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[Objective] Visual simultaneous localization and mapping (VSLAM) is invaluable in applications like autonomous driving, underground robot exploration, and robot search and rescue operations. However, traditional VSLAM struggles in dim dynamic environments, prompting researchers to develop specialized algorithms for such environments. Despite these efforts, many existing VSLAM algorithms often fail to meet real-time requirements. [Methods] In this study, a real-time VSLAM algorithm for dim dynamic scenes is proposed. First, this study uses an algorithm based on the Retinex theory to enhance the brightness of input red green blue(RGB) images. Because images captured by the red green blue-depth(RGB-D) cameras cannot directly obtain illumination maps, this study transforms the illumination solving problem into an optimization problem. By developing an illumination cost function and taking its derivative, the derivative is yielded as zero, and a photometric graph is obtained. Second, this paper adds an object detection thread on top of ORB-SLAM3 using YOLO to identify targets labeled "person." Next, feature points in the YOLO detection frame are prefiltered using the Lucas-Kanade(LK) optical flow method. Further, the position of the feature point in the next frame is predicted using this method. If the distance between the actual and predicted positions is greater than the preset threshold, the feature point is set as a dynamic feature point, and the other feature points are set as static feature points. Additionally, Gaussian distribution analysis is used for secondary judgment. By analyzing the standard deviation of the depth of feature points in the YOLO box, the variance and standard deviation ratio of their depth that are greater than and less than the standard deviation are calculated, and the distribution of feature points is estimated. These metrics are compared against thresholds, and different thresholds are selected for subsequent standardized scores. Finally, the standardized score for each preset static feature point is calculated to evaluate the degree of deviation from the original data point, yielding a mean of 0 and a standard deviation of 1. In static scenes, the depth values of most feature points should be relatively consistent, and even if there are some deep changes, these changes should be distributed around a mean. The depth value of dynamic feature points may significantly deviate from the mean value of static feature points in the boundary box, resulting in higher standardization scores and larger square values than the set thresholds. Through this judgment method, all dynamic feature points are successfully selected and culled. [Results and Conclusions] Test results on the TUM and Bonn public datasets show that the proposed algorithm reduces the absolute trajectory error by at least 86.93% in high dynamic environments and 27.61% in low dynamic environments compared with ORB-SLAM3. Among similar VSLAM algorithm types, the proposed algorithm offers faster processing speeds while maintaining high precision and achieves a good balance between accuracy and real-time performance. The actual scene verification shows the excellent performance of the proposed algorithm in dim scenes. In summary, through advanced image processing and accurate dynamic point culling, the proposed algorithm addresses the shortcomings of traditional VSLAM algorithms and achieves accurate positioning in dim dynamic environments. [ABSTRACT FROM AUTHOR]

• This paper presents an efficient algorithm for classification of Epileptic Seizure from normal, inter-ictal, and seizure EEG signals. • A new classifier named EMRVFLN is proposed which is an improved version of both RVFLN and ELM. • Efficient features are extracted after pre-processing from the EEG signal using VMD and HT. • Also, this paper presents digital implementation of the proposed EMRVFLN classifier in FPGA environment. • Two real time datasets i.e. Bonn university dataset and Neurology & Sleep Centre, Hauz Khas, New Delhi are used to validate the proposed method. In this paper, variational mode decomposition (VMD), Hilbert transform (HT), and proposed error-minimized random vector functional link network (EMRVFLN) are integrated to detect and classify epileptic seizure from electroencephalogram (EEG) signals. VMD is applied to decompose the EEG signal into Band-limited intrinsic mode functions (BLIMFs). The five efficacious instantaneous features are computed using HT to construct the feature vector. Proposed EMRVFLN classifier is used to classify the epileptic seizure. The performances of the proposed EMRVFLN are compared with recently developed classifiers such as least-square support vector machine (LSSVM) and extreme learning machine (ELM). The combination of VMD and HT with proposed EMRVFLN classifier outperforms other state-of-the-art methods with classification accuracy of 100% for two class classification problem and 99.74% for three class classification problem. The remarkable classification accuracy facilitates the digital implementation of the proposed EMRVFLN classifier which may aid to design an embedded system for real-time disease diagnosis. [ABSTRACT FROM AUTHOR]

The paper shows that the Prussian General Synod of 1846 was a learning ground in which oratory practices were tested that parliamentarism in Prussia would take up. The General Synod created oratorical procedures that fostered the development of a consensus-based communication regime. At the same time, the General Synod tested a culture of debate that became decisive for parliamentary debate in Prussia. Formats of synodal negotiation created the speech-cultural basis for later working parliamentarism. This was also due to the influence of the presbyterial-synodal traditions that the two western provinces of Prussia brought to the synodal negotiations. This cultural transfer from West to East succeeded above all because Rhenish synod members – such as the Bonn theology professor Nitzsch – left their mark on the deliberations of the General Synod. [ABSTRACT FROM AUTHOR]

This paper introduces a novel architecture—bidirectional optical neural network (BONN)—for providing backward connections alongside forward connections in artificial neural networks (ANNs). BONN incorporates laser diodes and photodiodes and exploits the properties of Köhler illumination to establish optical channels for backward directions. Thus, it has bidirectional functionality that is crucial for algorithms such as the backpropagation algorithm. BONN has a scaling limit of 96 × 96 for input and output arrays, and a throughput of 8.5 × 1015 MAC/s. While BONN's throughput may rise with additional layers for continuous input, limitations emerge in the backpropagation algorithm, as its throughput does not scale with layer count. The successful BONN-based implementation of the backpropagation algorithm requires the development of a fast spatial light modulator to accommodate frequent data flow changes. A two-mirror-like BONN and its cascaded extension are alternatives for multilayer emulation, and they help save hardware space and increase the parallel throughput for inference. An investigation into the application of the clustering technique to BONN revealed its potential to help overcome scaling limits and to provide full interconnections for backward directions between doubled input and output ports. BONN's bidirectional nature holds promise for enhancing supervised learning in ANNs and increasing hardware compactness. [ABSTRACT FROM AUTHOR]

This paper investigates the sources of information and their quality as accessible to the government of the Federal Republic of Germany when trying to assess the situation of company funded pensions between 1949 and 1985 for the Company Pension Act of 1974. By taking a closer look at multiple government surveys as well as official statistics, the paper shows that the government only had very little access to verifiable and detailed information on the matter and relied largely on estimates. There were no coherent sources of information, and none were set up after the Act came into effect. The trend of a growing distribution and extent of corporate retirement plans from the late 1940s to the 1980s as seen in the government surveys is congruent with our findings. Furthermore, the Company Pension Act strengthened this already growing trend. [ABSTRACT FROM AUTHOR]

