Search Results (197)

Physics-informed neural networks (PINNs) have recently attracted considerable attention as a framework for solving partial differential equations. Underwater sound-field prediction fundamentally relies on solving acoustic wave equations, making PINNs a natural candidate for this application. This paper reviews recent developments in PINN-based modeling of underwater acoustic propagation, which we group into two main lines of research. The first introduces mathematically motivated simplifications of the governing equations and then employs PINNs as efficient solvers; examples include ray-based PINNs and PINN estimators of modal wavenumbers. The second focuses on improving computational performance by tailoring network architectures and hyperparameters, such as spatial domain-decomposition strategies. While PINNs demonstrate significant potential, challenges persist regarding computational efficiency and convergence in high-frequency regimes. Future research directions are identified, emphasizing a multi-faceted strategy that systematically addresses limitations at both the physical formulation level and the neural network architecture level. By integrating advanced hybrid physics-data modeling and scalable training algorithms, this review highlights the pathway toward bridging the gap between theoretical frameworks and realistic ocean applications. Full article

The European Union’s decarbonization strategy relies on transparent and accurate climate data across value chains. Yet, existing sustainability reporting frameworks mainly target large companies, often neglecting small and medium-sized enterprises (SMEs). Although SMEs are largely exempt from mandatory reporting under recent regulatory simplifications, they play a critical role in Scope 3 emissions, which dominate the carbon footprints of larger firms. This paper presents two complementary, freely accessible digital tools designed to support credible carbon accounting. The first tool, Climate Compass, is a government-sanctioned tool that aligns with the GHG Protocol and has been used by >10,000 SMEs in Denmark to calculate Scopes 1, 2, and 3 emissions through a user-friendly interface. The second, a newly developed online cradle-to-gate life cycle assessment (LCA) tool, supports product-level carbon footprinting using open-source emission factor databases. The cradle-to-gate approach reflects typical SME production profiles and emphasizes embodied CO₂e from raw materials, transport, and energy consumption. Together, these tools enable researchers to effectively assess SMEs emissions in the value chain and thus support decarbonization while supplying reliable data to larger companies. The tool democratizes emissions analysis and supports regulatory and market demands and strengthens SMEs contribution to Europe’s low-carbon transition. Full article

The use of virtual reality (VR) has made significant advancements, and now it is widely used across a range of applications. However, consumers’ capacity to fully enjoy VR experiences continues to be limited by a chronic problem known as cybersickness (CS). This study explores the feasibility of mitigating CS through geometric scene simplification combined with electroencephalography (EEG)-based monitoring. According to the sensory conflict theory, this issue is caused by the discrepancy between the visually induced self-motion (VIMS) through immersive displays and the real motion the vestibular system detects. While prior mitigation strategies have largely relied on hardware modifications or visual field restrictions, this paper introduces a novel framework that integrates geometric scene simplification with EEG-based neurophysiological activity to reduce VIMS during VR immersion. The proposed framework combines EEG neurophysiology, allowing us to monitor users’ brainwave activity and cognitive states during virtual immersion experience. The empirical evidence from our investigation shows a correlation between CS manifestation and neural activation in the parietal and temporal lobes. As an experiment with 15 subjects, statistical differences were significantly different with $P= 0.001$ and large effect size $\eta 2=0.28$, while preliminary trends suggest lower neural activation during simplified scenes. Notably, a decrease in neural activation corresponding to reduced optic flow (OF) suggests that VR environment simplification may help attenuate CS symptoms, providing preliminary support for the proposed strategy. Full article

