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Abstract Background For patients with advanced non‐small cell lung cancer (NSCLC) who have received frontline immunochemotherapy, subsequent treatment options are limited. As the first dual programmed cell death‐1 (PD‐1)/cytotoxic T lymphocyte‐associated antigen‐4 bispecific antibody approved globally, cadonilimab demonstrated potential antitumor activity in advanced NSCLC patients resistant to anti‐PD‐1/PD‐L1 antibodies. Methods We retrospectively collected efficacy and safety data from advanced NSCLC patients treated with cadonilimab‐based regimens in later therapy lines. Results A total of 41 advanced NSCLC patients refractory to anti‐PD‐1/PD‐L1 therapy were enrolled. More than half of the patients received cadonilimab‐based regimen as a fourth or later line of treatment. At the data cutoff date, treatment efficacy could be evaluated in 23 patients. One patient (4.3%) achieved partial response, eight patients (34.8%) experienced stable disease, and 14 patients (60.9%) progressed. The objective response rate and disease control rate were 4.3% and 39.1%, respectively. The median progression‐free survival for all evaluated patients was 108.0 days. Due to the short follow‐up period, the median overall survival has not yet been reached. Treatment‐related adverse events (TRAEs) and immune‐related AEs occurred in 63.4% and 22% patients, respectively. The most common TRAEs included gamma‐glutamyl transferase elevation (17.1%), coughing (14.6%), and fatigue (12.2%). Five patients (12.2%) experienced grade ≥3 TRAEs. Conclusions In this heavily pretreated cohort of advanced NSCLC patients, cadonilimab‐based regimens showed moderate antitumor efficacy with a generally tolerable and manageable safety profile. However, more evidence is needed to support the administration of cadonilimab in NSCLC patients refractory to previous anti‐PD‐1/PD‐L1 therapy.

Despite various excellent properties, sulfoaluminate cement (SAC) can meet special engineering needs only after its properties are regulated using additives. In this study, the effects of aluminium sulfate single-doping, boric acid single-doping, and boric acid-polycarboxylate superplasticiser co-doping on the hydration rate, mechanical properties, chloride penetration resistance, and sulfate corrosion resistance of SAC and its mortar were explored. The results revealed that aluminium sulfate accelerates the hydration of SAC, and the boric acid single-doping and boric acid and polycarboxylate superplasticiser co-doping both delay the hydration of SAC, which is especially delayed by the latter; the mechanical properties, chloride penetration resistance, and sulfate corrosion resistance of the mortar are always sorted as boric acid and polycarboxylate superplasticiser co-doped samples>boric acid single-doped samples>aluminium sulfate single-doped samples, which is closely related to their effects on the crystal size and matrix compactness of the cement hydration products.

Due to the fatigue and continuous energy input during high-speed dynamic friction polishing (DFP), the diamond crystal beneath the polished surface (roughness 50 nm) and even preferential crystal cleavage with the non-diamond phase (distributing at the position in micrometers range).

Abstract Under the background of digitization and greening in China, digital infrastructure offers new opportunities for developing green innovation. This paper investigates the effect of digital infrastructure on urban green innovation using panel data from 285 Chinese prefecture-level cities between 2011 and 2020, and explains the mechanism and its spatial-temporal dynamic effect. The results demonstrate that digital infrastructure promotes urban green innovation. Talent agglomeration, R&D investment increase, and industrial structure upgrading are crucial channels. Furthermore, depending on a city’s size, human capital, environmental regulations, and financial subsidies, digital infrastructure contributes to urban green innovation differently. Also, there is not only a positive spatial spillover effect of digital infrastructure but a threshold effect that presents a nonlinear trend of rising marginal effect. This study provides a new perspective for promoting digital infrastructure and urban green innovation, which makes a difference in facilitating its high-level development collaboratively.

The pulse laser emission circuit plays a crucial role as the emission unit of time-of-flight (TOF) LiDAR. This paper proposes a nanosecond-level pulse laser diode array drive circuit for LiDAR, primarily aimed at addressing the issue of high-speed scanning drive for the laser diode array at the emission end of solid-state LiDAR. Based on the single pulse laser diode drive circuit, this paper innovatively designs a circuit that includes modules such as a boost circuit, linear power supply, high-speed gate driver, GaN field-effect transistor, and pulse narrowing circuit, realizing an 8-channel laser diode array drive circuit. This circuit can achieve a pulse laser array drive with a single channel operating frequency of greater than 100 kHz, an output pulse width of less than 5 ns, a peak power greater than 75 W, and a channel switching time that does not exceed 1 μs. A field programmable gate array (FPGA) is used to control the operation of this circuit and perform a series of performance tests. Experimental results show that this circuit has a high repetition rate, large output power, a narrow pulse width, and fast switching speeds, making it highly suitable for use in the optical emission module of solid-state LiDAR.

Scientific evaluation of cultivated land quality (CLQ) is necessary for promoting rational utilization of cultivated land and achieving one of the Sustainable Development Goals (SDGs): Zero Hunger. However, the CLQ evaluation system proposed in previous studies was diversified, and the methods were inefficient. In this study, based on China’s first national standard “Cultivated Land Quality Grade” (GB/T 33469-2016), we constructed a unified county-level CLQ evaluation system by selecting 15 indicators from five aspects—site condition, environmental condition, physicochemical property, nutrient status and field management—and used the Delphi method to calculate the membership degree of the indicators. Taking Jimo district of Shandong Province, China, as a case study, we compared the performance of three machine learning models, including random forest, AdaBoost, and support vector regression, to evaluate CLQ using multi-temporal remote sensing data. The comprehensive index method was used to reveal the spatial distribution of CLQ. The results showed that the CLQ evaluation based on multi-temporal remote sensing data and machine learning model was efficient and reliable, and the evaluation results had a significant positive correlation with crop yield (r was 0.44, p < 0.001). The proportions of cultivated land of high-, medium- and poor-quality were 27.43%, 59.37% and 13.20%, respectively. The CLQ in the western part of the study area was better, while it was worse in the eastern and central parts. The main limiting factors include irrigation capacity and texture configuration. Accordingly, a series of targeted measures and policies were suggested, such as strengthening the construction of farmland water conservancy facilities, deep tillage of soil and continuing to construct well-facilitated farmland. This study proposed a fast and reliable method for evaluating CLQ, and the results are helpful to promote the protection of cultivated land and ensure food security.

