M. Amirul Islam, Baranitharan Ethiraj, Chin Kui Cheng et al.

Energy & Fuels • 2017

Mutual interactions between microorganisms play a vital role in the formation of electroactive biofilms, which is a key element in the longevity and success of bioelectrochemical systems. The present study was intended to examine both the electrogenic properties of B. cereus and its ability to inhibit methanogenesis in microbial fuel cells (MFCs). The potential influence of the incorporation of B. cereus into anaerobic sludge (AS) on the electrochemical activity was assessed using cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS) analyses. The CV of MFCs with B. cereus showed a strong redox peak, suggesting that B. cereus has electrogenetic properties. Moreover, the incorporation of B. cereus into AS provided an enhancement in the power generation (4.83 W/m3) and the CE (22%) of the MFC compared to the corresponding values for an MFC inoculated solely with AS (1.82 W/m3, 12%). The increase in power generation could be due to the antimethanogenic property of B. cereus, which was evident from the 54% reduction in methane production. The results of this study suggest that the incorporation of microorganisms with electrogenic and antimethanogenic properties into AS promotes the formation of electroactive biofilms and maximizes the power generation of MFCs by suppressing the methanogenesis.

Alireza Valipour, Nazanin Hamnabard, Seyed Mohammad Hadi Meshkati et al.

Dalton Transactions • 2019

Crystal phase and morphology variations obtained by simple high-temperature annealing offer promising strategies for employing nanostructured manganese oxide as a cathode catalyst for microbial fuel cells (MFCs). This study examines the effectiveness of simultaneous-phase and morphology-controlled manganese dioxide nanomaterials, designed by annealing a hydrothermally synthesized flower-like δ-manganese dioxide precursor at 300-800 °C, as cathode catalysts for MFCs. MFCs with the best-performing catalyst cathode (at a reasonable mass loading) were also analyzed through cyclic voltammetry and electrochemical impedance spectroscopy. Among MFCs with non-annealed and annealed manganese dioxide cathodes (0.5 mg cm-2), those with the catalyst annealed at 500 °C (148 ± 7 mW m-2, CE = 13 ± 1%) generated the most power (5-52%). MFCs with the catalyst annealed at 500 °C at a loading of 1 mg cm-2 as the cathode delivered the highest maximum power density (213 ± 9 mW m-2, CE = 13 ± 1%), representing 44% of that obtained using Pt/C at 0.5 mg Pt per cm2 (483 ± 11 mW m-2, CE = 18 ± 2%) and a comparatively low internal resistance (164 ± 2 Ω). Both cyclic voltammetry and electrochemical impedance spectroscopy results were consistent with empirical data. Compared with previously reported cathode materials, the annealed product from the precursor flower-like δ-manganese dioxide annealed at 500 °C (particularly at a loading of 1 mg cm-2) was a more reliable, efficient, and inexpensive sustainable cathode catalyst for scaled-up MFCs.

Mir Reza Majidi, Fatemeh Shahbazi Farahani, Mir Ghasem Hosseini et al.

Bioelectrochemistry • 2018

Qiang Wang, Liping Huang, Yuzhen Pan et al.

Journal of Hazardous Materials • 2016

Indrasis Das, Md Tabish Noori, Gourav Dhar Bhowmick et al.

International Journal of Hydrogen Energy • 2018

Md Tabish Noori, Makarand M. Ghangrekar, C. K. Mukherjee

International Journal of Hydrogen Energy • 2016

Praveena Mishra, Rajeev Jain

International Journal of Hydrogen Energy • 2016

In this study, we reported the nano-composite of multiwalled carbon nanotubes-manganese oxide/polypyrrole (MWCNT-MnO2/PPy) was used as anode in microbial fuel cell (MFC) for sewage waste water treatment and power generation. The nano-composite of MWCNT-MnO2/PPy was electrochemically deposited on the surface of carbon cloth (CC) electrode. The modified electrodes were characterized by scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy (EDX), transmission electron microscopy (TEM) and fourier transformed infrared spectroscopy (FTIR). The electrochemical properties of the electrode have been investigated by cyclic voltammetry (CV) and electrochemical impedance spectroscopy (EIS). The MWCNT-MnO2/PPy electrode showed good electrical conductivity of 0.1185 S m−1. This was also supported by band gap measurement value 0.8 eV. The MWCNT-MnO2/PPy based MFC obtained maximum power density of 1125.4 mW m−2. The CC modified MWCNT-MnO2/PPy nano-composite electrodes have a high potential and good biocompatibility that showed a great realized mediator less MFCs for bio-electricity production from sewage waste water.

Diana Hidalgo, Tonia Tommasi, Sergio Bocchini et al.

Energy • 2016

Yu Zhao, Yan Ma, Ting Li et al.

