Connection Domain Research Articles

Neural decoding and feature selection methods for closed-loop control of avoidance behavior.

Many psychiatric disorders involve excessive avoidant or defensive behavior, such as avoidance in anxiety and trauma disorders or defensive rituals in obsessive-compulsive disorders. Developing algorithms to predict these behaviors from local field potentials (LFPs) could serve as the foundational technology for closed-loop control of such disorders. A significant challenge is identifying the LFP features that encode these defensive behaviors.&#xD;Approach. We analyzed LFP signals from the infralimbic cortex and basolateral amygdala of rats undergoing tone-shock conditioning and extinction, standard for investigating defensive behaviors. We utilized a comprehensive set of neuro-markers across spectral, temporal, and connectivity domains, employing SHapley Additive exPlanations for feature importance evaluation within Light Gradient-Boosting Machine models. Our goal was to decode three commonly studied avoidance/defensive behaviors: freezing, bar-press suppression, and motion (accelerometry), examining the impact of different features on decoding performance.&#xD;Main results. Band power and band power ratio between channels emerged as optimal features across sessions. High-gamma (80-150 Hz) power, power ratios, and inter-regional correlations were more informative than other bands that are more classically linked to defensive behaviors. Focusing on highly informative features enhanced performance. Across 4 recording sessions with 16 subjects, we achieved an average coefficient of determination of 0.5357 and 0.3476, and Pearson correlation coefficients of 0.7579 and 0.6092 for accelerometry jerk and bar press rate, respectively. Utilizing only the most informative features revealed differential encoding between accelerometry and bar press rate, with the former primarily through local spectral power and the latter via inter-regional connectivity. Our methodology demonstrated remarkably low training/inference time and memory usage, requiring <310 ms for training, <0.051 ms for inference, and 16.6 kB of memory, using a single core of AMD Ryzen Threadripper PRO 5995WX CPU.&#xD;Significance. Our results demonstrate the feasibility of accurately decoding defensive behaviors with minimal latency, using LFP features from neural circuits strongly linked to these behaviors. This methodology holds promise for real-time decoding to identify physiological targets in closed-loop psychiatric neuromodulation.&#xD;&#xD.

Expression of thermostable MMLV reverse transcriptase in Escherichia coli by directed mutation

The functionality of Moloney murine leukemia virus reverse transcriptase (MMLV RT) will increase with the improvement of its solubility and thermal stability. Introduce directed mutation at specific positions of the MMLV RT sequence and codon optimization is needed to achieve these properties. The two RT coding sequences with (rRT-K) and without directed mutations (rRT-L) were versatility optimized and expressed to analyze the ribonuclease H (RNase H) inactivity and thermostable polymerase activity. For this purpose, the five-point mutations (438–442aa) and three-point mutations (530, 568, and 659 aa) were done at the RT connection domain and RNase H active site, respectively. High expression levels of rRT-L and rRT-K were obtained in E. coli BL21(DE3) and BL21(shuffle) strains, 0.5 mM IPTG concentration at 37 °C, and 8 hours’ post-induction condition. Then, recombinant enzymes were purified and verified by Ni-NTA resin and western blotting. Insilico analysis (IUpred 3.0) showed that the directed mutation in the RNase H domain caused the formation of disorder regions or instability in the RNase H domain of rRT-K compared to rRT-L. The modified RT-PCR and the RT-LAMP reactions proved the RNase H inactivity of rRT-K. In addition, increasing of thermostability of rRT-K compared to rRT-L and commercial RT was evaluated by the RT-PCR and RT-LAMP reactions. The results showed that rRT-K could successfully tolerate 60 ºC in the two methods. This study revealed that the directed mutations and the versatile sequence optimization can promise to produce thermostable commercial enzymes to decrease non-specific one-step RT-PCR and RT-LAMP products.

Functional Connectivity and Graph Embedding-Based Domain Adaptation for Autism Classification from Multi-site Data

Functional Connectivity and Graph Embedding-Based Domain Adaptation for Autism Classification from Multi-site Data

Neural Decoding and Feature Selection Techniques for Closed-Loop Control of Defensive Behavior.