In the era of the digital economy (DE), the traditional economic growth paradigm is no longer applicable. To explore whether the DE can improve the urban ecological problems left behind by rough economic growth, this study examines the effects of land resource mismatch (LRM) on urban ecological resilience (UER) and evaluates the mitigating influence of the DE. This analysis utilizes data from 280 prefectural-level cities in China over the period from 2007 to 2021 and reveals that LRM significantly undermines UER, with this conclusion remaining robust across a series of tests. Additionally, the detrimental impact of LRM on UER is more pronounced in megacities, cities with high levels of economic development, and those with a lower degree of advanced industrial structure. In further analysis, this study finds that the digital economy can optimize the allocation of land resources, thereby enhancing urban ecological resilience, which has the effect of “treating the root causes”. In addition, digital government and digital infrastructure, as key elements of the digital economy, also mitigate the negative impacts of land resource misallocation on urban ecological resilience, having the effect of “treating the symptoms”. Finally, this study proposes policy suggestions such as optimizing ecological layout, deepening land reform, and promoting digital government and infrastructure construction to provide a theoretical basis and practical guidance for local governments to enhance UER and help build a new model of greener, more resilient, and sustainable urban development.

Fintech is driving the revolution of finance and profoundly affecting the development of the financial sector. However, few studies examined how commercial banks’ fintech innovation affects total factor productivity (TFP). To build up the fintech index of commercial banks, we use web crawler technology to accumulate news related to the fintech innovation of commercial banks in Baidu news. We use the panel data of 72 banks in China from 2010 to 2020 to explore the impacts and mechanisms of fintech on commercial banks’ TFP. The results show that fintech innovation effectively improves TFP after a series of robustness tests. Further, we find that fintech innovation can improve commercial banks’ TFP by promoting innovations of financial products, increasing risk control capability, reducing cost, and improving profit. Also, the utility of fintech is more significant in banks with more assets, facilities, and human capital, which means that fintech innovation creates a “bigger is better” mindset. Meanwhile, the result of quantile regression shows that the higher the fintech innovation, the more significant the increase in TFP, which further reveals that there is ‘too big to fail’ among commercial banks under digitalization.

Abstract Objective To elucidate the potential benefits of combining radiotherapy and epidermal growth factor receptor‐tyrosine kinase inhibitors (EGFR‐TKIs) for individuals with Stage IV lung adenocarcinoma (LUAD) harboring either exon 19 deletion (19‐Del) or exon 21 L858R mutation (21‐L858R). Methods In this real‐world retrospective study, 177 individuals with Stage IV LUAD who underwent EGFR‐TKIs and radiotherapy at Shandong Cancer Hospital from June 2012 to August 2017 were included. The main focus of this real‐world study was overall survival (OS). Results The clinical characteristics of patients with Stage IV LUAD harboring 19‐Del were similar to those harboring 21‐L858R (p > 0.05). Overall, the patients had a median OS (mOS) of 32.0 months (95% confidence interval [CI]: 28.6–35.5). Subsequently, multivariate analysis indicated that both EGFR mutations and thoracic radiotherapy were independent predictors of OS (p = 0.001 and 0.013). Furthermore, subgroup analysis highlighted a longer OS for the 19‐Del group compared to the 21‐L858R group, especially when EGFR‐TKIs were combined with bone metastasis or thoracic radiotherapy (mOS: 34.7 vs. 25.1 months and 51.0 vs. 29.6 months; p = 0.0056 and 0.0013, respectively). However, no significant differences were found in OS when considering patients who underwent brain metastasis radiotherapy (mOS: 34.7 vs. 25.1 months; p = 0.088). Conclusions Patients with Stage IV LUAD harboring 19‐Del experience a notably prolonged OS following combined therapy with EGFR‐TKIs and radiotherapy, while this OS benefit is observed despite the absence of substantial differences in the clinical characteristics between the 19‐Del and 21‐L858R groups.

Abstract Antioxidant collagen hydrolysates refers to the peptides mixture with antioxidant properties identified from hydrolyzed collagen. Due to its specific structural, biological and physicochemical properties, collagen hydrolysates have been explored as a multifunctional antioxidant in the biomedical field. In this review, we summarize recent advances in antioxidant collagen hydrolysates development. Initially, the preparation process of antioxidant collagen hydrolysates is introduced, including the production and separation methods. Then the effects and the mechanisms of amino acid composition and collagen peptide structure on the antioxidant activity of collagen hydrolysates are reviewed. Finally, the applications of antioxidant collagen hydrolysates in biomedical domains are summarized, with critical discussions about the advantages, current limitations and challenges to be resolved in the future. Graphical abstract

Abstract Handling emotions in human‐computer dialogues has emerged as a challenging task which requires artificial intelligence systems to generate emotional responses by jointly perceiving the emotion involved in the input posts and incorporating it into the generation of semantically coherent and emotionally reasonable responses. However, most previous works generate emotional responses solely from input posts, which do not take full advantage of the training corpus and suffer from generating generic responses. In this study, we introduce a hierarchical semantic‐emotional memory module for emotional conversation generation (called HSEMEC), which can learn abstract semantic conversation patterns and emotional information from the large training corpus. The learnt semantic and emotional knowledge helps to enrich the post representation and assist the emotional conversation generation. Comprehensive experiments on a large real‐world conversation corpus show that HSEMEC can outperform the strong baselines on both automatic and manual evaluation. For reproducibility, we release the code and data publicly at: https://github.com/siat‐nlp/HSEMEC‐code‐data.

The development of urban digital platforms has changed its entrepreneurial environment and affected regional innovation vitality. We calculated the development index of digital platforms of 294 prefecture-level cities in China between 2013 and 2020 using the principal component analysis method. We used the microdata of enterprise registration information to describe the urban entrepreneurial activity. Digital platforms have promoted urban entrepreneurial activity significantly. Moreover, alleviating labour market distortions, optimizing the urban financial environment, and improving technological innovation are virtual channels for digital platforms to increase urban entrepreneurial activity. Furthermore, digital platforms play a more significant role in promoting urban entrepreneurial activity in the eastern region and cities with better industrial structures. This impact has a nonlinear increasing “marginal effect” in which the faster the development of digital platforms, the more significant the promoting effect on urban entrepreneurial activity.

