Search Results (1,002)

In this study, we present experimental investigations of the surface structure and water contact angles of magnetoactive elastomers (MAEs), which are controlled by an external magnetic field. Specifically, we examine how the polymer matrix architecture affects the surface roughness and wettability of MAEs in various magnetic fields. We performed a comparative analysis on MAEs based on a linear polysiloxane network and on a matrix of the same chemical nature containing side-grafted chains. We synthesized a series of magnetoactive elastomers containing 75 wt.% carbonyl iron and varying amounts of a low-molecular-weight plasticizer. Although the magnetorheological effect is higher for traditional linear MAEs, we found that the magnetic response in surface properties is higher for novel MAEs with side-grafted chains. The largest increase in water contact angle was observed in the side-chain MAEs with the highest 60 wt.% plasticizer content: rising from 112° in a zero field to 168° in a 490 mT magnetic field. Water contact angles exhibit greater stability over time for side-chain MAEs, and this stability further increases in the presence of a magnetic field. Our results demonstrate that the architecture of the polymer matrix serves as an effective tool for designing smart, magnetically responsive surfaces. Full article

With the increasing deployment of unmanned aerial vehicle (UAV) swarms in scenarios such as disaster response, environmental monitoring, and military reconnaissance, the need for secure and scalable formation control has become critical. Traditional centralized architectures face challenges such as limited scalability, communication bottlenecks, and single points of failure in large-scale swarm coordination. To address these issues, this paper proposes a blockchain-based decentralized formation control framework that integrates smart contracts to manage UAV registration, identity authentication, formation assignment, and positional coordination. The system follows a leader–follower structure, where the leader broadcasts formation tasks via on-chain events, while followers respond in real-time through event-driven mechanisms. A parameterized control model based on dynamic angle and distance adjustments is employed to support various formations, including V-shape, line, and circular configurations. The transformation from relative to geographic positions is achieved using Haversine and Euclidean methods. Experimental validation in a simulated environment demonstrates that the proposed method achieves lower communication latency and better responsiveness compared to polling-based schemes, while offering enhanced scalability and robustness. This work provides a feasible and secure decentralized control solution for future UAV swarm systems. Full article

In the context of mounting climate impacts and growing urgency to meet the Paris Agreement goals, the ocean is now increasingly being recognised not just as a victim of climate change, but as an indispensable part of the solution. Research has demonstrated that readily actionable ocean-based climate solutions can help close the emissions gap (the difference between the greenhouse gas emission reductions needed to limit global warming to 1.5 °C, and projected global emissions considering current national pledges and policies) by providing approximately a third of the mitigation needed to keep the Paris Agreement’s 1.5 °C goal within reach. This mitigation potential (of fully actioning these solutions) is unequally divided across seven key ocean-based action areas (listed in decreasing order of magnitude): phasing out offshore oil and gas; deploying offshore renewable energy infrastructure; decarbonising maritime transport and associated infrastructure; decarbonising ocean and aquatic food value chains; carbon capture and storage; marine and coastal conservation and restoration; and decarbonising coastal tourism. We argue that achieving the full potential of ocean climate solutions will require smart governance, drastically increased financial investment, and international cooperation. Accomplishing this, however, will bring strong co-benefits for biodiversity, food systems, and coastal resilience. The Third United Nations Ocean Conference and 30th United Nations Climate Change Conference of the Parties (COP 30) present rare opportunities to mainstream the ocean into global climate strategies. Full article

Old communities are the weak link in urban security resilience, and smart governance could be a useful tool to address this issue. However, the existing research does not provide a definitive explanation of the mechanisms through which smart governance affects resilience. Based on the Accident Causation Theory and the ABC Theory of Emotion, a mixed-methods approach utilizing NCA and SEM is used to analyze the impact of smart digital technology on the security resilience of old urban communities and to explore the mediating roles of residents’ sense of safety and safety behaviors. The findings from on old urban communities in China reveal that smart digital technology and residents’ safety compliance behaviors are essential for community security resilience. Smart digital technology significantly and positively influences the security resilience of old urban communities. Residents’ sense of safety and safety participation behaviors mediate the relationship between smart digital technology and security resilience of old urban communities; residents’ sense of safety, safety compliance behaviors, and safety participation behaviors also exhibit a chain mediating role in the relationship between smart digital technology and security resilience of old urban communities. Therefore, old urban communities need to strengthen the application of smart digital technologies, while considering the human factor and emphasizing the facilitating role of residents’ sense of safety and safety behaviors. Full article

