工程技术最新文献

Identifying the cognitive state can help educators understand the evolving thought processes of learners, and it is important in promoting the development of higher-order thinking skills (HOTS). Cognitive neuroscience research identifies cognitive states by designing experimental tasks and recording electroencephalography (EEG) signals during task performance. However, most of the previous studies primarily concentrated on extracting features from individual channels in single-type tasks, ignoring the interconnection across channels. In this study, three learning activities (i.e., video watching activity, keyword extracting activity, and essay creating activity) were designed based on a revised Bloom's taxonomy and the Interactive-Constructive-Active-Passive framework and used with 31 college students. The EEG signals were recorded when they were engaged in these activities. First, whole-brain network temporal dynamics were characterized by EEG microstate sequence analysis. Such dynamic changes rely on learning activity and corresponding functional brain systems. Subsequently, phase locking value was used to construct synchrony-based functional brain networks. The network characteristics were extracted to be inputted into different machine learning classifiers: Support Vector Machine, K-Nearest Neighbour, Random Forest, and eXtreme Gradient Boosting (XGBoost). XGBoost showed superior performance in the classification of cognitive states, with an accuracy of 88.07%. Furthermore, SHapley Additive exPlanations (SHAP) was adopted to reveal the connections between different brain regions that contributed to the classification of cognitive state. SHAP analysis reveals that the connections in the frontal, temporal, and central regions are most important for the high cognitive state. Collectively, this study may provide further evidence for educators to design cognitive-guided instructional activities to enhance learners' HOTS.

To feed the world's expanding population, crop breeders need to increase agricultural productivity and expand major crops base. Orphan crops are indigenously important crops with great potential because they are climate resilient, highly nutritious, contain nutraceutical compounds, and can improve the livelihood of smallholder farmers and consumers, but they have received little or no scientific attention. This review article examines several research and developmental strategies for hastening the improvement of these crops so that they can effectively play their role in securing food and nutrition. The integration of both research and developmental approaches will open up modern opportunities for crop improvement. We summarized ways in which advanced tools in phenotyping and genotyping, using high-throughput processes, can be used to accelerate their improvement. Finally, we suggest roles the genebanks can play in improving orphan crops, as the utilization of plant genetic resources is important for the genetic improvement of a crop.

In recent years, Wireless Sensor Networks (WSN) have become vital because of their versatility in numerous applications. Nevertheless, the attain problems like inherent noise, and limited node computation capabilities, result in reduced sensor node lifespan as well as enhanced power consumption. To tackle such problems, this study develops a Modified-Distributed Arithmetic-Offset Binary Coding-based Adaptive Finite Impulse Response (MDA-OBC based AFIR) framework. By leveraging Modified Distributed Arithmetic (MDA) which optimizes arithmetic operations by replacing the multipliers with lookup tables (LUT) hence minimizing energy consumption as well as computational complexity. Offset Binary Coding (OBC) enhanced the efficiency of data transmission by minimizing the data representation overhead. In addition to this, the adaptive strategy is incorporated with the Adaptive Finite Impulse Response (AFIR) framework permitting the filters to dynamically adjust to varying signal characteristics, thus offering high noise suppression and low distortion rates. Comprehensive simulations and comparative analysis validate the effectiveness of the proposed MDA-OBC-based AFIR method. The proposed method attained a lower energy consumption of 1.5 J and 130 W power consumption than the traditional implementations, resulting in significant energy efficiency and data transmission in signal preprocessing and noise suppression in WSNs.

Spiking neural networks (SNNs), known for their rich spatio-temporal dynamics, have recently gained considerable attention in EEG-based emotion recognition. However, conventional model training approaches often fail to fully exploit the capabilities of SNNs, posing challenges for effective EEG data analysis. In this work, we propose a novel bio-information-fused SNN (BISNN) model to enhance EEG-based emotion recognition. The BISNN model incorporates biologically plausible intrinsic parameters into spiking neurons and is initialized with a structurally equivalent pre-trained ANN model. By constructing a bio-information-fused loss function, the BISNN model enables simultaneous training under dual constraints. Extensive experiments on benchmark EEG-based emotion datasets demonstrate that the BISNN model achieves competitive performance compared to state-of-the-art methods. Additionally, ablation studies investigating various components further elucidate the mechanisms underlying the model's effectiveness and evolution, aligning well with previous findings.

The dentate gyrus (DG) in hippocampus is reported to perform pattern separation, converting similar inputs into different outputs and thus avoiding memory interference. Previous studies have found that human and mice with epilepsy have significant pattern separation defects and a portion of adult-born granule cells (abGCs) migrate abnormally into the hilus, forming hilus ectopic granule cells (HEGCs). For the lack of relevant pathophysiological experiments, how HEGCs affect pattern separation remains unclear. Therefore, in this paper, we will construct the DG neuronal circuit and focus on discussing effects of HEGCs on pattern separation numerically. The obtained results showed that HEGCs impaired pattern separation efficiency since the sparse firing of granule cells (GCs) was destroyed. We provided new insights into the underlining mechanisms of HEGCs impairing pattern separation through analyzing two excitatory circuits: GC-HEGC-GC and GC-Mossy cell (MC)-GC, both of which involve the participation of HEGCs within the DG. It is revealed that the recurrent excitatory circuit GC-HEGC-GC formed by HEGCs mossy fiber sprouting significantly enhanced GCs activity, consequently disrupted pattern separation. However, another excitatory circuit had negligible effects on pattern separation due to the direct and indirect influences of MCs on GCs, which in turn led to the GCs sparse firing. Thus, HEGCs impair DG pattern separation mainly through the GC-HEGC-GC circuit and therefore ablating HEGCs may be one of the effective ways to improve pattern separation in patients with epilepsy.

