We assess the value of calibrating forecast models for significant wave height HS, wind speed W and mean spectral wave period Tm for forecast horizons between zero and 168 h from a commercial forecast provider, to improve forecast performance for a location in the central North Sea. We consider two straightforward calibration models, linear regression (LR) and non-homogeneous Gaussian regression (NHGR), incorporating deterministic, control and ensemble mean forecast covariates. We show that relatively simple calibration models (with at most three covariates) provide good calibration and that addition of further covariates cannot be justified. Optimal calibration models (for the forecast mean of a physical quantity) always make use of the deterministic forecast and ensemble mean forecast for the same quantity, together with a covariate associated with a different physical quantity. The selection of optimal covariates is performed independently per forecast horizon, and the set of optimal covariates shows a large degree of consistency across forecast horizons. As a result, it is possible to specify a consistent model to calibrate a given physical quantity, incorporating a common set of three covariates for all horizons. For NHGR models of a given physical quantity, the ensemble forecast standard deviation for that quantity is skilful in predicting forecast error standard deviation, strikingly so for HS. We show that the consistent LR and NHGR calibration models facilitate reduction in forecast bias to near zero for all of HS, W and Tm, and that there is little difference between LR and NHGR calibration for the mean. Both LR and NHGR models facilitate reduction in forecast error standard deviation relative to naive adoption of the (uncalibrated) deterministic forecast, with NHGR providing somewhat better performance. Distributions of standardised residuals from NHGR are generally more similar to a standard Gaussian than those from LR.
Dwi Setijawati, Abdul Aziz Jaziri, Mohd Azrie Bin Awang
et al.
The study aimed to determine the effect of using a mixture of kappa-carrageenan/Chitosan and Iota carrageenan/Chitosan as encapsulating materials for Lactobacillus acidophilus and Bifidobacterium bifidum on viability, encapsulation yield, moisture content and aw of microcapsules. The research method is an experimental laboratory design, ANOVA, LSD using the Excel program. The results showed that using chitosan with a DD of 83.5% in the i-carrageenan/Chitosan mixture gave the highest viability of Bifidobacterium bifidum. The combination of i-carrageenan/chitosan coating ingredients with a concentration ratio of 0.5%:1.5% produced probiotic viability (L aci + B bifi) of 6.56 log CFU/g, while probiotic viability (B bifi) was the highest with a viability 6.70 log CFU/g. The highest probiotic encapsulation yield (L aci+B bifi) was 72.85%. The water content of the microcapsules (L aci+B bifi) was 8.96%, and the aw value was 0.63. It is recommended to carry out further testing using a processing factor for its viability.
Abstract Anode-free lithium batteries represent a promising avenue for high-energy-density storage, yet their practical application is hindered by lithium inventory loss from parasitic interfacial reactions, cathode degradation, and limited Li+ reversibility. Herein, we propose a polyolefin separator integrated with a Li2S@C sacrificial layer, achieving multiscale interfacial stabilization in Ah-class anode-free pouch cells. This approach simultaneously replenishes the customized Li+ inventory during the formation cycle and establishes the lithium polysulfide-containing cathode interface with high-voltage tolerance (till 4.5 V). Real-time tracking via in-situ electrochemical impedance spectroscopy and transmission-mode operando X-ray diffraction reveals accelerated Li+ diffusion kinetics and stabilized phase evolution in LiNi0.8Co0.1Mn0.1O2 cathode interfaced with Li2S@C|PE prelithiation separator. Consequently, a 1.22 Ah pouch cell with an Ag-modified Cu foil and LiNi0.8Co0.1Mn0.1O2 cathode is assembled with Li2S@C|PE separator and exhibits gravimetric and volumetric energy densities of 450 Wh kg-1 and 1355 Wh L-1, respectively. This prelithiation protocol demonstrates upscaling potential and generic applicability to secure the interfacial chemistries for anode free/less lithium metal batteries.
