[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
You seem to have javascript disabled. Please note that many of the page functionalities won't work as expected without javascript enabled.
 
 
Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Article Types

Countries / Regions

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Search Results (1,739)

Search Parameters:
Keywords = event-driven

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
23 pages, 1917 KiB  
Article
Event-Driven Prescribed-Time Tracking Control for Multiple UAVs with Flight State Constraints
by Xueyan Han, Peng Yu, Maolong Lv, Yuyuan Shi and Ning Wang
Machines 2025, 13(3), 192; https://doi.org/10.3390/machines13030192 - 27 Feb 2025
Abstract
Consensus tracking control for multiple UAVs demonstrates critical theoretical value and application potential, improving system robustness and addressing challenges in complex operational environments. This paper addresses the challenge of event-triggered prescribed-time synchronization tracking control for 6-DOF fixed-wing UAVs with state constraints. We propose [...] Read more.
Consensus tracking control for multiple UAVs demonstrates critical theoretical value and application potential, improving system robustness and addressing challenges in complex operational environments. This paper addresses the challenge of event-triggered prescribed-time synchronization tracking control for 6-DOF fixed-wing UAVs with state constraints. We propose a novel prescribed-time command filtered backstepping approach to effectively tackle the issues of complexity explosion and singularities. By utilizing a state-transition function, we manage asymmetric time-varying state constraints, including limitations on speed, roll, yaw, and pitch angles in UAVs. The theoretical analysis demonstrates that all signals in the 6-DOF UAV system remain bounded, with tracking errors converging to the origin within the prescribed time. Finally, simulation results validate the effectiveness of the proposed control strategy. Full article
(This article belongs to the Special Issue Intelligent Control Techniques for Unmanned Aerial Vehicles)
22 pages, 7426 KiB  
Article
Estuarine Salinity Intrusion and Flushing Time Response to Freshwater Flows and Tidal Forcing Under the Constricted Entrance
by Usman Khalil, Mariam Sajid, Muhammad Zain Bin Riaz, Shuqing Yang and Muttucumaru Sivakumar
Water 2025, 17(5), 693; https://doi.org/10.3390/w17050693 - 27 Feb 2025
Viewed by 17
Abstract
Coastal waters, particularly in micro-tidal estuaries, are highly vulnerable to water quality changes driven by salinity intrusion. Effective coastal water management requires a detailed understanding of the dynamic processes in estuaries to mitigate the effects of salinity intrusion. This study focuses on the [...] Read more.
Coastal waters, particularly in micro-tidal estuaries, are highly vulnerable to water quality changes driven by salinity intrusion. Effective coastal water management requires a detailed understanding of the dynamic processes in estuaries to mitigate the effects of salinity intrusion. This study focuses on the Brisbane River estuary (BRE), Australia, to investigate salinity intrusion and flushing time under varying freshwater inflows. A coupled MIKE 21 FM hydrodynamic (HD) and transport (TR) model was used to assess salinity transport during a neap–spring tidal cycle. The 2D model was calibrated and validated for the 2008 and 2011 flow events using field data on water levels and salinity. Results show an increase in tidal amplitude landward under low river flow conditions, while tidal damping was observed under higher river discharge, reducing the BRE salinity during spring tides. This study found that salinity intrusion is highly sensitive to freshwater availability, with river discharges of 150 m3/s and 175 m3/s identified as critical thresholds to maintain salinity levels below 1 PSU at the estuary mouth during ebb and flood tides, respectively. Flushing time analysis indicates that the BRE takes approximately 302 h to completely displace saline water when the river discharge is optimized at 150 m3/s. Modifying the BRE estuary mouth—through channel widening or deepening—enhanced the flushing process, significantly reducing salinity levels. This study demonstrates that optimizing freshwater discharge and modifying the estuary mouth can provide effective solutions for managing salinity intrusion in micro-tidal estuaries. Full article
Show Figures

Figure 1

Figure 1
<p>Brisbane River catchment and Moreton Bay.</p>
Full article ">Figure 2
<p>(<b>a</b>) Daily freshwater flow calculated at Brisbane River mouth (1958–2011). (<b>b</b>) Monthly distribution of streamflow at the Brisbane River mouth (1958–2020).</p>
Full article ">Figure 3
<p>(<b>a</b>) The Brisbane River estuary (BRE), Queensland, Australia, and locations of salinity observation sites along the estuary; (<b>b</b>) observed monthly averaged salinity data in the BRE over 16 years from 2002 to 2018.</p>
Full article ">Figure 4
<p>(<b>a</b>) Tidal data at Brisbane Bar and discharge at Moggill (2008–2013). (<b>b</b>) Tidal data at Brisbane Bar and discharge at Moggill during low flow (2008).</p>
Full article ">Figure 5
<p>(<b>a</b>) Model grid and unstructured elements; (<b>b</b>) the bathymetry of the entire domain.</p>
Full article ">Figure 6
<p>The BRE mouth with Gate A and dikes for mouth extension.</p>
Full article ">Figure 7
<p>Observed and simulated water levels for the 2008 flood event at (<b>a</b>) Brisbane City and (<b>b</b>) Brisbane Bar. (<b>c</b>) Observed and simulated water levels for the 2011 flood event at Brisbane City and (<b>d</b>) Brisbane Bar.</p>
Full article ">Figure 8
<p>(<b>a</b>) Comparison of field-observed salinity at 11:00 am, 15th August 2008 and simulated salinity; (<b>b</b>) correlation coefficients, August 2008; (<b>c</b>) comparison of field-observed salinity at 10:00 am, 11 September 2008 and simulated salinity; (<b>d</b>) correlation coefficients, September 2008; (<b>e</b>) comparison of field observed salinity at 12:00 p.m., 17 May 2011 and simulated salinity for a longitudinal profile of BRE; May 2011; (<b>f</b>) correlation coefficients, May 2011.</p>
Full article ">Figure 9
<p>(<b>a</b>) Simulation of tidal levels in the BRE at various gauge locations; (<b>b</b>) longitudinal salinity profile of the BRE, Sep 2008; (<b>c</b>) velocity variation at Brisbane city gauge; (<b>d</b>) water level variation at Brisbane city gauge.</p>
Full article ">Figure 10
<p>(<b>a</b>) Vertical distribution of salinity in the BRE in 2008 at chainage 70 km; (<b>b</b>) vertical distribution of salinity in the BRE in 2008 at chainage 1.1 km; (<b>c</b>) vertical distribution of salinity in the BRE in 2011 at chainage 70 km; (<b>d</b>) vertical distribution of salinity in the BRE in 2011 at chainage 1.1 km.</p>
Full article ">Figure 11
<p>Salinity intrusion lengths difference during neap tides (7 days) and spring tides (7 days) under the influence of incremental freshwater flows (<b>a</b>,<b>b</b>), 50 m<sup>3</sup>/s (<b>c</b>,<b>d</b>), 100 m<sup>3</sup>/s (<b>e</b>,<b>f</b>), 150 m<sup>3</sup>/s (<b>g</b>,<b>h</b>), 200 m<sup>3</sup>/s, and (<b>i</b>,<b>j</b>) 250 m<sup>3</sup>/s.</p>
Full article ">Figure 12
<p>(<b>a</b>) Longitudinal salinity distribution under the influence of incremental freshwater increase; (<b>b</b>) salinity interface and freshwater relationship.</p>
Full article ">Figure 13
<p>(<b>a</b>) Freshwater and saline water interface (FSI); (<b>b</b>) flushing time for various flows under spring tide.</p>
Full article ">Figure 14
<p>(<b>a</b>,<b>b</b>) Saline water interface (with depth average salinity values &lt; 1 PSU) under 50 m<sup>3</sup>/s discharge for the ebb and flood tides, respectively; (<b>c</b>,<b>d</b>) saline water interface under 150 m<sup>3</sup>/s discharge for the ebb and flood tides, respectively.</p>
Full article ">Figure 15
<p>(<b>a</b>) Comparison of tidal velocity with 150 m<sup>3</sup>/s freshwater discharge, the tidal velocity with 150 m<sup>3</sup>/s, and entrance constriction and entrance extension; (<b>b</b>) combined effect of 150 m<sup>3</sup>/s and estuary mouth modification on longitudinal salinity profile.</p>
Full article ">
18 pages, 3983 KiB  
Article
Influence of Thinning on Carbon Balance in Natural Regeneration of Pinus pinaster in Portugal
by André Sandim, Domingos Lopes, José Luis Louzada and Maria Emília Silva
Land 2025, 14(3), 493; https://doi.org/10.3390/land14030493 - 27 Feb 2025
Viewed by 98
Abstract
The maritime pine (Pinus pinaster) is the main conifer species in Portugal, occurring mainly in the central and northern regions of the country. In addition to its environmental significance, it plays an important socio-economic role, supported by a robust forest sector. [...] Read more.
The maritime pine (Pinus pinaster) is the main conifer species in Portugal, occurring mainly in the central and northern regions of the country. In addition to its environmental significance, it plays an important socio-economic role, supported by a robust forest sector. In the face of climate change driven by the release of CO2 into the atmosphere, forests play an essential role in mitigating these changes by storing large amounts of carbon in their biomass. This study assesses the impact of forest management, focusing on thinning, on carbon accumulation in naturally regenerating maritime pine forests in the municipality of Boticas, Portugal and compares scenarios with and without forest intervention. To simulate forest growth scenarios, the Modispinaster software is used, and through mathematical models adjusted for the species and input of initial field data, it generates scenarios of forest evolution regarding biomass and carbon accumulation. Additionally, it allows for the visualization of the forest’s dendrometric characteristics throughout the cycle, enabling the creation of the carbon balance and its analysis across multiple scenarios. The results demonstrate that management based on thinning increases carbon retention, reducing early mortality and promoting the growth of larger diameter trees. Although natural forests initially accumulate more carbon, the reduction in competition in managed forests allows for greater carbon accumulation from the 24th year onwards, reaching 178 tons at the end of the cycle, in contrast to 143 tons in unmanaged areas. The carbon balance result in the unmanaged (natural) forest was negative (−18 tons), while in the managed forest, the result was positive (54 tons). This supports the thesis that thinning, although more intense and less frequent than mortality events, is more effective than the absence of interventions. Thinned forests optimize the carbon balance in Pinus pinaster, improving long-term retention by reducing competition and mortality. Managed forests show a positive carbon balance, highlighting the importance of sustainable management in mitigating climate change and strengthening ecological resilience. Full article
(This article belongs to the Section Land – Observation and Monitoring)
Show Figures

