Search Results (65)

Unsaturated polyester resins (UPRs) represent challenging systems for reversible-deactivation radical polymerization (RDRP) because chain propagation, branching, and localized gelation may occur concurrently. This study systematically investigates the influence of the concentration of the RAFT agent 2-cyano-2-propyl dodecyl trithiocarbonate (CPDT), the chemical structure of the polyester prepolymer, and the nature of the acidic comonomer on the structural evolution of RAFT-modified unsaturated polyester copolymers. Three copolymer series synthesized at different CPDT concentrations were investigated: p-EGM:AA:[CPDT], p-EGM:MAA:[CPDT], and p-PGM:MAA:[CPDT]. Structural changes were characterized using H NMR, H–H COSY, UV–Vis spectroscopy, and gel permeation chromatography (GPC). Semi-quantitative analysis of normalized H NMR integral intensities was performed using Relative Vinyl Intensity (RVI), CPDT-associated methyl intensity (MI*), and normalized aliphatic intensity (AI*) to compare changes in selected proton environments among the investigated copolymer series. Increasing CPDT concentration was accompanied by a decrease in the normalized residual maleate vinyl signal, although the magnitude of this change depended strongly on copolymer composition. The most pronounced decrease in RVI was observed for the p-EGM:AA:[CPDT] series, from 0.6291 to 0.0528, whereas substantially smaller changes were observed for the p-EGM:MAA:[CPDT] series. The MI* and AI* profiles exhibited composition-dependent variations, reflecting changes in the relative contributions of CPDT-associated methyl and overlapping aliphatic proton environments, respectively. Because the aliphatic region used for AI* contains overlapping polymer- and CPDT-derived contributions, AI* is not interpreted as a quantitative measure of polymer-backbone branching. Overall, the combined NMR and GPC/SEC results reveal composition-dependent structural changes accompanying RAFT copolymerization and demonstrate that both the polyester matrix and the acidic comonomer influence the response of these heterogeneous unsaturated polyester systems to variations in CPDT concentration. Full article

With the advancement of smart city technologies and the proliferation of Mobility as a Service (MaaS), realizing seamless navigation that continuously connects heterogeneous mobility modes and indoor–outdoor spaces has emerged as a critical challenge. However, existing navigation services operate in a fragmented, siloed manner, segmented by transport mode and spatial environment, and thus possess fundamental limitations in supporting continuous mobility. This study establishes an analytical framework comprising the four core components of navigation systems (positioning, navigation data, route planning, and guidance) and critically reviews 108 prior studies identified through purposive sampling from Web of Science, Scopus, and Google Scholar to evaluate the technical requirements and the level of seamless integration achieved for each component. The analysis reveals that while each component has reached a high level of maturity within its individual domain, four critical technical gaps persist across all components: positioning handover discontinuities at indoor–outdoor transition zones, structural and semantic inconsistencies between heterogeneous spatial datasets, static route planning that fails to account for transition-space uncertainties, and guidance systems whose context resets upon changes in transport mode. These gaps originate not from insufficient performance of individual technologies but from a systematic lack of research at the interface points between components. Overcoming these challenges necessitates a comprehensive redesign of the integrated system architecture, encompassing dynamically adaptive multi-sensor fusion positioning, hierarchical heterogeneous data integration models, probabilistic cost modeling for transition spaces, and adaptive guidance systems based on automatic context handover. Full article

Achieving strong adhesion in underwater or humid environments remains challenging because the interfacial hydration layer prevents direct contact between the adhesive and the substrate. Conventional adhesives typically fail under these conditions, so new strategies are needed to actively displace the water layer and create stable interfacial interactions. In this study, we prepared a series of copolymers with different monomer ratios via photocuring, using methacrylic acid (MAA) and stearyl methacrylate (SMA) as monomers. We focused on their thermal transition behavior and adhesion performance under both dry and underwater conditions. The results show that at an SMA molar fraction of 85%, the copolymer exhibits crystalline melting between 30 and 40 °C, where the storage modulus drops from approximately 10⁷ Pa to 10⁴ Pa, indicating a stiff-to-soft transition. Under dry conditions, this composition shows an adhesion strength of 1.67 MPa to glass, which remains 1.2 MPa underwater, and it can support a hanging load of 5 kg. The copolymer adheres well to glass and aluminum but shows weak adhesion to PTFE. After surface abrasion, the adhesion strength to glass increases to 1.6–1.8 MPa. In summary, the copolymer achieves effective underwater adhesion through the synergy of hydrophobic water displacement, thermally induced stiff-to-soft switching, and hydrogen bonding. Full article

