Atmospheric and Oceanic Sciences

To address the performance degradation of the traditional Extended Kalman Filter (EKF) in state estimation for sensorless brushless DC motor (BLDC) control under dynamic operating conditions, such as sudden speed and load changes—a degradation caused primarily by model mismatches—this paper proposes an Adaptive Extended Kalman Filter (AEKF) algorithm. The proposed algorithm incorporates a robust weighting strategy based on the Mahalanobis distance and a dynamically adjusted adaptive forgetting factor. This integration establishes an estimation mechanism capable of online updating of the innovation covariance, thereby enhancing the state observer’s adaptability to system uncertainties and external disturbances. Simulation results demonstrate that, compared to the traditional EKF, the designed AEKF algorithm significantly improves the estimation accuracy of rotor position and speed under various operating conditions, including low-speed start-up, speed step changes, and sudden load applications. Furthermore, it accelerates dynamic response, suppresses overshoot, and enhances the system’s disturbance rejection robustness. This work provides an effective state estimation solution for high-dynamic performance sensorless control of BLDC.

Triboelectric nanogenerators (TENGs) have gradually been applied in various practical scenarios, mainly focusing on core areas such as wearable motion monitoring devices, medical security systems, and natural resource exploration technology. However, they have the problem of low output energy and have not yet formed effective integration with mature commercially available products, which has hindered the industrialization process. This situation still significantly limits its global promotion and application. In this study, TENG was used as the sensing module for intelligent automotive airbags. We tested the voltage and current output characteristics of the system under different impact forces and frequency conditions. During the testing process, the electrical energy generated under different operating conditions is transmitted to the control system via Metal-Oxide-Semiconductor Field-Effect Transistor (MOSFET) circuits. The system will quickly determine whether to trigger the airbag deployment based on the received electrical signals, and activate the ignition device when necessary to achieve rapid inflation and deployment of the airbag. Compared with traditional triggering mechanisms, the airbag system based on this designed sensor has higher sensitivity and reliability. The sensor can stably capture collision signals, and experiments have shown that as the collision speed increases, the slope of its open-circuit voltage gradually approaches infinity. Applying TENG to automotive airbags not only effectively improves the triggering efficiency and accuracy of airbags, but also provides more reliable safety protection for drivers and passengers. Finite element simulation of the automotive airbag was conducted to provide specific data support for evaluating its safety performance. With the continuous advancement of TENG technology and further expansion of its application scenarios, we believe that such innovative safety technologies will play a more critical role in the future automotive industry.

Defects such as gaps, delamination, and the misalignment of fibres impair the performance of carbon fibre-reinforced composites and can lead to structural failure during operation. Eddy current testing has proven to be a suitable method for detecting these defects early in the manufacturing process. However, validated electromagnetic modelling techniques are required to develop new eddy current sensors and gain a better understanding of the eddy current signals caused by different defect sizes. This paper proposes a novel finite element modelling approach to better account for fibre heterogeneity using spline approximation. Further, adaptive mesh refinement is used to reduce FEM solution errors. A defect in the form of a gap is modelled by adjusting the spline approximation accordingly. Finally, the model also accounts for inter-laminar current paths between carbon fibre layers, which are determined by four-terminal resistance measurement. The results show that the electromagnetic properties of the structure can be successfully modelled. The simulation is validated by comparing the virtual scans with eddy current scans of dry carbon fibre fabric with and without artificially manufactured gaps.

This paper proposes a frequency-reconfigurable and active beam-switching antenna based on an X-shaped slot array integrated with a diode-based switching network. The proposed antenna features four slots arranged at 90° intervals around the feed point. Each slot is integrated with two PIN diodes and one varactor diode. By selectively activating a specific slot through the PIN diodes, the radiation pattern can be switched in four directions at 90° intervals. Dual-band operation is achieved using varactor diodes, and by controlling their equivalent capacitance, the antenna covers two operating bands: a low-frequency band with a 29.51% bandwidth (2.6–3.5 GHz) and a high-frequency band with a 24.52% bandwidth (3.65–4.67 GHz). These frequency ranges include the 5G sub-6 GHz bands, specifically n77 and n78. Experimental results confirm stable beam-switching performance across the entire operating frequency range.