Leonard Horner (1785–1864) was a pioneer in the study of loess. His 1836 paper on the geology of Bonn contained detailed descriptions of loess in the Rhine valley. He identified and presented loess as an interesting material for geological study. He investigated loess in the crater of the Rodderberg with Charles Lyell in 1833. He presented the first significant paper on loess in Britain in 1833, but it was not published until 1836. With the assistance of G.A. Goldfuss and J.J. Noegerath he conducted early studies of the Siebengebirge and published the first geological map of the region, and the first picture of loess, at Rhondorf by the Drachenfels. He became the eleventh person to be included in the list of loess scholars which Charles Lyell published in volume 3 of the Principles of Geology. These were Leonhard, Bronn, Boue, Voltz, Steininger, Merian, Rozet, Hibbert in 1833, Noeggerath, von Meyer in 1835, Horner in 1837. Horner arrived after the publication of his studies on the loess at Bonn in 1836. [ABSTRACT FROM AUTHOR]

This article recounts a classroom application of a dialogue, story-telling process used at COPs beginning in 2017. In that year, the Fijian President of the United Nations climate summit, COP23 in Bonn, proposed the entire assembly use his nation's Talanoa Dialogue process for building ambition for national commitments to the Paris Agreement. When youth, women's and Indigenous voices were heard in unprecedented ways, many committed to continuing the practice. A florescence of the Asian-Pacific-inspired, conflict-resolution storytelling and dialogue practice ensued, with some continuing for years afterward. This paper recounts eight rounds of varied Talanoa Dialogues employed in higher education contexts. Purposes ranged from internal team-building, to forging community partnerships, to serving as vehicles for youth representation in international contexts like climate COPs. All dialogues nonetheless prioritized listening, recording, and representation through ethnographic, anthropological lenses. These experiences support recommendations to (1) Create chances for youth to listen empathetically to the stories of others in ways attentive to varied national, ethnic and other intersectional (e.g. gendered, class-based) social locations, expanding their awareness and linking their affective and intellectual selves; (2) Make opportunities for youth to voice their own stories and perspectives on climate challenges and work, considering their developmental trajectories in intergenerational contexts, while emphasizing the need to "scale" from individual through global dimensions; (3) Explore the implications of borrowing from exogenous cultural traditions, considering what factors may render this relatively legitimate, as opposed to appropriative. I explore particularities of the cultural origins of Talanoa Dialogues, which mediate hierarchy and egalitarianism, arguing that this function is especially apt for navigating conundrums of youth leadership and so-called "youth-washing." [ABSTRACT FROM AUTHOR]

Epilepsy is one of the most prevalent neurological diseases. Electroencephalography (EEG) is an essential tool for diagnosing and detecting epilepsy. In this paper, we aimed to provide an accurate system for classifying epileptic EEG signals using an information fusion-based approach. To this effect, an innovative feature-level fusion methodology was presented. First, utilizing the theory of information, the EEG signals were characterized by two similarity indicators, including correntropy and Cauchy–Schwarz divergence. Then, different combining modes, including, stacking, adding, weighted sum, and maximum rules were used to combine the information of both similarity measures. Concurrently, a novel algorithm was also proposed to define the weights of each feature based on the information gain ratio (IGR). Finally, the probabilistic neural network and the k-nearest neighbor were implemented to detect the epileptic conditions. The EEGs of the Bonn database were used to appraise the framework. Promising classification rates were obtained for epilepsy detection. The results revealed the potent ability of the fusion approach based on maximum IGR in the improvement of the classifier performances. [ABSTRACT FROM AUTHOR]

Copyright of Bildung und Erziehung is the property of Vandenhoeck & Ruprecht GmbH & Co. KG and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The access to goods and their allocation (who gets what, when, where and how) is one of the analytical problems of the 2009 Earth System Governance (ESG) Science and Implementation Plan (Biermann, in: Earth System Governance project report, IHDP no. 1, International Human Dimensions Program, Bonn). The 2009 Science Plan distinguishes three dimensions of access: fair outcomes, adequate pathways and effective reallocation. This paper answers the question: how have scholars used concepts related to access and allocation in their research related to international aid, trade and investment. In other words, what have we learnt from the environmental governance literature about who gets what, when and why in the areas of international trade, aid or investment? To date there has not been an attempt to synthesise the findings of this research and this is the gap that this paper addresses. The paper first highlights the key themes in the literature on access and allocation from a justice perspective. It analyses the literature of the ESG and related communities over the last 10 years on international aid, trade, and investment, drawing out the important issues and findings. This study found that most papers applied empirical case study methodologies, highlighted how governance deficits contributed to environmental injustice and were normatively premised on the need to solve the particular and case based problems of global inequality related to resource use and distribution. However, existing studies fell short of identifying the conceptual preconditions and ideal parameters for fair access and allocation of environmental resources in international economic systems. Future research is needed on the extent, nature and causes of the underlying normative and structural elements of the international economic systems that entrench unfair access and allocation of environmental goods and services; on the nature and protagonists of environmental rights and duties and on the elements of governance that may guarantee fair access and allocation in international trade, aid and investment. [ABSTRACT FROM AUTHOR]

As a kind of non-invasive, low-cost, and readily available brain examination, EEG has attached significance to the means of clinical diagnosis of epilepsy. However, the reading of long-term EEG records has brought a heavy burden to neurologists and experts. Therefore, automatic EEG classification for epileptic patients plays an essential role in epilepsy diagnosis and treatment. This paper proposes an Attention Mechanism-based Wavelet Convolution Neural Network for epilepsy EEG classification. Attention Mechanism-based Wavelet Convolution Neural Network firstly uses multi-scale wavelet analysis to decompose the input EEGs to obtain their components in different frequency bands. Then, these decomposed multi-scale EEGs are input into the Convolution Neural Network with an attention mechanism for further feature extraction and classification. The proposed algorithm achieves 98.89% triple classification accuracy on the Bonn EEG database and 99.70% binary classification accuracy on the Bern-Barcelona EEG database. Our experiments prove that the proposed algorithm achieves a state-of-the-art classification effect on epilepsy EEG. [ABSTRACT FROM AUTHOR]

