Search Results (1,825)

As the global population ages, the friendliness of urban spaces towards seniors becomes increasingly crucial. This research primarily investigates the environmental factors that influence the safety perception of elderly people in living street spaces. Taking Dingzigu Street in Tianjin, China, as an example, by employing deep learning fully convolutional network (FCN-8s) technology and the semantic segmentation method based on computer vision, the objective measurement data of street environmental elements are acquired. Meanwhile, the subjective safety perception evaluation data of elderly people are obtained through SD semantic analysis combined with the Likert scale. Utilizing Pearson correlation analysis and multiple linear regression analysis, the study comprehensively examines the impact of the physical environment characteristics of living street spaces on the spatial safety perception of seniors. The results indicate that, among the objective environmental indicators, ① the street greening rate is positively correlated with the spatial sense of security of seniors; ② there is a negative correlation between sky openness and interface enclosure; and ③ the overall safety perception of seniors regarding street space is significantly influenced by the spatial sense of security, the sense of security during walking behavior, and the security perception in visual recognition. This research not only uncovers the impact mechanism of the street environment on the safety perception of seniors, but also offers valuable references for the age-friendly design of urban spaces. Full article

Sensor-based Human Activity Recognition (HAR) is crucial in ubiquitous computing, analyzing behaviors through multi-dimensional observations. Despite research progress, HAR confronts challenges, particularly in data distribution assumptions. Most studies assume uniform data distributions across datasets, contrasting with the varied nature of practical sensor data in human activities. Addressing data heterogeneity issues can improve performance, reduce computational costs, and aid in developing personalized, adaptive models with fewer annotated data. This review investigates how machine learning addresses data heterogeneity in HAR by categorizing data heterogeneity types, applying corresponding suitable machine learning methods, summarizing available datasets, and discussing future challenges. Full article

Crowd behavior presents significant challenges due to intricate interactions. This research proposes an approach that combines the power of 3D Convolutional Neural Networks (ConvNet) and Multi-Support Vector Machines (Multi-SVM) to study and analyze crowd behavior in highly dense crowd videos. The proposed approach effectively utilizes the temporal information captured by the 3D ConvNet, which accounts for the spatiotemporal characteristics of crowd movement. By incorporating the third dimension as a temporal stack of images forming a clip, the network can learn and comprehend the dynamics and patterns of crowd behavior over time. In addition, the learned features from the 3D ConvNet are classified and interpreted using Multi-SVM, enabling a comprehensive analysis of crowd behavior. This methodology facilitates the identification and categorization of various crowd dynamics, including merging, diverging, and dense flows. To evaluate the effectiveness of the approach, experiments are conducted on the Crowd-11 dataset, which comprises over 6000 video sequences with an average length of 100 frames per sequence. The dataset defines a total of 11 crowd motion patterns. The experimental results demonstrate promising recognition rates and achieve an accuracy of 89.8%. These findings provide valuable insights into the complex dynamics of crowd behavior, with potential applications in crowd management. Full article

In response to the poor performance of long-distance small target recognition tasks and real-time intelligent monitoring, this paper proposes a deep learning-based recognition method aimed at improving the ability to recognize and monitor various behaviors of captive ewes. Additionally, we have developed a system platform based on ELFN-YOLO to monitor the behaviors of ewes. ELFN-YOLO enhances the overall performance of the model by combining ELFN with the attention mechanism CBAM. ELFN strengthens multiple layers with fewer parameters, while the attention mechanism further emphasizes the channel information interaction based on ELFN. It also improves the ability of ELFN to extract spatial information in small target occlusion scenarios, leading to better recognition results. The proposed ELFN-YOLO achieved an accuracy of 92.5%, an F1 score of 92.5%, and a [email protected] of 94.7% on the ewe behavior dataset built in commercial farms, which outperformed YOLOv7-Tiny by 1.5%, 0.8%, and 0.7% in terms of accuracy, F1 score, and [email protected], respectively. It also outperformed other baseline models such as Faster R-CNN, YOLOv4-Tiny, and YOLOv5s. The obtained results indicate that the proposed approach outperforms existing methods in scenarios involving multi-scale detection of small objects. The proposed method is of significant importance for strengthening animal welfare and ewe management, and it provides valuable data support for subsequent tracking algorithms to monitor the activity status of ewes. Full article