Unmanned aerial vehicles (UAVs) are increasingly used for crack inspection of civil infrastructure. However, crack interpretation from UAV imagery is constrained by trade-offs among imaging resolution, operational efficiency, and measurement uncertainty. Higher resolution generally requires reduced flight distance, increased image quantity, and greater data-processing effort, which can limit inspection efficiency. This study presents an exploratory analysis of UAV-based crack inspection from a measurement-oriented perspective. Empirical UAV flight experiments were conducted to examine the relationships among flight distance, ground sampling distance (GSD), image quantity, and photogrammetric processing effort under controlled acquisition conditions. In addition, a dataset-based segmentation analysis was performed to investigate pixel-level uncertainty associated with crack thickness representation near the resolution limit. This analysis does not aim to estimate physical crack width, but rather to identify intrinsic limitations of image-based crack interpretation. The results indicate that while flight distance and GSD follow expected geometric relationships, image quantity and processing effort are influenced by multiple interacting factors rather than resolution alone. Pixel-level analysis further reveals substantial segmentation uncertainty for thin cracks represented by only a few pixels. These findings highlight the importance of accounting for measurement uncertainty and operational trade-offs when planning efficient UAV-based crack inspections.

Introduction: Step counts are increasingly used to assess mobility and track recovery following total knee arthroplasty (TKA). The purpose of this study was to assess the convergent validity of step count data captured by a smart implantable device (SID) in comparison with step counts derived from established, validated sensor-based technology. Methods: A secondary analysis of an anonymized commercial database (N = 7861, median age: 68, female: 59%, median BMI: 31.7) of patients who received an SID and used a digital care management application (App) with or without a smart watch. The SID recorded “qualified steps”, defined as periods of walking for at least seven steps that met predefined acceleration and cadence thresholds between 7 am and 10 pm. The App collected total daily step counts via smartwatch and/or smartphone. Pearson correlations were calculated between SID and App data at 30, 90, and 180 days post-operative. Step counts at 30, 90, and 180 days post-operative were compared between groups with the Mann–Whitney U test. Statistical significance was assessed at p < 0.001. Results: Step counts increased throughout the recovery period as measured by all three devices. SID-captured fewer qualified steps than App-captured step counts from watch-wearers throughout the post-operative period (p ≤ 0.001). SID step counts were similar to App step counts at 30 days post-operative and greater than App step counts at 90 and 180 days post-operative (p < 0.001). There were significant (p < 0.001), moderate correlations (r = 0.62 to r = 0.74) between step counts collected by the SID and App for both watch-wearers and smartphone-carriers at 30, 90, and 180 days post-operative. Conclusions: The SID’s qualified step metric demonstrated consistent, moderate, correlations with app-based step counts across 30, 90, and 180 days. While smartwatch-based tools recorded higher absolute step counts, both technologies reflected similar recovery trajectories.

