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In recent years, transformers have shown great potential in hyperspectral image processing and have also been gradually applied in hyperspectral target detection (HTD). Nonetheless, applying a typical transformer to HTD remains challenging. The heavy computation burden of the multi-head self-attention (MSA) in transformers limits its efficient HTD, while the limited ability to extract local spectral features can reduce the discrimination of the learned spectral features. To further explore the potential of transformers for HTD, for balance of representation ability and computational efficiency, we propose a dual-branch Fourier-mixing transformer network for hyperspectral target detection (DBFTTD). First, this work explores a dual-branch Fourier-mixing transformer network. The transformer-style network replaces the MSA sublayer in the transformer with a Fourier-mixing sublayer, which shows advantages in improving computational efficiency and learning valuable spectral information effectively for HTD. Second, this work proposes learnable filter ensembles in the Fourier domain that are inspired by ensemble learning to improve detection performance. Third, a simple but efficient dropout strategy is proposed for data augmentation. Sufficient and balanced training samples are constructed for training the dual-branch network, and training samples for balanced learning can further improve detection performance. Experiments on four data sets indicate that our proposed detector is superior to the state-of-the-art detectors. [ABSTRACT FROM AUTHOR]

Deep Neural Networks have been successfully applied in hyperspectral image classification. However, most of prior works adopt general deep architectures while ignore the intrinsic structure of the hyperspectral image, such as the physical noise generation. This would make these deep models unable to generate discriminative features and provide impressive classification performance. To leverage such intrinsic information, this work develops a novel deep learning framework with the noise inclined module and denoise framework for hyperspectral image classification. First, the spectral signature of hyperspectral image is modeled with the physical noise model to describe the high intra‐class variance of each class and great overlapping between different classes in the image. Then, a noise inclined module is developed to capture the physical noise within each object and a denoise framework is then followed to remove such noise from the object. Finally, the CNN with noise inclined module and the denoise framework is developed to obtain discriminative features and provides good classification performance of hyperspectral image. Experiments are conducted over two commonly used real‐world datasets and the experimental results show the effectiveness of the proposed method. The implementation of the proposed method and other compared methods could be accessed at https://github.com/shendu‐sw/noise‐physical‐framework. [ABSTRACT FROM AUTHOR]

Rhinacanthin C (RC) is a naphthoquinone ester with an anti-inflammatory activity extracted from Rhinacanthus nasutus (L.) Kurz (Rn). It has been proven to improve hyperglycemia and hyperlipidemia, but the prevention and mechanism of RC in nonalcoholic fatty liver disease (NAFLD) are not clear. In the current study, we first extracted RC from Rn using ethyl acetate and identified it by HPLC, MS, and NMR. At the same time, molecular docking analysis of RC with AMPK and SREBP-1c was performed using AutoDock software. In addition, the mouse model of NAFLD was induced by a high-fat diet in vivo, and low, medium, and high concentrations of RC were used for intervention. The results showed that RC significantly reduced the body mass and liver body coefficient of NAFLD mice, inhibited liver inflammation and fat accumulation, and improved insulin resistance. Further studies showed that RC significantly reduced the levels of serum leptin and resistin, upregulated the expression levels of adiponectin and adiponectin receptor in the liver, and inhibited the expression levels of MCP-1, TNF-α, and IL-6. In terms of mechanism, RC upregulates the expression of p-AMPK and SIRT1 and downregulates the expression of p-p65, SREBP-1c, Fas, Acc-α, PPAR-γ, and SCD1. These studies suggest that RC improves insulin resistance and lipid accumulation in NAFLD by activating the AMPK/SIRT1 and SREBP-1c/Fas/ACC pathways, respectively. [ABSTRACT FROM AUTHOR]

This work aims to investigate the gasification characteristics of oily sludge in CO2 atmosphere. In this study, the gasification characteristics of two typical oily sludge (SSOS and SLOS) in CO2 atmosphere were compared and studied by fixed bed experiment and thermogravimetric analysis system. TG-DTG analysis results showed that the reaction was completed at 600 °C, while the termination temperature of SLOS was 650 °C. The thermal conversion of oil phase (excluding asphaltene) of SSOS and SLOS both maintained between 200 and 550 °C, showing high similarity. In kinetics analysis part, the average Ea of SSOS and SLOS was about 90 kJ mol−1 and 110 kJ mol−1, respectively, which means the gasification reaction difficulty of SSOS was slightly lower than SLOS. The gasification products are mainly composed of H2 and CO. For CO, as the temperature exceeded 800 °C, the output surged, and the H2 output only increased slightly. The effect of gasification agent was much less than that of temperature. When R exceeded 1, the dosage of CO2 has little effect on the output of gas products. [ABSTRACT FROM AUTHOR]

This paper investigates the causes for the interdecadal change in the relationships between early and late summer precipitation over South China (SC). It is found that the correlations of the precipitation over SC between June and August shift from weak positive in 1979–1995 to significantly negative in 1996–2019. Further analysis demonstrates that the distinct evolution of sea surface temperature (SST) pattern between the two periods accounts for the interdecadal variations of their relationships. Although the warming of the tropical western Indian Ocean (WIO) in June favors increasing precipitation over the SC via enhancing the Northwest Pacific Subtropical High (NWPSH) during the whole period, the associated SST anomalies in August are rather different between the two periods. Specifically, the WIO warming in June corresponds to slightly positive anomalies over the tropical central-eastern Pacific in August during the 1979–1995, which has weak impact on the NWPSH and results in a weak precipitation correlation between June and August. However, the WIO warming in June corresponds to La Niña's rapid development in August during the 1996–2019, which favors the enhancement of the NWPSH via increasing the regional Hadley circulation. Due to the climatologically northward movement of NWPSH from June to August, the enhanced NWPSH in August acts to decrease the precipitation over SC, causing a significantly negative correlation between precipitation in June and August. Overall, the distinct evolution of tropical SST pattern is the key factor inducing the change of the relationships between June and August precipitation in the two periods. [ABSTRACT FROM AUTHOR]

Based on the complex network method, this study reveals the temporal and spatial characteristics of the synchronization of extreme rainfall events in southwest China (SWC) during the main rainfall season (May–September). Our results show the significant synchronization pattern of extreme rainfall events in SWC, which is closely associated with the convergence of warm‐wet air and cold‐dry air and the corresponding occurrence of extreme rainfall in SWC. The network divergence indicates a dominated direction of the net influence of extreme rainfall from the Sichuan Basin to the Yunnan–Guizhou Plateau, implying the extreme rainfall synchronization moving from north to south in SWC is more frequent than the opposite direction. The development of the low vortex and the north wind system usually shows a movement from north to south, resulting in the evolution of extreme rainfall events more tend to present a southward movement. The low‐reinforced latitude cyclonic circulation leads to the strengthened transportation of water vapour from the tropical area to the north and converges with the anticyclonic circulation in the mid‐latitudes to form the movement of heavy rainfall events from the Yunnan–Guizhou Plateau to the Sichuan Basin. Therefore, the synchronization direction of extreme rainfall events in SWC has obvious asymmetry spatial feature due to the more frequent and stronger circulation system moving from north to south than the opposite. [ABSTRACT FROM AUTHOR]

This article proposed an original comprehensive thermal treatment coupled with gasification and combustion (CGC) of oil sludge (OS), which was designed to produce hydrogen-rich syngas. Based on the experimental results of OS gasification with steam, the combustion characteristics of char from OS gasification were analyzed by thermogravimetric experiments under different heating rates of 10, 20 and 30 °C/min. The combustion process of OS gasification char can be divided into three stages, including water evaporation, volatile combustion and heavy component combustion. The average values of activation energy (E) obtained by Friedman, FWO and Starink methods were 89.98 kJ/mol, 147.61 kJ/mol and 143.09 kJ/mol, respectively. According to OS gasification and OS gasification char combustion experiments, the comprehensive thermal treatment process CGC of OS was simulated by Aspen Plus. The simulation results showed that increasing both gasification temperature and the mass ratio of steam to OS (SOS) could promote the hydrogen production. Considering energy consumption, the recommended OS gasification temperature, SOS and char combustion temperature were 800 ∼ 900 °C, 0.3 ∼ 0.5, and 900 ∼ 1000 °C, respectively, which could ensure full burning of char and reduce the generation of pollutants. The CGC process could reduce CO2 emissions by 44.2% from carbon flow analysis. [ABSTRACT FROM AUTHOR]

