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PurposeThis paper verifies whether popular Internet information from Internet forum and search engine exhibit useful content for forecasting the volatility in Chinese stock market.Design/methodology/approachFirst, the authors’ study commences with several HAR-RV-type models, then the study amplifies them respectively with the posting volume and search frequency to construct HAR-IF-type and HAR-BD-type models. Second, from in-sample and out-of-sample analysis, the authors empirically investigate the interpretive ability, forecasting performance (statistic and economic). Third, various robustness checks are utilized to reconfirm the authors’ findings, including alternative forecast window, alternative evaluation method and alternative stock market. Finally, the authors further discuss the forecasting performance in different forecast horizons (h = 5, 10 and 20) and asymmetric effect of information from Internet forum.FindingsFrom in-sample perspective, the authors discover that posting volume exhibits better analytical ability for Chinese stock volatility than search frequency. Out-of-sample results indicate that forecasting models with posting volume could achieve a superior forecasting performance and increased economic value than competing models.Practical implicationsThese findings can help investors and decision-makers obtain higher forecasting accuracy and economic gains.Originality/valueThis study enriches the existing research findings about the volatility forecasting of stock market from two dimensions. First, the authors thoroughly investigate whether the Internet information could enhance the efficiency and accuracy of the volatility forecasting concerning with the Chinese stock market. Second, the authors find a novel evidence that the information from Internet forum is more superior to search frequency in volatility forecasting of stock market. Third, they find that this study not only compares the predictability of the posting volume and search frequency simply, but it also divides the posting volume into “good” and “bad” segments to clarify its asymmetric effect respectively.HighlightsThis study aims to verify whether posting volume and search frequency contain predictive content for estimating the volatility in Chinese stock market.The forecasting model with posting volume can achieve a superior forecasting performance and increases economic value than competing models.The results are robust in alternative forecast window, alternative evaluation method and alternative market index.The posting volume still can help to forecast future volatility for mid- and long-term forecast horizons. Additionally, the role of posting volume in forecasting Chinese stock volatility is asymmetric.

The synthesis of Mn 3 O 4 nanoparticles and activated carbon/reduced graphene oxide (AC/rGO) nanocomposite involved surfactant-assisted chemical precipitation and sonochemical methods, respectively, to produce high-quality electrode materials. The morphology of the spherical Mn 3 O 4 nanoparticles and the wrinkled sheet-like structure of rGO were found to enhance the electrochemical performance and stability of the electrodes significantly. Electrochemical investigations were conducted using two electrolytes: 2 M KOH and LiNO 3 . In half-cell analyses, Mn 3 O 4 and AC/rGO exhibited specific capacitances of 138 F g −1 and 609 F g −1 , respectively, with 2 M KOH, and 104 F g −1 and 49.8 F g −1 with 2 M LiNO 3 electrolyte, at 1 A g −1 . The observed differences in performance were discussed regarding ionic radius, ionic conductivity, and diffusional coefficient of ions. Furthermore, asymmetric supercapacitor pouch cell devices (Mn 3 O 4 //AC/rGO, MAGASC) were fabricated employing both electrolytes, demonstrating enhanced electrochemical performance. The MAGASC pouch cells exhibited specific capacitances of 273 F g −1 and 130 F g −1 at. 100 mV s −1 with KOH and LiNO 3 electrolytes, respectively. Energy and power density were measured to be 35.2 Wh kg −1 and 1.4 kW kg −1 for KOH electrolyte, and 10.9 Wh kg −1 and 1.6 kW kg −1 for LiNO 3 electrolyte at 0.6 A g −1 . Electrochemical impedance spectroscopy (EIS) analysis revealed a lower equivalent series and charge transfer resistance for MAGASC with KOH electrolyte than for ASC pouch cells with LiNO 3 electrolyte. Complex capacitance and relaxation time constant of the MAGASC were determined using EIS data to analyze frequency behavior. Moreover, the ASC pouch cell demonstrated excellent cyclic stability, retaining 90% of its initial capacitance over 5000 cycles in both electrolytes. These findings underscore the superior energy storage capacity of MAGASC with KOH electrolyte and its broader operating potential with LiNO 3 electrolyte. Graphical Abstract