High-precision phase difference measurement based on field-programmable gate arrays (FPGA) has important application requirements in fields such as high-stability time-frequency transmission, signal synchronization, and precision testing. Addressing the limitations of traditional methods in terms of temperature stability and measurement accuracy, this paper proposes two high-precision phase difference measurement schemes based on the FPGA platform. An eight-parallel-multi-carry chain time-to-digital converter (TDC) and digital dual-mixer time difference (DDMTD) measurement modules are constructed to perform high-precision phase difference measurements on the phase-shifted output signal of the MMCM dynamic phase-shifted module. Results show that at room temperature (25 °C), the single-carry chain TDC exhibits better measurement accuracy than the DDMTD, and the single-carry chain TDC’s measurement error range of 4.7–6.0 ps is superior to the DDMTD’s 20–75 ps error range. Under different temperature conditions, the eight-parallel-multi-carry chain TDC consistently demonstrates superior measurement accuracy, resolution, and temperature stability compared to the single-carry chain TDC. In terms of measurement accuracy, under room temperature conditions, in three sets of phase difference tests (178.5714 ps, 357.1428 ps, and 535.7142 ps), the measurement error of the eight-parallel-multi-carry chain TDC was controlled within 4.6 ps, which is better than the 4.7–6.0 ps error range of the single-carry chain TDC. Average resolution: The average resolution of the single-carry chain TDC was 6.329 ps, while the average resolution of the eight-parallel-multi-carry chain TDC improved to 0.833 ps. Temperature stability: Within the temperature range of 10 °C to 100 °C, the temperature coefficient of the single-carry chain TDC was 0.002127 ps/°C, while the temperature coefficient of the eight-parallel-multi-carry chain TDC decreased to 0.000564 ps/°C. This paper also summarizes the advantages and limitations of the above methods in terms of implementation complexity and robustness, providing a reference for the optimized design of high-precision phase difference measurement technology for FPGA platforms.

This article presents an experimental investigation of the effect of water aging on the static mechanical behavior and damage mechanisms of bio-based sandwich structures with auxetic cores using acoustic emission (AE) monitoring. Both the skins and the core are manufactured by 3D printing using polylactic acid (PLA) reinforced with short flax fibers. Four auxetic core configurations, differing in the number of unit cells across the core width, are considered. The specimens are immersed in water at room temperature to characterize their absorption behavior, which follows a Fickien’s diffusion law model with different saturation levels. Static three-point bending tests are performed at various immersion times to evaluate the influence of moisture on mechanical performance. The results show a progressive degradation of mechanical properties with increasing water exposure time, with the four-cell core configuration exhibiting the highest mechanical performance. Acoustic emission (AE) monitoring is employed to analyze damage evolution as a function of hydrothermal aging. AE parameters such as amplitude, energy, and cumulative event count are used to identify and classify the different damage mechanisms. This approach highlights the effectiveness of acoustic emission for structural health monitoring and for assessing the durability of auxetic core sandwich structures subjected to moisture.

This article presents the analysis of selected maximum power point tracking (MPPT) algorithms and their influence on developed energy harvester (EH) systems under uniform conditions. The energy harvester is an electronic system that converts available ambient energy to electrical energy and regulates its distribution to the output. The aim is to design an energy harvester with the highest integration rate possible with consideration of area requirements and low power consumption. To improve the overall energy conversion of the developed harvester, we implemented several MPPT algorithms (Pilot Cell, Constant Voltage, Perturb and Observe) into a dedicated MPPT controller that controls the DC-DC converter. Consequently, we experimentally analyzed their impact on the harvester system. Findings show that even simple algorithms with smaller chip areas and lower power consumption can achieve results comparable to more complex ones. The proposed, manufactured and experimentally evaluated EH chip prototype has proven its expected functionality and is therefore fully capable of supplying energy for low-power electronics and battery-operated devices.

Automated visual inspection of safety-critical metal assemblies such as automotive door lock strikes remains challenging due to their complex three-dimensional geometry, highly reflective surfaces, and scarcity of defect samples. While 3D sensing technologies are often constrained by cost and speed, traditional 2D optical methods struggle with severe imaging artifacts and poor generalization under few-shot conditions. This work constructs a complete system integrating defect imaging, generation, and detection. It proposes an integrated framework through the co-design of an image acquisition system and deep generative models to holistically enhance defect perception capability. First, we develop an imaging system using dome illumination and a small-aperture lens to acquire high-quality images of non-planar metal surfaces. Subsequently, we introduce a dual-stage generation strategy: stage one employs an improved FastGAN with Dynamic Multi-Granularity Fusion Skip-Layer Excitation (DMGF-SLE) and perceptual loss to efficiently generate high-quality local defect patches; stage two utilizes Poisson image editing and an optimized loss function to seamlessly fuse defect patches into specified locations of normal images. This strategy avoids modeling the complete complex background, concentrating computational resources on creating realistic defects. Experiments on a dedicated dataset demonstrate that our method can efficiently generate realistic defect samples under few-shot conditions, achieving 11–24% improvement in Fréchet Inception Distance (FID) scores over baseline models. The generated synthetic data significantly enhances downstream detection performance, increasing YOLOv8’s mAP@50:95 from 50.4% to 60.5%. Beyond proposing individual technical improvements, this research provides a complete, synergistic, and deployable system solution—from physical imaging to algorithmic generation—delivering a computationally efficient and practically viable technical pathway for defect detection in highly reflective, non-planar metal components.