Electroencephalography (EEG) is a commonly used clinical approach for the diagnosis of epilepsy which is a life-threatening neurological disorder. Many algorithms have been proposed for the automatic detection of epileptic seizures using traditional machine learning and deep learning. Although deep learning methods have achieved great success in many fields, their performance in EEG analysis and classification is still limited mainly due to the relatively small sizes of available datasets. In this paper, we propose an automatic method for the detection of epileptic seizures based on deep metric learning which is a novel strategy tackling the few-shot problem by mitigating the demand for massive data. First, two one-dimensional convolutional embedding modules are proposed as a deep feature extractor, for single-channel and multichannel EEG signals respectively. Then, a deep metric learning model is detailed along with a stage-wise training strategy. Experiments are conducted on the publicly-available Bonn University dataset which is a benchmark dataset, and the CHB-MIT dataset which is larger and more realistic. Impressive averaged accuracy of 98.60% and specificity of 100% are achieved on the most difficult classification of interictal (subset D) vs ictal (subset E) of the Bonn dataset. On the CHB-MIT dataset, an averaged accuracy of 86.68% and specificity of 93.71% are reached. With the proposed method, automatic and accurate detection of seizures can be performed in real time, and the heavy burden of neurologists can be effectively reduced. [ABSTRACT FROM AUTHOR]

Introduction: Intelligent recognition of electroencephalogram (EEG) signals can remarkably improve the accuracy of epileptic seizure prediction, which is essential for epileptic diagnosis. Extreme learning machine (ELM) has been applied to EEG signals recognition, however, the artifacts and noises in EEG signals have a serious effect on recognition efficiency. Deep learning is capable of noise resistance, contributing to removing the noise in raw EEG signals. But traditional deep networks suffer from time-consuming training and slow convergence. Methods: Therefore, a novel deep learning based ELM (denoted as DELM) motivated by stacking generalization principle is proposed in this paper. Deep extreme learning machine (DELM) is a hierarchical network composed of several independent ELM modules. Augmented EEG knowledge is taken as complementary component, which will then be mapped into next module. This learning process is so simple and fast, meanwhile, it can excavate the implicit knowledge in raw data to a greater extent. Additionally, the proposed method is operated in a single-direction manner, so there is no need to perform parameters fine-tuning, which saves the expense of time. Results: Extensive experiments are conducted on the public Bonn EEG dataset. The experimental results demonstrate that compared with the commonly-used seizure prediction methods, the proposed DELM wins the best average accuracies in 13 out of the 22 data and the best average F-measure scores in 10 out of the 22 data. And the running time of DELM is more than two times quickly than deep learning methods. Discussion: Therefore, DELM is superior to traditional and some state-of- the-art machine learning methods. The proposed architecture demonstrates its feasibility and superiority in epileptic EEG signal recognition. The proposed less computationally intensive deep classifier enables faster seizure onset detection, which is showing great potential on the application of real-time EEG signal classification. [ABSTRACT FROM AUTHOR]

The objective of this paper is to promote a better understanding of the link between normative climate justice claims—originating in Political Theory and Philosophy—and concrete social practices of the climate justice movement active at the international climate negotiations. We argue that the climate justice movement can be understood as a community of practice. Empirically, we zoom into this community of practice and comparatively analyse three case studies on human rights networks, faith-based groups and gender justice advocates. Methodologically, our analysis is based on a review of primary and secondary documents, participatory observations and expert interviews at the climate negotiations in Warsaw (2013), Bonn (2014), and Paris (2015) and via skype/phone (2013–2016). Our analysis reveals that each network within the community of practice—even those with more radical objectives—minimise the demands formulated at the outset in order to successfully cooperate with state negotiators. [ABSTRACT FROM AUTHOR]

Background: Epilepsy is a neurological disorder that is usually detected by electroencephalogram (EEG) signals. Since manual examination of epilepsy seizures is a laborious and time-consuming process, lots of automatic epilepsy detection algorithms have been proposed. However, most of the available classification algorithms for epilepsy EEG signals adopted a single feature extraction, in turn to result in low classification accuracy. Although a small account of studies have carried out feature fusion, the computational efficiency is reduced due to too many features, because there are also some poor features that interfere with the classification results. Methods: In order to solve the above problems, an automatic recognition method of epilepsy EEG signals based on feature fusion and selection is proposed in this paper. Firstly, the Approximate Entropy (ApEn), Fuzzy Entropy (FuzzyEn), Sample Entropy (SampEn), and Standard Deviation (STD) mixed features of the subband obtained by the Discrete Wavelet Transform (DWT) decomposition of EEG signals are extracted. Secondly, the random forest algorithm is used for feature selection. Finally, the Convolutional Neural Network (CNN) is used to classify epilepsy EEG signals. Results: The empirical evaluation of the presented algorithm is performed on the benchmark Bonn EEG datasets and New Delhi datasets. In the interictal and ictal classification tasks of Bonn datasets, the proposed model achieves an accuracy of 99.9%, a sensitivity of 100%, a precision of 99.81%, and a specificity of 99.8%. For the interictal-ictal case of New Delhi datasets, the proposed model achieves a classification accuracy of 100%, a sensitivity of 100%, a specificity of 100%, and a precision of 100%. Conclusion: The proposed model can effectively realize the high-precision automatic detection and classification of epilepsy EEG signals. This model can provide high-precision automatic detection capability for clinical epilepsy EEG detection. We hope to provide positive implications for the prediction of seizure EEG. [ABSTRACT FROM AUTHOR]

In this paper, we propose and evaluate Epilepsy-Net, a collection of deep learning EEG signal processing tools to detect epileptic seizures against non-epileptic seizures without any handcrafted features extraction. In the Epilepsy-Net model, the 1D-convolutional neural networks (CNN), the recurrent neural network (RNN) and the attention mechanism are combined, where each algorithm is represented by the ResNet and Inception, the gated recurrent unit and the convolutional block attention module respectively; without any handcrafted features. To the best of our knowledge, Epilepsy-Net is the first EEG signal processing work to detect epileptic seizures by combining the attention mechanism with the Inception deep network algorithm.We validate our Epilepsy-Net through several large public EEG signal datasets. The results of our experiments show that the proposed attention deep learning approach is an effective tool for epilepsy detection using EEG signals with high accuracy of 100%, 99.05% and 98.22% for the Bonn EEG dataset, variant of the Bonn EEG dataset, and CHB-MIT dataset, respectively. [ABSTRACT FROM AUTHOR]

