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In comparison to traditional wired and wireless communication scenarios, the underwater channel is peculiar, being significantly more difficult for communication and presenting a unique set of features and impairments, thus necessitating special care in selecting ad hoc encoding and modulation technologies to achieve successful transmissions. This process can be aided by simulations, which can be effectively carried out only using a good, detailed channel model validated through sea measurements. This study presents the results of a sea measurement campaign run in May 2024 off the Gulf of La Spezia, Italy, characterized by challenging shallow water conditions and the presence of shadow zones. The collected data is then used to model a simulated channel as faithful as possible to the one experienced during the sea trial. The obtained channel is then used to carry out a comparison of different forward error correction (FEC) codes, highlighting each scheme’s performance in our working context. Conclusive results show that a satisfactory simulated channel was obtained and that a different choice of FEC schemes could have improved the performance of the underwater acoustic communication. Full article

Network intrusion detection is a key technology for ensuring cybersecurity. However, current methods face two major challenges: reliance on manual feature engineering, which leads to the loss of discriminative information, and the vulnerability of deep learning models to adversarial sample attacks. To address these issues, this paper proposes STS-AT, a novel network intrusion detection method that integrates structured tensors with adversarial training. The method consists of three core components: first, structured tensor encoding, which fully converts raw hexadecimal traffic into a numerical representation; second, a hierarchical deep learning model that combines CNN and LSTM networks to simultaneously learn spatial and temporal features of the traffic; third, a multi-strategy adversarial training method that enhances model robustness by adaptively adjusting the mix of adversarial samples in different training phases. Experiments on the CICIDS2017 dataset show that the proposed method achieves an accuracy of 99.6% in normal traffic classification, significantly outperforming classical machine learning baselines such as Random Forest (93.1%) and Support Vector Machine (84.7%). Crucially, under various adversarial attacks (FGSM, PGD, and DeepFool), the accuracy of an undefended model drops to as low as 24.4%, whereas after multi-strategy adversarial training, the defense accuracy rises above 96.8%. Meanwhile, the total training time is reduced by approximately 67.6%. These results verify that structured tensor encoding effectively preserves original traffic information, the hierarchical model achieves comprehensive feature learning, and multi-strategy adversarial training significantly improves training efficiency while ensuring robust defense effectiveness. Full article

With the rapid advancement of technology in recent years, signatures on contracts and documents have increasingly shifted from traditional handwritten forms on paper to digital handwritten signatures executed on devices (hereafter referred to as digital tablets). This transition introduces new challenges for forensic document examination due to the differences in writing instruments. According to the European Network of Forensic Science Institutes (ENFSI), a Digital Capture Signature (DCS) refers to data points captured during the writing process on digital devices such as tablets, smartphones, or signature pads. In addition to retaining the visual image of the signature, DCS provides more information previously unavailable, including pen pressure, stroke order, and writing speed. These features possess potential forensic value and warrant further study and evaluation. This study employs three devices—Samsung Galaxy Tab S10, Apple iPad Pro, and Apple iPad Mini—together with their respective styluses as experimental tools. Using custom-developed handwriting capture software for both Android and iOS platforms, we simulated signature-writing scenarios common in the financial and insurance industries. Thirty participants were asked to provide samples of horizontal Chinese, English, and number writings (FUJ-IRB NO: C113187), which were subsequently normalized and segmented into characters. For analysis, we adopted distance-based time-series alignment algorithms (FastDTW and SC-DTW) to match writing data across different instances (intra- and inter-writer). The accumulated distances between corresponding data points, such as coordinates and pressure, were used to assess handwriting stability and to study the differences between same-writer and different-writer samples. The findings indicate that preprocessing through character centroid alignment, followed by the analysis, substantially reduces the average accumulated distance of handwriting. This procedure quantifies the stability of an individual’s handwriting and enables differentiation between same-writer and different-writer scenarios based on the distribution of DCS distances. Furthermore, the use of styluses provides more precise distinctions between same- and different-writer samples compared with direct finger-based writing. In the context of rapid advancements in artificial intelligence and emerging technologies, this preliminary study aims to contribute foundational insights into the forensic application of digital signature examination. Full article

