Search Results (486)

Distributed seismic sensor networks integrated into the Internet of Things (IoT) infrastructure enable continuous condition monitoring of large-scale engineering structures. During long-term operation, however, measurement channels are subject to sensitivity drift, increased noise, and pulse artifacts that statistically mimic real vibration events. Related deep-learning techniques for noisy and ill-posed inverse problems have demonstrated the value of combining principled physical priors with deep models. Although the application domain differs, the underlying methodological insight—that constrained, physics-aware feature mappings can stabilize learning under noisy and partially observed conditions—directly motivates the use of a parameterized quantum circuit as a nonlinear feature transformer in the present work, where Hilbert space mapping serves as an analogous structural prior for the latent representation. Three principal fault modes are considered in this work, corresponding to the dominant degradation mechanisms observed in long-term seismic instrumentation: sensor drift, increased noise, and sensor failure. Each fault mode produces a distinct signature in the windowed feature space; the proposed model is trained to discriminate between them based on the latent CNN-LSTM-VQC representation. We propose a hybrid quantum-inspired deep-learning model (QC-DL) for the detection and diagnosis of channel-degradation anomalies. The architecture combines a 1D-CNN+LSTM feature extractor with a parameterized variational quantum circuit (VQC) used as a nonlinear feature transformer. All quantum experiments were performed on the QPanda3 CPUQVM simulator. The data were split chronologically prior to windowing to avoid information leakage. On real-world labeled accelerometric data with four operating modes (normal/drift/high-noise/failure), the QC-DL model achieved a macro-averaged F1 score of approximately 0.69 and per-class AUC values in the range 0.88–0.99. The mean early-detection latency was 1.6 s versus 2.1 s for the CNN-LSTM baseline (~24% reduction). An ablation study against a parameter-matched classical MLP showed that the gain is modest and not solely attributable to additional nonlinearity. The reported p-values (p = 0.70, p = 0.29) do not establish statistical significance. The results support the feasibility of hybrid quantum-inspired deep learning for sensor-channel verification, while highlighting the need for evaluation on real NISQ hardware. This paper proposes a hybrid quantum-inspired approach for detecting and diagnosing such anomalies in the time series of distributed seismic networks. The architecture combines a classical temporal feature extraction module based on one-dimensional convolutional layers and a recurrent long short-term memory (LSTM) network, which generates a latent window representation of the signal, with a parameterized variational quantum circuit used as a nonlinear feature processor in a hybrid computational circuit. Experimental validation was performed on real-world labeled data with multiple sensor degradation modes. The evaluation was organized in a scoring framework aligned with autonomous operation through window ranking and threshold alarm generation. In the experiments, the proposed model provided a macro-averaged F1 score of approximately 0.69 and area under the receiver operating characteristic (AUC) curve values in the range of 0.88–0.99 across classes, outperforming baseline deep models. The average early detection latency was 1.6 s versus 2.1 s for the baseline recurrent model (a 24% reduction). An ablative comparison with a control model based on a classical multilayer perceptron of comparable dimension confirmed that the improvement is not limited to the addition of additional nonlinearity. The obtained results indicate the potential of quantum-supported deep learning for improving the reliability of long-term vibration monitoring and verifying the correctness of sensor channels in distributed seismic networks. Full article

Adolescent vaping has become a persistent health and behavioural challenge in schools, yet many institutions lack reliable tools to detect and respond to concealed e-cigarette use. This study addresses this problem by evaluating the real-world performance of a low-cost “Internet of Things” (IoT) vape detection system deployed across 37 high-risk restroom and change-room locations at a large Australian Independent school. The aim was to determine whether an IoT-based environmental monitoring platform could accurately identify vaping events, support timely staff intervention, and provide actionable insights into student behaviour patterns. A longitudinal case study design was used, collecting continuous particulate matter (PM_2.5 and PM₁₀) data at one-minute intervals over an 18-month period, where PM_2.5 and PM₁₀ refer to particulate matter with aerodynamic diameters ≤ 2.5 µm and ≤10 µm, respectively, reported in micrograms per cubic metre (µg/m³. Threshold-based alerting, cloud-based data processing, and school-led Closed-circuit television (CCTV) verification were combined to assess detection accuracy, temporal trends, and operational responses. The system recorded more than 300 vaping-related incidents, with clusters aligned to predictable times of day and higher prevalence among senior students. Operational detection performance was high, with alert events characterised by rapid, concurrent PM_2.5 and PM₁₀ excursions consistent with vaping-related aerosol profiles, although staff responsiveness declined over time due to alert fatigue and competing priorities. A major environmental smoke event demonstrated the need for context-aware logic to reduce false positives. The findings demonstrate that real-time aerosol monitoring is not only technically reliable but also highly effective in detecting vaping within school environments. These perspectives help explain why user engagement, alert fatigue, and institutional follow-through are as critical as sensor accuracy itself. Ultimately, the effectiveness of vape detection relies on strong organisational commitment, well-defined response workflows, and alignment with broader wellbeing and policy strategies. When these elements are in place, such systems can evolve from simple detection tools into intelligent, integrated components of school health governance. Full article