The traditional visual simultaneous localization and mapping (SLAM) systems have a high dependence on static world assumption, which makes them easy to fail to track in dynamic environments. In this paper, we propose a real-time dynamic visual SLAM system (RTDSLAM) based on ORB-SLAM3 to realize accurate pose estimation of the camera in indoor dynamic environments. We regard the static objects in the environments as a complete virtual rigid body and add two motion removal modules to handle the dynamic feature points without the aid of the camera's ego motion. The geometry-based motion removal module utilizes the point correlations and the structural invariance of rigid body to detect sparse dynamic feature points between two keyframes, and the clustering of depth images helps find the complete dynamic regions. Meanwhile, the template-based motion removal module uses template matching to fast track the known moving objects between ordinary frames. The dynamic feature points located on moving objects are removed and only static feature points are reserved for pose estimation. We evaluate our system on public TUM and Bonn datasets, and the comparison with state-of-the-art dynamic visual SLAM systems shows our advantages both in runtime and the accuracy of pose estimation. Besides, the test in real-world scenes shows the effectiveness of our system in dynamic environments. [ABSTRACT FROM AUTHOR]

Electroencephalography (EEG) records the electrical activity of the brain, which is an important tool for the automatic detection of epileptic seizures. It is certainly a very heavy burden to only recognize EEG epilepsy manually, so the method of computer-assisted treatment is of great importance. This paper presents a seizure detection algorithm based on variational modal decomposition (VMD) and a deep forest (DF) model. Variational modal decomposition is performed on EEG recordings, and the first three variational modal functions (VMFs) are selected to construct the time–frequency distribution of the EEG signals. Then, the log−Euclidean covariance matrix (LECM) is computed to represent the EEG properties and form EEG features. The deep forest model is applied to complete the EEG signal classification, which is a non-neural network deep model with a cascade structure that performs feature learning through the forest. In addition, to improve the classification accuracy, postprocessing techniques are performed to generate the discriminant results by moving average filtering and adaptive collar expansion. The algorithm was evaluated on the Bonn EEG dataset and the Freiburg long−term EEG dataset, and the former achieved a sensitivity and specificity of 99.32% and 99.31%, respectively. The mean sensitivity and specificity of this method for the 21 patients in the Freiburg dataset were 95.2% and 98.56%, respectively, with a false detection rate of 0.36/h. These results demonstrate the superior performance advantage of our algorithm and indicate its great research potential in epilepsy detection. [ABSTRACT FROM AUTHOR]

Polarimetric weather radars offer a wealth of new information compared to conventional technology, not only to enhance quantitative precipitation estimation, warnings, and short-term forecasts, but also to improve our understanding of precipitation generating processes and their representation in numerical weather prediction models. To support such research opportunities, this paper describes an open-access dataset between 2014–2019 collected by the polarimetric Doppler X-band weather radar in Bonn (BoXPol), western Germany. To complement this dataset, the technical radar characteristics, scanning strategy and the best-practice for radar data processing are detailed. In addition, an investigation of radar calibration is presented. Reflectivity measurements from the Dual-frequency Precipitation Radar operating on the core satellite of the Global Precipitation Mission are compared to those of BoXPol to provide absolute calibration offsets with the dataset. The Relative Calibration Adjustment technique is applied to identify stable calibration periods. The absolute calibration of differential reflectivity is determined using the vertical scan and provided with the BoxPol dataset. Measurement(s) Radar backscattering of precipitation Technology Type(s) Polarimetric Doppler X-band weather radar [ABSTRACT FROM AUTHOR]

Epilepsy is one of the neurological conditions that are diagnosed in the vast majority of patients. Electroencephalography (EEG) readings are the primary tool that is used in the process of diagnosing and analyzing epilepsy. The epileptic EEG data display the electrical activity of the neurons and provide a significant amount of knowledge on pathology and physiology. As a result of the significant amount of time that this method requires, several automated classification methods have been developed. In this paper, three wavelets such as Haar, dB4, and Sym 8 are employed to extract the features from A–E sets of the Bonn epilepsy dataset. To select the best features of epileptic seizures, a Particle Swarm Optimization (PSO) technique is applied. The extracted features are further classified using seven classifiers like linear regression, nonlinear regression, Gaussian Mixture Modeling (GMM), K-Nearest Neighbor (KNN), Support Vector Machine (SVM-linear), SVM (polynomial), and SVM Radial Basis Function (RBF). Classifier performances are analyzed through the benchmark parameters, such as sensitivity, specificity, accuracy, F1 Score, error rate, and g-means. The SVM classifier with RBF kernel in sym 8 wavelet features with PSO feature selection method attains a higher accuracy rate of 98% with an error rate of 2%. This classifier outperforms all other classifiers. [ABSTRACT FROM AUTHOR]

Epilepsy is a psychosocial neurological disorder, which emerges as a major threat to public health. In this age of the internet of things, the smart diagnosis of epilepsy has gained huge research attention with machine learning-based seizure detection in cloud-fog assisted environments. The present paper also proposes a cloud-fog integrated smart neurocare approach, which performs a temporal analysis of raw electroencephalogram (EEG) signals using deep learning to detect the occurrence of epileptic seizures. This patient-independent approach makes use of single-channel EEG signals to achieve real-time and computationally efficient seizure detection at fog layer devices. It employs a maximum variance-based channel selection procedure to select only one channel of raw scalp EEG signals, followed by their filtering and segmentation into various short-duration temporal segments. To analyse EEG patterns, these segments are further fed to the proposed models of convolutional neural network, recurrent neural network and stacked autoencoder deep learning classifiers. The performance analysis through simulation results evidently reveals that the proposed convolutional neural network-based temporal analysis approach performs better than other approaches. It realises an optimum accuracy of 96.43%, sensitivity of 100% and specificity of 93.33% for 30s duration EEG segments of CHB-MIT dataset and achieves 100% accuracy, sensitivity and specificity values for EEG segments of Bonn dataset for 23.6s EEG segments. Thus, the proposed convolutional neural network-based approach emerges as an appropriate method for rapid and accurate detection of epileptic seizures in fog-cloud integrated environment. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Simultaneous localization and mapping (SLAM), as one of the core prerequisite technologies for intelligent mobile robots, has attracted much attention in recent years. However, the traditional SLAM systems rely on the static environment assumption, which becomes unstable for the dynamic environment and further limits the real-world practical applications. To deal with the problem, this paper presents a dynamic-environment-robust visual SLAM system named YOLO-SLAM. In YOLO-SLAM, a lightweight object detection network named Darknet19-YOLOv3 is designed, which adopts a low-latency backbone to accelerate and generate essential semantic information for the SLAM system. Then, a new geometric constraint method is proposed to filter dynamic features in the detecting areas, where dynamic features can be distinguished by utilizing the depth difference with Random Sample Consensus (RANSAC). YOLO-SLAM composes the object detection approach and the geometric constraint method in a tightly coupled manner, which is able to effectively reduce the impact of dynamic objects. Experiments are conducted on the challenging dynamic sequences of TUM dataset and Bonn dataset to evaluate the performance of YOLO-SLAM. The results demonstrate that the RMSE index of absolute trajectory error can be significantly reduced to 98.13% compared with ORB-SLAM2 and 51.28% compared with DS-SLAM, indicating that YOLO-SLAM is able to effectively improve stability and accuracy in the highly dynamic environment. [ABSTRACT FROM AUTHOR]

