Search Results (1,518)

The use of resource-constrained Low-Power and Lossy Networks (LLNs), where the IPv6 Routing Protocol for LLNs (RPL) is the de facto routing standard, has increased due to the Internet of Things’ (IoT) explosive growth. Because of the dynamic nature of IoT deployments and the lack of in-protocol security, RPL is still quite susceptible to routing-layer attacks like Blackhole, Lowered Rank, version number manipulation, and Flooding despite its lightweight architecture. Lightweight, data-driven intrusion detection methods are necessary since traditional cryptographic countermeasures are frequently unfeasible for LLNs. However, the lack of RPL-specific control-plane semantics in current cybersecurity datasets restricts the use of machine learning (ML) for practical anomaly identification. In order to close this gap, this work models both static and mobile networks under benign and adversarial settings by creating a novel, large-scale multiclass RPL attack dataset using Contiki-NG’s Cooja simulator. To record detailed packet-level and control-plane activity including DODAG Information Object (DIO), DODAG Information Solicitation (DIS), and Destination Advertisement Object (DAO) message statistics along with forwarding and dropping patterns and objective-function fluctuations, a protocol-aware feature extraction pipeline is developed. This dataset is used to evaluate fifteen classifiers, including Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), k-Nearest Neighbors (KNN), Random Forest (RF), Extra Trees (ET), Gradient Boosting (GB), AdaBoost (AB), and XGBoost (XGB) and several ensemble strategies like soft/hard voting, stacking, and bagging, as part of a comprehensive ML-based detection system. Numerous tests show that ensemble approaches offer better generalization and prediction performance. With overfitting gaps less than 0.006 and low cross-validation variance, the Soft Voting Classifier obtains the greatest accuracy of 99.47%, closely followed by XGBoost with 99.45% and Random Forest with 99.44%. Full article

This research investigates how hybrid review systems integrating human-generated reviews and AI-generated summaries shape consumer trust and decision-related confidence. Across three controlled experiments conducted in simulated e-commerce environments, when and how hybrid reviews enhance consumer evaluations were examined. Study 1 demonstrates that hybrid reviews, which combine the emotional authenticity of human input with the analytical objectivity of AI, elicit greater levels of review trust and decision confidence than single-source reviews. Study 2 employs an experimental manipulation of presentation order and demonstrates that decision confidence increases when human reviews are presented before AI summaries, because this sequencing facilitates more effective cognitive integration. Finally, Study 3 shows that AI literacy strengthens the positive effect of perceived diagnosticity on confidence, while information overload mitigates it. By explicitly testing these processes across three experiments, this research clarifies the mechanisms through which hybrid reviews operate, identifying authenticity and objectivity as dual mediators, and sequencing, literacy, and cognitive load as critical contextual moderators. This research advances current theories on human–AI complementarity, information diagnosticity, and dual-process cognition by demonstrating that emotional and analytical cues can jointly foster trust in AI-mediated communications. This integrative evidence contributes to a nuanced understanding of how hybrid intelligence systems shape consumer decision-making within digital marketplaces. Full article

