Zhibin Qiu, Xuan Zhu, Caibo Liao et al.

Applied Sciences • 2022

Defective insulators seriously threaten the safe operation of transmission lines. This paper proposes an insulator defect detection method based on an improved YOLOv4 algorithm. An insulator image sample set was established according to the aerial images from the power grid and the public dataset on the Internet, combining with the image augmentation method based on GraphCut. The insulator images were preprocessed by Laplace sharpening method. To solve the problems of too many parameters and low detection speed of the YOLOv4 object detection model, the MobileNet lightweight convolutional neural network was used to improve YOLOv4 model structure. Combining with the transfer learning method, the insulator image samples were used to train, verify, and test the improved YOLOV4 model. The detection results of transmission line insulator and defect images show that the detection accuracy and speed of the proposed model can reach 93.81% and 53 frames per second (FPS), respectively, and the detection accuracy can be further improved to 97.26% after image preprocessing. The overall performance of the proposed lightweight YOLOv4 model is better than traditional object detection algorithms. This study provides a reference for intelligent inspection and defect detection of suspension insulators on transmission lines.

Anurag Sharma, Prabhat Kumar Singh, Rohitash Chandra

IEEE Access • 2022

Class imbalance in a dataset is a major problem for classifiers that results in poor prediction with a high true positive rate (TPR) but a low true negative rate (TNR) for a majority positive training dataset. Generally, the pre-processing technique of oversampling of minority class(es) are used to overcome this deficiency. Our focus is on using the hybridization of Generative Adversarial Network (GAN) and Synthetic Minority Over-Sampling Technique (SMOTE) to address class imbalanced problems. We propose a novel two-phase oversampling approach involving knowledge transfer that has the synergy of SMOTE and GAN. The unrealistic or overgeneralized samples of SMOTE are transformed into realistic distribution of data by GAN where there is not enough minority class data available for GAN to process them by itself effectively. We named it SMOTified-GAN as GAN works on pre-sampled minority data produced by SMOTE rather than randomly generating the samples itself. The experimental results prove the sample quality of minority class(es) has been improved in a variety of tested benchmark datasets. Its performance is improved by up to 9% from the next best algorithm tested on F1-score measurements. Its time complexity is also reasonable which is around $O(N^{2}d^{2}T)$ for a sequential algorithm.

Jin-Gyeom Kim, Bowon Lee

Energies • 2019

The imbalance of power supply and demand is an important problem to solve in power industry and Non Intrusive Load Monitoring (NILM) is one of the representative technologies for power demand management. The most critical factor to the NILM is the performance of the classifier among the last steps of the overall NILM operation, and therefore improving the performance of the NILM classifier is an important issue. This paper proposes a new architecture based on the RNN to overcome the limitations of existing classification algorithms and to improve the performance of the NILM classifier. The proposed model, called Multi-Feature Combination Multi-Layer Long Short-Term Memory (MFC-ML-LSTM), adapts various feature extraction techniques that are commonly used for audio signal processing to power signals. It uses Multi-Feature Combination (MFC) for generating the modified input data for improving the classification performance and adopts Multi-Layer LSTM (ML-LSTM) network as the classification model for further improvements. Experimental results show that the proposed method achieves the accuracy and the F1-score for appliance classification with the ranges of 95–100% and 84–100% that are superior to the existing methods based on the Gated Recurrent Unit (GRU) or a single-layer LSTM.

Xiao Zhang, Xunhua Zhao, Peng Zhu et al.

Nature Communications • 2022

Electrochemical oxygen reduction to hydrogen peroxide (H 2 O 2 ) in acidic media, especially in proton exchange membrane (PEM) electrode assembly reactors, suffers from low selectivity and the lack of low-cost catalysts. Here we present a cation-regulated interfacial engineering approach to promote the H 2 O 2 selectivity (over 80%) under industrial-relevant generation rates (over 400 mA cm -2 ) in strong acidic media using just carbon black catalyst and a small number of alkali metal cations, representing a 25-fold improvement compared to that without cation additives. Our density functional theory simulation suggests a "shielding effect" of alkali metal cations which squeeze away the catalyst/electrolyte interfacial protons and thus prevent further reduction of generated H 2 O 2 to water. A double-PEM solid electrolyte reactor was further developed to realize a continuous, selective (∼90%) and stable (over 500 hours) generation of H 2 O 2 via implementing this cation effect for practical applications.

Fenghui Ye, Shishi Zhang, Qingqing Cheng et al.

