Search Results (121)

The ratchet click mechanism in mechanical watch movements is a micro-component essential to prevent the unwinding of the caliber mainspring, providing secure energy storage during recharging. Despite its geometrical simplicity, the ratchet click undergoes to a complex distribution of stress, elevated strains, and cyclical mechanical deformations, affecting its long-term reliability. Despite being a crucial element in all mechanical watch movements, the non-return system appears to have been overlooked in scientific literature, with no studies available on its design, modeling, and micromachining. In this work, we introduce a novel Finite Element Method (FEM) -based design strategy for the ratchet click, systematically refining its geometry and dimensional parameters to minimize peak stress and improve durability. A mechanical simulation model was created to simulate the boundary conditions, contact interactions, and stress distributions on the part. If compared with the standard component, the optimized design exhibits a decrease in peak stress values. The mechanism was micro-machined, and it was experimentally tested to validate the numerical model outputs. The integrated digital–physical approach not only underscores the scientific contribution of coupling advanced simulation with experimental validation of complex micromechanisms but also provides a generalizable method for enhancing performance of micro-mechanical components while preserving their historical design heritage. Full article

This paper presents a conceptual and methodological framework for using Geographical Information Systems (GIS) to “deep map” cultural heritage sites along Ireland’s Wild Atlantic Way, with a focus on the 1588 Spanish Armada wrecks in County Kerry and archaeological landscapes in County Sligo’s “Yeats Country.” Drawing on interdisciplinary dialogues from the humanities, social sciences, and geospatial sciences, it illustrates how digital spatial technologies can excavate, preserve, and sustain intangible cultural knowledge embedded within such palimpsestic landscapes. Using MAXQDA 24 software to mine and code historical, literary, folkloric, and environmental texts, the study constructed bespoke GIS attribute tables and visualizations integrated with elevation models and open-source archaeological data. The result is a richly layered cartographic method that reveals the spectral and affective dimensions of heritage landscapes through climate, memory, literature, and spatial storytelling. By engaging with “deep mapping” and theories such as “Spectral Geography,” the research offers new avenues for sustainable heritage conservation, cultural tourism, and public education that are sensitive to both ecological and cultural resilience in the West of Ireland. Full article

Sea level rise (SLR) and increased urbanisation of coastal areas have exacerbated coastal flood threats, making them even more severe in important cultural sites. In this context, the role of hard coastal defences such as promenades and embankments needs to be carefully assessed. Here, a thorough investigation is conducted in Grado, one of the most significant coastal and historical towns in the Friuli Venezia Giulia region of Italy. Grado is located on a barrier island of the homonymous lagoon, the northernmost of the Adriatic Sea, and is prone to flooding from both the sea and the back lagoon. The mean and maximum sea levels from the historical dataset of Venice (1950–2023) were analysed using the Gumbel-type distribution, allowing for the identification of annual extremes based on their respective return periods (RPs). Grado and Trieste sea level datasets (1991–2023) were used to calibrate the statistics of the extremes and to calculate the local component (subsidence) of relative SLR. The research examined the occurrence of annual exceedance of the minimum threshold water level of 110 cm, indicating Grado’s initial notable marine ingression. The study includes a detailed analysis of flood impacts on the urban fabric, categorised into sectors based on the promenade elevation on the lagoon side, the most vulnerable to flooding. Inundated areas were obtained using a high-resolution digital terrain model through a GIS-based technique, assessing both the magnitude and exposure of the urban environment to flood risk due to storm surges, also considering relative SLR projections for 2050 and 2100. Currently, approximately 42% of Grado’s inhabited area is inundated with a sea level threshold value of 151 cm, which occurs during surge episodes with a 30-year RP. By 2100, with an optimistic forecast (SSP1-2.6) of local SLR of around +53 cm, the same threshold will be met with a surge of ca. 100 cm, which occurs once a year. Thus, extreme levels linked with more catastrophic events with current secular RPs will be achieved with a multi-year frequency, inundating more than 60% of the urbanized area. Grado, like Venice, exemplifies trends that may impact other coastal regions and historically significant towns of national importance. As a result, the generated simulations, as well as detailed analyses of urban sectors where coastal flooding may occur, are critical for medium- to long-term urban planning aimed at adopting proper adaptation measures. Full article

