A Survey of Coverage Problems in Wireless Sensor Networks

Coverage problem is an important issue in wireless sensor networks, which has a great impact on the performance of wireless sensor networks. Given a sensor network, the coverage problem is to determine how well the sensing field is monitored or tracked by sensors. In this paper, we classify the coverage problem into three categories: area coverage, target coverage, and barrier coverage, give detailed description of different algorithms belong to these three categories. Moreover, we specify the advantages and disadvantages of the existing classic algorithms, which can give a useful direction in this area

Neural Temporal Dynamics of Facial Emotion Processing: Age Effects and Relationship to Cognitive Function

This study used event-related potentials (ERPs) to investigate the effects of age on neural temporal dynamics of processing task-relevant facial expressions and their relationship to cognitive functions. Negative (sad, afraid, angry, and disgusted), positive (happy), and neutral faces were presented to 30 older and 31 young participants who performed a facial emotion categorization task. Behavioral and ERP indices of facial emotion processing were analyzed. An enhanced N170 for negative faces, in addition to intact right-hemispheric N170 for positive faces, was observed in older adults relative to their younger counterparts. Moreover, older adults demonstrated an attenuated within-group N170 laterality effect for neutral faces, while younger adults showed the opposite pattern. Furthermore, older adults exhibited sustained temporo-occipital negativity deflection over the time range of 200–500 ms post-stimulus, while young adults showed posterior positivity and subsequent emotion-specific frontal negativity deflections. In older adults, decreased accuracy for labeling negative faces was positively correlated with Montreal Cognitive Assessment Scores, and accuracy for labeling neutral faces was negatively correlated with age. These findings suggest that older people may exert more effort in structural encoding for negative faces and there are different response patterns for the categorization of different facial emotions. Cognitive functioning may be related to facial emotion categorization deficits observed in older adults. This may not be attributable to positivity effects: it may represent a selective deficit for the processing of negative facial expressions in older adults

Structural and functional connectivity of the whole brain and subnetworks in individuals with mild traumatic brain injury:Predictors of patient prognosis

Patients with mild traumatic brain injury have a diverse clinical presentation, and the underlying pathophysiology remains poorly understood. Magnetic resonance imaging is a non-invasive technique that has been widely utilized to investigate neurobiological markers after mild traumatic brain injury. This approach has emerged as a promising tool for investigating the pathogenesis of mild traumatic brain injury. Graph theory is a quantitative method of analyzing complex networks that has been widely used to study changes in brain structure and function. However, most previous mild traumatic brain injury studies using graph theory have focused on specific populations, with limited exploration of simultaneous abnormalities in structural and functional connectivity. Given that mild traumatic brain injury is the most common type of traumatic brain injury encountered in clinical practice, further investigation of the patient characteristics and evolution of structural and functional connectivity is critical. In the present study, we explored whether abnormal structural and functional connectivity in the acute phase could serve as indicators of longitudinal changes in imaging data and cognitive function in patients with mild traumatic brain injury. In this longitudinal study, we enrolled 46 patients with mild traumatic brain injury who were assessed within 2 weeks of injury, as well as 36 healthy controls. Resting-state functional magnetic resonance imaging and diffusion-weighted imaging data were acquired for graph theoretical network analysis. In the acute phase, patients with mild traumatic brain injury demonstrated reduced structural connectivity in the dorsal attention network. More than 3 months of followup data revealed signs of recovery in structural and functional connectivity, as well as cognitive function, in 22 out of the 46 patients. Furthermore, better cognitive function was associated with more efficient networks. Finally, our data indicated that small-worldness in the acute stage could serve as a predictor of longitudinal changes in connectivity in patients with mild traumatic brain injury. These findings highlight the importance of integrating structural and functional connectivity in understanding the occurrence and evolution of mild traumatic brain injury. Additionally, exploratory analysis based on subnetworks could serve a predictive function in the prognosis of patients with mild traumatic brain injury.</p