Despite ongoing population aging, older adults remain underrepresented in educational and digital media research, particularly individuals from the fourth and fifth ages. Available data suggest a decline in participation in formal education, as well as decreasing numbers of internet users with advancing age. However, detailed information on very old adults’ perspectives on learning and digital devices remain limited, especially regarding informal learning activities. This study aims to examine attitudes toward and the perceived relevance of informal and digital learning among individuals aged 90 years and over. In total, seven interviews with older adults aged 90 years and older were conducted using a combination of biographical-narrative and problem-centered interview methods. Data were analyzed using Reflexive Thematic Analysis. Two overarching themes, consisting of a total of seven themes, were developed: perspectives on learning developed over the course of life and current perspectives on learning. Results indicate that the aging process itself becomes a salient learning process. Although age-related stereotypes such as ‘You can’t teach an old dog new tricks’ persist, nursing homes were identified as environments that support learning. This study sheds light on informal learning processes in advanced age. Full article

Small and medium-sized enterprises (SMEs) are expected to contribute to sustainable manufacturing, yet they often lack the resources and capabilities needed to adopt advanced automation in a structured and scalable manner. While lean robotics have been widely studied, there is still limited empirical evidence on how their integration can be systematically designed to improve sustainability-oriented performance in SME contexts. This paper examines how a scalable lean robotics system can be conceived and implemented to enhance productivity and resource efficiency in an SME packaging process. We develop a lean robotics design approach that jointly considers lean principles, collaborative industrial robotics, and Industrial Internet of Things (IIoT) monitoring. The approach is applied in a real-world case study of a “Fold Station” robotic cell, where stone paper sheets are destacked, glued, and formed into cylindrical plant protectors. Key performance indicators related to cycle time, material utilization, process stability, and manual workload are measured before and after implementation. The results show a three- to four-fold reduction in preparation time per unit, more efficient use of stone paper and adhesive, and a decrease in repetitive manual handling, thereby contributing to both economic and environmental sustainability. TRIZ (Teoriya Resheniya Izobretatelskikh Zadach, Theory of Inventive Problem Solving) is used to structure the resolution of design contradictions that arise when embedding lean principles into the robotic system and to support its scalable adaptation to different production scenarios. This study advances the understanding of lean robotics for sustainable SME production and derives practical guidelines for designing scalable, resource-efficient robotic cells. Full article

In Social Internet of Vehicles (SIoV) environments, fog computing plays a crucial role in supporting real-time services by reducing the latency inherent in cloud-based architectures. However, fog nodes are typically deployed in physically exposed roadside environments and can be operated by several system operators, making them vulnerable to physical compromise and unauthorized access. Despite these threats, many existing authentication schemes assume fog nodes to be fully trusted or honest-but-curious, allowing them to decrypt transmitted data using a session key shared among vehicles, fog nodes, and cloud servers. To overcome these limitations, this paper proposes a quantum-secure pairwise key agreement scheme that establishes distinct session keys for vehicle–fog, fog–cloud, and vehicle–cloud communications. This design effectively prevents the disclosure of sensitive information even in the event of fog node compromise. Furthermore, Physical Unclonable Functions (PUFs) are employed to mitigate physical capture attacks, while lattice-based cryptography based on the Module Learning with Errors (MLWE) problem is integrated to ensure resistance against quantum computing attacks. The security of the proposed protocol is rigorously validated through formal analysis using AVISPA, BAN logic, and the Real-or-Random (RoR) model, in addition to informal security analysis. Comparative performance evaluations against related schemes demonstrate that the proposed approach achieves a balance between efficiency and security, making it well suited for practical deployment in SIoV environments. Full article