The effects of climate change and urbanisation, such as more intense rainfall and changing land use patterns, are putting increasing pressure on urban drainage systems. This study proposes a comprehensive methodology for evaluating the effectiveness of sustainable urban drainage systems (SUDSs) in mitigating flooding and managing stormwater in both current and future scenarios. The approach integrates geospatial data, including digital elevation models (DEMs) and land use information, to delineate catchments and characterise hydrological parameters. Historical rainfall records and hydrological modelling were employed to define two baseline storm events: an extreme storm involving 422 mm of rainfall over 2 h, and an average storm involving 2.84 mm of rainfall over 1 h and 18 min. Future scenarios were developed by updating these baseline events using annual rates of change in maximum and average precipitation derived from climate projections between 2025 and 2100. The analysis incorporates seven CMIP6 climate scenarios: SSP1-1.9, SSP1-2.6, SSP4-3.4, SSP4-2.5, SSP4-6.0, SSP3-7.0, and SSP5-8.5. A stochastic simulation of 1000 storms per year was carried out using a custom-built conceptual hydrological model based on CN and developed in Python, which reflects interannual variability. The results show that extreme storm volumes could increase by up to seven times and average storm volumes by up to two and a half times. Additionally, discharge peaks could exceed baseline values by up to 20% in some years, suggesting an increased occurrence of extreme runoff events. The methodology assesses SUDS performance by comparing runoff and hydrological responses between baseline and future estimates. This framework enables vulnerabilities and adaptation needs to be identified, ensuring the long-term effectiveness of SUDSs in managing urban flood risk. Addressing uncertainties in climate and land use projections emphasises the importance of integrating SUDS assessments into wider urban resilience strategies. Full article

Accurate short-term wave forecasting is critical for the safe and efficient operation of marine structures that rely on real-time, phase-resolved ocean wave information for control and monitoring purposes (e.g., digital twins). These systems often depend on environmental sensors (e.g., waverider buoys, wave-sensing LIDAR). Challenges arise when upstream sensor data are missing, sparse, or phase-shifted due to drift. This study investigates the performance of two machine learning models, time-series dense encoder (TiDE) and long short-term memory (LSTM), for forecasting phase-resolved ocean surface elevations under varying degrees of data degradation. We introduce the $\tau$-trimming algorithm, which adapts the prediction horizon based on uncertainty thresholds derived from historical forecasts. Numerical wave tank (NWT) and wave basin experiments are used to benchmark model performance under short- and long-term data masking, spatially coarse sensor grids, and upstream phase shifts. Results show under a 50% probability of upstream data loss, the $\tau$-trimmed TiDE model achieves a 46% reduction in error at the most upstream target, compared to 22% for LSTM. Furthermore, phase misalignment in upstream data introduces a near-linear increase in forecast error. Under moderate model settings, a ±3 s misalignment increases the mean absolute error by approximately 0.5 m, while the same error is accumulated at ±4 s using the more conservative approach. These findings inform the design of resilient, uncertainty-aware wave forecasting systems suited for realistic offshore sensing environments. Full article

Floods pose a substantial threat to both life and property, with their frequency and intensity escalating due to climate change. A comprehensive hydrological and hydraulic modeling approach is essential for understanding flood dynamics and developing effective future flood risk management strategies. The accuracy of Digital Elevation Models (DEMs) directly impacts the reliability of hydrologic simulations. This study focuses on evaluating the efficacy of two DEMs in hydrological modeling, specifically investigating their potential for daily discharge simulation in the Periyar River Basin, Kerala, India. Recognizing the limitations of the Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS) with the available dataset, a novel hybrid model was developed by integrating HEC-HMS outputs with an Artificial Neural Network (ANN). While precipitation, lagged precipitation, and lagged discharge served as inputs to the ANN, the hybrid model also incorporated HEC-HMS simulations as an additional input. The results demonstrated improved performance of the hybrid model in simulating daily discharge. The Hydrologic Engineering Center’s River Analysis System (HEC-RAS) was employed to predict flood inundation areas for both historical and future scenarios in the Aluva region of the Periyar River Basin, which was severely impacted during the 2018 Kerala floods. By integrating hydrological and hydraulic modeling approaches, this study aims to enhance flood prediction accuracy and contribute to the development of effective flood mitigation strategies. Full article