Figure 1

Figure 1
<p>Location of study areas in Portuguese territory.</p>
Full article ">Figure 2
<p>Granja area before intervention (<b>a</b>) is a high tree density, dense understory, and disorganized spatial arrangement, and (<b>b</b>) after intervention is with reduced tree density, clean understory, and spatial organization in vegetation-free inter strips and tree strips.</p>
Full article ">Figure 3
<p>Internal (red) and external (orange) plots of the intervention areas of the Granja (<b>a</b>) and Torneiros (<b>b</b>) areas (yellow).</p>
Full article ">Figure 4
<p>Evolution of the mass of carbon accumulated in the aerial parts of living trees in the forest in a scenario of natural growth and forest intervened by thinning (<b>a</b>). Evolution of the mass of carbon accumulated in the aerial parts and roots of living trees in the forest in a scenario of natural growth and forest intervened by thinning (<b>b</b>).</p>
Full article ">Figure 5
<p>Accumulated mass of carbon extracted due to each mortality or thinning event (<b>a</b>) and total mass of carbon extracted in the simulated thinning and mortality cycle in the natural and intervened forest scenarios (<b>b</b>).</p>
Full article ">Figure 6
<p>Carbon mass according to the diameter class of dead and thinned trees.</p>
Full article ">Figure 7
<p>Carbon balance per hectare simulated in the 45-year cycle for the natural forest and intervened forest scenarios.</p>
Full article ">Figure 8
<p>Average evolution of NDVI between 2020 and 2024 for plots inside and outside areas managed by thinning.</p>
Full article ">Figure A1
<p>NDVI image of the Granja area before thinning in May 2022 (<b>a</b>) and after thinning in January 2025 (<b>b</b>), plus their respective NDVI distributions and frequency histograms.</p>
Full article ">Figure A2
<p>NDVI image of the Torneiros area before thinning in May 2022 (<b>a</b>) and after thinning in January 2025 (<b>b</b>), plus their respective NDVI distributions and frequency histograms.</p>
Full article ">
19 pages, 3487 KiB  
Article
Evaluating the Effectiveness of Soil Profile Rehabilitation for Pluvial Flood Mitigation Through Two-Dimensional Hydrodynamic Modeling
by Julia Atayi, Xin Zhou, Christos Iliadis, Vassilis Glenis, Donghee Kang, Zhuping Sheng, Joseph Quansah and James G. Hunter
Hydrology 2025, 12(3), 44; https://doi.org/10.3390/hydrology12030044 - 26 Feb 2025
Viewed by 207
Abstract
Pluvial flooding, driven by increasingly impervious surfaces and intense storm events, presents a growing challenge for urban areas worldwide. In Baltimore City, MD, USA, climate change, rapid urbanization, and aging stormwater infrastructure are exacerbating flooding impacts, resulting in significant socio-economic consequences. This study [...] Read more.
Pluvial flooding, driven by increasingly impervious surfaces and intense storm events, presents a growing challenge for urban areas worldwide. In Baltimore City, MD, USA, climate change, rapid urbanization, and aging stormwater infrastructure are exacerbating flooding impacts, resulting in significant socio-economic consequences. This study evaluated the effectiveness of a soil profile rehabilitation scenario using a 2D hydrodynamic modeling approach for the Tiffany Run watershed, Baltimore City. This study utilized different extreme storm events, a high-resolution (1 m) LiDAR Digital Terrain Model (DTM), building footprints, and hydrological soil data. These datasets were integrated into a fully coupled 2D hydrodynamic model, the City Catchment Analysis Tool (CityCAT), to simulate urban flood dynamics. The pre-soil rehabilitation simulation revealed a maximum water depth of 3.00 m in most areas, with hydrologic soil groups C and D, especially downstream of the study area. The post-soil rehabilitation simulation was targeted at vacant lots and public parcels, accounting for 33.20% of the total area of the watershed. This resulted in a reduced water depth of 2.50 m. Additionally, the baseline runoff coefficient of 0.49 decreased to 0.47 following the rehabilitation, and the model consistently recorded a peak runoff reduction rate of 4.10 across varying rainfall intensities. The validation using a contingency matrix demonstrated true-positive rates of 0.75, 0.50, 0.64, and 0 for the selected events, confirming the model’s capability at capturing real-world flood occurrences. Full article
(This article belongs to the Special Issue Runoff Modelling under Climate Change)
Show Figures

Figure 1

Figure 1
<p>Map showing the study area and its geographical features.</p>
Full article ">Figure 2
<p>A contingency table was applied to validate modeled flood results (Source: [<a href="#B37-hydrology-12-00044" class="html-bibr">37</a>]).</p>
Full article ">Figure 3
<p>Water depth (m) changes resulting from different storm intensities.</p>
Full article ">Figure 4
<p>311 reports received on these extreme storm events.</p>
Full article ">Figure 5
<p>Social media and newspaper reports received on 10 June 2021 storm event.</p>
Full article ">Figure 6
<p>Social media and newspaper reports received on 12 September 2023 storm event.</p>
Full article ">Figure 7
<p>(<b>A</b>) Boundaries of public properties and vacant lots within the study area; (<b>B</b>) overlay of public parcels and vacant lots on the soil profile map, highlighting the areas targeted for soil rehabilitation.</p>
Full article ">Figure 8
<p>Spatial distribution of flood water depths post-soil rehabilitation.</p>
Full article ">
29 pages, 4768 KiB  
Article
Dynamic Scheduling in Identical Parallel-Machine Environments: A Multi-Purpose Intelligent Utility Approach
by Mahmut İbrahim Ulucak and Hadi Gökçen
Appl. Sci. 2025, 15(5), 2483; https://doi.org/10.3390/app15052483 - 25 Feb 2025
Viewed by 241
Abstract
This paper presents a robust and adaptable framework for predictive–reactive rescheduling in identical parallel-machine environments. The proposed Multi-Purpose Intelligent Utility (MIU) methodology utilizes heuristic methods to efficiently address the computational challenges associated with NP-hard scheduling problems. By incorporating 13 diverse dispatching rules, the [...] Read more.
This paper presents a robust and adaptable framework for predictive–reactive rescheduling in identical parallel-machine environments. The proposed Multi-Purpose Intelligent Utility (MIU) methodology utilizes heuristic methods to efficiently address the computational challenges associated with NP-hard scheduling problems. By incorporating 13 diverse dispatching rules, the MIU framework provides a flexible and adaptive approach to balancing critical production objectives. It effectively minimizes total weighted tardiness and the number of tardy jobs while maintaining key performance metrics like stability, robustness, and nervousness. In dynamic manufacturing environments, schedule congestion and unforeseen disruptions often lead to inefficiencies and delays. Unlike traditional event-driven approaches, MIU adopts a periodic rescheduling strategy, enabling proactive adaptation to evolving production conditions. Comprehensive rescheduling ensures system-wide adjustments to disruptions, such as stochastic changes in processing times and rework requirements, without sacrificing overall performance. Empirical evaluations show that MIU significantly outperforms conventional methods, reducing total weighted tardiness by 50% and the number of tardy jobs by 27% on average across various scenarios. Furthermore, this study introduces novel quantifications for nervousness, expanding the scope of stability and robustness evaluations in scheduling research. This work contributes to the ongoing discourse on scheduling methodologies by bridging theoretical research with practical industrial applications, particularly in high-stakes production settings. By addressing the trade-offs between improving the objective function or improving the rescheduling performance, MIU provides a comprehensive solution framework that enhances operational performance and adaptability in complex manufacturing environments. Full article
Show Figures

Figure 1

Figure 1
<p>Dynamic scheduling problems in the literature.</p>
Full article ">Figure 2
<p>Production process of company.</p>
Full article ">Figure 3
<p>Information flow of company.</p>
Full article ">Figure 4
<p>Predictive–reactive rescheduling using MIU.</p>
Full article ">Figure 5
<p>MIU procedure.</p>
Full article ">Figure 6
<p>Initial scheduling for TWT problem.</p>
Full article ">Figure 7
<p>Initial scheduling for NTJ problem.</p>
Full article ">Figure 8
<p>Completed TWT strategy Gantt chart.</p>
Full article ">Figure 9
<p>Completed NTJ strategy Gantt chart.</p>
Full article ">Figure 10
<p>Initial schedule when applying company’s current scheduling method.</p>
Full article ">Figure 11
<p>Final schedule when applying company’s current rescheduling method.</p>
Full article ">
28 pages, 4827 KiB  
Article
Influencing Factors of the Sub-Seasonal Forecasting of Extreme Marine Heatwaves: A Case Study for the Central–Eastern Tropical Pacific
by Lin Lin, Yueyue Yu, Chuhan Lu, Guotao Liu, Jiye Wu and Jingjia Luo
Remote Sens. 2025, 17(5), 810; https://doi.org/10.3390/rs17050810 - 25 Feb 2025
Viewed by 170
Abstract
Seven extreme marine heatwave (MHW) events that occurred in the central–eastern tropical Pacific over the past four decades are divided into high-(MHW#1 and #2), moderate-(MHW#3–5), and low-predictive (MHW#6 and #7) categories based on the accuracy of the 30–60d forecast by the Nanjing University [...] Read more.
Seven extreme marine heatwave (MHW) events that occurred in the central–eastern tropical Pacific over the past four decades are divided into high-(MHW#1 and #2), moderate-(MHW#3–5), and low-predictive (MHW#6 and #7) categories based on the accuracy of the 30–60d forecast by the Nanjing University of Information Science and Technology Climate Forecast System (NUIST CFS1.1). By focusing on high- and low-predictive MHWs, we found that metrics indicative of strong and severe warming (S > 2 and S > 3, where S is MHW severity index) pose greater challenges for accurate forecasting, with the biggest disparity observed for S > 2. All events are intertwined with the El Niño–Southern Oscillation (ENSO), yet a robust ENSO forecast does not guarantee a good MHW forecast. Heat budget analysis within the surface mixed layer during the rapid warming periods revealed that the moderate and severe warming in MHW#1, #2, #6 are primarily caused by heat convergence due to advection (Adv), whereas MHW#7 is mainly driven by air–sea heat flux into the sea surface (Q). The NUIST CFS1.1 model better captures Adv than Q. High-predictive events exhibit a greater contribution from Adv, especially the zonal component associated with the zonal gradient of sea surface temperature anomalies, which may explain their higher sub-seasonal forecast skills. Full article
Show Figures

