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High-performance concrete (HPC) is commonly utilized in the construction industry because of its strength and durability. The mechanical properties of HPC, specifically its compressive and tensile strength, are crucial indicators. Accurate prediction of concrete strength is crucial for optimizing the design as well as the performance of concrete structures. In this investigation, a novel approach for strength prediction of HPC is proposed, employing the Support Vector Regression (SVR) algorithm in conjunction with three optimizers: the Slime Mold Algorithm (SMA), Adaptive Opposition Slime Mold Algorithm (AOSM), and Equilibrium Slime Mold Algorithm (ESMA). The SVR algorithm is a robust machine-learning technique that has displayed promising results in various prediction tasks. The utilization of SVR allows for the effective modeling and prediction of the complex relationship between the strength properties of HPC and the influencing factors. To achieve this, a dataset comprising 344 samples of high-performance concrete was collected and utilized to train and assess the SVR algorithm. However, the choice of suitable optimization algorithms becomes crucial to enhance prediction accuracy and convergence speed. Through extensive experimentation and comparative analysis, the proposed framework’s performance is evaluated using real-world HPC strength data. The results demonstrate that combining SVR with AOSM, ESMA, and SMA outperforms traditional prediction accuracy and convergence speed optimization methods. The suggested framework provides an effective and reliable solution for accurately predicting the compressive strength (CS) of HPC, enabling engineers and researchers to optimize the design and construction processes of HPC structures.

In recent years, the world has been actively promoting the development of wind power, photovoltaic, and other new energy. The inherent randomness and intermittency of wind power output have led to the reduction of supply-side controllability and stability, and the power system is facing severe challenges. Aiming at the irregular fluctuation of wind power output and the restriction between the charge and discharge depth and service life of hybrid energy storage equipment, a hierarchical control strategy for a hybrid energy storage system based on improved GA-SVR wind power prediction is proposed. First of all, the short-term prediction of wind power output is carried out using Support Vector Regression (SVR), and the improved genetic algorithm is used for optimization. Then, the result obtained from the prediction calculation is used as the wind power output, and the internal initial power of each energy storage element is obtained through the hybrid energy storage capacity configuration method and further controlled through hierarchical control regulation. Finally, a simulation experiment is carried out on the proposed control strategy. The simulation algorithm shows that the proposed method can not only enhance the effective output of new energy but also extend the service life of energy storage and ensure the safe and stable operation of the power system.

Developing simple, efficient, and inexpensive method for trace amount organophosphorus pesticides’ (OPs) detection with high sensitivity and specificity is of significant importance for guaranteeing food safety. Herein, an Ag/Au bimetallic nanoparticle-based acetylcholinesterase (AChE) surface-enhanced Raman scattering (SERS) biosensor was constructed for in situ simple and sensitive detection of pesticide residues in food. The principle of this biosensor exploited 4-mercaptophenylboronic acid (4-MPBA)-modified Ag/Au bimetallic nanoprobes as SERS signal probe to improve sensitivity and stability. The combination of AChE and choline oxidase (CHO) can hydrolyze acetylcholine (ATCh) to generate H 2 O 2 . The product of H 2 O 2 selectively oxidizes the boronate ester of 4-MPBA, decreasing the Raman intensity of the B-O symmetric stretching. In the presence of OPs, it could inhibit the production of H 2 O 2 by destroying the AChE activity, so the reduction of the SERS signal was also alleviated. Based on the principle, an Ag/Au bimetallic nanoparticle-based AChE SERS sensor was established without any complicated pretreatments. Benefiting from the synergistic effects of Ag/Au bimetallic hybrids, a linear detection range from 5×10 −9 to 5×10 −4 M was achieved with a limit of detection down to 1.7×10 −9 M using parathion-methyl (PM) as the representative model of OPs. Moreover, the SERS biosensor uses readily available reagents and is simple to implement. Importantly, the proposed SERS biosensor was used to quantitatively analyze OP residues in apple peels. The levels of OPs detected in real samples by this method were consistent with those obtained using gas chromatography–mass spectrometry (GC–MS), suggesting the proposed assay has great potential applications for OPs in situ detection in food safety fields. Graphical abstract

