Search Results (6,297)

Predicting the behavior of Internet of Things (IoT) networks under irregular topologies and heterogeneous battery conditions remains a significant challenge. Simulation tools can capture these effects but can require high manual effort and computational capacity, motivating the use of machine learning surrogates. This work introduces an automated pipeline for generating large-scale IoT network datasets by bringing together the Contiki-NG firmware, parameterized topology generation, and Slurm-based orchestration of Cooja simulations. The system supports a variety of network structures, scalable node counts, randomized battery allocations, and routing protocols to reproduce diverse failure modes. As a case study, we conduct over 10,000 Cooja simulations with 15–75 battery-powered motes arranged in sparse grid topologies and operating the RPL routing protocol, consuming 1300 CPU-hours in total. The simulations capture realistic failure modes, including unjoined nodes despite physical connectivity and cascading disconnects caused by battery depletion. The resulting graph-structured datasets are used for two prediction tasks: (1) estimating the last successful message delivery time for each node and (2) predicting network-wide spatial coverage. Graph neural network models trained on these datasets outperform baseline regression models and topology-aware heuristics while evaluating substantially faster than full simulations. The proposed framework provides a reproducible foundation for data-driven analysis of energy-limited IoT networks. Full article

Artificial intelligence (AI) is increasingly used in marketing to deliver personalized messages and services. Although such tools create new opportunities, their acceptance by consumers depends on several factors that go beyond technology itself. This study examines how trust and ethical perceptions, familiarity and exposure to AI, digital consumer behavior, and identity concerns shape acceptance of AI-based personalized advertising. The analysis draws on data from 650 Greek consumers, collected through a mixed-mode survey (online and paper), and tested using logistic regression models with demographic characteristics included as controls. The results show trust and ethical perceptions of acceptance as factors, while familiarity with AI tools also supports positive attitudes once trust is established. In contrast, digital consumer behavior played a smaller role, and identity-related consumption was negatively associated with acceptance, reflecting concerns about autonomy and self-expression. Demographic factors, such as age and income, also influenced responses. Overall, the findings suggest that acceptance of AI in marketing is not only a technical matter but also a psychological and social process. This study highlights the importance for firms to build trust, act responsibly, and design personalization strategies that respect consumer identity and ethical expectations. Full article

A context-aware system is a system that adapts its behaviours in response to changes in the system’s environment (i.e., context). Ensuring the correctness of such a system is difficult because the state of the environment changes frequently in an unpredictable manner according to the laws of physics. Hence, formal verification techniques like model-checking and theorem proving do not work in many cases. Runtime Verification (RV) is a lightweight formal verification technique that consists of checking at runtime whether the execution of the system violates the requirements of the system. The Calculus of Context-aware Ambients (CCA) is a process calculus for modelling context-aware systems and reasoning about their behaviours. This paper proposes an RV tool for CCA, called ccaRV. Given a model of a system in CCA and a property of the system written in LTL (Linear Temporal Logic), ccaRV verifies automatically at runtime if the execution of the system violates the property. We propose a semantic approach to RV, where the RV mechanism is defined at the semantics level and not as an add-on. A consequence of this is that there is no need for generating a monitor from the property specification nor for the instrumentation of a system during verification. We define a labelled reduction relation for CCA, where the labels are used to capture the execution traces at the semantics level. Then we extend LTL with spatial operators and context expressions in order to formulate properties about the system context. We use a case study of the MQTT (Message Queue Telemetry Transport) protocol to evaluate the proposed RV approach. The results show that the ccaRV tool is scalable and its decisions are accurate. Full article

Background and objectives: Given the limited number of studies evaluating the relationship of ABO blood groups and Premature coronary artery disease (PCAD) as well as the lack of relevant literature in Saudi Arabia, a study to assess the association of ABO blood groups and PCAD in Saudi population was crucial. Methods: This is a retrospective comparative study, where controls are healthy individuals and cases are divided into: patients younger than 51 years (PCAD) with confirmed coronary artery disease and patients ≥ 51 years (CAD) with confirmed coronary artery disease, whose data are retrieved from 2015 to 2022. Severity of the disease is assessed by vessel score and Gensini score. Results: We have collected a total of 1167 samples; 466 individuals served as controls (39.9%), 346 were PCAD cases (29.6%), and 355 were CAD patients (30.4%). No significant overall difference was found in ABO distribution among healthy, PCAD, and CAD individuals, although blood group A is more common in PCAD and CAD patients than in healthy controls. Among males, there is a statistically significant difference in ABO distribution across healthy, PCAD, and CAD groups, with a higher frequency of blood group A and a lower frequency of O in patients compared to controls (A = 19.7%, 28.1%, 28.4%, B = 17.5%, 19.0%, 18.6%, O = 60.0%, 48.3%, 50.2%, AB = 2.8%, 4.6%, 2.8%, p = 0.041, respectively). Additionally, the difference in ABO is not statistically significant between the healthy females, PCAD female patients, and CAD female patients (A = 25.5%, 31.3%, 25.7%, B = 20.7%, 13.3%, 20.0%, O = 47.2%, 53.0%, 51.4%, AB = 6.6%, 2.4%, 2.9%, p = 0.541, respectively). The result reveals the severity of coronary vessel occlusion in PCAD group by using Gensini score as follows: A: 52.81 ± 31.30, B: 66.94 ± 45.57, O: 43.06 ± 32.95, AB: 49.00 ± 49.40 with p value = 0.131. Conclusions: The present findings suggest that higher frequency of blood group “A” was found among male patients with PCAD and CAD compared to other blood groups. In addition, blood group “O” is less associated with male PCAD and CAD in Saudi population. Identification of ABO blood groups might assist in the genetic screening as well as guiding prophylaxis for premature CAD. Full article