Higher education continues to encounter the challenge of redesigning writing pedagogy beyond the rapid adoption of emerging technologies. This challenge is particularly evident in English writing courses, which play a role in developing students’ writing and research skills in universities across the United Arab Emirates (UAE). While generative artificial intelligence (GenAI) tools offer practical affordances for writing instruction, their growing use has also raised concerns about academic integrity, authenticity, and critical engagement. Although early discourse has focused on the risks and potential of GenAI, there remains a clear dearth of frameworks to guide instructors in designing meaningful and engaging writing assignments. This paper introduces Ai-RACE, an adaptable pedagogical framework for designing purposeful and innovative writing assignments. Grounded in classroom-based insights, principles of writing pedagogy, constructivist and multimodal learning theories, Ai-RACE conceptualises assignment design around five interconnected components: AI integration, Relevance, Authenticity, the 4Cs, and Engagement. Employing a design-focused qualitative approach, the study uses classroom implementation and student reflections to examine the implementation of Ai-RACE in writing contexts. Although situated within a specific institutional context, the study offers transferable guidelines for designing writing assignments across international higher education settings. By positioning Ai-RACE as a design heuristic, the study highlights its significance in supporting engagement, critical thinking, writing skills and ethical use of AI, and highlights the importance of rethinking writing pedagogy and the role of professional development in AI- influenced contexts. Full article

Hilly and mountainous areas are important agricultural production regions globally. Their dramatic topography, dense fruit tree planting, and steep slopes severely restrict the application of traditional plant protection machinery. Pest and disease control has long relied on manual spraying, resulting in high labor intensity, low efficiency, and pesticide utilization rates of less than 30%. Plant protection UAVs, with their advantages of flexibility, high efficiency, and precise application, provide a feasible technical approach for plant protection operations in hilly and mountainous areas. However, steep slopes and dense orchard environments place higher demands on key technologies such as drone positioning and navigation, attitude control, trajectory planning, and terrain following. Achieving accurate identification and adaptive following of the undulating fruit tree canopy while maintaining a constant spraying distance to ensure uniform pesticide coverage has become a core technological bottleneck. This paper systematically reviews the key technologies and research progress of plant protection UAVs in hilly and mountainous operations, focusing on the principles, advantages, and limitations of core methods such as multi-sensor fusion positioning, intelligent SLAM navigation, nonlinear attitude control and intelligent control, three-dimensional trajectory planning, and multimodal terrain following. It also discusses the challenges currently faced by these technologies in practical applications. Finally, this paper discusses and envisions the future of plant protection UAVs in achieving intelligent, collaborative, and precise operations on steep slopes and in dense orchards, providing theoretical reference and technical support for promoting the mechanization and intelligentization of mountain agriculture. Full article

The rapid growth in the installation of new energy poses challenges to the stability of the power grid due to its volatility and intermittency. Coal-fired power plants have come to play an important role in flexible peak power regulation. Considering that the burner is the core of a pulverized coal boiler, this study proposes the application of reverse injection pulverized coal combustion technology to power plant burners to achieve better ignition and combustion stability. The results of numerical simulations combined with experimental verification indicate that for a single ignition stabilizer, recirculation zones can be formed on both sides of the primary pulverized coal pipe at the front cone, and a high-temperature flame is ejected at high speed at the outlet. As the secondary air temperature increases from 373 K to 533 K, the axial length of the high-temperature recirculation zone increases, corresponding to an increase in the average outlet flame temperature from 1510 K to 1672 K. Under different loads of the main pulverized coal burner, the high-temperature flame ejected from the stabilizer can quickly encounter and mix with the surrounding main pulverized coal airflow, thereby igniting it rapidly. This process establishes a high-temperature flame zone within the two-stage combustion chamber, demonstrating strong adaptability to load fluctuations. As the burner load decreases, the outlet airflow velocity decreases significantly and the high-speed zone area shrinks, and the two adjacent high-temperature zones initially formed at the outlet gradually merge into a larger high-temperature zone. Simultaneously, the upward deflection of the jet at the outlet weakens. Full article

