Semantic reasoning on the edge of internet of things

Abstract. The Internet of Things (IoT) is a paradigm where physical objects are connected with each other with identifying, sensing, networking and processing capabilities over the Internet. Millions of new devices will be added into IoT network thus generating huge amount of data. How to represent, store, interconnect, search, and organize information generated by IoT devices become a challenge. Semantic technologies could play an important role by encoding meaning into data to enable a computer system to possess knowledge and reasoning. The vast amount of devices and data are also challenges. Edge Computing reduces both network latency and resource consumptions by deploying services and distributing computing tasks from the core network to the edge. We recognize four challenges from IoT systems. First the centralized server may generate long latency because of physical distances. Second concern is that the resource-constrained IoT devices have limited computing ability in processing heavy tasks. Third, the data generated by heterogeneous devices can hardly be understood and utilized by other devices or systems. Our research focuses on these challenges and provide a solution based on Edge computing and semantic technologies. We utilize Edge computing and semantic reasoning into IoT. Edge computing distributes tasks to the reasoning devices, which we call the Edge nodes. They are close to the terminal devices and provide services. The newly added resources could balance the workload of the systems and improve the computing capability. We annotate meaning into the data with Resource Description Framework thus providing an approach for heterogeneous machines to understand and utilize the data. We use semantic reasoning as a general purpose intelligent processing method. The thesis work focuses on studying semantic reasoning performance in IoT system with Edge computing paradigm. We develop an Edge based IoT system with semantic technologies. The system deploys semantic reasoning services on Edge nodes. Based on IoT system, we design five experiments to evaluate the performance of the integrated IoT system. We demonstrate how could the Edge computing paradigm facilitate IoT in terms of data transforming, semantic reasoning and service experience. We analyze how to improve the performance by properly distributing the task for Cloud and Edge nodes. The thesis work result shows that the Edge computing could improve the performance of the semantic reasoning in IoT

lncRNA LOC100911717-targeting GAP43-mediated sympathetic remodeling after myocardial infarction in rats

ObjectiveSympathetic remodeling after myocardial infarction (MI) is the primary cause of ventricular arrhythmias (VAs), leading to sudden cardiac death (SCD). M1-type macrophages are closely associated with inflammation and sympathetic remodeling after MI. Long noncoding RNAs (lncRNAs) are critical for the regulation of cardiovascular disease development. Therefore, this study aimed to identify the lncRNAs involved in MI and reveal a possible regulatory mechanism.Methods and resultsM0- and M1-type macrophages were selected for sequencing and screened for differentially expressed lncRNAs. The data revealed that lncRNA LOC100911717 was upregulated in M1-type macrophages but not in M0-type macrophages. In addition, the lncRNA LOC100911717 was upregulated in heart tissues after MI. Furthermore, an RNA pull-down assay revealed that lncRNA LOC100911717 could interact with growth-associated protein 43 (GAP43). Essentially, immunofluorescence assays and programmed electrical stimulation demonstrated that GAP43 expression was suppressed and VA incidence was reduced after lncRNA LOC100911717 knockdown in rat hearts using an adeno-associated virus.ConclusionsWe observed a novel relationship between lncRNA LOC100911717 and GAP43. After MI, lncRNA LOC100911717 was upregulated and GAP43 expression was enhanced, thus increasing the extent of sympathetic remodeling and the frequency of VA events. Consequently, silencing lncRNA LOC100911717 could reduce sympathetic remodeling and VAs

Renal effects and safety between Asian and non‐Asian chronic kidney disease and type 2 diabetes treated with nonsteroidal mineralocorticoid antagonists

