A Novel Ranging Method Based on RSSI

AbstractThe ranging technique based on RSSI is often used in localization of wireless sensor network (WSN). Due to external interferences, the RSSI fluctuates a lot and then a novel ranging method is presented. It establishes a database of mapping relationship between the RSSI and the distance range, then the distance between the transmitter and the receiver can be drawn by summing weighted of the distance spaces obtained through querying the mapping database. Simulation results show that,this method can eliminate the negative effects on RSSI fluctuation as much as possible and provides high ranging precision. It's no environmental limitations and can be applied in range-based localization technique with high value

Synthesis of Alkyl Substituted Dicyclohexano-18-crown-6 Homologues for Strontium Extraction in HNO3 Media

AbstractA series of dicyclohexano-18-crown-6 (DCH18C6) homologues containing different alkyl substituents were synthesized for a comparative study of the extraction ability towards strontium. The synthesis and the structure characterization of the intermediates and the products were detailed. The crown ether homologues were labeled as CX-DCH18C6 (X=3∼7), where the X represents the number of the carbon atoms in the alkyl substituents. The extraction ability of the CX-DCH18C6 samples towards strontium in solvent extraction system was investigated. The substituent effect of the samples was discussed, and the factors affecting the separation such as solvent, acidity and initial metal concentration were examined

Karst collapse risk zonation and evaluation in Wuhan, China based on analytic hierarchy process, logistic regression, and insar angular distortion approaches

The current study presents a detailed assessment of risk zones related to karst collapse in Wuhan by analytical hierarchy process (AHP) and logistic regression (LR) models. The results showed that the LR model was more accurate with an area under the receiver operating characteristic (ROC) curve of 0.911 compared to 0.812 derived from the AHP model. Both models performed well in identifying high-risk zones with only a 3% discrepancy in area. However, for the medium-and low-risk classes, although the spatial distribution of risk zoning results were similar between two approaches, the spatial extent of the risk areas varied between final models. The reliability of both methods were reduced significantly by excluding the InSAR-based ground subsidence map from the analysis, with the karst collapse presence falling into the high-risk zone being reduced by approximately 14%, and karst collapse absence falling into the karst area being increased by approximately 6.5% on the training samples. To evaluate the practicality of using only results from ground subsidence maps for the risk zonation, the results of AHP and LR are compared with a weighted angular distortion (WAD) method for karst risk zoning in Wuhan. We find that the areas with relatively large subsidence horizontal gradient values within the karst belts are generally spatially consistent with high-risk class areas identified by the AHP-and LR-based approaches. However, the WAD-based approach cannot be used alone as an ideal karst collapse risk assessment model as it does not include geological and natural factors into the risk zonation. © 2021 by the authors. Licensee MDPI, Basel, Switzerland

Beyond the Obvious: Evaluating the Reasoning Ability In Real-life Scenarios of Language Models on Life Scapes Reasoning Benchmark~(LSR-Benchmark)

This paper introduces the Life Scapes Reasoning Benchmark (LSR-Benchmark), a novel dataset targeting real-life scenario reasoning, aiming to close the gap in artificial neural networks' ability to reason in everyday contexts. In contrast to domain knowledge reasoning datasets, LSR-Benchmark comprises free-text formatted questions with rich information on real-life scenarios, human behaviors, and character roles. The dataset consists of 2,162 questions collected from open-source online sources and is manually annotated to improve its quality. Experiments are conducted using state-of-the-art language models, such as gpt3.5-turbo and instruction fine-tuned llama models, to test the performance in LSR-Benchmark. The results reveal that humans outperform these models significantly, indicating a persisting challenge for machine learning models in comprehending daily human life

Apatinib inhibits tumor growth and angiogenesis in PNET models

Angiogenesis has a pivotal role in the growth and metastasis of pancreatic neuroendocrine tumors (PNETs). Apatinib inhibits angiogenesis as a highly selective KDR inhibitor and has been used to treat advanced gastric cancer and malignancies in clinical settings. However, the efficacy of apatinib in PNETs remains unclear. The aim of this study was to compare the antitumor efficacy of apatinib with that of the standard PNET drug sunitinib in our subcutaneous and liver metastasis models of insulinoma and non-functional PNET. Our results revealed that apatinib had a generally comparable or even superior antitumor effect to that of sunitinib on primary PNET, and it inhibited angiogenesis without directly causing tumor cell cytotoxicity. Apatinib inhibited the tumor in a dose-dependent manner, and the high dose was well tolerated in mice. We also found that the apatinib efficacy in liver metastasis models was cell-type (disease) selective. Although apatinib efficiently inhibited INR1G9-represented non-functional PNET liver metastasis, it led to the emergence of a hypoxic area in the INS-1-represented insulinoma and promoted liver metastasis. Our study demonstrated that apatinib has promise for clinical applications in certain malignant PNETs, and the application of anti-angiogenesis drugs to benign insulinomas may require careful consideration