Paper packaging materials are widely used as sustainable green materials in food packaging. The production or processing of paper materials is conducted in an environment that contains organic chlorides; therefore, potential food safety issues exist. In this study, the adsorption behavior of organic chlorides on paper materials was investigated. Chloropropanol, which has been extensively studied in the field of food safety, was employed as the research object. We studied the adsorption mechanism of chloropropanol on a crystalline nanocellulose (CNC) model. The results demonstrated that physical adsorption was the prevailing process, and the intermolecular hydrogen bonds acted as the driving force for adsorption. The adsorption effect assumed greatest significance under neutral and weakly alkaline conditions. A good linear relationship between the amount of chloropropanol adsorbed and the amount of CNC used was discovered. Thus, the findings of this study are crucial in monitoring the safety of products in systems containing chloropropanol and other chlorinated organic substances. This is particularly critical in the production of food-grade paper packaging materials. [ABSTRACT FROM AUTHOR]

Oil sludge is a typical hazardous waste in the petrochemical and electric power industry. It has complex components and special properties, and has serious hazards to humans, plants, water, and soil. Therefore, how to realize the effective disposal of oil sludge has become an urgent issue to be solved worldwide. Among the existing oil sludge treatment approaches, combustion has been considered to be a promising technology to realize the large-scale industrial application. In the present work, the characteristics of oil sludge were described in detail. The application and development of oil sludge combustion technology were critically summarized and discussed, including factors affecting combustion, drying process, combustion characteristics, synergistic treatment technology, and formation and control of secondary pollution. Besides, the development of combustion equipment, and integrated thermal treatment technology for oil sludge were prospected. This work can be used for guiding the industrial disposal of oil sludge. [ABSTRACT FROM AUTHOR]

In this paper, pyrolysis treatment was carried out with the tank bottom sludge of Liaohe Oilfield (LSOS) as the treatment object. Pyrolysis oil has the highest output of only 6% and the main components are gasoline and light diesel. Syngas which was the main form of energy recovery was mainly composed of CO/CO2, CH4, C2H4, etc. Temperature is the most important factor affecting energy recovery, and high-temperature environments have a positive impact on the cracking of macromolecular hydrocarbons. Increasing the temperature had a significant effect on improving the efficiency of energy recovery, which is beneficial to the recovery of energy in the form of gaseous and liquid oil. The content of organic matter in char greatly reduced, which was reflected in the significant reduction in C/H content. Part of the S/N compounds migrated and transformed, mixed into gas and liquid oil while heavy metals were enriched in char. The thermogravimetric method was carried out for the study on the pyrolysis behavior of LSOS. Starink method, KAS method and FWO method were used to calculate the kinetic parameters. The activation energy range and the average activation energy calculated by the three methods all showed high similarity, which were 61.75–237.33 kJ·mol−1, 52.58–205.63 kJ·mol−1and 59.35–207.54 kJ·mol−1 and the average values were 122.19 kJ·mol−1, 106.64 kJ·mol−1 and 112.13 kJ·mol−1, respectively. [ABSTRACT FROM AUTHOR]

Based on remote sensing data of vegetation coverage, observation data of basic meteorological elements, and support vector machine (SVM) method, this study develops an analysis model of meteorological elements influence on vegetation coverage (MEVC). The variations for the vegetation coverage changes are identified utilizing five meteorological elements (temperature, precipitation, relative humidity, sunshine hour, and ground temperature) in the SVM model. The performance of the SVM model is also evaluated on simulating vegetation coverage anomaly change by comparing with statistical model multiple linear regression (MLR) and partial least squares (PLS)-based models. The symbol agreement rates (SAR) of simulations produced by MLR, PLS, and SVM models are 55%, 57%, and 66%, respectively. The SVM model shows obviously better performance than PLS and MLR models in simulating meteorological elements-related interannual variation of vegetation coverage in North China. Therefore, the introduction of the intelligent analysis method in term of SVM in model development has certain advantages in studying the internal impact of meteorological elements on regional vegetation coverage. It can also be further applied to predict the future vegetation anomaly change. [ABSTRACT FROM AUTHOR]

Temperature reconstruction is vital for ensuring system reliability in electronic component design. However, current approaches struggle to effectively explore system information and physical relationships, thereby limiting their performance. This paper presents deep learning surrogate models for precise temperature field reconstruction, showcasing their effective discernment of system distribution laws. However, the scarcity of high-quality training data poses a significant challenge, often leading to issues like overfitting and compromised precision. To address this problem, the paper proposes an adaptive multi-source information fusion method (MFIF) for integrating physical information from various data sources in the frequency domain. By leveraging frequency domain analysis, a deeper understanding of underlying physical phenomena is achieved, facilitating effective integration of information. Furthermore, by utilizing deep surrogate models and high-quality training samples, the developed multi-source frequency fusion method enables the creation of a multi-source fusion driven deep learning method for temperature field reconstruction. The proposed method enhances the robustness, accuracy, and effectiveness of aircraft temperature field reconstruction in orbit. Experimental results demonstrate a substantial decrease in both noise and errors, while the Signal-to-Noise Ratio can be improved by up to more than 86%. [ABSTRACT FROM AUTHOR]

Oil productions can result in pore pressure drop in the reservoir, generating an increase in effective stress and leading to reservoir compaction. The compaction in the subsurface reservoir translates to the earth's surface, which is manifested as a loss of elevation (land subsidence), causing damages to oil production facilities and surface infrastructures. The Liaohe oilfield, located in Liaohe River Delta (LRD), northeast of China, is one of the most significant subsidence areas in China as a direct consequence of oil extraction from the reservoir. Previous studies carried out in this area assumed the oil production-induced displacement retrieved from Interferometric Synthetic Aperture Radar (InSAR) corresponds only to vertical deformation. In this work, for the first time, we proposed a method to retrieve the full three-dimensional (3D) displacement field over the oilfield. We retrieved the vertical and east-west displacement components by combining the multiple-geometry InSAR line-of-sight (LOS) observations and retrieved the north-south component based on the assumption of a physical relationship between the horizontal and vertical displacement. Two ascending and two descending datasets from Sentinel-1 satellite covering the area were processed by an InSAR time series analysis over the 2017 to 2021 period, providing consistent displacement rate maps and displacement time series in the LOS direction. Spatial local-scale land subsidence was found in several producing fields over the deltaic region, including Shuguang, Huanxiling, and Jinzhou oilfields. The 3D displacement decomposition was then conducted in Shuguang oilfield. The derived 3D displacement field exhibit a circular subsidence bowl with a maximum subsiding rate reaching 212 mm/year, accompanied by a centripetal pattern of horizontal displacements with maximum rates up to 50–60 mm/year moving towards the subsidence center. The retrieved 3D displacements are in good agreement with predictions from the geomechanical modeling by assuming a disk-shaped reservoir subject to a uniform reduction in pore fluid pressure. Finally, we show the importance of knowing both the vertical and horizontal displacement in characterizing the lateral boundary of the subsurface reservoir. [ABSTRACT FROM AUTHOR]

