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In challenging visibility conditions, the reliability of existing port lighting systems is significantly affected by abrupt changes in environmental factors (primarily stemming from ocean weather). This study proposes a cloud-edge collaborative dimming model that integrates a combined filter, enabling dynamic adaptation to these weather variations to ensure the stability of the lighting system. Additionally, the application of edge computing not only alleviates computational pressure but also facilitates the model’s ability to achieve effective regional adaptive dimming in accordance with environmental regulations. Experimental results indicate that this method is suitable for scenarios with unknown mutations under extreme conditions, providing a more reliable and intelligent solution for port lighting systems within the Internet of Things (IoT) framework.

Seeds will display different germination states during the germination process, and their good or bad state directly influences the subsequent growth and yield of the crop. This study aimed to address the difficulties of obtaining the images of seed germination process in all time series and studying the dynamic evolution law of seed germination state under stress conditions. A licorice sprouting experiment was performed using a seed sprouting phenotype acquisition system to obtain images of the sprouting process of licorice in full-time sequence. A labeled dataset of licorice full-time sequence sprouting process images was constructed based on the four states of unsprouted, sprouted, cracked, and shelled in the sprouting process. An optimized model, YOLOv8-Licorice, was developed based on the YOLOv8-n model and its effectiveness was demonstrated by comparative and ablation tests. Different salt stress environments were simulated via NaCl aqueous solution concentration, and germination experiments of licorice seeds were performed under different salt stresses. The germination state of licorice under different salt stress environments was detected using the YOLOv8-Licorice detection model. Percentage curve of licorice seeds in an unsprouted state displayed a continuous decreasing trend. For the percentage curve of licorice seeds in the sprouted state, an increasing and then decreasing trend was observed under the condition of 0-200 mmol/L NaCl solution, and a continuous increasing trend was observed under the condition of 240-300 mmol/L NaCl solution. Licorice seeds in the cracked state demonstrated percentage curves with an increasing and then decreasing trend under the condition of 0-140 mmol/L NaCl solution and a continuous increasing trend under the condition of 160-300 mmol/L NaCl solution. The percentage curve of licorice seeds in shelled state displayed a continuous increasing trend in 0-200 mmol/L NaCl solution condition and remained horizontal in 220-300 mmol/L NaCl solution condition. Overall, this study provides a valuable method involving the seed sprouting phenotype acquisition system and the proposed method for detecting the germination state of licorice seeds. This method serves as a valuable reference to comprehensively understand the seed sprouting process under triggering treatment.

Seed germination vigor is one of the important indexes reflecting the quality of seeds, and the level of its germination vigor directly affects the crop yield. The traditional manual determination of seed germination vigor is inefficient, subjective, prone to damage the seed structure, cumbersome and with large errors. We carried out a cucumber seed germination experiment under salt stress based on the seed germination phenotype acquisition platform. We obtained image data of cucumber seed germination under salt stress conditions. On the basis of the YOLOv8-n model, the original loss function CIoU_Loss was replaced by ECIOU_Loss, and the Coordinate Attention(CA) mechanism was added to the head network, which helped the model locate and identify the target. The small-target detection head was added, which enhanced the detection accuracy of the tiny target. The precision P, recall R, and mAP of detection of the model improved from the original values of 91.6%, 85.4%, and 91.8% to 96.9%, 97.3%, and 98.9%, respectively. Based on the improved YOLOv8-ECS model, cucumber seeds under different concentrations of salt stress were detected by target detection, cucumber seed germination rate, germination index and other parameters were calculated, the root length of cucumber seeds during germination was extracted and analyzed, and the change characteristics of root length during cucumber seed germination were obtained, and finally the germination activity of cucumber seeds under different concentrations of salt stress was evaluated. This work provides a simple and efficient method for the selection and breeding of salt-tolerant varieties of cucumber.

Existing seed germination detection technologies based on deep learning are typically optimized for hydroponic breeding environments, leading to a decrease in recognition accuracy in complex soil cultivation environments. On the other hand, traditional manual germination detection methods are associated with high labor costs, long processing times, and high error rates, with these issues becoming more pronounced in complex soil–based environments. To address these issues in the germination process of new cucumber varieties, this paper utilized a Seed Germination Phenotyping System to construct a cucumber germination soil–based experimental environment that is more closely aligned with actual production. This system captures images of cucumber germination under salt stress in a soil-based environment, constructs a cucumber germination dataset, and designs a lightweight real-time cucumber germination detection model based on Real-Time DEtection TRansformer (RT-DETR). By introducing online image enhancement, incorporating the Adown downsampling operator, replacing the backbone convolutional block with Generalized Efficient Lightweight Network, introducing the Online Convolutional Re-parameterization mechanism, and adding the Normalized Gaussian Wasserstein Distance loss function, the training effectiveness of the model is enhanced. This enhances the model’s capability to capture profound semantic details, achieves significant lightweighting, and enhances the model’s capability to capture embryonic root targets, ultimately completing the construction of the RT-DETR-SoilCuc model. The results show that, compared to the RT-DETR-R18 model, the RT-DETR-SoilCuc model exhibits a 61.2% reduction in Params, 61% reduction in FLOP, and 56.5% reduction in weight size. Its mAP@0.5, precision, and recall rates are 98.2%, 97.4%, and 96.9%, respectively, demonstrating certain advantages over the You Only Look Once series models of similar size. Germination tests of cucumbers under different concentrations of salt stress in a soil-based environment were conducted, validating the high accuracy of the RT-DETR-SoilCuc model for embryonic root target detection in the presence of soil background interference. This research reduces the manual workload in the monitoring of cucumber germination and provides a method for the selection and breeding of new cucumber varieties.