This work proposes a non-singular fast terminal sliding mode control (NFTSMC) strategy based on the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm and a nonlinear disturbance observer (NDO) to address the issues of modeling errors, motion disturbances, and transmission friction in robotic manipulators. Firstly, a novel modular serial 5-DOF robotic manipulator configuration is designed, and its kinematic and dynamic models are established. Secondly, a nonlinear disturbance observer is employed to estimate the total disturbance of the system and apply feedforward compensation. Based on boundary layer technology, an improved NFTSMC method is proposed to accelerate the convergence of tracking errors, reduce chattering, and avoid singularity issues inherent in traditional terminal sliding mode control. The stability of the designed control system is proved using Lyapunov stability theory. Subsequently, a deep reinforcement learning (DRL) agent based on the TD3 algorithm is trained to adaptively adjust the control gains of the non-singular fast terminal sliding mode controller. The dynamic information of the robotic manipulator is used as the input to the TD3 agent, which searches for optimal controller parameters within a continuous action space. A composite reward function is designed to ensure the stable and efficient learning of the TD3 agent. Finally, the motion characteristics of three joints for the designed 5-DOF robotic manipulator are analyzed. The results show that compared to the non-singular fast terminal sliding mode control algorithm based on a nonlinear disturbance observer (NDONFT), the non-singular fast terminal sliding mode control algorithm integrating a nonlinear disturbance observer and the Twin Delayed Deep Deterministic Policy Gradient algorithm (TD3NDONFT) reduces the mean absolute error of position tracking for the three joints by 7.14%, 19.94%, and 6.14%, respectively, and reduces the mean absolute error of velocity tracking by 1.78%, 9.10%, and 2.11%, respectively. These results verify the effectiveness of the proposed algorithm in enhancing the trajectory tracking accuracy of the robotic manipulator under unknown time-varying disturbances and demonstrate its strong robustness against sudden disturbances. Full article

This study uses Chinese A-share listed companies from 2014 to 2023 as the research sample (comprising a total of 18,053 firm-year observations) and explores the impact of executive tone manipulation on corporate sustainable growth capability from the perspective of corporate governance. Benchmark fixed-effects regression results indicate that the regression coefficient of tone manipulation on the sustainable growth rate is −0.141 (p < 0.01). Corporate financial sustainability is measured by the sustainable growth rate, an indicator reflecting a firm’s endogenous growth capacity while maintaining existing financial policies and operational efficiency. This differs distinctly from “broad-based sustainability” measured by ESG metrics. Therefore, this study focuses on sustainability in the financial dimension and further analyzes the moderating effects of corporate social responsibility information disclosure quality, institutional investor ownership ratio, and board independence. The findings reveal that executive tone manipulation significantly undermines corporate financial sustainability, while sound governance mechanisms can effectively mitigate this adverse impact. This research enriches the theoretical framework of financial sustainability studies from the perspectives of linguistic information and behavioral governance and provides policy implications for information disclosure regulation and governance optimization. Full article

The literature on derivative pricing in illiquid markets has mostly focused on computing optimal hedging controls, but empirical microstructure studies show that large order flow generates persistent and predictable price effects. Therefore, these controls can themselves induce endogenous market manipulation because traders can internalize the impact of their own trades. We identify the key shortcoming as the absence of a formal separation between a large trader’s informational advantage and the mechanical price impact and temporary cost-of-hedging. To address this gap, we introduce a counterfactual informed observer—an agent who knows the large trader’s strategy but does not face trading frictions—and use this device to isolate informational order-flow effects from mechanical price impact, a distinction explicitly observed in microstructure data. We prove the existence of information-neutral probability measures under which the discounted asset is a martingale for this observer and derive a hedging framework that jointly accounts for transaction costs and permanent market impact. Numerical experiments show that because price pressure and order-flow effects create non-linear execution costs, the optimal hedge for an out-of-the-money call can deviate substantially from the Black–Scholes hedge, with implications for risk management and regulatory monitoring. Full article

The integration of large-scale renewable energy sources has increased the complexity of operation and maintenance in modern power systems, causing on-site substation operation and maintenance activities to exhibit stronger continuity and dynamics, and thereby placing higher demands on real-time operational perception and safety judgment. However, existing behavior recognition methods have difficulty accurately identifying operational states in complex scenarios involving continuous actions, partial occlusions, and fine-grained manipulations. To address these challenges, this paper proposes a safety behavior recognition method for substation operations based on a dual-path spatiotemporal network. Personnel localization is achieved using YOLOv8, while behavior classification is performed through the SlowFast framework. In the Slow pathway, an ECA attention mechanism is integrated with residual structures to enhance the representation of sustained operational postures. In the Fast pathway, a multi-path excitation residual network is introduced to fuse temporal, channel, and motion information, improving the multi-scale representation of local action variations. Furthermore, to mitigate the issue of class imbalance in substation operation data, Focal Loss based on binary cross-entropy is incorporated to adaptively down-weight easily classified samples. Experimental results demonstrate that the proposed method achieves a recognition accuracy of 87.77% and an F1-score of 85.56% across multiple operation scenarios. The results further indicate improved recognition stability and adaptability, supporting safe substation operation and maintenance in renewable energy-integrated power systems. Full article

