Hasil untuk "Ocean engineering"

Anıl Çelik

Yu LIU, Hongjuan GE, Xinxin ZHANG et al.

To elucidate the quality characteristics of crabapple cultivars and facilitate resource utilization, we systematically evaluated 30 crabapple varieties using multidimensional analyses. Physicochemical parameters (fruit weight, moisture content, and soluble solids content) and bioactive profiles (xanthine oxidase inhibitory activity, expressed as IC50) were quantified. Antioxidant potential was assessed using three established methods: DPPH and ABTS+ radical scavenging capacity, and ferric ion-reducing antioxidant power. Multivariate statistical approaches, including correlation analysis, principal component analysis (PCA), and hierarchical cluster analysis, were used for data interpretation. Notably, the cultivars contained substantial levels of total phenolics (2.749~80.877 mg GAE/g DW) and flavonoids (3.522~167.312 mg RE/g DW), demonstrating strong negative correlations (P<0.05) with fruit morphometric parameters (diameter, length, and weight). These phytochemical profiles accounted for the remarkable antioxidant capacity and inhibition of xanthine oxidase. The PCA revealed that the first five principal components explained 82.570% of the total variance. Through the established quality evaluation model, cultivar '2010-6' emerged as the most promising candidate, characterized by the highest phenolic and flavonoid contents, superior antioxidant activities (0.827, 0.654, and 0.518 mmol TE/g DW), and potent xanthine oxidase inhibition (IC50=2.724 mg/mL). Despite the suboptimal sugar–acid ratio (0.47~5.48), these findings position crabapples as valuable raw materials for functional food processing (such as juice and wine production) and natural bioactive compound extraction. This study establishes a scientific framework for the comprehensive quality assessment of crabapple resources, providing critical insights for varietal selection in nutraceutical development and value-added processing.

Seyed Mohammadreza Tabatabaee Fard, Mohammad Javad Ketabdari, Hamid Reza Ghafari

In this paper, a new concept of the floating breakwater with zigzag geometry on the seaside of the floating breakwater body was studied numerically. The hydrodynamic analysis of the zigzag floating breakwater has been investigated using the boundary element method based on the three-dimensional diffraction radiation theory. The zigzag floating breakwater was designed by deforming the seaside wall of the breakwater with three different zigzag angles (60, 90, and 120 degrees). The numerical model was validated against experimental data from a pontoon-type breakwater, demonstrating strong agreement with a root mean square error (RMSE) of 0.093 for the transmission coefficient. The RMSE values for sway, heave, and roll response amplitude operators were 0.28, 0.20, and 0.34, respectively, confirming the reliability of the model. The results revealed that the zigzag geometry significantly increases turbulence within the wave field, disrupting typical wave reflection patterns and enhancing energy dissipation due to the greater upstream surface area of the breakwater compared to conventional straight breakwaters. Notably, the 90° zigzag configuration with a middle heave plate exhibited superior performance, achieving a transmission coefficient of 0.28 at w²B/2g = 0.8, compared to 0.84 for a conventional rectangular breakwater. At higher frequencies (w²B/2g ≥ 1.1), the 90° zigzag breakwater with a heave plate further outperformed other designs, achieving a Ct of 0.15 at w²B/2 g = 1.43, compared to 0.44 for the rectangular breakwater. The inclusion of the heave plate was found to enhance performance for mid-range wave conditions but had minimal impact during longer wave periods. For shorter wave periods, the zigzag design demonstrated significant advantages over traditional configurations, particularly in reducing wave transmission.

Abolghasem Akbari, Majid Rajabi Jaghargh, Azizan Abu Samah et al.