The relationship between the interactive behavior of industry–university–research subjects and the cooperative innovation performance: The mediating role of knowledge absorptive capacity

IntroductionIndustry–university–research cooperation innovation, which is often characterized by resource complementarity and the sharing technology, has become one of the most preferred innovation cooperation methods for enterprises. However, various problems still occur in the process of industry–university–research cooperations, such as poor innovation performance and difficulty in sustaining cooperation. Existing studies mostly focus on the macroscopic perspectives of geographic location, cooperation scale, concentration, and diversification of industry–university–research cooperation subjects, and fail to explore the microscopic behavioral mechanisms.MethodsTherefore, this paper establishes the interactive behavior of industry–university–research subjects and defines its concepts and dimensions in an attempt to provide a mechanism for improving the cooperative innovation performance of industry–university–research from the micro-behavioral perspective. On the basis of theoretical analysis, this paper develops a model of the relationship between cooperative trust, cooperative communication, and cooperative innovation performance for interactive behavior, while exploring the mediating role of knowledge absorptive capacity. The model was validated by stepwise regression using data from 325 questionnaires.ResultsThe paper found that cooperative trust and cooperative communication in the cooperative interactive behavior of industry–university–research positively contribute to the improvement of cooperative innovation performance. Knowledge absorptive capacity plays a partially mediating role between the interactive behaviors and cooperative innovation performance. More specifically, knowledge absorptive capacity partially mediates cooperative communication in cooperative innovation performance and completely mediates cooperative trust in cooperative innovation performance. The results are largely consistent with the results of the heterogeneity analysis of the sample.DiscussionThis paper not only explains why the cooperative innovation performance of industry–university–research is poor from the perspective of interactive behavior, but also enriches the research perspective of industry–university–research and provides theoretical support for enterprises to optimize the relationship between industry, university, and research institutes

ChatRadio-Valuer: A Chat Large Language Model for Generalizable Radiology Report Generation Based on Multi-institution and Multi-system Data

Radiology report generation, as a key step in medical image analysis, is critical to the quantitative analysis of clinically informed decision-making levels. However, complex and diverse radiology reports with cross-source heterogeneity pose a huge generalizability challenge to the current methods under massive data volume, mainly because the style and normativity of radiology reports are obviously distinctive among institutions, body regions inspected and radiologists. Recently, the advent of large language models (LLM) offers great potential for recognizing signs of health conditions. To resolve the above problem, we collaborate with the Second Xiangya Hospital in China and propose ChatRadio-Valuer based on the LLM, a tailored model for automatic radiology report generation that learns generalizable representations and provides a basis pattern for model adaptation in sophisticated analysts' cases. Specifically, ChatRadio-Valuer is trained based on the radiology reports from a single institution by means of supervised fine-tuning, and then adapted to disease diagnosis tasks for human multi-system evaluation (i.e., chest, abdomen, muscle-skeleton, head, and maxillofacial $\&$ neck) from six different institutions in clinical-level events. The clinical dataset utilized in this study encompasses a remarkable total of \textbf{332,673} observations. From the comprehensive results on engineering indicators, clinical efficacy and deployment cost metrics, it can be shown that ChatRadio-Valuer consistently outperforms state-of-the-art models, especially ChatGPT (GPT-3.5-Turbo) and GPT-4 et al., in terms of the diseases diagnosis from radiology reports. ChatRadio-Valuer provides an effective avenue to boost model generalization performance and alleviate the annotation workload of experts to enable the promotion of clinical AI applications in radiology reports

Connected Target Coverage for Fully Data Gathering in Wireless Sensor Networks

Abstract: Coverage problem is an important issue in wireless sensor networks, which has a great impact on the performance of wireless sensor networks. Many researches in this area only focus on how to cover all the targets in the network, which ignore considering the problem of how to transmit the sensing data to the sink. In this paper, we propose an energy-effective connected target coverage algorithm named ECTCG to guarantee the sensed data be effectively gathered in mobile wireless sensor networks. We theoretically analyze ECTCG algorithm and conduct extensive simulations to evaluate its performance. Simulation results show that ECTCG outperforms another existing algorithm CWGC by effectively prolonging the network lifetime. Copyright © 2013 IFSA