Many psychiatric disorders involve excessive avoidant or defensive behavior, such as avoidance in anxiety and trauma disorders or defensive rituals in obsessive-compulsive disorders. Developing algorithms to predict these behaviors from local field potentials (LFPs) could serve as foundational technology for closed-loop control of such disorders. A significant challenge is identifying the LFP features that encode these defensive behaviors. We analyzed LFP signals from the infralimbic cortex and basolateral amygdala of rats undergoing tone-shock conditioning and extinction, standard for investigating defensive behaviors. We utilized a comprehensive set of neuro-markers across spectral, temporal, and connectivity domains, employing SHapley Additive exPlanations for feature importance evaluation within Light Gradient-Boosting Machine models. Our goal was to decode three commonly studied avoidance/defensive behaviors: freezing, bar-press suppression, and motion (accelerometry), examining the impact of different features on decoding performance. Band power and band power ratio between channels emerged as optimal features across sessions. High-gamma (80-150 Hz) power, power ratios, and inter-regional correlations were more informative than other bands that are more classically linked to defensive behaviors. Focusing on highly informative features enhanced performance. Across 4 recording sessions with 16 subjects, we achieved an average coefficient of determination of 0.5357 and 0.3476, and Pearson correlation coefficients of 0.7579 and 0.6092 for accelerometry jerk and bar press rate, respectively. Utilizing only the most informative features revealed differential encoding between accelerometry and bar press rate, with the former primarily through local spectral power and the latter via inter-regional connectivity. Our methodology demonstrated remarkably low time complexity, requiring <110 ms for training and <1 ms for inference. Our results demonstrate the feasibility of accurately decoding defensive behaviors with minimal latency, using LFP features from neural circuits strongly linked to these behaviors. This methodology holds promise for real-time decoding to identify physiological targets in closed-loop psychiatric neuromodulation.

Quality-of-Life Instruments and Nutrition Screening Tools

Background Quality of life (QoL) is critical for healthy aging—both for older adults and for healthcare providers/health systems. Eating and nutritious food are essential for older adults to remain healthy/independent and maintain good nutrition status and also provide pleasure and enhance QoL. However, research on nutrition and QoL is limited, including for building understanding of the relationship between nutrition and QoL and of the instruments and tools used to help develop a research framework and evaluate the impact of United States community nutrition programs/services on QoL. Objectives The first objective is to review overlaps/gaps in previously identified/validated QoL instruments and nutrition screening tools used in research with community-living older adults and then, second, to use these findings to suggest opportunities for further research and implications for developing US health and nutrition policies/programs supporting healthy aging. Methods Twenty validated QoL instruments were reviewed to determine if they included nutrition-related items that corresponded to 8 QoL domains (physical health, emotional state, mental health, social connection, environment, personhood, autonomy, and spiritual feeling). Sixteen validated nutrition screening tools were reviewed to determine if any of their nutrition items corresponded with these same 8 QoL domains. Results Of the 20 QoL instruments reviewed, 75% included at least 1 nutrition-related item, most commonly fitting into the autonomy (n = 11), physical health (n = 7), social connection (n = 3), environment (n = 3), emotional state (n = 2), mental health (n = 2), and personhood (n = 1) domains, with none in the spiritual feeling domain. All 16 nutrition screening tools included at least 1 nutrition-related item corresponding to a QoL domain, most commonly the physical health (n = 16) domain. Other QoL domains represented by nutrition items in nutrition screening tools were autonomy (n = 9), emotional state (n = 5), social connection (n = 5), environment (n = 4), and mental health (n = 3). Commonalities existed between QoL instruments and nutrition screening tools in types of nutrition-related items included, but there were many inconsistencies/gaps. Conclusions Nutrition items corresponding to different QoL domains are found inconsistently among validated QoL instruments and nutrition screening tools. Nutrition can be potentially modified to benefit healthy aging and QoL outcomes; findings present opportunities for further research to help increase understanding of the relationship between QoL and nutrition and the effectiveness of nutrition interventions, as well as to help advance US policy development and programs supporting healthy aging.