Background: Autoimmune rheumatic diseases (ARDs) often present diagnostic challenges, particularly in undifferentiated disease or overlap syndromes. Autoantibodies (AABs) serve as early biomarkers, but their relationship with cardiac involvement during the prodromal phase remains unclear. We hypothesized that cardiac involvement is an early, unifying feature in AAB-seropositive patients with suspected ARD/overlap syndromes but an as-of-yet unclear diagnosis. Methods: We prospectively recruited 18 treatment-naïve patients (mean age 52 ± 17 years, 94.4% women) with suspected undifferentiated ARD/overlap syndromes who were seropositive for myositis-specific (MSAs), myositis-associated (MAAs), or scleroderma-specific autoantibodies (SScSAs). All underwent comprehensive rheumatologic, pulmonologic, and cardiac evaluations, including multiparametric cardiovascular magnetic resonance (CMR) to assess myocardial inflammation, edema, and fibrosis. Results: Despite normal echocardiograms, electrocardiograms, and inflammatory biomarkers, all patients exhibited CMR evidence of cardiac involvement. Active myocardial inflammation (revised Lake Louise criteria) was confirmed in 66.7%, while subepicardial fibrosis was universal (median 5.0% of LV mass). During the 12-month follow-up, all patients with evidence of inflammation received immunosuppressive and cardioprotective therapy, leading to symptomatic improvement in all and reduced inflammation in 75% of repeat CMRs (3/4 patients). A definitive rheumatologic diagnosis was established in all cases, with 50% classified as overlap syndromes. Conclusions: Cardiac involvement is a highly prevalent disease manifestation in AAB-seropositive patients with suspected ARD/overlap syndromes and can be detected by CMR during the prodromal phase, even before diagnostic criteria are met. These findings support early CMR integration in the workup of such patients to guide timely immunosuppressive and cardioprotective interventions. Full article

Chronic wounds, particularly those complicated by infection, present significant challenges in clinical management. The microenvironment of these wounds is typically characterized by the accumulation of reactive oxygen species (ROS) and abnormal local pH levels, both of which impede the healing process. Baicalin (BA), a natural flavonoid, exhibits anti-inflammatory activity, ROS-scavenging capability, and pro-healing effects. In this study, hydrogels were synthesized through photoinitiated radical polymerization of methacrylic anhydride (MAA) and dopamine (DA)-modified chondroitin sulfate (ChSMA-DA), grafting degrees of MA and DA were 58%, 23%, MPDA@MnO₂ nanoparticles (NPs), and methacrylated gelatin (GelMA). The gelation time, microtopography, swelling behavior, and water retention of the hydrogels were investigated, along with their degradation, rheological properties, and photothermal effects. The results indicate that swelling ratio (SR) and water retention (WR) of optimal HG-MPDA@MnO₂-M sample were 5.7, 82.42%, exhibited responsive behavior upon weakly acidic environment with pH 6.5 and elevated ROS levels, and exhibited a stable photothermal effect (photothermal conversion efficiency was 22.7%) under 808 nm near-infrared (NIR) light. Following the incorporation of the drug model BA, the cumulative release percentage over 24 h under the combined stimulation of pH 6.5, 1 mmol·L⁻¹ H₂O₂, and 808 nm NIR was 81.1%, significantly higher than either factor alone. These hydrogels show promise as an injectable dressing for chronic wounds, effectively integrating the internal microenvironment of the wound tissue with external NIR to modulate drug release. Full article