Abstract The Google Earth Engine (GEE) was used to investigate the performance of the Global Land Data Assimilation System (GLDAS) soil temperature (ST) data against observed ST from 13 synoptic stations over a semiarid region in Iran. Three‐hourly ST data were collected and analyzed in two depths (0–10 cm; 40–100 cm) and 5 years. In each depth, GLDAS‐Noah ST data were evaluated for daily minimum, maximum, and average ST (i.e., Tmin, Tmax, and Tavg). Based on the correlation coefficient, Kling–Gupta Efficiency, and Nash–Sutcliffe Efficiency the overall performance of the GLDAS‐Noah is 0.96, 0.66, and 0.79 for Tmin; 0.97, 0.84, and 0.89 for Tavg; and 0.95, 0.89, and 0.89 for Tmax, respectively in the first layer. Likewise, 0.97, 0.85, and 0.86 for Tmin; 0.97, 0.77, and 0.80 for Tavg; and 0.97, 0.69, and 0.69 for Tmax are obtained in the second layer. However, there is a significant negative bias which tends to underestimate ST in the two investigated layers, given by an average bias over all the stations analyzed of −24%, −12%, and −5% for Tmin, Tavg, and Tmax in the first layer, and average bias of −8%, −13%, and −17% for Tmin, Tavg, and Tmax in the second layer. This study reveals that GLDAS‐Noah‐derived ST can be used in arid regions where little or no observation data is available. Moreover, GEE performed as an advanced geospatial processing tool in regional scale analysis of ST in different layers.

Cong Lin, Xin Mao, Chenghao Qiu et al.

Super-resolution reconstruction technology is a crucial approach to enhance the quality of remote sensing optical images. Currently, the mainstream reconstruction methods leverage convolutional neural networks (CNNs). However, they overlook the global information of the images, thereby impacting the reconstruction effectiveness. Methods based on Transformer networks have demonstrated the capability to improve reconstruction quality, but the high model complexity renders them unsuitable for remote sensing devices. To enhance reconstruction performance while maintaining the model lightweight, a distillation Transform-CNN Network is proposed in this article. The strategy employs the Transformer network as a teacher network, guiding its long-range features into a compact CNN, achieving distillation across networks. Simultaneously, to rectify misinformation in the teacher network, prior information is introduced to ensure accurate information transfer. Concerning the student network, a novel upsampling approach is devised, utilizing inherent information in downsampled feature maps for padding, thereby avoiding the introduction of zero-information feature points in the traditional deconvolution process. Experimental evaluations conducted on multiple publicly available remote sensing image datasets demonstrate that the proposed method, while maintaining a smaller parameter count, achieves outstanding reconstruction quality for remote sensing images, surpassing existing approaches.

Zhenwei Wang, Yingbao Yang, Penghua Hu et al.

Land surface temperature (LST) is a vital parameter that reflects the land&#x2013;atmosphere interaction in the land surface energy process. However, due to the temporal effect induced by the wide swath of satellites, differences in local solar time for each pixel observation can result in large differences in LST. Although many studies have been conducted to address this problem, these methods still suffer from spatial discontinuities due to cloud contamination. In this context, this article aims to develop a hybrid method that is combined with surface solar irradiance parameterization and random forest regression. The developed method is suitable for the temporal normalization of LST under all-sky conditions. This method was tested with terra moderate resolution imaging spectroradiometer (MODIS) data and skin temperature in the land component of the fifth generation of European ReAnalysis under clear and cloudy conditions and was validated by three in-situ measurements. An enhancement can be observed with the root mean square error (RMSE) reduction from 2.73 K (3.39 K) to 2.45 K (2.66 K) under clear-sky conditions compared with the original MODIS LST. The RMSE of the normalized LSTs was reduced from 3.60 K (4.14 K) to 2.96 K (3.74 K) under cloudy-sky conditions. From a comparison between the hybrid method and current temporal normalization methods, it is found the former outperforms the latter in terms of spatial completeness and accuracy. These results demonstrate this method has good potential for the temporal normalization of LST under all-sky conditions.

Charles Aubeny

Haolin Yu, Haolin Yu, Haolin Yu et al.