Immigrant Perspectives of Social Connection in a Nontraditional Migration Area.

Social connection is a core dimension of health and wellness among all populations, yet the experience of moving to and living in a new country makes social and community-level influences particularly salient for immigrants. We interviewed 38 Latino immigrants living in a nontraditional migration area to explore the social and community foundations of health and wellness. Using hybrid (inductive/deductive) qualitative analysis, we identified seven domains of social connection from the perspective of the interviewed participants: (1) lens of the individual; (2) immigrant experience; (3) interpersonal support; (4) community belonging; (5) community capital; (6) community navigation; and (7) social acceptance. Social connection domains generated by participants are consistent with the scientific literature, but this study identifies the specific social factors that immigrants describe as most salient to their own health and wellness. Our community-generated understanding of social connection can be used by healthcare providers to reduce risks and build on assets that will improve the health of immigrants living in nontraditional migration areas. Additionally, these results might serve as a foundation for a quantitative measure that can be used by providers to more accurately and comprehensively assess the social connection of their patients and by researchers to evaluate the effectiveness of community-level interventions for immigrants.

Altered brain functional connectivity in patients with resistance to thyroid hormone ß.

To investigate changes in brain network organization and possible neurobehavioral similarities to attention-deficit hyperactivity disorder (ADHD), we measured changes in brain resting-state functional connectivity (rs-fMRI) and cognitive domains in patients with resistance to thyroid hormone β (RTHβ) and compared them with those in healthy control subjects. In this prospective case-control study, twenty-one participants with genetically confirmed RTHβ were matched with 21 healthy controls. The Adult ADHD Self-Report Scale (ASRS-v1.1) and ADHD Rating Scale-IV were used to assess self-reported symptoms of ADHD. A voxel-wise and atlas-based approach was used to identify changes in the brain networks. The RTHβ group reported behavioral symptoms similar to those of ADHD. We found evidence of weaker network integration of the lingual and fusiform gyri in the RTHβ group, which was mainly driven by weaker connectivity to the bilateral insula and supplementary motor cortex. Functional connectivity between regions of the default mode network (angular gyrus/middle temporal gyrus) and regions of the cognitive control network (bilateral middle frontal gyrus) was increased in RTHβ patients compared to healthy controls. Increased connectivity between regions of the default mode network and the dorsolateral prefrontal cortex is frequently reported in ADHD and is interpreted to be associated with deficits in attention. Our finding of weaker connectivity of the lingual gyrus to the bilateral insula (salience network) in RTHβ patients has also been reported previously in ADHD and may reflect decreased habituation to visual stimuli and increased distractibility. Overall, our observations support the notion of neuropsychological similarities between RTHβ and ADHD.

СУЧАСНИЙ СТАН ЦИФРОВІЗАЦІЇ КРАЇН-ЧЛЕНІВ ЄС

This article explores the present status of digitization across European Union member states. While the EU maintains common goals and overarching strategies for digitalization, individual member nations exhibit diverse levels of progress in its implementation. The asynchronous pace of digitalization among EU member states stems from various factors, including economic disparities, variations in technological infrastructure and legal frameworks, cultural and social influences, disparities in education and digital literacy, divergent government initiatives and funding levels, and the extent of collaboration between the public and private sectors. Each member state contributes to distinct dimensions of progress in the implementation of digitalization. The European Commission facilitates the monitoring of digital competitiveness and indicators across EU member states through the utilization of the Digital Economy and Society Index (DESI) report. This report evaluates diverse aspects of the digital economy and society, offering a holistic perspective on digital development within the EU. The current digital landscape in individual EU member states has been analyzed. The study examines the dynamics of changes in specific indicators (connectivity, human capital, integration of digital technologies, and state digital services) within the framework of the DESI from 2015 to 2022 in both the European Union as a collective entity and the individual member states. The research findings indicate that, on the whole, there has been noteworthy advancement in digitalization across all EU member states over the past decade. Nevertheless, the study highlights the contemporary challenges reflected in the objectives outlined in the "Digital Decade" program and the asynchronous fulfillment of these goals among member states. The most significant progress has been observed in accomplishing objectives related to digitalizing public services. All figures of this indicator are approaching the maximum. Notable advances have been made in the connectivity or digital infrastructure domain in implementing 5G and high-speed communications. However, there is a recognized need for further development, particularly in establishing distribution nodes and advancing the field of quantum computing. Examining the indicator of human capital, member states have made significant progress in imparting basic digital skills to their populations. Nevertheless, increasing the number of Information and Communication Technology (ICT) specialists remains required. Turning to the integration of digital technologies, specifically the digital transformation of businesses, discernible progress has been achieved in incorporating digital technologies into Small and Medium Enterprises (SMEs) activities. However, further advancement necessitates the introduction of cloud technologies and artificial intelligence into the operations of enterprises across the European Union.

