Portraying on-road CO2 concentrations using street view panoramas and ensemble learning.

A significant reduction in carbon dioxide (CO2) emissions caused by transportation is essential for attaining sustainable urban development. Carbon concentrations from road traffic in urban areas exhibit complex spatial patterns due to the impact of street configurations, mobile sources, and human activities. However, a comprehensive understanding of these patterns, which involve complex interactions, is still lacking due to the human perspective of road interface characteristics has not been taken into account. In this study, a mobile travel platform was constructed to collect both on-road navigation Street View Panoramas (OSVPs) and the corresponding CO2 concentrations. >100 thousand sample pairs that matched "street view-CO2 concentration" were obtained, covering 675.8 km of roads in Shenzhen, China. In addition, four ensemble learning (EL) models were utilized to establish nonlinear connections between the semantic and object features of streetscapes and CO2 concentrations. After performing EL fusion modeling, the predictive R2 in the test set exceeded 90 %, and the mean absolute error (MAE) was <3.2 ppm. The model was applied to Baidu Street View Panoramas (BSVPs) in Shenzhen to generate a map of average on-road CO2 with a 100 m resolution, and the Local Indicator of Spatial Association (LISA) was then used to identify high CO2 intensity spatial clusters. Additionally, the Light Gradient Boost-SHapley Additive exPlanation (LGB-SHAP) analysis revealed that vertically planted trees can reduce CO2 emissions from on-road sources. Moreover, the factors that affect on-road CO2 exhibit interaction and threshold effects. Street View Panoramas (SVPs) and Artificial Intelligence (AI) were adopted here to enhance the spatial measurement of on-road CO2 concentrations and the understanding of driving factors. Our approach facilitates the assessment and design of low-emission transportation in urban areas, which is critical for promoting sustainable traffic development.

Lasso-Cox interpretable model of AFP-negative hepatocellular carcinoma.

BACKGROUND: In AFP-negative hepatocellular carcinoma patients, markers for predicting tumor progression or prognosis are limited. Therefore, our objective is to establish an optimal predicet model for this subset of patients, utilizing interpretable methods to enhance the accuracy of HCC prognosis prediction. METHODS: We recruited a total of 508 AFP-negative HCC patients in this study, modeling with randomly divided training set and validated with validation set. At the same time, 86 patients treated in different time periods were used as internal validation. After comparing the cox model with the random forest model based on Lasso regression, we have chosen the former to build our model. This model has been interpreted with SHAP values and validated using ROC, DCA. Additionally, we have reconfirmed the model's effectiveness by employing an internal validation set of independent periods. Subsequently, we have established a risk stratification system. RESULTS: The AUC values of the Lasso-Cox model at 1, 2, and 3 years were 0.807, 0.846, and 0.803, and the AUC values of the Lasso-RSF model at 1, 2, and 3 years were 0.783, 0.829, and 0.776. Lasso-Cox model was finally used to predict the prognosis of AFP-negative HCC patients in this study. And BCLC stage, gamma-glutamyl transferase (GGT), diameter of tumor, lung metastases (LM), albumin (ALB), alkaline phosphatase (ALP), and the number of tumors were included in the model. The validation set and the separate internal validation set both indicate that the model is stable and accurate. Using risk factors to establish risk stratification, we observed that the survival time of the low-risk group, the middle-risk group, and the high-risk group decreased gradually, with significant differences among the three groups. CONCLUSION: The Lasso-Cox model based on AFP-negative HCC showed good predictive performance for liver cancer. SHAP explained the model for further clinical application.

Intelligent films based on dual-modified starch and microencapsulated Aronia melanocarpa anthocyanins: Functionality, stability and application.

This study aims to enhance the physical properties and color stability of anthocyanin-based intelligent starch films. Three dual-modified starches, namely crosslinked-oxidized starch (COS), acetylated distarch phosphate (ADSP), and hydroxypropyl distarch phosphate (HDSP), were utilized as film matrices. Aronia melanocarpa anthocyanins were incorporated through three different pre-treatments (free, spray-drying microencapsulation, and freeze-drying microencapsulation) to assess the prepared films' functionality, stability, and applicability. The results indicate that the ADSP film exhibited an approximately two-fold increase in elongation at break (EAB) compared to native starch film. Specifically, the ADSP film's water contact angle (WCA) reached 90°, demonstrating excellent flexibility and hydrophobicity. Scanning electron microscopy (SEM) revealed stronger interactions between anthocyanins and the film matrix after microencapsulation. Furthermore, after 30 days of exposure to 37 °C heat and light radiation, the freeze-dried anthocyanin-based intelligent film (FDA film) exhibited minimal fading, displaying the highest stability among the tested films. Notably, during beef freshness monitoring, the intelligent films underwent significant color changes as the beef deteriorated. In conclusion, the developed FDA film, with its outstanding stability and responsive pH characteristics, holds immense potential as a novel packaging material for food applications.