Hierarchical federated learning (HFL) combined with the Mobile Edge Computing (MEC) paradigm has attracted extensive research interest in the Industrial Internet of Things (IIoT) due to its ability to deploy computational resources near edge devices and effectively reduce communication overhead. However, in real-world applications, the dynamic participation of edge devices and their diverse training objectives can lead to instability in model convergence, affecting overall system performance. To address this challenge, this paper proposes a device selection strategy based on task completion probability to determine participating devices dynamically in each training round. Furthermore, to balance system resource consumption and model performance, we formulate an optimization objective to minimize the loss function under resource constraints. By leveraging theoretical analysis, we reformulate the objective as a loss upper bound minimization problem related to resource allocation, which is subsequently decomposed into multiple subproblems for iterative solving. Simulation results demonstrate that the proposed method achieves superior resource efficiency and training stability. Compared to the state-of-the-art HFL method, DSRA-HFL reduces the average training delay by approximately 18% and energy consumption by 22% under dynamic conditions, while maintaining a competitive model accuracy. This validates the effectiveness of our joint optimization strategy in practical IIoT scenarios. Full article

Blockchain technology, with its characteristics of decentralization, immutability, auditability, and traceability, has gradually become a core infrastructure in the digital economy era, demonstrating great potential in fields such as finance, government services, and the Internet of Things (IoT). However, as the scale of blockchain networks expands and data volumes surge, issues such as full-node storage redundancy, limited transaction throughput, and inefficient synchronization of historical data have become increasingly prominent, severely restricting the large-scale application of blockchain systems. The storage scalability problem faced by blockchain is therefore becoming more critical. To address the challenge in which on-chain storage expansion still cannot meet the demand for large-scale data storage, a storage method combining the InterPlanetary File System (IPFS) with blockchain, referred to as IPFS-BC, is proposed. In IPFS-BC, large-scale raw data are stored in the decentralized and content-addressable IPFS network, while the blockchain only retains the unique content identifier (CID) hash and related metadata. Through smart contracts enabling dynamic permission management and fine-grained access control, efficient interaction and collaborative storage between on-chain and off-chain systems are achieved. In this work, file upload simulation experiments were conducted, and two evaluation indicators—storage space consumption and storage performance (file read/write time and speed)—were used to compare three storage approaches: Distributed Hash Table (DHT)-based off-chain storage, Financial Blockchain Shenzhen Open Source (FISCO BCOS) on-chain storage, and the IPFS-BC on-chain/off-chain collaborative storage model. Experimental results show that the IPFS-BC model reduces storage space consumption by approximately 75% compared with FISCO BCOS blockchain storage when storing file data, significantly decreasing data redundancy. Moreover, IPFS-BC ensures system security during the on-chain process, and through the automated management and auditing provided by smart contracts, it effectively enhances system security and realizes scalable on-chain/off-chain collaborative storage. Full article

Over the last decade, there has been a significant increase in banks’ investment in technology, alongside a substantial rise in CEO compensation. Research on executive compensation has primarily focused on traditional performance metrics, such as return on assets and return on equity, as well as governance factors. Investigating the nexus between fintech adoption and CEO compensation introduces a new perspective on the determinants of CEO pay and how technological transformation influences executive remuneration structures. This study investigated the relationship between Chief Executive remuneration and fintech adoption among banks listed on the Johannesburg Stock Exchange. There is a lack of literature on the impact of technology adoption on CEO compensation in developing and emerging economies. The quantitative longitudinal study, conducted over 15 years from 2010 to 2024, collected secondary data from the annual reports of six banks and the IRESS database. A panel data fixed effects regression analysis was employed to analyze the data. CEO compensation included both salary and total compensation. Fintech variables used for the study included automated teller machines, mobile banking, and internet banking. The findings revealed a positive relationship between CEO salary and the rollout of ATMs and mobile banking, while an inverse relationship was noted between salary and internet banking. Similarly, total compensation showed an inverse relationship with the adoption of ATMs and internet banking, whereas mobile banking had a positive effect on total compensation. Understanding how technology impacts CEO compensation can help remuneration committees ensure that CEO pay is linked to the value that infrastructure investments bring to an organization, rather than simply the number of innovations introduced. This understanding will also help solve the principal-agent problem, as it will ensure technology innovations that enhance firm performance are rewarded. In the context of emerging markets, the study’s findings suggest that organizations should recognize and formalize pay linked to digital transformation, rather than focusing solely on short-term financial metrics. This also suggests the need to develop guidelines for executive remuneration disclosure related to the technology sector. The close connection between fintech adoption and technological and regulatory risks highlights the need to balance incentive structures that reward innovation with risk-adjusted performance measures. Full article

