Modeling the Spill in the Songhua River after the Explosion in the Petrochemical Plant in Jilin

An explosion in a petrochemical plant in Jilin in the northeast of China on 13 November 13 2005 was responsible for the discharge of large quantities of benzene and nitrobenzene into Songhua River. This endangered the water supply of Harbin city and influenced the daily life for millions of people. The dispersion-advection equation was solved analytically and numerically and used to simulate the concentration of benzene and nitrobenzene in the Songhua River after the accident. Both solutions gave practically identical results. The main elimination process for both compounds was volatilization. The model results are quite close to the results obtained by measurements at monitoring stations. Arrival time of the pollutant wave, peak concentrations and end of the pollutant wave at Harbin and along the river were predicted successfully. The peak concentrations of nitrobenzene at Harbin were more than 30 times above the permissible limits for drinking water. This paper describes the distribution of benzene and nitrobenzene in the Songhua River after the accident in Jilin

Turbulent Heat Transfer Analysis of Silicon Carbide Ceramic Foam as a Solar Volumetric Receiver

A volumetric solar receiver receives the concentrated radiation generated by a large number of heliostats. Turbulent heat transfer occurs from the solid matrix to the air as it passes through the porous receiver. Such combined heat transfer within the receiver, including radiation, convection and conduction, is studied using a local thermal non-equilibrium model. Both the Rosseland approximation and the P1 model are applied to consider the radiative heat transfer through the solar receiver. Furthermore, the low Mach approximation is exploited to investigate the compressible flow through the receiver. Analytic solutions are obtained for the developments of air and ceramic temperatures as well as the pressure along the flow direction. Since the corresponding fluid and solid temperature variations generated under the Rosseland approximation agree fairly well with those based on the P1 model, the Rosseland approximation is used for further analysis. The results indicate that the pore diameter must be larger than its critical value to obtain high receiver efficiency. Moreover, it has been found that optimal pore diameter exists for achieving the maximum receiver efficiency under the equal pumping power. The solutions provide effective guidance for a novel volumetric solar receiver design of silicon carbide ceramic foam

Experience-driven well-being: the case of unmanned smart hotels

Purpose: Drawing on the theory of memory-dominant logic, this study aims to examine how the substantive staging of the servicescape, experience co-creation, experiential satisfaction and experience intensification affect experience memorability and hedonic well-being in the case of unmanned smart hotels. Design/methodology/approach: An online survey was used, with the target respondents being hotel guests people aged 18 years and older who had been recent guests of the FlyZoo Hotel in Hangzhou, China. Data were collected online from 429 guests who had stayed in the hotel between April and June 2023. Data analysis was undertaken using structural equation modelling. Findings: The results suggest that all the proposed four constructs are positive drivers of a memorable unmanned smart hotel experience. The relationship between the memorability of the hotel experience and hedonic well-being was found to be significant and positive. Practical implications: Unmanned smart hotels should ensure that all smart technologies function effectively and dependably and offer highly personalised services to guests, allowing them to co-create their experiences. This will lead to the guest receiving a satisfying and memorable experience. To enable experience co-creation using smart technologies, unmanned smart hotels could provide short instructional videos for guests, as well as work closely with manufacturers and suppliers to ensure that smart technology systems are regularly updated. Originality/value: This study investigates the antecedents and outcomes of a novel phenomenon and extends the concept of memorable tourism experiences to the context of unmanned smart hotels

Risk factors for overcorrection of severe hyponatremia: a post hoc analysis of the SALSA trial

Background Hyponatremia overcorrection can result in irreversible neurologic impairment such as osmotic demyelination syndrome. Few prospective studies have identified patients undergoing hypertonic saline treatment with a high risk of hyponatremia overcorrection. Methods We conducted a post hoc analysis of a multicenter, prospective randomized controlled study, the SALSA trial, in 178 patients aged above 18 years with symptomatic hyponatremia (mean age, 73.1 years; mean serum sodium level, 118.2 mEq/L). Overcorrection was defined as an increase in serum sodium levels by >12 or 18 mEq/L within 24 or 48 hours, respectively. Results Among the 178 patients, 37 experienced hyponatremia overcorrection (20.8%), which was independently associated with initial serum sodium level (≤110, 110–115, 115–120, and 120–125 mEq/L with 7, 4, 2, and 0 points, respectively), chronic alcoholism (7 points), severe symptoms of hyponatremia (3 points), and initial potassium level (<3.0 mEq/L, 3 points). The NASK (hypoNatremia, Alcoholism, Severe symptoms, and hypoKalemia) score was derived from four risk factors for hyponatremia overcorrection and was significantly associated with overcorrection (odds ratio, 1.41; 95% confidence interval, 1.24–1.61; p < 0.01) with good discrimination (area under the receiver-operating characteristic [AUROC] curve, 0.76; 95% CI, 0.66–0.85; p < 0.01). The AUROC curve of the NASK score was statistically better compared with those of each risk factor. Conclusion In treating patients with symptomatic hyponatremia, individuals with high hyponatremia overcorrection risks were predictable using a novel risk score summarizing baseline information

