Search Results (356)

Background: COVID-19 can have adverse effects on individuals’ lung functions for up to 6 months or more after the episode. As a result, people may be reluctant to exercise, and this can have further adverse effects on their lung capacity and fitness. This study aimed to examine the effectiveness of a telemedicine program designed to increase the exercise participation of young adults after COVID-19 and evaluate the changes in lung function after exercise training. Methods: The quasi-experimental pre–post study recruited sixty university students who had suffered from COVID-19 within the past 12 months. Four pulmonary outcomes were compared: forced expiratory volume in one second (FEV1), forced vital capacity (FVC), peak expiratory flow rate (PEFR), and the ratio of FEV1 to FVC. The telemedicine exercise (TE) group (n = 36) received an intervention to carry out regular stepping exercise (up to 10,000 steps) via online video instruction and frequent WhatsApp reminder messages. The control group (n = 24) only received an initial WhatsApp message to carry out regular stepping exercise, with no further follow-up. Results: The FVC, FEV1, and FEV1/FVC ratio revealed significant overall improvement both within groups and between groups (p < 0.001), with moderate effect sizes. PEFR showed a significant improvement within groups (p = 0.007) but not between groups (p = 0.533). The TE group recorded a significant increase in daily step count (from 7165 to 9733, p < 0.001) after 4 weeks of training. The control group showed a significant reduction in step count (from 6975 to 6442, p = 0.049). Conclusions: The results confirmed the beneficial effects of the telemedicine exercise program in contributing to increased exercise participation and improved lung functions. Full article

LoRaWAN holds immense potential in smart applications for its low-power, long-range communication capabilities and in-built AES-128 encryption for end-to-end security. However, prior research has identified critical security vulnerabilities, most notably its use of AES-128 encryption in ECB mode, which lacks semantic security. Sertainty UXP (Unbreakable Exchange Protocol) technology enhances AES by embedding intelligence directly into the data. Sertainty Corporation’s UXP encryption employs AES-256-GCM, which offers authenticated encryption with integrated access control and policy enforcement at the data level, making it a promising candidate for securing sensitive IoT data. The objective of this study is to evaluate whether Sertainty UXP can operate effectively within the strict payload and performance constraints of LoRaWAN. To benchmark performance and overhead, several encryption algorithms, including AES-256-GCM, ASCON-128, SPECK, and XTEA, were implemented for comparison. For experimentation, smart meter data is encrypted with these algorithms and transmitted over LoRaWAN using the LoRa-E5 development board by Seeed Studio. The system’s performance is evaluated based on latency, payload size, and message integrity. Payloads are strategically split into LoRaWAN-compatible chunks and reassembled upon reception to meet network constraints. The results show that integrating UXP encryption within LoRaWAN is technically feasible, though it introduces additional overhead and latency. Despite this, the ability to embed robust encryption and controls directly within the data object offers significant potential to enhance end-to-end IoT security. The research concludes that Sertainty UXP can offer a viable and forward-looking solution for securing resource-constrained networks, provided implementation strategies carefully manage the trade-offs between security strength and transmission efficiency. Full article

Background/Objectives: The characterization of varying levels of parental engagement is important for increasing understanding of how to tailor and maximize the effectiveness of parent-based eHealth interventions. In this study, we aimed to determine if parental engagement in the WeChat group of a parent-based eHealth intervention affected preschoolers’ physical activity, diet, or sleep. Methods: We utilized baseline, post-test (12 weeks after baseline), and follow-up (12 weeks after post-test measurement) data from the intervention group in a parent-based eHealth intervention concerning children aged from three to six years, designed as a single-blinded randomized controlled trial with two parallel arms to explore the intervention’s influence on preschoolers’ physical activity, diet, and sleep. The parents in the intervention group were categorized into two groups: (1) The actively engaged group (53 parent–child dyads), defined as parents who actively posted and commented on modules at least once a week, either in the WeChat groups or through private messages with the researchers. (2) The lurker group (67 parent–child dyads), defined as parents who only responded to the weekly self-assessment messages and who, aside from this, showed no interaction within WeChat groups and did not privately message the researchers. Preschoolers’ physical activity was measured using ActiGraph wGT3X-BT, while their dietary behaviors and sleep were measured using parent-reported questionnaires. Generalized Estimating Equations using group and time as main effects and adjusted demographic information for covariates were computed to examine the effects of parental engagement in the eHealth intervention on preschoolers’ physical activity, diet, and sleep. Results: At post-test, higher levels of parental engagement were significantly associated with a marked increase in preschoolers’ moderate-to-vigorous and vigorous physical activity, alongside a notable reduction in weekend screen time. Furthermore, active parental engagement was linked to greater decreases in satiety responsiveness, desire to drink, and food fussiness compared to the ‘lurker’ group. However, no significant associations were observed between the level of parental engagement and preschoolers’ sleep-related outcomes. Conclusions: Further research with larger sample sizes and longer durations is needed to better investigate the potential of social media in parent-based interventions for promoting healthy lifestyles in children. Full article