Historical buildings have important historical and social value, but they are generally difficult to identify, complicated to evaluate, and insufficiently addressed in digitization research. On 25 July 2021, Quanzhou successfully applied for World Heritage status. In this case study, Qiaonan Village in the Quanzhou World Heritage Area is selected, and a deep learning algorithm is proposed for the identification, evaluation, and digitization of historical buildings. By comparing multiple models, the optimal model is selected for intelligent identification and classification of building elevations. Combined with GIS, a distribution map of the village buildings is created for digitization research. An intelligent monitoring platform is built to enable dynamic monitoring and hierarchical protection of the buildings, aiding in the protection of historical structures and the sustainable development of the tourism industry. In the future, we will continue to optimize the integration of YOLO and GIS and explore a more universal model for the intelligent protection of historical buildings. Full article

This study examines the Roman burgus and medieval lowland castle ‘Zullestein’ near Biblis (Bergstraße district/Hessen/Germany) and its surrounding fluvio-scape. The aim of the study is to reassess the appearance of the fortifications and the surrounding area at the confluence of the River Weschnitz and the River Rhine based on the excavation results from the 1970s and current geoarchaeological research on site. Our approach encompasses electrical resistivity tomography, direct push sensing, sediment coring and the use of a high-resolution digital elevation model in combination with historical depictions of the Zullestein site from the 17th century AD. The findings of this integrative approach indicate that the Roman fort was likely located at a secondary channel of the River Rhine. With the renewed occupation of the Zullestein site by Lorsch Abbey during Carolingian times and the expansion into a lowland castle in the 11th century, the site was now located at the Weschnitz mouth into the Rhine, likely as part of anthropogenic interventions related to the Weschnitz fluvioscape. Traces of the final phase of the castle at the time of the Thirty Years’ War can still be seen in the terrain today and their attribution to individual elements of the historical account can be confirmed by the geoarchaeological results. The combination of methods presented in this study is a suitable option if excavations are not possible. Full article

This study highlights the importance of incorporating temporal landscape dynamics in landslide susceptibility assessments (LSAs). Two models are compared: one integrates multi-temporal data, while the other relies solely on present conditions. Artificial Digital Elevation Models (ADEMs) for 1960, 1980, 2000, and 2020 were generated using a landscape manipulation tool to simulate phases of land degradation and rehabilitation, thereby enabling the assessment of susceptibility over time. Landslide occurrences were simulated to increase over time—primarily as a result of anthropogenic changes, such as deforestation and land use alterations—with partial stabilization following conservation efforts. Both models achieved identical AUC values of 0.97, but the blind model misclassified stable areas and missed historically unstable regions. While conventional performance metrics such as ROC curves provide insights into model accuracy, they fail to detect misclassifications arising from temporal landscape changes, leading to overestimation in some areas and underestimation in others, especially in evolving environments. This study demonstrates that neglecting temporal landscape evolution leads to flawed susceptibility maps, potentially misguiding hazard mitigation efforts. To improve LSA accuracy, the study advocates for integrating multi-temporal thematic maps and adopting performance metrics that assess temporal robustness. It emphasizes the need for a shift from a static-to-hazard paradigm to a temporally evolved susceptibility-to-hazard framework for more accurate hazard and risk predictions. Full article