Disruptions threaten supply chains, creating a need for more resilient manufacturing networks. Manufacturing-as-a-Service (MaaS) has emerged as a promising Industry 4.0 approach to address this challenge. Yet, its effectiveness relies on interoperable digital twins (DTs), enabling the standardized exchange of manufacturing capabilities across organizational boundaries. The Asset Administration Shell (AAS) standards can be used to meet this requirement. However, modeling AAS-compliant DTs is considered challenging due to the standard’s complexity. This paper, therefore, investigates the automatic generation of AAS-compliant DTs for representing manufacturing capabilities. Requirements from MaaS use cases in two research projects reveal limitations in current approaches. To address these limitations, this paper introduces an automated, LLM-supported generation process that leverages ontologies as a domain-specific knowledge base. The approach is operationalized in a modular software architecture and demonstrated through two use cases. Full article

Metro train timetable rescheduling (TTR) is a critical task for ensuring the reliability of urban rail transit systems. However, with the increasing density of railway networks and the growing number of operational trains, TTR has evolved into a typical high-dimensional and large-scale optimization problem. Traditional mathematical programming and heuristic approaches often struggle with the “curse of dimensionality” and fail to provide real-time responses under stochastic disturbances. To address these challenges, this paper proposes a novel framework based on Multi-Agent Deep Reinforcement Learning (MADRL). Specifically, we model the TTR problem as a decentralized cooperative process and utilize the Multi-Agent Advantage Actor-Critic (MAA2C) algorithm to optimize train schedules dynamically. The proposed framework is implemented within the Flatland simulation environment, which allows for the representation of complex arbitrary topologies. We design a composite reward function that minimizes total delay deviation while maximizing passenger satisfaction, subject to constraints such as headway, operating time, and train capacity. Furthermore, to enhance the robustness of the model against high-dimensional state uncertainties, random disturbances following a negative exponential distribution are introduced during training. Experimental results across various scenarios—ranging from simple dual-track to complex random networks—demonstrate that the MAA2C-based approach significantly outperforms traditional baselines. It not only achieves faster convergence in small-scale scenarios but also demonstrates superior computational efficiency and scalability in large-scale environments, effectively minimizing passenger waiting times. This study validates the potential of MADRL in solving high-dimensional traffic control problems for intelligent transportation systems. Full article

Accurate cropland parcel mapping is essential for food security and sustainable land management in arid Africa, yet it remains challenging in Egypt due to edge blurring, spectral confusion, and fragmented fields in medium-resolution imagery. A novel dual-encoder deep learning method that integrates multi-scale axial attention and boundary constraints (MAA-BCNet) is proposed for the precise extraction of croplands in Egypt from Sentinel-2 multispectral images. A dual-path encoder is designed to fuse CNN-based local textures with an RMT global branch using spatial decay attention for complementary feature extraction. A multi-scale axial attention module is introduced to capture anisotropic parcel structures for improved spectral–spatial discrimination, and a multi-directional gradient edge enhancement module is developed for explicitly preserving boundary integrity. A U-Net++ decoder is employed for dense multi-scale aggregation. Experimental results in Egypt demonstrate that MAA-BCNet achieves superior performance in delineating cropland parcels, particularly for irregular or fragmented croplands with complex landscapes and fuzzy boundaries. Compared with the widely used segmentation models such as DeepLabV3_plus, PSPnet, Link_net, FCN_resnet101, and U-Net++ under the same training and evaluation settings, our model has the best performance, with Recall, Precision, IoU, and F1-Score reaching 94.92%, 90.77%, 86.57%, and 92.80%, respectively. These advancements make MAA-BCNet suitable for cropland mapping of large areas of Egypt, with applications in precision agriculture and sustainable land management. Full article