The increasing adoption of wearable technologies highlights the potential of electroencephalogram (EEG) signals for biometric recognition. However, the intrinsic variability in cross-session EEG data presents substantial challenges in maintaining model stability and reliability. Moreover, the diversity within single-task protocols complicates achieving consistent and generalized model performance. To address these issues, we propose the Joint Disentangled Representation with Domain Adversarial Training (JDR-DAT) framework for EEG-based cross-session biometric recognition within single-task protocols. The JDR-DAT framework disentangles identity-specific features through mutual information estimation and incorporates domain adversarial training to enhance longitudinal robustness. Extensive experiments on longitudinal EEG data from two publicly available single-task protocol datasets-RSVP-based (Rapid Serial Visual Presentation) and MI-based (Motor Imagery)-demonstrate the efficacy of the JDR-DAT framework, with the proposed method achieving average accuracies of 85.83% and 96.72%, respectively.

Maize is a major food crop in China, and drought is one of the major abiotic stresses that threaten the growth and development of the crop, seriously affecting the crop yield. 4-coumaric acid coenzyme A ligase (4CL) is a key enzyme in the phenylpropane metabolic pathway, which can regulate the lignin content of the plant and play an important role in the plant's resistance to drought stress, plays an important role in plant resistance to drought stress. In the present study, we screened the differentially expressed up-regulated gene Zm4CL-like9 under drought stress by pre-transcriptome sequencing data (PRJNA793522) in the laboratory, and analyzed the significant up-regulation of Zm4CL-like9 gene in roots under drought stress by qRT-PCR(Real-Time Quantitative Reverse Transcription PCR). The results of prokaryotic expression experiments showed that the protein encoded by the Zm4CL-like9 gene was able to be expressed in prokaryotic cells and could effectively improve the drought tolerance of E. coli. Phenotypic analysis of transgenic Arabidopsis plants under drought stress revealed that seed germination rate, root length, and plant survival after drought rehydration were significantly higher in transgenic Zm4CL-like9 Arabidopsis compared with wild-type Arabidopsis; physiological and biochemical indexes revealed that peroxidase activity, proline (Pro) content, and chlorophyll content were significantly higher in transgenic Arabidopsis compared with wild-type Arabidopsis. Under drought stress, the expression of drought-related genes was significantly up-regulated in transgenic Arabidopsis compared with wild-type Arabidopsis. Taken together, the Zm4CL-like9 gene enhances plant resistance to drought stress by reducing reactive oxygen species accumulation in plants.

Respiration is a fundamental physiological function in humans, often synchronized with movement to enhance performance and efficiency. Recent studies have underscored the modulatory effects of respiratory rhythms on brain oscillations and various behavioral responses, including sensorimotor processes. In light of this connection, our study aimed to investigate the influence of different respiratory patterns on beta corticomuscular coherence (CMC) during isometric hand flexion and extension. Utilizing electroencephalogram (EEG) and surface electromyography (sEMG), we examined three breathing conditions: normal breathing, deep inspiration, and deep expiration. Two experimental protocols were employed: the first experiment required participants to simultaneously breathe and exert force, while the other involved maintaining a constant force while varying breathing patterns. The results revealed that deep inspiration significantly enhanced beta CMC during respiration-synchronized tasks, whereas normal breathing resulted in higher CMC compared to deep respiration during sustained force exertion. In the second experiment, beta CMC was cyclically modulated by respiratory phase across all breathing conditions. The difference in the outcomes from the two protocols demonstrated a task-specific modulation of respiration on motor control. Overall, these findings indicate the complex dynamics of respiration-related effects on corticomuscular neural communication and provide valuable insights into the mechanisms underpinning the coupling between respiration and motor function.

Supplementary information: The online version contains supplementary material available at 10.1007/s11571-025-10245-x.

Brain signals and muscle movements have been analyzed using electroencephalogram (EEG) data in several studies. EEG signals contain a lot of noise, such as electromyographic (EMG) waves. Further studies have been done to improve the quality of the results, though it is thought that the combination of these two signals can lead to a significant improvement in the synergistic analysis of muscle movements and muscle connections. Using graph theory, this study examined the interaction of EMG and EEG signals during hand movement and estimated the synergy between muscle and brain signals. Mapping of the brain diagram was also developed to reconstruct the muscle signals from the muscle connections in the brain diagram. The proposed method included noise removal from EEG and EMG signals, graph feature analysis from EEG, and synergy calculation from EMG. Two methods were used to estimate synergy. In the first method, after calculating the brain connections, the features of the communication graph were extracted and then synergy estimating was made with neural networks. In the second method, a convolutional network created a transition from the matrix of brain connections to the synergistic EMG signal. This study reached the high correlation values of 99.8% and maximum MSE error of 0.0084. Compared to other graph-based methods, this method based on regression analysis had a very significant performance. This research can lead to the improvement of rehabilitation methods and brain-computer interfaces.