Figure 1

Figure 1
<p>The anomaly correlation coefficient (ACC) and root mean square error (RMSE) skill of metrics corresponding to (<b>a</b>) S &gt; 1 (P<sub>S&gt;1</sub> and I<sub>S&gt;1</sub>, units: % and °CM km<sup>2</sup>), (<b>b</b>) S &gt; 2 (P<sub>S&gt;2</sub> and I<sub>S&gt;2</sub>, units: % and °CM km<sup>2</sup>), (<b>c</b>) S &gt; 3 (P<sub>S&gt;3</sub> and I<sub>S&gt;3</sub>, units: % and °CM km<sup>2</sup>), and (<b>d</b>) their averages based on the ensemble mean forecasts of the NUIST CFS1.1 model averaged in the period from 30 days before the onset to 30 days after the end of each MHW event. The red, pink, light gray, dark gray, black, light blue and blue dots represent the P parameter in MHW#1–7, respectively. The sign “×” with the above colors represents the I parameter in MHW#1–7. The black dashed lines represent the average ACCs and RMSEs for S &gt; 1, S &gt; 2, S &gt; 3, and all metrics in MHW#1~7, respectively. If the symbol that represents an MHW event is located in the top-left corner, it signifies that MHW has an above-average ACC and a below-average RMSE, indicating better forecast skills. Oppositely, the symbols in the bottom-right corner indicate MHWs with poorer forecast skills.</p>
Full article ">Figure 2
<p>The time series of MHW metrics including (<b>a</b>) the difference between the regional average SST and 90th SST (units: °C), (<b>b</b>) the regional mean S index, and metrics (<b>c</b>) P<sub>S&gt;1</sub> (units: %), (<b>d</b>) I<sub>S&gt;1</sub> (units: °CM km<sup>2</sup>), (<b>e</b>) P<sub>S&gt;2</sub> (units: %), (<b>f</b>) I<sub>S&gt;2</sub> (units: °CM km<sup>2</sup>), (<b>g</b>) P<sub>S&gt;3</sub> (units: %), and (<b>h</b>) I<sub>S&gt;3</sub> (units: °CM km<sup>2</sup>), which are derived from observations (black bold curves) and 30–60 d forecasts (red curves represent the ensemble mean forecasts, and blue curves represent forecasts of members with different nudging coefficient in SST) by the NUIST CFS1.1 model during the period around the MHW#1 event. The period of observed MHW event is marked by gray rectangular. The rapid warming period, featured by distinct upward trends in I<sub>S&gt;1</sub> and I<sub>S&gt;2</sub>, along with the inclusion of the I<sub>S&gt;3</sub> peak, is visually identified and demarcated by the space between two black dashed lines. Corresponding indicators. The occurrence region of the corresponding MHW event is shown in <a href="#remotesensing-17-00810-f0A1" class="html-fig">Figure A1</a>.</p>
Full article ">Figure 3
<p>As in <a href="#remotesensing-17-00810-f002" class="html-fig">Figure 2</a>, the time series of MHW metrics including (<b>a</b>) the difference between the regional average SST and 90th SST (units: °C), (<b>b</b>) the regional mean S index, and metrics (<b>c</b>) P<sub>S&gt;1</sub> (units: %), (<b>d</b>) I<sub>S&gt;1</sub> (units: °CM km<sup>2</sup>), (<b>e</b>) P<sub>S&gt;2</sub> (units: %), (<b>f</b>) I<sub>S&gt;2</sub> (units: °CM km<sup>2</sup>), (<b>g</b>) P<sub>S&gt;3</sub> (units: %), and (<b>h</b>) I<sub>S&gt;3</sub> (units: °CM km<sup>2</sup>), which are derived from observations (black bold curves) and 30–60 d forecasts (red curves represent the ensemble mean forecasts, and blue curves represent forecasts of members with different nudging coefficient in SST) by the NUIST CFS1.1 model during the period but for the event MHW#2.</p>
Full article ">Figure 4
<p>As in <a href="#remotesensing-17-00810-f002" class="html-fig">Figure 2</a>, the time series of MHW metrics including (<b>a</b>) the difference between the regional average SST and 90th SST (units: °C), (<b>b</b>) the regional mean S index, and metrics (<b>c</b>) P<sub>S&gt;1</sub> (units: %), (<b>d</b>) I<sub>S&gt;1</sub> (units: °CM km<sup>2</sup>), (<b>e</b>) P<sub>S&gt;2</sub> (units: %), (<b>f</b>) I<sub>S&gt;2</sub> (units: °CM km<sup>2</sup>), (<b>g</b>) P<sub>S&gt;3</sub> (units: %), and (<b>h</b>) I<sub>S&gt;3</sub> (units: °CM km<sup>2</sup>), which are derived from observations (black bold curves) and 30–60 d fore-casts (red curves represent the ensemble mean forecasts, and blue curves represent forecasts of members with different nudging coefficient in SST) by the NUIST CFS1.1 model during the period but for the event MHW#6.</p>
Full article ">Figure 5
<p>As in <a href="#remotesensing-17-00810-f002" class="html-fig">Figure 2</a>, the time series of MHW metrics including (<b>a</b>) the difference between the regional average SST and 90th SST (units: °C), (<b>b</b>) the regional mean S index, and metrics (<b>c</b>) P<sub>S&gt;1</sub> (units: %), (<b>d</b>) I<sub>S&gt;1</sub> (units: °CM km<sup>2</sup>), (<b>e</b>) P<sub>S&gt;2</sub> (units: %), (<b>f</b>) I<sub>S&gt;2</sub> (units: °CM km<sup>2</sup>), (<b>g</b>) P<sub>S&gt;3</sub> (units: %), and (<b>h</b>) I<sub>S&gt;3</sub> (units: °CM km<sup>2</sup>), which are derived from observations (black bold curves) and 30–60 d fore-casts (red curves represent the ensemble mean forecasts, and blue curves represent forecasts of members with different nudging coefficient in SST) by the NUIST CFS1.1 model during the period but for the event MHW#7.</p>
Full article ">Figure 6
<p>The spatial distribution of D<sub>S&gt;1</sub>, D<sub>S&gt;2</sub>, and D<sub>S&gt;3</sub> (units: days) derived from observations for MHW#1 (<b>a</b>–<b>c</b>), MHW#2 (<b>d</b>–<b>f</b>), MHW#6 (<b>g</b>–<b>i</b>), and MHW#7 (<b>j</b>–<b>l</b>), respectively. The black contour lines show the spatial extent of the occurrence of MHW#1, #2, #6, and #7, which is selected based on the maximum yearly mean SSTA from 1983 to 2020, strictly following Lin et al. [<a href="#B49-remotesensing-17-00810" class="html-bibr">49</a>].</p>
Full article ">Figure 7
<p>As in <a href="#remotesensing-17-00810-f006" class="html-fig">Figure 6</a>, but from NUIST CFS1.1 forecasts. The positive and negative values of forecast-minus-observation differences are depicted by the black solid and black dashed contours for MHW#1 (<b>a</b>–<b>c</b>), MHW#2 (<b>d</b>–<b>f</b>), MHW#6 (<b>g</b>–<b>i</b>), and MHW#7 (<b>j</b>–<b>l</b>), respectively. The black contour lines show the spatial extent of the occurrence of MHW#1, #2, #6, and #7.</p>
Full article ">Figure 8
<p>The (<b>a</b>) temporal correlation coefficient (TCC) and (<b>b</b>) ACC of Niño 3.4, NiñoEP and mega ENSO index between the observations and forecasts at lead times of 30–60 days for the period from 30 days before the onset of MHW to 30 days after the end of MHW.</p>
Full article ">Figure 9
<p>The contribution rates of Q and Adv to the SST’ tendency (<math display="inline"><semantics> <mrow> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <mrow> <mi mathvariant="normal">Q</mi> </mrow> <mrow> <mo>∂</mo> <msup> <mrow> <mi mathvariant="normal">S</mi> <mi mathvariant="normal">S</mi> <mi mathvariant="normal">T</mi> </mrow> <mrow> <mi mathvariant="normal">′</mi> </mrow> </msup> <mo>/</mo> <mo>∂</mo> <mi mathvariant="normal">t</mi> </mrow> </mfrac> </mstyle> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <mrow> <mi mathvariant="normal">A</mi> <mi mathvariant="normal">d</mi> <mi mathvariant="normal">v</mi> </mrow> <mrow> <mo>∂</mo> <mi mathvariant="normal">S</mi> <mi mathvariant="normal">S</mi> <msup> <mrow> <mi mathvariant="normal">T</mi> </mrow> <mrow> <mi mathvariant="normal">′</mi> </mrow> </msup> <mo>/</mo> <mo>∂</mo> <mi mathvariant="normal">t</mi> </mrow> </mfrac> </mstyle> </mrow> </semantics></math>) in the equation corresponding to the grids where MHW occurrences meet the criteria for (<b>a</b>) moderate warming (S &gt; 1), (<b>b</b>) strong warming (S &gt; 2), and (<b>c</b>) severe warming (S &gt; 3) in MHW#1, #2, #6 and #7, respectively. The red, pink, light blue, and blue dots represent observations from MHW#1, #2, #6 and #7, respectively. The stars of the same colors represent the corresponding ensemble mean forecasts in MHW#1, #2, #6 and #7. The gray dashed line represents the line y = x. Since the percentage of grid points experiencing severe warming in MHW#2 is less than 5% of the total region, MHW#2 is excluded in panel (<b>c</b>).</p>
Full article ">Figure 10
<p>As in <a href="#remotesensing-17-00810-f009" class="html-fig">Figure 9</a>, but for the contribution rates of Adv-u and Adv-v to SST’ tendency (<math display="inline"><semantics> <mrow> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <mrow> <mi mathvariant="normal">A</mi> <mi mathvariant="normal">d</mi> <mi mathvariant="normal">v</mi> <mo>−</mo> <mi mathvariant="normal">u</mi> </mrow> <mrow> <mo>∂</mo> <msup> <mrow> <mi mathvariant="normal">S</mi> <mi mathvariant="normal">S</mi> <mi mathvariant="normal">T</mi> </mrow> <mrow> <mi mathvariant="normal">′</mi> </mrow> </msup> <mo>/</mo> <mo>∂</mo> <mi mathvariant="normal">t</mi> </mrow> </mfrac> </mstyle> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <mrow> <mi mathvariant="normal">A</mi> <mi mathvariant="normal">d</mi> <mi mathvariant="normal">v</mi> <mo>−</mo> <mi mathvariant="normal">v</mi> </mrow> <mrow> <mo>∂</mo> <msup> <mrow> <mi mathvariant="normal">S</mi> <mi mathvariant="normal">S</mi> <mi mathvariant="normal">T</mi> </mrow> <mrow> <mi mathvariant="normal">′</mi> </mrow> </msup> <mo>/</mo> <mo>∂</mo> <mi mathvariant="normal">t</mi> </mrow> </mfrac> </mstyle> </mrow> </semantics></math>) which meet the criteria for (<b>a</b>) moderate warming (S &gt; 1), (<b>b</b>) strong warming (S &gt; 2), and (c) severe warming (S &gt; 3) in MHW#1, #2, #6 and #7, respectively. The red, pink, light blue, and blue dots represent observations from MHW#1, #2, #6 and #7, respectively. The stars of the same colors represent the corresponding ensemble mean forecasts in MHW#1, #2, #6 and #7. The gray dashed line represents the line y = x. MHW#2 is excluded in panel (<b>c</b>), as in <a href="#remotesensing-17-00810-f009" class="html-fig">Figure 9</a>c.</p>
Full article ">Figure A1
<p>The spatial distribution of grids reaching the standard of moderate, strong, and severe warming (i.e., S &gt; 1, S &gt; 2, and S &gt; 3) during the rapid warming period for MHW#1 (<b>a</b>–<b>c</b>), MHW#2 (<b>d</b>–<b>f</b>), MHW#6 (<b>g</b>–<b>i</b>), and MHW#7 (<b>j</b>–<b>l</b>), respectively.</p>
Full article ">
21 pages, 1364 KiB  
Review
Towards Predictive Maintenance in the Maritime Industry: A Component-Based Overview
by Alexandros S. Kalafatelis, Nikolaos Nomikos, Anastasios Giannopoulos, Georgios Alexandridis, Aikaterini Karditsa and Panagiotis Trakadas
J. Mar. Sci. Eng. 2025, 13(3), 425; https://doi.org/10.3390/jmse13030425 - 25 Feb 2025
Viewed by 146
Abstract
The maritime industry has a significant influence on the global economy, underscoring the need for operational availability and safety through effective maintenance practices. Predictive maintenance emerges as a promising solution compared to conventional maintenance schemes currently employed by the industry, offering proactive failure [...] Read more.
The maritime industry has a significant influence on the global economy, underscoring the need for operational availability and safety through effective maintenance practices. Predictive maintenance emerges as a promising solution compared to conventional maintenance schemes currently employed by the industry, offering proactive failure predictions, reduced downtime events, and extended machinery lifespan. This paper addresses a critical gap in the existing literature by providing a comprehensive overview of the main data-driven PdM systems. Specifically, the review explores common issues found in vessel components (i.e., propulsion, auxiliary, electric, hull), examining how different state-of-the-art PdM architectures, ranging from basic machine learning models to advanced deep learning techniques aim to address them. Additionally, the concepts of centralized machine learning, federated, and transfer learning are also discussed, demonstrating their potential to enhance PdM systems as well as their limitations. Finally, the current challenges hindering adoption are discussed, together with the future directions to advance implementation in the field. Full article
(This article belongs to the Special Issue Maritime Communication Networks and 6G Technologies)
Show Figures