RSC Advances • 2018

Carbon felt is widely used as an anode material in microbial fuel cells (MFCs) because of its high specific surface area, low cost, good electrical conductivity, and biocompatibility. In this paper, carbon felt samples were thermally treated with a mixed solution of concentrated HNO 3 and 30% H 2 O 2 with different volume ratios of 1 : 3 (MFC-1), 1 : 1 (MFC-2), and 3 : 1 (MFC-3). The electrochemical performance of the resulting MFCs were investigated by cyclic voltammetry, electrochemical impedance spectroscopy, chronoamperometry and polarization curve measurement. Fourier transform infrared spectroscopy and scanning electron microscopy were conducted to characterize the functional groups and the morphology of the carbon felts. After modification, the number of oxygen-containing functional groups in MFC-1, MFC-2, and MFC-3 increased compared with MFC-4 (bare anode MFC), the start-up time of the obtained MFCs was markedly shortened, and the charge transfer resistance of the bioanode was decreased. In MFC-2, the maximum power density was 758.2 mW m -2 , which was 51.1% higher than MFC-4. Increases of oxygen-containing functional groups on the modified anodes favored the adsorption and growth of bacteria and acceleration of electron transport between the electrode and bacteria. Thus, the electrochemical characteristics of MFCs employing these anodes were improved.

Lutian Wang, Yun Wu, Zhenkun You et al.

Water Research • 2022

Arunodaya Raj Mishra, Pratibha Rani, Fausto Cavallaro et al.

Facta Universitatis Series Mechanical Engineering • 2023

Effective wastewater treatment has significant effects on saving water and preventing unnecessary water scarcity. An appropriate wastewater treatment technology (WWTT) brings economic benefits through reuse in different sectors and benefits the society and environment. This study aims to develop a decision-making framework for evaluating the sustainable WWTTs under interval-valued intuitionistic fuzzy set (IVIFS) environment. The proposed MCDM framework is divided into two stages. First, a new Hellinger distance measure is developed to determine the degree of difference between IVIFSs and also discussed its desirable characteristics. Second, an interval-valued intuitionistic fuzzy extension of multi-attribute ideal-real comparative analysis (MAIRCA) model is developed using the proposed Hellinger distance measure-based weighting tool. Further, the proposed model is implemented on an empirical study of sustainable WWTTs evaluation problem. Sensitivity and comparative studies are made. The results indicate that odor impacts, sludge production, maintenance and operation are the most effective sustainable factors and Microbial fuel cell (MFC) technology is the best WWTT followed by natural treatment methods.

Chengyong Yang, Bolong Huang, Shuxing Bai et al.

Advanced Materials • 2020

Electrocatalytic nitrogen reduction reaction (NRR) is a promising process relative to energy-intensive Haber-Bosch process. While conventional electrocatalysts underperform with sluggish paths, achieving dissociation of N 2 brings the key challenge for enhancing NRR. This study proposes an effective surface chalcogenation strategy to improve the NRR performance of pristine metal nanocrystals (NCs). Surprisingly, the NH 3 yield and Faraday efficiency (FE) (175.6 ± 23.6 mg h -1 g -1 Rh and 13.3 ± 0.4%) of Rh-Se NCs is significantly enhanced by 16 and 15 times, respectively. Detailed investigations show that the superior activity and high FE are attributed to the effect of surface chalcogenation, which not only can decrease the apparent activation energy, but also inhibit the occurrence of the hydrogen evolution reaction (HER) process. Theoretical calculations reveal that the strong interface strain effect within core@shell system induces a critical redox inversion, resulting in a rather low valence state of Rh and Se surface sites. Such strong correlation indicates an efficient electron-transfer minimizing NRR barrier. Significantly, the surface chalcogenation strategy is general, which can extend to create other NRR metal electrocatalysts with enhanced performance. This strategy open a new avenue for future NH 3 production for breakthrough in the bottleneck of NRR.

Rongli Zhao, Douxi Yan, Qiang Liu et al.

IEEE Access • 2019

Microstructure functional surface is widely used in an optical system because of its special micro-topological structure and particular physical properties. This paper presents a context-aware autonomously controlling method of micro-dots punching machine tool via establishing the digital twin-driven cyber-physical system. Key enabling techniques on twinning of cyberspace and physical equipment are discussed. A dynamic adjustment model of piezoelectric ceramics for micro-dots punching is presented based on the high-precision online detection and control system. A novel staggered punching approach is proposed for improving the punching speed. A joint optimization model is proposed for coordinating micro-punching system and staggered process. Context-aware autonomous adjusting of the system with errors analysis and compensations in the punching process is realized. Finally, a positioning accuracy of $2~\mu \text{m}$ and a high punching speed of 20–65 dots/s are achieved. This paper is expected to provide a new approach for incorporating smart-enabling techniques in the ultra-precision machining of microstructure arrays.

Kit Yan Chan, Bilal Abu-Salih, Raneem Qaddoura et al.

