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Due to the fundamental differences between the quantum world and the classical world, some phenomena, such as entanglement and wave–particle duality, only exist in the quantum realm. These peculiar phenomena cannot be demonstrated by classical means: Quantum networks, quantum cryptography, and quantum precision measurements all require quantum sources. Photons are particularly well-suited as quantum sources owing to their minimal interaction with the environment, high flight speed, and ease of interaction with current typical quantum systems. Single-photon sources include pulsed excitation of quantum dots, spontaneous parametric down-conversion, and photon blockade. Herein, we propose that the anti-Jaynes–Cummings model can induce a pronounced photon antibunching effect when subjected to intense cavity dissipation. Similar to the photon blockade caused by strong photon–photon interaction, this antibunching effect is referred to as 'dissipation-induced blockade'. Our findings indicate that the minimum decay rate of a qubit, coupled with a high decay rate for photons, is conducive to achieving strong antibunching within the system. Notably, g (2) (0) < g (2) (τ) , a characteristic of photon antibunching, is only valid under the optimal condition Δ = 0 . Conversely, g (2) (0) < 1 is satisfied across all parameters, indicating that g (2) (0) < 1 is not a prerequisite for antibunching in the anti-Jaynes–Cummings model. Moreover, under the optimal conditions of the antibunching effect, the average photon number attains its peak value. Consequently, the current anti-Jaynes–Cummings model is promising for developing single-photon sources characterized by excellent purity and average photon number. [ABSTRACT FROM AUTHOR]

China aims to reduce carbon dioxide emissions and achieve peak carbon and carbon neutrality goals. Natural gas, as a high-quality fossil fuel energy, is an important transition resource for China in the process of carbon reduction, so it is necessary to predict China's natural gas demand. In this paper, a novel natural gas demand combination forecasting model is constructed to accurately predict the future natural gas demand. The Lasso model and the polynomial model are used to build a combinatorial model, which overcomes the shortcomings of traditional models, which have low data dimensions and poor prediction abilities. In the modeling process, the cross-validation method is used to adjust the modeling parameters. By comparing the performance of the combinatorial forecasting model, the single forecasting model and other commonly used forecasting models, the results show that the error (2.99%) of the combinatorial forecasting model is the smallest, which verifies the high accuracy and good stability advantages of the combinatorial forecasting model. Finally, the paper analyzes the relevant data from 1999 to 2022 and predicts China's natural gas demand in the next 10 years. The results show that the annual growth rate of China's natural gas demand in the next 10 years will reach 13.33%, at 8.3 × 1011 m3 in 2033, which proves that China urgently needs to rapidly develop the gas supply capacity of gas supply enterprises. This study integrates the impact of multiple factors on the natural gas demand, predicts China's natural gas demand from 2023 to 2033, and provides decision-making support for China's energy structure adjustment and natural gas import trade. [ABSTRACT FROM AUTHOR]

Understanding the evolution characteristics and driving mechanisms of eutrophic lake ecosystems, especially over long time scales, remains a challenge. Little research on lake ecosystem mutation has been conducted using long-term time series data. In this study, long-term water quality indicators, as well as ecological indexes, natural meteorological factors, and socio-economic indexes, were collected for Tai Lake to enable us to study the environmental evolution of the lake ecosystem. The key time nodes and early warning signals of the steady-state transformation of Tai Lake were also identified, which could provide a theoretical basis for early indication of the transformation of lake ecosystems. Furthermore, the characteristics and driving mechanism of the lake's ecosystem evolution were analyzed based on the physical and chemical indexes of its sediments and its long-term water quality indexes. The results show that the early warning signals (variance, autocorrelation, and skewness) of ecosystem mutation included abnormal changes 10 years before the steady-state change, and the evolution of Tai Lake was driven by the complex nonlinear effects of biological, physical, chemical, and socio-economic factors in the lake basin. These results have important theoretical and practical value for pollution control and the management of eutrophic lakes. [ABSTRACT FROM AUTHOR]

In this work, rutin (RT)–loaded zein–carboxymethyl starch (CMS) nanoparticles were successfully prepared by the antisolvent precipitation method. The effect of CMS on composite nanoparticles at different concentrations was studied. When the ratio of zein–RT–CMS was 10:1:30, the encapsulation efficiency (EE) was the highest, reaching 73.5%. At this ratio, the size of the composite nanoparticles was 196.47 nm, and the PDI was 0.13, showing excellent dispersibility. The results of fluorescence spectroscopy, FTIR, XRD, and CD showed that electrostatic interaction, hydrogen bonding, and hydrophobic interaction were the main driving forces for the formation of nanoparticles. It can be seen from the FE–SEM images that the zein–RT–CMS nanoparticles were spherical. With the increase in the CMS concentration, the particles gradually embedded in the cross–linked network of CMS (10:1:50). After RT was loaded on zein–CMS nanoparticles, the thermal stability and pH stability of RT were improved. The results showed that zein–CMS was an excellent encapsulation material for bioactive substances. [ABSTRACT FROM AUTHOR]