Semantic communication systems leverage deep neural networks to extract and transmit essential information, achieving superior performance in bandwidth-constrained wireless environments. However, their vulnerability to backdoor attacks poses critical security threats, where adversaries can inject malicious triggers during training to manipulate system behavior. This paper introduces Selective Communication Unlearning (SCU), a novel defense mechanism based on Variational Information Bottleneck (VIB) principles. SCU employs a two-stage approach: (1) joint unlearning to remove backdoor knowledge from both encoder and decoder while preserving legitimate data representations, and (2) contrastive compensation to maximize feature separation between poisoned and clean samples. Extensive experiments on the RML2016.10a wireless signal dataset demonstrate that SCU achieves 629.5 ± 191.2% backdoor mitigation (5-seed average; 95% CI: [364.1%, 895.0%]), with peak performance of 1486% under optimal conditions, while maintaining only 11.5% clean performance degradation. This represents an order-of-magnitude improvement over detection-based defenses and fundamentally outperforms existing unlearning approaches that achieve near-zero or negative mitigation. We validate SCU across seven signal processing domains, four adaptive backdoor types, and varying SNR conditions, demonstrating unprecedented robustness and generalizability. The framework achieves a 243 s unlearning time, making it practical for resource-constrained edge deployments in 6G networks. Full article

Financial statement fraud is a socio-technical risk that arises from coupled organizational, informational, and regulatory processes. To address the Identification Paradox in financial fraud detection, where existing models cannot simultaneously recognize both chronic manipulation and acute outbreaks in financial data, this study proposes the Causal–Temporal Asynchrony (CTA) theory as a process-oriented conceptual framework that guides feature construction and model design in a predictive setting. CTA defines fraud motive as a chronic, multi-period accumulation and fraud action as an acute, single-year event. To operationalize CTA within a predictive setting, we build a deployable Decoupling-Fusion System that encodes CTA as an Acute–Chronic Binary Feature Dimensions schema and performs detection via Decoupling-Fusion FraudNet. Within this system, parallel Long Short-Term Memory networks (LSTM) capture chronic motive signals from longitudinal sequences, while parallel Convolutional Neural Networks (CNN) and a Feed-forward Neural Network (FNN) identify acute action signals from multimodal snapshots; the resulting asynchronous probabilities are integrated via an adaptive decision-level fusion mechanism. Empirical tests on China’s A-share market (2001–2021) show the system (AUC = 0.967) outperforms baseline models. Furthermore, eXplainable AI analysis reveals patterns consistent with the classic fraud triangle (pressure, opportunity and rationalization). This study develops a theory-grounded decision-support system that unifies acute and chronic evidence streams and provides a deployable blueprint for continuous auditing and governance. Full article

This study addresses specific gaps in current research on user-experience interface design for news and information apps targeted at elderly users, particularly in the context of human factors and ergonomics. To investigate how interface design features of mobile news clients affect the cognitive load of elderly users, an in-depth analysis was conducted using a combination of objective eye movement tests and subjective evaluation scales. Mobile news client interfaces with systematically varied visual complexity were designed by orthogonally manipulating three core elements identified from top-ranked Chinese news apps and prior literature, and within-subject repeated experiments were performed to collect subjective cognitive load data, objective eye movement data, and behavioral data, validating the proposed hypothesis model. The results indicate that the visual complexity of mobile news client interfaces significantly impacts the cognitive load of elderly users, with keyword color substantially modulating this effect. These findings contribute to the knowledge base on mobile news client interface design for elderly users and provide practical recommendations for designers to create more equitable interfaces, enhancing usability for this demographic. Full article