Promoting and increasing sustainable mobility has become more of a focus in transport and mobility policies and plans. However, challenges remain in its implementation in low-density urban areas, which are usually highly dependent on private motorised transport. This study investigates how local actors and citizens in a low-density suburban area perceive the main mobility challenges and opportunities, contributing empirical evidence on how collaborative planning operationalises accessibility-oriented mobility models in low-density suburban territories, an under-researched context in sustainable mobility. It also examines how co-creation processes contribute to identifying barriers and priorities and to what extent proximity-based concepts such as the 15-Minute City, Transit-Oriented Development (TOD), and Mobility as a Service (MaaS) can be reinterpreted for low-density suburban realities. The methodological approach involved three focus groups with local actors and citizens to identify barriers, priorities, and strategies through collective discussion and co-creation. This process resulted in an agreement on eight (8) co-created strategies, revealing convergence towards promoting active modes and public transport and emphasising that accessibility depends on territorial redesign, digital integration, and inclusive governance. The findings contribute to the empirical evidence that participatory and context-sensitive approaches can enable sustainable mobility transitions in suburban areas by efficiently meeting people’s needs and aspirations. Full article

Broadband sound absorption has long been a concern in noise control engineering, but the inverse design of multi-parallel microperforated panels (MPPs) for broadband sound absorption remains challenging. To address this issue, we propose a deep learning model that combines a variational autoencoder (VAE) with a physics-informed neural network (PINN) to accelerate the inverse design process of a multi-parallel MPP. Following Maa’s theory, we generated a dataset of 500,000 samples to train the model. By incorporating the PINN, we added an acoustic physical constraint to the loss function, promoting model convergence and the derivation of stable, unified parameters. The efficacy of the inverse design model was validated through theoretical analysis, finite element simulations, and impedance tube experiments. The experimental results show that the average sound absorption coefficient of multi-parallel MPPs within the frequency range of 500–1200 Hz is 0.85. Our work contributes to accelerating the inverse design of multi-parallel acoustic metamaterials. Full article

Pickering emulsions have emerged as promising multiphase systems owing to their high stability and diverse applications in materials and chemical engineering. However, achieving precise and stimuli-responsive regulation of emulsion type, particularly reversible phase inversion between oil-in-water and water-in-oil states under fixed formulation without additional stabilizers, remains a considerable challenge. In this work, we developed a sol–gel strategy, i.e., in situ hydrolysis and condensation of silane precursors to form a silica shell directly on responsive microgels, to produce H-SiO₂@P(NIPAM-co-MAA) hybrid microgels. The resulting hybrid particles simultaneously retained pH and temperature responsiveness, enabling the transfer of these properties from the polymeric network to the emulsion interface. When employed as stabilizers, the hybrid microgels allowed the controlled formation of Pickering emulsions that remained stable for one week under testing conditions. More importantly, they facilitated in situ reversible phase inversion under external stimuli. Overall, this work establishes a sol–gel approach to fabricate organic–inorganic hybrid microgels with well-defined dispersion and uniform silica deposition, while preserving dual responsiveness and enabling controlled phase inversion of Pickering emulsions. Full article

Continuous technological, ecological, and digital transformations reshape urban mobility systems. While sustainable mobility has become a dominant keyword, there are many different approaches and policies to help achieve lasting and properly functioning change. This study applies a comprehensive qualitative policy analysis to influential and leading sustainable mobility approaches (i.a. Mobility Justice, Avoid–Shift–Improve, spatial models like the 15-Minute City and Superblocks, governance frameworks such as SUMPs, and tools ranging from economic incentives to service architectures like MaaS and others). Each was assessed across structural barriers, psychological resistance, governance constraints, and affective dimensions. The results show that, although these approaches provide clear normative direction, measurable impacts, and scalable applicability, their implementation is often undermined by fragmentation, Policy Layering, limited intermodality, weak Future-Readiness, and insufficient participatory engagement. Particularly, the lack of sequencing and pacing mechanisms leads to policy silos and societal resistance. The analysis highlights that the main challenge is not the absence of solutions but the absence of a unifying paradigm. To address this gap, the paper introduces CalmMobility, a conceptual framework that integrates existing strengths while emphasizing comprehensiveness, pacing–sequencing–inclusion, and Future-Readiness. CalmMobility offers adaptive and co-created pathways for mobility transitions, grounded in education, open innovation, and a calm, deliberate approach. Rather than being driven by hasty or disruptive change, it seeks to align technological and spatial innovations with societal expectations, building trust, legitimacy, and long-term resilience of sustainable mobility. Full article