With the fast development of the 5G technology and IoV, a vehicle has become a smart device with communication, computing, and storage capabilities. However, the limited on-board storage and computing resources often cause large latency for task processing and result in degradation of system QoS as well as user QoE. In the meantime, to build the environmentally harmonious transportation system and green city, the energy consumption of data processing has become a new concern in vehicles. Moreover, due to the fast movement of IoV, traditional GSI-based methods face the dilemma of information uncertainty and are no longer applicable. To address these challenges, we propose a T2VC model. To deal with information uncertainty and dynamic offloading due to the mobility of vehicles, we propose a MAB-based QEVA-UCB solution to minimize the system cost expressed as the sum of weighted latency and power consumption. QEVA-UCB takes into account several related factors such as the task property, task arrival queue, offloading decision as well as the vehicle mobility, and selects the optimal location for offloading tasks to minimize the system cost with latency energy awareness and conflict awareness. Extensive simulations verify that, compared with other benchmark methods, our approach can learn and make the task offloading decision faster and more accurately for both latency-sensitive and energy-sensitive vehicle users. Moreover, it has superior performance in terms of system cost and learning regret. Full article

With rapid advancements in sub-meter satellite and aerial imaging technologies, high-resolution remote sensing imagery has become a pivotal source for geospatial information acquisition. However, current semantic segmentation models encounter two primary challenges: (1) the inherent trade-off between capturing long-range global context and preserving precise local structural details—where excessive reliance on downsampled deep semantics often results in blurred boundaries and the loss of small objects and (2) the difficulty in modeling complex scenes with extreme scale variations, where objects of the same category exhibit drastically different morphological features. To address these issues, this paper introduces MAFMamba, a multi-scale adaptive fusion visual Mamba network tailored for high-resolution remote sensing images. To mitigate scale variation, we design a lightweight hybrid encoder incorporating an Adaptive Multi-scale Mamba Block (AMMB) in each stage. Driven by a Multi-scale Adaptive Fusion (MSAF) mechanism, the AMMB dynamically generates pixel-level weights to recalibrate cross-level features, establishing a robust multi-scale representation. Simultaneously, to strictly balance local details and global semantics, we introduce a Global–Local Feature Enhancement Mamba (GLMamba) in the decoder. This module synergistically integrates local fine-grained features extracted by convolutions with global long-range dependencies modeled by the Visual State Space (VSS) layer. Furthermore, we propose a Multi-Scale Cross-Attention Fusion (MSCAF) module to bridge the semantic gap between the encoder’s shallow details and the decoder’s high-level semantics via an efficient cross-attention mechanism. Extensive experiments on the ISPRS Potsdam and Vaihingen datasets demonstrate that MAFMamba surpasses state-of-the-art Convolutional Neural Network (CNN), Transformer, and Mamba-based methods in terms of mIoU and mF1 scores. Notably, it achieves superior accuracy while maintaining linear computational complexity and low memory usage, underscoring its efficiency in complex remote sensing scenarios. Full article

In the pursuit of high-quality economic development, addressing the challenge of high pollution and carbon emissions has become a critical issue. The rapid advancement of digital technology offers novel opportunities and tools to effectively mitigate these challenges. This study examines how digital technology empowerment can enhance the effectiveness of low-carbon city pilot (LCCP) policies in mitigating high pollution and carbon emissions, thereby improving green economic efficiency (GEE), using data from 283 Chinese cities between 2006 and 2021. The method adopted is a DID framework tailored for settings with staggered treatment adoption. Our analysis focuses on the low-carbon city pilot initiative, examining its consequences and how it interacts with digital technology. The results indicate that (1) the LCCP policy significantly promotes green economic efficiency; (2) digital technology empowerment demonstrates a substantial positive moderating impact upon the policy outcome, thus considerably reinforcing low-emission pilot policies’ improvement effect on GEE; (3) there are regional variations in the policy effectiveness, with the eastern region showing the most pronounced improvement, followed by the central region, while the western region exhibits a relatively lower response. This study provides theoretical and empirical support for further integrating digital technology with low-carbon policies and advancing urban green and high-quality development. Full article