In today's digital era, households are empowered by digital servitization, which could potentially impact their ability to become entrepreneurs. Using data from the China Family Panel Studies (CFPS) and the National Bureau of Statistics of China, we construct the digital servitization index to analyze the impact of digital servitization on household entrepreneurship. The results show that the utilization of digital servitization by households significantly increases the likelihood of them engaging in entrepreneurial endeavors. This result remains robust after a series of robustness tests. We also find that digital servitization can help households identify entrepreneurial opportunities and access entrepreneurial resources by alleviating information and financial constraints, and increasing social capital. Further, the effect is more among households with rural residences, low material assets, elderly household heads, and household heads with low cognitive abilities, which means that digital servitization can promote underdog entrepreneurship. In conclusion, this paper provides micro-level evidence supporting the idea that digital servitization can foster household entrepreneurship, particularly among underprivileged groups. Ultimately, this paper highlights the potential of digitization as an essential resource to drive economic growth and help households in need become successful entrepreneurs.

There is a lack of micro evidence on whether medical insurance may optimize the household financial asset allocation by transferring health risk, despite the fact that health risk is a significant component driving families’ precautionary savings. This article empirically examines the impact of health risk and social medical insurance on household risky financial asset allocation using a Probit model, based on data from the 2015–2019 China Household Finance Survey (CHFS). The findings indicate that social medical insurance, with its lower level of security, reduces the likelihood, but it can alter households’ preferences for risk by lowering marginal effect of health risk. According to the findings of the heterogeneity analysis, people who live in rural and less developed areas are more likely to experience the risk-inhibiting effects of social medical insurance and health risk. The eroding and risk-suppressing impacts of social medical insurance are likewise less pronounced for households headed by women and older people, as is the health risk’s suppressive influence on household involvement in risky financial markets. Compared with social medical insurance, commercial medical insurance with a higher level of coverage can dramatically increase household participation in riskier financial markets. This article provides micro-empirical evidence for the household asset allocation effect of medical insurance.

ABSTRACTTimely and accurate wetland information is necessary for wetland resource management. Recent advances in machine learning and remote sensing have facilitated cost-effective monitoring of wetlands. However, reliable methods for fine-grained and rapid wetland mapping are still lacking. To address the issue, a wetland sample set with 20 categories for China was collected based on a sampling strategy that combines automatic sample generation and visual interpretation. Simultaneously, a novel multi-stage method for fine-grained wetland classification was proposed, which integrates pixel-based and object-based strategies using ensemble learning algorithms and multi-source remote sensing data. First, a pixel-based ensemble learning algorithm was implemented to classify five rough wetland categories and six non-wetland categories. Second, an object-based ensemble learning approach was designed to separate the water cover in the pixel-based classification results into eight detailed categories. Third, the merged pixel-based and object-based classification results were refined with knowledge-based post-processing procedures to identify 14 fine-grained wetland categories. Results using the Pixel Information Expert Engine (PIE-Engine) cloud platform proved the effectiveness of the proposed wetland classification method. The overall accuracy, kappa, and weighted F1 reached 87.39%, 82.80%, and 86.02%, respectively. The adopted ensemble learning algorithm yielded better performance than classifiers such as CatBoost, random forest, and XGBoost. The incorporation of spectral, texture, shape, topographic, and geographic features from multi-source data contributed to differentiating wetland categories. According to the relative contribution, spectral indexes (NDVI and NDWI), texture features (sum average and contrast), and topographic features (slope and elevation) were identified as important leading predictors for the first-stage pixel-based classification. Shape features (shape index and compactness) and auxiliary features (geographic location) were crucial predictors for the second-stage object-based classification. Compared with other products, our 10-m wetland mapping results for national wetland reserves were rich in detail and fine in categories. Overall, the constructed sample set and developed classification method show promise in laying a foundation for large-scale wetland mapping. The derived wetland maps can provide support for wetland protection and restoration.

Currently, the high-purity single crystal diamond preparation is still a challenging task, which restricts the application of diamond in electronics. In this paper, the chemical vapor deposition (CVD) IIa type single crystal diamond was prepared on the IIa type high pressure high temperature (HPHT) substrate by combining the chamber design with the plasma purifying technology, and the performance of diamond detectors using both IIa type single crystal diamonds were compared based on the defect analysis. The dislocations in diamonds were directly observed by synchrotron radiation X-ray white-beam topography. The CVD diamond shows the comparatively low dislocation density resulting from the high-quality HPHT diamond substrate. No observable NV impurity could be found in photoluminescence (PL) spectrum. The trace impurity of substitutional nitrogen (Ns) was characterized by electron paramagnetic resonance (EPR). It shows the very low concentration in the CVD diamond (2.1 ppb) compared with the several tens ppb in HPHT diamond. This value is comparable with the electronic-grade diamond reported by Element Six. Furthermore, both of the HPHT and CVD single crystal diamonds were packaged as the α-particle radiation detectors with the typical sandwich structure and used Ni/Al (50 nm/500 nm) as metal electrodes. The energy resolution of HPHT and CVD diamond detectors were 2.04% and 0.86%, respectively. Based on Schottky contact and defect regulation, at 0 voltage, the CVD diamond detector achieved response of α-particle radiation and the charge collection efficiency (CCE) was 10.76%. Under positive voltage, the CCE of HPHT and CVD diamond detectors were 38.11% and 86.65%, respectively. The deep level trapping effect of Ns impurities on charge carriers resulted in low CCE of HPHT diamond detector. The spatial polarization effect caused by defects leaded to the formation of an internal electric field, which was the main reason affecting the transport of charge carriers in CVD diamond.

The PIESAT-01 constellation is the world’s first multi-baseline distributed synthetic aperture radar (SAR) constellation with a “Cartwheel” formation. The “Cartwheel” formation is a unique formation in which four satellites fly in companion orbits, ensuring that at any given moment, the main satellite remains at the center, with three auxiliary satellites orbiting around it. Due to this unique configuration of the PIESAT-01 constellation, four images of the same region and six pairs of baselines can be obtained with each shot. So far, there has been no imaging and interference research based on four-satellite constellation measured data, and there is an urgent need to explore algorithms for the “Cartwheel” configuration imaging and digital surface model (DSM) production. This paper introduces an improved bistatic SAR imaging algorithm under the four-satellites interferometric mode, which solves the problem of multi-orbit nonparallelism in imaging while ensuring imaging coherence and focusing ability. Subsequently, it presents an interferometric processing method for the six pairs of baselines, weighted fusion based on elevation ambiguity from different baselines, to obtain a high-precision DSM. Finally, this paper selects the Dingxi region of China and other regions with diverse terrains for imaging and DSM production and compares the DSM results with ICESat-2 global geolocated photon data and TanDEM DSM data. The results indicate that the accuracy of PIESAT-01 DSM meets the standards of China’s 1:50,000 scale and HRTI-3, demonstrating a high level of precision. Moreover, PIESAT-01 data alleviate the reliance on simulated data for research on multi-baseline imaging and multi-baseline phase unwrapping algorithms and can provide more effective and realistic measured data.