Phishing attacks often rely on impersonating a legitimate entity, such as a well-known company or a bank, with the intent to deceive individuals. A common tactic used by cybercriminals to conduct such an attack is to register a specific domain to host a phishing website on it. In this paper, we propose BadDomains, a system for the early detection of phishing domains’ registration. BadDomains utilizes domain registry data about newly registered domains combined with knowledge about the current phishing situation, such as information about the most frequent impersonation targets, or suspicious domain contact information. An analysis of .pl phishing domain registry data, combined with the authors’ CSIRT operational experience, helped in the design of new features. It also facilitated the extension of features already used in other solutions. The system’s evaluation has been performed using information from .pl Top Level Domain (TLD) registry combined with CERT Polska’s (Polish national CSIRT) public list of phishing domains, used as a ground truth. BadDomains has been compared to a similar detection system designed for .eu TLD called Premadoma, which was adapted to this work. The results showed that BadDomains achieved higher F1 scores than Premadoma. After operational deployment, the system proved to provide timely detections, uncovering unknown phishing domains.

This study examines whether environmentally specific authentic leadership promotes organizational citizenship behavior for the environment and contributes to organizational environmental sustainability. The study employed a three-wave survey design to collect data from 262 full-time employees in Chinese organizations, ensuring the results are both representative and reliable. The results show that environmentally specific authentic leadership is positively associated with employees’ organizational citizenship behavior for the environment. Environmentally specific authentic leadership is also positively related to environmental commitment, highlighting the importance of leadership for sustainable workplace behaviors. Additionally, the study found that environmental commitment partially mediates the relationship between environmentally specific authentic leadership and organizational citizenship behavior, underscoring the vital role of environmental commitment in fostering pro-environmental actions. Taken together, these findings clarify how environmentally specific authentic leadership influences organizational citizenship behavior for the environment through environmental commitment. Practically, the results suggest that organizations can encourage organizational citizenship behavior for the environment by developing leaders’ environmentally specific authentic leadership and implementing green human resource management practices. Consequently, the study offers actionable guidance for achieving environmental sustainability.

Nature-based Solutions (NbS) are essential for peri-urban resilience; however, a critical research gap exists regarding the lack of species-specific eco-physiological validation for interventions within complex biocultural systems. This study addresses this gap by assessing the vulnerability of Quararibea funebris, a shade-tolerant tree and biocultural keystone for the tejate economy in Oaxaca, Mexico, currently caught in an anthropogenic ecological trap. A mixed-methods approach was employed, integrating a geospatial analysis of land-use change (1992–2021), microclimatic monitoring, and ethnographic assessment of gendered management. Results reveal the loss of 1552 ha of forest buffer, which has degraded the thermal niche below the species optimum. Urban specimens are subjected to a Daily Light Integral exceeding 38 mol m−2 d−1, triggering biometric stunting and oxidative stress. Furthermore, given that seed recalcitrance limits ex situ conservation, the species’ persistence relies strictly on a domestic monopoly of irrigation managed by women, who effectively subsidize the environmental deficit. The study concludes that the current backyard conservation model has hit its ecological ceiling; sustainability requires a transition toward landscape-scale NbS—specifically biocultural corridors governed by local female knowledge—to restore the multi-strata canopy required to regulate the species’ eco-physiological limits.