Reef habitat in coastal ecosystems is increasingly being augmented with artificial reefs (ARs) and is simultaneously experiencing increasing hypoxia due to eutrophication and climate change. Relatively little is known about the effects of hypoxia on organisms that use complex habitat arrangements and how the presence of highly preferred AR habitat can affect the exposure of organisms to low dissolved oxygen (DO). We performed two laboratory experiments that used video recording of behavioral movement to explore 1) habitat usage and staying duration of individuals continuously exposed to 3, 5, and 7 mg/L dissolved oxygen (DO) in a complex of multiple preferred and avoided habitat types, and 2) the impact of ARs on exposure to different DO concentrations under a series of two-way replicated choice experiments with or without AR placement on the low-oxygen side. Six common reef-dependent species found in the northeastern sea areas of China were used (i.e., rockfish Sebastes schlegelii and Hexagrammos otakii, filefish Thamnaconus modestus, flatfish Pseudopleuronectes yokohamae, sea cucumber Stichopus japonicus, and crab Charybdis japonica). Results showed that lower DO levels decreased the usage of preferred habitats of the sea cucumber and the habitat-generalist filefish but increased the habitat affinity to preferred habitat types for the two habitat-specific rockfishes. Low DO had no effect on the crab’s habitat usage. In the choice experiment, all three fish species avoided 1 mg/L, and the rockfish S. schlegelii continued to avoid the lower DO when given choices involving pairs of 3, 5, and 7 mg/L, while H. otakii and the flatfish showed less avoidance. The availability of ARs affected exposure to low DO for the habitat-preferring rockfishes but was not significant for the flatfish. This study provides information for assessing the ecological effects and potential for adaptation through behavioral movement for key reef-dependent species under the increasing overlap of ARs and hypoxia anticipated in the future.

Javed Ali, Thomas Wahl, Alejandra R. Enriquez et al.

Natural hazards such as hurricanes, floods, and wildfires cause devastating socio-economic impacts on communities. In South Florida, most of these hazards are becoming increasingly frequent and severe because of the warming climate, and changes in vulnerability and exposure, resulting in significant damage to infrastructure, homes, and businesses. To better understand the drivers of these impacts, we developed a bottom-up impact-based methodology that takes into account all relevant drivers for different types of hazards. We identify the specific drivers that co-occurred with socio-economic impacts and determine whether these extreme events were caused by single or multiple hydrometeorological drivers (i.e., compound events). We consider six types of natural hazards: hurricanes, severe storm/thunderstorms, floods, heatwaves, wildfire, and winter weather. Using historical, socio-economic loss data along with observations and reanalysis data for hydrometeorological drivers, we analyze how often these drivers contributed to the impacts of natural hazards in South Florida. We find that for each type of hazard, the relative importance of the drivers varies depending on the severity of the event. For example, wind speed is a key driver of the socio-economic impacts of hurricanes, while precipitation is a key driver of the impacts of flooding. We find that most of the high-impact events in South Florida were compound events, where multiple drivers contributed to the occurrences and impacts of the events. For example, more than 50% of the recorded flooding events were compound events and these contributed to 99% of total property damages and 98% of total crop damages associated with flooding in Miami-Dade County. Our results provide valuable insights into the drivers of natural hazard impacts in South Florida and can inform the development of more effective risk reduction strategies for improving the preparedness and resilience of the region against extreme events. Our bottom-up impact-based methodology can be applied to other regions and hazard types, allowing for more comprehensive and accurate assessments of the impacts of compound hazards.

Haoyang Du, Manchun Li, Yunyun Xu et al.

Accurate classifications of land use&#x002F;land cover (LULC) in arid regions are vital for analyzing changes in climate. We propose an ensemble learning approach for improving LULC classification accuracy in Xinjiang, northwest China. First, multisource geographical datasets were applied, and the study area was divided into Northern Xinjiang, Tianshan, and Southern Xinjiang. Second, five machine learning algorithms&#x2014;k-nearest neighbor, support vector machine (SVM), random forest (RF), artificial neural network (ANN), and C4.5&#x2014;were chosen to develop different ensemble learning strategies according to the climatic and topographic characteristics of each subregion. Third, stratified random sampling was used to obtain training samples and optimal parameters for each machine learning algorithm. Lastly, each derived approach was applied across Xinjiang, and subregion performance was evaluated. The results showed that the LULC classification accuracy achieved across Xinjiang via the proposed ensemble learning approach was improved by &#x2265;6.85&#x0025; compared with individual machine learning algorithms. By specific subregion, the accuracies for Northern Xinjiang, Tianshan, and Southern Xinjiang increased by &#x2265;6.70&#x0025;, 5.87&#x0025;, and 6.86&#x0025;, respectively. Moreover, the ensemble learning strategy combining four machine learning algorithms (i.e., SVM, RF, ANN, and C4.5) was superior across Xinjiang and Tianshan; whereas, the three-algorithm (i.e., SVM, RF, and ANN) strategy worked best for the Northern and Southern Xinjiang. The innovation of this study is to develop a novel ensemble learning approach to divide Xinjiang into different subregions, accurately classify land cover, and generate a new land cover product for simulating climate change in Xinjiang.