Digital evidence custody requires consensus protocols that guarantee immediate and deterministic finality. Legal admissibility depends on proof that no party can alter or delay confirmation of evidence transfers. Conventional Byzantine fault tolerance protocols scale poorly because of quadratic communication overhead, while probabilistic ledger systems such as IOTA and SPECTRE produce confirmation uncertainty that weakens custody verification. This paper introduces SelectVote Byzantine Fault Tolerance, a deterministic consensus protocol that infers virtual votes from graph structure instead of exchanging explicit messages. The protocol operates in permissioned forensic networks and assigns validation witnesses through a fixed, hash-based selection process. Empirical evaluation demonstrates sub-quadratic communication scaling ($O\left(n^{1.7}\right)$) compared to traditional $O\left(n^2\right)$ Byzantine protocols and maintains Byzantine resilience. To ensure physical integrity, the paper also presents an environmental compensation framework for precision weight verification. The framework models temperature, humidity, and pressure effects on load cells and corrects measurement drift to preserve sub-gram accuracy across normal storage conditions. Experimental evaluation confirms that the integrated system sustains high throughput with deterministic finality and maintains consistent measurement precision under environmental variation. The combined result supports reliable, legally defensible custody of digital evidence across distributed institutions. Full article

The conventional OLSR protocol faces substantial challenges in highly dynamic and interference-intensive UAV environments, including high mobility, frequent topology changes, and insufficient adaptability to electromagnetic interference. This paper proposes a cross-layer improved OLSR protocol, OLSR-LCN, that integrates three evaluation metrics—link lifetime (LL), channel interference index (CII), and node load (NL)—to enhance communication stability and network performance. The proposed protocol extends the OLSR control message structure and employs enhanced MPR selection and routing path computation algorithms. LL prediction enables proactive selection of stable communication paths, while the CII helps avoid heavily interfered nodes during MPR selection. Additionally, the NL metric facilitates load balancing and prevents premature node failure due to resource exhaustion. Simulation results demonstrate that across different UAV flight speeds and network scales, OLSR-LCN protocol consistently outperforms both the OLSR and the position-based OLSR in terms of end-to-end delay, packet loss rate, and network efficiency. The cross-layer optimization approach effectively addresses frequent link disruptions, interference, and load imbalance in dynamic environments, providing a robust solution for reliable communication in complex FANETs. Full article

Cloud storage uses proofs of ownership to avoid redundant uploads while keeping file contents secret. Many existing schemes need extra round trips, or rely on predictable sampling. These choices reduce security when an adversary knows part of the file. We present MiS-PoW, a zero knowledge and non-interactive proof of ownership. The protocol derives a synchronized challenge seed from the existing HTTPS/TLS session. The seed binds a discretized time window and the file identifier. Both parties compute the same challenges locally, and the protocol adds no new messages. MiS-PoW samples blocks with a stratified policy without duplicates. The policy enforces coverage across partitions and reduces the advantage of contiguous knowledge and near duplicate files. The proof layer uses STARKs with simple AIR constraints. The constraints check that indices come from the seed, lie in range, are unique, and meet per partition counts. We analyze security and show seed unpredictability, resistance to replay, and bounds under partial knowledge with limited grinding. A prototype shows that verification time does not grow with file size, and proof and bandwidth costs remain modest. MiS-PoW is deployable, privacy preserving, and scalable for cloud storage. Full article

