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Identifying monetary policy shocks with Divisia money in the United Kingdom
Get PDFWe construct a Divisia money measure for U.K. households and private non-financial corporations and a corresponding dual user cost index employing a consistent methodology from 1977 up to the present. Our joint construction of both the Divisia quantity index and the Divisia price dual facilitates an investigation of structural vector autoregresssion models (SVARs) over a long sample period of the type of non-recursive identifications explored by Belongia and Ireland (2016, 2018), as well as the block triangular specification advanced by Keating et al. (2019). An examination of the U.K. economy reveals that structures that consider a short-term interest rate to be the monetary policy indicator generate unremitting price puzzles. In contrast, we find sensible economic responses in various specifications that treat our Divisia measure as the indicator variable
Enhancing Customer Engagement Through Artificial Intelligence Authenticity
Get PDFGiven the limited research on the factors and mechanisms underlying artificial intelligence (AI) authenticity, we examine its use in fostering breakthrough knowledge and enhancing customer engagement. We devised a robust model grounded in mind perception and social exchange theories, with a focus on the outcomes of AI authenticity. Tested across 452 virtual health home stations, the findings reveal that both performance expectation and effort expectation serve as mediators between AI authenticity and customer engagement. This research provides managers with comprehensive insights into the defining attributes and operational mechanics of AI authenticity, thereby highlighting its critical importance in boosting customer engagement
Defining surgical indication for instability in spinal tuberculosis: validation analysis of Tuberculosis Spine Instability Score (TSIS)
No full textAims: In the absence of neurological deficits, the decision for surgery in spinal tuberculosis (STB) depends on the individual experiences of the surgeons, which may differ widely. There is currently no universal consensus on an objective definition of instability in STB. The Tuberculosis Spine Instability Score (TSIS) was developed to discriminate between the stable and unstable spine in STB. In the current study, we analyze the reliability, responsiveness, and construct validity of the TSIS to define clear guidelines for management of STB. Methods: Individuals presenting with STB were evaluated to assess the quality of the TSIS along with testing for validity, reproducibility, and responsiveness. Construct validity was expressed as the Pearson correlation coefficient. Intraobserver test-retest reliability and interobserver reliability was expressed using intraclass correlation. Longitudinal validity was assessed through responsiveness and an effect size calculation. Results: There were 162 individuals (98 females, 64 males) with STB identified with a mean age of 33.39 years (SD 16.58). The TSIS showed good construct validity with substantial correlation with the Spinal Instability Neoplastic Score (Pearson coefficient 0.827). Near perfect interobserver and intraobserver reliability was obtained with intraclass correlation coefficient values of 0.941 (95% CI 0.921 to 0.957) and 0.985 (95% CI 0.980 to 0.989), respectively. Evaluation of longitudinal validity was performed in 64 individuals at six months apart. With the smallest detectable change measure smaller than the minimal important change measure, the instrument was found to be responsive. The effect size over the six-month period was 1.039. Conclusion: The TSIS proves to be an excellent discriminative scoring tool with good validity, reliability, responsiveness, and high specificity. It can differentiate between the stable and unstable spine in STB and will provide objective assessments to aid surgical decision-making in this scenario
An investigation of multimodal EMG-EEG fusion strategies for upper-limb gesture classification
