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Photovoltaic (PV) solar energy generation attracts considerable attention to archive carbon neutrality goals worldwide. Geospatial data describing the PV system based on satellite images are critical for PV deployment. However, it remains challenging to obtain relative data in coastal zones due to frequent cloud cover. There have also been relatively few studies on spatial analysis of PV development in terms of solar resources and the energy demands. Therefore, this study took Chinese coastal provinces as example, and proposed a random forest classification method with information on morphology, optics, and SAR to address cloud pollution. The results obtained were analyzed with geographical and socioeconomic factors to determine its development status. The results have shown that (1) by October 2022, the total PV area in Chinese coastal provinces reached 837.3 km2, with an overall accuracy of 96.9 % and a Kappa coefficient of 0.91. Water-based PV represents 33.7 % which tended to be distributed near the shoreline. (2) Bounded by the Huai River, the PV density in the north was 9.0 km2 per 104 km2, which is more than twice that in the south. The development of water-based PV is a key reason for the high PV construction density in coastal areas. (3) PV distribution was slightly mismatched with solar resource and power demand, especially in Liaoning and Guangdong. Liaoning has relatively high potential for PV development. The study findings can play a key role in evaluation of PV power generation, development potential, and ecological environmental impacts.

In this study, the governing equation for one-dimensional large strain consolidation of soft soils under electro-osmosis–surcharge preloading is derived with some assumptions, and the general analytical solution is proposed for the first time by using the methods of algebraic transformation and separation variable. The expressions for the specific analytical solutions under several common cases in foundation treatment are developed. Besides, the correctness of the proposed analytical solutions is verified by comparing them with the existing analytical solutions and finite difference solution. Finally, based on the proposed analytical solutions, the effects of effective voltage Vm, the ratio of electro-osmosis (EO) conductivity to hydraulic conductivity ke/kv and volume compressibility coefficient mv1 on the consolidation behaviors are analyzed. It is found that the increase of Vm increases the absolute values of excess pore water pressure (EPWP) and hence the settlement increases. Furthermore, the increasing ratio of ke/kv leads to larger absolute values of EPWP generated in the soil, resulting in an increase of settlement. The consolidation degree Us defined by settlement based on small strain and large strain models decreases with the increase of mv1, and both the differences of Us and settlement between the two consolidation theories increase with the increase of mv1. In conclusion, the proposed analytical solution is simple in form and practical, which can be adopted to analyze the one-dimensional large strain consolidation behaviors under electro-osmosis–surcharge preloading.

In large smelting plants such as steel plant and coking plant the utilization rate of the byproduct gas is generally low, especially in developing countries. Usually, multiple low-parameter boilers are combined with multiple low-parameter steam turbines, which are built in phases, to drive the smelting accessory machineries. In terms of energy utilization efficiency, this model is definitely not economical. Based on the law of thermodynamics, the direction of efficiency improvement was proposed, and the solution of gas utilization system was put forward, according to the Rankine cycle principle. Finally, with a steel plant as the research object, the proposed scheme was applied in the transformation of this plant, and the economic benefit was estimated. The results show that the proposed scheme in this paper can generate more electricity with the same gas consumption.

Aimed at the limited engineering condition that only gas calorific value but not gas composition can be provided, a new theoretical combustion calculation method based on gas calorific value was proposed, which is suitable for calculating the theoretical dry flue gas volume of metallurgical gas. In the new method, three simplified models were proposed, including blast furnace gas (BFG) model, Linz–Donawitz converter gas (LDG) model and coke oven gas (COG) model. The simplified models were respectively derived from the original model of theoretical combustion calculation, according to the composition characteristics of each metallurgical gas. Finally, the proposed models were verified through large sample of BFG, LDG and COG. The verification results show that the new models for calculating the theoretical dry flue gas volume of metallurgical gas all have high accuracy. The average relative errors of BFG model, LDG model and COG model are only 0.35%, 0.12%, 0.64% respectively. Thus, the new method proposed in this paper is reasonable and feasible. It can meet the accuracy requirement of engineering and can provide a new way for theoretical combustion calculation of metallurgical gas.