Melatonin is a conserved indolamine implicated in circadian and developmental timing, but its role in social-insect task allocation is unclear. Here, we show that melatonergic signaling modulates the nurse → forager transition in the honey bee (Apis mellifera). A single hemocoelic dose of melatonin (100 ng) markedly reduced hive retention and advanced the age at first waggle dance by ≈9 days (median 11.8 vs. 20.9 days; common-language effect size = 0.94). Complementary manipulations—pharmacological antagonism with luzindole and RNA interference (RNAi)-mediated knockdown of a candidate melatonin receptor (AmMTR/AmMT2; transcript reduction ≈65–79% at 24–72 h)—produced reciprocal suppression of foraging, indicating pathway dependence. Transcriptional profiling revealed a forager-age peak in the arylalkylamine N-acetyltransferase ortholog AmNAT2 (≈10-fold increase near day 23), while AmNAT1 remained unchanged; melatonin treatment was associated with a trend toward increased Amα-glucosidase expression. Computational analyses classify AmMTR as a class-A GPCR and identify plausible melatonin-compatible pockets; promoter scans reveal high-confidence circadian motif matches upstream of AmMTR. These in silico results are presented as hypothesis-generating. Together, the behavioral, molecular, pharmacological and computational lines of evidence support melatonin as a circadian-informed modulatory signal that helps align neuroendocrine and metabolic states with the timing of extranidal behavior. Confirmation via receptor functional assays and broader colony replication will be important. Full article

We report on the realization of a platform for trapping and manipulating individual ⁸⁸Sr atoms in optical tweezers. A first cooling stage based on a blue shielded magneto-optical trap (MOT) operating on the ${|}^1{\mathrm{S}}_0⟩\to {|}^1{\mathrm{P}}_1⟩$ transition at $461 \mathrm{n}\mathrm{m}$ enables us to trap approximately 4 × 10⁶ atoms at a temperature of 6.8 mK. Further cooling is achieved in a narrow-line red MOT using the ${|}^1{\mathrm{S}}_0⟩\to {|}^3{\mathrm{P}}_1⟩$ intercombination transition at 689 nm, bringing 5 × 10⁵ atoms down to $5\mu \mathrm{K}$ and reaching a density of 4 × 10¹⁰ cm³. Atoms are then loaded into 813 nm tweezer arrays generated by crossed acousto-optic deflectors and tightly focused onto the atoms with a high-numerical-aperture objective. Through light-assisted collision processes we achieve the collisional blockade, which leads to single-atom occupancy with a probability of about 50%. The trapped atoms are detected via fluorescence imaging with a fidelity of $99.986\left(6\right)\%$, while maintaining a survival probability of $97\left(2\right)\%$. The release-and-recapture measurement provides a temperature of $12.92\left(5\right)\mu \mathrm{K}$ for the atoms in the tweezers, and the ultra-high-vacuum environment ensures a vacuum lifetime higher than 7 min. These results demonstrate a robust alkaline-earth tweezer platform that combines efficient loading, cooling, and high-fidelity detection, providing the essential building blocks for scalable quantum simulation and quantum information processing with Sr atoms. Full article

The deep integration of digital technologies has significantly improved the operational efficiency of electricity markets, but it has also introduced increasingly severe and sophisticated cybersecurity challenges. As a highly coupled cyber–physical system (CPS), the electricity market is increasingly vulnerable to attacks that exploit weaknesses in both market mechanisms and information infrastructure. Unlike existing reviews, this study makes three key contributions: First, it provides a hierarchical analysis of cyberattacks targeting electricity market operations, detailing how such attacks manipulate outcomes for profit or disruption. Second, it proposes a novel full-lifecycle dynamic defense framework tailored to the cyber–physical–market nature of the electricity market, coordinating defenses across the entire attack lifecycle to ensure market stability and financial integrity. Third, it analyzes key enabling technologies for attack–defense games and identifies fundamental challenges to market resilience. Looking ahead, the manuscript outlines a strategic research agenda, emphasizing breakthroughs in intelligent and collaborative technologies. These advancements are expected to drive the evolution of the electricity market’s defense from a passive–reactive model to a state of active immunity, which can anticipate, withstand, and autonomously recover from complex cyber threats. Full article

