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In container terminal, the container trucks should go by a “quay crane - container trucks -import and export block” routing, and the optimization of this routing has become the focus of this paper. This study divided the problem of routing optimization into two levels: one is a routing optimization model of single container truck which takes the lowest total cost as the target, without considering the distribution of overall transportation task; the other is a routing optimization model concerning the allocation of no-load trucks, aiming at the lowest cost of transportation task. We designed an improved Particle Swarm Optimization (PSO) algorithm to solve the two-level optimization model, and the influence of emission policy on the two-level optimization model and the influence of the number of container trucks on the transportation distance of level II in optimization model are analyzed. The results show that the increase of carbon tax will lead to the decrease of emissions in level I, but has no effect on the emissions in level II. The increase in the number of container trucks will lead to a reduction of the no-load transportation distance in level II.

There has been an upsurge of green reductants for the preparation of graphene materials taking consideration of human health and the environment in recent years. In this paper, reduced graphene oxides (RGOs) were prepared by chemical reduction of graphene oxide (GO) with three green reductants, L-ascorbic acid (L-AA), D-glucose (D-GLC) and tea polyphenol (TP), and comparatively characterized by X-ray photoelectron spectroscopy (XPS), Fourier transform infrared (FTIR) spectra, Raman spectra and electrical conductivity analysis. Results showed that all these three reductants were effective to remove oxygen-containing functional groups in GO and restore the electrical conductivity of the obtained RGO. The RGO sample with L-ascorbic acid as a reductant and reduced with the existence of ammonia had the highest electrical conductivity (9.8 S·cm(-1)) among all the obtained RGO samples. The mechanisms regarding to the reduction of GO and the dispersion of RGO in water were also proposed. It is the good dispersibility of reduced graphene oxide in water that will facilitate its further use in composite materials and conductive ink.

Incinerating agricultural residues to recover energy is a popular method for reducing solid waste pollution and resource waste caused by the huge amount of coconut petiole residues. However, this process not only has the risk of secondary air pollution, but also the added value of comprehensive utilization is not high. In this paper, coconut petiole residues were used as eco-friendly and economical raw materials to synthesize carbon dots (CCDs) via a simple hydrothermal method. Furthermore, given the important reference significance of Fe3+ concentration in assessing the quality of water environment and human body health, the obtained CCDs were utilized for Fe3+ detection. The prepared spherical CCDs with an average diameter of 1.39 nm and a carbon skeleton dominated by benzene ring structure have surface functional groups that endow them with good water solubility. The CCDs with a quantum yield of 1.39% emit strong blue fluorescence under ultraviolet radiation (365 nm), and show favorable fluorescence stability with excitation-dependent and concentration-dependent emission behaviors. Based on the synergetic effect of static and dynamic quenching, the CCDs exhibit favorable selectivity and detection sensitivity toward Fe3+ with a linear range of 0.005 to 0.2 mM and a detection limit of 2.3 μM, and also show the feasibility of Fe3+ detection in real water samples. This work is in favor of the high value-added conversion of coconut petiole residues. In addition, the development and use of bio-based CCDs as fluorescence sensors may be beneficial to monitoring and protecting public health and the environment.Graphic abstract

Since dual channel supply chain has become one of the main modes of supply chain, its research has acquired great significance. This paper constructs a low-carbon dual channel supply chain composed of one manufacturer and one retailer. The manufacturer produces low-carbon product and high carbon product with substitution relationship. The retailer sells high carbon product in traditional channel. The manufacturer also sells low-carbon product in direct channel. The government, manufacturer and retailer conduct a three-level Stackelberg game. This paper studies the optimal decisions of the government, manufacturer and retailer under the three modes of carbon tax + subsidy, carbon tax only and subsidy only. It has been found that for social welfare, the carbon tax + subsidy model is higher than the subsidy model and carbon tax model. For manufacturer profit, the subsidy mode is the highest, followed by the carbon tax + subsidy mode. For retailer profit, the carbon tax + subsidy model is equal to the carbon tax model. The increase in the proportion of consumers who prefer high carbon product in the total market or product cost of low-carbon product, will increase the profit of traditional channel and reduce the profit of direct channel.