Southwest China (SWC) is located in the transition zone affected by the East Asia summer monsoons, India monsoon, and the Tibetan Plateau thermal activity. SWC is also an important hub for water vapor transport in the Asia–Pacific (AP) region. Here we study the dynamical and spatiotemporal characteristics of extreme rainfall events in AP by a synchronization network approach, providing insights for understanding of the relevant synchronization mechanisms of extreme rainfall between the SWC and AP. It is found that the probability of significant synchronization rainfall events between SWC and AP as a function of distance changes from a power-law decay behavior to a teleconnection one at around 2000 km. Specifically, the SWC—the middle and lower reaches of the Yangtze River—Japan band (SYJ) is an important teleconnection pattern of extreme rainfall in AP region. This SYJ teleconnection pattern is associated with the water vapor flux convergence along the main water vapor transport route from the low latitude ocean. Moreover, we find that the inter-annual variation of the SYJ teleconnections index can reflect the evolution of synchronization rainfall features from SWC to Japan, which could be useful for operational climate monitoring and prediction. It is also found that the SYJ index is strongly correlated with the sea surface temperature anomalies (SSTA) over the North Indian Ocean and the Western Pacific. The warm Indian Ocean and Western Pacific SST can motivate the transfer of water vapor from SWC to Japan, yielding the synchronization of extreme rainfall along the SYJ band. [ABSTRACT FROM AUTHOR]

Recently, more attention has been focused on the surrogate model using deep learning methods due to its powerful representational ability. However, it is usually challenging to obtain sufficient labeled samples for effective training because of the time-consuming numerical calculation, especially for the temperature field prediction of heat source layout (HSL-TFP). This work develops a novel and effective training method to overcome this problem for the deep surrogate model in HSL-TFP. First, the prediction of temperature field is modeled as an image-to-image regression problem, and the feature pyramid network (FPN) is chosen as the backbone of the deep network. Then, considering the inter-sample difference and the limited number of training samples, pairwise temperature field difference is utilized as data augmentation to train the surrogate model. Finally, deep transfer learning is introduced to take advantage of the valuable information from similar tasks and accelerate learning among different HSL-TFP problems. Experiments employing physic simulation data are conducted to validate the effectiveness of the proposed method. The results demonstrate that the proposed methods have significantly improved the prediction precision of the deep surrogate model with a small sample. [ABSTRACT FROM AUTHOR]

Combustion of High-sulfur oil sludge (OS) blended with CaO can significantly reduce the emission of sulfur gas pollutants, but its combustion and kinetic characteristics need to be further studied. TGA experiments showed the combustion characteristics of OS were significantly changed after adding CaO. As reflected by comprehensive combustion index (S), the combustion performance of OS decreased after adding CaO, and significantly improved with the increase of heating rate. The kinetic parameters of the main combustion process of OS with CaO were calculated by the iso-conversion methods of Friedman, FWO, and Starink, respectively. Kinetic analysis results indicated the energy required for OS combustion with CaO first increased and then decreased with deepening of reaction degree. The thermodynamic parameters of ΔH, ΔG and ΔS were determined on the basis of kinetics. The negative ΔH, positive ΔG, and negative ΔS validated the combustion of OS with CaO was an exothermic and nonspontaneous process. [ABSTRACT FROM AUTHOR]

Background: The relationship between the NHHR and kidney stone risk remains unknown. The purpose of this study was to evaluate the association between adult NHHR and kidney stone occurrence in USA. Methods: This study used a variety of statistical techniques such as threshold effects, subgroup analysis, smooth curve fitting, multivariate logistic regression, and data from the National Health and Nutrition Examination Survey (NHANES) from 2007 to 2014. We aimed to clarify the relationship between the NHHR and kidney stone risk. Results: The average age of the 21,058 individuals in this research was 49.70 ± 17.64 years. The mean NHHR was 3.00 ± 1.47, and the overall prevalence of kidney stone occurrence was 9.05%. The prevalence within the quartile ranges (Q1–Q4) was 7.01%, 8.71%, 9.98%, and 10.49%, respectively. The overall average recurrence rate of kidney stones was 3.05%, demonstrating a significant increase with increasing NHHR (Q1: 1.92%, Q2: 2.92%, Q3: 3.35%, Q4: 4.00%, P < 0.01). The occurrence of kidney stones increased by 4% (95% CI: 1.00-1.08, P = 0.0373) and the chance of recurrence increased by 9% (95% CI: 1.03–1.14, P < 0.01) with each unit increase in NHHR. The interaction analysis results demonstrated that the relationship between the NHHR and the risk of kidney stones was not significantly impacted by the following factors: sex, body mass index, poverty income ratio, diabetes, or hypertension. Curve fitting and threshold effect analysis also demonstrated a non-linear association, with a breakpoint found at 3.17, between the NHHR and the risk of kidney stones. Conclusions: In adults in the USA, there is a substantial correlation between elevated NHHR levels and a higher probability of kidney stones developing and recurring. Timely intervention and management of NHHR may effectively mitigate the occurrence and recurrence of kidney stones. [ABSTRACT FROM AUTHOR]

Oil sludge (OS) is a typical dangerous waste in petroleum industry, which also contains abundant energy. The purpose of this study was to recover resources from OS gasification. To figure out the gasification process, a thermal analyzer was used to carry out OS gasification experiments under steam/nitrogen atmosphere with different heating rates. Results showed that the OS gasification reaction could be divided into five stages: free water volatilization, the escape of light components, the cracking of heavy components and steam reforming, the charring process and water gas shift (WGS) reactions, and inorganic mineral decomposition. The activation energy (E), pre‐exponential factor (A), enthalpy change (ΔH), Gibbs free energy change (ΔG), and entropy change (ΔS) of three main reaction stages (Stages 2–4) were analyzed through kinetic and thermodynamic analysis. From Stage 2 to Stage 4, E, ΔH, and ΔS decreased from 118.80 kJ·mol−1, 113.92 kJ·mol−1, and −4.09 J·(K mol)−1 to 13.19 kJ·mol−1, 5.92 kJ·mol−1, and −234.06 J·(K mol)−1, respectively. Nevertheless, ΔG increased from 116.01 to 210.04 kJ·mol−1. From these parameters, it could be found that the decrease of E was beneficial to the reaction. However, the values of ΔG were always positive and the reaction could not happen spontaneously. [ABSTRACT FROM AUTHOR]

• The heavy metals of OS, OSA and OSA-CaO were analyzed. • The residual of Cr, Cu and Zn of bottom ash can be restrained by the increase of temperature. • The promotion effect of CaO on metal residue was in the order of Cu, Cr, Zn. • Cao can promote the transformation of Cu and Zn to more stable speciation. The volatilization of heavy metals in oil sludge (OS) restricts the development of OS incineration. In order to explore the effect of CaO on the migration characteristics of heavy metals during OS incineration, incineration experiments of OS with and without CaO were carried out respectively in a horizontal tube furnace at the temperature ranging from 800 °C to 1000 °C. The total amount, speciation fractions, and leachability of Cr, Cu and Zn in OS and bottom ash were investigated and presented in this work. The results showed that the total amount of Cr, Cu, and Zn in the bottom ash decreased with the increasing of experimental temperature. CaO could solidify the heavy metals in the bottom ash with an order of Cu, Cr, and Zn from strong to weak. In the presence of CaO additive, the exchangeable and acid-soluble fractions of Cu and Zn in bottom ash were significantly reduced. Moreover, the leaching levels of Cr, Cu and Zn in bottom ash were also obviously lowered. The results will supply essential data and theoretical support for the innocuous treatment of heavy metals in the bottom ash of OS incineration. [ABSTRACT FROM AUTHOR]