Kostas J. Spyrou

Sangseop Lim, Chang-hee Lee, Won-Ju Lee et al.

A rapid transition toward a decarbonized economy is underway, following the Paris Agreement and the International Maritime Organization 2030 decarbonization goals. However, due to the high cost of the rapid transition to eco-friendly energy and the geopolitical conflict in eastern Europe, liquefied natural gas (LNG), which emits less carbon than other fossil fuels, is gaining popularity. As the spot market grows due to increased LNG demand, the usage of period extension options in time charter (T/C) contracts is increasing; however, these options are generally provided free of charge in practice, without economic evaluation; this is because some shipowners want to make their time charter contracts more attractive to the more credible charterers. Essentially, the reason for why this option has not been evaluated is because there is no reliable evaluation model currently used in practice. That is, research on the evaluation model for the T/C extension option has been insufficient. Therefore, this study evaluates the economic value of the extended period option in LNG time charter contracts using machine learning models, such as artificial neural networks, support vector machines, and random forest, and then compares them with the Black–Scholes model that is used for option valuations in financial markets. The results indicate superior valuation performance of the random forest model compared with the other models; particularly, its performance was significantly better than the Black–Scholes model. Since T/C extension options involve significant sums in the balance sheets of both shipowners and charterers, the fair value of these options should be assessed. In this regard, this paper has meaning in proposing valid machine models to efficiently reflect the fair value of period extension options that are provided at no charge in the LNG market.

Jui-Yi Ho, Che-Hsin Liu, Wei-Bo Chen et al.

Abstract Heavy rainfall brought by typhoons has been recognised as a major trigger of landslides in Taiwan. On average, 3.75 typhoons strike the island every year, and cause large amounts of shallow landslides and debris flow in mountainous region. Because landslide occurrence strongly corresponds to the storm dynamics, a reliable typhoon forecast is therefore essential to landslide hazard management in Taiwan. Given early warnings with sufficient lead time, rainfall-induced shallow landslide forecasting can help people prepare disaster prevention measures. To account for inherent weather uncertainties, this study adopted an ensemble forecasting model for executing precipitation forecasts, instead of using a single-model output. A shallow landslide prediction model based on the infinite slope model and TOPMODEL was developed. Considering the detailed topographic characteristics of a catchment, the proposed model can estimate the change in saturated water table during rainstorms and then link with the slope-instability analysis to clarify whether shallow landslides can occur in the catchment. Two areas vulnerable to landslide in Taiwan were collected to test the applicability of the model for landslide prediction. Hydrological data and landslide records derived from 15 typhoons events were used to verify the applicability of the model. Three indices, namely the probability of detection (POD), false alarm ratio (FAR), and threat score (TS), were used to assess the performance of the model. The results indicated that for landslide prediction through the proposed model, the POD was higher than 0.73, FAR was lower than 0.33, and TS was higher than 0.53. The proposed model has potential for application in landslide early warning systems to reduce loss of life and property.

Dracos Vassalos, Donald Paterson

Nurul Uyun Azman, Mohd Khairi Abu Husain, Noor Irza Mohd Zaki et al.