The development of the construction industry has generated a large amount of construction waste, and resource utilization of construction waste is an effective means of recycling. However, such recycled construction waste products still lack market competitiveness and recognition. Consumers’ psychological activities are often influenced by emotions, and the sense of awe plays an important role in green consumption. This study aims to investigate how the sense of awe affects consumers’ willingness to pay for construction waste recycled products. The study used functional near-infrared spectroscopy (fNIRS) with a willingness-to-pay task paradigm for experiments, which aims to reveal how different types of awe affect willingness to pay for construction waste recycled products. The behavioral results showed that two conditions effectively induced awe and enhanced consumers’ willingness to pay, but the difference between nature awe and social awe was not significant. The neural activation results showed significant activation in the inferior prefrontal gyrus and dorsolateral prefrontal cortex. In particular, dorsolateral prefrontal cortex activity was significantly enhanced in the social awe condition. The functional connectivity results showed that, compared to the control condition experiment, the awe condition experiment triggered stronger functional connectivity. Therefore, exploring the effect of awe on the willingness to pay for construction waste recycled products can provide a basis reference for companies to develop marketing strategies and corporate pricing and promote the promotion and application of construction waste recycled products in the market. Full article

Visual biosignals can be used to analyze human behavioral activities and serve as a primary resource for Facial Expression Recognition (FER). FER computational systems face significant challenges, arising from both spatial and temporal effects. Spatial challenges include deformations or occlusions of facial geometry, while temporal challenges involve discontinuities in motion observation due to high variability in poses and dynamic conditions such as rotation and translation. To enhance the analytical precision and validation reliability of FER systems, several datasets have been proposed. However, most of these datasets focus primarily on spatial characteristics, rely on static images, or consist of short videos captured in highly controlled environments. These constraints significantly reduce the applicability of such systems in real-world scenarios. This paper proposes the Facial Biosignals Time–Series Dataset (FBioT), a novel dataset providing temporal descriptors and features extracted from common videos recorded in uncontrolled environments. To automate dataset construction, we propose Visual–Temporal Facial Expression Recognition (VT-FER), a method that stabilizes temporal effects using normalized measurements based on the principles of the Facial Action Coding System (FACS) and generates signature patterns of expression movements for correlation with real-world temporal events. To demonstrate feasibility, we applied the method to create a pilot version of the FBioT dataset. This pilot resulted in approximately 10,000 s of public videos captured under real-world facial motion conditions, from which we extracted 22 direct and virtual metrics representing facial muscle deformations. During this process, we preliminarily labeled and qualified 3046 temporal events representing two emotion classes. As a proof of concept, these emotion classes were used as input for training neural networks, with results summarized in this paper and available in an open-source online repository. Full article

The goji fruit fly, Neoceratitis asiatica, is a major pest on the well-known medicinal plant Lycium barbarum. Dissecting the molecular mechanisms of the oviposition selection of N. asiatica regarding the host plant will help to identify new strategies for pest fly control. However, the molecular mechanism of chemical communication between the goji fruit fly and the host goji remains unclear. Hence, our study found that young goji fruit volatiles induced the oviposition response of gravid female N. asiatica. After N. asiatica was exposed to young goji fruit volatiles, the expression of six chemosensory genes (NasiOBP56h3 and OBP99a1 in the antennae; OBP99a2, OBP99a3 and CSP2 in the legs; and OBP56a in the ovipositor) was significantly upregulated in different organs of female N. asiatica compared with the group without odor treatment according to transcriptome data. Further results of qPCR verification show that the expression levels of the six selected upregulated genes after the flies were exposed to host plant volatiles were mostly consistent with the results of transcriptome data. We concluded that six upregulated genes may be involved in the recognition of young goji fruit volatiles by gravid female N. asiatica. Our study preliminarily identifies young goji fruit volatiles as a key factor in the oviposition behavior of N. asiatica, which will facilitate further studies on the mechanisms of host oviposition selection in N. asiatica. Full article