Recycling of waste biomass energy has attracted more and more attention. Oil sludge char (OS char) combustion blended with microalgae residue (MR) is a clean, effective method for recycling energy. The co-combustion and kinetics analysis were conducted on a thermogravimetric analyzer (TGA). The simulation calculation and potential toxicity of heavy metals in the co-combustion process were investigated. Results showed that adding MR could improve the combustion performance of OS char. The average activation energy of OS char, MR and the mixture was 121.28 kJ mol−1, 153.43 kJ mol−1, and 142.24 kJ mol−1, respectively. The KAS and Starink methods were more accurate than the FWO method. Besides, the risk assessment results indicated the risk index (RI) values of MR and fly ash at 1100 °C were at a considerable risk level of 140.33 and 109.50, respectively. Simulation results demonstrated that the synergistic removal of arsenic (As) and nitrogen (N) could be achieved by moderately controlling temperature. Furthermore, MR addition could significantly reduce the content of chlorine (Cl) in the mixture, decreasing the generation of gaseous ZnCl 2 and PbCl 2. The results will supply essential data and theoretical support for the high-value utilization and low heavy metal emissions of biomass waste. • OS char combustion mixed with MR is a promising method for energy recovery. • Simulation calculation of heavy metals migration was studied by FactSage. • MR addition improve the combustion characteristics and stability of OS char. • A moderate MR addition could significantly reduce the heavy metals ecotoxicity. • The synergistic removal of As and N elements can be achieved above 1000 °C. [ABSTRACT FROM AUTHOR]

Pyrolysis characteristics of chemical extraction microalgae residue in the presence and absence of catalysts (KCl, KOH, K 2 CO 3 , Al 2 O 3 , CaO, MgO and pyrolysis char of microalgae residue) were evaluated using a thermal analyzer and a tube furnace reactor. These catalysts can effectively improve the oil yield during microalgae residue pyrolysis. The order of the strength on increasing the oil yield was: pyrolysis char of microalgae residue > Al 2 O 3 >CaO > K 2 CO 3 >MgO > KCl > KOH. The addition of all the catalysts can effectively reduce the potential ecological risk of heavy metals in pyrolysis chars. The addition of KCl, MgO and Al 2 O 3 can increase the content of gasoline components in pyrolysis oil. All the catalysts can reduce the content of CO 2 in the pyrolysis gas, and increase the content of CO and hydrocarbon gases. KCl addition showed considerable increase in both yield and quality of pyrolysis oil and gas with a proportion of 5 wt % during microalgae residue pyrolysis. Image 104 • Catalytic pyrolysis performance of microalgae extraction residue was studied. • Catalysts reduced the potential ecological risk of the heavy metals in the char. • The order of increasing oil yield was microalgae residue pyrolysis char>Al 2 O 3 >CaO>K 2 CO 3 >MgO>KCl>KOH. • KCl showed a best catalytic performance with a proportion of 5 wt %. [ABSTRACT FROM AUTHOR]

Pyrolysis characteristics of microalgae (MA) and its chemical extraction residue (MR) were evaluated and compared using a thermal analyzer and a tube furnace reactor. Results showed that the pyrolysis process of MA and MR can be divided into three stages, which corresponded to the volatile of free water, the decomposition of organic compounds and the stabilization of residues, respectively. Due to the removal of lipids after MA extraction, only one weight loss peak was recorded in the second stage of MR. Using the methods of Friedman, FWO and Starink, the average activation energies of MA and MR were calculated as 204.72 and 178.51 kJ/mol, respectively. Pyrolysis oil, gas, and char products were obtained from both MA and MR pyrolysis. Main gas products from pyrolysis of MA and MR contained CO 2 , CO, H 2 and CHs. Compared with MA pyrolysis, the relative contents of CHs were lower (<59%) during MR pyrolysis, but the contents of CH 4 were higher. Results for higher hydrocarbons (C 4-6 , C 6+) were lower in MR than in MA pyrolysis. MR pyrolysis can be a promising method for the waste treatment with high value-added pyrolysis liquid and gases products. Image 1 • Pyrolysis kinetics of MA and MR were investigated and compared. • Distribution of pyrolysis products was comprehensively studied. • Average activation energies of MA and MR were 63.29 kJ/mol and 50.28 kJ/mol. • Higher hydrocarbons (C 4-6 , C 6+) were lower in MR than in MA pyrolysis. [ABSTRACT FROM AUTHOR]

Schematic of PC and NPC fabrication in this work. • Nitrogen-doped porous carbon from oil sludge was prepared and applied to CO 2 adsorption. • Nitrogen-doped porous carbon synthesized from oil sludge via a facile and low-cost approach. • The CO 2 adsorption capacity of nitrogen-doped porous carbon prepared at 700 °C was as high as 3 mmol·g−1. • Nitrogen-doped porous carbon possessed good reproducibility and thermal stability. As an adsorbent material, porous carbon (PC) with high specific surface area, renewability and excellent thermal stability has been certified to be a potential candidate for carbon dioxide (CO 2) adsorption. However, its high manufacturing cost and insufficient CO 2 adsorption capacity are still significant problems to be solved urgently. The present work reported a nitrogen-doped porous carbon (NPC) synthesized from hazardous waste oil sludge (OS) via a facile and low-cost approach. The NPC possessed favorable features for excellent adsorbent, such as outstanding specific surface area (1224 m2·g−1), large pore volume (1.738 cm3·g−1) as well as tunable and large range of pore size distribution, among which the specific surface area and pore volume of porous carbon (PC) were just 632 m2·g−1 and 0.826 cm3·g−1, respectively. The carbon material NPC prepared by mixing oil sludge pyrolysis char with KOH and urea at 700 °C achieved the formation of nitrogen-containing functional groups, with CO 2 adsorption capacity as high as 3 mmol·g−1. Furthermore, NPC possessed excellent reproducibility and thermal stability. It is demonstrated that the NPC synthesized from OS via a facile and low-cost approach was a promising adsorbent material for CO 2 capture. This work provides a new strategy for synergetic processing of hazardous industrial waste and greenhouse gas. [ABSTRACT FROM AUTHOR]

Experimental study on combustion and emission characteristics of microalgae (M) and the extraction residue (MR) was carried out on a coupled thermogravimetric and mass spectrometer system (TG-MS). TG results showed that combustion process of M and MR can be divided into three stages, including water volatilization, the release and combustion of proteins, carbohydrates and lipids, and minerals decomposition. As the heating rate increased, thermal hysteresis was occurred during combustion of M and MR, especially for M. Friedman method, FWO method and Starink method were used to calculate the apparent activation energy during combustion of M and MR. As the conversion rates increased, the apparent activation energy of M and MR showed different trends. The average apparent activation energy of M and MR in the main combustion stage was 183.41 kJ mol−1 and 144.41 kJ mol−1, respectively. MS results demonstrated that the release of nitrogen-containing compounds of M was more than MR while the release of sulfur-containing compounds was similar during M and MR combustion. • Combustion of microalgae and the residue was carried out on a TG-MS system. • The average apparent activation energy of M and MR was 183.41 and 144.41 kJ mol−1, respectively. • Nitrogen-containing compounds released during the combustion in M were more than MR. • The release of sulfur-containing compounds were similar during combustion of M and MR. [ABSTRACT FROM AUTHOR]

Temperature field prediction is of great importance in the thermal design of systems engineering, and building a surrogate model is an effective method for the task. Ensuring a high prediction performance for the surrogate models, especially deep learning models with high representational power and numerous parameters, typically requires a significant amount of labeled data. However, obtaining labeled data, particularly high-fidelity data can be prohibitively expensive. To solve this problem, this paper proposes a novel deep multi-fidelity modeling method for temperature field prediction, which takes advantage of low-fidelity data to boost performance with less high-fidelity data. First, a pithy pre-train and fine-tune paradigm is proposed for constructing the deep multi-fidelity model, which is straightforward and efficient, allowing for the effective utilization of information from various fidelity levels. Then, a physics-driven self-supervised learning method is proposed to learn the deep multi-fidelity model, which fully utilizes the physics characteristics of the heat transfer system and further reduces the dependence on large amounts of labeled low-fidelity data in the training process. Two diverse temperature field prediction problems are presented to validate the effectiveness of the proposed method. The results show that our approach can significantly improve the model's accuracy, reducing the required high-fidelity data for model construction. [ABSTRACT FROM AUTHOR]