The high-throughput and full-time acquisition of images of crop growth processes, and the analysis of the morphological parameters of their features, is the foundation for achieving fast breeding technology, thereby accelerating the exploration of germplasm resources and variety selection by crop breeders. The evolution of embryonic soybean radicle characteristics during germination is an important indicator of soybean seed vitality, which directly affects the subsequent growth process and yield of soybeans. In order to address the time-consuming and labor-intensive manual measurement of embryonic radicle characteristics, as well as the issue of large errors, this paper utilizes continuous time-series crop growth vitality monitoring system to collect full-time sequence images of soybean germination. By introducing the attention mechanism SegNext_Attention, improving the Segment module, and adding the CAL module, a YOLOv8-segANDcal model for the segmentation and extraction of soybean embryonic radicle features and radicle length calculation was constructed. Compared to the YOLOv8-seg model, the model respectively improved the detection and segmentation of embryonic radicles by 2% and 1% in mAP50-95, and calculated the contour features and radicle length of the embryonic radicles, obtaining the morphological evolution of the embryonic radicle contour features over germination time. This model provides a rapid and accurate method for crop breeders and agronomists to select crop varieties.

Pear trees must be artificially pollinated to ensure yield, and the efficiency of pollination and the quality of pollen germination affect the size, shape, taste, and nutritional value of the fruit. Detecting the pollen germination vigor of pear trees is important to improve the efficiency of artificial pollination and consequently the fruiting rate of pear trees. To overcome the limitations of traditional manual detection methods, such as low efficiency and accuracy and high cost, and to meet the requirements of screening high-quality pollen to promote the yield and production of fruit trees, we proposed a detection method for pear pollen germination vigor named YOLOv8-Pearpollen, an improved version of YOLOv8-n. A pear pollen germination dataset was constructed, and the image was enhanced using Blend Alpha to improve the robustness of the data. A combination of knowledge distillation and model pruning was used to reduce the complexity of the model and the difficulty of deployment in hardware facilities while ensuring that the model achieved or approached the detection effect of a large-volume model that can adapt to the actual requirements of agricultural production. Various ablation tests on knowledge distillation and model pruning were conducted to obtain a high-quality lightweighting method suitable for this model. Test results showed that the mAP of YOLOv8-Pearpollen reached 96.7%. The Params, FLOPs, and weights were only 1.5 M, 4.0 G, and 3.1 MB, respectively, and the detection speed was 147.1 FPS. A high degree of lightweighting and superior detection accuracy were simultaneously achieved.

The Surface Waves Investigation and Monitoring instrument (SWIM) provides the directional wave spectrum within the wavelength range of 23–500 m, corresponding to a frequency range of 0.056–0.26 Hz in deep water. This frequency range is narrower than the 0.02–0.485 Hz frequency range of buoys used to validate the SWIM nadir Significant Wave Height (SWH). The modulation transfer function used in the current version of the SWIM data product normalizes the energy of the wave spectrum using the nadir SWH. A discrepancy in the cut-off frequency/wavelength ranges between the nadir and off-nadir beams can lead to an overestimation of off-nadir cut-off SWHs and, consequently, the spectral densities of SWIM wave spectra. This study investigates such errors in SWHs due to the wavelength cut-off effect using buoy data. Results show that this wavelength cut-off error of SWH is small in general thanks to the high-frequency extension of the resolved frequency range. The corresponding high-frequency cut-off errors are systematic errors amenable to statistical correction, and the low-frequency cut-off error can be significant under swell-dominated conditions. By leveraging the properties of these errors, we successfully corrected the high-frequency cut-off SWH error using an artificial neural network and mitigated the low-frequency cut-off SWH error with the help of a numerical wave hindcast. These corrections significantly reduced the error in the estimated cut-off SWH, improving the bias, root-mean-square error, and correlation coefficient from 0.086 m, 0.111 m, and 0.9976 to 0 m, 0.039 m, and 0.9994, respectively.

Mooring design for floating wave energy converters (WECs) is crucial for station maintaining, efficient power collection, and economic concerns. In order to study the dynamic response of the floating-point absorber under the coupling action of the catenary in regular waves, this research presents the numerical modeling of the floating-point absorber alone with a catenary mooring system. Hydrodynamic behavior of the floating-point absorber is analyzed with respect to wave height, wave period, and current velocity. From the computational fluid dynamics (CFD) results, it can be deduced that the wave height has a much more pronounced impact on the longitudinal motion properties of WEC, such as the longitudinal force and the surge motion, and essentially no impact on the vertical force and the heave motion. The dynamic performance of the WEC under small wave periods are quite different from those under large wave periods. The current velocity also significantly affects the hydrodynamic performance of the WEC. The larger current velocity brings strong nonlinearity for the forces of the WEC. Under the combination of waves and current, the WEC and its mooring system will achieve a dynamic balance.

Large industrial estates for electrical and electronic waste (e-waste) mechanical dismantling and recycling are gradually replacing outmoded small factories and intensive domestic workshops for e-waste manual and chemical dismantling. However, the air pollution and health risks of persistent organic pollutants during the modern mechanical processing of e-waste, especially obsolete electrical equipment, still remain unclear. Here, unexpectedly high levels (409.3 ng/m3) and health risks of airborne polychlorinated biphenyls (PCBs) were found during the mechanical processing of obsolete electric equipment or parts in a large integrated dismantling industrial estate, which is comparable to or a dozen times higher than those reported during chemical processing. In contrast, the levels (936.0 pg/m3) and health risks of particulate polybrominated diphenyl ethers (PBDEs) were all lower than those of previous studies. PCB emissions (44.9–3300.5 ng/m3) varied significantly across six mechanical dismantling places specifically treating waste motors, electrical appliances, hardware, transformers, and metals, respectively. The high PCB content and mass processing number of obsolete electrical equipment probably result in the highest PCB emissions from the mechanical dismantling of obsolete motors, followed by waste electrical appliances and metals. The PCB non-cancer and cancer risks associated with inhalation and dermal exposure in different mechanical dismantling places were all above the given potential risk limits. In particular, the health risks of dismantling obsolete motor exceeded the definite risk levels. Little difference in PCB emissions and health risks between working and non-working time suggested the importance of PCB volatilization from most e-waste. Such high PCB emissions and health risks of PCBs undoubtedly posed a severe threat to frontline workers, but fortunately, they decreased significantly with the increasing distance from the industrial estate. We highlight that PCB emissions and associated health risks from obsolete electrical equipment with high PCB content during mechanical dismantling activities should be of great concern.

