Search Results (37,833)

Catalytic pyrolysis is a crucial technology for lignin valorization, where the catalyst support itself can play a pivotal role in influencing the catalytic process. This study systematically investigates and compares the distinct catalytic effects of two commonly used catalyst supports, HZSM-5 zeolite and activated carbon (AC), during lignin pyrolysis. Macrokinetic analysis was conducted using TGA coupled with the Friedman kinetic model to determine the apparent activation energies (Ea) and coke yields. The evolution of functional groups was analyzed using Py-GC/MS coupled with quantitative functional group indexing. Additionally, the evolution of small-molecule gases during catalytic pyrolysis was monitored using TGA-FTIR. The results demonstrate differences in the catalytic pathways promoted by HZSM-5 and AC. HZSM-5 effectively deoxygenated lignin by removing methoxy and hydroxyl groups, resulting in a reduction in Ea by 83 kJ/mol at 80% conversion and suppression of coke formation. In contrast, AC, exploiting its large specific surface area as a reaction platform, promoted the conversion of methoxy groups into methyl and hydroxyl functional groups, rather than directly removing them. Moreover, the use of AC led to a marked increase in Ea, and the coke yield increased by 2.5%. This study provides valuable insights for the rational design of efficient catalyst systems for biomass conversion. Full article

The digital era represents a paradigm shift in gifted education, moving at an accelerating pace away from traditional models toward flexible and personalized technology-based pathways. This study investigates the impact of a model implemented via the FutureX platform in Saudi Arabia on the autonomy and self-regulated learning (SRL) of 63 gifted high school students. Using a quasi-experimental design, the study integrated quantitative measures (paired t-tests) with phenomenological analysis of interviews. The quantitative results showed statistically significant improvements (p < 0.001) in the dimensions of autonomy and self-regulated learning, with large Cohen’s d effect sizes for planning (d = 1.05), monitoring (d = 1.05), and cognitive control (d = 1.30). These gains were supported by a pedagogical design intentionally embedded within the platform to scaffold self-regulation. These findings were reinforced by qualitative results, with 88% of gifted students reporting that the platform provided appropriately challenging content and promoted self-learning and goal-setting behaviors. Full article

Airport digital transformation is commonly approached through technological integration and data-driven optimization, yet such perspectives provide limited insight into system-level reasoning and governance. This paper introduces the cognitive airport paradigm (CAP) as a mathematically grounded framework that models the airport as a domain-specific cognitive digital twin within a complex aviation ecosystem. Methodologically, the study follows a conceptual–analytical and design-science research approach, combining system analysis, conceptual modeling, ontology engineering, and formal mathematical representation of cognitive transitions and governance constraints. CAP represents airport cognition as an explicit state space characterized by cognitive maturity, governance integrity, and semantic stability. Analytical reasoning, adaptive learning, and orchestration mechanisms are formalized through instrument dominance profiles and cognitive performance functionals, enabling analytical comparison of airport configurations and identification of cognitive regimes. The results include (i) a formalization of airports as cognitive digital twins with measurable cognitive and governance properties; (ii) quantitative indices such as the cognitive readiness index, governance integrity index, and ethical alignment coefficient supporting structured evaluation of airport cognitive maturity; and (iii) illustrative expert-based parameterizations and a geometric interpretation in a cognitive simplex demonstrating that governance-oriented orchestration stabilizes airport cognition under increasing system complexity. Airport development is interpreted as continuous cognitive evolution rather than discrete stages of digitalization. The paper further proposes a cognitive roadmap for guiding airport evolution through structured cognitive rebalancing. The framework contributes to the theoretical foundations of cognitive digital twins and is transferable to other safety-critical and institutionally governed socio-technical systems. Full article