Abstract Background Asians bear a heavier burden of chronic kidney disease (CKD), a common comorbidity of type 2 diabetes mellitus (T2DM), than non‐Asians. Nonsteroidal mineralocorticoid receptor antagonists (MRAs) have garnered attention for their potential advantages in renal outcomes. Nevertheless, the impact on diverse ethnic groups remains unknown. Methods The PubMed, Embase, Cochrane Library, China National Knowledge Infrastructure (CNKI), Wanfang database, and clinical trial registries were searched through August 2023 with the following keywords: nonsteroidal MRAs (finerenone, apararenone, esaxerenone, AZD9977, KBP‐5074), CKD, T2DM, and randomized controlled trial (RCT). A random effects model was used to calculate overall effect sizes. Results Seven RCTs with 14 997 participants were enrolled. Nonsteroidal MRAs reduced urinary albumin to creatinine ratio (UACR) significantly more in Asians than non‐Asians: (weighted mean difference [WMD], −0.59, 95% CI, −0.73 to −0.45, p  .05). Regarding systolic blood pressure (SBP), nonsteroidal MRAs had a better antihypertension performance in Asians (WMD, −5.12, 95% CI, −5.84 to −4.41, p < .01) compared to non‐Asians (WMD, −3.64, 95% CI, −4.38 to −2.89, p < .01). A higher incidence of hyperkalemia and eGFR decrease ≥30% was found in Asians than non‐Asians (p < .01). Conclusions Nonsteroidal MRAs exhibited significant renal benefits by decreasing UACR and lowering SBP in Asian than that of non‐Asian patients with CKD and T2DM, without increase of adverse events except hyperkalemia and eGFR decrease ≥30%

Transferring remote ontologies to the edge of Internet of Things Systems

Abstract Edge computing paradigm allows computation to be moved from the central high powered Cloud or data center to the edge of the network. This paradigm often enables more efficient data processing near its source and sends only the data and knowledge that have value over the network. Our study focuses on performing semantic reasoning at the edge computing devices, which requires transferring ontologies to the edge devices. This paper presents different representations for transferring Web Ontology language (OWL) version 2 ontologies to the edge. We evaluate different representations in an experimental IoT system with edge nodes and compare lengths of different syntaxes and their computation effort of building models in Cloud and edge computing devices in terms of processing time

Evaluating Real Driving Emissions of Compressed Natural Gas Taxis in Chongqing, China—A Typical Mountain Cities

Compressed natural gas (CNG) taxis represent the most ubiquitous and dynamically active passenger vehicles in urban settings. The pollutant emission characteristics of in-use CNG taxis driving on a typical mountain city before and after three-way catalyst (TWC) replacement was examined using a modular on-board portable emissions measurement system (PEMS), the OBS-ONE developed by Horiba. The results showed that the exhaust NO of CNG taxis equipped with deactivation TWC exceeded the emission limits, even higher than gasoline vehicles. The high emission rate of CNG taxis is mainly concentrated on road slopes between a 2% and 6% gradient and a deceleration rate in the interval of [0.5, 4], respectively, which results in higher emissions from CNG taxis traveling in the mountain city of Chongqing than other cities and vehicles. Moreover, the pollutant emission rates of the in-use CNG taxis were highly correlated with the velocity and the vehicle specific power (VSP). After a new TWC replacement, the emission factors of carbon monoxide (CO), total hydrocarbons (THC), nitrogen oxides (NOx), and particle number (PN) decreased by 85.21–89.11%, 68.71–85.49%, 60.91–81.11%, and 62.26–68.39%, respectively. Our results will provide guidance for urban environments to carry out the comprehensive management of in-use vehicles and emphasize the importance of TWC replacement for CNG taxis

Distribution of semantic reasoning on the edge of Internet of Things

Abstract Semantics associates meaning with Internet of Things (IoT) data and facilitates the development of intelligent IoT applications and services. However, the big volume of the data generated by IoT devices and resource limitations of these devices have given rise to challenges for applying semantic technologies. In this article, we present Cloud and edge based IoT architectures for semantic reasoning. We report three experiments that demonstrate how edge computing can facilitate IoT systems in terms of data transfer and semantic reasoning. We also analyze how distributing reasoning tasks between the Cloud and edge devices affects system performance