Abstract A high step‐up DC‐DC converter for renewable energy applications is proposed. Based on a three‐winding coupled inductor and two voltage multiplier cells, the proposed converter obtains a high voltage conversion ratio. Through the passive clamp circuit, the voltage stress of the main switch is suppressed and the leakage energy of the coupled inductor is recycled. This leads to utilize a low on‐state power resistance and low voltage‐rating power switch that decreases the conduction losses. Meanwhile, the current stress of the diodes is minimized and the reverse recovery problem is alleviated. Thus, high efficiency can be achieved. Under continuous conduction mode (CCM) and discontinuous conduction mode (DCM), different operating principles and the mathematical derivations of the proposed converter are described in detail. To validate the feasibility of the proposed converter, a 320 W prototype with 25–38 V input voltage and 400 V output voltage is implemented. The measured maximum and full load efficiencies are 95.83 % and 94.96 %, respectively.

Abstract The configuration of marine towing cable changes significantly during the turning process, with the rotating procedure with fixed cable length being the most frequent. To overcome these challenges, the configuration and dynamic properties of the marine towing cable must be addressed. However, under some particular operating situations, the tugboat must release the marine towed cable during rotation, resulting in a constant change in the length of the marine cable. In view of this, the towed cable is discretized into a lumped mass model based on the lumped mass method, and the dynamic analysis model of the rotation process of towed cable with variable length under different release speeds and different depths is established. This is done with reference to the specific parameters of a towed system, combined with the specific sea conditions of a particular sea area. Time-domain coupling analysis is used to determine the dynamic changes in configuration and stress of marine towing cable at various release speeds and depths. The results of the calculations have some guiding relevance for a certain engineering practice.

IntroductionDrought stress has become an important factor affecting global food production. Screening and breeding new varieties of peas (Pisum sativum L.) for drought-tolerant is of critical importance to ensure sustainable agricultural production and global food security. Germination rate and germination index are important indicators of seed germination vigor, and the level of germination vigor of pea seeds directly affects their yield and quality. The traditional manual germination detection can hardly meet the demand of full-time sequence nondestructive detection. We propose YOLOv8-Peas, an improved YOLOv8-n based method for the detection of pea germination vigor.MethodsWe constructed a pea germination dataset and used multiple data augmentation methods to improve the robustness of the model in real-world scenarios. By introducing the C2f-Ghost structure and depth-separable convolution, the model computational complexity is reduced and the model size is compressed. In addition, the original detector head is replaced by the self-designed PDetect detector head, which significantly improves the computational efficiency of the model. The Coordinate Attention (CA) mechanism is added to the backbone network to enhance the model's ability to localize and extract features from critical regions. The neck used a lightweight Content-Aware ReAssembly of FEatures (CARAFE) upsampling operator to capture and retain detailed features at low levels. The Adam optimizer is used to improve the model's learning ability in complex parameter spaces, thus improving the model's detection performance.ResultsThe experimental results showed that the Params, FLOPs, and Weight Size of YOLOv8-Peas were 1.17M, 3.2G, and 2.7MB, respectively, which decreased by 61.2%, 61%, and 56.5% compared with the original YOLOv8-n. The mAP of YOLOv8-Peas was on par with that of YOLOv8-n, reaching 98.7%, and achieved a detection speed of 116.2FPS. We used PEG6000 to simulate different drought environments and YOLOv8-Peas to analyze and quantify the germination vigor of different genotypes of peas, and screened for the best drought-resistant pea varieties.DiscussionOur model effectively reduces deployment costs, improves detection efficiency, and provides a scientific theoretical basis for drought-resistant genotype screening in pea.

Remarkable effectiveness has been demonstrated by deep neural networks in the despeckling task for synthetic aperture radar (SAR) images. However, blurring and loss of fine details can result from many despeckling models due to upsampling and mean-square-error (MSE) loss. Additionally, existing degradation models and prior information are ignored by existing despeckling models, which directly learn the mapping from degraded to clear images. To address these issues, an optimization algorithm for the SAR despeckling task based on the integral-Newton method is proposed in this article. Then, a prior-driven despeckling network is proposed, which can automatically capture the implicit priors in SAR images to replace traditional manually made priors. Furthermore, to make the network focus more on learning the structural prior information of images, a structure-preserving loss function based on the MSE and the Canny edge detection operator is designed, which improves the detail of the network retention ability and speeds up convergence. Outstanding results on both simulated datasets and real SAR images are achieved by the proposed method, as shown by a large number of experimental results. Moreover, significant advantages of the proposed method both visually and quantitatively are revealed by comparison with classical and state-of-the-art despeckling algorithms.

Abstract Due to the excellent radiation hardness and high–temperature endurance, diamond detectors are suitable for intense neutron measurements and promising for neutron diagnostics of scientific fusion devices. In the present work, simultaneous measurement of energy spectrum and fluence of neutrons using a diamond detector was realized for the first time. The absolute response matrix of the diamond detector was simulated based on detailed analysis of the nuclear reactions and the proper selection of nuclear reaction data. Neutron energy spectra as well as neutron fluences for 5.0, 5.5, 8.5, 9.5 and 10.5 MeV neutrons from d–d reaction were measured using the diamond detector based on the absolute response matrix. The measured neutron energy spectra and neutron fluences are reasonable compared with those detected using a EJ-309 liquid scintillator and a 238U fission chamber, respectively, which verifies the reliability of the present work. Furthermore, the energy spectrum and fluence of a 14.2 MeV d–t neutron source were also measured using the diamond detector. The present work demonstrates the ability of simultaneous measurement of energy spectrum and fluence as well as for both d–d and d–t neutrons using a diamond detector, which is of great significance for neutron diagnostics of scientific fusion devices.