Nature Communications • 2023

Electrochemical coupling of biomass valorization with carbon dioxide (CO 2 ) conversion provides a promising approach to generate value-added chemicals on both sides of the electrolyzer. Herein, oxygen-vacancy-rich indium oxyhydroxide (InOOH-O V ) is developed as a bifunctional catalyst for CO 2 reduction to formate and 5-hydroxymethylfurfural electrooxidation to 2,5-furandicarboxylic acid with faradaic efficiencies for both over 90.0% at optimized potentials. Atomic-scale electron microscopy images and density functional theory calculations reveal that the introduction of oxygen vacancy sites causes lattice distortion and charge redistribution. Operando Raman spectra indicate oxygen vacancies could protect the InOOH-O V from being further reduced during CO 2 conversion and increase the adsorption competitiveness for 5-hydroxymethylfurfural over hydroxide ions in alkaline electrolytes, making InOOH-O V a main-group p-block metal oxide electrocatalyst with bifunctional activities. Based on the catalytic performance of InOOH-O V , a pH-asymmetric integrated cell is fabricated by combining the CO 2 reduction and 5-hydroxymethylfurfural oxidation together in a single electrochemical cell to produce 2,5-furandicarboxylic acid and formate with high yields (both around 90.0%), providing a promising approach to generate valuable commodity chemicals simultaneously on both electrodes.

Mahmuda Khatun Mishu, Md. Rokonuzzaman, Jagadeesh Pasupuleti et al.

Electronics • 2020

In the past few years, the internet of things (IoT) has garnered a lot of attention owing to its significant deployment for fulfilling the global demand. It has been seen that power-efficient devices such as sensors and IoT play a significant role in our regular lives. However, the popularity of IoT sensors and low-power electronic devices is limited due to the lower lifetime of various energy resources which are needed for powering the sensors over time. For overcoming this issue, it is important to design and develop better, high-performing, and effective energy harvesting systems. In this article, different types of ambient energy harvesting systems which can power IoT-enabled sensors, as well as wireless sensor networks (WSNs), are reviewed. Various energy harvesting models which can increase the sustainability of the energy supply required for IoT devices are also discussed. Furthermore, the challenges which need to be overcome to make IoT-enabled sensors more durable, reliable, energy-efficient, and economical are identified.

Timothy A. Vincent, Julian W. Gardner

Sensors and Actuators B Chemical • 2016

Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos et al.

International Journal of Computer Vision • 2016

Visual textures have played a key role in image understanding because they convey important semantics of images, and because texture representations that pool local image descriptors in an orderless manner have had a tremendous impact in diverse applications. In this paper we make several contributions to texture understanding. First, instead of focusing on texture instance and material category recognition, we propose a human-interpretable vocabulary of texture attributes to describe common texture patterns, complemented by a new describable texture dataset for benchmarking. Second, we look at the problem of recognizing materials and texture attributes in realistic imaging conditions, including when textures appear in clutter, developing corresponding benchmarks on top of the recently proposed OpenSurfaces dataset. Third, we revisit classic texture represenations, including bag-of-visual-words and the Fisher vectors, in the context of deep learning and show that these have excellent efficiency and generalization properties if the convolutional layers of a deep model are used as filter banks. We obtain in this manner state-of-the-art performance in numerous datasets well beyond textures, an efficient method to apply deep features to image regions, as well as benefit in transferring features from one domain to another.

Hassan Elahi, Khushboo Munir, Marco Eugeni et al.

Energies • 2020

The internet of things (IoT) manages a large infrastructure of web-enabled smart devices, small devices that use embedded systems, such as processors, sensors, and communication hardware to collect, send, and elaborate on data acquired from their environment. Thus, from a practical point of view, such devices are composed of power-efficient storage, scalable, and lightweight nodes needing power and batteries to operate. From the above reason, it appears clear that energy harvesting plays an important role in increasing the efficiency and lifetime of IoT devices. Moreover, from acquiring energy by the surrounding operational environment, energy harvesting is important to make the IoT device network more sustainable from the environmental point of view. Different state-of-the-art energy harvesters based on mechanical, aeroelastic, wind, solar, radiofrequency, and pyroelectric mechanisms are discussed in this review article. To reduce the power consumption of the batteries, a vital role is played by power management integrated circuits (PMICs), which help to enhance the system’s life span. Moreover, PMICs from different manufacturers that provide power management to IoT devices have been discussed in this paper. Furthermore, the energy harvesting networks can expose themselves to prominent security issues putting the secrecy of the system to risk. These possible attacks are also discussed in this review article.

Kaipeng Liu, Xintian Zhao, Guoqing Ren et al.