Feature selection (FS) is a key process in many pattern-recognition tasks, which reduces dimensionality by eliminating redundant or irrelevant features. However, for complex high-dimensional issues, traditional FS methods cannot find the ideal feature combination. To overcome this disadvantage, this paper presents a multispiral whale optimization algorithm (MSWOA) for feature selection. First, an Adaptive Multipopulation merging Strategy (AMS) is presented, which uses exponential variation and individual location information to divide the population, thus avoiding the premature aggregation of subpopulations and increasing candidate feature subsets. Second, a Double Spiral updating Strategy (DSS) is devised to break out of search stagnations by discovering new individual positions continuously. Last, to facilitate the convergence speed, a Baleen neighborhood Exploitation Strategy (BES) which mimics the behavior of whale tentacles is proposed. The presented algorithm is thoroughly compared with six state-of-the-art meta-heuristic methods and six promising WOA-based algorithms on 20 UCI datasets. Experimental results indicate that the proposed method is superior to other well-known competitors in most cases. In addition, the proposed method is utilized to perform feature selection in human fall-detection tasks, and extensive real experimental results further illustrate the superior ability of the proposed method in addressing practical problems. Full article

This study investigates neural activity changes in the medial prefrontal cortex (mPFC) of a lipopolysaccharide (LPS)-induced acute depression mouse model using flexible polymer multichannel electrodes, local field potential (LFP) analysis, and a convolutional neural network-long short-term memory (CNN-LSTM) classification model. LPS treatment effectively induced depressive-like behaviors, including increased immobility in the tail suspension and forced swim tests, as well as reduced sucrose preference. These behavioral outcomes validate the LPS-induced depressive phenotype, providing a foundation for neurophysiological analysis. Flexible polymer-based electrodes enabled the long-term recording of high-quality LFP and spike signals from the mPFC. Time-frequency and power spectral density (PSD) analyses revealed a significant increase in theta band (3–8 Hz) amplitude under depressive conditions. Using theta waveform features extracted via empirical mode decomposition (EMD), we classified depressive states with a CNN-LSTM model, achieving high accuracy in both training and validation sets. This study presents a novel approach for depression state recognition using flexible polymer electrodes, EMD, and CNN-LSTM modeling, suggesting that heightened theta oscillations in the mPFC may serve as a neural marker for depression. Future studies may explore theta coupling across brain regions to further elucidate neural network disruptions associated with depression. Full article

Segregation of household waste is an important means of achieving resource recovery, minimization and harmlessness of waste, which is of great significance in addressing the dilemma of the “rubbish siege”. However, at present, urban community residents still face many challenges in the practice of household waste classification, such as lack of classification knowledge, imperfect classification facilities and weak and persistent classification behavior, which seriously restrict the effective promotion of garbage classification work. In this paper, a model of the factors influencing community residents’ willingness to separate household waste was developed based on the theory of planned behavior and tested by using structural equation modeling (SEM) with a sample of 218 surveys conducted among residents of community X in Nanchang, Jiangxi Province. It was found that urban community residents were generally willing to sort their household waste subjectively. The five factors of waste sorting recognition, intrinsic moral constraints, group behavioral incentives, time and space factors and waste sorting facilities positively influenced urban community residents’ willingness to sort household waste. Government job satisfaction and legal and regulatory constraints had no significant influence on urban community residents’ willingness to sort household waste and did not reach a statistically significant level. Based on this, in the future, we should strengthen public education, enhance group behavioral incentives, improve supporting infrastructure, standardize and improve laws and regulations to improve residents’ willingness to separate household waste and promote the process of urban household waste segregation in China. Full article

In communication systems, the signal and preference for the signal have to match, limiting phenotypic variation. Yet, communication systems evolve, but the mechanisms of how phenotypic variation can come into existence while not disrupting the match are poorly understood. Geographic variation in communication can provide insights into the diversification of these systems. Females of the katydid Neoconocephalus triops use the pulse rate and call structure for call recognition. Using behavioral experiments, we determined preferences for pulse rate at two relevant ambient temperatures and preferences for call structure (continuous, versed) in females from Puerto Rico and Costa Rica. Puerto Rican females had closed preference at both tested temperatures, indicating high selectivity for pulse rate. In contrast, Costa Rican females had a closed preference only at 20 °C; at 25 °C the females were unselective toward higher than natural pulse rates. Additionally, Puerto Rican females were not selective for call structure, whereas Costa Rican females preferred versed calls. It is not clear whether the differences in pulse preference were due to neural constraints or different selective pressures, however, they may facilitate further divergence and reproductive isolation. Importantly, the reduced selectivity for call structure or pulse rate allows calls to display the necessary variation for the communication system to evolve. Full article