Phyllanthus emblica Linn. fruit that primarily contains many polyphenols, have been considered homologous food for centuries and have been used as a remedy for a variety of ailments, including maintaining brain function. It is, however, unclear what is the mechanism of protective action of Phyllanthus emblica Linn. fruit polyphenols (PEFPs) against cognitive impairment and anxiety induced by acute paradoxical sleep deprivation (SD). Behavioral abnormalities induced by acute paradoxical SD were effectively alleviated by PEFPs, which also prevented histopathological and morphological damage to hippocampal neurons. Acute paradoxical SD-induced cognitive impairments and anxiety are treated by PEFPs through modulating a variety of biological functional modules, such as oxidative stress, inflammation/immune, cell proliferation and differentiation, metabolism, and neurotransmitter signaling. Further results showed that PEFPs markedly counteracted oxidative stress damage and neuroinflammation by activating the Nrf2 pathway. In summary, PEFPs are attractive nutraceutical ingredients for preventing cognitive impairment and anxiety associated with sleep loss.

The emergence of immunotherapy, particularly programmed cell death 1 (PD-1) and programmed cell death ligand-1 (PD-L1) produced profound transformations for treating non-small cell lung cancer (NSCLC). Nevertheless, not all NSCLC patients can benefit from immunotherapy in clinical practice. In addition to limited response rates, exorbitant treatment costs, and the substantial threats involved with immune-related adverse events, the intricate interplay between long-term survival outcomes and early disease progression, including early immune hyperprogression, remains unclear. Consequently, there is an urgent imperative to identify robust predictive and prognostic biological markers, which not only possess the potential to accurately forecast the therapeutic efficacy of immunotherapy in NSCLC but also facilitate the identification of patient subgroups amenable to personalized treatment approaches. Furthermore, this advancement in patient stratification based on certain biological markers can also provide invaluable support for the management of immunotherapy in NSCLC patients. Hence, in this review, we comprehensively examine the current landscape of individual biological markers, including PD-L1 expression, tumor mutational burden, hematological biological markers, and gene mutations, while also exploring the potential of combined biological markers encompassing radiological and radiomic markers, as well as prediction models that have the potential to better predict responders to immunotherapy in NSCLC with an emphasis on some directions that warrant further investigation which can also deepen the understanding of clinicians and provide a reference for clinical practice.

Microorganisms promoted corrosion has caused significant loss to marine engineering and the antibacterial coatings have served as a solution that has gained attention. In this study, the chemical vapour deposition technique has been employed to grow three different types of diamond coatings, namely, ultrananocrystalline diamond (UNCD), nanocrystalline diamond (NCD), and microcrystalline diamond (MCD) coatings. The evolution of associated surface morphology and the surface functional groups of the grown coatings have demonstrated antibacterial activity in seawater environments. It is found that different ratio of sp3/sp2 carbon bonds on the diamond coatings influences their surface property (hydrophobic/hydrophilic), which changes the anti-adhesion behaviour of diamond coatings against bacteria. This plays a critical role in determining the antibacterial property of the developed coatings. The results show that the diamond coatings arising from the deposition process kill the bacteria via a combination of the mechanical effects and the functional groups on the surface of UNCD, NCD, and MCD coatings, respectively. These antibacterial coatings are effective to both Gram-negative bacteria (E. coli) and Gram-positive bacteria (B. subtilis) for 1–6 h of incubation time. When the contact duration is prolonged to 6 h or over, the MCD coatings begin to reduce the bacteria colonies drastically and enhance the bacteriostatic rate for both E. coli and B. subtilis.

ObjectiveAs one of the cancers that seriously threatens women’s health, ovarian cancer has a high morbidity and mortality rate. Surgery and chemotherapy are the basic treatment strategies for ovarian cancer, and chemotherapy resistance is a significant factor in affecting the prognosis, survival cycle, and recurrence of ovarian cancer. This article aims to analyze articles about ovarian cancer and drug resistance via bibliometric software, offering new ideas and directions for researchers in this field.MethodsBoth Citespace and Vosviewer are bibliometric software on the Java platform. Articles were collected on ovarian cancer and drug resistance in the Web of Science Core Collection database from 2013 to 2022. The countries, institutions, journals, authors, keywords, and references were analyzed, and the development status of this field was indicated from multiple perspectives.ResultsStudies on ovarian cancer and drug resistance generally showed an increasing trend from 2013 to 2022. The People’s Republic of China and Chinese institutions contributed more to this field. Gynecologic Oncology published the most articles, and the journal with the most citations was Cancer Research. Li Li was the author with the most publications, and Siegel RL was the author with the most citations. Through burst detection, it can be found that the research hotspots in this field mainly focused on the in-depth exploration of the drug resistance mechanism of ovarian cancer and the progress of PARP inhibitors and bevacizumab in the treatment of ovarian cancer.ConclusionsMany studies on the mechanism of drug resistance in ovarian cancer have been discovered; however, the deeper mechanism remains to be explored. Compared with traditional chemotherapy drugs, PARP inhibitors and bevacizumab have shown better efficacy, but PARP inhibitors have initially shown drug resistance. The future direction of this field should be to overcome the resistance of existing drugs and actively develop new ones.

The low-carbon city pilot (LCCP) policy is an essential strategy for China to combat climate change, attain the carbon neutrality target, and achieve comprehensive green transformation, which will inevitably have an impact on many areas of the economic system. The digital economy has given a new impetus to green development and sustainable growth in China, becoming an essential indicator of green transformation. Promoting the digital economy's growth is essential to successful green development. Does China's LCCP policy support digital economy growth while reducing energy use and emissions? This study evaluates digital economy growth in China's 277 cities from 2002 to 2019 using the principal component analysis method and employing the difference-in-differences method to assess how the LCCP policy affects digital economy growth. The empirical results demonstrate that the LCCP policy encourages digital economy growth, which then promotes green development. The conclusion remains tenable following several robustness tests, including parallel trend, placebo, and endogeneity tests. A heterogeneity analysis demonstrates that the LCCP policy has a more profound influence on digital economy growth in coastal, non-resource-based, and large-scale cities. A mechanism test indicates that the LCCP policy positively encourages digital economy growth by promoting technological innovation and changing the industrial structure. This study offers new empirical evidence that LCCP policy can advance the achievement of green development.