In recent years, there has been increasing investment in the deployment of massive commercial Low Earth Orbit (LEO) constellations to provide global Internet connectivity. These constellations, now equipped with inter-satellite links, can serve as low-latency Internet backbones, requiring LEO satellites to act not only as access nodes for ground stations, but also as in-orbit core routers. Due to their high velocity and the resulting frequent handovers of ground gateways, LEO networks highly stress mobility procedures at both the sender and receiver endpoints. On the other hand, a growing trend in networking is the use of technologies based on the Information Centric Networking (ICN) paradigm for servicing IoT networks and sensor networks in general, as its addressing, storage, and security mechanisms are usually a good match for IoT needs. Furthermore, ICN networks possess additional characteristics that are beneficial for the massive LEO scenario. For instance, the mobility of the receiver is helped by the inherent data-forwarding procedures in their architectures. However, the mobility of the senders remains an open problem. This paper proposes a comprehensive solution to the mobility problem for massive LEO constellations using the Named-Data Networking (NDN) architecture, as it is probably the most mature ICN proposal. Our solution includes a scalable method to relate content to ground gateways and a way to address traffic to the gateway that does not require cooperation from the network routing algorithm. Moreover, our solution works without requiring modifications to the actual NDN protocol itself, so it is easy to test and deploy. Our results indicate that, for long enough handover lengths, traffic losses are negligible even for ground stations with just one satellite in sight. Full article

Emergency scenarios in the Internet of Vehicles (IoV) face significant challenges due to the stringent requirements for ultra-reliable and low-latency communication under high-mobility conditions. This paper proposes a cooperative transmission framework for semantic communication to address these challenges. We introduce a knowledge graph-based approach to represent information as semantic triples (structured entity-relation-attribute representations), whose importance is quantified using a Zipf distribution, enabling prioritized transmission. At the physical layer, a semantic-aware cooperative communication scheme is proposed to combat fading and enhance transmission reliability. The joint optimization of the number of transmitted triples and node power allocation is formulated as a cross-layer problem. To tackle this Mixed-Integer Nonlinear Programming (MINLP) problem with a hybrid action space, we employ the Multi-Pass Deep Q-Network (MP-DQN) algorithm, which is specifically designed for problems with hybrid discrete-continuous action spaces. Simulation results demonstrate that our framework dynamically adapts to channel states and semantic value, achieving up to 85% end-to-end success rate and improving convergence speed by approximately 40% compared to conventional methods. Full article

Background: Although smartphone usage is inevitable and convenient in recent days, numerous potential problems due to excessive smartphone use (ESU) have been highlighted. With the rising concern about ESU, the focus on exploring the relationship between ESU and personality traits and their neural correlations also increased; however, studies that explore these factors simultaneously are lacking. Objective: This study investigated whether altered resting state functional connectivity (rsFC) is related to personality traits in adolescents exhibiting ESU compared to healthy controls (HCs). Methods: Thirty-one adolescents exhibiting ESU and 31 HCs (62 adolescents) aged 12–18 years were included in this study. Seed-to-voxel connectivity analysis was used to examine group differences in rsFC in the middle cingulate cortex (MCC) and insula, key parts of the salience network, in relation to personality traits. Results: Adolescents exhibiting ESU showed trends toward low persistence and high harm avoidance in terms of personality traits. Additionally, they exhibited enhanced rsFC between the MCC and insula but reduced rsFC between the precentral and postcentral gyri compared with HCs. Notably, increased rsFC between the MCC and insula in the ESU group was negatively correlated with low persistence. Conclusions: ESU was associated with low persistence at the uncorrected threshold in terms of personality traits and involved in neuro-functional alterations between the key hubs of the salience network, MCC, insula, and several other brain regions. These findings may provide a neurobiological basis for intervention targeting behavioral addiction in youth. Accordingly, adolescents with low persistence may need tailored education on appropriate and controlled use of smartphones and internet-based technologies. Full article