Nature Communications • 2020

Single-atom catalysts (SACs) have demonstrated superior catalytic performance in numerous heterogeneous reactions. However, producing thermally stable SACs, especially in a simple and scalable way, remains a formidable challenge. Here, we report the synthesis of Ru SACs from commercial RuO 2 powders by physical mixing of sub-micron RuO 2 aggregates with a MgAl 1.2 Fe 0.8 O 4 spinel. Atomically dispersed Ru is confirmed by aberration-corrected scanning transmission electron microscopy and X-ray absorption spectroscopy. Detailed studies reveal that the dispersion process does not arise from a gas atom trapping mechanism, but rather from anti-Ostwald ripening promoted by a strong covalent metal-support interaction. This synthetic strategy is simple and amenable to the large-scale manufacture of thermally stable SACs for industrial applications.

Patrick Kline, Christopher R. Walters

The Quarterly Journal of Economics • 2016

Abstract We use data from the Head Start Impact Study (HSIS) to evaluate the cost-effectiveness of Head Start, the largest early childhood education program in the United States. Head Start draws roughly a third of its participants from competing preschool programs, many of which receive public funds. We show that accounting for the fiscal impacts of such program substitution pushes estimates of Head Start’s benefit-cost ratio well above one under a wide range of assumptions on the structure of the market for preschool services and the dollar value of test score gains. To parse the program’s test score impacts relative to home care and competing preschools, we selection-correct test scores in each care environment using excluded interactions between experimental assignments and household characteristics. We find that Head Start generates larger test score gains for children who would not otherwise attend preschool and for children who are less likely to participate in the program.

Peter H. Rudebeck, Joshua A. Ripple, Andrew R. Mitz et al.

Journal of Neuroscience • 2017

Orbitofrontal cortex (OFC), medial frontal cortex (MFC), and amygdala mediate stimulus-reward learning, but the mechanisms through which they interact are unclear. Here, we investigated how neurons in macaque OFC and MFC signaled rewards and the stimuli that predicted them during learning with and without amygdala input. Macaques performed a task that required them to evaluate two stimuli and then choose one to receive the reward associated with that option. Four main findings emerged. First, amygdala lesions slowed the acquisition and use of stimulus-reward associations. Further analyses indicated that this impairment was due, at least in part, to ineffective use of negative feedback to guide subsequent decisions. Second, the activity of neurons in OFC and MFC rapidly evolved to encode the amount of reward associated with each stimulus. Third, amygdalectomy reduced encoding of stimulus-reward associations during the evaluation of different stimuli. Reward encoding of anticipated and received reward after choices were made was not altered. Fourth, amygdala lesions led to an increase in the proportion of neurons in MFC, but not OFC, that encoded the instrumental response that monkeys made on each trial. These correlated changes in behavior and neural activity after amygdala lesions strongly suggest that the amygdala contributes to the ability to learn stimulus-reward associations rapidly by shaping encoding within OFC and MFC. SIGNIFICANCE STATEMENT Altered functional interactions among orbital frontal cortex (OFC), medial frontal cortex (MFC), and amygdala are thought to underlie several psychiatric conditions, many related to reward learning. Here, we investigated the causal contribution of the amygdala to the development of neuronal activity in macaque OFC and MFC related to rewards and the stimuli that predict them during learning. Without amygdala inputs, neurons in both OFC and MFC showed decreased encoding of stimulus-reward associations. MFC also showed increased encoding of the instrumental responses that monkeys made on each trial. Behaviorally, changes in neural activity were accompanied by slower stimulus-reward learning. The findings suggest that interactions among amygdala, OFC, and MFC contribute to learning about stimuli that predict rewards.

Murat Aykanat, Özkan Kılıç, Bahar Kurt et al.

EURASIP Journal on Image and Video Processing • 2017

In the field of medicine, with the introduction of computer systems that can collect and analyze massive amounts of data, many non-invasive diagnostic methods are being developed for a variety of conditions. In this study, our aim is to develop a non-invasive method of classifying respiratory sounds that are recorded by an electronic stethoscope and the audio recording software that uses various machine learning algorithms. In order to store respiratory sounds on a computer, we developed a cost-effective and easy-to-use electronic stethoscope that can be used with any device. Using this device, we recorded 17,930 lung sounds from 1630 subjects. We employed two types of machine learning algorithms; mel frequency cepstral coefficient (MFCC) features in a support vector machine (SVM) and spectrogram images in the convolutional neural network (CNN). Since using MFCC features with a SVM algorithm is a generally accepted classification method for audio, we utilized its results to benchmark the CNN algorithm. We prepared four data sets for each CNN and SVM algorithm to classify respiratory audio: (1) healthy versus pathological classification; (2) rale, rhonchus, and normal sound classification; (3) singular respiratory sound type classification; and (4) audio type classification with all sound types. Accuracy results of the experiments were; (1) CNN 86%, SVM 86%, (2) CNN 76%, SVM 75%, (3) CNN 80%, SVM 80%, and (4) CNN 62%, SVM 62%, respectively. As a result, we found out that spectrogram image classification with CNN algorithm works as well as the SVM algorithm, and given the large amount of data, CNN and SVM machine learning algorithms can accurately classify and pre-diagnose respiratory audio.