Abstract Diamond exhibits large application potential in the field of power electronics, owing to its excellent and desirable electronic properties. However, the main obstacles to its development originate from the small‐sized single‐crystal wafers and the instability of the electrical conductivity. This work presents a metal‐oxide‐semiconductor field‐effect transistor (MOSFET) on a diamond substrate derived from a five‐inch (110) highly preferred polycrystalline diamond film. The MOSFETs with excellent performance are fabricated by combining an H‐terminated channel and an epitaxially grown boron‐doped layer as the source/drain contacts of the diamond devices. According to the electrical statistical results of ≈110 devices on the polycrystalline diamond substrate, 44% of devices show normally‐off operation with a maximum current density of 400 mA mm−1, while 56% of devices demonstrate normally‐on operation with a maximum current density of 525 mA mm−1. The normally‐off characteristics are more related to the higher amounts of nitrogen concentration than the grain boundaries. The stable boron‐doped source and drain provide a high concentration of holes, which facilitate transport in the surface p‐type channel induced by the H‐termination. The characteristics of the MOSFETs are inspiring for the fabrication of complementary inverter circuits on large diamond wafers.

Collagen is a biopolymer in animals that has been widely used in the food field to improve the value of health benefits in products. Chicken skin, which is a collagen-containing food, is popular as a healthy fat replacer for processed meat. To enable high information transparency of the products, the chicken-derived components need to be traceable to guarantee food safety and quality. However, the current methods for identifying the chicken-derived components in food still lack accuracy. Here a combination of two marker peptides were discovered and employed to establish a sensitive method based on LC-MS for the detection of chicken-derived components in collagen-containing foods. This method has a very low detection limit of 0.33 pg and 0.16 pg for two marker peptides, respectively. The results illustrated that the combination of these maker peptides could be utilized for the authentication of the chicken skin-derived components in processed meat.

Urban digital infrastructure is the cornerstone of optimizing resource allocation and promoting sustainable economic development in the era of digital economy, and it will also affect corporate ESG performance. Based on the data of Chinese A-share listed companies from 2011 to 2021, an asymptotic difference-in-difference model is used to investigate the impact of urban digital infrastructure on corporate ESG performance based on the “broadband China” strategy and its underlying mechanism. This paper finds that urban digital infrastructure can promote corporate ESG performance. Further, urban digital infrastructure can contribute to corporate ESG performance by increasing research and development (R&D) investment, improving corporate governance, and increasing information transparency. Through heterogeneity analysis, the results show urban digital infrastructure contributes more significantly to the ESG performance of state-owned, small and medium, growth-stage, and low-profit companies and is more pronounced in non-heavy polluting companies and companies in the central and western regions. This paper has enhanced the theoretical framework of urban digital infrastructure and corporate ESG (environmental, social, and governance) performance, paving the way for a new approach to the collaborative development of cities and enterprises in pursuit of green and sustainable growth.

Digital transformation is seen as an “elixir” for companies to improve their economic performance and expand their market power in the digital economy. Therefore, how does digital transformation affect enterprises’ market power? This paper used machine learning to construct a digital transformation index and used panel data of listed enterprises from 2008 to 2020 to study the impact of digital transformation on market power and its mechanism of action. The findings showed that digital transformation significantly increases market power, and this conclusion still holds after considering potential endogeneity issues and conducting robustness tests. The results of mechanism analysis revealed that digital transformation facilitates endogenous scale expansion and promotes merger and acquisition (M&A), which reshapes firm boundaries and, thus, enhances market power. This paper revealed new changes in the micro-organization of enterprises in the context of digital transformation and provided micro-evidence for the industrial organization effect of digital transformation.

The orthogonal experiment of laser planarization of CVD polycrystalline diamond film was carried out, and the morphology was analyzed by using scanning electron microscope (SEM). The surface roughness Ra, the surface roughness Sa and the taper of kerf were measured by confocal laser scanning microscope. The influence of laser parameters on cutting quality was analyzed. The results show that the factors affecting kerf taper are pulse width, pulse frequency, feed speed and laser current in turn, and the factors affecting line roughness are feed speed, laser current, pulse frequency and pulse width in turn. After optimization by orthogonal test, the best groove surface morphology can be obtained with laser current of 64 A, pulse width of 400 μs, pulse frequency of 275 Hz and feed speed of 100 mm/min. Using the optimized parameters, the surface roughness Sa was measured to be 11.7 μm. When the incident angle increased to 75°, the surface roughness Sa decreased to 1.9 μm, and the actual removal efficiency reached 1.1 mm3/min.

Active phased array antenna typically featured high performance, high device integration, and high heat flux, making it difficult to dissipate heat. Diamond, the substance with the closest arrangement of atoms in nature, has the advantages of a high thermal conductivity and strong adaptability to the space environment. The batch applications of high-thermal-conductivity diamonds for the thermal management of the phased array antennas of the inter-satellite links were introduced in this paper. The diamond was developed by the direct-current arc-plasma chemical vapor deposition method. The product size, thermal conductivity, precision, and application scale all met the engineering requirements. The high-precision assembly of the diamond and the structural frame enabled the efficient heat collection and transfer from the distributed point heat sources of multiple transmit/receive (T/R) modules. Verified on the ground, the thermal matching design between the diamond and the metal frame exhibited an outstanding heat dissipation performance. After four satellites using the diamonds were launched, the flight data showed good antenna thermal control, with temperature gradients of the T/R modules less than 2.2 °C, further verifying the rationality and effectiveness of using high-thermal-conductivity diamonds in the thermal design and implementation of antennas.

The burgeoning multi-field applications of diamond concurrently bring up a foremost consideration associated with nitrogen. Ubiquitous nitrogen in both natural and artificial diamond in most cases as disruptive impurity is undesirable for diamond material properties, eg deterioration in electrical performance. However, the feat of this most common element-nitrogen, can change diamond growth evolution, endow diamond fancy colors and even give quantum technology a solid boost. This perspective reviews the understanding and progress of nitrogen in diamond including natural occurring gemstones and their synthetic counterparts formed by high temperature high pressure (HPHT) and chemical vapor deposition (CVD) methods. The review paper covers a variety of topics ranging from the basis of physical state of nitrogen and its related defects as well as the resulting effects in diamond (including nitrogen termination on diamond surface), to precise control of nitrogen incorporation associated with selective post-treatments and finally to the practical utilization. Among the multitudinous potential nitrogen related centers, the nitrogen-vacancy (NV) defects in diamond have attracted particular interest and are still ceaselessly drawing extensive attentions for quantum frontiers advance.