As an integral component of China’s intangible cultural heritage (ICH), sports intangible cultural heritage (SICH) holds immense significance and importance in cultural inheritance, social cohesion, health promotion, values education, cultural innovation. However, the spatial distribution characteristics and influencing factors of SICH have not been extensively explored. Therefore, we conducted an in-depth analysis of the spatial patterns and influencing factors of SICH utilizing Geographic Information System (GIS) spatial analysis methods such as geographic concentration index and kernel density estimation. The results reveal that SICH exhibits a spatially clustered distribution, with the highest concentrations in Hebei, Guangdong, and Zhejiang provinces. Notably, the Beijing-Tianjin-Hebei region and the Yangtze River Delta region are identified as areas with particularly high densities of SICH. The analysis of natural and human factors indicates that altitude, climate, rivers, GDP, and population density significantly influence the distribution of SICH, while the presence of core cities does not have a notable impact. This research provides valuable insights into the spatial distribution patterns of SICH and offers a foundation for future preservation and promotion strategies.
Ammonia is thought to be the main chemical contaminant that stresses fish during keep-live transport. Taurine has antioxidant properties. This research investigated the oxidative stress, immune response, and apoptosis of large yellow croaker (Larimichthys crocea) during keep-live transport, and the alleviation effect of taurine. The findings demonstrated that ammonia stress might cause the transcription of the stress-related parameter (Hsp70) gene and antioxidant enzymes (SOD, CAT, and GPx) to be upregulated. Taurine administration could lessen the negative impact of ammonia stress. The results showed that ammonia stress might also lead to the up-regulation of stress-related parameter (Hsp70) gene and antioxidant enzymes (SOD, CAT and GPx). Taurine could alleviate the negative effects of ammonia stress. Taurine might reduce the production of reactive oxygen species (ROS), and the content of malondialdehyde (nmol/mL) could be decreased from 96.09 (AM group) to 62.15 (50TAU + AM group), thus reducing DNA damage. SOD and GPx activities increased from 3.47 to 13.23 (AM group) to 9.4 and 55.48 (50TAU + AM group), immunosuppression (lysozyme activity increased). In conclusion, taurine may alleviate the adverse effects of ammonia stress on fish physiology. This research will help elucidate the mechanism of taurine in alleviating ammonia stress. This discovery offers a potential new approach for reducing ammonia stress of large yellow croaker in the future.
Agriculture (General), Nutrition. Foods and food supply
Given the inherent limitations of camera-only and LiDAR-only methods in performing semantic segmentation tasks in large-scale complex environments, multimodal information fusion for semantic segmentation has become a focal point of contemporary research. However, significant modal disparities often result in existing fusion-based methods struggling with low segmentation accuracy and limited efficiency in large-scale complex environments. To address these challenges,we propose a semantic segmentation network with camera–LiDAR cross-attention fusion based on fast neighbor feature aggregation (MFSA-Net), which is better suited for large-scale semantic segmentation in complex environments. Initially, we propose a dual-distance attention feature aggregation module based on rapid 3-D nearest neighbor search. This module employs a sliding window method in point cloud perspective projections for swift proximity search, and efficiently combines feature distance and Euclidean distance information to learn more distinctive local features. This improves segmentation accuracy while ensuring computational efficiency. Furthermore, we propose a cross-attention fusion two-stream network based on residual, which allows for more effective integration of camera information into the LiDAR data stream, enhancing both accuracy and robustness. Extensive experimental results on the large-scale point cloud datasets SemanticKITTI and Nuscenes demonstrate that our proposed algorithm outperforms similar algorithms in semantic segmentation performance in large-scale complex environments.
Ji-Woo Hong, Rafat I.A. Simanto, Byoung-Kwon Ahn
et al.
The dynamics acting upon thin flat plates submerged in a fluid are chiefly governed by the delicate boundary layer enveloping their surfaces. Through a series of experiments, we investigated the impact of surface roughness elements on the boundary layer adjacent to a flat plate across a range of Reynolds numbers. The experiments were performed in the Chungnam National University-Cavitation Tunnel (CNU-CT). Three flat plates, each characterized by distinct surface roughness heights denoted by k, were subjected to scrutiny. One boasted a pristine smoothness, while the others bore the deliberate roughness of sandpaper, each with its own unique texture. With precision instrumentation, including Laser Doppler Velocimetry (LDV), we meticulously documented the axial velocity profile and the RMS (Root Mean Square) velocity at strategic points along the flat plates. Through these measurements, we unveiled the boundary layer's thickness, δ, and momentum thickness, θ, elucidating their variations under differing free-stream velocities. As our exploration deepened, the relationship between the local Reynolds number, Rnx, and the non-dimensional velocity profiles, u+ − y+, became apparent. A systematic shift along the log-law line ensued, with both u+ and y + increasing in tandem with the rise in Rnx. Yet, our inquiry did not conclude with observation alone. Employing empirical rigor, we quantified the drag forces acting upon flat plates of varying roughness heights, deriving them from the measured momentum thickness across a range of local Reynolds numbers, Rnx.