Shoreline variations in closed-basin lakes are closely linked to hydrological fluctuations and long-term changes in water balance, making them important indicators of environmental change. This study analyzes historical shoreline dynamics in Lake Van (Türkiye), the world’s largest soda lake, and provides scenario-based shoreline projections for 2032 and 2042 to support hydrological assessment and water-related management. Multi-temporal Landsat satellite images from 1982, 1992, 2002, 2012, and 2022 were processed using the Digital Shoreline Analysis System (DSAS 5.0) to quantify shoreline retreat and accretion, while future shoreline positions were estimated using the Kalman filter model. The results show pronounced spatial variability, with the most significant shoreline retreat observed in the Çelebibağ and Karahan regions, where sediment supplied by major inflowing streams contributes to shoreline instability through reworking and redistribution rather than stable accretion. Net shoreline movement values reached −2580.1 m for erosion and up to 1700 m for accretion. Model projections indicate an increasing trend of shoreline retreat by 2032 and 2042, accompanied by localized accretion zones. These hydrological-driven shoreline changes have potential implications for littoral habitats, water–land interactions, and human use of the shoreline, including fisheries infrastructure. The study demonstrates the value of integrating remote sensing and statistical forecasting for monitoring shoreline dynamics in closed-basin lake systems.

Islands are vital geographical units with significant economic, ecological, and sovereign value. However, their development often faces challenges such as fragile ecosystems, limited resources, and inadequate infrastructure. In response, China proposed the “Harmonious and Beautiful Islands” initiative in 2022, aiming to achieve a balance between production, living, and ecological functions on islands. This study evaluates the implementation of this initiative in Zhejiang Province, a key maritime region with numerous islands. Using a comprehensive indicator system comprising 7 first-level and 36 s-level indicators, we assessed 13 well-developed islands. Results show disparities in overall scores, which correlate with the economic development levels of their administering cities. Among the seven dimensions, Cultural Advancement and Development of Unique Local Industries performed best, while Ecological Protection and Restoration and Green and Low-Carbon Development lagged behind. The study highlights the importance of tailored policies, ecological restoration, and integrated planning in promoting sustainable island development. China’s experience offers valuable insights for global small island developing states (SIDS) seeking sustainable pathways.

The assessment of the degree of accessibility of urban green spaces for the population of the city of Timișoara (Romania) was carried out by taking into account the recommendations of the World Health Organization (WHO). These recommendations address the proximity accessibility of urban green spaces, operationalized through two main indicators: (1) proximity accessibility defined through two metrics–spatial distance and walking time between urban green spaces and residents’ dwellings; and (2) proximity accessibility defined by the area of urban green space available per urban resident capita. Based on the distance and walking time between residential areas and urban green spaces, accessibility classes were established, according to which the city’s green spaces were classified into distinct categories. Even under a simplified Euclidean centroid-to-centroid approach, the measured distances of urban green space accessibility exceed the World Health Organization’s recommended 300 m threshold for optimal access by a factor of 2 to 9 in the city of Timișoara. The measurements showed that none of the 48 studied neighborhoods of the city of Timișoara benefits from access to a public urban green space located at a distance of less than 200 m from the dwelling, according to the classification used in this study, and that only a single neighborhood has access to a public urban green space located at a distance of up to 300 m, as recommended by the WHO. The analysis indicated that for each resident of the city of Timișoara, an area of 8.4 m2 of urban green space is allocated, a value below the WHO recommendation of 9 m2 and below the legal threshold of 26 m2 established by Romanian national legislation. Consequently, the city of Timișoara does not meet either the values established by national legislation or the authoritative international recommendations (WHO) regarding the standard of urban green space per capita, nor the accessibility criteria expressed as distance and walking time from residents’ dwellings to the nearest public urban green space. The results of the study show that, in relation to international standards and national obligations, Timișoara faces a severe deficit of urban green space, which affects the ecological, social, and health functions of the city. The obtained values highlight both a quantitative problem and a structural one, characterized by an uneven distribution and reduced accessibility of green spaces in most neighborhoods, with green spaces concentrated in the central area and limited access for many residents. This situation underscores the need for a strategic reconfiguration of urban policies, oriented toward increasing green capital and ensuring balanced, sustainable urban development aligned with contemporary standards. Urban policy implications include the strategic development of new green spaces in underserved neighborhoods, the establishment of pedestrian and green corridors to reduce travel time, and the redesign of pedestrian connectivity to major parks. These interventions would help reduce territorial inequalities and strengthen the city’s resilience.

