Investigating factors influencing oil volatility: a GARCH-MIDAS model analysis

This study explores the main factors influencing international oil price fluctuations, selecting five influential variables: the consumer price index (CPI), industrial production index (IPI), global rig count (ADU), economic policy uncertainty index (EPU), and geopolitical risk index (GRI) based on previous literature. Employing the GARCH-MIDAS model, this research analyzes comparative effects on WTI international oil prices. Our findings highlight the varying degrees of influence, with IPI showing a stronger impact and EPU indicating broader economic implications. The GRI index responds primarily to specific geopolitical events with delayed fluctuations. Our study’s novelty lies in the empirical investigation using the GARCH-MIDAS model, offering valuable insights for policymakers to manage oil price volatility effectively, particularly by addressing economic policy uncertainty as a critical factor

Behaviour and design of duplex stainless steel bolted connections failing in block shear

Duplex stainless steel (DSS) is an emerging construction material for structural engineering, which is featured with high mechanical strength and superior corrosion resistance. Compared with considerable research on DSS structural members, available research is relatively limited for structural joints/connections between these members. In line with this concern, this paper presents a comprehensive experimental and numerical study of duplex stainless steel bolted connections (DSSBCs), focusing on the behaviour and design related to block shear failure. Eleven specimens are tested to investigate the effect of different bolt arrangements on the block shear behaviour. Furthermore, a detailed numerical study was performed as a supplement to the experimental tests, where the anisotropic mechanical properties of DSS are considered in the finite element modelling. Based on the test and analysis results, it is found that the block shear failure mode of DSSBCs resembles that of carbon steel bolted connections, which can be characterised as necking of the tensile section and yielding of the shear sections. Using the experimental and numerical data obtained in this and previous studies, the applicability of various block shear design methods to stainless steel bolted connections is assessed. An updated design method is proposed for predicting the block shear capacity of duplex and austenitic stainless steel bolted connections. A proper partial safety factor/resistance factor is suggested for the proposed method based on the results of reliability analyses

On the Robustness of Reading Comprehension Models to Entity Renaming

We study the robustness of machine reading comprehension (MRC) models to entity renaming -- do models make more wrong predictions when the same questions are asked about an entity whose name has been changed? Such failures imply that models overly rely on entity information to answer questions, and thus may generalize poorly when facts about the world change or questions are asked about novel entities. To systematically audit this issue, we present a pipeline to automatically generate test examples at scale, by replacing entity names in the original test sample with names from a variety of sources, ranging from names in the same test set, to common names in life, to arbitrary strings. Across five datasets and three pretrained model architectures, MRC models consistently perform worse when entities are renamed, with particularly large accuracy drops on datasets constructed via distant supervision. We also find large differences between models: SpanBERT, which is pretrained with span-level masking, is more robust than RoBERTa, despite having similar accuracy on unperturbed test data. We further experiment with different masking strategies as the continual pretraining objective and find that entity-based masking can improve the robustness of MRC models.Comment: Accepted to NAACL 202

Effectiveness and safety of Danshen injections in treatment of cardiac failure: a network meta-analysis

Objective: The purpose of this network meta-analysis (NMA) was to compare the therapeutic effects of various Danshen (Salvia miltiorrhiza Bunge [Lamiaceae; Salviae miltiorrhizae radix et rhizoma]) injections on heart failure to determine the optimal Danshen injection combined with conventional treatment.Methods: 8 databases were searched from the inception of these databases to May 2023 to collect randomized controlled trials (RCTs) on the effectiveness and safety of Danshen injections in the treatment of heart failure. This NMA was performed using Stata 16.0 software and R 4.1.3 software.Results: A total of 24 RCTs involving 2,186 subjects were included. The intervention group received Danshen injections plus conventional treatment, involving the following 7 Danshen injections. The results of the NMA showed that Compound Danshen injection + Common (SUCRA: 79.6%) and Sodium tanshinone ⅡA sulfonate injection + Common (SUCRA: 78.0%) exhibited higher total effective rates. Sodium tanshinone ⅡA sulfonate injection + Common (SUCRA: 94.3%) and Danshen injection + Common (SUCRA: 68.2%) were superior to other traditional Chinese medicines in improving left ventricular ejection fraction (LVEF). Danshen injection + Common (SUCRA: 99.9%) and Shenxiong glucose injection + Common (SUCRA: 77.2%) were the most effective in reducing brain natriuretic peptide (BNP). In addition, compared with conventional treatment, all Danshen injections did not increase the risk of adverse reactions.Conclusion: Current evidence shows that all seven Danshen injections are effective for heart failure. Due to the limited quantity and quality of the included studies, our findings need to be verified by more high-quality studies

Biological Immune System Applications on Mobile Robot for Disabled People

To improve the service quality of service robots for the disabled, immune system is applied on robot for its advantages such as diversity, dynamic, parallel management, self-organization, and self-adaptation. According to the immune system theory, local environment condition sensed by robot is considered an antigen while robot is regarded as B-cell and possible node as antibody, respectively. Antibody-antigen affinity is employed to choose the optimal possible node to ensure the service robot can pass through the optimal path. The paper details the immune system applications on service robot and gives experimental results

Robust Transceiver Design for Covert Integrated Sensing and Communications With Imperfect CSI

We propose a robust transceiver design for a covert integrated sensing and communications (ISAC) system with imperfect channel state information (CSI). Considering both bounded and probabilistic CSI error models, we formulate worst-case and outage-constrained robust optimization problems of joint trasceiver beamforming and radar waveform design to balance the radar performance of multiple targets while ensuring communications performance and covertness of the system. The optimization problems are challenging due to the non-convexity arising from the semi-infinite constraints (SICs) and the coupled transceiver variables. In an effort to tackle the former difficulty, S-procedure and Bernstein-type inequality are introduced for converting the SICs into finite convex linear matrix inequalities (LMIs) and second-order cone constraints. A robust alternating optimization framework referred to alternating double-checking is developed for decoupling the transceiver design problem into feasibility-checking transmitter- and receiver-side subproblems, transforming the rank-one constraints into a set of LMIs, and verifying the feasibility of beamforming by invoking the matrix-lifting scheme. Numerical results are provided to demonstrate the effectiveness and robustness of the proposed algorithm in improving the performance of covert ISAC systems

CollabCoder: A GPT-Powered Workflow for Collaborative Qualitative Analysis

The Collaborative Qualitative Analysis (CQA) process can be time-consuming and resource-intensive, requiring multiple discussions among team members to refine codes and ideas before reaching a consensus. To address these challenges, we introduce CollabCoder, a system leveraging Large Language Models (LLMs) to support three CQA stages: independent open coding, iterative discussions, and the development of a final codebook. In the independent open coding phase, CollabCoder provides AI-generated code suggestions on demand, and allows users to record coding decision-making information (e.g. keywords and certainty) as support for the process. During the discussion phase, CollabCoder helps to build mutual understanding and productive discussion by sharing coding decision-making information with the team. It also helps to quickly identify agreements and disagreements through quantitative metrics, in order to build a final consensus. During the code grouping phase, CollabCoder employs a top-down approach for primary code group recommendations, reducing the cognitive burden of generating the final codebook. An evaluation involving 16 users confirmed the usability and effectiveness of CollabCoder and offered empirical insights into the LLMs' roles in CQA