Detection of epileptic seizures on the basis of Electroencephalogram (EEG) recordings is a challenging task due to the complex, non-stationary and non-linear nature of these biomedical signals. In the existing literature, a number of automatic epileptic seizure detection methods have been proposed that extract useful features from EEG segments and classify them using machine learning algorithms. Some characterizing features of epileptic and non-epileptic EEG signals overlap; therefore, it requires that analysis of signals must be performed from diverse perspectives. Few studies analyzed these signals in diverse domains to identify distinguishing characteristics of epileptic EEG signals. To pose the challenge mentioned above, in this paper, a fuzzy-based epileptic seizure detection model is proposed that incorporates a novel feature extraction and selection method along with fuzzy classifiers. The proposed work extracts pattern features along with time-domain, frequency-domain, and non-linear analysis of signals. It applies a feature selection strategy on extracted features to get more discriminating features that build fuzzy machine learning classifiers for the detection of epileptic seizures. The empirical evaluation of the proposed model was conducted on the benchmark Bonn EEG dataset. It shows significant accuracy of 98% to 100% for normal vs. ictal classification cases while for three class classification of normal vs. inter-ictal vs. ictal accuracy reaches to above 97.5%. The obtained results for ten classification cases (including normal, seizure or ictal, and seizure-free or inter-ictal classes) prove the superior performance of proposed work as compared to other state-of-the-art counterparts. [ABSTRACT FROM AUTHOR]

Deep convolutional neural networks (DCNNs) have dominated as the best performers on almost all computer vision tasks over the past several years. However, it remains a major challenge to deploy these powerful DCNNs in resource-limited environments, such as embedded devices and smartphones. To this end, 1-bit CNNs have emerged as a feasible solution as they are much more resource-efficient. Unfortunately, they often suffer from a significant performance drop compared to their full-precision counterparts. In this paper, we propose a novel Bayesian Optimized compact 1-bit CNNs (BONNs) model, which has the advantage of Bayesian learning, to improve the performance of 1-bit CNNs significantly. BONNs incorporate the prior distributions of full-precision kernels, features, and filters into a Bayesian framework to construct 1-bit CNNs in a comprehensive end-to-end manner. The proposed Bayesian learning algorithms are well-founded and used to optimize the network simultaneously in different kernels, features, and filters, which largely improves the compactness and capacity of 1-bit CNNs. We further introduce a new Bayesian learning-based pruning method for 1-bit CNNs, which significantly increases the model efficiency with very competitive performance. This enables our method to be used in a variety of practical scenarios. Extensive experiments on the ImageNet, CIFAR, and LFW datasets show that BONNs achieve the best in classification performance compared to a variety of state-of-the-art 1-bit CNN models. In particular, BONN achieves a strong generalization performance on the object detection task. [ABSTRACT FROM AUTHOR]

Social reference points have been identified to be important determinants of individuals' welfare. We investigate the consequences of social reference points for risk taking in a laboratory experiment. In the main treatments, risk-taking subjects observe the predetermined earnings of peer subjects when making a risky choice. We exogenously manipulate peers' earnings and find a significant treatment effect: decision makers make less risk-averse choices in the case of larger peers' earnings. The treatment effect is consistent with an application of prospect theory to social reference points and cannot be explained by reference points based on counterfactual information, anchoring, and experimenter demand effects. In additional analyses, we show that diminishing sensitivity seems to play an important role in subjects' risky choices. We explore also whether inequity aversion and expectations-based reference points can account for our findings and conclude that they do not provide plausible alternative explanations for them. This paper was accepted by Yan Chen, behavioral economics and decision analysis. Funding: This research was financially supported by the Bonn Graduate School of Economics and the Center for Economics and Neuroscience in Bonn. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4698. [ABSTRACT FROM AUTHOR]

The standard treatments for epilepsy are drug therapy and surgical resection. However, around 1/3 of patients with intractable epilepsy are drug-resistant, requiring surgical resection of the epileptic focus. To address the issue of drug-resistant epileptic focus localization, we have proposed a transfer learning method on multi-modal EEG (iEEG and sEEG). A 10-fold cross-validation approach was applied to validate the performance of the pre-trained model on the Bern-Barcelona and Bonn datasets, achieving accuracy rates of 94.50 and 97.50%, respectively. The experimental results have demonstrated that the pre-trained model outperforms the competitive state-of-the-art baselines in terms of accuracy, sensitivity, and negative predictive value. Furthermore, we fine-tuned our pre-trained model using the epilepsy dataset from Chongqing Medical University and tested it using the leave-one-out cross-validation method, obtaining an impressive average accuracy of 90.15%. This method shows significant feature differences between epileptic and non-epileptic channels. By extracting data features using neural networks, accurate classification of epileptic and non-epileptic channels can be achieved. Therefore, the superior performance of the model has demonstrated that the proposed method is highly effective for localizing epileptic focus and can aid physicians in clinical localization diagnosis. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) signals, generated during the neuron firing, are an effective way of predicting such seizure and it is used widely in recent days for classifying and predicting seizure activity. But EEG signals generated during an epileptic seizure are highly nonstationary and dynamic in nature and contain very crucial information about the state of the brain. Due to this randomness, the accuracy of analysis of EEG data by conventional and visual methods is reduced drastically. This paper aims at enhancing epilepsy seizure detection using deep learning models with an FPGA implementation of the short-time Fourier transform block. Detection of seizure has been achieved in the following stages: (1) time–frequency analysis of EEG segments using STFT; (2) extraction of frequency bands and features of interest; and (3) seizure detection using convolutional neural network (CNN) and bidirectional long short-term memory (Bi-LSTM). For this work, the Bonn EEG dataset has been used. The maximum error of ~ 0.13% was encountered while the comparison of STFT output generated via proposed hardware architecture vs the output generated via simulation was done. The average classification accuracy of 93.9% and 97.2% was achieved by CNN and Bi-LSTM models, respectively, considering all frequency bands for epileptic and non-epileptic patients. [ABSTRACT FROM AUTHOR]