In the control of air pollutant emissions, the measured emission concentration should be converted into the standard condition. The converted concentration is obtained in order to determine whether the emissions meet requirements. At present, there are several conversion methods available, and the EAR (excess air ratio) conversion method is one of them. Based on the conversion mechanism of air pollutant concentration, the origin of EAR conversion method was deduced and analyzed in this paper. The results show that the EAR method is a simplified method that is only suitable for the fuels with fuel composition factor close to zero, such as coal, biofuel, solid waste, oil, etc. It is not suitable for the fuels with big fuel composition factor, such as blast furnace gas, converter gas and producer gas, etc.

Chronic and excessive alcohol consumption can lead to the development of alcoholic liver disease (ALD), which is characterized by oxidative damage, inflammation, and impaired intestinal barrier function. To evaluate the effect of exopolysaccharides (EPS) from Sporidiobolus pararoseus on ALD, alcohol was used in this study to cause liver damage in mice. Then, the antioxidant and anti-inflammation activities and protective effect of EPS on the intestinal barrier function were further explored in an ALD model. The results show that the histopathological change and liver injury were significantly improved in the EPS group. EPS treatment significantly increased the levels of superoxide dismutase (SOD), glutathione (GSH), and glutathione peroxidase (GSH-Px) (P

A common limitation of neuroimaging studies is their small sample sizes. To overcome this hurdle, the Enhancing Neuro Imaging Genetics through Meta-Analysis (ENIGMA) Consortium combines neuroimaging data from many institutions worldwide. However, this introduces heterogeneity due to different scanning devices and sequences. ENIGMA projects commonly address this heterogeneity with random-effects meta-analysis or mixed-effects mega-analysis. Here we tested whether the batch adjustment method, ComBat, can further reduce site-related heterogeneity and thus increase statistical power. We conducted random-effects meta-analyses, mixed-effects mega-analyses and ComBat mega-analyses to compare cortical thickness, surface area and subcortical volumes between 2897 individuals with a diagnosis of schizophrenia and 3141 healthy controls from 33 sites. Specifically, we compared the imaging data between individuals with schizophrenia and healthy controls, covarying for age and sex. The use of ComBat substantially increased the statistical significance of the findings as compared to random-effects meta-analyses. The findings were more similar when comparing ComBat with mixed-effects mega-analysis, although ComBat still slightly increased the statistical significance. ComBat also showed increased statistical power when we repeated the analyses with fewer sites. Results were nearly identical when we applied the ComBat harmonization separately for cortical thickness, cortical surface area and subcortical volumes. Therefore, we recommend applying the ComBat function to attenuate potential effects of site in ENIGMA projects and other multi-site structural imaging work. We provide easy-to-use functions in R that work even if imaging data are partially missing in some brain regions, and they can be trained with one data set and then applied to another (a requirement for some analyses such as machine learning).

Bipolar disorder (BD) is a severe manic-depressive illness. Patients with BD have been shown to have gray matter (GM) deficits in prefrontal, frontal, parietal, and temporal regions; however, the relationship between structural effects and clinical profiles has proved elusive when considered on a region by region or voxel by voxel basis. In this study, we applied parallel independent component analysis (pICA) to structural neuroimaging measures and the positive and negative syndrome scale (PANSS) in 110 patients (mean age 34.9 ± 11.65) with bipolar disorder, to examine networks of brain regions that relate to symptom profiles. The pICA revealed two distinct symptom profiles and associated GM concentration alteration circuits. The first PANSS pICA profile mainly involved anxiety, depression and guilty feelings, reflecting mood symptoms. Reduced GM concentration in right temporal regions predicted worse mood symptoms in this profile. The second PANSS pICA profile generally covered blunted affect, emotional withdrawal, passive/apathetic social withdrawal, depression and active social avoidance, exhibiting a withdrawal or apathy dominating component. Lower GM concentration in bilateral parietal and frontal regions showed worse symptom severity in this profile. In summary, a pICA decomposition suggested BD patients showed distinct mood and apathy profiles differing from the original PANSS subscales, relating to distinct brain structural networks.