The tokamak is a toroidal device that utilizes magnetic confinement to achieve controlled nuclear fusion. One of the major technical challenges hindering the development of this technology lies in effectively dissipating the generated heat. In this study, the inner blanket structure of a tokamak is selected as the research object, and a multi–physics numerical model coupling magnetic field, temperature field, and flow field is established. The effects of background magnetic field strength, blanket channel width, and inlet velocity of the liquid metal coolant on the thermal–flow characteristics of the blanket were systematically investigated. The results indicate that compared with the L-shaped channel, the U-shaped channel reduces flow resistance in the turning region by 6%, exhibits a more uniform temperature distribution, and decreases the outlet–inlet temperature difference by 4%, thereby significantly enhancing the heat transfer efficiency. An increase in background magnetic field strength suppresses coolant flow but has only a limited impact on the temperature field. When the background magnetic field reaches a certain strength, the magnetic field has a certain hindering effect on the flow of the working fluid. Increasing the thickness of the blankets appropriately can alleviate the hindering effect of the magnetic field on the flow and improve the velocity distribution in the outlet area. Full article

The glomerular filtration barrier poses a significant challenge for circulating proteins, with molecules below ~60–70 kDa facing rapid renal clearance. Endogenous proteins have evolved sophisticated evasion mechanisms including oligomerization, carrier binding, electrostatic repulsion, and FcRn-mediated recycling. Understanding these natural strategies provides blueprints for engineering therapeutic proteins with improved pharmacokinetics. This review examines how endogenous proteins resist filtration, evaluates their application in protein engineering, and discusses clinical translation including established technologies (PEGylation, Fc-fusion) and emerging strategies (albumin-binding domains, glycoengineering). We address critical challenges of balancing half-life extension with tissue penetration, biological activity, and immunogenicity—essential considerations for the rational design of next-generation therapeutics with optimized dosing and enhanced efficacy. Full article

Wellbore integrity maintenance constitutes a fundamental safety and technological challenge throughout the entire lifecycle of oil and gas wells (including production, injection, and CO₂ sequestration operations). As a critical completion phase, perforation generates a high-temperature, high-pressure shaped charge jet that impacts and compromises wellbore structural integrity. This process may induce failure in both the cement sheath body and its interfacial zones, potentially creating fluid migration pathways along the cement-casing interface through perforation tunnels. Current research remains insufficient in quantitatively evaluating cement sheath damage resulting from perforation operations. Addressing this gap, this study incorporates dynamic jet effects during perforation and establishes a numerical model simulating high-velocity jet penetration through casing–cement target–formation composites using a rock dynamics-based constitutive model. The investigation analyzes failure mechanisms within the cement sheath matrix and its boundaries during perforation penetration, while examining the influence of mechanical parameters (compressive strength and shear modulus) of both cement sheath and formation on damage characteristics. Results demonstrate that post-perforation cement sheath aperture exhibits convergent–divergent profiles along the tunnel axis, containing exclusively radial fractures. Primary fractures predominantly initiate at the inner cement wall, whereas microcracks mainly develop at the outer boundary. Enhanced cement compressive strength significantly suppresses fracture initiation at both boundaries: when increasing from 55 MPa to 75 MPa, the undamaged area ratio rises by 16.6% at the inner wall versus 11.2% at the outer interface. Meanwhile, increasing the formation shear modulus from 10 GPa to 15 GPa reduces cement target failure radius by 0.4 cm. Cement systems featuring high compressive strength and low shear modulus demonstrate superior performance in mitigating perforation-induced debonding. Full article