Over recent decades, various projects—especially at the European level—have developed platforms for storing 2D and 3D digital models of cultural heritage. These platforms aim to preserve, organise, and make heritage data accessible for research, education, and public engagement. However, they face challenges due to diverse data formats, increasing user demands, and a lack of standardisation and metadata consistency. Advancements in digital technologies have enabled more efficient systems for acquiring, processing, and preserving cultural heritage data. Three-dimensional digitisation, in particular, supports multidimensional analysis and modernises documentation practices. Despite significant experience in creating 3D data repositories, comprehensive Information Systems for managing the full lifecycle of cultural heritage—especially those that integrate existing platforms—or web-based platforms designed to support collaborative scientific research by integrating data, tools, and computational resources remain limited and are not established at national levels. This paper explores this evolving landscape, highlighting key methodological and technological foundations for future systems. It also addresses open questions, opportunities, limitations, and ongoing challenges, emphasizing the need for semantic-based approaches to integrate fragmented data and foster collaboration between public and private stakeholders. Full article

As offshore wind power development advances into deeper waters, tension leg platform (TLP) floating wind turbines stand out for their excellent motion performance, lightweight structure design, and minimal seabed footprint. This paper reviews the advancements in TLP technology, covering structural configurations, dynamic characteristics and control strategies. Particular emphasis is given to analyzing dynamic response under combined environmental loads, including nonlinear motions induced by higher-order wave forces and parametric excitations, as well as the multiphysics coupling mechanisms involving aerodynamics, hydrodynamics, servo control, and structural dynamics. The review concludes by outlining future trends in platform scaling, intelligent operation and maintenance, and multi-energy integration. Overall, this review provides strategic insights for further research and engineering applications of TLP floating wind turbines. Full article

Background: In recent years, virtual consultations have emerged as a crucial approach for continuity of chronic care provision, indicating a promising avenue for the future of smart healthcare systems. However, reversions to in-person care highlight persistent limitations, despite notable advantages of remote modalities. In parallel, recent developments in artificial intelligence (AI) indicate the potential to enhance remote chronic care, but user perceptions of such assistance and the corresponding human factors remain underexplored. Objective: This mixed methods study aims to better understand the virtual consultation experiences and attitudes toward AI-assisted tools in remote care among patients with noncommunicable chronic conditions and their healthcare professionals (HCPs). It conducts an in-depth examination of the associated human–computer interaction and usability elements of virtual consultations and of potential AI assistance. Methods: Public and Patient Involvement was integrated to run pilots and refine documentations. Semi-structured interviews with patients (n = 10), focus groups with HCPs (n = 15), and an online survey (n = 83) were conducted. Qualitative data was analysed through a reflexive thematic approach. The survey comprised the Telehealth Usability Questionnaire (TUQ) and bespoke items on user AI views, and the data was used to triangulate the qualitative findings. Nonparametric Kruskal–Wallis tests and ε² effect sizes compared TUQ and AI views scores between current and former virtual consultation user groups. Results: Seven themes emerged from the qualitative data, which were supported by the quantitative findings. The statistical analyses resulted in a mean TUQ total score of 90.6 (SD = 15.0), which indicates high usability and user satisfaction; however, they failed to detect a difference between groups (p > 0.05; ε² = 0.002–0.032). There was a clear preference for hybrid models, while a lack of empathy was identified during remote interactions. While a notable proportion of users indicated a literacy gap towards AI use in healthcare settings, they expressed cautious openness towards AI assistance, contingent upon transparency, human oversight, and data integrity; indicating a potential gap between competence to judge the technology and willingness to use it. Significant differences in views on AI assistance across groups failed to be detected (p > 0.05; ε² = 0.005–0.065). Conclusions: Virtual consultations for chronic conditions are widely usable and acceptable, particularly through hybrid approaches. Addressing empathic engagement, holistic patient status, and transparent AI integration can enhance clinical quality and user experiences during remote interactions. However, the low statistical power and failure to detect a difference between groups (likely due to the small sample size) indicate the need for caution when interpreting the quantitative findings. There is also the implicit need to address potential AI literacy gap among users, indicating the need for robust safeguard measures. This study has also identified evidence-based assistive AI features that can potentially enhance virtual consultations. These insights can inform the co-design of evidence-based virtual care platforms, policies and supportive AI tools to sustain remote chronic care delivery. Full article