Owing to the recent attention towards the growing issue of global warming, the automotive industry is shifting towards more capable and eco-friendly vehicles with longer ranges than conventional vehicles. Although the transition to eco-friendly vehicles faces several challenges, including component failures due to mechanical wear, electrical voltage fluctuations, motor damage from overloads, infrastructure, and external environmental disturbances. The four-wheel independent drive electric vehicle (4WID-EV) is often used as an alternative to the single-drive electric vehicle, providing improved traction control and reducing the increased load on the individual motors. This study proposes an optimally enhanced controller to control the linear and nonlinear trajectories of four independent motors to evaluate the electric vehicle’s speed and address challenges involved in torque distribution to the independent drive, especially under various motor failure conditions. The computed results reveal that the proposed optimal linear quadratic regulator (LQR) controller accurately predicts better than the conventional proportional integral derivative (PID) controller in terms of the vehicle’s speed under various motor failures. Specifically, the optimal LQR controller achieves a faster settling time of 2.5 s, a lower overshoot of 0.8%, a mean error of 0.0441 rad/s, and a mean squared error (MSE) of 0.0820 (rad/s2). These results indicate that the proposed controller enhances stability and accuracy, improving adaptability even under motor failure conditions in 4WID-EVs.

An Intrusion detection system is an essential security tool for protecting services and infrastructures of wireless sensor networks from unseen and unpredictable attacks. Few works of machine learning have been proposed for intrusion detection in wireless sensor networks and that have achieved reasonable results. However, these works still need to be more accurate and efficient against imbalanced data problems in network traffic. In this paper, we proposed a new model to detect intrusion attacks based on a genetic algorithm and an extreme gradient boosting (XGBoot) classifier, called GXGBoost model. The latter is a gradient boosting model designed for improving the performance of traditional models to detect minority classes of attacks in the highly imbalanced data traffic of wireless sensor networks. A set of experiments were conducted on wireless sensor network-detection system (WSN-DS) dataset using holdout and 10 fold cross validation techniques. The results of 10 fold cross validation tests revealed that the proposed approach outperformed the state-of-the-art approaches and other ensemble learning classifiers with high detection rates of 98.2%, 92.9%, 98.9%, and 99.5% for flooding, scheduling, grayhole, and blackhole attacks, respectively, in addition to 99.9% for normal traffic.

The study investigates volatility persistence, long-term memory and time-varying conditional correlations among the stock markets of the Gulf Cooperation Council (GCC) countries. Daily equity index data between 2012 and 2024 were analyzed using univariate fractionally integrated generalized autoregressive conditional heteroskedasticity (FIGARCH) models to examine long-memory behavior and multivariate dynamic conditional correlation (DCC) models to assess conditional correlations between these markets. For each of the GCC equity markets, the analysis highlighted large degrees of long-memory and volatility persistence. Finally, the DCC model shows that strong and dynamic Intermarket links among the GCC, especially between KSA and UAE, exist and reflect significant volatility spillover from good economic ties. This study fills the gap in the literature by providing a comprehensive understanding of long-run volatility memory and inter-market associations in the GCC stock markets.