Figure 1

Figure 1
<p>Review structure.</p>
Full article ">Figure 2
<p>Comparison of ML training schemes: (<b>A</b>) CML, (<b>B</b>) FL, and (<b>C</b>) TL.</p>
Full article ">Figure 3
<p>Distinction between RUL, TTF, and TTR.</p>
Full article ">Figure 4
<p>Key factors contributing to propulsion system failures.</p>
Full article ">Figure 5
<p>Hybrid propulsion architectural configurations: (<b>A</b>) serial hybrid system, (<b>B</b>) serial–parallel hybrid system, and (<b>C</b>) parallel hybrid system [<a href="#B40-jmse-13-00425" class="html-bibr">40</a>].</p>
Full article ">Figure 6
<p>Comparison of traditional and electric power system configurations. (<b>A</b>) Internal Combustion Engine (ICE), (<b>B</b>) Lithium–Nickel–Manganese–Cobalt–Oxide (NMC) batteries, (<b>C</b>) Lithium–Iron–Phosphate (LFP) batteries, (<b>D</b>) Lithium–Titanium–Oxide (LTO) batteries [<a href="#B88-jmse-13-00425" class="html-bibr">88</a>].</p>
Full article ">Figure 7
<p>Illustration of marine vessel hull corrosion [<a href="#B105-jmse-13-00425" class="html-bibr">105</a>].</p>
Full article ">
23 pages, 13840 KiB  
Article
A Convection-Permitting Regional Climate Simulation of Changes in Precipitation and Snowpack in a Warmer Climate over the Interior Western United States
by Yonggang Wang, Bart Geerts, Changhai Liu and Xiaoqin Jing
Climate 2025, 13(3), 46; https://doi.org/10.3390/cli13030046 - 24 Feb 2025
Viewed by 197
Abstract
This study investigates the impacts of climate change on precipitation and snowpack in the interior western United States (IWUS) using two sets of convection-permitting Weather Research and Forecasting model simulations. One simulation represents the ~1990 climate, and another represents an ~2050 climate using [...] Read more.
This study investigates the impacts of climate change on precipitation and snowpack in the interior western United States (IWUS) using two sets of convection-permitting Weather Research and Forecasting model simulations. One simulation represents the ~1990 climate, and another represents an ~2050 climate using a pseudo-global warming approach. Climate perturbations for the future climate are given by the CMIP5 ensemble-mean global climate models under the high-end emission scenario. The study analyzes the projected changes in spatial patterns of seasonal precipitation and snowpack, with particular emphasis on the effects of elevation on orographic precipitation and snowpack changes in four key mountain ranges: the Montana Rockies, Greater Yellowstone area, Wasatch Range, and Colorado Rockies. The IWUS simulations reveal an increase in annual precipitation across the majority of the IWUS in this warmer climate, driven by more frequent heavy to extreme precipitation events. Winter precipitation is projected to increase across the domain, while summer precipitation is expected to decrease, particularly in the High Plains. Snow-to-precipitation ratios and snow water equivalent are expected to decrease, especially at lower elevations, while snowpack melt is projected to occur earlier by up to 26 days in the ~2050 climate, highlighting significant impacts on regional water resources and hydrological management. Full article
Show Figures