The Galileo High Accuracy Service (HAS) has been operational since January 2023, offering good and stable performance. The next phase of HAS is currently being implemented, offering enhanced performance and new functionalities. One of the improvements in HAS Phase 2 will be the provisioning of ionospheric parameters to users in the European Coverage Area (ECA). This paper focuses on the new Message Type 2 (MT2) which will contain the ionospheric parameters, i.e., ionospheric vertical delays (IVDs) and ionospheric vertical accuracies (IVAs). IVDs and IVAs will be provided for ionospheric grid points (IGPs) which receivers in the ECA can see down to a certain elevation. Data for two ionospheric layers is planned to be provided. Because transmitting the IVDs and IVAs for a vast number of IGPs requires a significant amount of bandwidth, an investigation was also launched into different approaches for compressing the IVD data. To assess the efficacy of the compression, the percentage decrease in size was assessed through post-processing of historical data. Compared to non-optimized encoding of the IVDs using a fixed number of bits, processing of historical data showed a median IVD block size reduction of about 27% and 41% under solar maximum and solar minimum conditions, respectively. The IVD block compression approach will be evaluated further during the HAS Phase 2 implementation. Full article

In this study, we evaluated the performance of the Advanced Encryption Standard (AES)-128, AES-256, and Blowfish algorithms on WearOS for messages ranging from 8 to 128 bytes, which are typical message sizes for contemporary smartwatch applications. Using a WearOS emulator, we measured encryption time, memory usage, central processing unit (CPU) utilization, and battery consumption across 16 messages sizes with 10 repetitions over each configuration. The AES-128 algorithm consistently outperformed the others with approximately 1.0 ms of encryption time at 128 bytes, less than 6 KB memory, and less than 39% peak CPU utilization. The AES-256 algorithm added 25–30% processing overhead and higher energy consumption with negligible extra memory cost. The Blowfish algorithm consumed approximately three times more memory and exhibited the highest battery consumption per operation. It also scales poorly due to its 64-bit block size and large key scheduling approach. In addition, all performance differences are highly statistically significant (p < 0.001). Given the widespread hardware AES acceleration on WearOS devices and memory constraints, AES-128 is recommended as the default symmetric encryption algorithm for confidentiality in smartwatch applications. Full article

This exploratory eye-tracking study investigates how the emotional content of food bank advertisements influences food donor perception and visual attention. It does so by addressing a gap in the literature on eye-tracking applications in food donation contexts and social neuroscience. Visual attention represents a fundamental behavioural precursor to decision-making, yet its role in charitable communications remains underexplored. The objective of this research was to investigate how the content of food bank advertisements is associated with the way that potential food donors perceive food bank posters on a cognitive level. This study adopted a social neuroscience approach, using the methodology of eye-tracking to examine the visual attention patterns that form while viewing food bank posters. Participants (N = 96) viewed four posters varying in their emotional appeal, i.e., positive, neutral, negative and cognitive dissonance, while their eye movements were being recorded. Results revealed the robust attentional prioritisation of generic pictorial content over specific organisational logos or abstract symbols across all metrics and posters with large effect sizes (r = 0.69–0.87). It was found that pictures captured participants’ attention three to seven times faster than logos and also received two to seven times more fixations. The poster carrying a negative appeal elicited the strongest pictorial advantage, consistent with the negativity bias in attention allocation. Exploratory analysis found no significant correlation between participants’ past charitable behaviour and visual attention patterns, thus suggesting that the Picture Superiority Effect operates universally, regardless of individual past charitable behaviours. This is the first eye-tracking study examining donor-facing food bank communications in Poland, contributing to social neuroscience approaches in prosocial behaviour research. Findings suggest charitable organisations should prioritise emotionally engaging pictures’ inclusion over logo prominence in their visual communications messaging. Full article