Neurocomputing • 2023

Deep neural networks (DNNs) are currently being deployed as machine learning technology in a wide range of important real-world applications. DNNs consist of a huge number of parameters that require millions of floating-point operations (FLOPs) to be executed both in learning and prediction modes. A more effective method is to implement DNNs in a cloud computing system equipped with centralized servers and data storage sub-systems with high-speed and high-performance computing capabilities. This paper presents an up-to-date survey on current state-of-the-art deployed DNNs for cloud computing. Various DNN complexities associated with different architectures are presented and discussed alongside the necessities of using cloud computing. We also present an extensive overview of different cloud computing platforms for the deployment of DNNs and discuss them in detail. Moreover, DNN applications already deployed in cloud computing systems are reviewed to demonstrate the advantages of using cloud computing for DNNs. The paper emphasizes the challenges of deploying DNNs in cloud computing systems and provides guidance on enhancing current and new deployments.

Sophie Tomaz, Dongyu Wang, Nicolás Zabalegui et al.

Nature Communications • 2021

Organic peroxy radicals (RO 2 ) play a pivotal role in the degradation of hydrocarbons. The autoxidation of atmospheric RO 2 radicals produces highly oxygenated organic molecules (HOMs), including low-volatility ROOR dimers formed by bimolecular RO 2 + RO 2 reactions. HOMs can initiate and greatly contribute to the formation and growth of atmospheric particles. As a result, HOMs have far-reaching health and climate implications. Nevertheless, the structures and formation mechanism of RO 2 radicals and HOMs remain elusive. Here, we present the in-situ characterization of RO 2 and dimer structure in the gas-phase, using online tandem mass spectrometry analyses. In this study, we constrain the structures and formation pathway of several HOM-RO 2 radicals and dimers produced from monoterpene ozonolysis, a prominent atmospheric oxidation process. In addition to providing insights into atmospheric HOM chemistry, this study debuts online tandem MS analyses as a unique approach for the chemical characterization of reactive compounds, e.g., organic radicals.

Ryan M. Kelley, Oliver Flouty, Eric B. Emmons et al.

Brain • 2017

The subthalamic nucleus is a key site controlling motor function in humans. Deep brain stimulation of the subthalamic nucleus can improve movements in patients with Parkinson's disease; however, for unclear reasons, it can also have cognitive effects. Here, we show that the human subthalamic nucleus is monosynaptically connected with cognitive brain areas such as the prefrontal cortex. Single neurons and field potentials in the subthalamic nucleus are modulated during cognitive processing and are coherent with 4-Hz oscillations in medial prefrontal cortex. These data predict that low-frequency deep brain stimulation may alleviate cognitive deficits in Parkinson's disease patients. In line with this idea, we found that novel 4-Hz deep brain stimulation of the subthalamic nucleus improved cognitive performance. These data support a role for the human hyperdirect pathway in cognitive control, which could have relevance for brain-stimulation therapies aimed at cognitive symptoms of human brain disease.awx300media15660002226001.

Mingming Yin, He Miao, Ruigan Hu et al.

Journal of Power Sources • 2021

Xuan Gong, Fengbao Dong, Mohamed A. Mohamed et al.

IEEE Access • 2020

This article assesses the energy management of reconfigurable residential smart hybrid AC/DC microgrids considering the combined heat and power (CHP) loads as well as the electric vehicles charging/discharging behaviors. A holistic model is developed for the proton exchange membrane fuel cell to retrieve the unwanted thermal energy generated at the operation time. The proposed model makes use of the unoccupied capacity of the fuel cell for producing/storing hydrogen for the later usage and increasing its efficiency. A stochastic framework is designed using point estimate method (PEM) to capture the uncertainties of the photovoltaic and wind turbine forecast error, power company price, the operating temperature of the proton exchange membrane fuel cell, the price for natural gas, price for selling hydrogen, and the pressure of the H2 and O2 in the fuel cell stack. The PEM approach has shown superior advantages in terms of accuracy and running time. Considering the complex and nonlinear structure of the proposed framework, a proficient optimization technique based on the teacher learning algorithm (TLA) is devised. A two-phase modification method is proposed to increase the algorithm variety and help its convergence characteristics. The performance of the proposed algorithm is compared with the TLA, particle swarm optimization (PSO) algorithm and genetic algorithm (GA). For enhancing the security of the energy and data transaction within the system, a directed acyclic graph (DAG)-based security framework is introduced to guarantee the performance of the system against the subversive accesses. By using this scheme, the essential data of the units are recorded and secured in the form of public, private and transaction blockchains. The economic characteristics of the proposed method are assessed on a residential hybrid AC-DC microgrid test system.

Sebastian Funk, Anton Camacho, Adam J. Kucharski et al.

