Search Results (5,028)

High-Resolution Range Profile (HRRP) represents the electromagnetic backscattering distribution of targets and plays a pivotal role in remote-sensing-based Automatic Target Recognition (RATR). However, in non-cooperative sensing scenarios, acquiring sufficient measured data is severely constrained by operational costs and physical limitations, leading to data scarcity that hampers model robustness. To overcome this, we propose SpecM-DDPM, a spectral-aware Denoising Diffusion Probabilistic Models (DDPM) tailored for generating high-fidelity HRRPs that preserve physical scattering properties. Unlike generic generative models, SpecM-DDPM incorporates radar signal physics into the diffusion process. Specifically, a parallel multi-scale block is designed to adaptively capture both local scattering centers and global target resonance structures. To ensure spectral fidelity, a spectral gating mechanism serves as a physics-constrained filter to calibrate the energy distribution in the frequency domain. Furthermore, a Frequency-Aware Curriculum Learning (FACL) strategy is introduced to guide the progressive reconstruction from low-frequency structural components to high-frequency scattering details. Experiments on measured aircraft data demonstrate that SpecM-DDPM generates samples with high physical consistency, significantly enhancing the generalization performance of radar recognition systems in data-limited environments. Full article

Tunnel boring machine (TBM) hydraulic cylinders operate under pronounced start–stop shocks and load uncertainties, making it difficult to simultaneously achieve smooth start-up and high-precision tracking. This paper proposes a two-phase switching adaptive sliding mode control (ASMC) strategy for TBM hydraulic actuation. Phase I targets a soft start by introducing smooth gating and a ramped start-up mechanism into the sliding surface and equivalent control, thereby suppressing pressure spikes and displacement overshoot induced by oil compressibility and load transients. Phase II targets precise tracking, combining adaptive laws with a forgetting factor design to maintain robustness while reducing chattering and steady-state error. We construct a state-space model that incorporates oil compressibility, internal/external leakage, and pump/valve dynamics, and provide a Lyapunov-based stability analysis proving bounded stability and error convergence under external disturbances. Comparative simulations under representative TBM conditions show that, relative to conventional PID Controller and single ASMC Controller, the proposed method markedly reduces start-up pressure/velocity peaks, overshoot, and settling time, while preserving tracking accuracy and robustness over wide load variations. The results indicate that the strategy can achieve the unity of smooth start and high-precision trajectory of TBM hydraulic cylinder without additional sensing configuration, offering a practical path for high-performance control of TBM hydraulic actuators in complex operating environments. Full article

The growing complexity and dynamism of industrial and organizational projects require management approaches that can effectively adapt to uncertainty and rapidly changing operational environments. In this context, this study proposes a methodology to identify the most suitable management approach—predictive, agile, or hybrid—in complex projects. Building on the “Approach suitability tool” of the Project Management Institute’s (PMI) , the methodology integrates quantitative assessments of complexity and systemic risk. This is achieved through the analysis of stakeholder and risk networks, using metrics such as cyclomatic complexity and the coevolution parameter g, which allow for a deeper understanding of interactions and the evolution of project elements. The methodology was validated in three peacekeeping missions of the United Nations: UNMISS in South Sudan, MONUSCO in the Democratic Republic of Congo, and MINUSTAH in Haiti. The results confirm that the methodology accurately identifies the most appropriate management approach, emphasizing the effectiveness of hybrid approaches in complex and volatile environments. The proposed methodology serves as a valuable tool for optimizing project management in diverse contexts, enabling a quantitative and systematic evaluation of complexity and risk. It is adaptable and applicable to a wide range of complex projects, improving decision-making and planning in uncertain settings. Furthermore, by incorporating resilience as a cross-cutting principle, the methodology strengthens the ability of projects and their teams to maintain functionality and sustain learning even in highly volatile environments, where continuous adaptation becomes a critical success factor. In this sense, resilience emerges as the property that allows projects to absorb disruptions, reorganize, and preserve their core purpose without losing cohesion or direction. Full article