Figure 1

Figure 1
<p>Model domain with topography. Four subregions are highlighted as rectangular boxes: the Montana Rockies (1), the Greater Yellowstone area (2), the Wasatch Range (3), and the Colorado Rockies (4). Adopted from [<a href="#B10-climate-13-00046" class="html-bibr">10</a>].</p>
Full article ">Figure 2
<p>(<b>a</b>) CMIP5 model ensemble-mean seasonal difference of sea level pressure between future (2036–2065) and past (1976–2005) periods for DJF. (<b>b</b>) Same as (<b>a</b>), but of surface temperature. (<b>c</b>) Same as (<b>a</b>), but of 2 m relative humidity. (<b>d</b>–<b>f</b>) Same as (<b>a</b>–<b>c</b>), but for MAM. (<b>g</b>–<b>i</b>) Same as (<b>a</b>–<b>c</b>), but for JJA. (<b>j</b>–<b>l</b>) Same as (<b>a</b>–<b>c</b>), but for SON. The numbers at upper right corners are the mean differences over the IWUS domain. DJF stands for December, January, and February; MAM stands for March, April, and May; JJA stands for June, July, and August; and SON stands for September, October, and November.</p>
Full article ">Figure 3
<p>(<b>a</b>) The 30-year mean annual precipitation from the past climate simulation. (<b>b</b>) Same as (<b>a</b>), but for the future simulation. (<b>c</b>) Absolute difference between the past and future climates. (<b>d</b>) Percentage difference between the past and future climates. The contours in (<b>c</b>,<b>d</b>) are terrain elevation, at 800 m intervals starting at 800 m MSL, whereas higher elevations are associated with darker colors.</p>
Full article ">Figure 4
<p>(<b>a</b>) The 30-year mean seasonal precipitation difference between the past and future climates for DJF. (<b>b</b>) Same as (<b>a</b>), but for percentage difference. (<b>c</b>,<b>d</b>) Same as (<b>a</b>), but for MAM. (<b>e</b>,<b>f</b>) Same as (<b>a</b>), but for JJA. (<b>g</b>,<b>h</b>) Same as (<b>a</b>), but for SON. The contours are terrain elevation, at 800 m intervals starting at 800 m MSL, whereas higher elevations are associated with darker colors.</p>
Full article ">Figure 5
<p>Variation of annual and seasonal precipitation changes between past and future climates as a function of elevation over the subregion of (<b>a</b>) the Montana Rockies, (<b>b</b>) the Greater Yellowstone area, (<b>c</b>) the Wasatch Range, and (<b>d</b>) the Colorado Rockies. The boxes represent the frequency of elevation bins. Elevation is normalized between the lowest and highest grid point within each subregion.</p>
Full article ">Figure 6
<p>(<b>a</b>) The 30-year mean annual snowfall from the past simulation. (<b>b</b>) The 30-year mean annual snowfall difference between the past and future climates. (<b>c</b>,<b>d</b>) Same as (<b>a</b>,<b>b</b>), but for annual rainfall.</p>
Full article ">Figure 7
<p>(<b>a</b>) The 30-year mean SR averaged over the cold season (October-April) from the past climate simulation. (<b>b</b>) Same as (<b>a</b>), but for the future simulation. (<b>c</b>) Absolute difference between the past and future climates. (<b>d</b>) Percentage difference between the past and future climates. The contours in (<b>c</b>,<b>d</b>) are terrain elevation, at 800 m intervals starting at 800 m MSL, whereas higher elevations are associated with darker colors.</p>
Full article ">Figure 8
<p>Variation of SR as a function of elevation for past climate (black upward triangles), future climate (red downward triangles), and the difference between past and future climates (purple circles) for (<b>a</b>) the Montana Rockies, (<b>b</b>) the Greater Yellowstone area, (<b>c</b>) the Wasatch Range, and (<b>d</b>) the Colorado Rockies. The boxes represent the frequency of elevation bins. Elevation is normalized between the lowest and highest grid point.</p>
Full article ">Figure 9
<p>(<b>a</b>) The 30-year mean SWE on Apr 1st from the past climate simulation. (<b>b</b>) Same as (<b>a</b>), but for the future simulation. (<b>c</b>) Absolute difference between past and future climates. (<b>d</b>) Percentage difference between past and future climates, with grey shade representing past SWE less than 10 mm.</p>
Full article ">Figure 10
<p>(<b>a</b>) Variation of mean SWE as the function of elevation for the Montana Rockies. (<b>b</b>) Same as (<b>a</b>), but for the difference between the past and future climates. (<b>c</b>,<b>d</b>) Same as (<b>a</b>,<b>b</b>), but for the Greater Yellowstone area. (<b>e</b>,<b>f</b>) Same as (<b>a</b>,<b>b</b>), but for the Wasatch Range. (<b>g</b>,<b>h</b>) Same as (<b>a</b>,<b>b</b>), but for the Colorado Rockies. The blue boxes represent the frequency of elevation bins.</p>
Full article ">Figure 11
<p>Seasonal snowpack cycle as a function of elevation for (<b>a</b>) the Montana Rockies, (<b>b</b>) the Greater Yellowstone area, (<b>c</b>) the Wasatch Range, and (<b>d</b>) the Colorado Rockies. The solid curves represent the past climate and the dashed ones represent the future climate. The black color represents grid boxes with the lowest 1/3 elevations, the red color for the middle 1/3, and the blue color for the highest 1/3. The numbers of days shown in each panel are the difference in calendar days between the past and future climates that 10% of the current SWE maximum is reached, and the numbers in mm are the difference in SWE maximum from the past and future climates.</p>
Full article ">Figure 12
<p>(<b>a</b>) Histogram and (<b>b</b>) percentile–percentile plot of daily precipitation from the past and future simulations for the subregion of the Montana Rockies. Days with zero precipitation are included in (<b>a</b>) but not in (<b>b</b>). The 16 dots in (<b>b</b>) represent the following precipitation distribution percentiles: 2.5, 10, 20, 25, 30, 40, 50 (median), 60, 70, 75, 80, 90, 95, 97.5, 99, and 99.9%. (<b>c</b>,<b>d</b>) Same as (<b>a</b>,<b>b</b>), but for the Greater Yellowstone area. (<b>e</b>,<b>f</b>) Same as (<b>a</b>,<b>b</b>), but for the Wasatch Range. (<b>g</b>,<b>h</b>) Same as (<b>a</b>,<b>b</b>), but for the Colorado Rockies.</p>
Full article ">
29 pages, 7104 KiB  
Article
The Importance of Humidity in the Afternoon Local-Scale Precipitation Intensity over Eastern China and Its Impacts on the Aerosol Effects
by Xinlei Tang, Qian Chen, Jianping Guo, Jing Yang, Zeyong Zou, Jinghua Chen and Yue Sun
Remote Sens. 2025, 17(5), 778; https://doi.org/10.3390/rs17050778 - 23 Feb 2025
Viewed by 227
Abstract
Thermally driven local-scale precipitation (LSP) is an important type of summer precipitation over China, but the prestorm environmental conditions remain unclear. In order to investigate the major factors controlling the LSP intensity, the meteorological parameters preceding the occurrence of light and heavy afternoon [...] Read more.
Thermally driven local-scale precipitation (LSP) is an important type of summer precipitation over China, but the prestorm environmental conditions remain unclear. In order to investigate the major factors controlling the LSP intensity, the meteorological parameters preceding the occurrence of light and heavy afternoon LSP over Eastern China during 2018–2022 are examined using rain gauge, radiosonde sounding, and satellite observations. The temperature differences between heavy and light LSP events are relatively small, but heavy LSP events exhibit larger water vapor mixing ratios (Qv) below a 5 km altitude than light LSP. With an almost identical vertical temperature distribution, an increment in Qv increases the relative humidity (RH) in the lower troposphere. Furthermore, large eddy simulations with spectral bin microphysics are performed to investigate the impacts of humidity and aerosols on the LSP intensity. Increased low-level RH leads to larger mass concentrations of rain and graupel at the expense of cloud droplets due to enhanced drop collisions and the riming of ice particles, respectively, thereby reinforcing the LSP. However, an increased aerosol concentration leads to more cloud water but reduced rain water content, resulting mainly from suppressed drop collisions. The graupel mixing ratio exhibits a non-monotonic trend with aerosols, mostly contributed by riming. As a result, the LSP intensity first increases and then decreases with an increment in the aerosol concentration in both dry and humid air. Moreover, more aerosols lead to the humidification of the surrounding air due to the enhanced evaporation of cloud droplets, particularly under lower-RH conditions. These findings provide an enhanced understanding of the effects of covariations in humidity and aerosol concentrations on the afternoon LSP intensity over Eastern China. Full article
Show Figures