The lack of safety awareness and the irregular behavior of chemical laboratory personnel are major contributors to laboratory accidents which pose significant risks to both the safety of laboratory environments and the efficiency of laboratory work. These issues can lead to accidents, equipment damage, and jeopardize personnel health. To address this challenge, this study proposes a method for recognizing irregular behavior in laboratory personnel by utilizing an improved DeepSORT algorithm tailored to the specific characteristics of a chemical laboratory setting. The method first extracts skeletal keypoints from laboratory personnel using the Lightweight OpenPose algorithm to locate individuals. The enhanced DeepSORT algorithm tracks human targets and detects the positions of the relevant objects. Finally, an SKPT-LSTM network was employed to integrate tracking data for behavior recognition. This approach was designed to enhance the detection and prevention of unsafe behaviors in chemical laboratories. The experimental results on a self-constructed dataset demonstrate that the proposed method accurately identifies irregular behaviors, thereby contributing to the reduction in safety risks in laboratory environments. Full article

Moths use pheromones to ensure intraspecific communication. Nevertheless, few studies are focused on both intra- and intersexual communication based on pheromone recognition. Pheromone-binding proteins (PBPs) are generally believed pivotal for male moths in recognizing female pheromones. Our research revealed that PBP1 of Agriphila aeneociliella (AaenPBP1) serves a dual function in both intra- and intersexual pheromone recognition. Here, a total of 20 odorant-binding protein (OBP) family genes from A. aeneociliella were identified and subjected to transcriptional analysis. Among these, AaenPBP1 was primarily highly expressed in the antennae. Competitive fluorescence binding assays and molecular docking analyses demonstrated that AaenPBP1 exhibits a strong binding affinity for the female sex pheromone (Z)-9-Hexadecenyl acetate and the male pheromone 1-Nonanal. Notably, hydrogen bonds were observed between Ser56 and the ligands. The analysis of pheromone components and PBPs in lepidopteran lineage suggested that their strong and precise interactions, shaped by coevolution, may play a crucial role in facilitating reproductive isolation in moths. Our findings provide valuable insight into the functional significance of PBPs in invertebrates and support the development of behavioral regulation tools as part of an integrated pest management strategy targeting crambid pests. Full article

The sleeping and eating behaviors of horses are important indicators of their health. With the development of the modern equine industry, timely monitoring and analysis of these behaviors can provide valuable data for assessing the physiological state of horses. To recognize horse behaviors in stalls, this study builds on the SlowFast algorithm, introducing a novel loss function to address data imbalance and integrating an SE attention module in the SlowFast algorithm’s slow pathway to enhance behavior recognition accuracy. Additionally, YOLOX is employed to replace the original target detection algorithm in the SlowFast network, reducing recognition time during the video analysis phase and improving detection efficiency. The improved SlowFast algorithm achieves automatic recognition of horse behaviors in stalls. The accuracy in identifying three postures—standing, sternal recumbency, and lateral recumbency—is 92.73%, 91.87%, and 92.58%, respectively. It also shows high accuracy in recognizing two behaviors—sleeping and eating—achieving 93.56% and 98.77%. The model’s best overall accuracy reaches 93.90%. Experiments show that the horse behavior recognition method based on the improved SlowFast algorithm proposed in this study is capable of accurately identifying horse behaviors in video data sequences, achieving recognition of multiple horses’ sleeping and eating behaviors. Additionally, this research provides data support for livestock managers in evaluating horse health conditions, contributing to advancements in modern intelligent horse breeding practices. Full article

A considerable number of the physiological functions of extracellular vesicles are conditioned by the protein corona attached to their surface. The composition of this corona is initially defined during their intracellular synthesis, but it can be subsequently modified by interactions with the microenvironment. Here, we evaluated how the corona of small extracellular vesicles exposed to the inflammatory environment generated in acute pancreatitis is modified and what functional changes occur as a result of these modifications. Small extracellular vesicles obtained from a pancreatic cell line were incubated with the ascitic fluid generated in experimental acute pancreatitis in rats. Using proteomic techniques, we detected the appearance of new proteins and an increase the uptake of extracellular vesicles by certain cell types and the response induced in inflammatory cells. The inhibition of different pattern recognition receptors reversed this activation, indicating that some of these effects could be due to binding of damage-associated molecular patterns to the corona. All of this indicates that in pathologies such as acute pancreatitis, characterized by an inflammatory response and intense tissue damage, the microenvironment substantially influences the corona of extracellular vesicles, thus altering their behavior and enhancing their inflammatory activity. Full article