232Th/233U fuel cycle is an alternate candidate for future nuclear energy. Neutron-induced fission cross section of 232Th is one of the important nuclear data if using thorium as the fuel in the future. 232Th(n, f) cross sections from 1 to 300 MeV were measured at China Spallation Neutron Source (CSNS) Back-streaming neutron facility (Back-n) relative to 235U(n, f) and n-p scattering. A fast ionization chamber for fission cross section measurement (FIXM) and a proton recoil telescope (PRT) were used to perform the measurement. The neutron energy in this measurement reaches up to 300 MeV which is supposed to be the highest among the current Experimental Nuclear Reaction Data (EXFOR) library. It stands as the only experimental data in the region of 200-300 MeV in EXFOR as well. The experimental and evaluated data of 232Th(n, f) cross section above 60 MeV are scarce, the measurement data of this work will be valuable references for future evaluation in high energy. For the data below 60 MeV, most of our data are more consistent with ENDF/B-VIII.0, excepting in the region of 1-2 MeV where our data are more in favor of CENDL-3.2.

The lazy-wave riser is an input and output riser for a flexible development system, which is widely used in all the riser and pipeline systems. Because of the influence of various factors, its configuration description, control and motion which have a strong nonlinear character are complex during the running process of the lazy-wave riser. Reference to the specific structure and environmental parameters of a certain lazy-wave risers system with a 300 thousand tons FPSO, with the basis of the specific process of the flexible riser system at work, the lazy-wave risers were discretized into lumped mass models, combined with AQWA, the simplified dynamic model of the whole system at the depth of 2100m has been established by the large hydrodynamic analysis software OrcaFlex. The dynamic response characteristics of the lazy-wave risers have been given by using time domain coupling method. With and without the consideration of the 2nd wave drift load in the simulation process, the effects of the 2nd wave drift load on the results are obtained. The simulation results reveal the difficulty of simulation convergence caused by a large number of risers and flexible components. The 2nd order wave drift loads have a significant effect on the riser system, resulting in the increasement of the effective tension at each end of each riser. To counteract the magnitude of the FPSO response caused by such loads, the number of mooring lines needed to be increased or combined with dynamic positioning techniques to optimize the design.

Field-based sampling can provide more accurate evaluation than MODIS in regional biomass burning (BB) emissions given the limitations of MODIS on unresolved fires. Polyurethane foam-based passive air samplers (PUF-PASs) are a promising tool for collecting atmospheric monosaccharides. Here, we deployed PUF-PASs to monitor monosaccharides and other BB-related biomarkers and presented a dataset of 31 atmospheric BB-related biomarkers in the Indo-China Peninsula (ICP) and Southwest China. The peak concentrations of monosaccharides in the ICP occurred before monsoon season. The highest concentrations were in the eastern Mekong plain, while the lowest were along the eastern coast. BB-related biomarkers displayed elevated concentrations after April, particularly in the monsoon season; however, fewer active fires were recorded by MODIS. This revealed the importance of MODIS unresolved fires (e.g., indoor biofuel combustion, small-scale BB incidents, and charcoal fires) to the regional atmosphere. The PAS derived levoglucosan concentrations indicated that, with the inclusion of MODIS unresolved fires, the estimated top-down emissions of PM (4194–4974 Gg/yr), OC (1234–1719 Gg/yr) and EC (52–384 Gg/yr) would be higher than previous bottom-up estimations in the ICP. Future studies on these MODIS unresolved fires and regional monitoring data of BB are vital for improving the modeling of regional BB emissions.

The conventional mooring positioning technique is cost-effective; however, it shows poor maneuverability and positioning precision. In this study, to calculate the mooring tension, mooring cables were discretized into lumped mass models using the lumped mass technique. Dock fender nonlinear response forces were modeled in OrcaFlex using the Link unit. The multi-body system’s entire coupling vibrant time-frequency features have been examined. The effect of the side thrusters on removing the vessel motion carried on by the first-order wave loads has been determined under mooring conditions by comparing the difference in horizontal degree of freedom motion and the mooring line’s mooring tension between dynamic and non-dynamic positioning vessels (DPV). The impact of the wharf-cable and target position on side thrusters and positioning capabilities are analyzed, considering the results of both vessels under identical environmental loads. The results demonstrate that deep evaluation of the target position can greatly improve side thruster performance and ship positioning precision. DP systems are weak to cancel linear wave forces, and the missing ship motions for DP ships may be due to the combined action of the fender, moorings and the selection of a good target position. When the selection of the target position is unreasonable, the size of the pre-tension of the mooring line cannot meet the requirement of absorbing the first-order wave load on the vessel.

Engineering and scientific applications are frequently affected by turbulent phenomena, which are associated with a great deal of uncertainty and complexity. Therefore, proper modeling and simulation studies are required. Traditional modeling methods, however, pose certain difficulties. As computer technology continues to improve, machine learning has proven to be a useful solution to some of these problems. The purpose of this paper is to further promote the development of turbulence modeling using data-driven machine learning; it begins by reviewing the development of turbulence modeling techniques, as well as the development of turbulence modeling for machine learning applications using a time-tracking approach. Afterwards, it examines the application of different algorithms to turbulent flows. In addition, this paper discusses some methods for the assimilation of data. As a result of the review, analysis, and discussion presented in this paper, some limitations in the development process are identified, and related developments are suggested. There are some limitations identified and recommendations made in this paper, as well as development goals, which are useful for the development of this field to some extent. In some respects, this paper may serve as a guide for development.

The formation processes and potential sources of particulate nitrate can be revealed by nitrogen (δ15N-NO3−) and oxygen (δ18O-NO3−) isotopes; however, the linkage and comparative information over a large scale is limited. In this work, the feasibility of using quartz wool disk passive air samplers (Pas-QW) to identify and quantify the nitrate concentrations and their isotopic compositions was demonstrated. The results of a simultaneous sampling campaign from March to June showed that the NO3− concentration was largely attributed to the development of the regional economies. The regional distribution of δ15N-NO3− values was due to the source changes. The decreasing trend of δ18O-NO3− values with latitude from south to north was mainly a combination of oxygen isotopic fractionation of the oxidant induced by natural factors and anthropogenic changes in O3 concentrations. Coal combustion (CC) and mobile sources (MS) have a significant contribution to NOx in the typical urban agglomerations, while the high contribution from biomass burning (BB) and biogenic soil emission (BS) was mainly in areas with high natural productivity and intensive agricultural activities. By allowing simultaneous monitoring at multiple sites and over extended periods, passive sampling complements existing techniques for studying nitrate aerosol, and the results can provide a reference for the spatial distribution of its sources and formation in the China–Indochina Peninsula (CICP).