This study examines the effects of supply chain pressure, smart AI, and socio-economic fairness on long-term economic sustainability. To this end, this study uses quarterly data from 1999 Q1 through 2024 Q4 for the United States and employs the recently introduced Wavelet Cross-Quantile Regression (WCQR) to analyze this relationship. This study finds that smart AI, supply chain pressure (SC), and renewable energy consumption (REC) significantly drive U.S. economic growth, with the strongest short-term effects appearing when adoption and output are in the lower quantiles, reflecting threshold and diffusion dynamics. SC enhances growth once supply chain networks reach a critical level of connectivity, while REC generates substantial gains at low penetration levels, illustrating a “catch-up” effect. In contrast, economic inequality (EI) generally dampens growth, especially at moderate to high inequality levels; however, long-term reductions in EI yield positive returns in high-growth states by improving social cohesion and workforce productivity. Based on these findings, this study proposes funding low-adoption AI now, scaling to mid-adoption users mid-term, and entrenching long-term gains through economy-wide upskilling. Full article

Food packaging systems play a critical role in reducing resource wastage, extending shelf-life, and enhancing supply chain sustainability. Carbon dots (CDs) have emerged as promising nanofillers for sustainable active and smart packaging due to their exceptional optical properties, biocompatibility, and antimicrobial activity. This review synthesizes recent advances in CD-based food packaging technologies, focusing on their multifunctional applications and performance enhancements. We systematically analyze how CDs improve packaging materials’ mechanical strength, gas barrier properties, and functional performance (antioxidant, antimicrobial, and smart sensing capabilities). Current research demonstrates CDs’ ability to enable intelligent functions such as pH responsiveness and freshness monitoring while maintaining excellent biocompatibility. However, challenges remain in scaling up production, long-term toxicological evaluation, and matrix compatibility. Future research directions should address these limitations while exploring the full potential of CD-based multifunctional films as sustainable alternatives for next-generation food packaging systems. Full article

Agroforestry landscape has undergone changes, namely land abandonment, which when combined with negative attitudes towards fire, is associated with the eradication of agroforestry leftovers and acts towards the proliferation of fires, threatening sustainability concerns. Agroforestry leftovers recovery presents high potential to act on this problem; however, the logistical costs associated with the recovery chain make it unfeasible. The lack of coordination/transparency between stakeholders is one of the main explanations for these costs. This study develops a digital tool to enhance the residual biomass supply chain for energy recovery and fire risk mitigation. In addition to this concept, this work also proposes conceptual models and a prototype, two essential contributions to software development. Methodologically, this study consulted 10 experts to validate a concept previously presented in the literature, supplemented with UML modeling and prototyping with Figma^®. The main results point to the creation of a disruptive concept that will allow access to information/transparency about agroforestry services, with the goal that this will improve the functioning of the RBSC, resulting in a reduction in fire risk and, consequently, improvements in sustainability concerns associated with this hazard. Full article

The increasing complexity and digitization of modern supply chains have created an urgent demand for transparent, traceable, and trustworthy systems of data management. Blockchain, with its core features of immutability, decentralization, and smart contracts, has emerged as a promising solution for strengthening data-driven supply chain operations. This paper presents a narrative review synthesizing insights from academic research, industry reports, and regulatory documents to examine blockchain’s role in enhancing transparency, traceability, and trust. References were identified through targeted searches of major databases and gray literature sources, with emphasis on diverse sectors and global perspectives, rather than exhaustive coverage. The review maps how blockchain’s technical capabilities—such as data integrity preservation, access control, automated validation, and provenance tracking—support these outcomes, and assesses the empirical indicators used to evaluate them. A sectoral applicability analysis distinguishes contexts in which blockchain adoption offers clear advantages from those where benefits are limited. The review also identifies critical research gaps, including inconsistent definitions of core concepts, insufficient interoperability standards, overreliance on subjective performance measures, and lack of longitudinal cost–benefit evidence. Finally, it proposes directions for future research, including the development of sector-specific adoption frameworks, integration with complementary technologies, and cross-border regulatory harmonization. Full article