Deoxygenative alkynylation of amides via CO bond cleavage.

A novel deoxygenative alkynylation of amides promoted by a synergistic action of a divalent rare-earth element and a transition metal has been developed. In this method, α-alkynyl substituted amines are synthesized from unactivated amides and alkynes in a single transformation. Broad substrate scope and excellent selectivity for CO cleavage has been demonstrated. This approach represents a general method for the construction of versatile α-alkynyl substituted amines from unactivated amide bonds.

Palladium-catalyzed Suzuki-Miyaura cross-coupling of carboxylic-phosphoric anhydrides via C-O bond cleavage.

A robust palladium-catalyzed Suzuki-Miyaura reaction of carboxylic-phosphoric anhydrides via highly selective C(O)-O bond cleavage under inorganic base-free conditions has been reported. Carboxylic-phosphoric anhydrides, generated through activating carboxylic acids using phosphates by esterification or direct dehydrogenative reaction with phosphites, have been employed as highly reactive electrophiles for Suzuki-Miyaura cross-coupling reactions. Broad substrate scope and excellent functional group tolerance have been demonstrated to be a general and practical approach for the synthesis of highly valuable ketones.

Effects of adding proteins from different sources during heat-moisture treatment on corn starch structure, physicochemical and in vitro digestibility.

Proteins impact starch digestion, but the specific mechanism under heat-moisture treatment remains unclear. This study examined how proteins from various sources-white kidney bean, soybean, casein, whey-altered corn starch's structure, physicochemical properties, and digestibility during heat-moisture treatment (HMT). HMT and protein addition could significantly reduce starch's digestibility. The kidney bean protein-starch complex under HMT had the highest resistant starch at 19.74 %. Most proteins effectively inhibit α-amylase, with kidney bean being the most significantly (IC50 = 1.712 ± 0.085 mg/mL). HMT makes starch obtain a more rigid structure, limits its swelling ability, and reduces paste viscosity and amylose leaching. At the same time, proteins also improve starch's short-range order, acting as a physical barrier to digestion. Rheological and low-field NMR analyses revealed that protein enhanced the complexes' shear stability and water-binding capacity. These findings enrich the understanding of how proteins from different sources affect starch digestion under HMT, aiding the creation of nutritious, hypoglycemic foods.

Advances in MXene surface functionalization modification strategies for CO2 reduction.

MXenes, 2D transition metal carbides and nitrides, show great potential in electrocatalytic CO2 reduction reaction (ECO2RR) applications owing to their tunable structure, abundant surface functional groups, large specific surface area and remarkable conductivity. However, the ECO2RR has a complex pathway involving various reaction intermediates. The reaction process yields various products alongside a competitive electrolytic water-splitting reaction. These factors limit the application of MXenes in ECO2RRs. Therefore, this review begins by examining the functionalized modification of MXenes to enhance their catalytic activity and stability via the regulation of interactions between carriers and the catalytic centre. The review firstly covers the synthesis methods and characterisation techniques for functionalized MXenes reported in recent years. Secondly, it presents the methods applied for the functionalized modification of carriers through surface loading of single atoms, clusters, and nanoparticles and construction of composites. These methods regulate the stability, active sites, and metal-carrier electronic interactions. Finally, the article discusses the challenges, opportunities, pressing issues, and future prospects related to MXene-based electrocatalysts.

Octenyl succinic anhydride tigernut starch: Structure, physicochemical properties and stability of curcumin-loaded Pickering emulsion.

In recent years, there has been increasing attention to starch particle-stabilized Pickering emulsions. In this study, the tigernut starch (TNS) was isolated from the tigernut meal, and further octenyl succinic anhydride tigernut starch (OSATNS) was prepared by a semi-dry method. The structure of OSATNS was analyzed and characterized by degrees of substitution (DS), contact angle, SEM, and FTIR. OSATNS was then used to stabilize the curcumin-loaded Pickering emulsion to improve the water solubility and stability of the curcumin. The results showed that OSATNS with 3 %-9 % OSA exhibited a DS range of 0.012 to 0.029, and its contact angle increased from 69.23° to 84.76°. SEM revealed that TNS consisted of small starch particles averaging 7.71 µm, and esterification did not significantly alter their morphology or size. FTIR analysis confirmed successful OSA incorporation by revealing two new peaks at 1732 cm-1 and 1558 cm-1. After 7 days of storage, Pickering emulsions stabilized with OSATNS-9 % exhibited superior stability and curcumin retention compared to Tween 80 emulsions, maintaining retention rates above 80 % even after different heat treatments. In conclusion, this study shows the potential application of OSATNS in stabilizing Pickering emulsions and demonstrates its good thermal stability and protection against curcumin during storage.