British Columbia, Canada, is recognized for its abundant natural resources, including agricultural and aquaculture products, sustained by its diverse climate and geography. Water resource allocation in BC is governed by the Water Sustainability Act, enacted on 29 February 2016, replacing the historic Water Act. However, limited gauging of streams across the province poses challenges for ensuring water allocation while meeting Environmental Flow Needs. Overallocated watersheds and data-scarce watersheds in need of licensing highlight the need for robust streamflow prediction methods. To address these challenges, we developed a methodology that integrates the Drainage Area Ratio and Nearest Neighbors techniques to predict streamflows efficiently, without incurring additional financial costs. We utilized Digital Elevation Models and flow data from provincially and municipally managed hydrometric stations, as well as from the Water Survey of Canada, to normalize streamflows based on area, slope, and elevation. This approach ensures hydrological predictions that account for variability in hydrological processes resulting from differences in lumped-scale watershed characteristics. The method was validated using streamflow data from hydrometric stations maintained by the aforementioned entities. For validation, each station was iteratively treated as ungauged by temporarily removing it from the dataset and then predicting its streamflow using the proposed methodologies. The results demonstrated that the amalgamated Drainage Area Ratio–Nearest Neighbors approach outperformed the traditional Drainage Area Ratio method, offering reliable predictions for diverse watersheds. This study provides an adaptable and cost-effective framework for enhancing water resource management across BC. Full article

Understanding glacier and climate variations since pre-Industrial times is crucial for evaluating the present-day glacier response to climate change. Here, we focus on twelve small glaciers (≤2.0 km²) on both the northern and southern slopes of the Greater Caucasus to assess post-Little Ice Age glacier–climate fluctuations in this region. We reconstructed the Little Ice Age glacier extent using a manual detection method based on moraines. More recent glacier fluctuations were reconstructed using historical topographical maps and satellite imagery. Digital elevation models were used to estimate the topographic characteristics of glaciers. We also used the accumulation area ratio (AAR) method and a regional temperature lapse rate to reconstruct glacier snowlines and corresponding temperatures since the 1820s. The results show that all selected glaciers have experienced area loss, terminus retreat, and equilibrium line altitude (ELA) uplift over the last 200 years. The total area of the glaciers has decreased from 19.1 ± 0.9 km² in the 1820s to 9.7 ± 0.2 km² in 2020, representing a −49.2% loss, with an average annual reduction of −0.25%. The most dramatic reduction occurred between the 1960s and 2020, when the glacier area shrank by −35.5% or −0.59% yr⁻¹. The average terminus retreat for all selected glaciers was −1278 m (−6.4 m/yr⁻¹) during the last 200 years, while the average retreat over the past 60 years was −576 m (−9.6 m/yr⁻¹). AAR-based (0.6 ± 0.05) ELA reconstructions from all twelve glaciers suggest that the average ELA in the 1820s was about 180 m lower (3245 ± 50 m a.s.l.) than today (3425 ± 50 m a.s.l.), corresponding to surface air temperatures <1.1 ± 0.3 °C than today (2001–2020). The largest warming occurred between the 1960s and today, when snowlines rose by 105 m and air temperatures increased by <0.6 ± 0.3 °C. This study represents a first attempt at using glacier evidence to estimate climate changes in the Caucasus region since the Little Ice Age, and it can be used as a baseline for future studies. Full article

As the largest inland river in China, the Tarim River’s flood events significantly influence its riverbed formation. This paper took the Alar to Xinquman section of the Tarim River as the study area. The study area’s digital elevation model of the river was constructed using historical Google images and Copernicus DEM 30. Six different flood events were selected, corresponding to flood events with varying sediment loads, flood volumes, and peak flow volumes. The MIKE 21 software was used to simulate and investigate the response of the riverbed shape to different flood events. The experimental findings indicate that the sand content constitutes a pivotal factor in the formation of the riverbed during flood events. Flood sediment load goes through stages linked to changes in riverbed erosion and deposition. The combination of high peak flow and bed-forming flow after the peak effectively shapes the central channel’s morphology. The fourth type of flood event had the highest sediment transport coefficient Φ among the six types of floods and caused the most significant scouring effect on the riverbed under low sediment load conditions. Full article