Background/Objectives: Sleep stage classification is crucial for assessing sleep quality and diagnosing related disorders. Electroencephalography (EEG) is currently recognized as a primary method for sleep stage classification. High-performance automatic sleep staging methods based on EEG leverage the powerful contextual modeling capabilities of Transformer Encoder architectures. However, the global self-attention mechanism in Transformers incurs significant computational overhead, substantially hindering the training and inference efficiency of automatic sleep staging algorithms. Methods: To address these issues, we introduce an end-to-end framework for automatic sleep stage classification using single-channel EEG: SleepMFormer. At the algorithmic level, SleepMFormer adopts a task-driven simplification of the Transformer encoder to improve attention efficiency while preserving sequence modeling capability. At the training level, supervised contrastive learning is incorporated as an auxiliary strategy to enhance representation robustness. From an engineering perspective, these design choices enable efficient training and inference under resource-constrained settings. Results: When integrated with the SleePyCo backbone, the proposed framework achieves competitive performance on three widely used public datasets: Sleep-EDF, PhysioNet, and SHHS. Notably, SleepMFormer reduces training and inference time by up to 33% compared to conventional self-attention-based models. To further validate the generalizability of MaxFormer, we conduct additional experiments using DeepSleepNet and TinySleepNet as alternative feature extractors. Experimental results demonstrate that MaxFormer consistently maintains performance across different model architectures. Conclusions: Overall, SleepMFormer introduces an efficient and practical framework for automatic sleep staging, demonstrating strong potential for related clinical applications. Full article

Microparticle detection technology uses materials that can specifically recognize complex biostructures, such as antibodies and aptamers, as trapping agents. The development of antibody production technology and simplification of sensing signal output methods have facilitated commercialization of disposable biosensors, making rapid diagnosis possible. Although this contributed to the early resolution of pandemics, traditional biosensors face issues with sensitivity, durability, and rapid response times. We aimed to fabricate microspaces using metallic materials to further enhance durability of mold fabrication technologies, such as molecular imprinting. Low-damage metal deposition was performed on target protozoa and Norovirus-like particles (NoV-LPs) to produce thin metallic films that adhere to the material. The procedure for fitting the object into the bio structured space formed on the thin metal film took less than a minute, and sensitivity was 10 fg/mL for NoV-LPs. Furthermore, because it was a metal film, no decrease in reactivity was observed even when the same substrate was stored at room temperature and reused repeatedly after fabrication. These findings underscore the potential of integrating stable metallic structures with bio-recognition elements to significantly enhance robustness and reliability of environmental monitoring. This contributes to public health strategies aimed at early detection and containment of infectious diseases. Full article

Building extraction underpins land-use assessment, urban planning, and disaster mitigation, yet dense urban scenes still cause missed small objects, target adhesion, and ragged contours. We present High-Resolution-Mamba (HR-Mamba), a high-resolution semantic segmentation network that augments a High-Resolution Network (HRNet) parallel backbone with edge-aware and sequence-state modeling. A Canny-enhanced, median-filtered stem stabilizes boundaries under noise; Involution-based residual blocks capture position-specific local geometry; and a Mamba-based State Space Models (Mamba-SSM) global branch captures cross-scale long-range dependencies with linear complexity. Training uses a composite loss of binary cross entropy (BCE), Dice loss, and Boundary loss, with weights selected by joint grid search. We further design a feature-driven adaptive post-processing pipeline that includes geometric feature analysis, multi-strategy simplification, multi-directional regularization, and topological consistency verification to produce regular, smooth, engineering-ready building outlines. On dense urban imagery, HR-Mamba improves F1-score from 80.95% to 83.93%, an absolute increase of 2.98% relative to HRNet. We conclude that HR-Mamba jointly enhances detail fidelity and global consistency and offers a generalizable route for high-resolution building extraction in remote sensing. Full article