Artificial intelligence (AI) is rapidly reshaping thoracic surgery, advancing from decision support to the threshold of autonomous intervention. AI-driven technologies—including machine learning (ML), deep learning (DL), and computer vision—have demonstrated significant improvements in diagnostic accuracy, surgical planning, intraoperative navigation, and postoperative outcome prediction. In lung cancer and thoracic oncology, AI enhances imaging analysis, histopathological classification, and risk stratification, supporting multidisciplinary decision-making and personalized therapy. Robotic-assisted and AI-guided systems are optimizing surgical precision and workflow efficiency, while real-time decision-support tools and augmented reality are improving intraoperative safety. Despite these advances, widespread adoption is limited by challenges in algorithmic bias, data integration, regulatory approval, and ethical transparency. The literature emphasizes the need for multicenter validation, explainable AI, and robust governance frameworks to ensure safe and effective clinical integration. Future research should focus on digital twin technology, federated learning, and transparent AI outputs to further enhance reliability and accessibility. AI is poised to transform thoracic surgery, but responsible implementation and ongoing evaluation are essential for realizing its full potential. The aim of this review is to evaluate and synthesize the current landscape of artificial intelligence (AI) applications across the thoracic surgical pathway, from preoperative decision-support to intraoperative guidance and emerging autonomous interventions. Full article

The rise in Blockchain-based digital assets has transformed the financial ecosystems, which has also created complex governance and taxation challenges. The pseudonymous and cross-border nature of crypto transactions undermines traditional tax enforcement, leaving regulators such as the South African Revenue Service (SARS) reliant on voluntary disclosures with limited verification mechanisms, while existing Blockchain forensic tools and regulatory technologies (RegTechs) have advanced in anti-money laundering and institutional compliance, their integration into issues related to taxpayer compliance and locally adapted solutions remains underdeveloped. Therefore, this study conducts a state-of-the-art review of Blockchain forensics, RegTech innovations, and crypto tax frameworks to identify gaps in the crypto tax compliance space. Then, this study builds on these insights and proposes a conceptual model that integrates digital forensics, cost basis automation aligned with SARS rules, wallet interaction mapping, and non-fungible tokens (NFTs) as verifiable audit anchors. The contributions of this study are threefold: theoretically, which reconceptualise the adoption of Blockchain forensics as a proactive compliance mechanism; practically, it conceptualises a locally adapted proof-of-concept for diverse transaction types, including DeFi and NFTs; and lastly, innovatively, which introduces NFTs to enhance auditability, trust, and transparency in digital tax compliance. Full article

Biofuels have emerged as a promising alternative to conventional fuels, offering improved environmental sustainability. Nevertheless, inadequate control of their physicochemical properties can lead to increased emissions and potential engine damage. Existing methods for regulating these properties depend on costly and sophisticated laboratory equipment, which poses significant challenges for integration into industrial production processes. Three-dimensional printing technology provides a cost-effective alternative to traditional fabrication methods, offering particular benefits for the development of low-cost designs for detecting liquid properties. In this work, we present a sensor system for assessing biofuel solutions. The presented device employs piezoelectric sensors integrated with 3D-printed, liquid-filled cells whose structural design is refined through experimental validation and novel optimization strategies that account for sensitivity, recovery and resolution. This system incorporates discrete electronic circuits and a microcontroller, within which artificial intelligence algorithms are implemented to correlate sensor responses with fluid viscosity and density. The proposed approach achieves calibration and resolution errors as low as 0.99% and $1.48\times {10}^{-2}$ mPa·s for viscosity, and 0.00485% and $1.9\times {10}^{-4}$ g/mL for density, enabling detection of small compositional variations in biofuels. Additionally, algorithmic methodologies for dimensionality reduction and data treatment are introduced to address temporal drift, enhance sensor lifespan and accelerate data acquisition. The resulting system is compact, precise and applicable to diverse industrial liquids. Full article