Background Humans are widely exposed to mixtures of environmental perchlorate, nitrate, and thiocyanate. However, the associations of perchlorate, nitrate, and thiocyanate with pulmonary function in children were unclear. Methods To investigate the associations of urinary perchlorate, nitrate, and thiocyanate concentrations with pulmonary function in children, a total of 2,271 children and adolescents aged 6–19 years with complete data on perchlorate, nitrate, and thiocyanate and pulmonary function were analyzed from the U.S. NHANES 2007–2012. Exposure to perchlorate, nitrate, and thiocyanate was estimated by measuring urinary concentration, and pulmonary function was assessed by spirometry. Linear regression and generalized linear models were performed to estimate the associations of perchlorate, nitrate, and thiocyanate concentrations with pulmonary function measures. Stratified subgroup analyses were performed to examine whether age (6–12 and 13–19 years) and sex differences modified these associations. Results In the overall population, concentrations of perchlorate and nitrate, except for thiocyanate, were associated with a modest decrease in pulmonary function. After stratification by age and sex, those negative associations were more pronounced among the children aged 6–12 years. In children aged 6–12 years, perchlorate concentration was negatively associated with FEV1 (p-trend = 0.04) among boys. In children aged 6–12 years, nitrate concentration was negatively associated with FEV1, FVC, and PEF, respectively (all p-trends < 0.05), in both boys and girls. Conclusions Our study suggested that exposures to perchlorate, nitrate, except for thiocyanate were associated with impaired pulmonary function in children and adolescents aged 6–19 years, and those associations were more pronounced among children aged 6–12 years. Further replications of the associations in a large prospective cohort study are warranted.

We investigated if emotion regulation can be improved through self-regulation training on non-emotional brain regions, as well as how to change the brain networks implicated in this process. During the training period, the participants were instructed to up-regulate their right dorsolateral prefrontal cortex (rDLPFC) activity according to real-time functional near-infrared spectroscopy (fNIRS) neurofeedback signals, and there was no emotional element. The results showed that the training significantly increased emotion regulation, resting-state functional connectivity (rsFC) within the emotion regulation network (ERN) and frontoparietal network (FPN), and rsFC between the ERN and amygdala; however, training did not influence the rsFC between the FPN and the amygdala. However, self-regulation training on rDLPFC significantly improved emotion regulation and generally increased the rsFCs within the networks; the rsFC between the ERN and amygdala was also selectively increased. The present study also described a safe approach that may improve emotion regulation through self-regulation training on non-emotional brain regions.

With the continuous improvement of wind power penetration in the power system, the volatility and unpredictability of wind power generation have increased the burden of system frequency regulation. With its flexible control mode and fast power adjustment speed, energy storage has obvious advantages in participating in power grid frequency regulation. Therefore, this paper studies the control strategy of wind energy storage combined with primary frequency regulation and proposes a control method of wind energy storage combined with primary frequency regulation based on the operation state of wind turbines. This paper analyzes the reserve power and rotor reserve kinetic energy of wind turbines operating in different wind speed ranges, introduces the fan frequency regulation operation state coefficient to quantify the real-time frequency regulation ability of the unit, regards the fan and energy storage system as independent frequency regulation sources, and designs the control strategy of joint frequency regulation of wind and storage in different wind speed ranges. The energy storage system is employed to participate in frequency control in the low-wind-speed range, thereby addressing the “blind spot” issue of wind turbine unit frequency control alone. In the medium- and high-wind-speed ranges, the real-time complementary output of wind and energy storage power is achieved by assigning weights based on the frequency control operation status coefficient. Finally, the effectiveness of the joint frequency modulation control strategy of wind storage in low-, medium-, and high-wind-speed regions is verified in the simulation model.

Pipeline steel is highly susceptible to hydrogen embrittlement (HE) in hydrogen environments, which compromises its structural integrity and operational safety. Existing studies have primarily focused on the degradation trends of mechanical properties in hydrogen environments, but there remains a lack of quantitative failure prediction models. To investigate the failure behavior of X65 pipeline steel under hydrogen environments, this paper utilized notched round bar specimens with three different radii and smooth round bar specimens to examine the effects of pre-charging time, the coupled influence of stress triaxiality and hydrogen concentration, and the coupled influence of strain rate and hydrogen concentration on the HE sensitivity of X65 pipeline steel. Fracture surface morphologies were characterized using scanning electron microscopy (SEM), revealing that hydrogen-enhanced localized plasticity (HELP) dominates failure mechanisms at low hydrogen concentrations, while hydrogen-enhanced decohesion (HEDE) becomes dominant at high hydrogen concentrations. The results demonstrate that increasing stress triaxiality or decreasing strain rate significantly intensifies the HE sensitivity of X65 pipeline steel. Based on the experimental findings, failure prediction models for X65 pipeline steel were developed under the coupled effects of hydrogen concentration and stress triaxiality as well as hydrogen concentration and strain rate, providing theoretical support and mathematical models for the engineering application of X65 pipeline steel in hydrogen environments.