Figure 1

Figure 1
<p>The spatial distribution of atmospheric sounding stations (indicated by red diamonds) and automatic rain gauges (represented by blue dots) in Eastern China. The black dashed line denotes the approximate boundary between the second and third staircases of China’s terrain.</p>
Full article ">Figure 2
<p>The spatial distribution of (<b>a</b>) the mean rain rate (mm h<sup>−1</sup>) and (<b>b</b>) the occurrence frequency (%) for all precipitation events during the period 11:00–19:00 LT throughout the summer season (June–August) from 2018 to 2022 over Eastern China; the spatial distribution of (<b>c</b>) the rain rate (mm h<sup>−1</sup>) and (<b>d</b>) the occurrence of local-scale precipitation (LSP), with red circles and squares highlighting regions with high values in (<b>c</b>) and (<b>d</b>), respectively; (<b>e</b>) the spatial distribution of the LSP duration (hour). The black dashed lines represent the boundary of the second and third staircases of China’s terrain. The occurrence frequency in (<b>b</b>) is calculated as the ratio of the rain duration to the entire research period, while the occurrence number in (<b>d</b>) represents the number of LSP events. The maximum hourly rainfall recorded at the sampled stations during an LSP event is regarded as the LSP intensity, and the average LSP intensity for a given station shown in (<b>c</b>) is calculated as the mean rain rate over all LSP events experienced by that station.</p>
Full article ">Figure 3
<p>(<b>a</b>) The percentage distribution of various cloud types associated with the identified LSP events, which are derived from data obtained from the Himawari-8 geostationary satellite. The abbreviations DC, Ac, As, Ns, Cu, Sc, and St refer to deep convection, altocumulus, altostratus, nimbostratus, cumulus, stratocumulus, and stratus, respectively. Also shown is the normalized frequency of the maximum cloud top height of (<b>b</b>) all cloud types and (<b>c</b>) DC during light (grey) and heavy LSP events (blue), respectively. (<b>d</b>) Probability density functions of the aerosol optical depth (AOD) preceding the occurrence of light (grey) and heavy LSP events (blue); (<b>e</b>) variation in light (grey) and heavy LSP events (blue) as a function of AOD.</p>
Full article ">Figure 4
<p>(<b>a</b>) The normalized frequency of convective available potential energy (CAPE) under light (grey curves) and heavy LSP (blue curves) conditions, with mean values marked by the grey and blue text, respectively. (<b>b</b>–<b>i</b>) are similar to (<b>a</b>), but for (<b>b</b>) convective inhibition (CIN), (<b>c</b>) precipitation water (PW), (<b>d</b>) the lifting condensation level (LCL), (<b>e</b>) the level of free convection (LFC), (<b>f</b>) the equilibrium level (EL), (<b>g</b>) the water vapor mixing ratio (Q<sub>v</sub>) at the surface, (<b>h</b>) the K index, and (<b>i</b>) the moist static energy (MSE) between the surface and an altitude of 1 km, respectively.</p>
Full article ">Figure 5
<p>Vertical distributions of the median (<b>a</b>) temperature, (<b>b</b>) water vapor mixing ratio, and (<b>c</b>) relative humidity for light (gray curves) and heavy LSP (blue curves) over Eastern China. The shaded areas represent the range between the 10th and 90th percentiles of the sampled values. (<b>d</b>–<b>f</b>) are similar to (<b>a</b>–<b>c</b>), but for the absolute differences in the (<b>d</b>) temperature, (<b>e</b>) water vapor mixing ratio, and (<b>f</b>) relative humidity between heavy and light LSP.</p>
Full article ">Figure 6
<p>(<b>a</b>) The time series of the averaged rain rate (solid curves) for convective clouds developed under low- (L-RH) and high-humidity conditions (H-RH) in CCN1800, with the dotted and dashed curves representing the intensities of liquid and ice precipitation, respectively. (<b>b</b>) P-mean values of the updraft velocity over the cloud core area (w &gt; 1 m s<sup>−1</sup>) vs. the CCN number concentration for L-RH (grey lines) and H-RH cases (blue lines). (<b>c</b>–<b>i</b>) are similar to (<b>b</b>), but for (<b>c</b>) the onset time of precipitation, (<b>d</b>) the maximum rain rate, and the P-mean values of (<b>e</b>) the mixing ratios of cloud droplets, (<b>f</b>) raindrops, (<b>g</b>) ice crystals and snow, and (<b>h</b>) graupel and (<b>i</b>) the melting rate of ice particles, respectively.</p>
Full article ">Figure 7
<p>(<b>a</b>) Time–height plot of the mean updraft velocity (shaded, m s<sup>−1</sup>) and the mixing ratios of cloud droplets (red contours, g kg<sup>−1</sup>), raindrops (orange contours, g kg<sup>−1</sup>), ice/snow (green contours, g kg<sup>−1</sup>), and graupel (blue contours, g kg<sup>−1</sup>) averaged over the cloudy region (defined as condensate mass exceeding 0.01 g kg<sup>−1</sup>) for the L-RH CCN90 case; (<b>b</b>–<b>d</b>) similar to (<b>a</b>), but for CCN concentrations of (<b>b</b>) 300 cm<sup>−3</sup>, (<b>c</b>) 900 cm<sup>−3</sup>, and (<b>d</b>) 1800 cm<sup>−3</sup> for L-RH cases (<b>left</b> column); (<b>e</b>–<b>h</b>) similar to (<b>a</b>–<b>d</b>), but for H-RH cases (<b>right</b> column).</p>
Full article ">Figure 8
<p>Time–height plot of the mean (<b>a</b>,<b>b</b>) condensation rate of cloud droplets, (<b>c</b>,<b>d</b>) melting rate of ice particles, and (<b>e</b>,<b>f</b>) collection rate of liquid drops averaged over the cloudy region for L-RH (<b>left</b> column) and H-RH cases (<b>right</b> column).</p>
Full article ">Figure 9
<p>The vertical profiles of the mean effective radius of (<b>a</b>) cloud droplets and (<b>b</b>) raindrops averaged over the cloudy region during the simulation period of 60–210 min for the L-RH (<b>top</b> row) and (<b>c</b>,<b>d</b>) for the H-RH cases (<b>bottom</b> row).</p>
Full article ">Figure 10
<p>The vertical profiles of the mean mass changing rates of ice crystals/snow (<b>top</b> row) and graupel (<b>bottom</b> row) due to (<b>a</b>,<b>d</b>) depositional growth, (<b>b</b>,<b>e</b>) collection, and (<b>c</b>,<b>f</b>) droplet freezing, averaged over the cloudy region during a simulation period of 60–210 min for L-RH (grey curves) and for H-RH cases (blue curves).</p>
Full article ">Figure 11
<p>The vertical profiles of the (<b>a</b>) mean total buoyancy (m s<sup>−2</sup>), (<b>b</b>) thermal buoyancy (m s<sup>−2</sup>), (<b>c</b>) water vapor buoyancy (m s<sup>−2</sup>), and (<b>d</b>) water loading buoyancy (m s<sup>−2</sup>) for L-RH cases over the convective core region (w &gt; 1 m s<sup>−1</sup>) during the simulation period of 60–210 min (<b>left</b> column). (<b>e</b>–<b>h</b>) Similar to (<b>a</b>–<b>d</b>), but for H-RH cases (<b>right</b> column).</p>
Full article ">Figure 12
<p>The vertical profiles of (<b>a</b>) the averaged net heating and cooling rate, latent heat release from (<b>b</b>) condensation and evaporation, (<b>c</b>) deposition and sublimation, and (<b>d</b>) riming and freezing over the convective core region (w &gt; 1 m s<sup>−1</sup>) during a simulation period of 60–210 min for the L-RH cases (<b>left</b> column). (<b>e</b>–<b>h</b>) Similar to (<b>a</b>–<b>d</b>), but for the H-RH cases (<b>right</b> column).</p>
Full article ">Figure 13
<p>The vertical profiles of the (<b>a</b>) mean collection rate, (<b>b</b>) evaporation rate of liquid drops, and (<b>c</b>) sublimation rate of ice particles over the convective core region (w &gt; 1 m s<sup>−1</sup>) during a simulation period of 60–210 min for L-RH cases (<b>top</b> row); (<b>d</b>–<b>f</b>) the same as (<b>a</b>–<b>c</b>), but for H-RH cases (<b>bottom</b> row).</p>
Full article ">Figure 14
<p>(<b>a</b>) Time–height plot of the mean water vapor mixing ratio for the L−RH−CCN90 case; (<b>b</b>–<b>d</b>) time–height plots of the differences in the mean water vapor mixing ratio across various aerosol scenarios under lower-RH conditions compared to the L−RH−90 case; (<b>e</b>–<b>h</b>) similar to (<b>a</b>–<b>d</b>), but for H-RH cases; (<b>i</b>–<b>l</b>) and (<b>m</b>–<b>p</b>) are similar to (<b>a</b>–<b>d</b>) and (<b>e</b>–<b>h</b>), but showing the mean temperature for L-RH and H-RH cases, respectively.</p>
Full article ">Figure A1
<p>The flow chart of the experiments in this study.</p>
Full article ">
25 pages, 618 KiB  
Article
Festivals in Age of AI: Smarter Crowds, Happier Fans
by João M. Lopes, Ilda Massano-Cardoso and Camila Granadeiro
Tour. Hosp. 2025, 6(1), 35; https://doi.org/10.3390/tourhosp6010035 - 21 Feb 2025
Viewed by 373
Abstract
Artificial intelligence (AI) stands out as a transformative force in various sectors, offering both new opportunities and challenges. In tourism and music events, AI has proven to be a powerful tool for improving the attendee experience, personalizing artist recommendations, optimizing event logistics in [...] Read more.
Artificial intelligence (AI) stands out as a transformative force in various sectors, offering both new opportunities and challenges. In tourism and music events, AI has proven to be a powerful tool for improving the attendee experience, personalizing artist recommendations, optimizing event logistics in real time, and enhancing audience interaction through virtual assistants and immersive visual effects, thus highlighting its transformative potential. This study aims to analyze the impact of applying AI to the experience of consumers at music festivals. In particular, the research examines the impact of AI on the quality of information delivered, the extent of consumer engagement with brands at the event, and the level of trust in the technology. A quantitative methodology was used, collecting 400 responses from Portuguese consumers who attended music festivals. The results show that the quality of information and the AI positively influence customer engagement with the brand. Greater customer engagement, in turn, increases the willingness to use AI solutions. Trust in AI is significantly shaped by the quality of the information and the reliability of the system, which further promotes electronic word-of-mouth (eWOM) and the willingness to adopt AI. In addition, eWOM plays a key role in encouraging the use of AI technologies. Finally, memorable tourist experiences positively influence the willingness to adopt AI, underlining the importance of experiential factors in promoting adoption. These results highlight the interconnected roles of information quality, trust, involvement, and user experiences in shaping attitudes toward artificial intelligence applications. This study expands the literature by analyzing how AI-driven information quality influences consumer trust and engagement, thus emphasizing the need to optimize these factors for better festival strategies. It highlights the link between trust and positive eWOM, showing that trust based on high-quality information enhances the festival’s reputation and attracts participants. A key contribution is its exploration of how trust and eWOM influence AI adoption at future festivals, which offers insights to boost credibility and acceptance. Lastly, it provides strategic guidelines that improve attendee experience and festival management. Full article
Show Figures