Biomass-based carbon quantum dots (CQDs) have become a significant carbon materials by their virtues of being cost-effective, easy to fabricate and low in environmental impact. However, there are few reports regarding using cyanobacteria as a carbon source for the synthesis of fluorescent CQDs. In this study, the low-cost biomass of cyanobacteria was used as the sole carbon source to synthesize water-soluble CQDs by a simple hydrothermal method. The synthesized CQDs were mono-dispersed with an average diameter of 2.48 nm and exhibited excitation-dependent emission performance with a quantum yield of 9.24%. Furthermore, the cyanobacteria-derived CQDs had almost no photobleaching under long-time UV irradiation, and exhibited high photostability in the solutions with a wide range of pH and salinity. Since no chemical reagent was involved in the synthesis of CQDs, the as-prepared CQDs were confirmed to have low cytotoxicity for PC12 cells even at a high concentration. Additionally, the CQDs could be efficiently taken up by cells to illuminate the whole cell and create a clear distinction between cytoplasm and nucleus. The combined advantages of green synthesis, cost-effectiveness and low cytotoxicity make synthesized CQDs a significant carbon source and broaden the application of cyanobacteria and provide an economical route to fabricate CQDs on a large scale.

Aims Heart failure (HF) is one of the common adverse cardiovascular events after acute myocardial infarction (AMI), but the predictive efficacy of numerous machine learning (ML) built models is unclear. This study aimed to build an optimal model to predict the occurrence of HF in AMI patients by comparing seven ML algorithms. Methods Cohort 1 included AMI patients from 2018 to 2019 divided into HF and control groups. All first routine test data of the study subjects were collected as the features to be selected for the model, and seven ML algorithms with screenable features were evaluated. Cohort 2 contains AMI patients from 2020 to 2021 to establish an early warning model with external validation. ROC curve and DCA curve to analyze the diagnostic efficacy and clinical benefit of the model respectively. Results The best performer among the seven ML algorithms was XgBoost, and the features of XgBoost algorithm for troponin I, triglycerides, urine red blood cell count, γ-glutamyl transpeptidase, glucose, urine specific gravity, prothrombin time, prealbumin, and urea were ranked high in importance. The AUC of the HF-Lab9 prediction model built by the XgBoost algorithm was 0.966 and had good clinical benefits. Conclusions This study screened the optimal ML algorithm as XgBoost and developed the model HF-Lab9 will improve the accuracy of clinicians in assessing the occurrence of HF after AMI and provide a reference for the selection of subsequent model-building algorithms.

Severe Mycoplasma pneumoniae pneumonia (SMPP) poses significant diagnostic challenges due to its clinical features overlapping with those of other common respiratory diseases. This study aims to develop and validate machine learning (ML) models for the early identification of SMPP and the risk prediction for liver and heart damage in SMPP using accessible laboratory indicators. Cohort 1 was divided into SMPP group and other respiratory diseases group. Cohort 2 was divided into myocardial damage, liver damage, and non-damage groups. The models built using five ML algorithms were compared to screen the best algorithm and model. Receiver Operating Characteristic (ROC) curves, accuracy, sensitivity, and other performance indicators were utilized to evaluate the performance of each model. Feature importance and Shapley Additive Explanation (SHAP) values were introduced to enhance the interpretability of models. Cohort 3 was used for external validation. In Cohort 1, the SMPP differential diagnostic model developed using the LightGBM algorithm achieved the highest performance with AUC ROC = 0.975. In Cohort 2, the LightGBM model demonstrated superior performance in distinguishing myocardial damage, liver damage, and non-damage in SMPP patients (accuracy = 0.814). Feature importance and SHAP values indicated that ALT and CK-MB emerged as pivotal contributors significantly influencing Model 2’s output magnitude. The diagnostic and predictive abilities of the ML models were validated in Cohort 3, demonstrating the models had some clinical generalizability. The Model 1 and Model 2 constructed by LightGBM algorithm showed excellent ability in differential diagnosis of SMPP and risk prediction of organ damage in children.