Bridging the Gap: Enhancing Maritime Vessel Cyber Resilience through Security Operation Centers.

Increasingly disruptive cyber-attacks in the maritime domain have led to more efforts being focused on enhancing cyber resilience. From a regulatory perspective, there is a requirement that maritime stakeholders implement measures that would enable the timely detection of cyber events, leading to the adoption of Maritime Security Operation Centers (M-SOCs). At the same time, Remote Operation Centers (ROCs) are also being discussed to enable increased adoption of highly automated and autonomous technologies, which could further impact the attack surface of vessels. The main objective of this research was therefore to better understand both enabling factors and challenges impacting the effectiveness of M-SOC operations. Semi-structured interviews were conducted with nine M-SOC experts. Informed by grounded theory, incident management emerged as the core category. By focusing on the factors that make M-SOC operations a unique undertaking, the main contribution of this study is that it highlights how maritime connectivity challenges and domain knowledge impact the M-SOC incident management process. Additionally, we have related the findings to a future where M-SOC and ROC operations could be converged.

Psilocybin-induced default mode network hypoconnectivity is blunted in alcohol-dependent rats

Alcohol Use Disorder (AUD) adversely affects the lives of millions of people, but still lacks effective treatment options. Recent advancements in psychedelic research suggest psilocybin to be potentially efficacious for AUD. However, major knowledge gaps remain regarding (1) psilocybin’s general mode of action and (2) AUD-specific alterations of responsivity to psilocybin treatment in the brain that are crucial for treatment development. Here, we conducted a randomized, placebo-controlled crossover pharmaco-fMRI study on psilocybin effects using a translational approach with healthy rats and a rat model of alcohol relapse. Psilocybin effects were quantified with resting-state functional connectivity using data-driven whole-brain global brain connectivity, network-based statistics, graph theory, hypothesis-driven Default Mode Network (DMN)-specific connectivity, and entropy analyses. Results demonstrate that psilocybin induced an acute wide-spread decrease in different functional connectivity domains together with a distinct increase of connectivity between serotonergic core regions and cortical areas. We could further provide translational evidence for psilocybin-induced DMN hypoconnectivity reported in humans. Psilocybin showed an AUD-specific blunting of DMN hypoconnectivity, which strongly correlated to the alcohol relapse intensity and was mainly driven by medial prefrontal regions. In conclusion, our results provide translational validity for acute psilocybin-induced neural effects in the rodent brain. Furthermore, alcohol relapse severity was negatively correlated with neural responsivity to psilocybin treatment. Our data suggest that a clinical standard dose of psilocybin may not be sufficient to treat severe AUD cases; a finding that should be considered for future clinical trials.

