotor unmanned aerial vehicles (UAVs). The proposed approach employs an adaptive estimator based on filtered signals to approximate the nonlinear dynamic functions of the system. This approximator allows the development of a robust decentralized control law able to separately manage the position and attitude dynamics of the drone. The controller design is free of any prior knowledge of the system dynamics, and the control inputs are computed solely from instantaneous input and output measurements. Indeed, this can significantly reduce the computational burden and improve the efficiency of the control algorithm while preserving its simplicity. The design gains of the control law are selected using the metaheuristic flower pollination algorithm to achieve greater trajectory tracking performance and ensure closed-loop system stability. Simulation tests conducted on the Parrot mini drone platform validate the effectiveness and superior performance of FPA-MFC, compared to similar controllers without optimization and using the particle swarm optimization algorithm.

Daokuan Cheng, Hanlu Xu, Liang Zhao et al.

Process Safety and Environmental Protection • 2024

Andrzej Koszewnik, Kacper Lesniewski, Vikram Pakrashi

Sensors • 2021

This paper investigates damage identification metrics and their performance using a cantilever beam with a piezoelectric harvester for Structural Health Monitoring. In order to do this, the vibrations of three different beam structures are monitored in a controlled manner via two piezoelectric energy harvesters (PEH) located in two different positions. One of the beams is an undamaged structure recognized as reference structure, while the other two are beam structures with simulated damage in form of drilling holes. Subsequently, five different damage identification metrics for detecting damage localization and extent are investigated in this paper. Overall, each computational model has been designed on the basis of the modified First Order Shear Theory (FOST), considering an MFC element consisting homogenized materials in the piezoelectric fiber layer. Frequency response functions are established and five damage metrics are assessed, three of which are relevant for damage localization and the other two for damage extent. Experiments carried out on the lab stand for damage structure with control damage by using a modal hammer allowed to verify numerical results and values of particular damage metrics. In the effect, it is expected that the proposed method will be relevant for a wide range of application sectors, as well as useful for the evolving composite industry.

Eun Jung Chae, Amin Moosavian, Alexander M. Pankonien et al.

Volume 2: Modeling, Simulation and Control of Adaptive Systems; Integrated System Design and Implementation; Structural Health Monitoring • 2017

Along with recent advancements in novel materials and manufacturing processes, the interest in morphing wings has increased in order to further improve the aerodynamic performance of flying bodies. The morphing wing can be tailored to deliver superior performance, compared to its non-morphing counterparts, for multiple operating conditions and in varying flows. In particular, the morphing wing is implemented for drag reduction and lift enhancement, and hence, the maneuverability, adaptability, and capability of the morphing wing can encompass an even wider spectrum by changing the wing shape. In this research, an existing morphing UAV wing design, Spanwise Morphing Trailing Edge (SMTE), actuated by bending Macro Fiber Composites (MFCs), is considered to generate the spanwise sinusoidal variations on the trailing edge of the morphing wing. A comparative aerodynamic study of the morphing wing by varying the spatial frequency (i.e., number of waves along the span) and the phase shift (i.e., wave shape along the span) at different angles of attack is conducted through analytical approaches and numerical Computational Fluid Dynamic (CFD) simulations, which are validated with previous experimental measurements. The analytical approach uses the three-dimensional (3D) Prandtl lifting line theory, and the CFD modeling in turbulence flow solves the 3D Reynolds-Averaged Navier-Stokes (RANS) equations with the k-ω Shear Stress Transport (SST) turbulence model. Note that the numerical simulations of a morphing wing focus on the pre-stall condition to estimate the aerodynamic performance. This work extends a prior study about a nominal flight condition testing a morphing wing at multiple flight conditions to evaluate multi-point 1 performance. The results show that there are governing aerodynamic efficiency zones of the lift-to-drag ratio, endurance, and aircraft range within a zone of angles of attack. Therefore, the morphing wing is shown to have a good aerodynamic performance as compared to the non-morphing wing.