This research proposes an artificial intelligence (AI)-powered digital twin framework for highway slope stability risk monitoring and prediction. For highway slope stability, a digital twin replicates the geological and structural conditions of highway slopes while continuously integrating real-time monitoring data to refine and enhance slope modeling. The framework employs instance segmentation and a random forest model to identify embankments and slopes with high landslide susceptibility scores. Additionally, artificial neural network (ANN) models are trained on historical drilling data to predict 3D subsurface soil type point clouds and groundwater depth maps. The USCS soil classification-based machine learning model achieved an accuracy score of 0.8, calculated by dividing the number of correct soil class predictions by the total number of predictions. The groundwater depth regression model achieved an RMSE of 2.32. These predicted values are integrated as input parameters for seepage and slope stability analyses, ultimately calculating the factor of safety (FoS) under predicted rainfall infiltration scenarios. The proposed methodology automates the identification of embankments and slopes using sub-meter resolution Light Detection and Ranging (LiDAR)-derived digital elevation models (DEMs) and generates critical soil properties and pore water pressure data for slope stability analysis. This enables the provision of early warnings for potential slope failures, facilitating timely interventions and risk mitigation. Full article

Urban heritage areas are characterized by unique architectural and cultural elements, often coupled with specific challenges such as vulnerability to climate change and urban heat islands (UHIs). Investigating thermal performance at the neighborhood scale is crucial for preserving these areas while enhancing thermal comfort and sustainability. The aim of this research is to prove that the application of passive cooling techniques and urban green spaces can reduce the urban temperature and upgrade the conditions of thermal comfort, even in densely populated areas with small urban void spaces. ENVI-Met, a microclimate modeling software for evaluating the thermal performance of heritage urban neighborhoods, is applied in order to assess current thermal conditions, identify hotspots, perform simulations, and propose mitigation strategies to improve thermal comfort while preserving the architectural and cultural integrity of these areas. The test bed of this study is a historical urban area in central Athens, “Academia Platonos”. The methodology is mainly based on the design of different parametric scenarios for the study area, by integrating specific parameters that characterize the area of Academia Platonos (elevation distribution, materials, vegetation, etc.) and the microclimatic simulations of the area, designed in the digital environment of ENVI-Met. Five scenarios are implemented and studied in the study area, four of which are based on the existing situation of the study area, either by changing the construction materials of the built environment (passive cooling through cool material techniques) or by enhancing the area with vegetation. One of the most important findings of this study is that the use of plants with a high foliage density is more effective in reducing air temperature than the selection of species with sparse foliage. Full article

The compressor blades of the gas turbine continually operate under extreme conditions, including elevated temperature, increased pressure, rapid rotation speed, and high-load environments, and are also subjected to complex vibrations, which inevitably lead to performance degradation and failures. Early fault warning based on historical operation data and real-time working conditions can enhance the safety and economy of gas turbines, preventing severe accidents. However, previous studies often faced challenges, such as a lack of fault data, imbalanced datasets, and low data utilization, which limited the accuracy of the algorithms. This study proposes a fault warning technique for gas turbine compressor blades based on AAKR-ACGAN. First, a digital twin model of the gas turbine is constructed using long-term operation data and simulation data from the mechanism model. Then, an auto-associative kernel regression (AAKR) model is used for the fault warning, monitoring multiple parameters to provide effective early warnings of potential faults. Additionally, an auxiliary classifier generative adversarial network (ACGAN) is employed to fully extract hidden data features of the fault points, balance the dataset, and accurately simulate the process of fault occurrence and development. The proposed approach is utilized for the early detection of faults in the compressor blades of a high-capacity gas turbine, and its precision and applicability are confirmed. The multisource early warning indicator can provide an early warning of a failure up to one year in advance of its occurrence. It was also able to detect a severe surge that occurred six months before the failure, which is speculated to be one of the causes that led to the failure. Full article