Recently, researchers have shown the powerful ability of deep methods with multilayers to extract high-level features and to obtain better performance for hyperspectral image classification. However, a common problem of traditional deep models is that the learned deep models might be suboptimal because of the limited number of training samples, especially for the image with large intraclass variance and low interclass variance. In this paper, novel convolutional neural networks (CNNs) with multiscale convolution (MS-CNNs) are proposed to address this problem by extracting deep multiscale features from the hyperspectral image. Moreover, deep metrics usually accompany with MS-CNNs to improve the representational ability for the hyperspectral image. However, the usual metric learning would make the metric parameters in the learned model tend to behave similarly. This similarity leads to obvious model’s redundancy and, thus, shows negative effects on the description ability of the deep metrics. Traditionally, determinantal point process (DPP) priors, which encourage the learned factors to repulse from one another, can be imposed over these factors to diversify them. Taking advantage of both the MS-CNNs and DPP-based diversity-promoting deep metrics, this paper develops a CNN with multiscale convolution and diversified metric to obtain discriminative features for hyperspectral image classification. Experiments are conducted over four real-world hyperspectral image data sets to show the effectiveness and applicability of the proposed method. Experimental results show that our method is better than original deep models and can produce comparable or even better classification performance in different hyperspectral image data sets with respect to spectral and spectral–spatial features. [ABSTRACT FROM AUTHOR]

• Integrated thermal treatment of OS was designed and simulated using Aspen Plus. • Thermal and exergy efficiency of the process were analyzed and discussed. • The thermal and exergy efficiency of the system was 61–68% and 56–71%, respectively. • System's efficiency reached the maximum when pyrolysis temperature was 650 °C. An integrated thermal treatment (coupled pyrolysis and combustion) of oil sludge (OS) was proposed for enhancing the OS resource utilization. According to the pyrolysis experiment results, the integrated thermal treatment process of OS was modeled and simulated using Aspen Plus. Carbon flow, heat distribution, energy flow, energy efficiency and exergy efficiency of the process were analyzed and discussed. The results indicated that the simulation values were consistent with the experimental ones within the pyrolysis temperature of 00–780 °C With the increase of pyrolysis temperature, the carbon flow of pyrolysis oil was firstly increased and then sharply decreased, and the maximum value was achieved at 600–650 °C. Carbon flow of pyrolysis gas was initially increased rapidly followed by a continuously decreasing growth rate, and then increased sharply above 650 °C. The maximum heating value of pyrolysis oil was obtained at 600 °C. When pyrolysis temperature exceeded 650 °C, both the pyrolysis gas production rate and the gas heating value were improved. The energy and the exergy efficiency of the system can reach values as high as 61–68% and 56–71%, respectively, and the maximum efficiencies were achieved at the pyrolysis temperature of 650 °C. The present work could provide valuable insights towards promoting the development of effective OS utilization processes in the near future. [ABSTRACT FROM AUTHOR]

Highlights • Co-combustion of OS char and microalgae residue exhibited a good performance. • High temperature promoted the conversion of N-5 and N-6 to N-X. • NO x emissions reached the minimum value 96 mg/m3 with a MR ratio of 50% • When temperature reached 1100 °C, more S4+ was converted to S6+ and S2− Abstract Integrated thermal treatment (coupled pyrolysis and combustion) has great potential to be a clean and effective method for massive oil sludge (OS) utilization. Produced OS pyrolysis char (OS char) can be effectively utilized through fluidized bed incinerator with auxiliary fuels. A study on co-combustion of OS char and microalgae extraction residue (MR) were conducted with a coupled thermogravimetric-mass spectroscopy system (TG-MS), and a fluidized bed reactor system. Co-combustion process of OS char and MR could be divided into three stages, including water evaporation, volatiles release and combustion, fixed carbon combustion and minerals decomposition. SO 2 emissions decreased with the increase of MR ratio and NO x emissions initially decreased followed by an increase until the MR ratio exceeded 50%. Both NO x and SO 2 emissions initially increased before a decrease with combustion temperature raised. Higher excessive air ratio (α) promoted the pollutants emission. Pyrrole (N-5) content increased with the increase of MR ratio and more N-5 and pyridine (N-6) could be converted to oxidized nitrogen (N-X) in the bottom ash under a higher temperature during OS combustion. Besides, the temperature range (900–1000 °C) was conductive to SO 2 release, and S4+ formation. [ABSTRACT FROM AUTHOR]

The anomalous characteristics of summer precipitation and atmospheric circulation in the East Asia-West Pacific Region (EA-WP) associated with the co-action of East Asia/Pacific teleconnection-Silk Road teleconnection (EAP-SR) are investigated in this study. The compositions of EAP-SR phase anomalies can be expressed as pattern I (+ +), pattern II (+ −), pattern III (− −), and pattern IV (− +) using EAP and SR indices. It is found that the spatial distribution of summer precipitation anomalies in EA-WP corresponding to pattern I (III) shows a tripole structure in the meridional direction and a zonal dipole structure in the subtropical region, while pattern II (IV) presents a tripole pattern in meridional direction with compressed and continuous anomalies in the zonal direction over the subtropical region. The similar meridional and zonal structures are also found in the geopotential height anomalies at 500-hPa, as well as wind anomalies and moisture convergence at 850-hPa. Finally, a schematic mechanism for the EAP-SR co-action upon the summer precipitation in EA-WP is built: (1) Pattern I (III) exhibits that the negative (positive) sea surface temperature (SST) anomalies over tropical East Pacific may cause the enhanced (weakened) convective activity dominating the West Pacific, trigger the positive (negative) EAP teleconnection and produce more (less) precipitation. Besides, the negative (positive) SST anomalies over the Indonesia Maritime Continent (IMC) may further weaken (strengthen) anomalous downward (upward) motion over the South China Sea (SCS), cause negative (positive) geopotential height anomalies at the middle troposphere and surrounding regions through the function of the tropical Hadley circulation. Then the negative (positive) geopotential height anomalies could motivate the positive (negative) EAP teleconnection through the northward propagation of wave-activity perturbation. Meanwhile, a positive (negative) geopotential height anomalous pattern over Eastern Europe motivates a Rossby wave train propagation from Western Europe to west-central Asia. This circumstance can cause suppressed (enhanced) convection and less (more) precipitation over northwestern India and Pakistan, which could strengthen the negative (positive) geopotential height and positive (negative) vorticity anomalies over central East Asia, resulting in a negative (positive) SR teleconnection along the Asian jet stream. A positive (negative) lobe over the Korean Peninsula and Japan corresponding to SR overlaps with a positive (negative) lobe of EAP, which strengthens the anomalous phase contrast on both sides of 120°E. Accordingly, summer precipitation anomalies in EA-WP exhibit the meridional tripole pattern and the zonal dipole pattern. (2) Pattern II (IV) indicates that the normal SST anomalies over the tropical East Pacific cause the weak tele-impact on the tropical West Pacific, while the positive (negative) SST anomalies over the IMC will lead to a negative (positive) lobe of EAP over the subtropical region. This circumstance can weaken the positive (negative) lobe of SR over subtropical region, causing compressed and continuous negative (positive) anomalies of 500-hPa geopotential height and positive (negative) surface precipitation anomalies from central East China to Japan. [ABSTRACT FROM AUTHOR]