Public acceptance remains a critical barrier to the adoption of genetically modified (GM) foods. This study investigates whether communication strategies that establish different forms of technological legitimacy, specifically regulative, normative, and cognitive legitimacy, can effectively overcome this barrier. Using the contingent valuation method (CVM) with a nationally representative sample of 1194 individuals, this study examined the effect of communication strategies on Chinese consumers’ willingness to pay for GM soybean oil. The results revealed a striking asymmetry. Information emphasizing the safety regulations of GM foods, which aims to build regulative legitimacy, significantly reduced WTP, likely by activating consumer anxieties. Conversely, narratives highlighting technology’s role in ensuring national food security, which builds normative legitimacy, effectively increased WTP for domestic GM oil. Information about the advanced level of GM technology, intended to establish cognitive legitimacy, had no significant impact. The effects were heterogeneous. Females and less knowledgeable consumers were most sensitive to safety messages. Our findings demonstrate that building legitimacy through normative appeals to collective welfare is more effective than relying on regulatory assurances. This study provides a legitimacy-based framework for understanding public perception and offers policymakers crucial insights for communicating about controversial agricultural technologies. Full article

The paradigm of cellular vehicle-to-everything (C-V2X) communications assisted by unmanned aerial vehicles (UAVs) is poised to revolutionize the future of sixth-generation (6G) intelligent transportation systems, as outlined by the international mobile telecommunication (IMT)-2030 vision. This integration of UAV-assisted C-V2X communications is set to enhance mobility and connectivity, creating a smarter and reliable autonomous transportation landscape. The UAV-assisted C-V2X networks enable hyper-reliable and low-latency vehicular communications for 6G applications including augmented reality, immersive reality and virtual reality, real-time holographic mapping support, and futuristic infotainment services. This paper presents a Markov chain model to study a third-generation partnership project (3GPP)-specified C-V2X network communicating with a flying UAV for task offloading in a Federated Learning (FL) environment. We evaluate the impact of various factors such as model update frequency, queue backlog, and UAV energy consumption on different types of communication latency. Additionally, we examine the end-to-end latency in the FL environment against the latency in conventional data offloading. This is achieved by considering cooperative perception messages (CPMs) that are triggered by random events and basic safety messages (BSMs) that are periodically transmitted. Simulation results demonstrate that optimizing the transmission intervals results in a lower average delay. Also, for both scenarios, the optimal policy aims to optimize the available UAV energy consumption, minimize the cumulative queuing backlog, and maximize the UAV’s available battery power utilization. We also find that the queuing delay can be controlled by adjusting the optimal policy and the value function in the relative value iteration (RVI). Moreover, the communication latency in an FL environment is comparable to that in the gross data offloading environment based on Kullback–Leibler (KL) divergence. Full article

Batteryless devices offer an unparalleled opportunity for long-term, low-maintenance, and sustainable operation. These opportunities are especially attractive in the context of the Internet of Things (IoT). Such devices, however, rely on a stringent energy budget for their operation. As a result, batteryless devices often operate intermittently. Therefore, energy-intensive functionalities such as wireless communication, though valuable in practical applications, are still a significant challenge to realize on such devices. This work proposes wake-up receiver-based solutions for facilitating energy-efficient communication among batteryless, energy-harvesting devices. As a foundation for realizing wireless communication, we introduce BEWARE-MAC, a MAC protocol that exploits the capabilities of the underlying WuR to enable efficient message exchanges among batteryless devices. To demonstrate the broadcast capabilities of BEWARE-MAC, we propose WEND, a WuR-based neighbor discovery protocol that can operate on intermittently powered, batteryless devices. Finally, we present an evaluation of the proposed protocols using both experimental and simulation-based results. Our results suggest that BEWARE-MAC can improve the goodput of energy-constrained devices by up to 61.1%. Full article

Human papillomavirus (HPV) vaccination is a cornerstone of cancer prevention across genders. In the United Kingdom (UK), the programme now includes boys, yet uptake remains below target, with persistent disparities by gender and region. This commentary examines the drivers of these gaps, including the historical framing of the HPV vaccine as a vaccine for girls, limited public awareness of boys’ eligibility, and challenges in school-based delivery. Gendered misconceptions, cultural norms, and inadequate communication continue to limit uptake in boys, while healthcare professionals, including general practitioners, dentists, and pharmacists, remain underused in supporting vaccine access and tackling parental hesitancy. Schools are central to equitable delivery, but teachers often lack training and possess low-to-moderate knowledge of HPV-related topics, including HPV vaccination availability for boys and HPV-related cancers affecting men. Drawing on health behaviour theory, we propose evidence-informed, multi-level recommendations to improve uptake, from gender-inclusive messaging and more efficient consent processes to digital engagement tools that support parents. We also highlight our ongoing research into parental attitudes toward HPV vaccination for boys aged 9–12 in England, which will inform future targeted interventions and policy development. Full article