The socio-ecological transition requires not only technological innovation but also new ways of recognizing the social, environmental, and territorial value generated by collective action. Many of these positive externalities remain invisible in conventional assessment frameworks, limiting the legitimacy, financing, and scaling of local sustainability initiatives. This article presents a strategic framework designed to identify and structure positive externalities in collective self-consumption and other transformative projects. The method combines four components: (i) normative identification through the Sustainable Development Goals; (ii) balanced multi-stakeholder participation to surface diverse perspectives; (iii) perceptive mapping using an adapted Kano model; and (iv) strategic articulation. The framework was applied in two contrasting contexts: an energy community centered on shared renewable production, and a women’s empowerment program focused on capability-building and social innovation. These applications do not aim at empirical replication or the validation of results, but at examining how the framework supports collective recognition and strategic structuring in different organizational settings. Across these distinct settings, it led to the formulation of coherent and actionable strategic roadmaps, illustrating how positive externalities can inform governance choices, strengthen institutional legitimacy, and support long-term project consolidation. These results suggest that collective recognition enables externalities to structure strategic action beyond their original sector, demonstrating the potential transferability of the approach. Developed within a research program supported by the French Agency for Ecological Transition (ADEME) and the national urban-transition initiative (PUCA), the framework provides a practical decision architecture for structuring shared value within coordinated strategies. Full article

Paper-based analytical devices have emerged as a versatile and cost-effective platform for on-site chemical and biological analysis. The integration of fluorescence detection with smartphone imaging has significantly enhanced the analytical performance and portability of these systems, enabling sensitive, rapid, and user-friendly detection of diverse analytes. This review highlights recent advancements in paper-based fluorescence sensing technologies, focusing on their design principles, materials, and detection strategies. Emphasis is placed on the use of nanomaterials, quantum dots, and carbon-based fluorophores that improve sensitivity and selectivity in food, bioanalytical, and environmental applications. The role of smartphones as optical detectors and data processing tools is explored, underscoring innovations in image analysis, calibration algorithms, and app-based quantification methods. Full article

This paper presents the development of a robot-oriented Distributed Acoustic Sensing (DAS) system designed for environmental monitoring and hazard detection on ground robotic platforms. Unlike conventional DAS solutions primarily intended for stationary or quasi-stationary infrastructures, the proposed approach explicitly accounts for robot-induced mechanical vibrations, mobility constraints, and limited onboard resources. A dedicated anti-jitter signal processing pipeline combined with edge-based data processing is introduced to suppress motion-induced strain components while preserving weak external acoustic signals. The system integrates optical fiber deployment along the robot structure using flexible guides and vibration-isolated clamps, ensuring stable mechanical coupling under continuous motion. Experimental validation, including laboratory tests and preliminary outdoor field trials, demonstrates reliable detection of acoustic events in the 10–200 Hz frequency range, with reduced processing latency of 80–100 ms and a detection reliability of up to 95%. Comparative analysis with conventional sensors confirms the advantages of the proposed DAS-based approach in terms of sensitivity, spatial coverage, and robustness. The results demonstrate the feasibility and effectiveness of DAS technology for real-time sensing applications on mobile robotic platforms. Full article

This essay contends that Jean-Luc Godard’s late digital cinema elaborates a geopolitical aesthetics in which Europe confronts the return of its repressed histories through the very instability of the digital image. While Europe has long functioned in Godard’s work as both theme and epistemic horizon—echoing the Hegelian cartographies—Film Socialisme (2010) and The Image Book (2018) transform this Eurocentrism into a site of crisis. In these films, what Fredric Jameson terms the “political unconscious” (1981) emerges through the spectral return of Palestine and the Arab world, compelling a reckoning with colonial legacies and the limits of representation. The digital turn proves decisive. Godard mobilizes pixelation, saturation, glitch, and decomposed sound to reveal what might be called the technological unconscious of the medium. I develop the concept of “Digital Orientalism” to designate how Orientalist chronotopes persist in the digital age yet are unsettled by Godard’s experimental manipulation of audiovisual fragments. Through close readings of Film Socialisme and The Image Book, which incorporates works by Arab filmmakers including Youssef Chahine, Nacer Khemir, Ossama Mohammed, and Wiam Simav Bedirxan, I show how Godard’s fractured montages produce symptomatic cartographies of the world-system where repression, memory, and accident collide. Full article