Population aging intensifies the demand for rehabilitation services, which are already suffering from staff shortages. In response to this challenge, the implementation of new technologies in physiotherapy is needed. For such a task, rehabilitation exoskeletons can be used. While designing such tools, their functionality and safety must be ensured. Therefore, simulations of their strength and kinematics must meet set criteria. This paper aims to present a methodology for simulating the dynamics of rehabilitation exoskeletons during activities of daily living and determining the reactions in the construction’s joints, as well as the required driving torques. The methodology is applied to the SmartEx-Home exoskeleton. Two versions of a multibody model were developed in the Matlab/Simulink environment—a rigid-only version and one with deformable components. The kinematic chain of construction was reflected with the driven rotational joints and modeled passive sliding open bearings. The simulation outputs include the driving torques and joint reaction forces and the torques for various input trajectories registered using IMU sensors on human participants. The results obtained in the investigation show that in general, to mobilize shoulder flexion/extension or abduction/adduction, around 30 Nm of torque is required in such a lightweight exoskeleton. For elbow flexion/extension, around 10 Nm of torque is needed. All of the reactions are presented in tables for all of the characteristic points on the passive and active joints, as well as the attachments of the extremities. This methodology provides realistic load estimations and can be universally used for similar structures. The presented numerical results can be used as the basis for a strength analysis and motor or force sensor selection. They will be directly implemented for the process of mass minimization of the SmartEx-Home exoskeleton based on computational optimization. Full article

This paper presents a hybrid computational architecture for efficient and robust digital transmission inspired by helical genetic structures. The proposed system integrates advanced modulation schemes, such as multi-pulse-position modulation (MPPM), high-order quadrature amplitude modulation (QAM), and chirp spread spectrum (CSS), along with Reed–Solomon error correction and quantum-assisted search, to optimize performance in noisy and non-line-of-sight (NLOS) optical environments, including VLC channels modeled with log-normal fading. Through mathematical modeling and simulation, we demonstrate that the number of helical transmissions required for genome-scale data can be drastically reduced—up to 95% when using parallel strands and high-order modulation. The trade-off between redundancy, spectral efficiency, and error resilience is quantified across several configurations. Furthermore, we compare classical genetic algorithms and Grover’s quantum search algorithm, highlighting the potential of quantum computing in accelerating decision-making and data encoding. These results contribute to the field of operations research and supply chain communication by offering a scalable, energy-efficient framework for data transmission in distributed systems, such as logistics networks, smart sensing platforms, and industrial monitoring systems. The proposed architecture aligns with the goals of advanced computational modeling and optimization in engineering and operations management. Full article

The integration of blockchain technology into healthcare offers a robust solution to challenges in secure data sharing, privacy protection, and operational efficiency. Effective exchange of sensitive patient information among hospitals, clinics, insurers, and researchers is essential for better outcomes and medical advancements. Traditional centralized systems often suffer from data breaches, inefficiency, and poor interoperability. This paper presents a blockchain-based secure data-sharing framework tailored for healthcare, addressing these limitations. The framework employs a hybrid blockchain model, combining private and public blockchains: the private chain ensures fast transactions and controlled access, while the public chain fosters transparency and trust. Advanced cryptographic methods—such as asymmetric encryption, hashing, and digital signatures—safeguard patient data and maintain integrity throughout the datalifecycle. Smart contracts automate processes like consent management, access control, and auditing, ensuring dynamic permission enforcement without intermediaries. Role-based access control (RBAC) further limits access to authorized entities, enhancing privacy. To tackle interoperability, standardized data formats and protocols enable smooth communication across diverse healthcare systems. Large files, such as medical images, are stored off-chain, with only essential metadata and logs on the blockchain. This approach optimizes performance, scalability, and suitability for large-scale healthcare deployments. Full article

The cold-chain supply of perishable fruits continues to face challenges such as fuel wastage, fragmented stakeholder coordination, and limited real-time adaptability. Traditional solutions, based on static routing and centralized control, fall short in addressing the dynamic, distributed, and secure demands of modern food supply chains. This study presents a novel end-to-end architecture that integrates multi-agent reinforcement learning (MARL), blockchain technology, and generative artificial intelligence. The system features large language model (LLM)-mediated negotiation for inter-enterprise coordination, Pareto-based reward optimization balancing spoilage, energy consumption, delivery time, and climate and emission impact. Smart contracts and Non-Fungible Token (NFT)-based traceability are deployed over a private Ethereum blockchain to ensure compliance, trust, and decentralized governance. Modular agents—trained using centralized training with decentralized execution (CTDE)—handle routing, temperature regulation, spoilage prediction, inventory, and delivery scheduling. Generative AI simulates demand variability and disruption scenarios to strengthen resilient infrastructure. Experiments demonstrate up to 50% reduction in spoilage, 35% energy savings, and 25% lower emissions. The system also cuts travel time by 30% and improves delivery reliability and fruit quality. This work offers a scalable, intelligent, and sustainable supply chain framework, especially suitable for resource-constrained or intermittently connected environments, laying the foundation for future-ready food logistics systems. Full article