Abstract A multi‐microgrid economic dispatching strategy based on adaptive mutation genetic algorithm is proposed for multi‐microgrid systems with different load types and power demands. Based on the analysis of industrial, residential and commercial loads, considering the synergy and complementarity between multi‐microgrids, an optimal dispatching model of multi‐microgrids based on the minimum operation cost and environmental protection cost of multi‐microgrids is established from the point of view of environmental protection and economy. At the same time, an adaptive mutation genetic algorithm is proposed to optimize the system model and find the optimal economic dispatching scheme.

Abstract It is of significant practical importance to ensure the operational safety of the smart grid, which requires real‐time fault diagnosis and identifying what causes it based on an enormous amount of data. This article further studies the intelligent fault‐identification method based on the combination of multi‐machine learning methods on the bases of researching on Fault Diagnosis of Smart Grid Data Acquisition System. Firstly, we should apply statistical analysis and feature extraction for fault data. Then, we can use fuzzy neural network (FNN) to calculate the probability of fault prediction of power distribution stations, manufacturers and operation businesses, and use the membership function to calculate the corresponding fault membership and uncertainty. Secondly, it makes use of Dempster/Shafer (DS) evidence sequential fusion method to realize fault membership fusion, and gives the corresponding decision criteria for failure causes. Thirdly, a fault‐identification method of smart grid data‐acquisition system is established based on FNN and DS Evidence Fusion. Finally, the experimental results based on the actual operation data of smart grid show that the new method has a very good application effect at fault cause identification.

When analyzing the dynamic characteristics of marine cables, the static equilibrium state must first be calculated; a dynamic analysis can then be carried out based on the static equilibrium. Since the calculation of the static equilibrium is the basis of dynamic calculation, different methods for the calculation of the static equilibrium will have important impacts on the dynamic response characteristics of cables, which result in cables having different dynamic characteristics. This paper summarizes the research progress in the effects of different methods for the calculation of the static equilibrium on the dynamic response of marine cables during the releasing process, and current methods for their static calculation are more comprehensively developed as well as being more accurate. To study the influence of different static equilibrium calculation methods, with the reference of some specific parameters of a mooring cable, combined with specific sea conditions, through the necessary simplification of the cable releasing process, based on the lumped mass method, mooring cables are discretized into the lumped mass model, and dynamic analysis models of the releasing process of mooring cables under three methods for the calculation of the static equilibriums (the fast static equilibrium calculation method, the analytic catenary calculation method, and the catenary calculation method) are established. The dynamic characteristics of the spatial configurations of mooring cables based on different static equilibrium calculation methods are obtained through time domain coupling analyses. It was found that if the static equilibrium of a cable is calculated, taking into account gravity, buoyancy, wave current resistance, inertia forces, and the axial stiffness of a cable, then the characteristics of a cable during dynamic descent are closest to that of a real situation. The calculation results have a certain guiding significance for specific engineering practices.

Torpedoes play an irreplaceable role in naval warfare; therefore, it is significant to study the dynamic response of the direct navigation of torpedoes. In order to study the dynamic response of torpedoes under different Munk moment coefficients, the dynamic equation of torpedoes is established based on the momentum theorem and the momentum moment theorem. The linear motion mathematical model of torpedoes is obtained. The relationship between the torpedo and the Munk moment coefficient is derived. The straight-line motion model of the torpedo under different Munk moments is established, and the dynamic properties of the space motion of the torpedo are analyzed. It is found that the Munk moment coefficient increase will lead to an increase in the deflection of the torpedo’s direct motion on each degree of freedom, and the Munk moment coefficient is related to the additional mass matrix. During the design of the torpedo, the added mass should be reduced by changing the shape of the torpedo as much as possible so as to reduce the pitch moment, yaw, and roll moments of the torpedo.

As a typical multi-hull ship, the pentamaran has a wider deck, higher damage stability, and low-speed seaworthiness in harsh sea conditions compared with other multi-hull types such as the catamaran and trimaran, having more potential for becoming a new type of freight merchant ship in line with the development trend of the marine economy. In this paper, on the basis of the finite volume method, the numerical simulations and detailed comparative analysis of the monohull and pentamaran in viscous flow were carried out. The resistance prediction in calm water and regular waves and the changes of each resistance component of the pentamaran were explored. The dynamic fluid body interaction (DFBI) module was used for the calculations in calm water, and the overset mesh was used for the calculations in regular waves. The results show that in calm water, the adverse effect of the side hulls on the resistance was mainly reflected in the low-speed range. In the high-speed range, the influence of the side hulls on the resistance can be ignored. At high speeds, the main hull and side hulls of the pentamaran will produce favorable wave-making interference, thereby improving the wave-making performance of the pentamaran. In regular waves, although the added resistance of the pentamaran increased most of the time, its amplitude was smaller than that of the monohull under the same wave condition. This work provides a technical basis for the research on the design and hydrodynamic performance of the pentamaran.

Rice blast caused by pathogenic fungus Magnaporthe oryzae is one of the most serious diseases in rice. The pyramiding of effective resistance genes into rice varieties is a potential approach to reduce the damage of blast disease. In this study, combinations of three resistance genes, Pigm, Pi48 and Pi49, were introduced into a thermo-sensitive genic male sterile (PTGMS) line Chuang5S through marker-assisted selection. The results showed that the blast resistance of improved lines increased significantly compared with Chuang5S, and the three gene pyramiding lines (Pigm + Pi48 + Pi49) had higher rice blast resistance level than monogenic line and digenic lines (Pigm +Pi48, Pigm + Pi49). The genetic backgrounds of the improved lines were highly similar (>90%) to the recurrent parent Chuang5S by using the RICE10K SNP chip. In addition, agronomic traits evaluation also showed pyramiding lines with two or three genes similar to Chuang5S. The yields of the hybrids developed from improved PTGMS lines and Chuang5S are not significantly different. The newly developed PTGMS lines can be practically used for the breeding of parental lines and hybrid varieties with broad spectrum blast resistance.