PLoS Computational Biology • 2019

Real-time forecasts based on mathematical models can inform critical decision-making during infectious disease outbreaks. Yet, epidemic forecasts are rarely evaluated during or after the event, and there is little guidance on the best metrics for assessment. Here, we propose an evaluation approach that disentangles different components of forecasting ability using metrics that separately assess the calibration, sharpness and bias of forecasts. This makes it possible to assess not just how close a forecast was to reality but also how well uncertainty has been quantified. We used this approach to analyse the performance of weekly forecasts we generated in real time for Western Area, Sierra Leone, during the 2013-16 Ebola epidemic in West Africa. We investigated a range of forecast model variants based on the model fits generated at the time with a semi-mechanistic model, and found that good probabilistic calibration was achievable at short time horizons of one or two weeks ahead but model predictions were increasingly unreliable at longer forecasting horizons. This suggests that forecasts may have been of good enough quality to inform decision making based on predictions a few weeks ahead of time but not longer, reflecting the high level of uncertainty in the processes driving the trajectory of the epidemic. Comparing forecasts based on the semi-mechanistic model to simpler null models showed that the best semi-mechanistic model variant performed better than the null models with respect to probabilistic calibration, and that this would have been identified from the earliest stages of the outbreak. As forecasts become a routine part of the toolkit in public health, standards for evaluation of performance will be important for assessing quality and improving credibility of mathematical models, and for elucidating difficulties and trade-offs when aiming to make the most useful and reliable forecasts.

M. Chowdhury, Khem Poudel, Yating Hu

IEEE Access • 2020

Phonocardigraphy (PCG) is the graphical representation of heart sounds. The PCG signal contains useful information about the functionality and the condition of the heart. It also provides an early indication of potential cardiac abnormalities. Extracting cardiac information from heart sounds and detecting abnormal heart sounds to diagnose heart diseases using the PCG signal can play a vital role in remote patient monitoring. In this paper, we have combined different signal processing techniques and a deep learning method to denoise, compress, segment, and classify PCG signals effectively and accurately. First, the PCG signal is denoised and compressed by using a multi-resolution analysis based on the Discrete Wavelet Transform (DWT). Then, a segmentation algorithm, based on the Shannon energy envelope and zero-crossing, is applied to segment the PCG signal into four major parts: the first heart sound (S1), the systole interval, the second heart sound (S2), and the diastole interval. Finally, Mel-scaled power spectrogram and Mel-frequency cepstral coefficients (MFCC) are employed to extract informative features from the PCG signal, which are then fed into a classifier to classify each PCG signal into a normal or an abnormal signal by using a deep learning approach. For the classification, a 5-layer feed-forward Deep Neural Network (DNN) model is used, and overall testing accuracy of around 97.10% is achieved. Besides providing valuable information regarding heart condition, this signal processing approach can help cardiologists take appropriate and reliable steps toward diagnosis if any cardiovascular disorder is found in the initial stage.

Deepak Punetha, Manoranjan Kar, Saurabh Kumar Pandey

Scientific Reports • 2020

This paper reports on reduced graphene oxide (rGO), tin oxide (SnO 2 ) and polyvinylidene fluoride (PVDF) tertiary nanocomposite thick film based flexible gas sensor. The nanocomposite of 0.90(PVDF) - 0.10[x(SnO 2 ) - (1 - x)rGO] with different weight percentages (x = 0, 0.15, 0.30, 0.45, 0.6, 0.75, 0.90 and 1) have been prepared by the hot press method. Chromium (Cr) has been deposited on the surface by using E-beam evaporation system, which is used as electrode of the device. Crystal structure, morphology, and electrical characteristics of the device have been explored for the technological application. A correlation between crystallinity, morphology, and electrical properties with these thick films has also been established. The device has been tested at different hydrogen (H 2 ) gas concentration as well as at different response times. A superior response of 0.90(PVDF) - 0.10[0.75(SnO 2 ) - 0.25 rGO] nanocomposite thick film has been observed. Hence, this composition is considered as optimized tertiary nanocomposite for the hydrogen gas sensor application. The sensor response of 49.2 and 71.4% with response time 34 sec and 52 sec for 100 PPM and 1000 PPM H 2 gas concentration respectively have been obtained. First time a new kind of low cost and flexible polymer based nanocomposite thick film gas sensor has been explored.

Gregory D. Sepich‐Poore, Daniel McDonald, Evguenia Kopylova et al.

Oncogene • 2024

In 2020, we identified cancer-specific microbial signals in The Cancer Genome Atlas (TCGA) [1]. Multiple peer-reviewed papers independently verified or extended our findings [2-12]. Given this impact, we carefully considered concerns by Gihawi et al. [13] that batch correction and database contamination with host sequences artificially created the appearance of cancer type-specific microbiomes. (1) We tested batch correction by comparing raw and Voom-SNM-corrected data per-batch, finding predictive equivalence and significantly similar features. We found consistent results with a modern microbiome-specific method (ConQuR [14]), and when restricting to taxa found in an independent, highly-decontaminated cohort. (2) Using Conterminator [15], we found low levels of human contamination in our original databases (~1% of genomes). We demonstrated that the increased detection of human reads in Gihawi et al. [13] was due to using a newer human genome reference. (3) We developed Exhaustive, a method twice as sensitive as Conterminator, to clean RefSeq. We comprehensively host-deplete TCGA with many human (pan)genome references. We repeated all analyses with this and the Gihawi et al. [13] pipeline, and found cancer type-specific microbiomes. These extensive re-analyses and updated methods validate our original conclusion that cancer type-specific microbial signatures exist in TCGA, and show they are robust to methodology.