In this study, cellulose nanofibrils (CNFs) were successfully isolated from coconut palm petiole residues falling off naturally with chemical pretreatments and mechanical treatments by a grinder and a homogenizor. FTIR spectra analysis showed that most of hemicellulose and lignin were removed from the fiber after chemical pretreatments. The compositions of CNFS indicated that high purity of nanofibrils with cellulose contain more than 95% was obtained. X-ray diffractogram demonstrated that chemical pretreatments significantly increased the crystallinity of CNFs from 38.00% to 70.36%; however, 10-15 times of grinding operation followed by homogenizing treatment after the chemical pretreatments did not significantly improve the crystallinity of CNFs. On the contrary, further grinding operation could destroy crystalline regions of the cellulose. SEM image indicated that high quality of CNFs could be isolated from coconut palm petiole residues with chemical treatments in combination of 15 times of grinding followed by 10 times of homogenization and the aspect ratio of the obtained CNFs ranged from 320 to 640. The result of TGA-DTG revealed that the chemical-mechanical treatments improved thermal stability of fiber samples, and the CNFs with 15 grinding passing times had the best thermal stability. This work suggests that the CNFs can be successfully extracted from coconut palm petiole residues and it may be a potential feedstock for nanofiber reinforced composites due to its high aspect ratio and crystallinity.

The harmful cyanobacteria blooms which usually form in spring and summer, cause global eutrophication of freshwater and coastal marine ecosystems. This study tried to utilize cyanobacteria as a raw material to produce biological poly(vinyl)alcohol-based films. Cyanobacteria was firstly modified with poly(ethylene glycol), guanidine hydrochloride, carboxymethyl cellulose and 3-glycidoxypropyltrimethoxysilane as plasticizer, modifier, toughening agent and coupling agent, respectively. And then the modified cyanobacteria was introduced to poly(vinyl)alcohol and cellulose nanofibers/poly(vinyl)alcohol matrix to improve the barrier properties of poly(vinyl)alcohol to light and water. Compared with poly(vinyl)alcohol and cellulose nanofibers/poly(vinyl)alcohol films, the obtained cyanobacteria/poly(vinyl)alcohol and the cyanobacteria/cellulose nanofibers/poly(vinyl)alcohol composites exhibit better resistance to light and water. More interestingly, we found that after adding cyanobacteria, the poly(vinyl)alcohol-based films present better barrier properties to blue-violet light and red light. In adddition, introducing cyanobacteria into poly(vinyl)alcohol or cellulose nanofibers/poly(vinyl)alcohol matrix increases the surface roughness and contact angle to water of the composites.

The optimization of empty container repositioning nets has become an essential problem in low-carbon port cooperation. This paper proposed three optimization models of multi-port low-carbon empty container repositioning considering threshold under input and output of empty containers as random variables. Non repositioning strategy means the highest threshold, and complete-repositioning strategy means the lowest threshold; threshold-repositioning strategy is in the middle. The probability of empty-container inventory in each port and the storage cost, repositioning cost, lease cost, and carbon emission cost of empty containers are calculated. This paper mainly compares each cost of three models. The results have shown that: (1) Compared with the non repositioning strategy, the threshold-repositioning strategy and complete-repositioning strategy can reduce the ports storage costs and lease costs of empty containers and also reduce carbon emissions. The lower the repositioning threshold of empty containers between ports is, the more obvious the advantages of the threshold-repositioning strategy become. (2) When the cost of storage per empty container increases, under three strategies, the total cost, storage cost, lease cost, and carbon emission cost of the port will all increase. The ports proportion of dependence on its own empty-container storage will decrease, and the proportion of dependence on other ports and leasing companies will both increase.