While climate is known to regulate forest productivity, the mechanistic contribution of soil microbial communities—and whether it differs between natural and plantation forests—remains poorly quantified at broad scales. Here, we provide a synthesis-level, unified analysis that jointly evaluates climate, edaphic conditions, and soil microbes to compare mechanistic pathways underlying productivity divergence between forest types. We synthesized 237 observations across China and integrated productivity metrics—gross primary productivity (GPP) and net primary productivity (NPP)—with microbial diversity, dominant taxa, and soil drivers to compare natural and plantation forests within the current environmental coverage. Plantation productivity showed nonlinear responses to microbial diversity and appeared more sensitive than natural forests. Natural forests exhibited higher bacterial Shannon and Chao1 but lower fungal Chao1 and were characterized by taxa such as Nitrobacter, Bradyrhizobium, and Cortinarius. In contrast, plantations were characterized by taxa often associated with disturbance tolerance and opportunistic life-history strategies (e.g., Sphingomonas, Fusarium, Gemmatimonas), consistent with potential functional simplification. Structural equation models identified climate as the strongest correlate of productivity, while soil properties showed contrasting associations with microbial diversity across forest types. Random forest models further highlighted cation-exchange capacity and total nitrogen as key predictors of microbial diversity in plantations. Overall, our results indicate that soil microbial communities are differentially associated with forest productivity across forest types and environmental contexts and underscore the need for future climate-comparable designs and management-intensity information to more robustly isolate microbial contributions. Full article

Urban building energy modeling (UBEM) is crucial for assessing energy consumption patterns at the city-scale and for supporting data driven planning and decarbonization strategies. However, its practical deployment is often hindered by the need to balance detailed physics-based simulations with acceptable computation times when thousands of buildings are involved. This work presents a large-scale real world UBEM case study and proposes a workflow that combines EnergyPlus simulations, high-performance computing (HPC), and open urban datasets to model the energy consumption of the building stock in the Municipality of Bologna, Italy. Geometric data such as building footprints and heights were acquired from the Bologna Open Data portal and complemented by aerial light detection and ranging (LiDAR) measurements to refine elevations and roof geometries. Non-geometrical building characteristics, including wall materials, insulation levels, and window properties, were derived from local building regulations and the European TABULA project, enabling the assignment of archetypes in contexts where granular information about building materials is not available. The pipeline’s modular design allows us to analyze different combinations of retrofitting scenarios, making it possible to identify the groups of buildings that would benefit the most. A key feature of the workflow is the use of Leonardo, the supercomputer hosted and managed by Cineca, which made it possible to simulate the energy consumption of approximately 25,000 buildings in less than 30 min. In contrast to approaches that mainly reduce computation time by simplifying the physical model or aggregating representative buildings, the HPC-based workflow allows the entire building stock to be individually simulated (within the intrinsic simplifications of UBEM) without introducing further compromises in model detail. Overall, this case study demonstrates that the combination of open data and HPC-accelerated UBEM can deliver city-scale energy simulations that are both computationally tractable and sufficiently detailed to inform municipal decision-making and future digital twin applications. Full article

Several major studies require that students understand and master the concepts and procedures of mathematics. More specifically, an area of mathematics such as algebra requires students to be able to simplify, operate with, or solve fractions. Many students entering university show numerous shortcomings and errors, especially when simplifying algebraic fractions. This is why we conducted a study using eye-tracking techniques to better understand how students process these types of exercises in attentional terms, comparing students who can handle them successfully against those who show errors in their procedures. For this purpose, we evaluated the eye movements of 64 students from different university majors to characterize the attentional–visual strategies they use to simplify four different algebraic fraction exercises. We found that each type of simplification exercise needs a specific strategy where some parts of the rational algebraic expressions are cognitively relevant. Students with correct answers tend to allocate attention to these elements. Students with incorrect answers tend to find similar expressions with the intention to cancel them out, without applying any metacognitive thinking. The rational algebraic expression needs to be taught in a more conceptual manner than procedural. Full article

This paper proposes an innovative methodological framework for integrating Cultural Ecosystem Services (CES) into landscape planning, with the aim of enhancing the conservation and adaptive management of rural historical landscapes. Grounded in the principles of the European Landscape Convention and the recent Nature Restoration Law, the study advocates for a shift from prescriptive and sectoral approaches toward performance-based and ecosystem-oriented models. The research focuses on the Marche Region (Italy), where the historical landscape shaped by the mezzadria (sharecropping) system provides a representative case for testing the proposed methodology. Six spatial layers have been selected as ecosystem-based indicators to identify new potential landscape CES’ hotspots as agricultural landscape high-value areas, and to redefine protection and management strategies. The analysis integrates historical, ecological, and cultural dimensions to construct a spatially explicit value matrix, supporting the definition of differentiated management zones. Results reveal the persistence of high landscape and ecosystem values in mid- and upper-hill areas, contrasted by the progressive loss of structural and functional diversity in lowland and peri-urban contexts. The findings highlight the need for more adaptive and flexible planning models, capable of incorporating nature-based actions, climate-smart agriculture, and performance-oriented evaluation criteria. The proposed approach demonstrates potential for replicability and policy integration, providing a decision-support framework to align landscape planning with rural development strategies and climate adaptation objectives. Despite limitations related to data availability and model simplification, the methodology contributes to the ongoing paradigm shift toward dynamic, evidence-based, and transdisciplinary landscape governance across Mediterranean regions. Full article