In increasingly complex Engineer-to-Order (EtO) supply chains, accurately predicting supplier delivery delays is essential for ensuring operational resilience. This study proposes an intelligent Internet of Things (IoT)-enhanced probabilistic framework for early warning and dynamic prediction of supplier lead times in smart manufacturing contexts. Within this framework, three novel Early Warning Systems (EWS) are introduced: the Baseline Probabilistic Alert System (BPAS) based on fixed thresholds, the Smart IoT-Calibrated Alert System (SIoT-CAS) leveraging IoT-driven calibration, and the Adaptive IoT-Driven Risk Alert System (AID-RAS) featuring real-time threshold adaptation. Supplier lead times are modeled using statistical distributions and dynamically adjusted with IoT data to capture evolving disruptions. A comprehensive Monte Carlo simulation was conducted across varying levels of lead time uncertainty (σ), alert sensitivity ($P_{threshold}$), and delivery constraints ($L_{max}$), generating over 1000 synthetic scenarios per configuration. The results highlight distinct trade-offs between predictive accuracy, sensitivity, and robustness: BPAS minimizes false alarms in stable environments, SIoT-CAS improves forecasting precision through IoT calibration, and AID-RAS maximizes detection capability and resilience under high-risk conditions. Overall, the findings advance theoretical understanding of adaptive, data-driven risk modeling in EtO supply chains and provide practical guidance for selecting appropriate EWS mechanisms based on operational priorities. Furthermore, they offer actionable insights for integrating predictive EWS into MES (Manufacturing Execution System) and digital control tower platforms, thereby contributing to both academic research and industrial best practices. Full article

The Internet of Things (IoT) is widely used across domains such as smart homes, healthcare, and grids. As billions of devices become connected, strong privacy and security measures are essential to protect sensitive information and prevent cyber-attacks. However, IoT devices often have limited computing power and storage, making it difficult to implement robust security and manage large volumes of data. Existing studies have explored integrating blockchain and Deep Neural Networks (DNNs) to address security, storage, and data dissemination in IoT networks, but they often fail to fully leverage the mutual enhancement between them. This paper proposes DeepChainIoT, a blockchain–DNN integrated framework designed to address centralization, latency, throughput, storage, and privacy challenges in generic IoT networks. It integrates smart contracts with a Long Short-Term Memory (LSTM) autoencoder for anomaly detection and secure transaction encoding, along with an optimized Practical Byzantine Fault Tolerance (PBFT) consensus mechanism featuring transaction prioritization and node rating. On a public pump sensor dataset, our LSTM autoencoder achieved 99.6% accuracy, 100% recall, 97.95% precision, and a 98.97% F1-score, demonstrating balanced performance, along with a 23.9× compression ratio. Overall, DeepChainIoT enhances IoT security, reduces latency, improves throughput, and optimizes storage while opening new directions for research in trustworthy computing. Full article

As the global digital transformation of agriculture accelerates, the widespread deployment of farming equipment has triggered an exponential surge in agricultural production data. Consequently, traditional cloud computing frameworks face critical challenges: communication latency in the field, the demand for low-power devices, and stringent real-time decision constraints. These bottlenecks collectively exacerbate bandwidth constraints, diminish response efficiency, and introduce data security vulnerabilities. In this context, edge computing offers a promising solution for smart agriculture. By provisioning computing resources to the network periphery and enabling localized processing at data sources adjacent to agricultural machinery, sensors, and crops, edge computing leverages low-latency responses, bandwidth optimization, and distributed computation capabilities. This paper provides a comprehensive survey of the research landscape in agricultural edge computing. We begin by defining its core concepts and highlighting its advantages over cloud computing. Subsequently, anchored in the “terminal sensing-edge intelligence-cloud coordination” architecture, we analyze technological evolution in edge sensing devices, lightweight intelligent algorithms, and cooperative communication mechanisms. Additionally, through precision farming, intelligent agricultural machinery control, and full-chain crop traceability, we demonstrate its efficacy in enhancing real-time agricultural decision-making. Finally, we identify adaptation challenges in complex environments and outline future directions for research and development in this field. Full article