Get PDFObjective: Upper-limb gesture identification is an important problem in the advancement of robotic prostheses. Prevailing research into classifying electromyographic (EMG) muscular data or electroencephalographic (EEG) brain data for this purpose is often limited in methodological rigour, the extent to which generalisation is demonstrated, and the granularity of gestures classified. This work evaluates three architectures for multimodal fusion of EMG & EEG data in gesture classification, including a novel Hierarchical strategy, in both subject-specific and subject-independent settings. Approach: We propose an unbiased methodology for designing classifiers centred on Automated Machine Learning through Combined Algorithm Selection & Hyperparameter Optimisation (CASH); the first application of this technique to the biosignal domain. Using CASH, we introduce an end-to-end pipeline for data handling, algorithm development, modelling, and fair comparison, addressing established weaknesses among biosignal literature. Main results: EMG-EEG fusion is shown to provide significantly higher subject-independent accuracy in same-hand multi-gesture classification than an equivalent EMG classifier. Our CASH-based design methodology produces a more accurate subject-specific classifier design than recommended by literature. Our novel Hierarchical ensemble of classical models outperforms a domain-standard CNN architecture. We achieve a subject-independent EEG multiclass accuracy competitive with many subject-specific approaches used for similar, or more easily separable, problems. Significance: To our knowledge, this is the first work to establish a systematic framework for automatic, unbiased designing and testing of fusion architectures in the context of multimodal biosignal classification. We demonstrate a robust end-to-end modelling pipeline for biosignal classification problems which if adopted in future research can help address the risk of bias common in multimodal BCI studies, enabling more reliable and rigorous comparison of proposed classifiers than is usual in the domain. We apply the approach to a more complex task than typical of EMG-EEG fusion research, surpassing literature-recommended designs and verifying the efficacy of a novel Hierarchical fusion architecture
TFOS DEWS III 管理与治疗报告
Get PDF本报告基于循证医学, 综述了当前干眼病(DED)的治疗策略.干眼病的一线治疗主要聚焦于泪液补充,保存和刺激泪液分泌, 尤其是泪液补充剂, 这些仍是干眼病治疗的基石.睑板腺功能障碍 (MGD) 作为干眼病的主要病因, 通常采用热敷及多种门诊治疗手段, 包括设备驱动的眼睑加热技术,强脉冲光治疗,低强度光疗法及其他新兴技术.睑缘清洁治疗则包括睑缘清洁湿巾,抗蠕形螨治疗,睑缘去角质术及局部抗生素的应用. 部分病因导致的干眼病可采用包括皮质类固醇,局部T细胞免疫调节药物及多种药理制剂的抗炎治疗, 以及生物性泪液替代物(如自体血清和富血小板血浆)治疗.经鼻神经刺激的神经调节及新型药物治疗, 为未来干眼病的治疗提供了更多选择.对于严重或难治性病例, 羊膜移植和复杂手术方法等进阶治疗可提供解决方案.生活方式调整(包括改善眨眼习惯,膳食补充和环境调节)在干眼病的长期管理中发挥重要作用.患者教育及对治疗的依从性对干眼症状的缓解同样至关重要. TFOS DEWS III 治疗指南提供了一个循证框架, 帮助临床医生根据不同病因选择相应干预措施, 以实现针对不同类型干眼病的精准管理
Inadequate foundational decoding skills constrain global literacy goals for pupils in low- and middle-income countries
Get PDFLearning to read is the most important outcome of primary education. However, despite substantial improvements in primary school enrolment, most students in low- and middle-income countries (LMICs) fail to learn to read by age 10. We report reading assessment data from over half a million pupils from 48 LMICs tested primarily in a language of instruction and show that these pupils are failing to acquire the most basic skills that contribute to reading comprehension. Pupils in LMICs across the first three instructional years are not acquiring the ability to decode printed words fluently and, in most cases, are failing to master the names and sounds associated with letters. Moreover, performance gaps against benchmarks widen with each instructional year. Literacy goals in LMICs will be reached only by ensuring focus on decoding skills in early-grade readers. Effective literacy instruction will require rigorous systematic phonics programmes and assessments suitable for LMIC contexts
Producing micro-finite element models from real-time clinical CT scanners:calibration, validation and material mapping strategies
Get PDFFinite element (FE) models from living anatomical structures to produce patient-specific models offer improved diagnosis, precision pre-op planning for surgeries, and reliable biofidelic stress loading analysis. These models require the use of clinical scanners that are safe to use in-vivo but offer relatively lower resolution than in-vitro micro-CT ones. To capitalise on the clinical advantages, this route offers certain technical challenges which must be ironed out to derive a reliable validated route from scanning to in silico modelling. In the present study, sheep vertebrae were used to create biofidelic phantoms for scanning by using one of the latest technology high-resolution (300 micron) clinical standing scanners (HiRise, Curvebeam). Geometric information was used to produce FEA models (Abaqus/CAE), which were then validated under compression loading in the lab. The main challenges had to do first with reading and converting the scan data from voxels to material property assignment for each FE element, which was performed by using a number of different conversion equations from the