This work aims to demonstrate the successful, long-term human use of an automatic scheduling-manual operation (AS-MO) precision irrigation tool by farmers on a medium-scale Jordanian farm. Innovation in low-cost, accessible, and water-efficient irrigation technologies is critical as water resources become scarce, especially on resource-constrained farms in the drought-prone Middle East and North Africa (MENA) region. Prior work has shown that a proposed AS-MO decision support tool could bridge the gap between fully manual irrigation—a common practice on many MENA farms—and existing precision agriculture solutions, which are often too expensive or complex for medium-scale farmers to adopt. Recent developments have also demonstrated that the scheduling theory behind the proposed AS-MO tool uses up to 44% less water compared to fully manual irrigation. However, a functional design of the AS-MO tool has not been realized nor has it been demonstrated on a farm with farmer users. This work documents the detailed design of an AS-MO tool’s human–machine interaction (HMI) and validates the human execution of the tool in context. Through an 11-week case study conducted on a Jordanian farm, we show that farmers used a functional prototype of the AS-MO tool as intended. The functional tool prototype was designed to deliver a long-term AS-MO user experience to study participants. The prototype monitored local weather conditions, generated water-efficient schedules using an existing scheduling theory, and notified users’ phones when they should manually open or close valves. The irrigation practices of participants using the AS-MO prototype were measured, and participants demonstrated successful use of the tool. Users correctly confirmed 93% of the scheduled events using the tool’s HMI. Despite manual operation, a majority of confirmed irrigation event durations fell within 15% of the automatically scheduled durations; relative to the length of scheduled irrigation event durations, the medians of confirmed and scheduled durations were 102% and 88%, respectively. These results demonstrate the success of the tool’s decision support ability. Feedback from study participants can support the AS-MO tool’s next design iteration and can inform the development of other decision support systems designed for resource-constrained, medium-scale farms. This work presents an important step towards developing a precision irrigation tool that, if adopted at scale, could increase the adoption of water-efficient irrigation practices on resource-constrained farms that are not served by existing technology, improving sustainable agriculture in MENA. Full article

The safe and efficient integration of small unmanned aircraft systems (sUAS) into the National Airspace System (NAS) requires a systems-based understanding of the interrelations among human, technological, and regulatory components. Existing Federal Aviation Administration (FAA) guidelines restrict most operations to visual line of sight (VLOS), which constrains operational scalability and underscores the need for system-level innovations supporting beyond-visual-line-of-sight (BVLOS) operations. This study adopted a socio-technical systems approach to evaluate how first-person view (FPV) technologies influence operator workload and situational awareness (SA), key human performance elements within the broader sUAS safety system. Participants meeting FAA Part 107 eligibility criteria were assigned to one of three visual configurations: (a) traditional VLOS, (b) FPV using a 21-inch monitor, or (c) FPV with immersive goggles. Workload was measured with the NASA Task Load Index (NASA-TLX), and Level 1 SA was assessed via post-task recall. ANOVA results revealed no statistically significant differences across visual conditions, indicating no evidence that FPV integration either increased cognitive load or impaired perceptual awareness compared to traditional methods. Complementary analysis of NASA’s Aviation Safety Reporting System (ASRS) identified SA as the most recurrent human-factor issue, suggesting system-level implications for human–machine interaction and training design. These findings contribute to the systemic understanding of human factors in UAS operations, supporting FPV’s potential as a viable subsystem for achieving safe and effective BVLOS integration within complex socio-technical aviation systems. Full article