The clinical use of anticancer drugs necessitates new technologies for their safe, sensitive, and selective detection. In this article, lanthanide (Eu3+ and Tb3+)-loaded γ-cyclodextrin nano-aggregates (ECA and TCA) are reported, which sensitively detects the anticancer drug irinotecan by fluorescence intensity changes. Fluorescent lanthanide (Eu3+ and Tb3+) complexes exhibit high fluorescence intensity, narrow and distinct emission bands, long fluorescence lifetime, and insensitivity to photobleaching. However, these lanthanide (Eu3+ and Tb3+) complexes are essentially hydrophobic, toxic, and non-biocompatible. Lanthanide (Eu3+ and Tb3+) complexes were loaded into naturally hydrophilic γ-cyclodextrin to form fluorescent nano-aggregates. The biological nontoxicity and cytocompatibility of ECA and TCA fluorescent nanoparticles were demonstrated by cytotoxicity experiments. The ECA and TCA fluorescence nanosensors can detect irinotecan selectively and sensitively through the change of fluorescence intensity, with detection limits of 6.80 μM and 2.89 μM, respectively. ECA can safely detect irinotecan in the cellular environment, while TCA can detect irinotecan intracellularly and is suitable for cell labeling. [ABSTRACT FROM AUTHOR]

Oil depots in the coastal areas of China are prone to disasters caused by heavy rain due to the monsoon climate. Studies focusing on heavy rainfall vulnerability in coastal oil depots are limited. Therefore, we evaluated the safety of oil depots based on four factors in this study: personnel, equipment and facility, environment, and resilience. Complex networks, analytic hierarchy processes, and information entropy theory were used to establish an evaluation index system including four first-level indicators, nine second-level indicators, and 40 third-level indicators. Scores of 40 evaluation indicators were taken as the input, a vulnerability level of oil depots affected by heavy rain was gained as the output, and results were presented visually (different warning levels distinguished by color) to help oil depot enterprises improve their safety performance under extreme weather conditions. [ABSTRACT FROM AUTHOR]

Island-type petrochemical parks have gradually become the 'trend' in establishing new parks because of the security advantages brought by their unique geographical locations. However, due to the frequent occurrence of natural disasters and difficulties in rescue in island-type parks, an early warning model is urgently needed to provide a basis for risk management. Previous research on early warning models of island-type parks seldom considered the particularity. In this study, the early warning indicator system is used as the input parameter to construct the early warning model of an island-type petrochemical park based on the back propagation (BP) neural network, and an actual island-type petrochemical park was used as a case to illustrate the model. Firstly, the safety influencing factors were screened by designing questionnaires and then an early warning indicator system was established. Secondly, particle swarm optimization (PSO) was introduced into the improved BP neural network to optimize the initial weights and thresholds of the neural network. A total of 30 groups of petrochemical park data were taken as samples—26 groups as training samples and 4 groups as test samples. Moreover, the safety status of the petrochemical park was set as the output parameter of the neural network. The comparative analysis shows that the optimized neural network is far superior to the unoptimized neural network in evaluation indicators. Finally, the Zhejiang Petrochemical Co., Ltd., park was used as a case to verify the accuracy of the proposed early warning model. Ultimately, the final output result was 0.8324, which indicates that the safety status of the case park was "safer". The results show that the BP neural network introduced with PSO can effectively realize early warning, which is an effective model to realize the safety early warning of island-type petrochemical parks. [ABSTRACT FROM AUTHOR]

Edible packaging is a sustainable product and technology that uses one kind of "food" (an edible material) to package another kind of food (a packaged product), and organically integrates food with packaging through ingenious material design. Polysaccharides are a reliable source of edible packaging materials with excellent renewable, biodegradable, and biocompatible properties, as well as antioxidant and antimicrobial activities. Using polysaccharide-based materials effectively reduces the dependence on petroleum resources, decreases the carbon footprint of the "product-packaging" system, and provides a "zero-emission" scheme. To date, they have been commercialized and developed rapidly in the food (e.g., fruits and vegetables, meat, nuts, confectioneries, and delicatessens, etc.) packaging industry. However, compared with petroleum-based polymers and plastics, polysaccharides still have limitations in film-forming, mechanical, barrier, and protective properties. Therefore, they need to be improved by reasonable material modifications (chemical or physical modification). This article comprehensively reviews recent research advances, hot issues, and trends of polysaccharide-based materials in edible packaging. Emphasis is given to fundamental compositions and properties, functional modifications, food-packaging applications, and safety risk assessment of polysaccharides (including cellulose, hemicellulose, starch, chitosan, and polysaccharide gums). Therefore, to provide a reference for the development of modern edible packaging. [ABSTRACT FROM AUTHOR]

Three-dimensional ordered macroporous (3-DOM) IrO2 material was prepared using PMMA as a template and ammonia as a chelator. These 3-DOM IrO2 honeycomb arrays showed a large surface area and ordered macropores (155 nm in diameter) cross-linked by secondary mesopores. Internal structures of 3-DOM IrO2 material were observed microscopically through these secondary pores. According to the X-ray diffraction (XRD) and X-ray photoelectron spectroscopy (XPS) spectra, 3-DOM IrO2 has a rutile crystal structure and is mainly composed of iridium dioxide. In acidic electrolytes, the overpotential of 3-DOM IrO2 material at 0.5 mV cm−2 was only 0.22 V. Accelerated durability tests demonstrated excellent durability of 3-DOM IrO2 as an oxygen evolution reaction (OER) catalyst. [ABSTRACT FROM AUTHOR]