Topology optimization of connectable microstructure using enlarged connective domain

Topology optimization of connectable microstructure using enlarged connective domain

Interchange reconnection dynamics in a solar coronal pseudo-streamer

Context.The generation of the slow solar wind remains an open problem in heliophysics. One of the current theories among those aimed at explaining the injection of coronal plasma in the interplanetary medium is based on interchange reconnection. It assumes that the exchange of magnetic connectivity between closed and open fields allows the injection of coronal plasma in the interplanetary medium to travel along the newly reconnected open field. However, the exact mechanism underlying this effect is still poorly understood.Aims.Our objective is to study this scenario in a particular magnetic structure of the solar corona: a pseudo-streamer. This topological structure lies at the interface between open and closed magnetic field and is thought to be involved in the generation of the slow solar wind.Methods.We performed innovative 3D magnetohydrodynamic (MHD) simulations of the solar corona with a pseudo-streamer, using the Adaptively Refined MHD Solver (ARMS). By perturbing the quasi-steady ambient state with a simple photospheric, large-scale velocity flow, we were able to generate a complex dynamics of the open-and-closed boundary of the pseudo-streamer. We studied the evolution of the connectivity of numerous field lines to understand its precise dynamics.Results.We witnessed different scenarios of opening of the magnetic field initially closed under the pseudo-streamer: one-step interchange reconnection dynamics, along with more complex scenarios, including a coupling between pseudo-streamer and helmet streamer, as well as back-and-forth reconnections between open and closed connectivity domains. Finally, our analysis revealed large-scale motions of a newly opened magnetic field high in the corona that may be explained by slipping reconnection.Conclusions.By introducing a new analysis method for the magnetic connectivity evolution based on distinct closed-field domains, this study provides an understanding of the precise dynamics underway during the opening of a closed field, which enables the injection of closed-field, coronal plasma in the interplanetary medium. Further studies shall provide synthetic observations for these diverse outgoing flows, which could be measured by Parker Solar Probe and Solar Orbiter.

Ultra-fast 3D imaging by a big codewords space division multiplexing binary coding.

Patternless binary coding strategies have been a challenge for ultra-fast 3D imaging with structured light. This Letter proposes a big codewords space division multiplexing binary coding method. From the third to the multiple order, a special spatial binary coding instead of the Gray code is created for the first time, to the best of our knowledge, to achieve an ultra-wide unambiguous range with only one auxiliary pattern. Advantageously, a connection domain segmentation technique with anomaly detection is proposed to achieve decoding of the fringe order, which cleverly avoids the misalignment problem. Additionally, a center of gravity method is applied to compensate for the codewords of the residual connected domain. The robustness and effectiveness of the proposed method for complex, isolated, and non-uniform reflectivity objects, as well as the ultra-fast 3D imaging of dynamic measurements, are experimentally verified.

Energy Meter Patch Resistance and Welding Spot Anomaly Detection Method Based on Machine Vision

Based on the problems, such as the difficulty of detecting and obtaining evidence of patch resistance replacement and welding spot anomaly in the current field of meter anomaly detection, a patch resistance and welding spot anomaly detection algorithm is proposed based on machine vision. The resistance anomaly detection algorithm combines the K-D tree and Ransac to complete the high-efficiency energy meter registration. It detects the suspected resistance abnormal area through the difference shadow method and then judges the resistance abnormal situation according to the resistance value recognized by the classification network. The welding spot anomaly detection algorithm enhances the feature of the image through saliency detection, then obtains the target information of the welding spot by segmentation, and finally determines the welding spot anomaly condition in combination with the connection domain analysis. Experimental results show that the precision of patch resistance anomaly of this method reaches 95.28%, and the detection time is about 1.52s; the precision of welding spot anomaly reaches 96.74%, and the detection time is about 0.74s. The method can meet the requirements of spot detection accuracy and speed and has good application value.

Prediction of Tinnitus Treatment Outcomes Based on EEG Sensors and TFI Score Using Deep Learning