literature, and second, to a lesser degree, with the minor challenges of seeking convergence and refining the boundary conditions. The fit between the model and the experimental results was best for two equations from the literature, while others were less reliable. The selection of the most suitable and universally applicable material conversion equation is significant because it can streamline the route to produce scanner to computer patient-specific models, and make these widely available and ultimately more easily immediately obtainable post-scans. Some known clinical examples highlight the potential use of this methodology for situations where loading and unloading configurations are equally challenging for modelling (i.e., standing CT scans of feet), and this paper discusses the importance of the approach for such examples. Unlike previous studies using micro-CT or non-clinical setups, this work validates a real-time, weight-bearing CT-based workflow for biomechanically consistent finite element modelling
A hybrid robust optimization and simulation model to establish temporary emergency stations for earthquake relief
Get PDFEarthquakes pose a constant threat to human communities. A key step in improving preparedness against such disasters is to determine the optimal location of Temporary Emergency Stations (TESs) and allocate them to affected areas. Decisions in the preparedness phase ensure optimal performance by TESs and minimize potential delays in rescue operations. During crises, TESs have a significant role in minimizing human causalities. In this research, a robust simulation-optimization approach is proposed to ensure appropriate planning in the preparedness phase. We develop a mathematical model for simultaneous and hierarchical location-allocation of the injured to the available medical facilities under disaster conditions. Since natural disasters are inherently unpredictable, the uncertainty of the data should inevitably be taken into account. We thus employ a Robust Optimization (RO) technique to tackle the uncertainty in the number of the injured and use simulation to create the first seven days of the crisis and determine the optimal capacities of medical facilities. The findings indicate that by eliminating the unnecessary transfer of mildly-injured victims to high-level medical facilities, the model causes a 15% reduction in treatment costs
A socio-economic and multi-criterion decision analysis approach for a holistic sustainability assessment of mango waste-based integrated biorefineries
Get PDFMango waste valorisation in integrated biorefineries supports circular bioeconomies, sustainable development goals, and climate change mitigation by diverting waste from landfills for value addition. The biorefinery concept potentially supports full valorisation, product diversification, cleaner energy provision, and proper waste management in mango processing facilities. Preferred biorefinery options for commercialisation depend on sustainability attributes (social-economic benefits and acceptance, economic competitiveness, and environmental performance). However, sustainability assessments are often limited to technical and economic feasibility and/or environmental impacts. The study combined the Life Cycle Sustainability Assessment, Jobs and Economic Development Impact Assessment model, and Multicriteria-Decision Analysis approaches for a holistic sustainability assessment of six simulated commercial integrated mango waste biorefineries (MWBs) co-producing bioproducts, and combined heat and power (CHP). The MWBs are annexed to a dried mango chips processing facility processing 27.8 tonnes/hr of mangoes, generating 5.56 and 6.94 tonnes/hr of peels and seeds, respectively, and 50 tonnes/hr of wastewater. Scenario 1 (S1) produces CHP, S2 co-produces CHP and pectin, S3 includes polyphenols, and S4 produces bioethanol to S2, whereas S5 co-produces polyphenols, pectin, bioethanol, and CHP, and S6 co-produces bioethanol and CHP. Notably, system-wide socio-economic benefits (job creation and value-addition to GDP) increase with the recovery of more bioproducts (pectin and polyphenols). However, selecting sustainable biorefinery pathways depends on preferred sustainability metrics for the stakeholders’ developmental goals instead of the trade-off between environmental and economic benefits. Therefore, the results from this study would inform investment and policy decisions regarding the social benefits and MWB’s acceptance, which complement the economic competitiveness and environmental performance
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