Gray matter disruptions have been found consistently in Attention-deficit/Hyperactivity Disorder (ADHD). The organization of these alterations into brain structural networks remains largely unexplored. We investigated 508 participants (281 males) with ADHD (N = 210), their unaffected siblings (N = 108), individuals with subthreshold ADHD (N = 49), and unrelated healthy controls (N = 141) with an age range from 7 to 18 years old from 336 families in the Dutch NeuroIMAGE project. Source based morphometry was used to examine structural brain network alterations and their association with symptoms and cognitive performance. Two networks showed significant reductions in individuals with ADHD compared to unrelated healthy controls after False Discovery Rate correction. Component A, mainly located in bilateral Crus I, showed a ADHD/typically developing difference with subthreshold cases being intermediate between ADHD and typically developing controls. The unaffected siblings were similar to controls. After correcting for IQ and medication status, component A showed a negative correlation with inattention symptoms across the entire sample. Component B included a maximum cluster in the bilateral insula, where unaffected siblings, similar to individuals with ADHD, showed significantly reduced loadings compared to controls; but no relationship with individual symptoms or cognitive measures was found for component B. This multivariate approach suggests that areas reflecting genetic liability within ADHD are partly separate from those areas modulating symptom severity.

While gray matter (GM) anomalies have been reported for attention-deficit/hyperactivity disorder (ADHD), investigating their associations with cognitive deficits and individual symptom domains can help pinpoint the neural underpinnings critical for the pathology of ADHD, particularly the persist form of ADHD. In this work, we performed both independent component analysis and voxel-based morphometry analysis on whole brain GM of 486 adults including 214 patients, 96 unaffected siblings, and 176 healthy controls, in relation to cognition and symptoms. Independent component analysis revealed that higher GM volume in inferior semilunar lobule, inferior frontal gyri, and superior and middle frontal gyri was associated with better working memory performance, and lower GM volume in cerebellar tonsil and culmen was associated with more severe inattention symptoms. Consistently, voxel-based morphometry analysis showed that higher GM volume in multiple regions of frontal lobe, cerebellum and temporal lobe was related to better working memory performance. Focusing on the networks derived from ICA, our results integrated prefrontal regions and cerebellar regions through associations with working memory and inattention symptoms, lending support for the theory of ‘cool’-cognition dysfunction being mediated by inferior fronto-striato-cerebellar networks in ADHD. Siblings showed intermediate cognitive impairments between patients and controls but presented GM anomalies in unique focal regions, suggesting they are a separate group potentially affected by the shared genetic and environmental risks with ADHD patients. Keywords: Adult ADHD, Independent component analysis, Prefrontal cortex, Cerebellum, Working memory

Conversion reactions of SnO2-based anode materials have recently been confirmed reversible by using well-designed nanostructures, while the detailed mechanism during charging and discharging is still ambiguous. Unwrapping the real mechanism is helpful to design high-performance SnO2-based anode materials. In this work, we designed and fabricated a nanoarchitecture of ultrafine SnO2 (3–5 nm) anchored on the surfaces of carbon nanofibers (denoted as u-TOCNFs). The u-TOCNFs electrode exhibits high reversible capacity of 1006.4 mAh·g−1, high initial Coulombic efficiency of 71.3% and good cycle stability mainly due to good dispersion of ultrafine SnO2 on the conductive carbon nanofibers. The structure evolution is investigated by TEM observations that confirm the presence of amorphous SnO2 on the delithiated nanoanode at 1.28 V after 100 cycles. Charge/discharge induced amorphization of SnO2 nanocrystals is firstly observed that readily explain the capacity increasing around 100 cycles. In particular, we propose the mechanism of reversible conversion reactions of SnO2 and Sn induced by the amorphization of ultrafine SnO2 during lithiation/delithiation. We believe that it is a new direction to study SnO2-based anode materials to improve the electrochemical performance for lithium ion batteries. Keywords: Ultrafine SnO2, Reversible conversion, Amorphization, Lithium ion battery