Multi-scale coherent structures have been observed in ocean currents, which are induced by the interaction of shear flows with different velocities. Understanding the spatial configuration and scale characteristics of coherent structures will promote the explanation of physical ocean phenomena. Considering the self-similarity, we propose a spatial correlation identification model for coherent structure extraction and three-dimensional visualization based on the wavelet transform and time-dependent intrinsic correlation method. The spatial and scale distributions of coherent structures are related to the dissipation rate variation. Most large-scale coherent structures, with the largest length scale of 13 m, are found to exist in stable fluid, such as the water column below 50 m. However, small-scale structures are found in chaotic fluids, such as the upper layer. Furthermore, we found that coherent structures of different scales coexist simultaneously in the same depth range, indicating a simultaneous multi-scale structure pattern for turbulent flow investigations.
ObjectiveThis paper seeks to solve the problem of the partial failure of the actuator of an underwater salvage robot caused by a decrease in the shipborne power supply voltage or corrosion of the control system circuit. Methods Considering the influence of complex ocean conditions on deep-sea torpedoes, cargo shipwrecks, and other engineering salvage operations, the terminal sliding mode observer is used to observe the uncertain disturbance of the system, while the fault-tolerant control method and finite-time control method are used to estimate the fault coefficient of the actuator on-line, and a finite-time trajectory tracking fault-tolerant control scheme with a terminal sliding mode observer is designed. ResultThe system output of this scheme is smooth, stable, and can reach the desired trajectory quickly. At the same time, compared with the traditional fault-tolerant control scheme, the steady-state time of the underwater salvage robot's control system is reduced (the lateral displacement is reduced by 10 s and the longitudinal displacement by 15 s).ConclusionThis study has practical engineering significance and can provide theoretical references for the trajectory tracking of underwater construction machinery.
Tomohiro Tanaka, Hiroaki Kawase, Yukiko Imada
et al.
Two methods exist to address the degree to which past extreme events and associated disasters will be intensified due to climate change: storyline approaches and risk-based approaches. However, the risk-based approach applied to weather similar to the target event (typhoons, a stationary weather front,…etc) becomes theoretically similar to the storyline approach. We examine this theory for the climate change impact of a real event, Typhoon Hagibis, which caused devastating flood damage to eastern Japan in 2019, while focusing on basin-averaged accumulated rainfall (BAAR) in major eastern river basins. A risk-based approach was conducted to determine the future change of BAAR by calculating the quantile change corresponding to Hagibis from the probability distribution of typhoon-induced events in a large ensemble climate simulation dataset database for Policy Decision-making for Future climate change (past, +2K and +4K future climates). A storyline approach for Typhoon Hagibis was realized using a pseudo global warming (PGW) experiment with a 5 km non-hydrostatic model. The projected BAAR in the two approaches were consistent for all target basins, supporting the robustness of the calculated changes in extreme catchment precipitation. This presents an important practical benefit: one can assess future climate change impact on a past symbolic event using either PGW experiments or large ensemble climate projections for the target weather.