Figure 1

Figure 1
<p>Research model.</p>
Full article ">
28 pages, 6444 KiB  
Systematic Review
Weather-Related Disruptions in Transportation and Logistics: A Systematic Literature Review and a Policy Implementation Roadmap
by Dimos Touloumidis, Michael Madas, Vasileios Zeimpekis and Georgia Ayfantopoulou
Logistics 2025, 9(1), 32; https://doi.org/10.3390/logistics9010032 - 20 Feb 2025
Viewed by 427
Abstract
Background: The increasing frequency and severity of extreme weather events (EWEs) as a consequence of climate change pose critical challenges on the transport and logistics sector, hence requiring systematic evaluation and strategic adaptation. Methods: This study conducts a comprehensive systematic literature [...] Read more.
Background: The increasing frequency and severity of extreme weather events (EWEs) as a consequence of climate change pose critical challenges on the transport and logistics sector, hence requiring systematic evaluation and strategic adaptation. Methods: This study conducts a comprehensive systematic literature review (SLR) of 147 peer-reviewed articles and reports through a PRISMA framework to comprehensively identify key weather-induced challenges, quantify their operational, infrastructural and economic impacts, and explore alternative mitigation strategies. Results: With a greater focus on rainfall, flooding and snowfall, this study highlights their notable impacts causing reductions in transport efficiency, increased maintenance costs and substantial financial losses. Also, it emphasizes the role of advanced technologies, resilient infrastructure, and adaptive policy frameworks as critical enablers for enhancing sector resilience while simultaneously formulating a robust roadmap for cities and companies with actions ranging from direct operational adjustments to long-term transformational changes in policy and infrastructure. Conclusions: This work underscores the importance of using a data-driven approach to safeguard transport and logistics systems against evolving climate risks contributing to the broader goal of sustainable urban resilience and operational continuity. Full article
Show Figures

Figure 1

Figure 1
<p>The PRISMA statement followed within the SLR of the current study.</p>
Full article ">Figure 2
<p>Network of interconnections between keywords.</p>
Full article ">Figure 3
<p>(<b>a</b>) Classification of publications based on their type and (<b>b</b>) temporal distribution of the selected articles’ publication year.</p>
Full article ">Figure 4
<p>(<b>a</b>) Division of studies according to jurisdiction levels and (<b>b</b>) division of studies by country under analysis.</p>
Full article ">Figure 5
<p>Division of studies by type and focus of analysis.</p>
Full article ">Figure 6
<p>Weather events assessed in the studies (* incl. sea level rise, humidity and sunlight hours).</p>
Full article ">Figure 7
<p>The methods utilized through the studies to identify/estimate the quantitative impacts of extreme weather events on transport and logistics.</p>
Full article ">Figure 8
<p>Impact of specific weather events on traffic variables, with diamonds indicating outliers.</p>
Full article ">Figure 9
<p>A comprehensive roadmap for achieving resilient urban transport and logistics systems through multi-stakeholder collaboration.</p>
Full article ">
24 pages, 13219 KiB  
Article
Deformation Mechanisms and Rainfall Lag Effects of Deep-Seated Ancient Landslides in High-Mountain Regions: A Case Study of the Zhongxinrong Landslide, Upper Jinsha River
by Xue Li, Changbao Guo, Wenkai Chen, Peng Wei, Feng Jin, Yiqiu Yan and Gui Liu
Remote Sens. 2025, 17(4), 687; https://doi.org/10.3390/rs17040687 - 18 Feb 2025
Viewed by 296
Abstract
In high-mountain canyon regions, many settlements are located on large, deep-seated ancient landslides. The deformation characteristics, triggering mechanisms, and long-term developmental trends of these landslides significantly impact the safety and stability of these communities. However, the deformation mechanism under the influence of human [...] Read more.
In high-mountain canyon regions, many settlements are located on large, deep-seated ancient landslides. The deformation characteristics, triggering mechanisms, and long-term developmental trends of these landslides significantly impact the safety and stability of these communities. However, the deformation mechanism under the influence of human engineering activities remains unclear. SBAS-InSAR (Small Baseline Subset-Interferometric Synthetic Aperture Radar) technology, UAV LiDAR, and field surveys were utilized in this study to identify a large ancient landslide in the upper Jinsha River Basin: the Zhongxinrong landslide. It extends approximately 1220 m in length, with a vertical displacement of around 552 m. The average thickness of the landslide mass ranges from 15.0 to 35.0 m, and the total volume is estimated to be between 1.48 × 107 m3 and 3.46 × 107 m3. The deformation of the Zhongxinrong landslide is primarily driven by a combination of natural and anthropogenic factors, leading to the formation of two distinct accumulation bodies, each exhibiting unique deformation characteristics. Accumulation Body II-1 is predominantly influenced by rainfall and road operation, resulting in significant deformation in the upper part of the landslide. In contrast, II-2 is mainly affected by rainfall and river erosion at the front edge, causing creeping tensile deformation at the toe. Detailed analysis reveals a marked acceleration in deformation following rainfall events when the cumulative rainfall over a 15-day period exceeds 120 mm. The lag time between peak rainfall and landslide displacement ranges from 2 to 28 days. Furthermore, deformation in the high-elevation accumulation area consistently exhibits a slower lag response compared to the tensile deformation area at lower zones. These findings highlight the importance of both natural and anthropogenic factors in landslide risk assessment and provide valuable insights for landslide prevention strategies, particularly in regions with similar geological and socio-environmental conditions. Full article
Show Figures

Figure 1

Figure 1
<p>Spatial distribution and overview of representative landslides in the upper Jinsha River Basin, Batang segment. (<b>a</b>) Regional location of the study area in China; (<b>b</b>) Spatial location of the Zhongxinrong Township section; (<b>c</b>) Photograph of the Baige landslide (camera toward SE); (<b>d</b>) Photograph of the Gonghuo landslide (camera toward SE); (<b>e</b>) Photograph of the Zhongxinrong landslide (camera toward S).</p>
Full article ">Figure 2
<p>Flowchart of the deformation analysis methodology for the large deep-seated Zhongxinrong landslide in the upper Jinsha River Basin, based on multi-source remote sensing data.</p>
Full article ">Figure 3
<p>Sketch map of data acquisition based on descending orbit satellite data with SBAS-InSAR technology. (<b>a</b>) Schematic diagram of Sentinel-1A satellite acquiring data; (<b>b</b>) Schematic diagram of the Sentinel-1A satellite affected by terrain.</p>
Full article ">Figure 4
<p>Temporal and spatial baseline diagram of SAR descending orbit data.</p>
Full article ">Figure 5
<p>Remote sensing and UAV image interpretation of the Zhongxinrong landslide. (<b>a</b>) Basic characteristics of the Zhongxinrong landslide and interpretation of remote sensing imagery (based on UAV data); (<b>b</b>) Development of collapse features at the front edge of the Zhongxinrong landslide (camera toward SW); (<b>c</b>) Cracking characteristics along the road in the middle of the landslide (camera toward NE).</p>
Full article ">Figure 6
<p>Engineering geological map and profile of the Zhongxinrong landslide along section A-A’. (<b>a</b>) Engineering geological map of the Zhongxinrong landslide. (<b>b</b>) Engineering geological profile along section A-A’ of the Zhongxinrong landslide.</p>
Full article ">Figure 7
<p>Deformation rate distribution map and profile rate statistics of the Zhongxinrong landslide based on SBAS-InSAR descending orbit data. (<b>a</b>) Deformation rate distribution map of the Zhongxinrong landslide; (<b>b</b>) Rate statistics of monitoring points in the landslide deposits.</p>
Full article ">Figure 8
<p>Profile rate distribution map of the Zhongxinrong landslide. (<b>a</b>) Rate distribution along the A-A’ profile; (<b>b</b>) Rate distribution along the B-B’ profile.</p>
Full article ">Figure 9
<p>Photos from the field survey of the Zhongxinrong landslide. (<b>a</b>) Collapse beside the road in the middle of the landslide (camera toward SE); (<b>b</b>) Development of a scarp in the middle-lower part of the landslide (camera toward SE); (<b>c</b>) Development of a scarp in the middle-upper part of the landslide (camera toward W); (<b>d</b>) Cracking of rock beside the road in the middle-upper part of the landslide (camera toward SE); (<b>e</b>) Presence of slip marks in the middle-lower part of the landslide (camera toward S); (<b>f</b>) Exposure of slip zone soil in the middle of the landslide.</p>
Full article ">Figure 10
<p>Cumulative deformations (LOS direction) of the Zhongxinrong landslide in different periods. (<b>a</b>) 14 October 2015; (<b>b</b>) 14 October 2016; (<b>c</b>) 3 October 2017; (<b>d</b>) 10 October 2018; (<b>e</b>) 5 October 2019; (<b>f</b>) 11 October 2020; (<b>g</b>) 6 October 2021; (<b>h</b>) 1 October 2022; (<b>i</b>) 8 October 2023.</p>
Full article ">Figure 11
<p>Relationship between cumulative deformation at typical monitoring points and precipitation for the Zhongxinrong landslide. (<b>a</b>) Relationship between typical monitoring points of the Zhongxinrong landslide and daily precipitation; (<b>b</b>) Relationship between daily precipitation and cumulative precipitation from 2018 to 2022 for the Zhongxinrong landslide; (<b>c</b>) Daily precipitation statistics for the Zhongxinrong landslide.</p>
Full article ">Figure 12
<p>Relationship between deformation trend and rainfall lag in the Zhongxinrong landslide. (<b>a</b>) Deformation rate at typical monitoring points in the Zhongxinrong landslide; (<b>b</b>) Relationship between rainfall and cumulative deformation in the Zhongxinrong landslide.</p>
Full article ">Figure 13
<p>Jinsha River large deep-seated landslide deformation model. (<b>a</b>) Budding stage; (<b>b</b>) Instability stage of deformation area; (<b>c</b>) Instability sliding of the lower deformation zone of the mass and initial stage of high-elevation deformation; (<b>d</b>) Engineering impact on the high-elevation deformation area.</p>
Full article ">
27 pages, 7459 KiB  
Article
Flood Modelling of the Zhabay River Basin Under Climate Change Conditions
by Aliya Nurbatsina, Zhanat Salavatova, Aisulu Tursunova, Iulii Didovets, Fredrik Huthoff, María-Elena Rodrigo-Clavero and Javier Rodrigo-Ilarri
Hydrology 2025, 12(2), 35; https://doi.org/10.3390/hydrology12020035 - 15 Feb 2025
Viewed by 420
Abstract
Flood modelling in snow-fed river basins is critical for understanding the impacts of climate change on hydrological extremes. The Zhabay River in northern Kazakhstan exemplifies a basin highly vulnerable to seasonal floods, which pose significant risks to infrastructure, livelihoods, and water resource management. [...] Read more.
Flood modelling in snow-fed river basins is critical for understanding the impacts of climate change on hydrological extremes. The Zhabay River in northern Kazakhstan exemplifies a basin highly vulnerable to seasonal floods, which pose significant risks to infrastructure, livelihoods, and water resource management. Traditional flood forecasting in Central Asia still relies on statistical models developed during the Soviet era, which are limited in their ability to incorporate non-stationary climate and anthropogenic influences. This study addresses this gap by applying the Soil and Water Integrated Model (SWIM) to project climate-driven changes in the hydrological regime of the Zhabay River. The study employs a process-based, high-resolution hydrological model to simulate flood dynamics under future climate conditions. Historical hydrometeorological data were used to calibrate and validate the model at the Atbasar gauge station. Future flood scenarios were simulated using bias-corrected outputs from an ensemble of General Circulation Models (GCMs) under Representative Concentration Pathways (RCPs) 4.5 and 8.5 for the periods 2011–2040, 2041–2070, and 2071–2099. This approach enables the assessment of seasonal and interannual variability in flood magnitudes, peak discharges, and their potential recurrence intervals. Findings indicate a substantial increase in peak spring floods, with projected discharge nearly doubling by mid-century under both climate scenarios. The study reveals a 1.8-fold increase in peak discharge between 2010 and 2040, and a twofold increase from 2041 to 2070. Under the RCP 4.5 scenario, extreme flood events exceeding a 100-year return period (2000 m3/s) are expected to become more frequent, whereas the RCP 8.5 scenario suggests a stabilization of extreme event occurrences beyond 2071. These findings underscore the growing flood risk in the region and highlight the necessity for adaptive water resource management strategies. This research contributes to the advancement of climate-resilient flood forecasting in Central Asian river basins. The integration of process-based hydrological modelling with climate projections provides a more robust framework for flood risk assessment and early warning system development. The outcomes of this study offer crucial insights for policymakers, hydrologists, and disaster management agencies in mitigating the adverse effects of climate-induced hydrological extremes in Kazakhstan. Full article
(This article belongs to the Section Hydrological and Hydrodynamic Processes and Modelling)
Show Figures