Siyi Liu, Лэй Гуо, Kate K. Mays et al.

Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL) • 2019

Different news articles about the same topic often offer a variety of perspectives: an article written about gun violence might emphasize gun control, while another might promote 2nd Amendment rights, and yet a third might focus on mental health issues. In communication research, these different perspectives are known as "frames", which, when used in news media will influence the opinion of their readers in multiple ways. In this paper, we present a method for effectively detecting frames in news headlines. Our training and performance evaluation is based on a new dataset of news headlines related to the issue of gun violence in the United States. This Gun Violence Frame Corpus (GVFC) was curated and annotated by journalism and communication experts. Our proposed approach sets a new state-of-the-art performance for multiclass news frame detection, significantly outperforming a recent baseline by 35.9% absolute difference in accuracy. We apply our frame detection approach in a large scale study of 88k news headlines about the coverage of gun violence in the U.S. between 2016 and 2018.

Emma C. Thomson, Laura E. Rosen, James G. Shepherd et al.

Cell • 2021

SARS-CoV-2 can mutate and evade immunity, with consequences for efficacy of emerging vaccines and antibody therapeutics. Here, we demonstrate that the immunodominant SARS-CoV-2 spike (S) receptor binding motif (RBM) is a highly variable region of S and provide epidemiological, clinical, and molecular characterization of a prevalent, sentinel RBM mutation, N439K. We demonstrate N439K S protein has enhanced binding affinity to the hACE2 receptor, and N439K viruses have similar in vitro replication fitness and cause infections with similar clinical outcomes as compared to wild type. We show the N439K mutation confers resistance against several neutralizing monoclonal antibodies, including one authorized for emergency use by the US Food and Drug Administration (FDA), and reduces the activity of some polyclonal sera from persons recovered from infection. Immune evasion mutations that maintain virulence and fitness such as N439K can emerge within SARS-CoV-2 S, highlighting the need for ongoing molecular surveillance to guide development and usage of vaccines and therapeutics.

Randa Almadhoun, Tarek Taha, Lakmal Seneviratne et al.

SN Applied Sciences • 2019

Chenwei Ma, José Rodríguez, Cristian García et al.

2021 IEEE International Conference on Predictive Control of Electrical Drives and Power Electronics (PRECEDE) • 2021

Finite control set model predictive control (FCS-MFC) has been widely used in permanent magnet synchronous machine (PMSM) drive systems. FCS-MPC takes advantage of the discrete nature of power converters and selects an optimal switching state based on an evaluation of all possible ones. However, FCS-MPC suffers from an unsatisfactory steady-state performance due to the lack of input variants and suffers from a low computational efficiency due to the evaluation of all possible switching states. In this paper, a reference-variant based model predictive torque control (MPTC) is introduced. In the proposed strategy, by transforming the reference torque and reference flux to a reference voltage vector, an optimal solution can be obtained in a fast way. However, due to the single reference point and the lack of input variants, additional constraints cannot be included. Thus, reference variants are constructed based on the original reference point, thus resulting in more optimal solutions. A MPTC scheme with flexible structure to handle constraints is achieved. Simulations results show the improvements by comparison of the proposed MPTC and the single reference voltage vector based MPTC.

Gbeminiyi John Oyewole, George Alex Thopil

Artificial Intelligence Review • 2022

Guangfen Wei, Gang Li, Jie Zhao et al.

Sensors • 2019

A new LeNet-5 gas identification convolutional neural network structure for electronic noses is proposed and developed in this paper. Inspired by the tremendous achievements made by convolutional neural networks in the field of computer vision, the LeNet-5 was adopted and improved for a 12-sensor array based electronic nose system. Response data of the electronic nose to different concentrations of CO, CH₄ and their mixtures were acquired by an automated gas distribution and test system. By adjusting the parameters of the CNN structure, the gas LeNet-5 was improved to recognize the three categories of CO, CH₄ and their mixtures omitting the concentration influences. The final gas identification accuracy rate reached 98.67% with the unused data as test set by the improved gas LeNet-5. Comparison with results of Multiple Layer Perceptron neural networks and Probabilistic Neural Network verifies the improvement of recognition rate while with the same level of time cost, which proved the effectiveness of the proposed approach.