This paper presents an all-digital fractional-N phase-locked loop (ADPLL) operating in the 2.86–3.2 GHz range, optimized for IoT and high-frequency RF transceiver applications demanding stringent phase noise performance, fast settling time, and high integration capability. The key innovation lies in the introduction of a bandpass delta-sigma time-to-digital converter (BPDSTDC) that achieves high-resolution phase detection, an extended detection range of ±2$\pi$, and superior noise-shaping characteristics, completely eliminating the complex calibration procedures typically required in conventional TDC designs. The proposed architecture synergistically combines the BPDSTDC with digital down-conversion blocks to extract phase error at baseband, a divider chain integrated with phase interpolators achieving 1/4 fractional resolution to suppress in-band quantization noise, and a wide-bandwidth digital loop filter (>1 MHz) ensuring fast dynamic response and robust stability. The bandpass delta-sigma modulator is implemented with compact resonator structures and a flash quantizer, achieving an optimal balance among resolution, power consumption, and silicon area. The incorporation of highly linear phase interpolators extends fractional frequency synthesis capability without requiring complex digital-to-time converters (DTCs), significantly reducing design complexity and calibration overhead. Fabricated in a 180-nm CMOS technology, the proposed chip demonstrates robust measured performance. The band-pass delta-sigma TDC achieves a low integrated rms timing noise of 183 fs within a 1-MHz bandwidth. Leveraging this low TDC noise, the complete ADPLL exhibits a measured in-band phase noise of −120 dBc/Hz at a 1-MHz offset for a 3.2-GHz output frequency while operating with a loop bandwidth exceeding 1 MHz. This corresponds to a normalized phase noise of −216 dBc/Hz. The system operates from a 1.8-V supply and consumes 10 mW, achieving competitive performance compared with prior noise-shaping TDC-based all-digital PLLs. Full article

‘Korlaxiangli’ pear occupies a pivotal position in the pear industry of Xinjiang, with both its cultivation area and total output ranking first in the region. However, ‘Korlaxiangli’ is vulnerable to freezing injury and has suffered frequent frost damage in recent years. A cold-tolerant superior individual plant was identified via preliminary field surveys and designated as cold-tolerant superior line 15-25-15 (abbreviated as ‘HY15’). To facilitate the large-scale application of this germplasm, tissue culture technology was adopted for its rapid propagation. Using spring-collected young shoots and autumn-collected dormant buds as explants, the optimal proliferation medium was determined to be DKW supplemented with 1.0 mg/L of 6-BA and 0.5 mg/L of NAA, while the optimal rooting medium was identified as 1/2 MS containing 2.5 mg/L of NAA and 15 g/L of sucrose. Transcriptome analysis revealed eight candidate genes potentially associated with shoot proliferation, among which ARF3 and ARR12 might be associated with high proliferation efficiency. This study provides a theoretical foundation and practical technical support for the conservation, genetic improvement, and establishment of efficient propagation systems of elite pear germplasm resources. Full article

This study is grounded in the macro-context of smart agriculture and global food security. Due to population growth and climate change, precise and efficient monitoring of crop distribution and growth is vital for stable production and optimal resource use. Remote sensing combined with deep learning enables multi-scale agricultural monitoring from field identification to disease diagnosis. However, current models face three deployment bottlenecks: high complexity hinders operation on edge devices; scarce labeled data causes overfitting in small-sample cases; and there is insufficient generalization across regions, crops, and imaging conditions. These issues limit the large-scale adoption of intelligent agricultural technologies. To tackle them, this paper proposes a lightweight crop recognition model, MAF-RecNet. It aims to achieve high accuracy, efficiency, and strong generalization with limited data through structural optimization and attention mechanism fusion, offering a viable path for deployable intelligent monitoring systems. Built on a U-Net with a pre-trained ResNet18 backbone, MAF-RecNet integrates multiple attention mechanisms (Coordinate, External, Pyramid Split, and Efficient Channel Attention) into a hybrid attention module, improving multi-scale feature discrimination. On the Southern Hebei Farmland dataset, it achieves 87.57% mIoU and 95.42% mAP, outperforming models like SegNeXt and FastSAM, while maintaining a balance of efficiency (15.25 M parameters, 21.81 GFLOPs). The model also shows strong cross-task generalization, with mIoU scores of 80.56% (Wheat Health Status Dataset in Southern Hebei), 90.20% (Global Wheat Health Dataset), and 84.07% (Corn Health Status Dataset). Ablation studies confirm the contribution of the attention-enhanced skip connections and decoder. This study not only provides an efficient and lightweight solution for few-shot agricultural image recognition but also offers valuable insights into the design of generalizable models for complex farmland environments. It contributes to promoting the scalable and practical application of artificial intelligence technologies in precision agriculture. Full article