Various research groups are working on the automated detection of epileptic seizures using Electroencephalogram (EEG) data. EEG waveforms are composed of distinct bands of frequencies. Most of the researchers have used a wide range of frequencies or every frequency band of EEG for detection process of epileptic seizures to obtain high accuracy. However, not all frequency bins contain relevant information about seizures, thereby degrading the performance of the detection system. This paper demonstrates the suitability of only delta band (0.5–4 Hz) for the detection of seizures due to epilepsy. The work has been performed in four stages: (1) Short-time Fourier transform (STFT) of EEG data, (2) extraction of delta band from the time–frequency (t–f) plane, (3) calculation of four statistical features (4) performance analysis using Random Forest (RF) classifier. The proposed methodology achieved an average accuracy, specificity and sensitivity of 99.6%, 99.5% and 99.67% respectively between persons suffering from epilepsy and healthy people on Bonn EEG dataset. Proposed work is computationally efficient as it uses only single band which results in small data computation. Its detection time is very short (< 0.5 s) which makes it suitable for real-time clinical application. [ABSTRACT FROM AUTHOR]

Copyright of Cuadernos Europeos de Deusto is the property of Universidad de Deusto and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The reaction γ p → p π 0 η has been studied with the CBELSA/TAPS detector at the electron stretcher accelerator ELSA in Bonn for incident photon energies from threshold up to 3.1 GeV. This paper has been motivated by the recently claimed observation of a narrow structure in the M N η invariant mass distribution at a mass of 1678 MeV / c 2 . The existence of this structure cannot be confirmed in the present work. Instead, for E γ = 1400–1500 MeV and the cut M p π 0 ≤ 1190 MeV / c 2 a statistically significant structure in the M p η invariant mass distribution near 1700 MeV / c 2 is observed with a width of Γ ≈ 35 MeV / c 2 while the mass resolution is σ res = 5 MeV / c 2 . Increasing the incident photon energy from 1420 to 1540 MeV this structure shifts in mass from ≈ 1700 MeV / c 2 to ≈ 1725 MeV / c 2 ; the width increases to about 50 MeV / c 2 and decreases thereafter. The cross section associated with this structure reaches a maximum of ≈ 100 nb around E γ ≈ 1490 MeV (W ≈ 1920 MeV), which coincides with the p a 0 threshold. Three scenarios are discussed which might be the origin of this structure in the M p η invariant mass distribution. The most likely interpretation is that it is due to a triangular singularity in the γ p → p a 0 → p π 0 η reaction. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) is often used in clinical epilepsy treatment to monitor electrical signal changes in the brain of patients with epilepsy. With the development of signal processing and artificial intelligence technology, artificial intelligence classification method plays an important role in the automatic recognition of epilepsy EEG signals. However, traditional classifiers are easily affected by impurities and noise in epileptic EEG signals. To solve this problem, this paper develops a noise robustness low-rank learning (NRLRL) algorithm for EEG signal classification. NRLRL establishes a low-rank subspace to connect the original data space and label space. Making full use of supervision information, it considers the local information preservation of samples to ensure the low-rank representation of within-class compactness and between-classes dispersion. The asymmetric least squares support vector machine (aLS-SVM) is embedded into the objective function of NRLRL. The aLS-SVM finds the maximum quantile distance between the two classes of samples based on the pinball loss function, which further improves the noise robustness of the model. Several classification experiments with different noise intensity are designed on the Bonn data set, and the experiment results verify the effectiveness of the NRLRL algorithm. [ABSTRACT FROM AUTHOR]

EEG signal classification has been a research hotspot recently. The combination of EEG signal classification with machine learning technology is very popular. Traditional machine leaning methods for EEG signal classification assume that the EEG signals are drawn from the same distribution. However, the assumption is not always satisfied with the practical applications. In practical applications, the training dataset and the testing dataset are from different but related domains. How to make best use of the training dataset knowledge to improve the testing dataset is critical for these circumstances. In this paper, a novel method combining the non-negative matrix factorization technology and the transfer learning (NMF-TL) is proposed for EEG signal classification. Specifically, the shared subspace is extracted from the testing dataset and training dataset using non-negative matrix factorization firstly and then the shared subspace and the original feature space are combined to obtain the final EEG signal classification results. On the one hand, the non-negative matrix factorization can assure to obtain essential information between the testing and the training dataset; on the other hand, the combination of shared subspace and the original feature space can fully use all the signals including the testing and the training dataset. Extensive experiments on Bonn EEG confirmed the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Bessenrodt and Ono's work on additive and multiplicative properties of the partition function and DeSalvo and Pak's paper on the log-concavity of the partition function have generated many beautiful theorems and conjectures. In January 2020, the first author gave a lecture at the MPIM in Bonn on a conjecture of Chern–Fu–Tang, and presented an extension (joint work with Neuhauser) involving polynomials. Partial results have been announced. Bringmann, Kane, Rolen, and Tripp provided complete proof of the Chern–Fu–Tang conjecture, following advice from Ono to utilize a recently provided exact formula for the fractional partition functions. They also proved a large proportion of Heim–Neuhauser's conjecture, which is the polynomization of Chern–Fu–Tang's conjecture. We prove several cases, not covered by Bringmann et. al. Finally, we lay out a general approach for proving the conjecture. [ABSTRACT FROM AUTHOR]

Copyright of Voluntaris: Journal of Volunteer Services & Civic Engagement / Zeitschrift für Freiwilligendienste und Zivilgesellschaftliches Engagement is the property of Nomos Verlagsgesellschaft mbH & Co. KG and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article focuses on the mid-term symposium of the International Society of Photogrammetry and Remote Sensing (ISPRS) Commission III which was held in Bonn, Germany on September 20-22, 2006. Entitled "Photogrammetric Computer Vision 2006," the event was attended by 123 delegates from 20 countries. Majority of the papers focused on feature extraction from both images and laser scanning data. Among the highlights is a speech by Reinhard Klein on shape detection in point clouds.