Massimo Silvetti, Eliana Vassena, Elger Abrahamse et al.

PLoS Computational Biology • 2018

Optimal decision-making is based on integrating information from several dimensions of decisional space (e.g., reward expectation, cost estimation, effort exertion). Despite considerable empirical and theoretical efforts, the computational and neural bases of such multidimensional integration have remained largely elusive. Here we propose that the current theoretical stalemate may be broken by considering the computational properties of a cortical-subcortical circuit involving the dorsal anterior cingulate cortex (dACC) and the brainstem neuromodulatory nuclei: ventral tegmental area (VTA) and locus coeruleus (LC). From this perspective, the dACC optimizes decisions about stimuli and actions, and using the same computational machinery, it also modulates cortical functions (meta-learning), via neuromodulatory control (VTA and LC). We implemented this theory in a novel neuro-computational model-the Reinforcement Meta Learner (RML). We outline how the RML captures critical empirical findings from an unprecedented range of theoretical domains, and parsimoniously integrates various previous proposals on dACC functioning.

Bram Beckers, Michiel Op De Beeck, Sofie Thijs et al.

Frontiers in Microbiology • 2016

Next-generation sequencing technologies have revolutionized the methods for studying microbial ecology by enabling high-resolution community profiling. However, the use of these technologies in unraveling the plant microbiome remains challenging. Many bacterial 16S rDNA primer pairs also exhibit high affinity for non-target DNA such as plastid (mostly chloroplast) DNA and mitochondrial DNA. Therefore, we experimentally tested a series of commonly used primers for the analysis of plant-associated bacterial communities using 454 pyrosequencing. We evaluated the performance of all selected primer pairs in the study of the bacterial microbiomes present in the rhizosphere soil, root, stem and leaf endosphere of field-grown poplar trees (Populus tremula × Populus alba) based on (a) co-amplification of non-target DNA, (b) low amplification efficiency for pure chloroplast DNA (real-time PCR), (c) high retrieval of bacterial 16S rDNA, (d) high operational taxonomic unit (OTU) richness and Inverse Simpson diversity and (e) taxonomic assignment of reads. Results indicate that experimental evaluation of primers provide valuable information that could contribute in the selection of suitable primer pairs for 16S rDNA metabarcoding studies in plant-microbiota research. Furthermore, we show that primer pair 799F-1391R outperforms all other primer pairs in our study in the elimination of non-target DNA and retrieval of bacterial OTUs.

Francesco Sabatino, Alexa Grimm, Fausto Gallucci et al.

Joule • 2021

Bin Liu, Cheng Tan, Shuqin Li et al.

IEEE Access • 2020

The identification of grape leaf diseases based on deep learning is critical to controlling the spread of diseases and ensuring the healthy development of the grape industry. Focusing on the lack of training images of grape leaf diseases, this paper proposes a novel model named Leaf GAN, which is based on generative adversarial networks (GANs), to generate images of four different grape leaf diseases for training identification models. A generator model with degressive channels is first designed to generate grape leaf disease images; then, the dense connectivity strategy and instance normalization are fused into an efficient discriminator to identify real and fake disease images by utilizing their excellent feature extraction capability on grape leaf lesions. Finally, the deep regret gradient penalty method is applied to stabilize the training process of the model. Using a total of 4,062 grape leaf disease images, the Leaf GAN model ultimately generates 8,124 grape leaf disease images. The generated grape leaf disease images based on Leaf GAN model can obtain better performance than DCGAN and WGAN in terms of the Fréchet inception distance. The experimental results show that the proposed Leaf GAN model generates sufficient grape leaf disease images with prominent lesions, providing a feasible solution for the data augmentation of grape leaf disease images. For the eight prevailing classification models with the expanded dataset, the identification performance based on CNNs indicated higher accuracies, whereby all the accuracies were better than those of the initial dataset with other data augmentation methods. Among them, Xception achieves a recognition accuracy of 98.70% on the testing set. The results demonstrate that the proposed data augmentation method represents a new approach to overcoming the overfitting problem in disease identification and can effectively improve the identification accuracy.

Thanh Thi Nguyen, Thanh Thi Nguyen, Quoc Viet Hung Nguyen et al.

Computer Vision and Image Understanding • 2022

Thomas Töllner, Yijun Wang, Scott Makeig et al.