Objectives: To assess risk factors for post-extubation stridor in children and its impact on clinical outcomes. Methods: Prospective cohort study with children aged from 0 to 13 years who were intubated or underwent orotracheal intubation in the pediatric intensive care units (PICU) of two tertiary public hospitals. The outcome of interest was the occurrence of post-extubation stridor. The information collected included patient characteristics, comorbidities, history of airway manipulation, and factors related to orotracheal intubation. A logistic regression was used to identify potential risk factors for post-extubation stridor; data were analyzed until hospital discharge, death, or referral to another facility. Results: A total of 239 children were included, with a median age of 1.3 years and a duration of intubation of three days. Post-extubation stridor was observed in 57.3% of children. A multivariate analysis included prehospital or non-specialized hospital intubation, trauma or complications during intubation, and orotracheal intubation longer than seven days as risk factors for stridor. Children with stridor had a longer PICU length of stay, longer duration of invasive mechanical ventilation, and were often managed with non-invasive ventilation (p < 0.05). Most children with extubation failure (p = 0.001) and cardiorespiratory arrest (p = 0.03) presented with stridor. Conclusions: Risk factors for post-extubation stridor included intubation performed in prehospital or non-specialized hospitals, orotracheal intubation longer than seven days, and trauma or complications during intubation. Children with stridor had a worse prognosis, with longer stays in the PICU and on mechanical ventilation and higher rates of extubation failure. Full article

Inspired by the adaptive flexible motion coordination of biological systems, this study presents a bioinspired control strategy that enables robotic manipulators to achieve precise and compliant motion–force coordination for embodied intelligence and dexterous interaction in physically constrained environments. To this end, a learning-based motion–force hybrid control (LMFC) framework is proposed, which unifies learning and kinematic-level control to regulate both motion and interaction forces under incomplete or implicit kinematic information, thereby enhancing robustness and precision. The LMFC formulation recasts motion–force coordination as a time-varying quadratic programming (TVQP) problem, seamlessly incorporating multiple practical constraints—including joint limits, end-effector orientation maintenance, and obstacle avoidance—at the acceleration level, while determining control decisions at the velocity level. An RNN-based controller is further designed to integrate adaptive learning and control, enabling online estimation of uncertain kinematic parameters and mitigating joint drift. Simulation and experimental results demonstrate the effectiveness and practicality of the proposed framework, highlighting its potential for adaptive and compliant robotic control in constraint-rich environments. Full article

The transition to sustainable food systems requires communication strategies that resonate with consumers’ values, not only technological innovation. This study examines how values-centric communication can shape German consumers’ responses to alternative proteins, focusing on insect-based snacks. A desk-based synthesis of recent studies, guided by Schwartz’s value theory, identified Tradition and Security as dominant drivers of food choice and yielded five communication requirements: Cultural familiarity, Emotional safety, Simplicity and clarity, Trust and credibility, and Routine integration. These were operationalised into communication guidelines and short on-pack claims, which were applied to a refined packaging prototype. An exploratory focus group (N = 7) then compared reactions to the original versus the refined packaging, analysed using McGuire’s communication–persuasion stages. Within this small exploratory group, participants reported that familiar formats, a reassuring tone, clear visual hierarchy, and salient trust cues made them more willing to consider trying the product, whereas information overload, claim–image incongruence, value-incongruent brand naming, and delayed recognition of insect content appeared to impede acceptance. The study contributes an integrative analytic lens combining Schwartz’s value theory with McGuire’s model and a set of testable guidelines for value-aligned food communication. Because the empirical evidence is based on a single small student focus group with fixed presentation order, bundled manipulations, and hypothetical intentions, these results are exploratory and self-reported and should be interpreted cautiously; future research should employ counterbalanced factorial designs with behavioural outcomes. Full article