Chatbots have emerged as a promising interface for facilitating access to complex datasets, allowing users to pose questions in natural language rather than relying on specialized technical workflows. At the same time, remote sensing has transformed archaeological practice by producing vast amounts of imagery from LiDAR, drones, and satellites. While these advances have created unprecedented opportunities for discovery, they also pose significant challenges due to the scale, heterogeneity, and interpretative demands of the data. In related scientific domains, multimodal conversational systems capable of integrating natural language interaction with image-based analysis have advanced rapidly, supported by a growing body of survey and review literature documenting their architectures, datasets, and applications across multiple fields. By contrast, archaeological applications of chatbots remain limited to text-based prototypes, primarily focused on education, cultural heritage mediation or archival search. This review synthesizes the historical development of chatbots, examines their current use in remote sensing, and evaluates the barriers to adapting such systems for archaeology. Four major challenges are identified: data scale and heterogeneity, scarcity of training datasets, computational costs, and uncertainties around usability and adoption. By comparing experiences across domains, this review highlights both the opportunities and the limitations of integrating conversational AI into archaeological workflows. The central conclusion is that domain-specific adaptation is essential if multimodal chatbots are to become effective analytical partners in archaeology. Full article

Daily nighttime light (NTL) observations made by remote sensing satellites can monitor human activity at high temporal resolution, but are often constrained by residual physical disturbances. Even in standard products, such as NASA’s Black Marble VNP46A2, factors related to sensor viewing geometry, lunar illumination, atmospheric conditions, and seasonality can introduce noise into daily radiance retrievals. This study develops a locally adaptive framework to diagnose and correct residual disturbances in daily NTL data. By estimating location-specific regression models, we quantify the residual sensitivity of VNP46A2 radiance to multiple disturbance factors and selectively remove statistically significant components. The results show that the proposed approach effectively removes statistically significant residual disturbances from daily NTL data in the VNP46A2 product. An application for COVID-19 containment periods in China demonstrates the effectiveness of the proposed approach, where corrected daily NTL data exhibit enhanced temporal stability and improved interpretability. Further analysis based on event study approaches demonstrates that corrected daily NTL data enable the identification of short-run policy effects that are difficult to detect with lower-frequency indicators. Overall, this study enhances the suitability of daily NTL data for high-frequency socioeconomic applications and extends existing preprocessing approaches for daily NTL observations. Full article

Seventy 28-day-old weaned barrow piglets (Duroc × Landrace × Large White; 7.2 ± 0.20 kg) were used to determine the effects of 25-hydroxyvitamin D₃ (25-OH-VD₃) combined with phytase and probiotics on calcium and phosphorus metabolism and bone development. Five dietary groups were tested: basal diet + 50 µg/kg 25-OH-VD₃ (CON); basal diet with 17% reduced calcium and phosphorus + 50 µg/kg 25-OH-VD₃ (LCP); LCP + 50 mg/kg phytase (LH); LCP + 10 mg/kg probiotics (LC); LCP + 50 mg/kg phytase + 10 mg/kg probiotics (LHC). The experiment lasted for 31 days, including 3 days adaptation period. Apparent phosphorus digestibility was higher in the LH and LHC groups than in the CON group (p < 0.05). Bone mineral density and calcium content in metacarpal and rib bones were also higher in the LHC group compared with the CON, LCP, LC, and LH groups (p < 0.05). The jejunal mRNA expression of solute carrier family 34 members (SLC34A1, SLC34A2, and SLC34A3) members was higher in the LHC group than the CON, LCP, LC, and LH groups (p < 0.05), while the relative protein expression of the calcium-sensing receptor in the kidneys was lower in the CON group than in the LCP, LH, LC, and LHC groups (p < 0.05). Additionally, supplementation with 25-OH-VD₃, either alone or in combination with phytase and probiotics, was associated with an increased abundance of beneficial gut bacteria. Overall, combined supplementation of 25-OH-VD₃, phytase and probiotics enhanced bone development in weaned piglets fed a low-calcium, low-phosphorus diet by improving calcium and phosphorus utilization and calcium–phosphorus metabolic regulation. Full article