Information on a paper discussed at a symposium held on July 15-17, 2010 in Bonn, Germany is presented. The paper discussed the cultural technology of law. It further discusses the cultural histories with reference to Georg Wilhelm Friedrich Hegel and also the depiction of law as a part of culture. Information on the technology of law and law as a concept of culture is also presented.

Epilepsy is a prevalent neurological disorder that threatens human health in the world. The most commonly used method to detect epilepsy is using the electroencephalogram (EEG). However, epilepsy detection from the EEG is time-consuming and error-prone work because of the varying levels of experience we find in physicians. To tackle this challenge, in this paper, we propose a multi-scale non-local (MNL) network to achieve automatic EEG signal detection. Our MNL-Network is based on 1D convolution neural network involving two specific layers to improve the classification performance. One layer is named the signal pooling layer which incorporates three different sizes of 1D max-pooling layers to learn the multi-scale features from the EEG signal. The other one is called a multi-scale non-local layer, which calculates the correlation of different multi-scale extracted features and outputs the correlative encoded features to further enhance the classification performance. To evaluate the effectiveness of our model, we conduct experiments on the Bonn dataset. The experimental results demonstrate that our MNL-Network could achieve competitive results in the EEG classification task. [ABSTRACT FROM AUTHOR]

Epilepsy is a nervous disorder that causes arbitrary recurrent seizures within the cerebral cortex region of the encephalon. The early diagnosis of a seizure is important in clinical therapy. An automatic epileptic seizure detection method for electroencephalogram (EEG) signals can significantly enhance the patient's life in clinical aspect. The proposed paper is principally based on a completely unique approach of epileptic seizure detection using Q-Tuned Wavelet Transform (QTWT) and Approximate entropy (ApEn). This work focuses by utilizing and testing the common sense of Extreme Learning Adaptive Neuro-Fuzzy Inference System Model (EXL-ANFIS) which foresees the elements of the mind states as a trajectory that results in the seizure event. QTWT is used for decomposing EEG signals into sub-band frequency signals. Approximate entropy is carried out to those sub-band signals as a discriminatory function because of its indefinite disordered feature. The solutions obtained by directing towards EXL- ANFIS shows an incredible advancement in the perpetual performance outlay for the classification of an epileptic seizure. The proposed classification method is implemented on publicly available Bonn dataset. The outcome confirms that by combining extreme learning and ANFIS model improves the classification accuracy and decrease the feature dimension with reduced computational complexity. This method achieves 99.72% of classification accuracy over existing models. [ABSTRACT FROM AUTHOR]

Neural network (NN) finds role in variety of applications due to combined effect of feature extraction and classification availability in deep learning algorithms. In this paper, we have chosen SVM, logistic regression machine learning algorithms and NN for EEG signal classification. Two-layer LSTM and four-layer improved NN deep learning algorithms are proposed to improve the performance in EEG classification. Novelty lies in one-dimensional gradient descent activation functions with radial basis operations used in the initial layers of improved NN which help in achieving better performance. Statistical features namely mean, standard deviation, kurtosis and skewness are extracted for input EEG collected from Bonn database and then applied for various classification techniques. Accuracy, precision, recall and F1 score are the performance metrics used for analyzing the algorithms. Improved NN and LSTM give better performance compared to all other architectures. The simulations are carried out with variety of activation functions, optimizers and loss models to analyze the performance using Python in keras. [ABSTRACT FROM AUTHOR]

This work uses Fuzzy Lattices and Neural Reinforcement Learning techniques for seizure classification. EEG database of Bonn University and CHB-MIT has been used for the evaluation of the proposed method. Here, features are represented in the form of fuzzy lattices, and feature vectors are created in the form of Kinetic Energy (K.E.) using Schrödinger equation. Then, the highest K.E.-based seven fuzzy lattices have been used for classification using Neural Reinforcement Learning classifier. Neural Reinforcement Learning classifier is a self-learner method that classifies different seizure sub-classes (healthy eyes open, healthy eye closed, Epileptogenic Zone, hippocampal formation of opposite hemisphere, epileptic seizure). The effectiveness of the proposed method has been tested on two different public datasets. The average classification accuracy achieved is 97.6% and 97.5% for Bonn and CHB-MIT datasets, respectively. Results are compared with existing techniques to show the precedence of proposed approach. Also, computation speed of proposed classifier is more than 1.5 times compared to Fuzzy Q Learning classifier. The objective is to develop a hybrid model integrating fuzzy lattices and neural reinforcement learning (adaptive methods) for accurate classification, aiming to enhance and speeding up seizure detection and diagnosis to improve patient care through advanced computational techniques. [ABSTRACT FROM AUTHOR]

Copyright of Nachrichten aus der Chemie is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, a new epileptic seizure detection method using fuzzy-rules-based sub-band specific features and layered directed acyclic graph support vector machine (LDAG-SVM) is proposed for classification of electroencephalogram (EEG) signals. Wavelet transformation is used to decompose the input EEG signals into various sub-bands. The nonlinear features, namely approximate entropy, largest Lyapunov exponent and correlation dimension, are extracted from each sub-band. In this proposed work, sub-band specific feature subset that is reduced in size and capable of discriminating samples is selected by employing fuzzy rules. For classification purpose, a new LDAG-SVM is used for detecting epileptic seizure. Every sub-band has its own characteristics. If appropriate features which characterize the specific sub-band are selected, then the classification accuracy is improved and computational complexity is reduced. The important advantage of the fuzzy logic is its close relation to human thinking. Due to the lengthy record and intra-professional variability, automation of epileptic detection is inevitable. Fuzzy rules are the natural choice of employing human expertise to build machine learning system. Performances of the proposed methods are evaluated using two different benchmark EEG datasets, namely Bonn and CHB-MIT. The performance measures such as classification accuracy, sensitivity, specificity, execution time and receiver operating characteristics are used to measure and analyze the performances of the proposed classifier. The proposed LDAG-SVM with fuzzy-rules-based selected sub-band specific features provides better performance in terms of improved classification accuracy with reduced execution time compared to existing methods. [ABSTRACT FROM AUTHOR]