Inflation expectations play a central role in monetary policy effectiveness, yet relatively little is known about how individual behavioral traits shape expectation formation. This study examines whether time discounting and hyperbolic discounting, key dimensions of intertemporal preferences, are systematically associated with household inflation expectations. Using large-scale survey data from Japan that elicit both time preference measures and qualitative inflation expectations, we analyze expectations over one-, three-, and five-year horizons. The empirical analysis employs ordered probit models that fit well with the categorical nature of survey-based inflation expectations and controls for a rich set of demographic, socioeconomic, and behavioral characteristics, including financial literacy and risk preferences. The results reveal clear horizon-dependent patterns. Hyperbolic discounting is positively associated with short-term inflation expectations, suggesting that present-biased individuals place greater weight on recent inflation developments. In contrast, higher time discount rates are associated with higher inflation expectations at medium and longer horizons, indicating that impatience is more relevant for beliefs about distant future prices. These findings provide novel evidence on the behavioral micro-foundations of inflation expectation formation and highlight the importance of heterogeneity in time preferences. From a policy perspective, the results suggest that one-size-fits-all communication strategies may be insufficient and that effective expectation management may require tailoring messages to account for differences in individuals’ time orientation across forecast horizons. Full article

The rapid proliferation of small satellite missions demands flight software that combines reliability, reusability, and rapid development cycles. NASA’s Core Flight System (cFS), with its layered architecture and component-based design, offers a promising solution. However, its resource-intensive design poses significant challenges for deployment on microcontroller (MCU) platforms commonly used in nanosatellites. This paper presents a comprehensive approach to porting cFS to the TMS570 safety microcontroller running FreeRTOS. We address critical challenges including Operating System Abstraction Layer (OSAL) adaptation for lightweight real-time operating systems and file system virtualization using RAM disk. As a core architectural contribution, we propose a hierarchical memory architecture that partitions high-speed internal RAM from external SDRAM, enabling all five cFE core services to operate within 256 KB on-chip RAM by offloading latency-tolerant data structures to SDRAM and releasing 37.5% of internal memory for mission applications. Performance evaluation yields two key quantitative findings: (1) Software Bus latency on SDRAM scales non-linearly from 1.85× to 7.67× relative to internal RAM as message size increases from 64 B to 4 KB, revealing that memory bandwidth—not fixed routing overhead—dominates large-transfer cost; (2) the cFS framework introduces a constant additive overhead of approximately 82.5 $\mathsf{\mu }$s per task cycle, independent of computational load, remaining below 0.1% of the execution budget at typical 1–10 Hz control rates. System stability is validated through 72 h continuous operation encompassing over 2.5 million task cycles with zero unplanned resets. This work establishes quantitative design guidelines—including memory placement criteria and task granularity thresholds—that provide a reusable technical pathway for deploying reliable, extensible flight software on resource-constrained embedded platforms. Full article

Intrusion detectors are often evaluated using average metrics at unconstrained thresholds, yet deployments require explicit control over false alarms. We investigate zero-day (out-of-distribution, OOD) intrusion detection under a target-FPR calibrated protocol, where a threshold is set on benign validation traffic to satisfy a target false positive rate α and transferred, unchanged, to a seen-test and OOD-test. Using CICIDS2017-derived host-session nodes aggregated in 1 min and 5 min windows, we compare tabular baselines, message-passing GNNs on a rule-based graph, and employ a method that builds a k-nearest-neighbor similarity graph with lightweight feature pre-smoothing. Robustness is measured using the OOD violation ratio, percentile tail risk, and feasibility under explicit false-alarm budgets. Base-graph GNNs exhibit heavy-tailed false-alarm amplification under OOD shifts: at α = 0.001, the p95 violation ratio reaches 68.50 (1 m) and 67.95 (5 m). In contrast, the proposed method reduces p95 to 3.41 (1 m) and 1.15 (5 m) and improves budget feasibility. We further verify robustness beyond a single held-out family by evaluating additional unseen-family splits (e.g., DDoS and DDoS+DoS) under the same calibrated operating point. We also quantify deployment-oriented cost via edge-list size and practical parsing/loading time. These findings suggest that similarity-based graphs with light pre-smoothing improve deployability under distribution shifts. Full article