Today, vaccines play a crucial role in ensuring personal safety and are the most effective method for preventing related diseases. The ages over which vaccines are efficacious, from infancy to the old, is of utmost importance. With the recent outbreak of COVID-19 in 2019, the demand for vaccines and their usage has significantly increased. This surge in demand has led to issues such as vaccine counterfeiting and related problems, which have raised concerns among the public regarding vaccine administration. As a result, this has also resulted in a lack of trust in vaccine manufacturing companies and raised doubts about production processes. To address these concerns, this study proposed a vaccine production supervision system with Internet of Things (IoT) device based on blockchain. By utilizing IoT devices, vaccine-sensitive production data can be collected and encrypted and leaks that could lead to great benefit losses for vaccine manufacturing companies can also be prevented. This system adopts a digital signature technique to import immutable characteristics to the data, offering conclusive evidence in case any issues occur with the vaccine in the future. Finally, the system also integrates with the Inter Planetary File System (IPFS) with a blockchain solution, storing manufacturing plant vaccine production records in a secure, publicly accessible, and decentralized storage space, and also enabling public verification. Full article

Background/Objectives: The number of overweight and obese people has significantly increased in the world, and this phenomenon is referred to as globesity. Globally increasing obesity has become one of the major problems to be dealt with for countries, given obesity-related health problems, including nutrition-related noncommunicable diseases and some types of cancer, and the economic and social costs of obesity. Therefore, countries try to combat obesity through diverse strategies related to nutrition, physical activity, and education. In this regard, identifying the factors behind obesity is critical to making progress in the fight against obesity. Methods: This study explores the interplay amongst ICT (information and communication technologies) indicators, including Internet and mobile phone usage, unemployment, and adult obesity in the BRICS states from 1995 to 2022, using recently developed cointegration techniques and causality tests. Results: The outcomes of causality tests uncover an interaction between Internet and mobile phone usage, unemployment, and adult obesity. In addition, the cointegration coefficients reveal that Internet and mobile phone usage positively impact adult obesity, while unemployment has a negative effect on adult obesity. Conclusions: Our outcomes uncover that improper use of the Internet and mobile phones foster adult obesity, but proper utilization of the Internet and mobile phones can be effective instruments in combatting adult obesity through increasing the awareness of healthy lifestyles and online weight loss programs. Full article

Background/Objectives: The aim of this study was to investigate the experiences of Swedish patients with inflammatory bowel disease (IBD) regarding intimacy and sexuality-related issues, and to explore both patients’ and healthcare professionals’ perspectives on discussing these topics. Methods: This cross-sectional cohort study used two internet-based questionnaires: one targeting patients and the other healthcare professionals. The patient survey examined the impact of IBD and its treatment on relationships and sexuality, as well as expectations on healthcare support. The survey of healthcare professionals focused on experiences of discussing sexuality-related topics with IBD patients. Responses were analyzed using both quantitative and content analysis. Results: A total of 556 IBD patients and 118 healthcare professionals responded. Among patients, 78% reported difficulties related to relationships and sexuality, with physical symptoms like pain, fecal urgency, and bloating, and psychological problems such as fear of leakage and reduced sexual desire. Over half wished for these issues to be addressed in routine care, yet 84% had never initiated such discussions themselves. Among healthcare professionals, 23% never addressed issues of relationship and sexuality with patients, and another 50% did so only occasionally. Only 15% had access to qualified sexologists for referrals, and just 8% offered sexual rehabilitation after pelvic surgery. Conclusions: Sexual health is frequently compromised in IBD patients, especially in women, but remains insufficiently addressed in clinical practice. Both patients and healthcare professionals expressed a need for more open discussions about relationships and sexuality. Improving care requires routine screening, multidisciplinary support, and the development of guidelines for managing sexual dysfunction in IBD. Full article

Driven by advancements in communication technology, the Internet of Vehicles (IoV) has gained significant importance. Its capability for real-time information exchange and processing substantially enhances data transmission performance within multi-node distributed systems. Among core physical layer transmission technologies, beamforming and power allocation are crucial for optimizing system efficiency. However, the real-time joint optimization of the transmitter, receiver, and power allocation in MIMO-based IoV systems remains insufficiently addressed in existing research. To bridge this gap, this paper proposes a framework for the real-time joint optimization of beamforming and power allocation, aiming to maximize transmission efficiency while satisfying constant modulus constraints and power limitations. The proposed framework decomposes the problem and utilizes the CVX library to obtain a local optimum for the joint scheme. The simulation results show that compared with traditional beamforming methods, this scheme has better performance in multiple indicators, increasing the transmission rate of the system by 43%, having faster convergence speed, and improving spectral efficiency. Thus, this study achieves real-time joint optimization of MIMO beamforming and power allocation for IoV scenarios, providing crucial technical support for related designs. Full article