Patent Analytics Based on Feature Vector Space Model: A Case of IoT

The number of approved patents worldwide increases rapidly each year, which requires new patent analytics to efficiently mine the valuable information attached to these patents. Vector space model (VSM) represents documents as high-dimensional vectors, where each dimension corresponds to a unique term. While originally proposed for information retrieval systems, VSM has also seen wide applications in patent analytics, and used as a fundamental tool to map patent documents to structured data. However, VSM method suffers from several limitations when applied to patent analysis tasks, such as loss of sentence-level semantics and curse-of-dimensionality problems. In order to address the above limitations, we propose a patent analytics based on feature vector space model (FVSM), where the FVSM is constructed by mapping patent documents to feature vectors extracted by convolutional neural networks (CNN). The applications of FVSM for three typical patent analysis tasks, i.e., patents similarity comparison, patent clustering, and patent map generation are discussed. A case study using patents related to Internet of Things (IoT) technology is illustrated to demonstrate the performance and effectiveness of FVSM. The proposed FVSM can be adopted by other patent analysis studies to replace VSM, based on which various big data learning tasks can be performed

Emerging trends and focus of research on the relationship between traumatic brain injury and gut microbiota: a visualized study

BackgroundTraumatic brain injury (TBI) is one of the most serious types of trauma and imposes a heavy social and economic burden on healthcare systems worldwide. The development of emerging biotechnologies is uncovering the relationship between TBI and gut flora, and gut flora as a potential intervention target is of increasing interest to researchers. Nevertheless, there is a paucity of research employing bibliometric methodologies to scrutinize the interrelation between these two. Therefore, this study visualized the relationship between TBI and gut flora based on bibliometric methods to reveal research trends and hotspots in the field. The ultimate objective is to catalyze progress in the preclinical and clinical evolution of strategies for treating and managing TBI.MethodsTerms related to TBI and gut microbiota were combined to search the Scopus database for relevant documents from inception to February 2023. Visual analysis was performed using CiteSpace and VOSviewer.ResultsFrom September 1972 to February 2023, 2,957 documents published from 98 countries or regions were analyzed. The number of published studies on the relationship between TBI and gut flora has risen exponentially, with the United States, China, and the United Kingdom being representative of countries publishing in related fields. Research has formed strong collaborations around highly productive authors, but there is a relative lack of international cooperation. Research in this area is mainly published in high-impact journals in the field of neurology. The “intestinal microbiota and its metabolites,” “interventions,” “mechanism of action” and “other diseases associated with traumatic brain injury” are the most promising and valuable research sites. Targeting the gut flora to elucidate the mechanisms for the development of the course of TBI and to develop precisely targeted interventions and clinical management of TBI comorbidities are of great significant research direction and of interest to researchers.ConclusionThe findings suggest that close attention should be paid to the relationship between gut microbiota and TBI, especially the interaction, potential mechanisms, development of emerging interventions, and treatment of TBI comorbidities. Further investigation is needed to understand the causal relationship between gut flora and TBI and its specific mechanisms, especially the “brain-gut microbial axis.

Patent analytics based on feature Vector Space Model: a case of IoT

The number of approved patents worldwide increases rapidly each year, which requires new patent analytics to efficiently mine the valuable information attached to these patents. The vector space model (VSM) represents documents as high-dimensional vectors, where each dimension corresponds to a unique term. While originally proposed for information retrieval systems, VSM has also seen wide applications in patent analytics and is used as a fundamental tool to map patent documents to structured data. However, the VSM method suffers from several limitations when applied to patent analysis tasks, such as loss of sentence-level semantics and curse-of-dimensionality problems. In order to address the above limitations, we propose patent analytics based on feature VSM (FVSM), where the FVSM is constructed by mapping patent documents to feature the vectors extracted by convolutional neural networks (CNNs). The applications of FVSM for three typical patent analysis tasks, i.e., patents similarity comparison, patent clustering, and patent map generation, are discussed. A case study using patents related to the Internet of Things (IoT) technology is illustrated to demonstrate the performance and effectiveness of FVSM. The proposed FVSM can be adopted by other patent analysis studies to replace VSM, based on which various big data learning tasks can be performed

Federated reinforcement learning: techniques, applications, and open challenges

This paper presents a comprehensive survey of federated reinforcement learning (FRL), an emerging and promising field in reinforcement learning (RL). Starting with a tutorial of federated learning (FL) and RL, we then focus on the introduction of FRL as a new method with great potential by leveraging the basic idea of FL to improve the performance of RL while preserving data-privacy. According to the distribution characteristics of the agents in the framework, FRL algorithms can be divided into two categories, i.e., Horizontal Federated Reinforcement Learning and vertical federated reinforcement learning (VFRL). We provide the detailed definitions of each category by formulas, investigate the evolution of FRL from a technical perspective, and highlight its advantages over previous RL algorithms. In addition, the existing works on FRL are summarized by application fields, including edge computing, communication, control optimization, and attack detection. Finally, we describe and discuss several key research directions that are crucial to solving the open problems within FRL

The Role of Rheological Additives on Fresh and Hardened Properties of Cemented Paste Backfill