The clothing and textile industry is under increasing pressure to comply with European sustainability directives, including the European Strategy for Sustainable and Circular Textiles, the Circular Economy Action Plan, and the revised Waste Framework Directive, effective October 2025. While global interest in sustainable textile practices grows, limited research has examined clothing consumption and disposal behaviors in Greece, particularly through the lens of practice theory. This study addresses that gap by exploring the dynamics of these practices using a structured questionnaire distributed online via Google Forms in 2024 with 250 valid responses. Chi-square (χ2) tests and regressions analyses were used to assess associations among certain categorical variables. Our findings reveal that older consumers tend to spend more on clothing and show a preference for fast fashion. Frequent shoppers also lean toward fast fashion, yet they demonstrate greater concern for material composition. Higher sustainability awareness is associated with a preference for purchasing fewer garments or opting for higher-quality items. Notably, discomfort with recycled materials predicts reluctance toward industrial recycling and reinforces the tendency to choose durable clothing that lasts longer.

This conceptual article examines the shift of circular business models from policy-driven sustainability initiatives to commercially viable strategies in fast-moving product categories, with particular attention to repair, refurbishment, remanufacturing, and end-of-life recovery. Drawing on a structured narrative review and theoretical synthesis, it argues that circular models seldom scale within a single firm because slowing and closing resource loops require ecosystems that integrate product design, reverse logistics, and secondary markets. The paper develops an analytical framework that combines ecosystem strategy, complex adaptive systems, and common agency theory to explain how distributed complementarities, feedback dynamics, and multi-principal incentives jointly shape ecosystem trajectories. Reinforcing and balancing loops can accelerate, stabilise, or lock ecosystems into low-value routines, while incomplete contracts and divergent metrics may fragment effort and produce measurement traps. To address these coordination externalities, the framework introduces the super-principal as a meta-governance role that aligns principals through shared performance indicators, pooled funding rules, and investments in enabling infrastructures such as traceability. The framework offers implications for circular economy policy and ecosystem strategy aimed at sustaining higher-value circular loops.

Sustainability is now central to corporate legitimacy; yet, its implementation remains uneven—particularly in emerging and fragile institutional contexts characterized by weak enforcement, shifting stakeholder expectations, and fragmented governance. Although research acknowledges that senior executives shape sustainability outcomes, it often relies on structural or demographic proxies and overlooks how leaders actually interpret and address these demands. This conceptual paper develops Executive Sustainability Cognition (ESC) as cognitive governance: the capability through which C-suite leaders select, frame, prioritize, and embed sustainability imperatives when formal institutional guidance is weak or ambiguous. Integrating Upper Echelons Theory, Institutional Theory, Stakeholder Theory, Strategic Leadership Theory, and sensemaking research, the paper develops a four-stage ESC process comprising: (1) attention to sustainability cues (selective noticing and issue admission), (2) framing (meaning construction), (3) prioritization (authorization of strategic trade-offs through commitment and resource allocation), and (4) translation (institutionalizing sustainability through structures, incentives, and culture). Eight testable propositions specify how ESC mediates between external pressures and organizational responses, and how institutional fragility, stakeholder fragmentation, and organizational learning orientation moderate these effects to produce symbolic versus substantive outcomes. By framing executive cognition as a substitute governance mechanism in fragile contexts, the paper offers a context-sensitive framework to guide research and improve sustainability practices in emerging and weak-governance markets.