Snakebite envenomation remains an important, yet a neglected public health issue in most tropical and subtropical countries. Underdeveloped medical infrastructure, suboptimal medical services, poor documentation and failure to make snake-related injury a mandatory notifiable disease are important contributing factors. The King Cobra (Ophiophagus hannah ) is a medically significant species encountered in Malaysia however, there have been few publications from the clinical perspective. The objectives of this study were to determine the frequency of King Cobra related injuries, geographical distribution, clinical presentation, type and frequency of antivenom utilization and the management outcome. This is a cross-sectional study of confirmed King Cobra related injuries consulted to Remote Envenomation Consultation Services (RECS) from 2015 to 2020. Data were extracted from the RECS database and descriptively analyzed. A total of 32 cases of King Cobra bite were identified. Most cases were from Peninsular Malaysia with the most frequent from the state of Pahang ( n = 9, 28.1%). Most patients got bitten while attempting to catch or play with the snake (68.8%). Signs and symptoms of envenomation were documented in 24 (75.0%) cases and the most frequent systemic manifestation was ptosis ( n = 13, 40.6%). Tracheal intubation and ventilatory support were required in 13 (40.6%) patients. Antivenom was administered to 22 (68.8%) patients with most (25.0%) receiving 10 vials (1 dose). The commonest antivenom used was monospecific King Cobra antivenom (50.0%) from Thai Red Cross. There was one death documented due to complications from necrotizing fasciitis and septicemia. Public awareness of the dangers and proper handling of King Cobras needs to be emphasised. Timely administration of the appropriate antivenom is the definitive treatment and leads to favorable outcomes.

Automated action recognition has become essential in the surveillance, healthcare, and multimedia retrieval industries owing to the rapid proliferation of video data. This paper introduces YOLO-Act, a novel spatiotemporal action detection model that enhances the object detection capabilities of YOLOv8 to efficiently manage complex action dynamics within video sequences. YOLO-Act achieves precise and efficient action recognition by integrating keyframe extraction, action tracking, and class fusion. The model depicts essential temporal dynamics without the computational overhead of continuous frame processing by leveraging the adaptive selection of three keyframes representing the beginning, middle, and end of the actions. Compared with state-of-the-art approaches such as the Lagrangian Action Recognition Transformer (LART), YOLO-Act exhibits superior performance with a mean average precision (mAP) of 73.28 in experiments conducted on the AVA dataset, resulting in a gain of +28.18 mAP. Furthermore, YOLO-Act achieves this higher accuracy with significantly lower FLOPs, demonstrating its efficiency in computational resource utilization. The results highlight the advantages of incorporating precise tracking, effective spatial detection, and temporal consistency to address the challenges associated with video-based action detection.

PurposeThis study aims to investigate Sri Lankan Government university students’ acceptance of Chat Generative Pretrained Transformer (ChatGPT) for educational purposes. Using the unified theory of acceptance and use of technology 2 (UTAUT2) model as the primary theoretical lens, this study incorporated personal innovativeness as both a dependent and moderating variable to understand students’ ChatGPT use behaviour.Design/methodology/approachThis quantitative study used a questionnaire survey to collect data. A total of 500 legitimate undergraduates from 17 government universities in Sri Lanka were selected for this study. Items for the variables were adopted from previously validated instruments. Partial least squares structural equation modelling (PLS-SEM) using SmartPLS 4 was used to investigate latent constructs’ relationships. Furthermore, the variables’ relative relevance was ranked using a two-stage artificial neural network analysis with the SPSS 27 application.FindingsThe results of the analysis revealed that eight of the nine proposed hypotheses were confirmed. The most significant determinants of behavioural intention were habit and performance expectancy, closely followed by hedonic motivation and perceived ease of use. Use behaviour was highly influenced by both behavioural intention and personal inventiveness. Though personal innovativeness (PI) was suggested as a moderator, the relationship was not significant.Research limitations/implicationsThe research highlights the impact of habit, performance expectancy and perceived ease of use on students’ acceptance of AI applications such as ChatGPT, emphasising the need for efficient implementation techniques, individual variations in technology adoption and continuous support and training to improve students’ proficiency.Originality/valueThis study enhances the comprehension of how undergraduate students adopt ChatGPT in an educational setting. The study emphasises the significance of certain variables in the UTAUT2 model and the importance of PI in influencing the adoption of ChatGPT in educational environments.