Rapid Internet of Things (IoT) expansion creates security risks due to resource limits and evolving botnets. While Graph Neural Networks (GNNs) offer accuracy, their computational demands hinder real-time edge deployment. This study presents IoTGuard, based on a ‘Hetero-MLP’ architecture. The model replaces costly message passing with 8-dimensional categorical embeddings to capture protocol semantics. To avoid topology overfitting, L3 identifiers were excluded, relying on 13 L4 attributes selected via Pearson correlation. Evaluations on the NF-BoT-IoT-v2 dataset (37.7 M samples) demonstrate a 12.17 KB (INT8) footprint via post-training quantization. This represents a 1.9× size reduction, enabling independent operation on ARM Cortex-M7 platforms (Arm Ltd., Cambridge, UK) at 37,093 requests per second. The framework achieves a DDoS F1-score of 0.9943 with a false-positive rate of 0.0054. Comparative analysis confirms that while Random Forest is accurate, Hetero-MLP reduces parameters by 25.4× versus standard GAT models. The proposed approach balances detection depth with edge constraints, offering scalable critical infrastructure protection. Full article

Background and Objectives: Severe asthma poses significant clinical and economic burdens, with adherence to monitoring and treatment remaining a challenge despite biologic therapies. This pilot study aimed to evaluate the feasibility of telemedicine-based home monitoring using the AioCare system in patients with severe asthma and to determine if weekly reminder messages improved adherence compared to standard monitoring. Materials and Methods: In this prospective, single-center randomized controlled pilot study, 30 adults with severe asthma were assigned to either a reminder group (weekly SMS or in-app messages) or a control group without reminders. All participants performed weekly home spirometry for 12 weeks using the AioCare system. Lung function parameters, Asthma Control Test (ACT) scores, adherence to monitoring, and patient satisfaction were assessed. Longitudinal data were analyzed using mixed-effects and generalized estimating equation models. Results: Adherence to home monitoring was significantly higher in the reminder group (11.47 ± 0.92 vs. 9.13 ± 3.16 sessions; p = 0.044). Overall, patient satisfaction was higher in the intervention group (p = 0.0044), with universal endorsement of the reminders and perceived educational benefit. No significant between-group differences were observed in lung function parameters. ACT scores showed a favorable trend in both groups, with a medium between-group effect size favoring the intervention (d = 0.42), although this did not reach statistical significance. Conclusions: Home monitoring with reminders is feasible, safe, and enhances adherence and satisfaction in severe asthma, although it did not significantly affect short-term changes in lung function or symptom control. Larger, longer-term studies are warranted to determine whether these engagement benefits translate into improved long-term clinical outcomes. Full article

Accurate prediction of network-wide delay is crucial for air traffic management and passenger service. However, the inherent complexity of large-scale air traffic networks, with their dense interconnectivity and multi-dimensional operational dynamics such as flight connectivity, makes this task highly challenging. While Graph Neural Networks (GNNs) offer a promising framework, prevailing models are constrained by a “node → edge → node” representation paradigm, which fails to preserve the high-fidelity, edge-centric operational data that encodes delay propagation paths. To overcome this limitation, we propose a novel edge-based GNN. Our approach begins with a flight-connectivity-informed delay characterization, introducing delay width and delay strength as core metrics. The model implements an “edge → node” message-passing mechanism that explicitly encodes inbound and outbound flights, enabling direct learning of delay diffusion dynamics along air routes. Extensive experiments on real-world datasets demonstrate that our method outperforms state-of-the-art benchmarks, achieving the lowest RMSE, MAE, and MSE. A layered performance analysis reveals a key strength: the model delivers superior accuracy at major hub airports—which are critical to network performance—while maintaining robust precision at small-to-medium-sized airports. This balanced capability underscores the model’s practical utility and its enhanced capacity to capture the essential spatial–temporal dependencies governing delay propagation across diverse airport tiers. Full article