Transcranial magnetic stimulation (TMS), a non-invasive technique to stimulate human brain, has been widely used in stroke treatment for its capability of regulating synaptic plasticity and promoting cortical functional reconstruction. As shown in previous studies, the high electric field (E-field) intensity around the lesion helps in the recovery of brain function, thus the spatial location and angle of coil truly matter for the significant correlation with therapeutic effect of TMS. But, the error caused by coil placement in current clinical setting is still non-negligible and a more precise coil positioning method needs to be proposed. In this study, two kinds of real brain stroke models of ischemic stroke and hemorrhagic stroke were established by inserting relative lesions into three human head models. A coil position optimization algorithm, based on the genetic algorithm (GA), was developed to search the spatial location and rotation angle of the coil in four 4 × 4 cm search domains around the lesion. It maximized the average intensity of the E-field in the voxel of interest (VOI). In this way, maximum 17.48% higher E-field intensity than that of clinical TMS stimulation was obtained. Besides, our method also shows the potential to avoid unnecessary exposure to the non-target regions. The proposed algorithm was verified to provide an optimal position after nine iterations and displayed good robustness for coil location optimization between different stroke models. To conclude, the optimized spatial location and rotation angle of the coil for TMS stroke treatment could be obtained through our algorithm, reducing the intensity and duration of human electromagnetic exposure and presenting a significant therapeutic potential of TMS for stroke.

Abstract Glioblastoma (GBM) is the most common and malignant cancer of the central nervous system, and radiotherapy is widely applied in GBM treatment; however, the sensitivity to radiotherapy varies in different patients. To solve this clinical dilemma, a radiosensitivity prediction signature was constructed in the present study based on genomic methylation. In total, 1044 primary GBM samples with clinical and methylation microarray data were involved in this study. LASSO-COX, GSVA, Kaplan–Meier survival curve analysis, and COX regression were performed for the construction and verification of predictive models. The R programming language was used as the main tool for statistical analysis and graphical work. Via the integration analysis of methylation and the survival data of primary GBM, a novel prognostic and radiosensitivity prediction signature was constructed. This signature was found to be stable in prognosis prediction in the TCGA and CGGA databases. The possible mechanism was also explored, and it was found that this signature is closely related to DNA repair functions. Most importantly, this signature could predict whether GBM patients could benefit from radiotherapy. In summary, a radiosensitivity prediction signature for GBM patients based on five methylated probes was constructed, and presents great potential for clinical application.

Abstract Background Glioblastoma is the most malignant tumor of the central nervous system. Several prediction models have been produced to aid in prognosis assessment. Age, a primary decision factor for prognosis, is associated with increased genomic alterations, however the exact link between increased age and poor prognosis is unknown. Objective In this study, we aimed to reveal the underlying cause of poor prognosis in elderly patients. Methods This study included data on 616 primary GBM tumor samples from the CGGA and TCGA databases and 41 nontumor brain tissue samples obtained from GSE53890. Hallmarks and clinicopathological characteristics were evaluated in both tumor and nontumor brain tissues. The association between choice of treatment regimen and age was measured using ANOVA and Student's t test. Results Age was a robust predictor of poor prognosis in glioma. No age‐related hallmarks of cancer were detected, including pathological characteristics or mutations. However, treatment choice was strongly significantly different between old and young patients. Combined chemo‐radiation therapy could benefit old and young GBM patients, however, old patients are currently less likely to choose it. Conclusion The vast divergence in prognosis between young and old GBM patients is largely caused by choice of treatment rather than age‐related tumor genomic characteristics. Postoperative standard radio‐ and chemotherapy provide strong benefits to primary glioblastoma patients of all ages.

It is very difficult to predict the Maximum Power Demand (MPD) of customers in high performance because of various factors. In this paper, the problem of MPD prediction is studied by using fused machine learning algorithms. Firstly, an improved grey relation analysis method is adopted to analyze relevant influencing factors. Secondly, a modified prediction algorithm based on an adaptive cubature Kalman filter combined with Fbprophet is proposed according to the characteristics of customers' MPD. Finally, the proposed algorithm of this paper is applied to predict MPD and cost is evaluated. Experiment results show that the improved MPD prediction algorithm can comprehensively consider the relevant factors, and has good performance in time series prediction.

Accurate and efficient ultra-short-term load forecasting is crucial for industrial power users to have stabilized and optimized operations. In this paper, we develop novel strategies for industrial power users to handle their challenges in ultra-short-term load forecasting. Firstly, this paper proposes a two-way Genetic Algorithm Back Propagation Neural Networks (GABPNN) missing data completion model to handle data loss, which is common power load data mining. A particle swarm optimization - supporting vector regression (PSO-SVR) algorithm is further used to integrate the two-way completion results with better accuracy. In addition, the paper introduces a combined ultra-short-term load forecasting model for industrial power users. The proposed model combines the Cubature Kalman filter (CKF) prediction model with good performance in nonlinear dynamic systems and the least square support vector machine (LS-SVM) prediction model with good performance in small-scale data prediction. The grey neural network is used to integrate the two algorithms, which further improves the accuracy of ultra-short-term load forecasting. Lastly, we test the proposed strategy in case study with real industrial data and demonstrate that the proposed model has a high degree of precision in load forecasting.

Spaceborne altimeters are an important data source for obtaining global sea surface wind speeds (U10). Although many altimeter U10 algorithms have been proposed and they perform well, there is still room for improvement. In this study, the data from ten altimeters were collocated with buoys to investigate the error of the altimeter U10 retrievals. The U10 residuals were found to be significantly dependent on many oceanic and atmospheric parameters. Because these oceanic and atmospheric parameters are intercorrelated, an asymptotic strategy was used to isolate the impact of different parameters and establish a neural-network-based correction model of altimeter U10. The results indicated that significant wave heights and mean wave periods are effective in correcting U10 retrievals, probably due to the tilting modulation of long-waves on the sea surface. After the wave correction, the root-mean-square error between the U10 from altimeters and buoys was reduced from 1.45 to 1.24 m/s and the impacts of thermodynamic parameters, such as sea surface (air) temperate, became negligible. The U10 residuals after correction showed that the atmospheric instability can lead to errors on extrapolated buoy U10. The buoy measurements with large air-sea temperature differences need to be excluded in the Cal/Val of remotely sensed U10.