Technological advancements are reshaping New Mobility Services (NMS) by enhancing trip planning, booking, and payment processes, while also improving fleet management, infrastructure utilization, and data-driven decision-making. Despite these developments, challenges persist in integrating technologies into cohesive and interoperable mobility systems. This study draws insights from 163 stakeholders across the NMS ecosystem to examine both the opportunities and barriers associated with the effective integration of technology into NMS, particularly within urban and metropolitan contexts. Using statistical methods, these responses were analyzed across eight stakeholder groups to determine whether their views converge or diverge. Findings reveal a broad consensus on the technologies expected to have the greatest impact, as well as on the main challenges of integrating these technologies into NMS. Divergences arise in the perceived influence on specific mobility attributes, such as environmental sustainability, security, safety, equity, and social inclusion, and in the services considered most likely to benefit. Notably, investors express a more optimistic view across nearly all technologies, prioritizing shared vehicle services and anticipating the strongest impacts in environmental sustainability. The rest of the stakeholder groups emphasize the potential of technology to enhance modal integration and identify Mobility-as-a-Service (MaaS) as the NMS with the greatest expected benefits. These insights help identify strategic priorities and redirect efforts toward promoting investment in technologies with the highest potential to deliver transformative benefits across the NMS ecosystem. Full article

Over recent years, driven by intertwined economic, social, environmental, and technological factors, urbanization has accelerated at an unprecedented pace, posing complex challenges to metropolitan transport systems. This has intensified the demand for innovative mobility solutions, notably Mobility as a Service (MaaS), which promotes a paradigm shift from private vehicle ownership to mobility consumed as a service. With rapid advances in digital technologies, MaaS has gained substantial momentum, attracting significant scholarly attention for its potential to enable intelligent and sustainable transportation systems. This study aims to provide a comprehensive conceptual foundation of MaaS and its components, and to systematically examine how artificial intelligence (AI), machine learning (ML), and big data techniques are applied in this domain. Following PRISMA guidelines, a bibliometric and systematic review was conducted on peer-reviewed articles published between 2020 and 2024 and indexed in the Scopus and Web of Science databases. The analysis classifies AI applications across four MaaS integration levels: basic, intermediate, advanced, and full integration. The results show that machine learning and basic optimization dominate at the basic level; blockchain and big data are most prominent at the advanced and full levels; and deep learning is applied across all levels, with a particularly strong presence at the advanced stage for real-time, personalized mobility solutions. The findings also indicate that while most implementations focus on developed countries, there is substantial potential for adaptation in emerging markets. The paper concludes by discussing key challenges in regulatory compliance, inclusivity, and the protection of sensitive user data, and outlines future research avenues for building socially equitable, intelligent, and sustainable MaaS ecosystems. Full article

The industrialization of cybercrime, principally through Malware-as-a-Service (MaaS), has elevated HTTP cookie theft to a critical cybersecurity challenge, enabling attackers to bypass multi-factor authentication and perpetrate large-scale account takeovers. Employing a Holistic and Integrative Review methodology, this paper dissects the intricate, adaptive ecosystem of MaaS-driven cookie theft. We systematically characterize the co-evolving arms race between offensive and defensive strategies (2020–2025), revealing a critical strategic asymmetry where attackers optimize for speed and low cost, while effective defenses demand significant resources. To shift security from a reactive to an anticipatory posture, a multi-dimensional predictive framework is not only proposed but is also detailed as a formalized, testable algorithm, integrating technical, economic, and behavioral indicators to forecast emerging threat trajectories. Our findings conclude that long-term security hinges on disrupting the underlying cybercriminal economic model; we therefore reframe proactive countermeasures like Zero-Trust principles and ephemeral tokens as economic weapons designed to devalue the stolen asset. Finally, the paper provides a prioritized, multi-year research roadmap and a practical decision-tree framework to guide the implementation of these advanced, collaborative cybersecurity strategies to counter this pervasive and evolving threat. Full article