Existing approaches insufficiently analyze the quantitative relationship between frequency stability indices and wind power penetration rates. This limitation impedes rapid and accurate assessments of wind power’s grid integration potential. This paper first analyzed the impact mechanism of wind power integration on system inertia and static power-frequency characteristics from different aspects of the influence of double-fed induction generators (DFIGs) on the operating modes of synchronous machines. Subsequently, based on this analysis, the paper derived the quantitative relationship between key indicators reflecting transient frequency response characteristics during power shortages and wind power penetration rates. It also rapidly calculated the maximum wind power penetration rate based on constraints of frequency change rate and maximum frequency deviation, thereby enabling a quantitative evaluation of wind power grid connection capability. Finally, the IEEE 39-bus test system was used for case analysis. The research results indicate that the proposed method can accurately quantify the impact of wind power variation on system frequency stability and rapidly determine the maximum wind power penetration rate to ensure frequency stability, thereby improving the accuracy of the wind power grid connection capability assessment.

Understanding the spatial pattern of human fishing activity is very important for fisheries resource monitoring and spatial management. The environmental preferences of tropical tuna purse seine fleet in the Western and Central Pacific Ocean (WCPO) were constructed and compared at different spatial scales based on the fishing effort (FE) data from the available automatic identification system (AIS) and commercial fishery data compiled from the Western and Central Pacific Fisheries Commission (WCPFC), using maximum entropy (MaxEnt) methods. The MaxEnt models were fitted with FE and commercial fishery data and remote sensing environmental data. Our results showed that the area under the curve (AUC) value each month based on the commercial fishery data (1°) and FE at 0.25° and 0.5° spatial scales was greater than 0.8. The AUC values each month based on the FE data at a 1° scale ranged from 0.775 to 0.829. The AUC values based on commercial fishing data at the 1° scale were comparable to the model results based on FE data at the 0.5° scale and inferior to the model results based on FE data at the 0.25° scales. Overall, the sea surface temperature (SST), temperature at 100 metres (T100), oxygen concentration at 100 metres (O100) and total primary production (PP) had the greatest influence on the distribution of the purse seine tuna fleet. The oxygen concentration at 200 metres (O200), distance to shore (DSH), dissolved oxygen (Dox), EKE, mixed layer depth (Mld), sea surface salinity (SSS), salinity at 100 metres (S100) and salinity at 200 metres (S200) had moderate influences, and other environmental variables had little influence. The suitable habitat areas varied in response to environmental conditions. The purse seine tuna fleet was mostly present at locations where the SST, T100, O100, O200 and PP were 28–30 °C, 27–29 °C, 150–200 mmol/m3 and 5–10 mg/m−3, respectively. The MaxEnt models enable the integration of AIS data and high-resolution environmental data from satellite remote sensing to describe the spatiotemporal distribution of the tuna purse seine fishery and the influence of environmental variables on the distribution, and can provide forecasts for fishing ground distributions based on future remote sensing environmental data.

Understanding the dynamic spatial distribution and characteristics of fishing activities is crucial for fisheries management and sustainable development. In recent years, small pelagic fish and cephalopods in the Arabian Sea have become new targets for light purse seine fishing; however, there is a lack of publicly available reports. This study uses automatic identification system (AIS) data from January to May and October to December of 2021 to 2022 in the region between 58°–70° E and 10°–22° N to extract spatial distribution information through three methods. The results show that with a spatial resolution of 0.25° × 0.25°, the spatial similarity index between the fishing ground information extracted in 2022 and catch data was consistently above 0.60, reaching 0.76 in March 2021 and 0.79 in November 2022, while the spatial similarity index in March 2022 exceeded 0.71. The spatial distribution of fishing effort and kernel density was similar to that of the fishing grounds, and the fishing intensity information exhibited the highest spatiotemporal similarity with commercial catch data, making it more suitable as a substitute for fishery data. Therefore, effective international cooperation and efficient joint management mechanisms for fishing vessels are needed to enhance the regulatory oversight of fishing vessels in this region. Integrating AIS data with other technological methods is crucial for more effective monitoring and management of fishing vessels. The findings presented in this paper provide both quantitative and qualitative scientific support for resource conservation and sustainable development in the region.