Background: Maintaining daily optimal dental hygiene, especially in medically vulnerable patients with periodontitis, remains challenging in dental practice. Mobile apps and other digital tools might offer useful support alongside traditional advice. Objectives: This study aimed to develop a mobile health app, PerioSupportPro, that helps patients improve their daily plaque control habits. It also reports on the pilot testing of the app’s usability and users’ perception in a small patient group. Methods: The app was created by a mixed team including periodontists, psychologists, developers, and data protection specialists. The first version included reminders, gamified elements, video tutorials, and motivational messages. After internal testing, a group of 18 patients tested the app and completed a feedback questionnaire that assessed usability (Q3–Q5), educational impact (Q6–Q8), motivation (Q9–Q11), and overall satisfaction (Q12–Q14). Cronbach’s alpha was used to check internal consistency, and non-parametric tests were applied for basic statistical comparisons. Results: The motivation section of the questionnaire showed acceptable consistency (α = 0.784), while usability and educational impact had lower values (α = 0.418 and 0.438). No clear differences were found between age groups. Satisfaction was positively associated with reminders and motivational items. Most appreciated features included reminders, the simple interface, and short videos. Based on the input provided by the questionnaire, a few improvements were made, and a second version of the app was prepared. Conclusions: Early user responses show that PerioSupportPro may help motivate and guide patients in their oral hygiene routine. While still in an early phase, the app seems well-received and ready for future clinical validation with more users. Full article

This paper presents a Deep Autoencoder–LDPC–OFDM (DAE–LDPC–OFDM) transceiver architecture that integrates a learned belief propagation (BP) decoder to achieve robust, energy-efficient, and adaptive wireless communication. Unlike conventional modular systems that treat encoding, modulation, and decoding as independent stages, the proposed framework performs end-to-end joint optimization of all components, enabling dynamic adaptation to varying channel and noise conditions. The learned BP decoder introduces trainable parameters into the iterative message-passing process, allowing adaptive refinement of log-likelihood ratio (LLR) statistics and enhancing decoding accuracy across diverse SNR regimes. Extensive experimental results across multiple datasets and channel scenarios demonstrate the effectiveness of the proposed design. At 10 dB SNR, the DAE–LDPC–OFDM achieves a BER of 1.72% and BLER of 2.95%, outperforming state-of-the-art models such as Transformer–OFDM, CNN–OFDM, and GRU–OFDM by 25–30%, and surpassing traditional LDPC–OFDM systems by 38–42% across all tested datasets. The system also achieves a PAPR reduction of 26.6%, improving transmitter power amplifier efficiency, and maintains a low inference latency of 3.9 ms per frame, validating its suitability for real-time applications. Moreover, it maintains reliable performance under time-varying, interference-rich, and multipath fading channels, confirming its robustness in realistic wireless environments. The results establish the DAE–LDPC–OFDM as a high-performance, power-efficient, and scalable architecture capable of supporting the demands of 6G and beyond, delivering superior reliability, low-latency performance, and energy-efficient communication in next-generation intelligent networks. Full article

‘Deepfakes’ and other forms of digital communications disinformation are now on the virtual frontlines of many armed conflicts. Military commanders can potentially gain significant tactical advantages by misleading enemy forces, opposing governments, and civilian populations into believing X when Y is the true state of affairs. Distinct from military propaganda, deliberate deceptions and subterfuge have long been part of warfare. However, a powerful claim is advanced that deepfakes such as announcing surrender, truce declarations, or similar messages that place soldiers and civilians at greater risk are international humanitarian law (IHL) violations, notably under the 1907 Hague Convention and the 1977 Additional Protocol I to the Geneva Conventions. This four-section critical discussion considers whether, or to what extent, deepfakes are IHL compliant. Selected examples taken from the ongoing Russia–Ukraine war are highlighted to illustrate the potentially grave dangers that deepfakes represent for innocent civilian populations. IHL reform recommendations are made that would reduce deepfake harm—if such reforms are embraced by the international community (an admittedly doubtful prospect). Full article

Background: Social media has become a space for sharing personal experiences and shaping public opinion. This study explored how people respond to substance misuse recovery journeys shared on TikTok. Methods: The researchers collected 3583 comments from 350 TikTok videos under the hashtags #wedorecover, #recovery, and #sobertok using a scraper tool. A discourse analysis categorized comments into Narrative Strategies, Rhetorical Strategies, Linguistic Features, and Power Relationships, each with subcategories revealing public perceptions of substance use and recovery. A correlation analysis was also conducted to examine the role of emojis across narrative and linguistic features. Results: Most comments (94%) expressed support or positivity toward recovery videos. The heart emoji was the most common (93.35% of all emojis), symbolizing connection, encouragement, and solidarity. Four themes emerged reflecting public attitudes: encouragement and positive messaging, acknowledgment of struggle, the culture of sharing, and the influence of broader social narratives. Conclusions: These results provide insight into public responses to recovery content on TikTok, suggesting that peer support may be facilitated through the platform’s algorithmic design. While TikTok shows promise as a supportive digital space, further research is needed to understand its broader implications for substance use recovery support. Full article