ABSTRACT Human cytomegalovirus (HCMV) infects 36% to almost 100% of adults and causes severe complications only in immunocompromised individuals. HCMV viral surface trimeric (gH/gL/gO) and pentameric (gH/gL/UL128/UL130/UL131A) complexes play important roles in HCMV infection and tropism. Here, we isolated and identified a total of four neutralizing monoclonal antibodies (MAbs) derived from HCMV-seropositive blood donors. Based on their reactivity to HCMV trimer and pentamer, these MAbs can be divided into two groups. MAbs PC0012, PC0014, and PC0035 in group 1 bind both trimer and pentamer and neutralize CMV by interfering with the postattachment steps of CMV entering into cells. These three antibodies recognize antigenic epitopes clustered in a similar area, which are overlapped by the epitope recognized by the known neutralizing antibody MSL-109. MAb PC0034 in group 2 binds only to pentamer and neutralizes CMV by blocking the binding of pentamer to cells. Epitope mapping using pentamer mutants showed that amino acid T94 of the subunit UL128 and K27 of UL131A on the pentamer are key epitope-associated residues recognized by PC0034. This study provides new evidence and insight information on the importance of the development of the CMV pentamer as a CMV vaccine. In addition, these newly identified potent CMV MAbs can be attractive candidates for development as antibody therapeutics for the prevention and treatment of HCMV infection. IMPORTANCE The majority of the global population is infected with HCMV, but severe complications occur only in immunocompromised individuals. In addition, CMV infection is a major cause of birth defects in newborns. Currently, there are still no approved prophylactic vaccines or therapeutic monoclonal antibodies (MAbs) for clinical use against HCMV infection. This study identified and characterized a panel of four neutralizing MAbs targeting the HCMV pentamer complex with specific aims to identify a key protein(s) and antigenic epitopes in the HCMV pentamer complex. The study also explored the mechanism by which these newly identified antibodies neutralize HCMV in order to design better HCMV vaccines focusing on the pentamer and to provide attractive candidates for the development of effective cocktail therapeutics for the prevention and treatment of HCMV infection.

Abstract Background “Schistosomiasis” is a highly recurrent parasitic disease that affects a wide range of areas and a large number of people worldwide. In China, schistosomiasis has seriously affected the life and safety of the people and restricted the economic development. Schistosomiasis is mainly distributed along the Yangtze River and in southern China. Anhui Province is located in the Yangtze River Basin of China, with dense water system, frequent floods and widespread distribution of Oncomelania hupensis that is the only intermediate host of schistosomiasis, a large number of cattle, sheep and other livestock, which makes it difficult to control schistosomiasis. It is of great significance to monitor and analyze spatiotemporal risk of schistosomiasis in Anhui Province, China. We compared and analyzed the optimal spatiotemporal interpolation model based on the data of schistosomiasis in Anhui Province, China and the spatiotemporal pattern of schistosomiasis risk was analyzed. Methods In this study, the root-mean-square-error (RMSE) and absolute residual (AR) indicators were used to compare the accuracy of Bayesian maximum entropy (BME), spatiotemporal Kriging (STKriging) and geographical and temporal weighted regression (GTWR) models for predicting the spatiotemporal risk of schistosomiasis in Anhui Province, China. Results The results showed that (1) daytime land surface temperature, mean minimum temperature, normalized difference vegetation index, soil moisture, soil bulk density and urbanization were significant factors affecting the risk of schistosomiasis; (2) the spatiotemporal distribution trends of schistosomiasis predicted by the three methods were basically consistent with the actual trends, but the prediction accuracy of BME was higher than that of STKriging and GTWR, indicating that BME predicted the prevalence of schistosomiasis more accurately; and (3) schistosomiasis in Anhui Province had a spatial autocorrelation within 20 km and a temporal correlation within 10 years when applying the optimal model BME. Conclusions This study suggests that BME exhibited the highest interpolation accuracy among the three spatiotemporal interpolation methods, which could enhance the risk prediction model of infectious diseases thereby providing scientific support for government decision making.

A shortwave infrared Ge–Si photodetector will become the core device of the LiDAR optical receiver. In order to meet the urgent demand for photodetectors in the LiDAR field, we designed and produced a 32 × 32-pixel Ge–Si photodetector array, which was proposed and developed to meet the performance requirements of the detector array. A dark current detection system for fast scanning and detecting large-scale Ge–Si detector arrays was proposed and developed to achieve the rapid detection of dark current in each detector pixel, with a detection accuracy of less than 1 nA. The system validated the primary performance indicators of the detector array we designed, achieving rapid discrimination of the array performance and rapid localization of damaged pixels. The scanning test results showed that the average dark current of the designed detector array chip was on the nanoampere level, and the proportion of bad points was less than 1%. The consistency of the array chip was high, which meets the requirements of light detection at the receiving end of LiDAR. This work laid the foundation for our subsequent development of a LiDAR prototype system.

In the context of developing FinTech innovation, a commercial bank’s use of FinTech innovation can improve its risk management capability, thereby reducing its risk-taking. This paper explores the impact and mechanism of a bank’s FinTech innovation on its risk-taking using panel data of 65 commercial banks between 2008 and 2020. We innovatively construct a bank-level index based on web crawler technology and obtain the annual numbers of news items about a bank’s FinTech innovation from each bank in Baidu News. The empirical results show that improvement in the bank’s FinTech innovation significantly reduces its risk-taking. To overcome endogenous problems, including measurement errors and omitted variables, we use the instrumental variables (IV) and difference-in-differences (DID) methods to test the hypothesis and obtain consistent estimated results. The mechanism analysis shows that banks rely on FinTech innovation to reduce their risk-taking by improving their operating income and capital adequacy ratio, optimizing their operating performance, and improving their risk control capabilities. Further, a heterogeneity analysis shows that the effect of a bank’s FinTech innovation in reducing its risk-taking is more pronounced in larger, state-owned, joint-stock, and highly-competitive commercial banks. Our research results still hold after a series of robustness tests, including changing the construction methods of the bank’s FinTech innovation index, replacing the bank’s risk-taking indicators, tail-shrinking treatment, and changing samples. Our findings provide micro evidence for the application of FinTech innovation in commercial banks to reduce their risk-taking.

Cancer is a major public health problem around the world and the key leading cause of death in the world. It is well-known that glucolipid metabolism, immunoreaction, and growth/death pattern of cancer cells are markedly different from normal cells. Recently, acyl-CoA synthetase long-chain family 4 (ACSL4) is found be participated in the activation of long chain fatty acids metabolism, immune signaling transduction, and ferroptosis, which can be a promising potential target and biomarker for anticancer. Specifically, ACSL4 inhibits the progress of lung cancer, estrogen receptor (ER) positive breast cancer, cervical cancer and the up-regulation of ACSL4 can improve the sensitivity of cancer cells to ferroptosis by enhancing the accumulation of lipid peroxidation products and lethal reactive oxygen species (ROS). However, it is undeniable that the high expression of ACSL4 in ER negative breast cancer, hepatocellular carcinoma, colorectal cancer, and prostate cancer can also be related with tumor cell proliferation, migration, and invasion. In the present review, we provide an update on understanding the controversial roles of ACSL4 in different cancer cells.