Relationship between the Composition of Flavonoids and Flower Colors Variation in Tropical Water Lily (Nymphaea) Cultivars

Water lily, the member of the Nymphaeaceae family, is the symbol of Buddhism and Brahmanism in India. Despite its limited researches on flower color variations and formation mechanism, water lily has background of blue flowers and displays an exceptionally wide diversity of flower colors from purple, red, blue to yellow, in nature. In this study, 34 flavonoids were identified among 35 tropical cultivars by high-performance liquid chromatography (HPLC) with photodiode array detection (DAD) and electrospray ionization mass spectrometry (ESI-MS). Among them, four anthocyanins: delphinidin 3-O-rhamnosyl-5-O-galactoside (Dp3Rh5Ga), delphinidin 3-O-(2″-O-galloyl-6″-O-oxalyl-rhamnoside) (Dp3galloyl-oxalylRh), delphinidin 3-O-(6″-O-acetyl-β-glucopyranoside) (Dp3acetylG) and cyanidin 3- O-(2″-O-galloyl-galactopyranoside)-5-O-rhamnoside (Cy3galloylGa5Rh), one chalcone: chalcononaringenin 2′-O-galactoside (Chal2′Ga) and twelve flavonols: myricetin 7-O-rhamnosyl-(1→2)-rhamnoside (My7RhRh), quercetin 7-O-galactosyl-(1→2)-rhamnoside (Qu7GaRh), quercetin 7-O-galactoside (Qu7Ga), kaempferol 7-O-galactosyl-(1→2)-rhamnoside (Km7GaRh), myricetin 3-O-galactoside (My3Ga), kaempferol 7-O-galloylgalactosyl-(1→2)-rhamnoside (Km7galloylGaRh), myricetin 3-O-galloylrhamnoside (My3galloylRh), kaempferol 3-O-galactoside (Km3Ga), isorhamnetin 7-O-galactoside (Is7Ga), isorhamnetin 7-O-xyloside (Is7Xy), kaempferol 3-O-(3″-acetylrhamnoside) (Km3-3″acetylRh) and quercetin 3-O-acetylgalactoside (Qu3acetylGa) were identified in the petals of tropic water lily for the first time. Meanwhile a multivariate analysis was used to explore the relationship between pigments and flower color. By comparing, the cultivars which were detected delphinidin 3-galactoside (Dp3Ga) presented amaranth, and detected delphinidin 3′-galactoside (Dp3′Ga) presented blue. However, the derivatives of delphinidin and cyanidin were more complicated in red group. No anthocyanins were detected within white and yellow group. At the same time a possible flavonoid biosynthesis pathway of tropical water lily was presumed putatively. These studies will help to elucidate the evolution mechanism on the formation of flower colors and provide theoretical basis for outcross breeding and developing health care products from this plant

Soil Moisture Inversion Based on Data Augmentation Method Using Multi-Source Remote Sensing Data

Soil moisture is an important land environment characteristic that connects agriculture, ecology, and hydrology. Surface soil moisture (SSM) prediction can be used to plan irrigation, monitor water quality, manage water resources, and estimate agricultural production. Multi-source remote sensing is a crucial tool for assessing SSM in agricultural areas. The field-measured SSM sample data are required in model building and accuracy assessment of SSM inversion using remote sensing data. When the SSM samples are insufficient, the SSM inversion accuracy is severely affected. An SSM inversion method suitable for a small sample size was proposed. The alpha approximation method was employed to expand the measured SSM samples to offer more training data for SSM inversion models. Then, feature parameters were extracted from Sentinel-1 microwave and Sentinel-2 optical remote sensing data, and optimized using three methods, which were Pearson correlation analysis, random forest (RF), and principal component analysis. Then, three common machine learning models suitable for small sample training, which were RF, support vector regression, and genetic algorithm-back propagation neural network, were built to retrieve SSM. Comparison experiments were carried out between various feature optimization methods and machine learning models. The experimental results showed that after sample augmentation, SSM inversion accuracy was enhanced, and the combination of utilizing RF for feature screening and RF for SSM inversion had a higher accuracy, with a coefficient of determination of 0.7256, a root mean square error of 0.0539 cm3/cm3, and a mean absolute error of 0.0422 cm3/cm3, respectively. The proposed method was finally used to invert the regional SSM of the study area. The inversion results indicated that the proposed method had good performance in regional applications with a small sample size