Journal of Neuroscience • 2017

One of the most firmly established factors determining the speed of human behavioral responses toward action-critical stimuli is the spatial correspondence between the stimulus and response locations. If both locations match, the time taken for response production is markedly reduced relative to when they mismatch, a phenomenon called the Simon effect. While there is a consensus that this stimulus-response (S-R) conflict is associated with brief (4-7 Hz) frontal midline theta (fmθ) complexes generated in medial frontal cortex, it remains controversial (1) whether there are multiple, simultaneously active theta generator areas in the medial frontal cortex that commonly give rise to conflict-related fmθ complexes; and if so, (2) whether they are all related to the resolution of conflicting task information. Here, we combined mental chronometry with high-density electroencephalographic measures during a Simon-type manual reaching task and used independent component analysis and time-frequency domain statistics on source-level activities to model fmθ sources. During target processing, our results revealed two independent fmθ generators simultaneously active in or near anterior cingulate cortex, only one of them reflecting the correspondence between current and previous S-R locations. However, this fmθ response is not exclusively linked to conflict but also to other, conflict-independent processes associated with response slowing. These results paint a detailed picture regarding the oscillatory correlates of conflict processing in Simon tasks, and challenge the prevalent notion that fmθ complexes induced by conflicting task information represent a unitary phenomenon related to cognitive control, which governs conflict processing across various types of response-override tasks. SIGNIFICANCE STATEMENT Humans constantly monitor their environment for and adjust their cognitive control settings in response to conflicts, an ability that arguably paves the way for survival in ever-changing situations. Anterior cingulate-generated frontal midline theta (fmθ) complexes have been hypothesized to play a role in this conflict-monitoring function. However, it remains a point of contention whether fmθ complexes govern conflict processing in a unitary, paradigm-nonspecific manner. Here, we identified two independent fmθ oscillations triggered during a Simon-type task, only one of them reflecting current and previous conflicts. Importantly, this signal differed in various respects (cortical origin, intertrial history) from fmθ phenomena in other response-override tasks, challenging the prevalent notion of conflict-induced fmθ as a unitary phenomenon associated with the resolution of conflict.

Ahmad Akmaluddin Mazlan, Salwani Mohd Daud, Suriani Mohd Sam et al.

IEEE Access • 2020

Blockchain technology is a private, secure, trustworthy, and transparent information exchange performed in a decentralised manner. In this case, the coordination and validation efforts are simplified as the records are designed to update regularly and there is no difference in the two databases. This review focuses on how the blockchain addresses scalability challenges and provides solutions in the healthcare field through the implementation of blockchain technology. Accordingly, 16 solutions fell under two main areas, namely storage optimization and redesign of blockchain. However, limitations persist, including block size, high volume of data, transactions, number of nodes, and protocol challenges. This review consists of six stages, namely identification of research question, procedures of research, screening of relevant articles, keywording based on the abstract, data extraction, and mapping process. Through Atlas.ti software, the selected keywords were used to analyse through the relevant articles. As a result, 48 codes and 403 quotations were compiled. Manual coding was performed to categorise the quotations. The codes were then mapped onto the network as a mapping process. Notably, 16 solutions fell under two main areas, namely storage optimization and redesign of blockchain. Basically, there are 3 solutions compiled for storage optimization and 13 solutions for the redesign of the blockchain, namely blockchain modelling, read mechanism, write mechanism, and bi-directional network.

Yangfeng Ji, Noah A. Smith

Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) • 2017

We show that discourse structure, as defined by Rhetorical Structure Theory and provided by an existing discourse parser, benefits text categorization. Our approach uses a recursive neural network and a newly proposed attention mechanism to compute a representation of the text that focuses on salient content, from the perspective of both RST and the task. Experiments consider variants of the approach and illustrate its strengths and weaknesses.

Tom Verguts

Journal of Cognitive Neuroscience • 2017

A neural synchrony model of cognitive control is proposed. It construes cognitive control as a higher-level action to synchronize lower-level brain areas. Here, a controller prefrontal area (medial frontal cortex) can synchronize two cortical processing areas. The synchrony is achieved by a random theta frequency-locked neural burst sent to both areas. The choice of areas that receive this burst is determined by lateral frontal cortex. As a result of this synchrony, communication between the two areas becomes more efficient. The model is tested on the classical Stroop cognitive control task, and its operation is explored in several simulations. Both reactive and proactive controls are implemented via theta power modulation. Increasing theta power improves behavioral performance; furthermore, via theta-gamma phase-amplitude coupling, theta also increases gamma frequency power and synchrony in posterior processing areas. Thus, the model solves a central computational problem for cognitive control (how to allow rapid communication between arbitrary brain areas), while making rich contact with behavioral and neurophysiological data.