Runoff forecasting is essential for flood control, disaster mitigation, and sustainable water resources management. However, runoff processes are highly nonlinear and uncertain due to multiple interacting meteorological and underlying surface factors. Current models can be divided into process-driven and data-driven types. The former offers clear physical interpretability but involves complex calibration and simplifications, while the latter captures nonlinear relationships effectively but lacks physical consistency. To integrate their strengths, this study constructs process-based models and data-driven models, and proposes two hybrid strategies: (1) incorporating intermediate variables from physical models, such as soil moisture and runoff yield, as additional features for data-driven models, and (2) embedding physics-based constraints and synthetic data into loss functions. Using the Songxi River Basin as a case study, results show that both hybrid strategies significantly outperform standalone models. SHapley Additive exPlanations (SHAP)-based interpretability analysis further reveals the contribution mechanisms of key physical variables. This study demonstrates that coupling physical processes with data-driven learning effectively enhances runoff forecasting accuracy and offers a promising paradigm to support sustainable watershed management, climate-resilient water regulation, and flood risk reduction. Full article

Climate regulation ecosystem services (CRESs) play a crucial role in maintaining ecological balance and promoting regional sustainability. Previous studies have primarily focused on the total volume or per-unit-area quantity of CRESs, with limited attention given to their underlying driving mechanisms. This neglect overlooks their multidimensional attributes and dynamic complexity. Such simplifications often overlook the multidimensional attributes and dynamic complexity inherent in these services. Therefore, this study introduces a multidimensional evaluation framework to reveal the characteristic of the spatiotemporal evolution of CRESs. By integrating a multiscale geographically weighted regression (MGWR) model, the intensity and effective distance of theireffects are quantitatively identified, thereby providing a scientific and refined cognitive foundation for regional sustainable development. The results showed the following: (1) Between 2002 and 2022, CRESs in Guizhou Province showed an upward trend, with 64% of counties experiencing positive trends, whereas 51% of counties remained below average in terms of output and efficiency. (2) The spatial pattern of CRESs varied significantly, with stabilization in hotspots, improvement in coldspots, and the highest proportion of “A progress zones” in the east (45%). (3) Vegetation cover and annual precipitation were the two mainpositive factors that most strongly influenced the intensity of the CRESs, with values of 1.494 and 1.196, respectively; GDP had the most significant negative effect, with a value of −0.189; and population density had the largest range of effects, with a bandwidth of 1629. (4) Except for annual rainfall and aspect, the remaining eight influencingfactors, including population density, GDP, altitude, NPP, vegetation cover, annual temperature, and annual humidity, had positive and negative bidirectional effects on CRESs. Overall, this study emphasizes the need for differentiated, sustainability-oriented management strategies to better integrate ecosystem service evaluations into regional planning and sustainable policy development. Full article