Near-field acoustic holography (NAH) is an effective tool for realizing accurate sound field reconstruction in three-dimensional space on the prerequisite that appropriate elementary wave functions are selected or constructed to match the characteristics of the sound sources. However, for elongated sources, common wave functions, i.e., plane, cylindrical, or spherical waves, sometimes do not perform well during the sound field projections. To solve this problem, statistically optimized near-field acoustical holography combined with prolate spheroidal wave functions is proposed. In the approach, the sound field is expanded by a series of prolate spheroidal wave functions, whose wavefronts can be set nearly conformal to the elongated sources. Based on these wave functions, fewer expansion terms are required to model the sound field, and the need for regularization can be reduced during the inverse solving process. Therefore, the accuracy of the reconstruction results can be further improved. Numerical simulations are conducted by two types of elongated source models, namely, spatially separated and extended. The results show that the proposed method can effectively reconstruct the sound pressures of elongated sources and perform robustly across a wide frequency range. Simultaneously, a designed experiment is carried out in an anechoic chamber, which demonstrates the feasibility of the proposed method.
Nanoparticles can reduce the friction coefficient and present a self-restorative effect and MoDTC is important as a friction-reducing additive. Both are important for improving lubricating performance. In this study, the tribological performances of nanoparticles in the presence of MoDTC were studied. The chemical synthetic and ball-milled nanoparticles were selected as test samples, and tribological performances were evaluated by a block-ring friction test rig. Experimental results show that the synthetic serpentine particle with a 200–800 nm diameter exhibits the lowest friction coefficient and wear, while the ball-milled kaolin particle shows the highest friction and wear. A synergistic lubricating effect has been shown when mixing the synthetic nano serpentine particle and MoDTC. The friction coefficient of “BD + synthetic serpentine” reduced from 0.011 to 0.055 after the compound with MoDTC. At 150 °C, the “BD + synthetic serpentine + MoDTC” improves the production of MoS<sub>2</sub> on the friction surface, which further reduced the friction coefficient and wear, while the ball-milled kaolin reduced the production of MoS<sub>2</sub>, which leads to a high friction coefficient. The synthetic serpentine shows a round surface without any sharp edge, which shows the minimal ploughing effect on the friction surface. Based on the experimental results, the synthetic nanoparticles have the best antiwear and friction reduction performance when compounded with MoDTC.
The inversion of production performance after fracturing of coal-bed methane well is the key technology to realize the efficient development of gas reservoir. In order to improve the inversion efficiency of traditional numerical simulation methods, with the help of machine learning modeling technology and intelligent algorithm, this paper studies the automatic inversion and programmed design of key parameters such as coal-bed methane reservoir matrix permeability, gas saturation, fracture half length, fracture number and fracture conductivity. The multi-layer perceptron model is constructed with the training data generated by the nested discrete fracture coal-bed methane numerical simulator, and the collaborative inversion of reservoir-fracture parameters is realized by combining the intelligent algorithm. The results show that: (1) Using a small number of training samples (only 100 simulated samples are required for this case study), the machine learning model can accurately simulate the relationship between fracture/reservoir parameters and daily and cumulative gas production of shale gas wells; (2) The intelligent inversion algorithm based on machine learning agent assistance has high convergence efficiency and can quickly obtain a reasonable reservoir fracture parameter combination model with high inversion accuracy. It is concluded that the combination of machine learning modeling technology and intelligent inversion algorithm is helpful to promote the application and development of intelligent optimization technology of tight gas reservoirs, and provide theoretical guidance and technical support for accelerating the intelligent development process of unconventional oil and gas reservoirs in China.
Marine pollution is one of the biggest environmental problems, mainly due to single-use or disposable plastic waste fragmenting into microplastics (MPs) and nanoplastics (NPs) and entering oceans from the coasts together with human-made MPs. A rapidly growing worry concerning environmental and human safety has stimulated research interest in the potential risks induced by the chemicals associated with MPs/NPs. In this framework, the present review analyzes the recent advances in adsorption and desorption studies of different contaminants species, both organic and metallic, on MPs made of Poly(Ethylene terephthalate). The choice of PET is motivated by its great diffusion among plastic items and, unfortunately, also in marine plastic pollution. Due to the ubiquitous presence of PET MPS/NPs, the interest in its role as a vector of contaminants has abruptly increased in the last three years, as demonstrated by the very high number of recent papers on sorption studies in different environments. The present review relies on a chemical engineering approach aimed at providing a deeper overview of both the sorption mechanisms of organic and metal contaminants to PET MPs/NPs and the most used adsorption kinetic models to predict the mass transfer process from the liquid phase to the solid adsorbent.