Effect of Cyperus esculentus polysaccharide on Cyperus esculentus starch: Pasting, rheology and in vitro digestibility.

This study investigated the effects of varying amounts of added Cyperus esculentus polysaccharide (CEP) on the physicochemical and structural properties, as well as in vitro digestibility, of homologous Cyperus esculentus starch (CES). Compared to CES, the CES-CEP complexes showed reduced peak viscosity and breakdown value, and improved thermal paste stability of starch. Rheological properties showed that adding CEP reduced the consistency coefficient and pseudoelasticity of the complexes, thus increasing their resistance to shear thinning. FTIR analysis suggested the absence of covalent binding between CES and CEP. SEM showed a more homogeneous and dense gel structure, particularly in the CES-1.0%CEP sample. During in vitro digestion, the content of resistant starch in the complexes increased after CEP was added. Analysis of the interaction forces showed that the CES-CEP complexes had stronger hydrogen bonding and electrostatic interaction. This study offers valuable insights into the potential applications of CEP in starch-based foods.

Spatial Localization of a Transformer Robot Based on Ultrasonic Signal Wavelet Decomposition and PHAT-ß-γ Generalized Cross Correlation.

Because large oil-immersed transformers are enclosed by a metal shell, the on-site localization means it is difficult to achieve the accurate location of the patrol micro-robot inside a given transformer. To address this issue, a spatial ultrasonic localization method based on wavelet decomposition and PHAT-ß-γ generalized cross correlation is proposed in this paper. The method is carried out with a five-element stereo ultrasonic array for the location of a transformer patrol robot. Firstly, the localization signal is decomposed into wavelet coefficients of different scales, which would realize the adaptive decomposition of the frequency of the localization signal from low frequencies to high frequencies. Then, the wavelet coefficients are denoised and reconstructed by using the semi-soft threshold function. Second, a modified phase transform-beta-gamma (PHAT-ß-γ) method is used to calculate the exact time delay between different sensors by increasing the weights of the PHAT weighting function and introducing a correlation function. Finally, by using the proposed method, the accurate localization of the transformer patrol micro-robot is achieved with a five-element stereo ultrasonic array. The simulation and test results show that inside a transformer experimental oil tank (120 cm × 100 cm × 100 cm, L × W × H), the relative error of transformer patrol micro-robot spatial localization is within 4.1%, and the maximum localization error is less than 3 cm, which meets the requirement of engineering localization.

Retrospective study about clinical severity and epidemiological analysis of the COVID-19 Omicron subvariant lineage-infected patients in Hohhot, China.

BACKGROUND: Fear of a global public health issue and fresh infection wave in the persistent COVID-19 pandemic has been enflamed by the appearance of the novel variant Omicron BF.7 lineage. Recently, it has been seeing the novel Omicron subtype BF.7 lineage has sprawled exponentially in Hohhot. More than anything, risk stratification is significant to ascertain patients infected with COVID-19 who the most need in-hospital or in-home management. The study intends to understand the clinical severity and epidemiological characteristics of COVID-19 Omicron subvariant BF.7. lineage via gathering and analyzing the cases with Omicron subvariant in Hohhot, Inner Mongolia. METHODS: Based upon this, we linked variant Omicron BF.7 individual-level information including sex, age, symptom, underlying conditions and vaccination record. Further, we divided the cases into various groups and assessed the severity of patients according to the symptoms of patients with COVID-19. Clinical indicators and data might help to predict disadvantage outcomes and progression among Omicron BF.7 patients. RESULTS: In this study, in patients with severe symptoms, some indicators from real world data such as white blood cells, AST, ALT and CRE in patients with Omicron BF.7 in severe symptoms were significantly higher than mild and asymptomatic patients, while some indicators were significantly lower. CONCLUSIONS: Above results suggested that the indicators were associated with ponderance of clinical symptoms. Our survey emphasized the value of timely investigations of clinical data obtained by systemic study to acquire detailed information.