Waves have an important influence on the motion performances of offshore crane vessels. The floating crane vessel in waves gives rise to the motion of the lifted object which is connected to the hoisting wire. Based on the geometric parameters of a revolving offshore crane vessel, combined with the specific process of the floating crane vessel at work, a model of the offshore crane vessel under full-circle swing hoisting has been established by OrcaFlex. With the change in wave direction, the dynamic response of the system is made and the impact force between the support vessel and the hanging object and the tension of the crane wire under different wave directions is obtained. At the same time, the minimum impact forces between the support vessel and the hanging object and the tension of the crane wire and their wave directions are obtained. According to the calculated result, the optimal design of the full-circle swing hoisting operation of large revolving offshore crane vessel has been determined.

The trawl system plays an irreplaceable role in deep-sea fishing. In the towing process of the trawl system, many complex mechanical phenomena occurs, so it is necessary to analyze the dynamic response of the deep-sea trawl system during the towing process. In this paper, an equivalent mathematical model for predicating the movement of the ocean trawl system is established based on the equivalent net theory. In the proposed method, the lumped mass method is used to simulate the towed cable and some lines with hydrodynamic characteristics are used to simulate the fishing net. The effects of towing speeds on the dynamic characteristics of a rigid truss trawl system and a flexible trawl system during straight-line towing and rotation towing are studied. The results show that it is possible to simulate trawl motion, and the trawling process is well-presented using this equivalent mathematical model. The disadvantage of this method is also obvious, that is, it cannot simulate trawls with a large number of meshes because the proliferation of mesh numbers can lead to difficult computational convergence. The results also demonstrate that during straight-line towing, the higher the speed, the greater the tension of the cable. Due to the rigid truss, the shape of the rigid truss trawl under different towing speeds is not much different, while the shape of the flexible trawl system changes greatly. During rotating towing, the tension of the cable changes abruptly in the initial stage, and then fluctuates periodically in the time domain. With the increase of towing speed, the overall outward floating distance of the trawl increases gradually. This study has a certain reference and guiding role for deep-sea fishing operations.

In recent tsunami events like those happened in the Indian Ocean in 2004 and 2018 and in Japan in 2011, many ocean and coastal infrastructures have been damaged tremendously, revealing the need of the work to study the hydrodynamic interaction between real-world tsunami waves and offshore structures. Besides, the destructive energy of tsunami wave on the offshore structures should also be assessed. However, to study the impact of tsunami waves on offshore structures, solitary waves are used generally instead of real-world tsunami waves despite the vast differences in hydrodynamic characteristics. Using Computational Fluid Dynamics (CFD) approach, this paper investigates the hydrodynamic characteristics of the floating cylinders moored by chains, under the real-world tsunami-like waves (2011 Japan Tohoku tsunami). Comparisons of hydrodynamic characteristics between real-world tsunami-like wave and solitary wave propagating over the moored cylinders are analyzed and discussed, indicating that the flow fields, the tension forces of mooring chains, the impinging forces and the movement trajectories of the cylinder caused by the two types of waves are greatly different from each other. Moreover, the influences of single cylinder diameter and tandem cylinder spacing on the interaction between real-world tsunami-like waves and cylinders are considered through a series of simulation tests, while meaningful conclusions are drawn.

Dispatching the output of distributed power sources is the main task in the microgrid operation phase. This task is more concerned with the optimal dispatch of large electric vehicles connected to the grid-connected microgrid today.Full consider the influence of storage battery and peak-valley electricity price, its objective is to minimize the operating cost of microgrid and the cost of environmental protection, and establishing economic dispatching model of microgrid. To solve this constrained optimization problem, an annealing mutation particle swarm optimization algorithm is proposed. Through simulation and comparison, the dispatching cost results of microgrid are obtained under two dispatching modes of electric vehicle disorder and order. It is concluded that the orderly charging and discharging mode guided by electricity prices can effectively reduce the operating cost and environmental protection cost of microgrid. Improving the economy and reliability of microgrid operation.

Abstract Amphidrome refers to an oceanic point where the amplitude of one harmonic constituent of the tidal system is zero, and the phase of the harmonic constituent is undetermined. The concept of amphidrome can also be used in climatological studies because of the existence of annual amphidromes, points of zero amplitude, and ill‐defined phase of annual cycle. This study investigated the global atmospheric geopotential height from the ERA‐interim to identify annual amphidromes at different isobaric altitudes. Many well‐defined annual amphidromes were identified. These amphidromic points appear always as twins with different rotating directions with respect to phases. This phenomenon can be explained mathematically with the basic theory of spherical algebraic topology, suggesting amphidrome twins as a common feature for periodic variables in the atmosphere. Due to the spatial continuity of atmospheric parameters, amphidromes at different isobaric altitudes can be concatenated into amphidromic lines. Amphidromic lines have a three‐dimensional structure with both clockwise and anticlockwise vertical branches connected by one or two horizontal components. Particularly, some amphidromic lines in the atmosphere can be closed loops when they are connected by two horizontal components. The rotary phases around the amphidromic loops are similar to the magnetic induction lines around a closed electromagnetic coil.