Attribute-based encryption (ABE) is an advanced public key encryption mechanism that enables the precise control of access to encrypted data based on attributes assigned to users and data. Attribute-based access control (ABAC), which is built on ABE, is crucial in providing dynamic, fine-grained, and context-aware security management in modern Internet of Things (IoT) applications. ABAC controls access based on attributes associated with users, devices, resources, and environmental conditions rather than fixed roles, making it highly adaptable to the complex and heterogeneous nature of IoT ecosystems. ABE can significantly improve the security and manageability of modern military IoT systems. Nevertheless, its practical implementation requires obtaining a range of performance data and assessing the additional overhead, particularly regarding data transmission efficiency. This paper provides a comparative analysis of the performance of two cryptographic schemes for attribute-based encryption in the context of special Internet of Things (IoT) applications. This applies to special environments, both military and civilian, where infrastructure is unreliable and dynamic and decisions must be made locally and in near-real time. From a security perspective, there is a need for strong authentication, precise access control, and a zero-trust approach at the network edge as well. The CIRCL scheme, based on traditional pairing-based ABE (CP-ABE), is compared with the newer Covercrypt scheme, a hybrid key encapsulation mechanism with access control (KEMAC) that provides quantum resistance. The main goal is to determine which scheme scales better and meets the performance requirements for two different scenarios: large corporate networks (where scalability is key) and tactical edge networks (where minimal bandwidth and post-quantum security are paramount). The benchmark results are used to compare the operating costs in detail, such as the key generation time, message encryption and decryption times, public key size, and cipher overhead, showing that Covercrypt provides a reduction in ciphertext overhead in tactical scenarios, while CIRCL offers faster decryption throughput in large-scale enterprise environments. It is concluded that the optimal choice depends on the specific constraints of the operating environment. Full article

Linkable ring signatures are a type of ring signature scheme that can protect the anonymity of signers while allowing the public to verify whether the same signer has signed the same message multiple times. This functionality makes linkable ring signatures suitable for applications such as cryptocurrencies and anonymous voting systems, achieving the dual goals of identity privacy protection and misuse prevention. However, existing post-quantum linkable ring signature schemes often suffer from issues such as excessive linear data growth the adoption of post-quantum signature algorithms, and high circuit complexity resulting from the use of post-quantum zero-knowledge proof protocols. To address these issues, a logarithmic-size post-quantum linkable ring signature scheme based on aggregation operations is proposed. The scheme constructs a Merkle tree from ring members’ public keys via a hash algorithm to achieve logarithmic-scale signing and verification operations. Moreover, it introduces, for the first time, a post-quantum aggregate signature scheme to replace post-quantum zero-knowledge proof protocols, thereby effectively avoiding the construction of complex circuits. Scheme analysis confirms that the proposed scheme meets the correctness requirements of linkable ring signatures. In terms of security, the scheme satisfies the anonymity, unforgeability, and linkability requirements of linkable ring signatures. Moreover, the aggregation process does not leak information about the signing members, ensuring strong privacy protection. Experimental results demonstrate that, when the ring size scales to 1024 members, our scheme outperforms the existing Dilithium-based logarithmic post-quantum ring signature scheme, with nearly 98.25% lower signing time, 98.90% lower verification time, and 99.81% smaller signature size. Full article

Large multi-UAV mission systems operate over time-varying communication graphs with heterogeneous platforms, where classical distributed task assignment may incur excessive message passing and suboptimal task–resource matching. To address these challenges, this paper proposes CLAC-CBBA (Centrality-Driven and Load-Aware Adaptive Clustering CBBA), an enhanced variant of the Consensus-Based Bundle Algorithm (CBBA) for large heterogeneous swarms. The proposed method is biomimetic in the sense that it integrates swarm-inspired self-organization and load-aware self-regulation to improve scalability and robustness, resembling decentralized role emergence and negative-feedback workload balancing in natural swarms. Specifically, CLAC-CBBA first identifies key nodes via a centrality-based adaptive cluster-reconfiguration mechanism (CenCluster) and partitions the network into cooperation domains to reduce redundant communication. It then applies a load-aware cluster self-regulation mechanism (LCSR), which combines resource attributes and spatial information, uses K-medoids clustering, and triggers split/merge reconfiguration based on real-time load imbalance. CBBA bidding is executed locally within clusters, while anchors and cluster representatives synchronize winners/bids to ensure globally consistent, conflict-free assignments. Simulations across diverse network densities and swarm sizes show that CLAC-CBBA reduces communication overhead and runtime while improving total task score compared with CBBA and several advanced variants, with statistically significant gains. These results demonstrate that CLAC-CBBA is scalable and robust for large-scale heterogeneous UAV task allocation. Full article