Graphical abstract Highlights • Proposed an effective way of integrated thermal treatment of OS. • Both OS pyrolysis and OS pyrolysis char combustion were studied in this work. • OS char from lower pyrolysis temperature had better combustion characteristics. • OS char was simpler in composition and more homogeneous in chemical property. Abstract Integrated thermal treatment of OS is a promising method for massive and effective treatment of the waste. The present work investigated the tank OS pyrolysis by a thermogravimetric analyzer and a tube furnace reactor. Pyrolysis char combustion performance was studied with the thermogravimetric analyzer. Results showed that OS pyrolysis mainly occurred between 200 °C and 700 °C. With the increase of pyrolysis temperature, char yield decreased while gas yield increased. When pyrolysis temperature exceeded 600 °C, the oil yield decreased. Simulated distillation showed that oil products properties barely changed when pyrolysis temperature exceeded 600 °C. Light fractions (gasoline and diesel) from OS pyrolysis were more than 28% higher than those of oil from OS extraction. Major components of pyrolysis gas include H 2 , CO, CO 2 , and hydrocarbons (CH S ). CH S content was more than 40% in the pyrolysis gas in all cases. The molar H/C ratio and O/C ratio were lower for OS chars from higher temperature pyrolysis. The surface of OS char was rough with a well-developed pore structure formed during pyrolysis, which was conducive to the combustion reaction. The combustion characteristic of OS char from higher temperature pyrolysis was poorer than that of OS char from lower pyrolysis temperature, resulting from the less volatile content and more carbon crystal degree for OS char from higher temperature pyrolysis. With well-developed pore structure, relative simple composition, good ignition and burnout characteristics, OS char could perform well in actual combustion in CFB boilers. [ABSTRACT FROM AUTHOR]

• Proposing a method to smooth model attention and enhance attack transferability. • The smoothed attention map is separated by a scalable mask. • The method relies on reducing foreground attention and boosting that of background. • Both digital and physical attack verify the effectiveness of our approach. The research on attack transferability is of great importance as it can guide how to conduct an adversarial attack without knowing any information about target models. However, it remains challenging for adversarial examples to maintain a good attack transferability performance, especially for the black-box attack implemented in the physical world. To enhance black-box transferability of physical attacks on object detectors, we present a novel adversarial learning method to produce adversarial patches by redistributing separable attention maps. Concretely, we first develop smoothed multilayer attention maps by introducing serial composite transformations, which could suppress model-specific noise on the one hand, and cover objects to be concealed at various resolutions on the other hand. Besides, our method resorts to a scalable mask to separate object attention from the background and adjust their distribution with a novel loss function. Extensive experiments show that our approach outperforms state-of-the-art methods in both the digital space and the physical world. Our code is available at https://github.com/zhangyu13a/transPhyAtt. [ABSTRACT FROM AUTHOR]

Integrated thermal treatment (coupled pyrolysis and combustion) is considered to be an effective method for the utilization of oil sludge (OS). Characteristics of OS pyrolysis, OS combustion and OS char combustion were comprehensively investigated with a thermogravimetry-mass spectrum (TG-MS) system in this work. Small molecule hydrocarbons like CH 4 were main components in OS pyrolysis gas. Nitrogen and sulfur containing compounds, H 2 O, and CO 2 were detected out during OS and OS char combustion. The maximum intensities of NH 3 , HCN and NO all appeared in the OS combustion. H 2 S was the main precursor of SO 2 , and the peak of H 2 S occurred earlier with an increase in the pyrolysis temperature between 500 and 700 °C. Besides, with an increase in the pyrolysis temperature, NO 2 and SO 2 emissions increased during OS char combustion. [ABSTRACT FROM AUTHOR]

Activation of HSC is a pivotal step in hepatic fibrosis. In the activation of HSC, the TGF-β1 plays a key role that can promote the occurrence of hepatic fibrosis by combining with Smad proteins. Astragaloside is the main active component extracted from Radix Astragali that has the effect of antioxidation and hepatoprotection. In the present study, we investigated the mechanism of astragalosides inhibiting hepatic fibrosis in vitro and in vivo. In vitro, astragalosides inhibited the activation of HSC and regulated the expression of MMP-2 and TIMP-2 and reduced the formation of collagen fibers. In vivo, administration of astragalosides decreased the serum ALT, AST, and TBiL in rats by reducing oxidative stress. Astragalosides also attenuated hepatic fibrosis by reducing the concentration of hydroxyproline and inhibiting the formation of collagen fibers. The expressions of TGF-β1, TβR-I, p-Smad 2, and p-Smad 3 were downregulated after astragalosides treatments, while Smad 7 was upregulated compared to the control group. The results indicated that the effect of astragaloside on hepatic fibrosis was related to the inhibition of HSC activation and the modulation of the TGF-β1/Smad signaling pathway. [ABSTRACT FROM AUTHOR]

ABSTRACT: This article examines the ability of Beijing Climate Center Climate System Model (BCC_CSM) in demonstrating the prediction accuracy and the leading modes of the summer precipitation over North Asia (NA). A dynamic‐statistic combined approach for improving the prediction accuracy and the prediction of the leading modes of the summer precipitation over NA is proposed. Our results show that the BCC_CSM can capture part of the spatial anomaly features of the first two leading modes of NA summer precipitation. Moreover, BCC_CSM regains relationships such that the first and second mode of the empirical orthogonal function (EOF1 and EOF2) of NA summer precipitation, respectively, corresponds to the development of the El Niño and La Niña conditions in the tropical East Pacific. Nevertheless, BCC_CSM exhibits limited prediction skill over most part of NA and presents a deficiency in reproducing the EOF1's and EOF2's spatial pattern over central NA and EOF2's interannual variability. This can be attributed as the possible reasons why the model is unable to capture the correct relationships among the basic climate elements over the central NA, lacks in its ability to reproduce a consistent zonal atmospheric pattern over NA, and has bias in predicting the relevant Sea Surface Temperature (SST) modes over the tropical Pacific and Indian Ocean regions. Based on the proposed dynamic‐statistic combined correction approach, compared with the leading modes of BCC_CSM's original prediction, anomaly correlation coefficients of corrected EOF1/EOF2 with the tropical Indian Ocean SST are improved from 0.18/0.36 to 0.51/0.62. Hence, the proposed correction approach suggests that the BCC_CSM's prediction skill for the summer precipitation prediction over NA and its ability to capture the dominant modes could be certainly improved by choosing proper historical analogue information. [ABSTRACT FROM AUTHOR]

Experimental and simulation studies on the migration characteristics of heavy metals during the incineration of OS were conducted with a tube furnace reactor and thermodynamic equilibrium calculation software FactSage, respectively. The results showed that when the incineration temperature increased from 800°C to 1100°C, the release rates of heavy metals such as Cr, Pb, Cu, Zn and Ni increased gradually and reached the maximum at 1000°C. When the excess air ratio increased from 1.0 to 1.4, the release rates of heavy metals such as Cr, Pb, Cu, Zn and Ni first decreased and then became steady. The content of gaseous hydroxide of Cr and gaseous chlorides of Pb and Cu increased while the chemical forms of Zn and Ni did not change obviously with the increase of combustion temperature or excess air ratio. [ABSTRACT FROM PUBLISHER]

Bayer red mud was selected, and the NH3-SCR activity was tested in a fixed bed in which the typical flue gas atmosphere was simulated. Combined with XRF, XRD, BET, SEM, TG and NH3-Temperature Programmed Desorption (TPD) characterization, the denitration characteristics of Ce-doped red mud catalysts were studied on the basis of alkali-removed red mud. The results showed that typical red mud was a feasible material for denitration catalyst. Acid washing and calcining comprised the best treatment process for raw red mud, which reduced the content of alkaline substances, cleared the catalyst pore and optimized the particle morphology with dispersion. In the temperature range of 300-400 °C, the denitrification efficiency of calcined acid washing of red mud catalyst (ARM) was more than 70%. The doping of Ce significantly enhanced NH3 adsorption from weak, medium and strong acid sites, reduced the crystallinity of α-Fe2O3 in ARM, optimized the specific surface area and broadened the active temperature window, which increased the NOx conversion rate by an average of nearly 20% points from 250-350 °C. The denitration efficiency of Ce0.3/ARM at 300 °C was as high as 88%. The optimum conditions for the denitration reaction of the Ce0.3/ARM catalyst were controlled as follows: Gas Hourly Space Velocity (GHSV) of 30,000 h-1, O2 volume fraction of 3.5-4% and the NH3/NO molar ratio ([NH3/NO]) of 1.0. The presence of SO2 in the feed had an irreversible negative effect on the activity of the Ce0.3/ARM catalyst. [ABSTRACT FROM AUTHOR]