Objective. Evidence suggests that vitamin D deficiency correlated with metabolic disorders in women with polycystic ovary syndrome (PCOS). We conducted this systematic review and meta-analysis to evaluate the impact of vitamin D supplementation alone on glucose, lipid, and androgen parameters and inflammation biomarkers in women with PCOS. Methods. Literature research was conducted in Pubmed, Embase, Web of Science, Clinical Trials, and Cochrane Library to identify relevant randomized controlled trials (RCTs) up to March 2020. The effect of vitamin D supplementation alone on women with PCOS was compared with administration of placebo. The systematic review and meta-analysis protocol was registered in the International Prospective Register of Systematic Reviews (Prospero) as number CRD42020157444. Results. Thirteen randomized controlled trials with 824 patients in total were included. Serum FPG, fasting insulin, HOMA-IR, and VLDL-C were significantly decreased in the vitamin D group versus placebo. Vitamin D supplementation group also showed a significantly elevated level of QUICKI. No significant impact was seen on serum triglyceride, total-C, LDL-C, HDL-C, total testosterone, DHEAS, SHBG, or hs-CRP. Subgroup analysis demonstrated that oral vitamin D intake had significantly decreased serum triglyceride and total-C level in women with PCOS who have vitamin D deficiency (serum vitamin D

Abstract Background Increasing evidence has shown that long non-coding RNAs (lncRNAs) are important prognostic biomarkers and epigenetic regulators with critical roles in cancer initiation and progression. However, the expression and clinical prognostic value of antisense lncRNAs in diffuse glioma patients remain unknown. Methods Here, we profiled differentially expressed antisense lncRNAs in glioma using RNA sequencing data from Chinese Glioma Genome Atlas database. Cox regression was performed to evaluate the prognostic value. Gene oncology (GO) and gene set enrichment analysis (GSEA) were used for functional analysis of antisense LncRNAs. Results Expression profiling identified 169 aberrantly expressed antisense lncRNAs between lower grade glioma (LGG) (grade II and III) and glioblastoma multiforme (GBM), 113 antisense lncRNAs between LGG IDH-wt and IDH-mut samples, and 70 antisense lncRNAs between GBM IDH-wt and IDH-mut samples, respectively. Among them, three antisense lncRNAs (WDFY3-AS2, MCM3AP-AS1 and LBX2-AS1) were significantly associated with prognosis and malignant progression of patients. WDFY3-AS2, the top one of downregulated antisense lncRNAs in GBM with fold change of 0.441 (P

Using numerical model outputs as a bridge, an indirect validation method for remote sensing data was developed to increase the number of effective collocations between remote sensing data to be validated and reference data. The underlying idea for this method is that the local spatial-temporal variability of specific parameters provided by numerical models can compensate for the representativeness error induced by differences of spatial-temporal locations of the collocated data pair. Using this method, the spatial-temporal window for collocation can be enlarged for a given error tolerance. To test the effectiveness of this indirect validation approach, significant wave height (SWH) data from Envisat were indirectly compared against buoy and Jason-2 SWHs, using the SWH gradient information from a numerical wave hindcast as a bridge. The results indicated that this simple indirect validation method is superior to “direct” validation.

Tropical cyclone (TC)-induced wind waves are a major concern in coastal safety, therefore quantifying the long-term change in extreme TC waves is critical for the design of coastal infrastructures and for understanding variations in coastal morphology. In this study, a trend analysis is performed on the TC-induced extreme wave heights in the northern East China Sea using numerically simulated wave height data during the period of 1979 to 2018. The simulation was forced with historical TC winds constructed using a parametric TC wind model with satellite-observed TC best-track data as the input. The results show consistently increasing extreme wave heights throughout the study region, which are induced predominantly by the increasing TC intensity. The increase rates (0.01–0.08 m yr−1) are relatively large (small) in offshore (nearshore) waters and at relatively high (low) latitudes. The spatial variability of the wave height trend is highly sensitive to the type of TC track. An analytical model of extreme wave height trend is developed that can efficiently estimate the rate of change in the extreme wave heights using extreme wind speed information.

In nuclear reactors, inelastic neutron scattering is a significant energy-loss mechanism which has deep impacts on designments of nuclear reactor and radiation shielding. Iron is an important material in reactor. However, for the existing nuclear data for iron, there exists an obvious divergence for the inelastic scattering cross sections and the related gamma production sections. Therefore the precise measurements are urgently needed for satisfying the demanding to design new nuclear reactors (fast reactors), Accelerator Driven Subcritical System (ADS), and other nuclear apparatus. In this paper, we report a new system with an array of HPGe detectors, electronics and acquisition system. Experiments had been carried out on three neutron facilities.

In the field of Fuel Cell Electric Vehicles (FCEVs), a fuel-cell stack usually works together with a battery to improve powertrain performance. In this hybrid-power system, an Energy Management Strategy (EMS) is essential to configure the hybrid-power sources to provide sufficient energy for driving the FCEV in different traffic conditions. The EMS determines the overall performance of the power supply system; accordingly, EMS research has important theoretical significance and application values on the improvement of energy-utilization efficiency and the serviceability of vehicles’ hybrid-power sources. To overcome the deficiency of apparent filtering lag and improve the adaptability of an EMS to different traffic conditions, this paper proposes a novel EMS based on traffic-condition predictions, frequency decoupling and a Fuzzy Inference System (FIS). An Artificial Neural Network (ANN) was designed to predict traffic conditions according to the vehicle’s running parameters; then, a Hull Moving Average (HMA) algorithm, with filter-window width decided by the prediction result, is introduced to split the demanded power and keep low-frequency components in order to meet the load characteristics of the fuel cell; afterward, an FIS was applied to manage power flows of the FCEV’s hybrid-power sources and maintain the State of Change (SoC) of the battery in a predefined range. Finally, an FCEV simulation platform was built with MATLAB/Simulink and comparison simulations were carried out with the standard test cycle of the Worldwide harmonized Light vehicle Test Procedures (WLTPs). Simulation results showed that the proposed EMS could efficiently coordinate the hybrid-power sources and support the FCEV in following the reference speed with negligible control errors and sufficient power supply; the SoC of the battery was also maintained with good adaptability in different driving conditions.

Numerical wave models are powerful tools for investigating global wave climate. Here a global wave hindcast is employed to estimate the global pattern of crossing swells. However, the global patterns of crossing swells derived from the model are different from those derived from the synthetic aperture radar (SAR) wave mode products of quasi-linear inversion, indicating one of them is questionable. The comparison shows that the first two most energetic swells inversed by SAR are often not in accordance with the first two most energetic swells in the model, and this will have a large impact on the statistics of the data. Before this problem is solved, SAR wave products of quasi-linear inversion should be treated with care in wave climate studies.