Figure 1

Figure 1
<p>Location of the study area—Zhabay River basin.</p>
Full article ">Figure 2
<p>Interannual variation of seasonal values of air temperature and precipitation.</p>
Full article ">Figure 2 Cont.
<p>Interannual variation of seasonal values of air temperature and precipitation.</p>
Full article ">Figure 3
<p>SWIM model structure diagram (PIK, User Manual, 2024).</p>
Full article ">Figure 4
<p>Maps of land use and soil types of the Zhabay River catchment area.</p>
Full article ">Figure 5
<p>Difference-integral curve of maximum water runoff of the Zhabay-Atbasar region.</p>
Full article ">Figure 6
<p>Cumulative distribution function for maximum water discharge for 1984–2010 in the Zhabay-Atbasar region.</p>
Full article ">Figure 7
<p>Average water discharge for the period April–September in the Zhabay-Atbasar region.</p>
Full article ">Figure 8
<p>Seasonal distribution of water runoff in the Zhabay-Atbasar region during the historical period.</p>
Full article ">Figure 9
<p>Seasonal dynamics of runoff at the Atbasar gauge according to the RCP 4.5 and RCP 8.5 scenarios.</p>
Full article ">Figure 10
<p>Cumulative distribution function for maximum water discharge from 2011 to 2099 in the Zhabay-Atbasar region according to the GFDL-ESM2M RCP 4.5 and GFDL-ESM2M RCP 8.5 scenarios.</p>
Full article ">Figure 11
<p>Flood area estimation map using the FastFlood app on a 40 m grid.</p>
Full article ">
20 pages, 634 KiB  
Article
SATRN: Spiking Audio Tagging Robust Network
by Shouwei Gao, Xingyang Deng, Xiangyu Fan, Pengliang Yu, Hao Zhou and Zihao Zhu
Electronics 2025, 14(4), 761; https://doi.org/10.3390/electronics14040761 - 15 Feb 2025
Viewed by 206
Abstract
Audio tagging, as a fundamental task in acoustic signal processing, has demonstrated significant advances and broad applications in recent years. Spiking Neural Networks (SNNs), inspired by biological neural systems, exploit event-driven computing paradigms and temporal information processing, enabling superior energy efficiency. Despite the [...] Read more.
Audio tagging, as a fundamental task in acoustic signal processing, has demonstrated significant advances and broad applications in recent years. Spiking Neural Networks (SNNs), inspired by biological neural systems, exploit event-driven computing paradigms and temporal information processing, enabling superior energy efficiency. Despite the increasing adoption of SNNs, the potential of event-driven encoding mechanisms for audio tagging remains largely unexplored. This work presents a pioneering investigation into event-driven encoding strategies for SNN-based audio tagging. We propose the SATRN (Spiking Audio Tagging Robust Network), a novel architecture that integrates temporal–spatial attention mechanisms with membrane potential residual connections. The network employs a dual-stream structure combining global feature fusion and local feature extraction through inverted bottleneck blocks, specifically designed for efficient audio processing. Furthermore, we introduce an event-based encoding approach that enhances the resilience of Spiking Neural Networks to disturbances while maintaining performance. Our experimental results on the Urbansound8k and FSD50K datasets demonstrate that the SATRN achieves comparable performance to traditional Convolutional Neural Networks (CNNs) while requiring significantly less computation time and showing superior robustness against noise perturbations, making it particularly suitable for edge computing scenarios and real-time audio processing applications. Full article
Show Figures

Figure 1

Figure 1
<p>Overview of our proposed spike-based audio processing pipeline. The framework illustrates the complete processing flow from the raw audio input through time flow coding to the final classification, highlighting the integration of temporal feature extraction and spike-based neural processing.</p>
Full article ">Figure 2
<p>Overview of the proposed SATRN architecture. The model consists of three key components: (1) <b>the Spiking Potential Layer (SPL)</b>, which encodes input data into spike representations; (2) <b>hierarchical feature fusion (HFF)</b>, which integrates global and local features using Potential Feature Fusion (PFF) modules; and (3) <b>Spatio-Temporal Self-Attention (STSA)</b>, which captures spatial and temporal dependencies for enhanced feature representation. These components work together to enable efficient and effective spike-based neural network processing.</p>
Full article ">Figure 3
<p>Robustness evaluation on FSD50K across different SNR levels. Results demonstrate consistent performance advantage of time flow coding over static encoding, especially in challenging noise conditions.</p>
Full article ">Figure 4
<p>Performance comparison under varying noise levels on UrbanSound8k. Our time flow coding approach maintained higher accuracy across all SNR values, with particularly significant advantages in high-noise conditions (SNR &lt; 0 dB).</p>
Full article ">
14 pages, 3730 KiB  
Article
Near-Real-Time Event-Driven System for Calculating Peak Ground Acceleration (PGA) in Earthquake-Affected Areas: A Critical Tool for Seismic Risk Management in the Campi Flegrei Area
by Claudio Martino, Pasquale Cantiello and Rosario Peluso
GeoHazards 2025, 6(1), 8; https://doi.org/10.3390/geohazards6010008 - 15 Feb 2025
Viewed by 362
Abstract
Peak Ground Acceleration (PGA) is a measure of the maximum ground shaking intensity during an earthquake. The estimation of PGA in areas affected by earthquakes is a fundamental task in seismic hazard assessment and emergency response. This paper presents an automated service capable [...] Read more.
Peak Ground Acceleration (PGA) is a measure of the maximum ground shaking intensity during an earthquake. The estimation of PGA in areas affected by earthquakes is a fundamental task in seismic hazard assessment and emergency response. This paper presents an automated service capable of rapidly calculating the PGA’s values in regions impacted by seismic events and publishing its results on an interactive website. The importance of such a service is discussed, focusing on its contribution to timely response efforts and infrastructure resilience. The necessity for automatic and real-time systems in earthquake-prone areas is emphasized, enabling decision-makers to assess damage potential and deploy resources efficiently. Thanks to a collaboration agreement with the Civil Protection Department, we are able to acquire accelerometric data from the Italian National Accelerometric Network (RAN) in real time at the monitoring center of the Osservatorio Vesuviano. These data, in addition to those normally acquired by the INGV network, enable us to utilize all available accelerometric data in the Campi Flegrei area, enhancing our capacity to provide timely and accurate PGA estimates during seismic events in this highly active volcanic region. Full article
Show Figures

Figure 1

Figure 1
<p>The red triangles represent the INGV-OV seismic stations of the permanent seismic network, the blue triangles indicate the RAN accelerometers, and the yellow ones represent the accelerometers temporarily installed by INGV, which integrate the seismic network with proprietary dataloggers and high-quality MEMS accelerometers.</p>
Full article ">Figure 2
<p>Architecture of UrbanSM.</p>
Full article ">Figure 3
<p>Main page of UrbanSM. The page is also available on mobile devices with a simpler interface. The red star represents event location.</p>
Full article ">Figure 4
<p>Example of Pseudo-Spectral Acceleration for POZA site. ** they represent square power.</p>
Full article ">Figure 5
<p>Popup to show information on a single seismic station. The red star represents event location.</p>
Full article ">
Back to TopTop