With the rapid growth of multimodal remote sensing (RS) data, there is an increasing demand for intelligent onboard computing to alleviate the transmission and latency bottlenecks of traditional orbit-to-ground downlinking workflows. While many lightweight AI algorithms have been widely developed and deployed for onboard inference, their limited generalization capability restricts performance under the diverse and dynamic conditions of advanced Earth observation. Recent advances in remote sensing foundation models (RSFMs) offer a promising solution by providing pretrained representations with strong adaptability across diverse tasks and modalities. However, the deployment of RSFMs onboard resource-constrained devices such as nano satellites remains a significant challenge due to strict limitations in memory, energy, computation, and radiation tolerance. To this end, this review proposes the first comprehensive survey of onboard RSFMs deployment, where a unified deployment pipeline including RSFMs development, model compression techniques, and hardware optimization is introduced and surveyed in detail. Available hardware platforms are also discussed and compared, based on which some typical case studies for low Earth orbit (LEO) CubeSats are presented to analyze the feasibility of onboard RSFMs’ deployment. To conclude, this review aims to serve as a practical roadmap for future research on the deployment of RSFMs on edge devices, bridging the gap between the large-scale RSFMs and the resource constraints of spaceborne platforms for onboard computing. Full article

Understanding the evolution of the atmospheric boundary layer height (BLH) is essential for characterizing air–surface exchange and air pollution processes. This study investigates the consistency and applicability of three BLH retrieval methods based on multi-source remote sensing observations at Beijing Southern Suburb station during autumn–winter 2023. Using Doppler wind lidar (DWL) and microwave radiometer (MWR) data, the Haar wavelet covariance transform (HWCT), vertical velocity variance (Var), and parcel methods were applied, and 10 min averages were used to suppress short-term fluctuations. Statistical analysis shows good overall consistency among the methods, with the strongest correlation between HWCT and Var method (R = 0.62) and average systematic positive bias of 0.4–0.6 km for the parcel method. Case studies under clear-sky, cloudy, and hazy conditions reveal distinct responses: HWCT effectively captures aerosol gradients but fails under cloud contamination, the Var method reflects turbulent dynamics and requires adaptive thresholds, and the Parcel method robustly describes thermodynamic evolution. The results demonstrate that the three methods are complementary in capturing the material, dynamic, and thermodynamic characteristics of the boundary layer, providing a comprehensive framework for evaluating BLH variability and improving multi-sensor retrievals under diverse meteorological conditions. Full article

Rapid and accurate acquisition of landslide inventories is essential for effective disaster relief. Deep learning-based pixel-wise semantic segmentation of remote sensing imagery has greatly advanced in landslide mapping. However, the heavy dependance on extensive annotated labels and sensitivity to domain shifts severely constrain the model performance in unseen domains, leading to poor generalization. To address these limitations, we propose LandsDANet, an innovative unsupervised domain adaptation framework for cross-domain landslide identification. Firstly, adversarial learning is employed to reduce the data distribution discrepancies between the source and target domains, thereby achieving output space alignment. The improved SegFormer serves as the segmentation network, incorporating hierarchical Transformer blocks and an attention mechanism to enhance feature representation capabilities. Secondly, to alleviate inter-domain radiometric discrepancies and attain image-level alignment, a Wallis filter is utilized to perform image style transformation. Considering the class imbalance present in the landslide dataset, a Rare Class Sampling strategy is introduced to mitigate bias towards common classes and strengthen the learning of the rare landslide class. Finally, a contrastive loss is adopted to further optimize and enhance the model’s ability to delineate fine-grained class boundaries. The proposed model is validated on the Potsdam and Vaihingen benchmark datasets, followed by validation in two landslide scenarios induced by earthquakes and rainfall to evaluate its adaptability across different disaster domains. Compared to the source-only model, LandsDANet achieved improvements in IoU of 27.04% and 35.73% in two cross-domain landslide disaster recognition tasks, respectively. This performance not only showcases its outstanding capabilities but also underscores its robust potential to meet the demands for rapid response. Full article