Andrés Faíña, Brian Nejati, Kasper Støy

Applied Sciences • 2020

Commercial liquid handling robots are rarely appropriate when tasks change often, which is the case in the early stages of biochemical research. In order to address it, we have developed EvoBot, a liquid handling robot, which is open-source and employs a modular design. The combination of an open-source and a modular design is particularly powerful because functionality is divided into modules with simple, well-defined interfaces, hence customisation of modules is possible without detailed knowledge of the entire system. Furthermore, the modular design allows end-users to only produce and assemble the modules that are relevant for their specific application. Hence, time and money are not wasted on functionality that is not needed. Finally, modules can easily be reused. In this paper, we describe the EvoBot modular design and through scientific experiments such as basic liquid handling, nurturing of microbial fuel cells, and droplet chemotaxis experiments document how functionality is increased one module at a time with a significant amount of reuse. In addition to providing wet-labs with an extendible, open-source liquid handling robot, we also think that modularity is a key concept that is likely to be useful in other robots developed for scientific purposes.

Hafiz Muhammad Fahad Noman, Effariza Hanafi, Kamarul Ariffin Noordin et al.

IEEE Access • 2023

The upcoming 6G networks are sixth-sense next-generation communication networks with an ever-increasing demand for enhanced end-to-end (E2E) connectivity towards a connected, sustainable world. Recent developments in artificial intelligence (AI) have enabled a wide range of novel technologies through the availability of advanced machine learning (ML) models, large datasets, and high computational power. In addition, intelligent resource management is a key feature of 6G networks that enables self-configuration and self-healing by leveraging the parallel computing and autonomous decision-making ability of ML techniques to enhance energy efficiency and computational capacity in 6G networks. Consequently, ML techniques will play a significant role in addressing resource management and mobility management challenges in 6G wireless networks. This article provides a comprehensive review of state-of-the-art ML algorithms applied in 6G wireless networks, categorized into learning types, including supervised and unsupervised machine learning, Deep Learning (DL), Reinforcement Learning (RL), Deep Reinforcement Learning (DRL) and Federated Learning (FL). In particular, we review the ML algorithms applied in the emerging networks paradigm, such as device-to-device (D2D) networks, vehicular networks (Vnet), and Fog-Radio Access Networks (F-RANs). We highlight the ML-based solutions to address technical challenges in terms of resource allocation, task offloading, and handover management. We also provide a detailed review of the ML techniques to improve energy efficiency and reduce latency in 6G wireless networks. To this end, we identify the open research issues and future trends concerning ML-based intelligent resource management applications in 6G networks.

Johannes Bjerva, Barbara Plank, Johan Bos

arXiv (Cornell University) • 2016

We propose a novel semantic tagging task, sem-tagging, tailored for the purpose of multilingual semantic parsing, and present the first tagger using deep residual networks (ResNets). Our tagger uses both word and character representations and includes a novel residual bypass architecture. We evaluate the tagset both intrinsically on the new task of semantic tagging, as well as on Part-of-Speech (POS) tagging. Our system, consisting of a ResNet and an auxiliary loss function predicting our semantic tags, significantly outperforms prior results on English Universal Dependencies POS tagging (95.71% accuracy on UD v1.2 and 95.67% accuracy on UD v1.3).

Zhikun Wang, T. W. Ching, Shaojia Huang et al.

IEEE Access • 2020

This paper reviews motor techniques for reducing the cost of electric vehicles (EVs) and improving their range. In recent years, environmental issues, energy crises and the greenhouse effect have urged the popularization of clean energy EVs. In order to achieve this goal, it is necessary to overcome technical difficulties in vehicle cost and range. As a key component of an EV, the motor occupies a large proportion of the overall vehicle cost, and its efficiency directly affects the mileage. In this context, this article discusses the merits and challenges of three mainstream EV motors: permanent magnet synchronous motor (PMSM), induction motor (IM), and switched reluctance motor (SRM) in terms of vehicle cost and range. Then this paper compares the advanced techniques of these motors in terms of topology, material applications and control strategies. Finally, the development trends and opportunities of the three motors in EVs are predicted.

Xin Chang, Yangyang Chen, Sarah Qian Wang et al.

Journal of Financial Economics • 2019

Marrit B. Zuure, Leighton B. Hinkley, Paul Tiesinga et al.