Whether families using big data-based digital payments will increase household healthcare expenditure is a subject that needs to be investigated in the era of big data. Based on the data from China Family Panel Studies (CFPS), 24,126 samples from 2014 to 2018 are used to examine the impact and mechanism of big data-based digital payments on household healthcare expenditure. The empirical results of this paper show that the use of digital payments by households can significantly increase household healthcare expenditure with the empowerment of big data. This research employs the instrumental variable method to verify and produce consistent estimation results in order to address potential endogeneity issues such as measurement error and missing variables. We learn via mechanism analysis that household adoption of big data-driven digital payments can remove credit limitations and build social capital, resulting in higher household health-care spending. We also perform a heterogeneity analysis. The findings reveal that when a family's traditional financial accessibility is high, the head of the household is young or middle-aged, and the head of the household has a higher level of education, digital payment will play a larger role in encouraging household healthcare expenditure. The conclusions of this paper are still solid after changing the indicators of household healthcare expenditure substituting the indicators of digital payment, and adjusting the variables. As a result, this article provides micro-evidence for the usage of digital payments by households to enhance healthcare spending.JEL ClassificationD12 G21 O30 O53 I12

The tropical cyclone Idai caused severe floods in Mozambique in March 2019. Sentinel-1 and Sentinel-2 images are processed to monitor the flood disaster in Pungue and Buzi rivers by combining the object-based image analysis approach and the decision tree algorithm. Water is preliminarily extracted from Sentinel-1 image, and shadows of mountain and buildings in the study area are extracted from Sentinel-2 image. Mountain shadow is extracted based on the decision tree classification rules constructed by the digital elevation model and the index model, while building shadow is extracted by constructing the decision tree classification rules using the features of objects. Water extraction results are combined with shadows to eliminate confusing shadows in the preliminary extraction to obtain the accurate flood extent. Sentinel-2 images are used to classify land use types in multiple periods before and after the flood and analyze the disaster by combining with the change information of the water. Land use type changes are analyzed and predicted with the CA-Markov model. The results show that the maximum submerged area was 3.5 times of the normal water area, and that most areas were submerged for more than 10 d. The area between Pungue and Buzi rivers were severely affected, where a large number of crops and villages were submerged. Grassland and cropland were the most seriously submerged. The overall accuracy of extracted water results ranges from 86% to 92%. The results indicated that the combining method can effectively suppress the influence of shadows on water extraction results.

In the field of cartography and geographic information systems, eliminating self-intersection problems is a key step in improving the reliability and robustness of spatial data representations, especially for the multipart polygon with one outer ring (MPOOR). In traditional studies, self-intersected MPOORs were identified by the topological intersection relationships between the line segments, but during map generalization or map spatial calculations, identifying only topological self-intersections is insufficient. Elements (vertices and line segments) that are topologically separated but have a distance smaller than the minimum visible distance (MVD) on the map also need to be considered to avoid graphic conflicts. The former condition is known as a topological self-intersection (TS), while the latter is known as a cartographic self-intersection (CS). Both the above two cases are called visual self-intersections in this paper, and a method for the classification and cleaning of visually self-intersected MPOORs is proposed. First, four visual self-intersection patterns are distinguished and defined, namely, intersecting, separating, collinear and combined, which are further divided into ten refined patterns that can be identified automatically based on the topological and distance relationships between the vertices and line elements. Second, general displacement and shrinkage algorithms are proposed to clean visually self-intersected MPOORs by taking the conflict regions and the MVD as constraints. Finally, real data from the Second National Land Survey of five counties in Guizhou Province in China are used for validation. The experimental results show that all the TS patterns identified by the Martinez-Llario method were successfully captured by the proposed method, and two TS patterns and four CS patterns were newly identified. Moreover, the proposed method yields more reasonable cleaning results that are not only topologically correct but also morphologically consistent and visually distinguishable.

The local connection characteristics of convolutional neural network (CNN) are linked with the local spatial correlation of image pixels for water depth retrieval in this article. The method has greater advantages and higher precision than traditional retrieval methods. Traditional remote sensing empirical models require manual extraction of retrieval factors and the process is complex. This article proposes a model based on CNN, which uses different remote sensing images in four spectral bands, red, green, blue, and near-infrared, to retrieve the water depth. In general, CNN is mostly used for image recognition and classification tasks, which can make full use of the local spatial correlation between pixels. The method in this article exploits this feature of CNN for water depth retrieval, taking into consideration the nonlinear relationship between the radiance value and water depth value from adjacent and central pixels. In this article, remote sensing image data, measured water depth data, and lidar sounding data are used as input data to build the model. Then, the retrieval error is analyzed and the parameters are adjusted to further optimize the model. Quantitative analysis and experimental results show that the accuracy of the CNN model in shallow sea areas retrieval is improved by more than 50%. The mean absolute error can reach within 0.8 m. Finally, the model is shown to be highly portable and capable of retrieving water depth data with resolution equal to the spatial resolution of the remote sensing image using only a small amount of input water depth data.

Natural rubber is an essential raw material for industrial products and plays an important role in social development. A variety of diseases can affect the growth of rubber trees, reducing the production and quality of natural rubber. Therefore, it is of great significance to automatically identify rubber leaf disease. However, in practice, different diseases have complex morphological characteristics of spots and symptoms at different stages and scales, and there are subtle interclass differences and large intraclass variation between the symptoms of diseases. To tackle these challenges, a group multi-scale attention network (GMA-Net) was proposed for rubber leaf disease image recognition. The key idea of our method is to develop a group multi-scale dilated convolution (GMDC) module for multi-scale feature extraction as well as a cross-scale attention feature fusion (CAFF) module for multi-scale attention feature fusion. Specifically, the model uses a group convolution structure to reduce model parameters and provide multiple branches and then embeds multiple dilated convolutions to improve the model’s adaptability to the scale variability of disease spots. Furthermore, the CAFF module is further designed to drive the network to learn the attentional features of multi-scale diseases and strengthen the disease features fusion at different scales. In this article, a dataset of rubber leaf diseases was constructed, including 2,788 images of four rubber leaf diseases and healthy leaves. Experimental results show that the accuracy of the model is 98.06%, which was better than other state-of-the-art approaches. Moreover, the model parameters of GMA-Net are only 0.65 M, and the model size is only 5.62 MB. Compared with MobileNetV1, V2, and ShuffleNetV1, V2 lightweight models, the model parameters and size are reduced by more than half, but the recognition accuracy is also improved by 3.86–6.1%. In addition, to verify the robustness of this model, we have also verified it on the PlantVillage public dataset. The experimental results show that the recognition accuracy of our proposed model is 99.43% on the PlantVillage dataset, which is also better than other state-of-the-art approaches. The effectiveness of the proposed method is verified, and it can be used for plant disease recognition.