To address the pervasive challenges of resource inefficiency and static management in the hospitality sector, this study proposes a novel management framework that synergistically integrates Model Predictive Control (MPC) with Green Human Resource Management (GHRM). Methodologically, the framework establishes a dynamic closed-loop architecture that cyclically links environmental sensing, predictive optimization, plan execution and organizational learning. The MPC component generates data-driven forecasts and optimal control signals for resource allocation. Crucially, these technical outputs are operationally translated into specific, actionable directives for employees through integrated GHRM practices, including real-time task allocation via management systems, incentives-aligned performance metrics, and structured environmental training. This practical integration ensures that predictive optimization is directly coupled with human behavior. Theoretically, this study redefines hospitality operations as adaptive sociotechnical systems, and advances the hospitality energy-saving management framework by formally incorporating human execution feedback, predictive control theory, and dynamic optimization theory. Empirical validation across a sample of 40 hotels confirms the framework’s effectiveness, demonstrating significant reductions in daily average water consumption by 15.5% and electricity usage by 13.6%. These findings provide a robust, data-driven paradigm for achieving sustainable operational transformations in the hospitality industry. Full article

Vision-language models can leverage natural language descriptions to encode stable farmland characteristics, providing a new paradigm for farmland extraction, yet existing methods face challenges in ambiguous text-visual alignment and loss of high-frequency boundary details during fusion. To address this, this article utilizes the semantic prior knowledge provided by textual descriptions in vision–language models to enhance the model’s ability to recognize polymorphic features, and proposes an Uncertainty-Guided and Frequency-Fused Vision-Language Model (UGFF-VLM) for remote sensing farmland extraction. The UGFF-VLM combines the semantic representation ability of vision-language models, further integrates an Uncertainty-Guided Adaptive Alignment (UGAA) module to dynamically adjust cross-modal fusion based on alignment confidence, and a Frequency-Enhanced Cross-Modal Fusion (FECF) mechanism to preserve high-frequency boundary details in the frequency domain. Experimental results on the FarmSeg-VL dataset demonstrate that the proposed method delivers excellent and stable performance, achieving the highest mIoU across diverse geographical environments while showing significant improvements in boundary precision and robustness against false positives. Therefore, the proposed UGFF-VLM not only mitigates the issues of recognition confusion and poor generalization in purely vision-based models caused by farmland feature polymorphism but also effectively enhances boundary segmentation accuracy, providing a reliable method for the precise delineation of agricultural parcels in diverse landscapes. Full article

The emerging paradigm of “fusion of lifeforms” represents a transformative shift from conventional human–machine interfaces toward deeply integrated symbiotic systems, where biological and artificial components co-adapt structurally, energetically, informationally, and cognitively. This review systematically classifies multi-level perception systems within fusion of lifeforms into four functional categories: sensory and functional restoration, beyond-natural sensing, endogenous state sensing, and cognitive enhancement. We survey recent advances in neuroprosthetics, sensory augmentation, closed-loop physiological monitoring, and brain–computer interfaces, highlighting the transition from substitution to fusion. Despite significant progress, critical challenges remain, including multi-source heterogeneous integration, bandwidth and latency limitations, power and thermal constraints, biocompatibility, and system-level safety. We propose future directions such as layered in-body communication networks, sustainable energy strategies, advanced biointerfaces, and robust safety frameworks. Ethical considerations regarding self-identity, neural privacy, and legal responsibility are also discussed. This work aims to provide a comprehensive reference and roadmap for the development of next-generation fusion of lifeforms, ultimately steering human–machine integration from episodic functional repair toward sustained, multi-level symbiosis between biological and artificial systems. Full article