Journal of Neuroscience • 2020

Theta-band (∼6 Hz) rhythmic activity within and over the medial PFC ("midfrontal theta") has been identified as a distinctive signature of "response conflict," the competition between multiple actions when only one action is goal-relevant. Midfrontal theta is traditionally conceptualized and analyzed under the assumption that it is a unitary signature of conflict that can be uniquely identified at one electrode (typically FCz). Here we recorded simultaneous MEG and EEG (total of 328 sensors) in 9 human subjects (7 female) and applied a feature-guided multivariate source-separation decomposition to determine whether conflict-related midfrontal theta is a unitary or multidimensional feature of the data. For each subject, a generalized eigendecomposition yielded spatial filters (components) that maximized the ratio between theta and broadband activity. Components were retained based on significance thresholding and midfrontal EEG topography. All of the subjects individually exhibited multiple (mean 5.89, SD 2.47) midfrontal components that contributed to sensor-level midfrontal theta power during the task. Component signals were temporally uncorrelated and asynchronous, suggesting that each midfrontal theta component was unique. Our findings call into question the dominant notion that midfrontal theta represents a unitary process. Instead, we suggest that midfrontal theta spans a multidimensional space, indicating multiple origins, but can manifest as a single feature at the sensor level because of signal mixing. SIGNIFICANCE STATEMENT "Midfrontal theta" is a rhythmic electrophysiological signature of the competition between multiple response options. Midfrontal theta is traditionally considered to reflect a single process. However, this assumption could be erroneous because of "mixing" (multiple sources contributing to the activity recorded at a single electrode). We investigated the dimensionality of midfrontal theta by applying advanced multivariate analysis methods to a multimodal MEG/EEG dataset. We identified multiple topographically overlapping neural sources that drove response conflict-related midfrontal theta. Midfrontal theta thus reflects multiple uncorrelated signals that manifest with similar EEG scalp projections. In addition to contributing to the cognitive control literature, we demonstrate both the feasibility and the necessity of signal demixing to understand the narrowband neural dynamics underlying cognitive processes.

Paulina Radzik, Agnieszka Leszczyńska, Krzysztof Pielichowski

Polymer Bulletin • 2019

The wider use of renewable feedstock in structural applications, where high mechanical performance is required, can be achieved by the application of recently developed engineering biopolymers and their further modification by micro- and nanoparticles. In this review, we present the current state of the art of biopolyamide materials for structural and functional applications. The overview includes all stages of the manufacturing—from the synthesis of building blocks, through the synthesis of polymers and its physical modification, with special emphasis on the properties of the obtained engineering biocomposites as a final product of modern polymer technology. In the first part, the synthetic routes of bio-derived counterparts of common polyamides as well as specialty polymers with functional properties arising from the complex structure of biochemicals were exemplified. The development of environmentally friendly composites and nanocomposites based on biopolyamides and natural fillers, such as plant fibers or cellulosic nanofibers, was of particular interest due to preserved sustainable character of such materials.

Joaquı́n Tintoré, Nadia Pinardi, Enrique Álvarez-Fanjul et al.

Frontiers in Marine Science • 2019

The Mediterranean community represented in this paper is the result of more than 30 years of EU & nationally funded coordination that has led to key contributions in science concepts and operational initiatives. Together with the establishment of operational services, the community has been coordinating with Universities, research centers, research infrastructures and private companies to implement advanced multi-platform and integrated observing and forecasting systems that facilitate the advancement of operational services, scientific achievements and mission-oriented innovation. As a result, the community has been able to respond to societal challenges and stakeholders needs, developing a variety of fit for purpose services such as the Copernicus Marine Service. The combination of state-of-the-art observations and forecasting provides new opportunities for downstream services in response to the needs of the heavily populated Mediterranean coastal areas and climate change. The real challenge for the next decade is to sustain ocean observations within the research community, to monitor the variability at small scales, e.g. mesoscale/submesoscale, to resolve the sub-basin/seasonal and inter-annual variability of the circulation and by this establish the decadal variability, understand and correct the model associated biases and to enhance model-data integration and ensemble forecasting for uncertainty estimation. Better knowledge and understanding of the Mediterranean variability will enable subsequent evaluation of the impacts and mitigation of human activities and climate change on the biodiversity and ecosystem functioning to underpin environmental assessments and decision support. Furthermore, the challenges are to extend the science based added value products to societal relevant downstream services and engage the communities to build initiatives that will contribute to UN SDG14 and the UN Decade of Ocean Science. The Mediterranean observing and forecasting capacity was built on the basis of community best practices in monitoring and modelling and could serve as basis for the development of the integrated global ocean observing system.

Liang Li, Baojuan Li, Yuanhan Bai et al.

Brain and Behavior • 2017

These findings suggest that the reduced excitatory causal influence of the left parietal cortex is the key alteration of the DMN in patients with MDD, and the disrupted causal influences that parietal cortex exerts on the posterior cingulate cortex is responsive to antidepressant treatment.