PANoptosis-related genes function as efficient prognostic biomarkers in colon adenocarcinoma

BackgroundPANoptosis is a newly discovered cell death type, and tightly associated with immune system activities. To date, the mechanism, regulation and application of PANoptosis in tumor is largely unknown. Our aim is to explore the prognostic value of PANoptosis-related genes in colon adenocarcinoma (COAD).MethodsAnalyzing data from The Cancer Genome Atlas-COAD (TCGA-COAD) involving 458 COAD cases, we concentrated on five PANoptosis pathways from the Molecular Signatures Database (MSigDB) and a comprehensive set of immune-related genes. Our approach involved identifying distinct genetic COAD subtype clusters and developing a prognostic model based on these parameters.ResultsThe research successfully identified two genetic subtype clusters in COAD, marked by distinct profiles in PANoptosis pathways and immune-related gene expression. A prognostic model, incorporating these findings, demonstrated significant predictive power for survival outcomes, underscoring the interplay between PANoptosis and immune responses in COAD.ConclusionThis study enhances our understanding of COAD’s genetic framework, emphasizing the synergy between cell death pathways and the immune system. The development of a prognostic model based on these insights offers a promising tool for personalized treatment strategies. Future research should focus on validating and refining this model in clinical settings to optimize therapeutic interventions in COAD

Spatio-Temporal Estimation of Rice Height Using Time Series Sentinel-1 Images

Rice height, as the fundamental biophysical attribute, is a controlling factor in crop phenology estimation and yield estimation. The aim of this study was to use time series Sentinel-1A images to estimate the spatio-temporal distribution of rice height. In this study, a particle filter (PF) was applied for the real-time estimation of rice height compared with a simplified water cloud model (SWCM) on the basis of rice mapping and transplanting date. It was found that the VH backscatter (&sigma;vho) can potentially be applied to accurately estimate rice height compared with VV backscatter (&sigma;vvo), the &sigma;vho/&sigma;vv0 ratio, and the Radar Vegetation Index (RVI, 4* &sigma;vho/(&sigma;vho+&sigma;vvo)). The results show that the rice height estimation by PF generated a better result with a root-mean-square error (RMSE) equal to 7.36 cm and a determination factor (R2) of 0.95 compared with SWCM (RMSE = 12.59 cm and R2 = 0.86). Moreover, rice height in the south and east of the study area was higher than in the north and west. The reason for this is that the south and east are near to the South China Sea, and there are higher temperatures and earlier transplanting. Altogether, our results demonstrate the potential of PF and &sigma;vho to study the spatio-temporal distribution of crop height estimation. As a result, the PF method can contribute greatly to improvements in crop monitoring

Crop Classification Based on GDSSM-CNN Using Multi-Temporal RADARSAT-2 SAR with Limited Labeled Data

Crop classification is an important part of crop management and yield estimation. In recent years, neural networks have made great progress in synthetic aperture radar (SAR) crop classification. However, the insufficient number of labeled samples limits the classification performance of neural networks. In order to solve this problem, a new crop classification method combining geodesic distance spectral similarity measurement and a one-dimensional convolutional neural network (GDSSM-CNN) is proposed in this study. The method consisted of: (1) the geodesic distance spectral similarity method (GDSSM) for obtaining similarity and (2) the one-dimensional convolutional neural network model for crop classification. Thereinto, a large number of training data are extracted by GDSSM and the generalized volume scattering model which is based on radar vegetation index (GRVI), and then classified by 1D-CNN. In order to prove the effectiveness of the GDSSM-CNN method, the GDSSM method and 1D-CNN method are compared in the case of a limited sample. In terms of evaluation and verification of methods, the GDSSM-CNN method has the highest accuracy, with an accuracy rate of 91.2%, which is 19.94% and 23.91% higher than the GDSSM method and the 1D-CNN method, respectively. In general, the GDSSM-CNN method uses a small number of ground measurement samples, and it uses the rich polarity information in multi-temporal fully polarized SAR data to obtain a large number of training samples, which can quickly improve the accuracy of classification in a short time, which has more new inspiration for crop classification