Tsunami hazards pose persistent threats to low-lying coastal settlements in Indonesia, where physical exposure and social vulnerability often intersect. This study integrates tsunami inundation modelling using the Cornell Multi-grid Coupled Tsunami (COMCOT) model with a community preparedness assessment to develop a comprehensive understanding of tsunami risk in Tanjung Benoa, Bali, Indonesia. The COMCOT simulation, based on a potential Mw 8.5 earthquake scenario south of Bali, indicates a maximum inundation depth of up to 14 m, where the tsunami waves are projected to traverse the Tanjung Benoa peninsula, with the first tsunami arrival being expected within 24 min after rupture. A social survey involving 327 household heads across six neighborhoods was conducted using the Tsunami Ready Community framework (UNESCO–IOC) to evaluate awareness, preparedness, and response capacities. The overall Preparedness Index (PI) reached 78, categorized as “Ready”, indicating moderate readiness but uneven distribution across neighborhoods. This integrated approach highlights that physical modelling alone is insufficient to capture real tsunami risk without incorporating social preparedness dimensions. The study provides actionable insights for local disaster management authorities and supports the strengthening of the UNESCO–IOC Tsunami Ready Community indicators in Tanjung Benoa. The framework demonstrated here can serve as a replicable model for other coastal communities pursuing sustainable and data-driven tsunami resilience strategies.

In light of the rapid adoption of text-to-image (T2I) tools in higher education, this study develops a stimulus–organism–response (S-O-R) model to explain the sustainable and responsible use intentions of text-to-image generative AI tools in higher education. Focusing on both university students and faculty, the model conceptualizes perceptions of ease of use, information quality, and ethical awareness as external stimuli; technology- and ethics-related anxiety as internal emotional states; and algorithmic trust, perceived risk, and sustainable use intention as behavioral evaluations and responses. Grounded in the Stimulus–Organism–Response (S–O–R) framework, we integrate the Technology Acceptance Model (TAM), Technology Threat Avoidance Theory (TTAT), and the DeLone–McLean (D&M) model to propose a layered mechanism, with personal innovativeness serving as a moderator. Utilizing 807 valid survey responses, we employed structural equation modeling and fuzzy-set qualitative comparative analysis. The results reveal that (1) the overall chain is supported: perceived ease of use, information quality, and ethical awareness primarily influence sustainable use intention indirectly through anxiety, trust, and risk; (2) although higher usability and quality do not alleviate anxiety, they coexist within a complex pattern of trust amid anxiety; and (3) high levels of personal innovativeness diminish the linear effects of trust and risk on intention. Configurational evidence further indicates multiple pathways leading to high sustainable intention, whereas low intention is typically characterized by uniformly low perceptions, emotions, evaluations, and innovativeness. By framing sustainable adoption through a coupled trust–risk–anxiety lens, this study extends the understanding of generative AI use in education and offers actionable implications for promoting responsible and sustainable practices in universities.

Understanding residents’ support is essential for the social sustainability of tourism development, particularly in rapidly transforming destinations. Drawing on Social Exchange Theory, Social Identity Theory, and Destination Image Theory, this study proposes a process-oriented model in which perceived economic benefits and environmental concerns influence residents’ Behavioral Intention Support for Tourism through destination image and national identity. Using survey data from 418 young, educated Saudi residents (predominantly undergraduate university students) and structural equation modeling, the findings show that support is driven primarily by indirect pathways rather than direct cost–benefit evaluations. Economic benefits enhance destination image and strengthen national identity, which in turn foster supportive behavioral intentions. Environmental concerns do not directly reduce support; instead, they operate through sustainability-oriented perceptions of destination image and identity. The results extend existing models by showing how young residents’ behavioral intention support for tourism in Saudi Arabia is shaped through a cognitive–identity process in which destination image and national identity translate economic and environmental evaluations into behavioral intention. This framework offers actionable insights for destinations pursuing state-led tourism development.