Wei Tan, Shaohua Xie, Duy Le et al.

Nature Communications • 2022

Constructing single atom catalysts with fine-tuned coordination environments can be a promising strategy to achieve satisfactory catalytic performance. Herein, via a simple calcination temperature-control strategy, CeO 2 supported Pt single atom catalysts with precisely controlled coordination environments are successfully fabricated. The joint experimental and theoretical analysis reveals that the Pt single atoms on Pt 1 /CeO 2 prepared at 550 °C (Pt/CeO 2 -550) are mainly located at the edge sites of CeO 2 with a Pt-O coordination number of ca. 5, while those prepared at 800 °C (Pt/CeO 2 -800) are predominantly located at distorted Ce substitution sites on CeO 2 terrace with a Pt-O coordination number of ca. 4. Pt/CeO 2 -550 and Pt/CeO 2 -800 with different Pt 1 -CeO 2 coordination environments exhibit a reversal of activity trend in CO oxidation and NH 3 oxidation due to their different privileges in reactants activation and H 2 O desorption, suggesting that the catalytic performance of Pt single atom catalysts in different target reactions can be maximized by optimizing their local coordination structures.

Zhizheng Wu, Phillip L. De León, Cenk Demiroğlu et al.

IEEE/ACM Transactions on Audio Speech and Language Processing • 2016

In this paper, we present a systematic study of the vulnerability of automatic speaker verification to a diverse range of spoofing attacks. We start with a thorough analysis of the spoofing effects of five speech synthesis and eight voice conversion systems, and the vulnerability of three speaker verification systems under those attacks. We then introduce a number of countermeasures to prevent spoofing attacks from both known and unknown attackers. Known attackers are spoofing systems whose output was used to train the countermeasures, while an unknown attacker is a spoofing system whose output was not available to the countermeasures during training. Finally, we benchmark automatic systems against human performance on both speaker verification and spoofing detection tasks.

Abeer Ali Alnuaim, Mohammed Zakariah, Prashant Kumar Shukla et al.

Journal of Healthcare Engineering • 2022

Human-computer interaction (HCI) has seen a paradigm shift from textual or display-based control toward more intuitive control modalities such as voice, gesture, and mimicry. Particularly, speech has a great deal of information, conveying information about the speaker's inner condition and his/her aim and desire. While word analysis enables the speaker's request to be understood, other speech features disclose the speaker's mood, purpose, and motive. As a result, emotion recognition from speech has become critical in current human-computer interaction systems. Moreover, the findings of the several professions involved in emotion recognition are difficult to combine. Many sound analysis methods have been developed in the past. However, it was not possible to provide an emotional analysis of people in a live speech. Today, the development of artificial intelligence and the high performance of deep learning methods bring studies on live data to the fore. This study aims to detect emotions in the human voice using artificial intelligence methods. One of the most important requirements of artificial intelligence works is data. The Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS) open-source dataset was used in the study. The RAVDESS dataset contains more than 2000 data recorded as speeches and songs by 24 actors. Data were collected for eight different moods from the actors. It was aimed at detecting eight different emotion classes, including neutral, calm, happy, sad, angry, fearful, disgusted, and surprised moods. The multilayer perceptron (MLP) classifier, a widely used supervised learning algorithm, was preferred for classification. The proposed model's performance was compared with that of similar studies, and the results were evaluated. An overall accuracy of 81% was obtained for classifying eight different emotions by using the proposed model on the RAVDESS dataset.

Dharmendra Tripathi, Akash Sharma, O. Anwar Bég

International Journal of Heat and Mass Transfer • 2017

Liya Zhu, Huansheng Song, Xi Zhang et al.

IEEE Access • 2019

This paper devotes to the image compression and encryption problems. We develop a novel hybrid scheme based on block compressive sensing. Concentrate on taking full advantage of the different frequency coefficients sparsity, the nonuniform sampling strategy is adopted to improve the compression efficiency. First, the discrete cosine transform coefficients matrices of blocks are transformed into vectors by zigzag scanning. The different frequency components are extracted in the front, middle, and back of vectors, respectively. Using the measurement matrices with different dimensions, the combination of low- and high-frequency components, together with the medium-frequency coefficients are compressed simultaneously. Second, the recombinational block measurements are re-encrypted by the permutation-diffusion framework. The logistic map is introduced for key stream generation. In order to accomplish a sensitive and effective cryptosystem, the control strategy for secret keys is employed. The simulation results indicate that the proposed scheme forms a high balance between reconstruction performance, storage and computational complexity, and hardware implementation. Moreover, the security analyses demonstrate the satisfactory performance and effectiveness of the proposed cryptosystem. The scheme can work efficiently in the parallel computing environment, especially for the images with medium and large size.

Oleg Gaidai, Vladimir Yakimov, Fang Wang et al.