The structural integrity of offshore platforms is affected by degradation issues such as subsidence. Subsidence involves large settlement areas, and it is one of the phenomena that may be experienced by offshore platforms throughout their lives. Compaction of the reservoir is caused by pressure reduction, which results in vertical movement of soils from the reservoir to the mud line. The impact of subsidence on platforms will lead to a gradually reduced wave crest to deck air gap (insufficient air gap) and cause wave-in-deck. The wave-in-deck load can cause significant damage to deck structures, and it may cause the collapse of the entire platform. This study aims to investigate the impact of wave-in-deck load on structure response for fixed offshore structure. The conventional run of pushover analysis only considers the 100-year design crest height for the ultimate collapse. The wave height at collapse is calculated using a limit state equation for the probabilistic model that may give a different result. It is crucial to ensure that the reserve strength ratio (<i>RSR</i>) is not overly estimated, hence giving a false impression of the value. This study is performed to quantify the wave-in-deck load effects based on the revised <i>RSR</i>. As part of the analysis, the Ultimate Strength for Offshore Structures (USFOS) software and wave-in-deck calculation recommended by the International Organization for Standardization (ISO) as practised in the industry is adopted to complete the study. As expected, the new revised <i>RSR</i> with the inclusion of wave-in-deck load is lower and, hence, increases the probability of failure (<i>POF</i>) of the platform. The accuracy and effectiveness of this method will assist the industry, especially operators, for decision making and, more specifically, in outlining the action items as part of their business risk management.

Qinwang Xing, Qinwang Xing, Huaming Yu et al.

Tides are the dominant hydrodynamic processes in most continental shelf seas and have been proven to have a significant impact on both marine ecosystem dynamics and biogeochemical cycles. In situ and satellite observations have suggested that the spring-neap tide results in fluctuations of chlorophyll-a concentrations (Chl-a) with a fortnightly period in some shelf waters. However, a large number of missing values and low observation frequency in satellite-observed Chl-a have been recognized as the major obstacle to investigating the regional pattern showing where and to what extent of the effects of spring-neap tide on Chl-a and the seasonal variations in the effects within a relatively large region. Taking Himawari-8 as an example, a simple algorithm appropriate for geostationary satellites was proposed in this study with the purpose of obtaining a tide-related daily climatological Chl-a dataset (TDCD) and to quantitatively estimate the effects of the spring-neap tide on Chl-a variations. Based on the Chl-a time series from TDCD, significant fortnightly signals of Chl-a fluctuations and high contribution together with high explanations of the fortnightly fluctuations for Chl-a variations were found in some specific inshore waters, especially in the East China Sea, Bay of Bengal, South China Sea, and northern Australian waters. The spring-neap tide was found able to induce the spatio-temporal fortnightly fluctuations of Chl-a with an annual amplitude of 12–33% of the mean in these inshore areas. Significant seasonal variations in the fortnightly fluctuation of Chl-a were observed in the temperate continental shelf regions, while levels remained relatively stable in the tropical waters. Further analysis implied that the spatio-temporal fortnightly fluctuations of Chl-a were closely associated with the tidal current differences between the spring and neap tides. Seasonal variations in the tidal current differences were found to be a key driving factor for seasonal fluctuations of the spring-neap tidal effects on Chl-a in the temperate continental shelf regions. This study provides a better understanding of tide-related marine ecosystem dynamics and biogeochemical cycles and is helpful in improving physical–biogeochemical models.

Waseem Khodabux, Feargal Brennan

Corrosion in the marine environment is a complex mechanism. One of the most damaging forms of corrosion is pitting corrosion, which is difficult to design and inspect against. In the North Sea, multiple offshore wind structures have been deployed that are corroding from the inside out. One of the most notable corrosion mechanisms observed is pitting corrosion. This study addresses the lack of information both in the literature and the industry standards on the pitting corrosion profile for water depth from coupons deployed in the North Sea. Image processing was therefore conducted to extract the characteristics of the pit, which were defined as pit major length, minor length, area, aspect ratio, and count. The pit depth was measured using a pit gauge and the maximum pit depth was found to be 1.05 mm over 111 days of exposure. The goal of this paper is to provide both deterministic models and a statistical model of pit characteristics for water depth that can be used by wind farm operators and researchers to inform and simulate pits on structures based on the results of a real field experiment. As such, these models highlight the importance of adequate corrosion protection.