This study examines the ability of the Beijing Climate Center Climate System Model (BCC_CSM) to predict the meridional pattern of summer precipitation over East Asia-Northwest Pacific (EA-NWP) and its East Asia-Pacific (EAP) teleconnection. The differences of summer precipitation modes of the empirical orthogonal function and the bias of atmospheric circulations over EA-NWP are analyzed to determine the reason for the precipitation prediction errors. Results indicate that the BCC_CSM could not reproduce the positive-negative-positive meridional tripole pattern from south to north that differs markedly from that observed over the last 20 years. This failure can be attributed to the bias of the BCC_CSM hindcasts of the summer EAP teleconnection and the low predictability of 500 hPa at the mid-high latitude lobe of the EAP. Meanwhile, the BCC_CSM hindcasts' deficiencies of atmospheric responses to SST anomalies over the Indonesia maritime continent (IMC) resulted in opposite and geographically shifted geopotential anomalies at 500 hPa as well as wind and vorticity anomalies at 850 hPa, rendering the BCC_CSM unable to correctly reproduce the EAP teleconnection pattern. Understanding these two problems will help further improve BCC_CSM's summer precipitation forecasting ability over EA-NWP. [ABSTRACT FROM AUTHOR]

Temperature monitoring over heat source components in engineering systems, such as the energy system, electronic equipments, becomes essential to guarantee the working performance of these components. However, prior methods, which mainly use the interpolate estimation to reconstruct the overall temperature field from limited monitoring points, require large amounts of temperature tensors for an accurate estimation. This may affect the availability and reliability of the system. To solve the problem, this work develops a novel reconstruction method which joints the deep reversible regression model and physics-informed unsupervised learning for temperature field reconstruction of heat-source systems (TFR-HSS). Firstly, we define the TFR-HSS mathematically, numerically model the system with discrete grids, and hence transform the task as an image-to-image regression problem. Then, this work develops the deep reversible regression model which can better learn physical information, especially over the area near the boundaries of the system. Finally, this work proposes the physics-informed reconstruction loss with the physical characteristics of the system and learns the deep model without labelled samples. Experimental studies have conducted over typical two-dimensional heat-source systems to validate the effectiveness of the proposed method. Under the proposed method, the mean average error of the constructed temperature field can achieve about 0.1K, 50% lower than other methods. Besides, the proposed method takes 5.2 ms per sample for inference which can provide real-time predictions. • This work defines temperature field reconstruction of heat-source systems (TFR-HSS) task. • This work provides the mathematical formulation as well as numerically modelling of TFRHSS task. • This work further proposes the reversible regression model which can better fit for TFR-HSS task. • This work develops physics-informed reconstruction loss, which can train the deep model without labelled samples. [ABSTRACT FROM AUTHOR]

Recently, surrogate models based on deep learning have attracted much attention for engineering analysis and optimization. Since constructing data pairs in most engineering problems is time-consuming, data acquisition is becoming the predictive capability bottleneck of most deep surrogate models, which also exist in surrogate for thermal analysis and design. In contrast with data-driven learning, enforcing the physical laws in building surrogates has emerged as a promising alternative to reduce the dependence on annotated data. This paper develops a physics-informed convolutional neural network (CNN) for the thermal simulation surrogate without labeled data. Firstly, we leverage the finite difference method to integrate heat conduction equation and loss function construction, guiding surrogate model training to minimize the violation of physical laws. Since the solution is sensitive to boundary conditions, we properly impose hard constraints by padding in the Dirichlet and Neumann boundaries. The proposed network can learn a mapping from heat source layout to the steady-state temperature field without labeled data, which equals solving an entire family of partial difference equations (PDEs). Moreover, the neural network architecture is well-designed to improve the prediction accuracy of the problem at hand, and pixel-level online hard example mining is proposed to overcome the imbalance of optimization difficulty in the computation domain, which is beneficial to the network training of physics-informed learning. The experiments demonstrate that the proposed method can provide comparable predictions with numerical methods and data-driven deep learning models. We also conduct various ablation studies to investigate the effectiveness of the proposed network components and training methods in this paper. Furthermore, the developed methods can be applied to other design and optimization applications which need to solve parameterized PDEs. [ABSTRACT FROM AUTHOR]

Data-driven prediction of laminar flow and turbulent flow in marine and aerospace engineering has received extensive research and demonstrated its potential in real-time prediction recently. However, usually large amounts of high-fidelity data are required to describe and accurately predict the complex physical information, while reality, only limited high-fidelity data are available due to the high experimental/computational cost. Therefore, this work proposes a novel multi-fidelity learning method based on the Fourier neural operator by jointing abundant low-fidelity data and limited high-fidelity data under transfer learning paradigm. First, as a resolution-invariant operator, the Fourier neural operator is first and gainfully applied to integrate multi-fidelity data directly, which can utilize the limited high-fidelity data and abundant low-fidelity data simultaneously. Then, the transfer learning framework is developed for the current task by extracting the rich low-fidelity data knowledge to assist high-fidelity modeling training, to further improve data-driven prediction accuracy. Finally, three engineering application problems are chosen to validate the accuracy of the proposed multi-fidelity model. The results demonstrate that our proposed method has high effectiveness when compared with other high-fidelity models and has the high modeling accuracy of 99% for all the selected physical field problems. Additionally, the low-fidelity model without transfer learning has the modeling accuracy of 86%. Significantly, the proposed multi-fidelity learning method has the potential of a simple structure with high precision for fluid flow problems, which can provide a reference for the construction of the subsequent model. [ABSTRACT FROM AUTHOR]

In 2021/2022 winter, an intraseasonal surface air temperature (SAT) reversal occurred in China, shifting from regional warm to cold phase in late January 2022. The intraseasonal variations of winter SAT in China are tightly connected with 500 hPa potential height. In particular, the Arctic Oscillation (AO) positive phase is diagnosed to be contributing to the SAT reversal. Under its influence, the intraseasonal build‐up and collapse of blockings over Urals Mountain led to the cold surges in February 2022. Seasonal prediction systems (SPSs) such as NCEP_CFSv2, EC_SYSTEM5 and BCC_CSM1.1 are assessed to have poor predictability in this SAT reversal and similar historical events, owing to not only the poor skills in winter SAT predictions but also the integral inertia in SPSs. Prediction skill only increases as the forecasting time approaches, owing to initial values. Besides, the SPSs can simulate the physical process of the influence on SAT from circulation. The poor predictability is attributed to the poor prediction skills in mid–high latitude circulation pattern and potential height over China. [ABSTRACT FROM AUTHOR]

In the literature of remote sensing, deep models with multiple layers have demonstrated their potentials in learning the abstract and invariant features for better representation and classification of hyperspectral images. The usual supervised deep models, such as convolutional neural networks, need a large number of labeled training samples to learn their model parameters. However, the real-world hyperspectral image classification task provides only a limited number of training samples. This paper adopts another popular deep model, i.e., deep belief networks (DBNs), to deal with this problem. The DBNs allow unsupervised pretraining over unlabeled samples at first and then a supervised fine-tuning over labeled samples. But the usual pretraining and fine-tuning method would make many hidden units in the learned DBNs tend to behave very similarly or perform as “dead” (never responding) or “potential over-tolerant” (always responding) latent factors. These results could negatively affect description ability and thus classification performance of DBNs. To further improve DBN’s performance, this paper develops a new diversified DBN through regularizing pretraining and fine-tuning procedures by a diversity promoting prior over latent factors. Moreover, the regularized pretraining and fine-tuning can be efficiently implemented through usual recursive greedy and back-propagation learning framework. The experiments over real-world hyperspectral images demonstrated that the diversity promoting prior in both pretraining and fine-tuning procedure lead to the learned DBNs with more diverse latent factors, which directly make the diversified DBNs obtain much better results than original DBNs and comparable or even better performances compared with other recent hyperspectral image classification methods. [ABSTRACT FROM PUBLISHER]

