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In modelling volatility in financial time series, the double-threshold autoregressive conditional heteroscedastic (DTARCH) model has been demonstrated as a useful variant of the autoregressive conditional heteroscedastic (ARCH) models. In this paper, we propose a weighted composite quantile regression method for simultaneously estimating the autoregressive parameters and the ARCH parameters in the DTARCH model. This method involves a sequence of weights and takes a data-driven weighting scheme to maximize the asymptotic efficiency of the estimators. Under regularity conditions, we establish asymptotic distributions of the proposed estimators for a variety of heavy- or light-tailed error distributions. Simulations are conducted to compare the performance of different estimators, and the proposed approach is used to analyse the daily S&P 500 Composite index, both of which endorse our theoretical results.

Innovation is an important driving force for social and economic development, and an important way for enterprises to obtain competitive advantages in the market and realize sustainable development. The article takes 960 enterprises listed in Shanghai and Shenzhen A-shares as research samples, and utilizes the threshold regression model to explore the impact of the remuneration gap of returnee executives on the innovation ability of enterprises. After analyzing the results of the threshold panel regression, it was found that there is a double threshold effect between the remuneration gap of returnee executives and the innovation ability of enterprises. When the returnee executives’ pay gap is ≤2.463 times, the returnee executives’ pay gap has a negative impact on the innovation ability of enterprises. When the remuneration gap of returnee executives is in the range of (2.463, 4.134], the remuneration gap of returnee executives positively and significantly promotes the enterprise’s innovation ability at the 1% level. When the remuneration gap between returnee executives is higher than 4.134 times, it has a negative impact on the performance of enterprise innovation ability at the 1% level. Therefore, enterprises need to set a reasonable salary gap range when introducing returnee executives, which can not only motivate the innovation enthusiasm of returnee executives but also eliminate the negative emotions of employees, thus effectively promoting the development of enterprise innovation ability.

Aiming at the problems of poor coordination effect and low positioning accuracy of unmanned aerial vehicle (UAV) formation cooperative navigation in complex environments, an adaptive time-varying factor graph framework UAV formation cooperative navigation algorithm is proposed. The proposed algorithm uses the factor graph to describe the relationship between the navigation state of the UAV fleet and its own measurement information as well as the relative navigation information, and detects the relative navigation information at each moment by the double-threshold detection method to update the factor graph model at the current moment. And the robust estimation is combined with the factor graph, and the weight function measurements are used in the construction of the factor nodes for adaptive adjustment to make the system highly robust. The simulation results show that the proposed method realises the effective fusion of airborne multi-source sensing information and relative navigation information, which effectively improves the UAV formation cooperative navigation accuracy.

The purpose of this paper is to investigate the digital lens presentation effect and image dissemination effect of intangible cultural heritage. In order to test the lens boundary detection technology of ICH digital images, we focus on analyzing the number of image frames, the number of mutations and the number of gradual changes of ICH characters, space-time and events, and exploring the checking completeness, the checking accuracy and the comprehensive detection rate of this paper’s algorithm and the dual-threshold comparative method on different types of ICH image fragments. Combined with the descriptive analysis of valid samples, we explore the main types of transmission of NRM digital images and clarify the degree of media utilization and the ranking of transmission power of NRM digital images in the Yangtze River Delta region. Shanghai has the strongest online dissemination power of traditional theater, traditional fine arts and traditional arts and crafts, with 1284586, 310468, and 237818, respectively, while traditional music, Chinese opera and folklore have the weakest dissemination power in Shanghai. Traditional music makes up 18.73% of them. To ensure non-heritage culture’s inheritance, it is necessary to strengthen the use of digital imaging technology to achieve linkage communication between non-heritage cultures and build an overall communication system.

Type of the article: Research Article AbstractThis paper examines the influence of inventory management on firm performance, applying Hansen’s threshold estimation method across firm size. It uses panel data, including 149 industrial manufacturing firms listed on HOSE, HNX, and UPCOM markets in Vietnam from 2014 to 2024. In small firms (SIZE ≤ 24.4679), WIP (work in progress) and ITO (inventory turnover) positively affect ROA, while FIN (finished goods) has a negative effect. As SIZE increases (24.4679 < SIZE ≤ 25.0912), WIP reverses to a strong negative effect, FIN turns positive, and ITO loses statistical significance. In large firms (SIZE > 25.0912), RAW (raw materials) appears as a significant negative factor on ROA, WIP continues to have a negative effect but at a decreasing level, and FIN reverses to a negative effect. These findings suggest that SIZE is important in moderating the relationship between inventory and firm performance. The control variables also show significant effects: TANG (tangible assets) negatively affects firm performance, while CASH has a positive impact, confirming the role of working capital balance. Regarding managerial implications, SIZE is an important moderator in the relationship between inventory and firm performance. For small firms, exploiting the benefits of WIP and increasing inventory turnover can improve profitability. Meanwhile, maintaining a reasonable WIP level becomes urgent for medium and large firms to avoid wasting resources and delaying production. For the largest firms, more attention should be paid to RAW to limit the risk of capital congestion, while maintaining a suitable level of FIN to ensure a smooth supply chain.