Cemented paste backfill (CPB) has become a significant structural material in most mines across the world. In this study, the effects of chemical rheological additives including viscosity modifying agent (i.e., polyacrylamide) and polycarboxylate superplasticizer (PCE) on fresh and hardened properties of CPB with different water-to-solid (W/S) ratios and water-to-cement (W/C) ratios were investigated. The microstructure of CPB specimens was also characterized by X-ray diffraction (XRD), thermogravimetric analysis (TGA), scanning electron microscopy (SEM), and backscattered electron image (SEM-BSE). The obtained results indicate that PAM (polyacrylamide) dosage and W/S are the most significant parameters influencing the workability of fresh CPB mixtures. For the hardened CPB specimens, the decreasing W/S ratio leads to higher flexural and compressive strength values and lower dry shrinkage strains. The interfacial transition zone (ITZ) between the cement matrix and the tailings sand was also observed to be narrower, with fewer micro cracks and capillary pores. Meanwhile, the existence of PAM decreased the number of hydration products and retarded the hydration reaction. Overall, the CPBs with high W/C ratios (i.e., 1.0 and 1.2), low W/S ratios (i.e., 0.3), and moderate amounts of rheological additives (i.e., 0.05% PAM and 1.0% PCE) have excellent fresh and hardened properties. The findings of this study contribute to better optimization of CPB mixtures in backfill construction, bringing benefits of low costs and low environmental impacts

Spectral Camouflage Characteristics and Recognition Ability of Targets Based on Visible/Near-Infrared Hyperspectral Images

Hyperspectral imaging can simultaneously obtain the spatial morphological information of the ground objects and the fine spectral information of each pixel. Through the quantitative analysis of the spectral characteristics of objects, it can complete the task of classification and recognition of ground objects. The appearance of imaging spectrum technology provides great advantages for military target detection and promotes the continuous improvement of military reconnaissance levels. At the same time, spectral camouflage materials and methods that are relatively resistant to hyperspectral reconnaissance technology are also developing rapidly. In order to study the reconnaissance effect of visible/near-infrared hyperspectral images on camouflage targets, this paper analyzes the spectral characteristics of different camouflage targets using the hyperspectral images obtained in the visible and near-infrared bands under natural conditions. Two groups of experiments were carried out. The first group of experiments verified the spectral camouflage characteristics and camouflage effects of different types of camouflage clothing with grassland as the background; the second group of experiments verified the spectral camouflage characteristics and camouflage effects of different types of camouflage paint sprayed on boards and steel plates. The experiment shows that the hyperspectral image based on the near-infrared band has a good reconnaissance effect for different camouflage targets, and the near-infrared band is an effective “window” band for detecting and distinguishing true and false targets. However, the stability of the visible/near-infrared band detection for the target identification under camouflage paint is poor, and it is difficult to effectively distinguish the object materials under the same camouflage paint. This research confirms the application ability of detection based on the visible/near-infrared band, and points out the direction for the development of imaging detectors and camouflage materials in the future

Spectral Camouflage Characteristics and Recognition Ability of Targets Based on Visible/Near-Infrared Hyperspectral Images

Hyperspectral imaging can simultaneously obtain the spatial morphological information of the ground objects and the fine spectral information of each pixel. Through the quantitative analysis of the spectral characteristics of objects, it can complete the task of classification and recognition of ground objects. The appearance of imaging spectrum technology provides great advantages for military target detection and promotes the continuous improvement of military reconnaissance levels. At the same time, spectral camouflage materials and methods that are relatively resistant to hyperspectral reconnaissance technology are also developing rapidly. In order to study the reconnaissance effect of visible/near-infrared hyperspectral images on camouflage targets, this paper analyzes the spectral characteristics of different camouflage targets using the hyperspectral images obtained in the visible and near-infrared bands under natural conditions. Two groups of experiments were carried out. The first group of experiments verified the spectral camouflage characteristics and camouflage effects of different types of camouflage clothing with grassland as the background; the second group of experiments verified the spectral camouflage characteristics and camouflage effects of different types of camouflage paint sprayed on boards and steel plates. The experiment shows that the hyperspectral image based on the near-infrared band has a good reconnaissance effect for different camouflage targets, and the near-infrared band is an effective “window” band for detecting and distinguishing true and false targets. However, the stability of the visible/near-infrared band detection for the target identification under camouflage paint is poor, and it is difficult to effectively distinguish the object materials under the same camouflage paint. This research confirms the application ability of detection based on the visible/near-infrared band, and points out the direction for the development of imaging detectors and camouflage materials in the future

Status and factors related to post‐traumatic growth in continuous ambulatory peritoneal dialysis: A multi‐centre study

AimTo investigate the extent of post‐traumatic growth, and the correlation between post‐traumatic growth and self‐perceived stress, post‐traumatic growth and self‐perceived burden among CAPD patients.DesignA cross‐sectional study.MethodsThis was a multi‐centre study including 752 patients from 44 hospitals. Self‐perceived stress, self‐perceived burden and post‐traumatic growth were measured using the post‐traumatic growth inventory (PTGI), the Chinese version of the perceived stress questionnaire (CPSQ) and the self‐perceived burden scale (SPBS). A multiple stepwise regression analysis was fit with the total PTGI score as the outcome of interest.ResultsPatients concurrently experienced post‐traumatic growth and stress following peritoneal dialysis. The initiation of patients’ education level, employment status and self‐perceived stress were all found to relate to growth among Chinese CAPD patients. There was not sufficient evidence to suggest that self‐perceived burden was related to experiencing growth.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/171193/1/nop21096.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/171193/2/nop21096_am.pd