A Delay-Sensitive Connected Target Coverage Algorithm in Wireless Sensor Networks

Abstract: The issue of guaranteeing the network QoS (target coverage, network connectivity, etc.) to maximize the lifetime in wireless sensor networks (WSNs) has been widely studied in recent years. In some delaysensitive sensor networks (fires, gas leaks, explosions, etc.), sensor nodes must transmit their data to sink within a limited period to monitor the critical physical environment. In order to study connected target coverage in such delay-sensitive sensor networks, we are the first one to propose the Delay-Constraint Connected Target Coverage (DCCTC) problem and study the following works specifically: 1) we model DCCTC problem as a Height Limited Maximum Cover Tree (HLMCT) problem, and then give an upper bound on the network lifetime for HLMCT problem; 2) we develop a fast heuristic algorithm, named HLCWGC; 3) we study the performance of HLCWGC algorithm by comparing it with other existing algorithms improved to solve HLMCT problem. Simulation results show that HLCWGC algorithm can achieve a better performance than other improved algorithms in the delay-sensitive sensor networks. Copyright © 2014 IFSA Publishing, S. L. Key words: Connected target coverage, Lifetime maximization, Delay constraint. 1

Porous Single-Crystalline Monolith to Enhance Catalytic Activity and Stability

Engineering the catalytic activity and stability of materials would require the identification of the structural features that can tailor active sites at surfaces. Porous single crystals combine the ordered lattice structures and disordered interconnected pores, and they would therefore provide the advantages of precise structure features to identify and engineer the active sites at surfaces. Herein, we fabricate porous single-crystalline vanadium nitride (VN) at centimeter scale and further dope Fe (Fe0.1V0.9N) and Co (Co0.1V0.9N) in lattice to engineer the active sites at surface. We demonstrate that the active surface is composed of unsaturated coordination of V-N, Fe-N, and Co-N structures which lead to the generation of high-density active sites at the porous single-crystalline monolith surface. The interconnected pores aid the pore-enhanced fluxion to facilitate species diffusion in the porous architectures. In the nonoxidative dehydrogenation of ethane to ethylene, we demonstrate the outstanding performance with ethane conversion of 36% and ethylene selectivity of 99% at 660°C. Remarkably stability as a result of their single-crystalline structure, the monoliths achieve the outstanding performance without degradation being observed even after 200 hours of a continuous operation in a monolithic reactor. This work not only demonstrates the effective structural engineering to simultaneously enhance the stability and overall performance for practically useful catalytic materials but also provide a new route for the element doping of porous single crystals at large scale for the potential application in other fields

RCSS: A Real-Time On-Demand Charging Scheduling Scheme for Wireless Rechargeable Sensor Networks

With the emergence of edge computing, a large number of devices such as sensor nodes have been deployed in the edge network to sense and process data. However, how to provide real-time on-demand energy for these edge devices is a new challenge issue of edge networks. In real-world applications of edge computing, sensor nodes usually have different task burdens due to the environmental impact, which results in a dynamic change of the energy consumption rate at different nodes. Therefore, the traditional periodical charging mode cannot meet the nodes charging demand that have dynamic energy consumption. In this paper, we propose a real-time on-demand charging scheduling scheme (RCSS) under the condition of limited mobile charger capacity. In the process of building the charging path, RCSS adequately considers the dynamic energy consumption of different node, and puts forward the next node selection algorithm. At the same time, a method to determine the feasibility of charging circuit is also proposed to ensure the charging efficiency. During the charging process, RCSS is based on adaptive charging threshold to reduce node mortality. Compared with existing approaches, the proposed RCSS achieves better performance in the number of survival nodes, the average service time and charging efficiency