Tinnitus is a hearing disorder that is characterized by the perception of sounds in the absence of an external source. Currently, there is no pharmaceutical cure for tinnitus, however, multiple therapies and interventions have been developed that improve or control associated distress and anxiety. We propose a new Artificial Intelligence (AI) algorithm as a digital prognostic health system that models electroencephalographic (EEG) data in order to predict patients' responses to tinnitus therapies. The EEG data was collected from patients prior to treatment and 3-months following a sound-based therapy. Feature selection techniques were utilised to identify predictive EEG variables with the best accuracy. The patients' EEG features from both the frequency and functional connectivity domains were entered as inputs that carry knowledge extracted from EEG into AI algorithms for training and predicting therapy outcomes. The AI models differentiated the patients' outcomes into either therapy responder or non-responder, as defined by their Tinnitus Functional Index (TFI) scores, with accuracies ranging from 98%-100%. Our findings demonstrate the potential use of AI, including deep learning, for predicting therapy outcomes in tinnitus. The research suggests an optimal configuration of the EEG sensors that are involved in measuring brain functional changes in response to tinnitus treatments. It identified which EEG electrodes are the most informative sensors and how the EEG frequency and functional connectivity can better classify patients into the responder and non-responder groups. This has potential for real-time monitoring of patient therapy outcomes at home.

Exploring the Quality of Narrative Feedback Provided to Residents During Ambulatory Patient Care in Medicine and Surgery.

The transition to competency-based medical education (CBME) has increased the volume of residents' assessment data; however, the quality of the narrative feedback is yet to be used as feedback-on-feedback for faculty. Our objectives were (1) to explore and compare the quality and content of narrative feedback provided to residents in medicine and surgery during ambulatory patient care and (2) to use the Deliberately Developmental Organization framework to identify strengths, weaknesses, and opportunities to improve quality of feedback within CBME. We conducted a mixed convergent methods study with residents from the Departments of Surgery (DoS; n = 7) and Medicine (DoM; n = 9) at Queen's University. We used thematic analysis and the Quality of Assessment for Learning (QuAL) tool to analyze the content and quality of narrative feedback documented in entrustable professional activities (EPAs) assessments for ambulatory care. We also examined the association between the basis of assessment, time to provide feedback, and the quality of narrative feedback. Forty-one EPA assessments were included in the analysis. Three major themes arose from thematic analysis: Communication, Diagnostics/Management, and Next Steps. Quality of the narrative feedback varied; 46% had sufficient evidence about residents' performance; 39% provided a suggestion for improvement; and 11% provided a connection between the suggestion and the evidence. There were significant differences between DoM and DoS in quality of feedback scores for evidence (2.1 [1.3] vs. 1.3 [1.1]; p < 0.01) and connection (0.4 [0.5] vs. 0.1 [0.3]; p = 0.04) domains of the QuAL tool. Feedback quality was not associated with the basis of assessment or time taken to provide feedback. The quality of the narrative feedback provided to residents during ambulatory patient care was variable with the greatest gap in providing connections between suggestions and evidence about residents' performance. There is a need for ongoing faculty development to improve the quality of narrative feedback provided to residents.

Bayesian connective field modeling using a Markov Chain Monte Carlo approach

The majority of neurons in the human brain process signals from neurons elsewhere in the brain.Connective Field (CF) modelling is a biologically-grounded method to describe this essential aspect of the brain's circuitry. It allows characterizing the response of a population of neurons in terms of the activity in another part of the brain. CF modelling translates the concept of the receptive field (RF) into the domain of connectivity by assessing, at the voxel level, the spatial dependency between signals in distinct cortical visual field areas. Thus, the approach enables to characterize the functional cortical circuitry of the human cortex. While already very useful, the present CF modelling approach has some intrinsic limitations due to the fact that it only estimates the model's explained variance and not the probability distribution associated with the estimated parameters. If we could resolve this, CF modelling would lend itself much better for statistical comparisons at the level of single voxels and individuals. This is important when trying to gain a detailed understanding of the neurobiology and pathophysiology of the visual cortex, notably in rare cases. To enable this, we present a Bayesian approach to CF modeling (bCF). Using a Markov Chain Monte Carlo (MCMC) procedure, it estimates the posterior probability distribution underlying the CF parameters. Based on this, bCF quantifies, at the voxel level, the uncertainty associated with each parameter estimate. This information can be used in various ways to increase confidence in the CF model predictions. We applied bCF to BOLD responses recorded in the early human visual cortex using 3T fMRI. We estimated both the CF parameters and their associated uncertainties and show they are only weakly correlated. Moreover, we show how bCF facilitates the use of effect size (beta) as a data-driven parameter that can be used to select the most reliable voxels for further analysis. Finally, to further illustrate the functionality gained by bCF, we apply it to perform a voxel-level comparison of a single, circular symmetric, Gaussian versus a Difference-of-Gaussian model. We conclude that our bCF framework provides a comprehensive tool to study human functional cortical circuitry in health and disease.