Caiyan Zhang, Weihang Gao, Bin Du

Structures • 2025

Brad A. Radvansky, Daniel A. Dombeck

Nature Communications • 2018

All motile organisms use spatially distributed chemical features of their surroundings to guide their behaviors, but the neural mechanisms underlying such behaviors in mammals have been difficult to study, largely due to the technical challenges of controlling chemical concentrations in space and time during behavioral experiments. To overcome these challenges, we introduce a system to control and maintain an olfactory virtual landscape. This system uses rapid flow controllers and an online predictive algorithm to deliver precise odorant distributions to head-fixed mice as they explore a virtual environment. We establish an odor-guided virtual navigation behavior that engages hippocampal CA1 "place cells" that exhibit similar properties to those previously reported for real and visual virtual environments, demonstrating that navigation based on different sensory modalities recruits a similar cognitive map. This method opens new possibilities for studying the neural mechanisms of olfactory-driven behaviors, multisensory integration, innate valence, and low-dimensional sensory-spatial processing.

Robert M. G. Reinhart

Proceedings of the National Academy of Sciences • 2017

Rescuing executive functions in people with neurological and neuropsychiatric disorders has been a major goal of psychology and neuroscience for decades. Innovative computer-training regimes for executive functions have made tremendous inroads, yet the positive effects of training have not always translated into improved cognitive functioning and often take many days to emerge. In the present study, we asked whether it was possible to immediately change components of executive function by directly manipulating neural activity using a stimulation technology called high-definition transcranial alternating current stimulation (HD-tACS). Twenty minutes of inphase stimulation over medial frontal cortex (MFC) and right lateral prefrontal cortex (lPFC) synchronized theta (∼6 Hz) rhythms between these regions in a frequency and spatially specific manner and rapidly improved adaptive behavior with effects lasting longer than 40 min. In contrast, antiphase stimulation in the same individuals desynchronized MFC-lPFC theta phase coupling and impaired adaptive behavior. Surprisingly, the exogenously driven impairments in performance could be instantly rescued by reversing the phase angle of alternating current. The results suggest executive functions can be rapidly up- or down-regulated by modulating theta phase coupling of distant frontal cortical areas and can contribute to the development of tools for potentially normalizing executive dysfunction in patient populations.

Wim Thiery, Édouard L. Davin, Sonia I. Seneviratne et al.

Nature Communications • 2016

Weather extremes have harmful impacts on communities around Lake Victoria, where thousands of fishermen die every year because of intense night-time thunderstorms. Yet how these thunderstorms will evolve in a future warmer climate is still unknown. Here we show that Lake Victoria is projected to be a hotspot of future extreme precipitation intensification by using new satellite-based observations, a high-resolution climate projection for the African Great Lakes and coarser-scale ensemble projections. Land precipitation on the previous day exerts a control on night-time occurrence of extremes on the lake by enhancing atmospheric convergence (74%) and moisture availability (26%). The future increase in extremes over Lake Victoria is about twice as large relative to surrounding land under a high-emission scenario, as only over-lake moisture advection is high enough to sustain Clausius-Clapeyron scaling. Our results highlight a major hazard associated with climate change over East Africa and underline the need for high-resolution projections to assess local climate change.

Céline Amiez, Jérôme Sallet, William D. Hopkins et al.

Nature Communications • 2019

Although the relative expansion of the frontal cortex in primate evolution is generally accepted, the nature of the human uniqueness, if any, and between-species anatomo-functional comparisons of the frontal areas remain controversial. To provide a novel interpretation of the evolution of primate brains, sulcal morphological variability of the medial frontal cortex was assessed in Old World monkeys (macaque/baboon) and Hominoidea (chimpanzee/human). We show that both Hominoidea possess a paracingulate sulcus, which was previously thought to be unique to the human brain and linked to higher cognitive functions, such as mentalizing. Also, we show systematic sulcal morphological organization of the medial frontal cortex that can be traced from Old World monkeys to Hominoidea species, demonstrating an evolutionarily conserved organizational principle. These data provide a new framework to compare sulcal morphology, cytoarchitectonic areal distribution, connectivity, and function across the primate order, leading to clear predictions about how other primate brains might be anatomo-functionally organized.

Oren Kraus, Ben T. Grys, Jimmy Ba et al.

Molecular Systems Biology • 2017

Existing computational pipelines for quantitative analysis of high-content microscopy data rely on traditional machine learning approaches that fail to accurately classify more than a single dataset without substantial tuning and training, requiring extensive analysis. Here, we demonstrate that the application of deep learning to biological image data can overcome the pitfalls associated with conventional machine learning classifiers. Using a deep convolutional neural network (DeepLoc) to analyze yeast cell images, we show improved performance over traditional approaches in the automated classification of protein subcellular localization. We also demonstrate the ability of DeepLoc to classify highly divergent image sets, including images of pheromone-arrested cells with abnormal cellular morphology, as well as images generated in different genetic backgrounds and in different laboratories. We offer an open-source implementation that enables updating DeepLoc on new microscopy datasets. This study highlights deep learning as an important tool for the expedited analysis of high-content microscopy data.