Epileptic seizures have a significant impact on the well-being of a large number of individuals worldwide. Utilizing electroencephalographic (EEG) signals for automatic seizure detection proves to be a valuable solution. However, dealing with raw EEG signals is inherently complex, necessitating a preliminary step of feature extraction prior to detection. Traditional feature extraction methods often amalgamate various types of features for seizure detection, as each type typically captures specific properties. In contrast, this paper focuses on detecting seizures by analyzing the system dynamics. The proposed Deterministic Learning Feature Extraction (DLFE) method extracts a single type of nonlinear dynamical feature rooted in the EEG system dynamics. DLFE employs deterministic learning to discern the inherent system dynamics of the EEG under both seizure and normal conditions. Through the feature extraction process, the infinite-dimensional system dynamics are transformed into feature vectors, exhibiting distinct distributions in seizure and normal states. This disparity can be effectively utilized for classification using standard classifiers. The performance of the proposed seizure detection method was assessed using the CHB-MIT and Bonn datasets. The average classification accuracy was found to be 98.63% with a specificity of 99.19% and a sensitivity of 98.06% on CHB-MIT dataset. Compared with the latest similar methods, the accuracy, specificity and sensitivity are improved by 0.31%, 0.21% and 0.05% respectively. Moreover, the performance was achieved with the short-time interval EEG signals within a few channels. The average classification accuracy was found to be 99.90% with a 0.22% improvement on Bonn dataset, which indicates the good generalization performance. • We propose a new seizure detection method based on brain system dynamics. • We utilize dynamic distances to distinguish the seizure and normal conditions. • We use deterministic learning to approximate the system dynamics of EEG. • DLFE converts infinite-dimensional system dynamics into nonlinear feature vectors. • Features reflect seizure information from global and local perspectives. [ABSTRACT FROM AUTHOR]

Copyright of Nachhaltigkeitsrecht is the property of Verlag Oesterreich GmbH and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Visual simultaneous localization and mapping (SLAM) in dynamic environments has received significant attention in recent years, and accurate segmentation of real dynamic objects is the key to enhancing the accuracy of pose estimation in such environments. In this study, we propose a visual SLAM approach based on ORB-SLAM3, namely dynamic multiple object tracking SLAM (DMOT-SLAM), which can accurately estimate the camera's pose in dynamic environments while tracking the trajectories of moving objects. We introduce a spatial point correlation constraint and combine it with instance segmentation and epipolar constraint to identify dynamic objects. We integrate the proposed motion check method into DeepSort, an object tracking algorithm, to facilitate inter-frame tracking of dynamic objects. This integration not only enhances the stability of dynamic features detection but also enables the estimation of global motion trajectories for dynamic objects and the construction of object-level semi-dense semantic maps. We evaluate our approach on the public TUM, Bonn, and KITTI dataset, and the results show that our approach has a significant improvement over ORB-SLAM3 in dynamic scenes and performs better compared to other state-of-the-art SLAM approaches. Moreover, experiments in real-world scenarios further substantiate the effectiveness of our approach. [ABSTRACT FROM AUTHOR]

Machine learning is an expanding research area. Its main application is in the medical field and particularly the detection of epilepsy and epileptic seizures through electroencephalographic signals (EEG). It aims to design an intelligent framework that enables an immediate diagnosis of this disease without neurological consultation and thus saves the lives of the epileptic patients by detecting seizures and warning them before it happens. However, as a real-time application, this kind of framework faces several challenges such as accuracy, fast responses, and optimal memory usage. Within this context, our work was carried out. We propose a new machine learning framework based on chaos and fractal theories. Two main novelties are presented in this paper. Firstly, we propose a new method for signal preprocessing, and we reconstruct new versions of studied EEG signals using derivative determination and chaotic injection. Secondly, we suggest a new method for fractal analysis using Higuchi fractal dimension (HFD). In fact, HFDs extracted from EEG derivatives lead to detect epilepsy, whereas HFDs extracted from EEG with a chaotic signal injection lead to seizure detection. In addition, feature fusion helped to linearize all classification problems. An experimental study using the Bonn EEG database proves the efficiency of our contributions in comparison to published research. An accuracy of 100% was achieved in different classification cases using few features and a simple linear classifier. [ABSTRACT FROM AUTHOR]

Electroencephalogram (EEG) plays a pivotal role in the detection and analysis of epileptic seizures, which affects over 70 million people in the world. Nonetheless, the visual interpretation of EEG signals for epilepsy detection is laborious and time-consuming. To tackle this open challenge, we introduce a straightforward yet efficient hybrid deep learning approach, named ResBiLSTM, for detecting epileptic seizures using EEG signals. Firstly, a one-dimensional residual neural network (ResNet) is tailored to adeptly extract the local spatial features of EEG signals. Subsequently, the acquired features are input into a bidirectional long short-term memory (BiLSTM) layer to model temporal dependencies. These output features are further processed through two fully connected layers to achieve the final epileptic seizure detection. The performance of ResBiLSTM is assessed on the epileptic seizure datasets provided by the University of Bonn and Temple University Hospital (TUH). The ResBiLSTM model achieves epileptic seizure detection accuracy rates of 98.88–100% in binary and ternary classifications on the Bonn dataset. Experimental outcomes for seizure recognition across seven epilepsy seizure types on the TUH seizure corpus (TUSZ) dataset indicate that the ResBiLSTM model attains a classification accuracy of 95.03% and a weighted F1 score of 95.03% with 10-fold crossvalidation. These findings illustrate that ResBiLSTM outperforms several recent deep learning state-of-the-art approaches. [ABSTRACT FROM AUTHOR]

We investigate the effects of residual tensor force (TF) and pairing force on the Gamow–Teller (GT) transitions in four magic nuclei, 48Ca, 90Zr, 132Sn and 208Pb. The TF is taken into account by using the Brückner G -matrix theory with the charge-dependent (CD) Bonn potential as the residual interaction of charge-exchange quasiparticle random phase approximation (QRPA). We found that particle–particle (p–p) tensor interaction does not affect the GT transitions because of the closed shell nature in the nuclei, but repulsive particle–hole (p–h) residual interaction for the p–h configuration of spin-orbit partners dominates the high-lying giant GT states for all of the nuclei. It is also shown that appreciable GT strengths are shifted to a lower energy region by the attractive p–h TF for the same j π = j ν configuration, and produce the low-lying GT peak about 2.5 MeV in 48Ca. Simultaneously, in 90Zr and 132Sn, the low-energy GT strength appears as a lower energy shoulder near the main GT peak. On the other hand, the shift of the low-lying GT state is not seen clearly for 208Pb because of the strong spin-orbit splitting of high j orbits, which dominates the GT strength. [ABSTRACT FROM AUTHOR]