To address the challenges of low data validity and limited energy efficiency in agricultural IoT, we propose a deep predictive agricultural variable-frequency sensing method. First, we construct a hybrid prediction model, denoted as SVMD-TCN-R-GRU-T (STRGT). This model integrates successive variational mode decomposition (SVMD) with an optimized TCN-GRU architecture, thereby improving prediction accuracy. Building on this framework, we design a frequency conversion sampling method under dual detection analysis (FCSDDA). This approach employs wavelet transform to determine the minimum sampling rate and incorporates dynamic time warping evaluate data variation. The dual detection mechanism enables real-time adjustment of sensor acquisition frequency. Experimental results demonstrate that the proposed model significantly outperforms conventional models in terms of RMSE, MAE, and MAPE. When the STRGT outputs are applied as inputs to the FCSDDA algorithm, the system achieved optimal improvements in energy efficiency improvement rate (84.61%) and data value density (0.3368), exceeding the performance of other prediction model variants. These findings confirm that prediction accuracy directly influences adaptive sensing performance. This indicates that the method can effectively achieve dual optimization of energy saving and data validity in testing scenarios. In the future, more agricultural sensing scenarios can be validated. Full article

Agricultural robotics and artificial intelligence (AI) are becoming essential to building more sustainable, efficient, and resilient food systems. As climate change, food security pressures, and labour shortages intensify, the integration of intelligent technologies in agriculture has gained strategic importance. This systematic review provides a consolidated assessment of AI and robotics research in agriculture from 2000 to 2025, identifying major trends, methodological trajectories, and underexplored domains. A structured search was conducted in the Scopus database—which was selected for its broad coverage of engineering, computer science, and agricultural technology—and records were screened using predefined inclusion and exclusion criteria across title, abstract, keywords, and eligibility levels. The final dataset was analysed through descriptive statistics and science-mapping techniques (VOSviewer, SciMAT). Out of 4894 retrieved records, 3673 studies met the eligibility criteria and were included. As with all bibliometric reviews, the synthesis reflects the scope of indexed publications and available metadata, and potential selection bias was mitigated through a multi-stage screening workflow. The analysis revealed four dominant research themes: deep-learning-based perception, UAV-enabled remote sensing, data-driven decision systems, and precision agriculture. Several strategically relevant but underdeveloped areas also emerged, including soft manipulation, multimodal sensing, sim-to-real transfer, and adaptive autonomy. Geographical patterns highlight a strong concentration of research in China and India, reflecting agricultural scale and investment dynamics. Overall, the field appears technologically mature in perception and aerial sensing but remains limited in physical interaction, uncertainty-aware control, and long-term autonomous operation. These gaps indicate concrete opportunities for advancing next-generation AI-driven robotic systems in agriculture. Funding sources are reported in the full manuscript. Full article

Artificial Intelligence (AI) is increasingly reshaping how cultural heritage institutions design and deliver digital visitor experiences, particularly through adaptive Augmented Reality (AR) applications. However, most existing AR deployments in museums and galleries remain static, rule-based, and insufficiently responsive to visitors’ contextual, behavioral, and emotional diversity. This paper presents ARIA (Augmented Reality for Interpreting Artefacts), a conceptual and architectural framework for AI-supported, adaptive AR experiences in cultural heritage settings. ARIA is designed to address current limitations in personalization, affect-awareness, and ethical governance by integrating multimodal context sensing, lightweight affect recognition, and AI-driven content personalization within a unified system architecture. The framework combines Retrieval-Augmented Generation (RAG) for controlled, knowledge-grounded narrative adaptation, continuous user modeling, and interoperable Digital Asset Management (DAM), while embedding Human-Centered Design (HCD) and Fairness, Accountability, Transparency, and Ethics (FATE) principles at its core. Emphasis is placed on accountable personalization, privacy-preserving data handling, and curatorial oversight of narrative variation. ARIA is positioned as a design-oriented contribution rather than a fully implemented system. Its architecture, data flows, and adaptive logic are articulated through representative museum use-case scenarios and a structured formative validation process including expert walkthrough evaluation and feasibility analysis, providing a foundation for future prototyping and empirical evaluation. The framework aims to support the development of scalable, ethically grounded, and emotionally responsive AR experiences for next-generation digital museology. Full article