C. Li, H. Wang, H. Wang et al.

<p>We developed thermal dissociation cavity-enhanced absorption spectroscopy (TD-CEAS) for the in situ measurement of NO<span class="inline-formula">₂</span>, total peroxy nitrates (PNs, RO<span class="inline-formula">₂</span>NO<span class="inline-formula">₂</span>), and total alkyl nitrates (ANs, RONO<span class="inline-formula">₂</span>) in the atmosphere. PNs and ANs were thermally converted to NO<span class="inline-formula">₂</span> at the corresponding pyrolytic temperatures and detected by CEAS at 435–455 nm. The instrument sampled sequentially from three channels at ambient temperature, 453 and 653 K, with a cycle of 3 min, to measure NO<span class="inline-formula">₂</span>, NO<span class="inline-formula">₂+</span> PNs, and NO<span class="inline-formula">₂+</span> PNs <span class="inline-formula">+</span> ANs. The absorptions between the three channels were used to derive the mixing ratios of PNs and ANs by spectral fitting. The detection limit (LOD, 1<span class="inline-formula"><i>σ</i></span>) for retrieving NO<span class="inline-formula">₂</span> was 97 parts per trillion by volume (pptv) in 6 s. The measurement uncertainty of NO<span class="inline-formula">₂</span> was 9 %, while the uncertainties of PN and AN detection were larger than those of NO<span class="inline-formula">₂</span> due to chemical interferences that occurred in the heated channels, such as the reaction of NO (or NO<span class="inline-formula">₂</span>) with the peroxy radicals produced by the thermal dissociation of organic nitrates. Based on laboratory experiments and numerical simulations, we created a lookup table method to correct these interferences in PN and AN channels under various ambient organic nitrates, NO, and NO<span class="inline-formula">₂</span>. Finally, we present the first field deployment and compare it with other instruments during a field campaign in China. The advantages and limitations of this instrument are outlined.</p>

Jie-Bang Yan, Linfeng Li, Joshua A. Nunn et al.

Radio echo sounding of polar ice sheets provides important information on the ice bed topography and internal layers. These data have been used by scientists to create 3-D maps of polar ice sheets for climate modeling as well as to reconstruct the climate history that dates back to hundreds of thousands of years. In this article, we present the design and development of three surface-based multichannel radars in the VHF and UHF bands. We provide results from radar data multifrequency and polarization radar data collected over the Greenland ice sheet. All the three radars shared the same digital waveform generator and digitizer, and were installed in and operated on a tracked vehicle. The radars are operated with three different antenna arrays designed for operation over 170-230, 180-340, and 600-900 MHz. The results we obtained sounded more than 2.7 km thick ice with radars operating at frequencies as high as 850 MHz with more than 40 dB signal-to-noise ratio.

Ellysia Jumin, Nuratiah Zaini, Ali Najah Ahmed et al.

High level of tropospheric ozone concentration, exceeding allowable level has been frequently reported in Malaysia. This study proposes accurate model based on Machine Learning algorithms to predict Tropospheric ozone concentration in major cities located in Kuala Lumpur and Selangor, Malaysia. The proposed models were developed using three-year of historical data for different parameters as input to predict 24-hour and 12-hour of tropospheric ozone concentration. Different Machine Learning algorithms have been investigated, viz. Linear Regression, Neural Network and Boosted Decision Tree. The results revealed that wind speed, humidity, Nitrogen Oxide, Carbon Monoxide and Nitrogen Dioxide have significant influence on ozone formation. Boosted Decision Tree outperformed Linear regression and Neural Network algorithms for all stations. The performance of the proposed model improved by using 12-hours dataset instead of the 24-hour where R2 values were equal to 0.91, 0.88 and 0.87 for the three investigated stations. To assess the uncertainties of the Boosted Decision Tree model, 95% prediction uncertainties (95PPU) d-factors were introduced.95PPU showed about 94.4, 93.4, 96.7% and the d-factors were 0.001015, 0.001016 and 0.001124 which relate to S1, S2 and S3, respectively. The obtained results provide a reliable prediction model to mimic actual ozone concentration in different locations in Malaysia.