Sustainable Energy Research • 2023

Abstract Modern offshore and onshore green energy engineering includes energy harvesting—as a result, extensive experimental investigations, as well as safety and reliability analysis are crucial for design and engineering. For this study, several wind-tunnel experiments under realistic in situ wind speed conditions have been conducted to examine the performance of galloping energy harvester. Next, a novel structural reliability approach is presented here that is especially well suited for multi-dimensional energy harvesting systems that have been either numerically simulated or analog observed during the representative time lapse, yielding an ergodic system time record. As demonstrated in this study, the advocated methodology may be used for risk assessment of dynamic system structural damage or failure. Furthermore, traditional reliability methodologies dealing with time series do not easily cope with the system’s high dimensionality, along with nonlinear cross-correlations between the system’s components. This study’s objective was to assess state-of-the-art reliability method, allowing efficient extraction of relevant statistical information, even from a limited underlying dataset. The methodology described in this study aims to assist designers when assessing nonlinear multidimensional dynamic energy harvesting system’s failure and hazard risks.

Karim Aljawhari, Roberto Gentile, Fabio Freddi et al.

Bulletin of Earthquake Engineering • 2020

Abstract This study investigates the effects of ground-motion sequences on fragility and vulnerability of reinforced concrete (RC) moment-resisting frames (MRFs). Two four-storey, four-bay RC MRFs are selected as case studies. These structures, which share the same geometry, are representative of distinct vulnerability classes in the Mediterranean region and are characterized by different material properties, cross-section dimensions, and detailing. The first case study is a ductile MRF designed according to Eurocode 8 (i.e., a special-code frame), while the second is a non-ductile MRF designed to sustain only gravity loads (i.e., a pre-code frame). The influence of masonry infills on their seismic performance is also investigated. Advanced numerical models are developed to perform cloud-based sequential nonlinear time history analyses using ground-motion sequences assembled by randomly pairing two real records via Latin hypercube sampling. Different structure-specific damage states are considered to derive fragility curves for the undamaged structures, when subjected to a single ground-motion record, and state-dependent fragility curves by considering the additional damage induced by a second ground-motion record within the sequence. Damage-to-loss models are then used to derive mean vulnerability relationships. Results of the analysis show the importance of considering the effect of damage accumulation in the pre-code frames. Moreover, the presence of infills shows an overall positive contribution to the seismic performance of both frame types. Vector-valued vulnerability relationships accounting for the damaging effect of two ground-motion records are finally presented in the form of mean vulnerability surfaces.

Muhammad E. H. Chowdhury, Amith Khandakar, Khawla Alzoubi et al.

Sensors • 2019

One of the major causes of death all over the world is heart disease or cardiac dysfunction. These diseases could be identified easily with the variations in the sound produced due to the heart activity. These sophisticated auscultations need important clinical experience and concentrated listening skills. Therefore, there is an unmet need for a portable system for the early detection of cardiac illnesses. This paper proposes a prototype model of a smart digital-stethoscope system to monitor patient’s heart sounds and diagnose any abnormality in a real-time manner. This system consists of two subsystems that communicate wirelessly using Bluetooth low energy technology: A portable digital stethoscope subsystem, and a computer-based decision-making subsystem. The portable subsystem captures the heart sounds of the patient, filters and digitizes, and sends the captured heart sounds to a personal computer wirelessly to visualize the heart sounds and for further processing to make a decision if the heart sounds are normal or abnormal. Twenty-seven t-domain, f-domain, and Mel frequency cepstral coefficients (MFCC) features were used to train a public database to identify the best-performing algorithm for classifying abnormal and normal heart sound (HS). The hyper parameter optimization, along with and without a feature reduction method, was tested to improve accuracy. The cost-adjusted optimized ensemble algorithm can produce 97% and 88% accuracy of classifying abnormal and normal HS, respectively.

Yongchang Zhang, Wenjia Shen, Haitao Yang

Chinese Journal of Electrical Engineering • 2023

Deadbeat predictive current control (DPCC) has been widely applied in permanent magnet synchronous motor (PMSM) drives due to its fast dynamic response and good steady-state performance. However, the control accuracy of DPCC is dependent on the machine parameters' accuracy. In practical applications, the machine parameters may vary with working conditions due to temperature, saturation, skin effect, and so on. As a result, the performance of DPCC may degrade when there are parameter mismatches between the actual value and the one used in the controller. To solve the problem of parameter dependence for DPCC, this study proposes an improved model-free predictive current control method for PMSM drives. The accurate model of the PMSM is replaced by a first-order ultra-local model. This model is dynamically updated by online estimation of the gain of the input voltage and the other parts describing the system dynamics. After obtaining this ultra-local model from the information on the measured stator currents and applied stator voltages in past control periods, the reference voltage value can be calculated based on the principle of DPCC, which is subsequently synthesized by space vector modulation (SVM). This method is compared with conventional DPCC and field-oriented control (FOC), and its superiority is verified by the presented experimental results.