ABSTRACTObjectivesDiagnosis of patients suspected of mild dementia (MD) is a challenge and patient numbers continue to rise. A short test triaging patients in need of a neuropsychological assessment (NPA) is welcome. The Montreal cognitive assessment (MoCA) has high sensitivity at the original cutoff <26 for MD, but results in too many false-positive (FP) referrals in clinical practice (low specificity). A cutoff that finds all patients at high risk of MD without referring to many patients not (yet) in need of an NPA is needed. A difficulty is who is to be considered at risk, as definitions for disease (e.g. MD) do not always define health at the same time and thereby create subthreshold disorders. DesignIn this study, we compared different selection strategies to efficiently identify patients in need of an NPA. Using the MoCA with a double threshold tackles the dilemma of increasing the specificity without decreasing the sensitivity and creates the opportunity to distinguish the clinical (MD) and subclinical (MCI) state and hence to get their appropriate policy. Setting/participantsPatients referred to old-age psychiatry suspected of cognitive impairment that could benefit from an NPA ( n = 693). ResultsThe optimal strategy was a two-stage selection process using the MoCA with a double threshold as an add-on after initial assessment. By selecting who is likely to have dementia and should be assessed further (MoCA<21), who should be discharged (≥26), and who’s course should be monitored actively as they are at increased risk (21<26). ConclusionBy using two cutoffs, the clinical value of the MoCA improved for triaging. A double-threshold MoCA not only gave the best results; accuracy, PPV, NPV, and reducing FP referrals by 65%, still correctly triaging most MD patients. It also identified most MCIs whose intermediate state justifies active monitoring.

Structural health monitoring (SHM) systems have been widely applied in long-span bridges and a large amount of SHM data is continually collected. The harsh environment of sensors installed at structures causes multiple types of anomalies such as outlier, minor, missing, trend, drift, and break in the SHM data, which seriously hinders the further analysis of SHM data. In order to achieve anomaly detection from a large amount of SHM data, this paper proposes a long-short term memory (LSTM) network-based anomaly detection method. Firstly, the proposed method reduces the workload for preparing training sets. Secondly, the purpose of real-time anomaly detection can be met. Thirdly, the problem of high alarm rate can be avoided by utilizing double thresholds. To validate the effectiveness of the proposed method, a case study of finite element model simulation is firstly introduced, which illustrates the detailed implementation process. Finally, acceleration data from the SHM system of a long-span suspension bridge located in Jiangyin, China is employed. The results show that the proposed method can detect anomaly with high accuracy and identify abnormal accidents such as a ship collision quickly.

The artificial neural network-spiking neural network (ANN-SNN) conversion, as an efficient algorithm for deep SNNs training, promotes the performance of shallow SNNs, and expands the application in various tasks. However, the existing conversion methods still face the problem of large conversion error within low conversion time steps. In this paper, a heuristic symmetric-threshold rectified linear unit (stReLU) activation function for ANNs is proposed, based on the intrinsically different responses between the integrate-and-fire (IF) neurons in SNNs and the activation functions in ANNs. The negative threshold in stReLU can guarantee the conversion of negative activations, and the symmetric thresholds enable positive error to offset negative error between activation value and spike firing rate, thus reducing the conversion error from ANNs to SNNs. The lossless conversion from ANNs with stReLU to SNNs is demonstrated by theoretical formulation. By contrasting stReLU with asymmetric-threshold LeakyReLU and threshold ReLU, the effectiveness of symmetric thresholds is further explored. The results show that ANNs with stReLU can decrease the conversion error and achieve nearly lossless conversion based on the MNIST, Fashion-MNIST, and CIFAR10 datasets, with 6× to 250 speedup compared with other methods. Moreover, the comparison of energy consumption between ANNs and SNNs indicates that this novel conversion algorithm can also significantly reduce energy consumption.

Purpose Most of the linear encoders are based on optics. The accuracy and reliability of these encoders are greatly reduced in polluted and noisy environments. Moreover, these encoders have a complex structure and large sensor volume and are thus not suited to small application scenarios and do not have universality. This paper aims to present a new absolute magnetic linear encoder, which has a simple structure, small size and wide application range. Design/methodology/approach The effect of swing error is analyzed for the sensor structural arrangement. A double-threshold interval algorithm is then proposed to synthesize multiple interval electrical angles into absolute angles and convert them into actual displacement distances. Findings The final linear encoder measurement range is 15.57 mm, and the resolution reaches ± 2 µm. The effectiveness of the algorithm is demonstrated experimentally. Originality/value The linear encoder has good robustness, and high measurement accuracy, which is suitable for industrial production. The linear encoder has been mass-produced and used in an electric power-assisted braking system.