Alessandro Bongioanni, Davide Folloni, Lennart Verhagen et al.

Nature • 2021

Alfredo Arcos Jiménez, Carlos Quiterio Gómez Muñoz, Fausto Pedro Garcı́a Márquez

Energies • 2017

Delamination in Wind Turbine Blades (WTB) is a common structural problem that can generate large costs. Delamination is the separation of layers of a composite material, which produces points of stress concentration. These points suffer greater traction and compression forces in working conditions, and they can trigger cracks, and partial or total breakage of the blade. Early detection of delamination is crucial for the prevention of breakages and downtime. The main novelty presented in this paper has been to apply an approach for detecting and diagnosing the delamination WTB. The approach is based on signal processing of guided waves, and multiclass pattern recognition using machine learning. Delamination was induced in the WTB to check the accuracy of the approach. The signal is denoised by wavelet transform. The autoregressive Yule–Walker model is employed for feature extraction, and Akaike’s information criterion method for feature selection. The classifiers are quadratic discriminant analysis, k-nearest neighbors, decision trees, and neural network multilayer perceptron. The confusion matrix is employed to evaluate the classification, especially the receiver operating characteristic analysis by: recall, specificity, precision, and F-score.

Naveed Anwar Bhatti, Muhammad Hamad Alizai, Affan A. Syed et al.

ACM Transactions on Sensor Networks • 2016

Advances in micro-electronics and miniaturized mechanical systems are redefining the scope and extent of the energy constraints found in battery-operated wireless sensor networks (WSNs). On one hand, ambient energy harvesting may prolong the systems’ lifetime or possibly enable perpetual operation. On the other hand, wireless energy transfer allows systems to decouple the energy sources from the sensing locations, enabling deployments previously unfeasible. As a result of applying these technologies to WSNs, the assumption of a finite energy budget is replaced with that of potentially infinite , yet intermittent , energy supply, profoundly impacting the design, implementation, and operation of WSNs. This article discusses these aspects by surveying paradigmatic examples of existing solutions in both fields and by reporting on real-world experiences found in the literature. The discussion is instrumental in providing a foundation for selecting the most appropriate energy harvesting or wireless transfer technology based on the application at hand. We conclude by outlining research directions originating from the fundamental change of perspective that energy harvesting and wireless transfer bring about.

Bor‐Sheng Ko, Yu‐Fen Wang, Jeng-Lin Li et al.

EBioMedicine • 2018

Through large-scaled clinical validation, we showed that AI algorithms can produce efficient and clinically-relevant MFC analysis. This approach also possesses a great advantage of the ability to integrate other clinical tests. FUND: This work was supported by the Ministry of Science and Technology (107-2634-F-007-006 and 103-2314-B-002-185-MY2) of Taiwan.

Pai Peng, Xiaojin Zhao, Xiaofang Pan et al.

Sensors • 2018

In this work, we propose a novel Deep Convolutional Neural Network (DCNN) tailored for gas classification. Inspired by the great success of DCNN in the field of computer vision, we designed a DCNN with up to 38 layers. In general, the proposed gas neural network, named GasNet, consists of: six convolutional blocks, each block consist of six layers; a pooling layer; and a fully-connected layer. Together, these various layers make up a powerful deep model for gas classification. Experimental results show that the proposed DCNN method is an effective technique for classifying electronic nose data. We also demonstrate that the DCNN method can provide higher classification accuracy than comparable Support Vector Machine (SVM) methods and Multiple Layer Perceptron (MLP).

Mehdi Senoussi, Pieter Verbeke, Kobe Desender et al.

Nature Human Behaviour • 2022

Cognitive control allows to flexibly guide behaviour in a complex and ever-changing environment. It is supported by theta band (4-7 Hz) neural oscillations that coordinate distant neural populations. However, little is known about the precise neural mechanisms permitting such flexible control. Most research has focused on theta amplitude, showing that it increases when control is needed, but a second essential aspect of theta oscillations, their peak frequency, has mostly been overlooked. Here, using computational modelling and behavioural and electrophysiological recordings, in three independent datasets, we show that theta oscillations adaptively shift towards optimal frequency depending on task demands. We provide evidence that theta frequency balances reliable set-up of task representation and gating of task-relevant sensory and motor information and that this frequency shift predicts behavioural performance. Our study presents a mechanism supporting flexible control and calls for a reevaluation of the mechanistic role of theta oscillations in adaptive behaviour.