The performance of summer precipitation prediction skill of the Beijing Climate Center Climate System Model (BCC_CSM1.1; hereafter CSM) over the East Asia-west Pacific (EA-WP) region indicates the need for further development in order to improve model prediction skill. Two methods, namely the statistical alone prediction and the statistical and model combined prediction, are proposed to improve prediction skill. For the former, more than 2000 combinations of precursory predictors are considered in the predictive model; both most similar and dissimilar information are considered in the prediction process. For the latter, both the statistical analog information and the CSM's forecast are considered in the predictive model; this new prediction method is based on merging information from both a dynamical model and historical analogs. Cross validation of summer precipitation over EA-WP for the years 1991-2010 shows that the average spatial anomaly correlation coefficient (ACC) of the statistical alone prediction is 0.16 and the average spatial root-mean-standard error (RMSE) is 22.7, which are much improved compared to the systematic error correction with ACC and RMSE being 0.09 and 25.0 respectively. The ACC and RMSE results from the statistical and CSM combined prediction are 0.22 and 21.8, respectively, which also indicates an improvement compare to the statistical alone prediction. Independent sample validation for the years 2011-13 indicates that the average ACCs of the statistical alone prediction and the statistical and CSM combined prediction (0.25 and 0.24) are enhanced compared to the systematic error correction (−0.02). The average RMSE is also improved from 25.0 to 23.6 and 22.7. Therefore, the two new prediction methods proposed in this paper both demonstrate the possibilities for the operational prediction of summer precipitation over EA-WP. [ABSTRACT FROM AUTHOR]

[Display omitted] • The synergistic effect of nano-zinc oxide and silver ions can realize broad-spectrum antibacterial. • The silver ions in the hydroxyapatite whiskers will gradually dissolve as the whiskers degrade, realizing long-term antibacterial effects. Nano-zinc oxide as a new antibacterial modification of hydroxyapatite whiskers. Osteomyelitis remains one of the most challenging diseases for orthopedic doctors despite the advancement of therapeutic techniques. Biomaterials with antibacterial activities are beneficial to prevent infection post implantation. The aim of this study was to evaluate the antibacterial effects of three biomaterials including pure- hydroxyapatite (HA), Ag-HA and Ag-HA/ZnO. The prepared materials were analyzed by X-ray diffractometer (XRD), scanning electron microscope (SEM), cell adhesion and long-service antibacterial ability. The results showed that with the increase of silver content, the growth of whiskers was inhibited and the aspect ratio became smaller. Nano-zinc oxide is evenly attached on the surface of the whiskers. Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus) were used for antibacterial tests. The results showed that Ag-HA and Ag-HA/ZnO had good antibacterial ability against E. coli and S. aureus. Thus, the low amount of Ag-HA has a potential of minimizing the risk of bacterial contamination, without compromising the bioactivity, and is expected to display greater biological efficacy in terms of osseointegration. Ag+ and ZnO particles have a synergistic effect and can effectively kill most bacteria. At the same time, with the degradation of hydroxyapatite, Ag+ is slowly released, which can play a long-term antibacterial effect. [ABSTRACT FROM AUTHOR]

Under global warming, cold winters over Northern Asia are highly correlated with the subsequent hot summers over middle and lower reaches of the Yangtze River valley ( MLYRV). This high correlation may result from an oceanic bridge's effect in the Western Pacific. The mechanism may be described as follows: the atmospheric circulation anomalies of cold winters corresponds with a simultaneous warmer Western Pacific warm pool ( WPWP) by decreasing surface latent heat fluxes. The warmer sea surface temperature ( SST) can persist and move eastward gradually to the subsequent summer through the oceanic processes, and thus in turn affect the hot days ( HDs) over MLYRV. [ABSTRACT FROM AUTHOR]

Based on the 1983∼2011 CMAP data, the precipitation anomaly in East Asia and its nearby sea regions (hereafter called East Asia for short) demonstrates the '+−+' pattern before 1999 and the '−+−' pattern afterwards; this decadal change is contained principally in the corresponding EOF3 component. However, the NCC_CGCM forecast results are quite different, which reveal the '+−+−' pattern before 1999 and the '−+−+' pattern afterwards. Meanwhile, the probability of improving NCC_CGCM's forecast accuracy based on these key SST areas is discussed, and the dynamic-statistics combined forecast scheme is constructed for increasing the information of decadal change contained in the summer precipitation in East Asia. The independent sample forecast results indicate that this forecasting scheme can effectively modify the NCC_CGCM's decadal change information contained in the summer precipitation in East Asia (especially in the area of 30°N-55°N). The ACC is 0.25 and ACR is 61% for the forecasting result based on the V SST area, and the mean ACC is 0.03 and ACR is 51% for the seven key areas, which are better than NCC_CGCM's system error correction results (ACC is −0.01 and ACR is 49%). Besides, the modified forecast results also provide the information that the precipitation anomaly in East Asia mainly shows the '+−+' pattern before 1999 and the '−+−' pattern afterwards. [ABSTRACT FROM AUTHOR]

The regional extreme low temperature event from December 30, 2010 to February 2, 2011 was a very rare and protracted cold event with the largest integrated index ( Z) since 1979. Two meteorological factors could be responsible for this extreme winter event. First, a persistent blocking pattern existed in the mid-latitudes. This not only allowed cold air to persist in southern China but also enabled each perturbation from the west propagating around the blocking high to trigger downstream cold air intrusions from the north. Second, the consistently downward negative Arctic Oscillation (AO) was favorable for the eastward moving of Rossby waves in middle latitudes, which made the upper reaches positive center in SLP and negative center in Z500 move to East Asia. This stable and consistent situation favored the polar area cold air invasion to the mid-latitude region. Of these two factors, the blocking pattern was likely to be the direct cause, the co-effects of consistently strong downward negative AO from the stratosphere, and the corresponding eastward moving wave train in Z500 and SLP might be the prophase teleconnection culprit. [ABSTRACT FROM AUTHOR]

[Display omitted] • Biogas was used as the reactant for low-carbon H 2 generation. • A tailored syngas ratio of 1:1 can be produced by adjusting CO 2 amount. • CO 2 could inhibit carbon deposition effectively in Fe 3 O 4 -Fe 0.947 O stage. • The behavior of CO breakthrough at different temperature was investigated. • The proposed multi-reactor model can improve the efficiency of H 2 production. An integrated recycling waste bioenergy and manufacturing low-carbon hydrogen by a method to convert biogas to hydrogen based on the chemical looping concepts was proposed in this study. Biogas from biomass anaerobic digestion and pyrolysis was employed in the reduction step as the feedstock of fuel gas. Simulated marsh gas (CH 4 and CO 2) and pyrolysis gas (CO and H 2) were used to validate the feasibility of the reduction performance with iron (III) oxide at different reaction temperatures. The experiments were conducted by using a thermogravimetric analyzer and a tandem packed-bed reactor. As for marsh gas, the reaction behavior was completely different under different CO 2 concentrations and converting temperatures. The reduction reaction with CH 4 would be divided into three stages, including complete combustion, competition reaction, and partial oxidation stage. Furthermore, the syngas with a tailored ratio of 1:1 can be produced without CH 4 breakthrough by adjusting CO 2 concentration. Besides, the total H 2 production achieved 241.9 mL per gram of oxygen carrier with a purity of 98.29%. Regarding the pyrolysis gas, the solid conversion and CO breakthrough curve were investigated. Compared to a single reactor, seven reactors were required to increase the average solid conversion from 34.1% to 42% and reduced the total redox time from 705.6 min to 437.7 min. Overall, this study provided a link between waste bioenergy recycling and H 2 generation with a novel chemical looping hydrogen generation (CLHG) system and supplied theoretical support for scaling up the packed-bed reactor to improve H 2 production efficiency. [ABSTRACT FROM AUTHOR]