Digital technologies such as big data are reshaping resource allocation, raising interest in whether and how heterogeneous science and technology innovation (STI) policies can help unlock urban carbon lock-in. Using panel data for 286 prefecture-level cities in China from 2009 to 2023, this paper examines the relationship between heterogeneous STI policy intensity—classified as supply-side, demand-side, complementary-factor, and institutional-reform policies—and urban carbon unlocking efficiency. We develop a mechanism-based framework and empirically assess (i) the moderating roles of digital infrastructure, science and technology finance, and government green attention, and (ii) spatial spillover effects using spatial econometric models. The results show that all four policy types show a significant positive association with local carbon unlocking efficiency, with institutional-reform policies exhibiting the strongest association. When the four types are included jointly, only supply-side and demand-side policies retain statistically significant direct associations. Heterogeneity analyses indicate that demand-side, complementary-factor, and institutional-reform policies are more strongly associated with efficiency gains in low-pollution cities, whereas supply-side and demand-side policies have a stronger association in high energy-consuming cities. Mechanism analysis reveals that regional digital infrastructure exerts a selective moderating effect on the relationship between heterogeneous sci-tech innovation policies and urban carbon emission reduction efficiency. It positively reinforces the effectiveness of supply-side, demand-side, and institutional reform-oriented policies, while its interaction with complementary policies is statistically insignificant. Technology finance and government green policies function as a “resource catalyst” and an “institutional guarantee” respectively, significantly enhancing the correlation between heterogeneous sci-tech innovation policies and urban carbon emission reduction efficiency. Finally, carbon unlocking efficiency displays significant spatial dependence: the intensity of supply-side and institutional-reform policies is positively associated with carbon unlocking efficiency in neighboring cities, while complementary-factor policies exhibit a negative spatial association. Overall, the findings provide empirical evidence to inform the design and coordination of heterogeneous STI policy portfolios aimed at improving urban carbon unlocking efficiency.

The transition to sustainable agriculture is a key strategic objective at the European level; however, its effective implementation largely depends on farmers’ perceptions and the extent to which sustainable practices are integrated at the farm level. This study analyzes Romanian farmers’ attitudes towards sustainable agricultural practices, their self-reported level of integration, and the associations between these two dimensions. Data were collected through an online self-administered questionnaire (CAWI), yielding 264 valid responses. Nonparametric methods were applied, including the Kruskal–Wallis test with post hoc comparisons, principal component analysis (PCA) with promax rotation, and Kendall’s tau correlation. Significant differences in perceived importance of sustainable practices were observed by farming experience, with higher scores reported by farmers with 6–10 years of experience compared to those with 16–20 years (p = 0.0046). PCA confirmed a two-component structure reflecting attitudes and self-reported integration, explaining 72.4% of the total variance. The association between these constructs was modest but statistically significant (τ = 0.289, p < 0.001). Overall, the farmers report positive attitudes towards sustainability alongside a moderate and heterogeneous level of practice integration, with soil and water protection and long-term cost considerations emerging as more salient than market- or image-related factors. The findings provide a descriptive and correlational perspective relevant for advisory services and support measures aligned with farmers’ reported perceptions and experience.

The global demand for renewable energy is rapidly increasing in response to efforts to reduce greenhouse gas emissions, driving the development of novel technologies. Offshore solar energy is an emerging renewable technology with the potential to contribute to the energy transition and decarbonization of electricity generation. Although offshore solar projects are developing at an increasing pace, their ecological implications are not yet well-understood, including interactions with marine megafauna. Given the central ecological roles of birds and marine mammals, assessing and monitoring these interactions is essential before large-scale deployment. Despite extensive research on marine megafauna interactions with offshore wind farms, no studies have yet examined offshore interactions with solar installations. This study uses year-round time-lapse imagery and bird pellet analyses to record species presence, abundance, juvenile occurrence, and behavioral use of these structures in the southern North Sea. Seagulls, as well as grey and harbor seals, were frequently observed resting on the floating solar installations. Bird occurrence showed seasonal variation, likely reflecting breeding and migration patterns. The results indicate offshore solar structures may serve as temporary resting grounds for marine megafauna. These findings emphasize the importance of long-term ecological monitoring to ensure the sustainable co-existence of offshore renewable energy and marine biodiversity.