SWMM (Stormwater Management Model) is one of the most widely used computation tools in urban water resources management. Traditionally, the choice of rainfall data for calibrating the SWMM model has been arbitrary, lacking clarity on the most suitable rainfall types. In addition, the simplification in the SWMM hydrological module of the rainfall–runoff process, coupled with measurement errors, introduces a high level of uncertainty in the calibration. This study investigates the influences of rainfall types on the highly uncertain SWMM model calibration by implementing the Bayesian inference theory. A Bayesian SWMM calibration framework was established, in which an advanced DREAM(zs) (Differential Evolution Adaptive Metropolis, Version ZS) sampling method was used. The investigation focused on eight key hydrological parameters of SWMM. The impact of rainfall types was analyzed using nine rainfall intensities and three rainfall patterns. Results show that rainfall events equivalent to a one-year return period (R5, 42.70 mm total depth) or higher generally yield the most accurate parameters, with posterior distribution standard deviations reduced by 40–60% compared to low-intensity rainfalls. Notably, three parameters (impervious area percentage [Imperv], storage depth of impervious area [S-imperv], and Manning’s coefficient of impervious area [N-imperv]) demonstrated consistent accuracy irrespective of rainfall intensity, with a coefficient of variation below 0.05 for Imperv and S-imperv across all rainfall intensities. Furthermore, it was found that rainfall events with double peaks resulted in more satisfactory calibration compared to single or triple peaks, reducing the standard deviation of the Width parameter from 168.647 (single-peak) to 110.789 (double-peak). The findings from this study could offer valuable insights for selecting appropriate rainfall events before SWMM model calibration for more accurate predictions when it comes to urban non-point pollution control strategies and watershed management. Full article

Introduction: Cancer pain remains a critical issue for patients’ quality of life, affecting their physiology, psychology, and social relationships. Despite the widely recognized role of pharmacists in pain management, their involvement in palliative care in Greece remains limited. This study focuses on exploring the perceptions and experiences of pharmacists regarding their role in cancer pain management, identifying barriers, required skills, and proposing strategies for their integration in the multidisciplinary team. Μaterials and Μethods: Qualitative research was conducted through semi-structured interviews with seven pharmacists in the Attica region. The interviews were recorded, transcribed, and thematically analyzed. Results: The analysis revealed four main themes: (1) limited access to medical records and challenges in pharmaceutical decision-making, (2) lack of institutional frameworks and a culture of collaboration, (3) need for specialized education and continuous training, and (4) understaffing and bureaucracy, faced by pharmacists. Discussion: This study highlights the underutilized role of pharmacists in cancer pain management in Greece. Barriers such as restricted access to patient records, weak interdisciplinary collaboration, insufficient training, and bureaucratic constraints limit their contribution. Structured frameworks and collaborative cultures can enhance pharmacists’ involvement, while education and continuous training are essential to strengthen their legitimacy within care teams. Digital tools can improve access to patient information and support evidence-based decisions. Conclusions: Pharmacists’ integration in the patient’s management team has significant benefits for the patient’s quality of life. Strengthening pharmacists’ involvement in cancer pain management requires the establishment of collaborations, continuous education, bureaucratic simplification, and the integration of digital tools. The development of practical resources, such as educational guides, can play a pivotal role in enhancing the quality of care provided. Full article

The design parameters of the sucker rod pumping unit (SRPU) are influenced by multiple factors. Traditional methods based on oil production engineering theories involve numerous simplifications, making it difficult to effectively address the complex realities of oilfields, thereby requiring improvement in the reliability of pumping system design solutions. This paper, based on the massive design schemes and corresponding operational performance data accumulated during the long-term development of oilfields, innovatively proposes an intelligent optimization model combining Random Forest and Covariance Matrix Adaptation Evolution Strategy algorithm (CMA-ES). This model overcomes the shortcomings of insufficient data and incomplete design indicators in the establishment of lifting design models. By standardizing and processing the data from 5000 historical lifting scheme sets, a sample database of SRPU lifting system designs was created, covering dimensions such as well geology, fluid, and production. Based on this, aiming at system efficiency and pump life expectancy, geological development characteristic parameters and lifting design parameters were taken as variables to establish a predictive model for the operation effect of the lifting system. The dataset was divided into 8:1:1 subsets for training, hyperparameter tuning and performance testing. Subsequently, an optimization model was established to jointly optimize the lifting system design parameters. Case studies show that the intelligent optimization method can simultaneously optimize parameters such as pump setting depth, pump diameter, stroke, and frequency, with expected improvements in system efficiency of 6.75% and pump life expectancy of 29%. Full article