Local optimum underwater polarization imaging enhancement based on connected domain prior

Estimating the degree of polarization (DOP) of target signal light () is a critical step in the underwater polarization differential imaging process. It is challenging to accurately estimate the of objects containing multiple attributes in the imaging range. Inspired by the optical polarization characteristics of objects with different DOP, we find a prior of connected domains (CDs) of objects in polarization orthogonal images. Based on this prior, the CDs of low-DOP (LDOP) and high-DOP (HDOP) objects are extracted from the darkest image () and brightest image () without additional image acquisition. The of LDOP and HDOP objects in the scene can be estimated through the extracted CDs, which realizes the transition from global optimum estimation in the traditional method to local optimum estimation. According to the estimated , the enhanced results of the entire image marked by can be obtained after fusing the enhanced results of the objects in each CD. Experimental results of simple and complex combination scenes of objects show that our method can recognize CDs of LDOP and HDOP objects. We obtained the enhanced results of the entire image, verified the feasibility and robustness of our method, and found that the CDs of LDOP and HDOP objects could be effectively extracted in up to NTU of turbid water. The local optimum improves the application ability of underwater polarization imaging in unpredictable scenarios, while the enhanced result of target labeling has potential application value in underwater target recognition and classification.

Assessment of multi-scale pore structures and pore connectivity domains of marine shales by fractal dimensions and correlation lengths

Digital rock (DR) technology can offer three-dimensional distribution of pore networks, providing realistic pore structure information that is essential for shale reservoir evaluation. However, effective application of the DR technology in characterizing multi-scale pore size distribution (PSD) and pore connectivity has been hampered by the unresolvable conflict between the size and the detection resolution of samples being characterized, and the lack of effective characterization parameters. In this study, a novel method named FracD has been developed to obtain continuous and integrated multi-scale PSD curves of shales. The FracD method extracts two types of fractal pore structure information from pore network models at both micro- and nano- scales, respectively, to acquire two representative fractal equations. The fractal equations were then used to generate multi-scale PSD curves with pore-size ranges of ∼ 1 nm-1000 μm through a series of computations. The FracD method was used to successfully compute PSD curves for two shale samples with distinctly different pore structure characteristics from the Sichuan Basin, China. The results were verified to be realistic by microscopic pore observation and physical experiments. In addition, a percolation correlation length parameter is introduced to evaluate the pore connectivity of shales, which has the advantage of describing the size of the connected pore domains more accurately, suitable for the pore connectivity assessment of shales with locally-connected pore structure. The correlation length was effectively applied to distinguish the connectivity of different pores in the Longmaxi shales in the Sichuan Basin, China.

Electroencephalograph-Based Emotion Recognition Using Brain Connectivity Feature and Domain Adaptive Residual Convolution Model.

In electroencephalograph (EEG) emotion recognition research, obtaining high-level emotional features with more discriminative information has become the key to improving the classification performance. This study proposes a new end-to-end emotion recognition method based on brain connectivity (BC) features and domain adaptive residual convolutional network (short for BC-DA-RCNN), which could effectively extract the spatial connectivity information related to the emotional state of the human brain and introduce domain adaptation to achieve accurate emotion recognition within and across the subject’s EEG signals. The BC information is represented by the global brain network connectivity matrix. The DA-RCNN is used to extract high-level emotional features between different dimensions of EEG signals, reduce the domain offset between different subjects, and strengthen the common features between different subjects. The experimental results on the large public DEAP data set show that the accuracy of the subject-dependent and subject-independent binary emotion classification in valence reaches 95.15 and 88.28%, respectively, which outperforms all the benchmark methods. The proposed method is proven to have lower complexity, better generalization ability, and domain robustness that help to lay a solid foundation for the development of high-performance affective brain-computer interface applications.