Optical communication modulation technology and networking technology are two important technologies for constructing free-space optical (FSO) communication. In this paper, pulse width modulation (PWM) is used to realize free-space optical communication. The process of signal modulation and demodulation is implemented by means of a field programmable gate array (FPGA). An optical communication relay system is constructed to realize communication networking. The binary data bits in the communication process are converted into pulse signals of different widths, the data demodulation process is realized by sampling with a high-speed analog-to-digital converter (ADC), the data level is determined by counting the proportion of high and low voltages sampled in a pulse period. The relay system analyzes the routing target after receiving the pulse signal from the transmitter, and then sends the data to the target receiver. The experimental results show that the constructed system can achieve point-to-multipoint free-space optical communication. Additionally, using ADC to demodulate the received signal increases the stability of the free-space optical communication system. This system provides the design prototype system of FSO communication networking technology.

Electrocatalytic CO2 reduction is regarded as a green and promising technology because it can convert carbon dioxide into value-added fuel or chemicals in a flexible and sustainable way. This research aimed to comprehensively analyze the research hotspots and trends in the field of CO2 electroreduction from 2005 to 2022 using bibliometric methods based on the core database of Web of Science. The results showed that 4546 papers on CO2 electroreduction were retrieved from 2005 to 2022, with a clear increasing trend. The research direction was diversified, involving multiple disciplines, and it is a comprehensive research field. ACS Catalysis is the journal with the largest number of articles. China is the country with the largest number of documents and has made significant contributions to the development and progress of this field. Copper-based catalysts are still the research focus in recent years. It is of great practical significance to develop copper-based catalysts with high efficiency, low cost, high stability, and high selectivity for the preparation of C1 products.

Quadrotor unmanned aerial vehicles (UAVs) are widely used as flexible and mobile access points and information carriers for the future Internet of Things (IoT). This work studies a quadrotor UAV-assisted IoT network, where the UAV helps to collect sensing data from a group of IoT users. Our goal is to optimize the UAV’s overall energy consumption required to complete the sensing task. Firstly, we propose a more accurate and mathematically tractable model to characterize the UAV’s real-time energy consumption, which accounts for the UAV’s dynamics, brushless direct current (BLDC) motor dynamics and aerodynamics. Then, we can show that the UAV’s circular flight based on the proposed energy-consumption model consumes less energy than that of hover flight. Therefore, a fly–circle–communicate (FCC) trajectory design algorithm, adopting Dubins curves for circular flight, is proposed and derived to save energy and increase flight duration. Employing the FCC strategy, the UAV moves to each IoT user and implements a circular flight in the sequence solved by the travelling-salesman-problem (TSP) algorithm. Finally, we evaluate the efficiency of the proposed algorithm in a mobile sensing network by comparing the proposed algorithm with the conventional hover-communicate (HC) algorithm in terms of energy consumption. Numerical results show that the FCC algorithm reduces energy consumption by 1–10% compared to the HC algorithm, and also improves the UAV’s flight duration and the sensing network’s service range.

Clinically, antioxidant therapy is a potential strategy for myocardial ischemia-reperfusion injury (MI/RI), a common complication of acute myocardial ischemia. The H-D-Arg-Dmt-Ly-Phe-NH2 (SS31) peptide is shown to have amazing antioxidant properties, but its utilization is limited by the peptide characteristics, such as the destruction by proteases and rapid metabolism. Silica nanoparticles (MSNs) comprise an excellent material for peptide delivery, owing to the protection effect relating to peptides. Moreover, platelet membrane (PLTM) is shown to be advantageous as a coat for nanosystems because of its specific protein composition, such that a PLTM-coated nanosystem has a stealth effect in vivo, able to target injury in the cardiovascular system. Based on this feature, we designed and prepared a novel nanocarrier to target SS31 delivery. This carrier is encapsulated by a platelet membrane and loaded with SS31 peptide into MSNs. The results reveal that this delivery system can target SS31 to the injured cardiovascular site, exert antioxidant function, and alleviate MI/RI.

The rise of Industry 4.0 is pushing more machine devices into the connection of the communication network. Internet of Things (IoT) emerges as an enabling technique to support automatic information transmission and the universal interconnection among massive machine devices. Unlike the human type communication (HTC) in the conventional Internet, IoT features machine type communication (HTC) with small data granularity and massive connections, which impose new challenges on the network resource allocation earlier designed for HTC communication. One of the challenging issues is how we can manage the network resource for the IoT service provisioning. As a promising network architecture for the “last-mile” access in 5G communication, Fiber-Wireless (FiWi) access network compromises the high bandwidth capacity of the optical access network and the flexibility of wireless access network. Especially, network virtualization as an emerging technique provides FiWi with the feasibility for the coordinated bandwidth allocation of wireless access network and optical access network. The evolution of FiWi access network should be not only oriented to the technological breakthrough in regular traffic, but also be supportive of the emerging IoT traffic. In this paper, we focus on the resource allocation problem in FiWi access network supporting IoT service. We first analyze the dynamics of regular traffic and propose the traffic prediction method based on Q-Learning. Then, we propose a virtual network embedding algorithm to map exactly the virtual networks of IoT traffic to the idle resource of the virtual networks of regular traffic without degrading the performance of regular traffic. Thus, the proposed algorithm can improve the resource utilization more effectively. The simulation results demonstrate that the proposed algorithm can achieve a higher acceptance ratio and lower migration ratio of IoT traffic.

With the development of Internet of Vehicles (IoV) and the gradual maturity of 5th Generation Mobile Networks (5G) technology, the further development of the IoV highly relies on network energy and resources. However, basic methods of researching new energy or improving equipment result in high cost. This article focuses on researching how to minimize energy consumption and maximize resource utilization with the constraints of existing environment and equipment. We jointly discuss 5G technology, mobile edge computing and deep reinforcement learning in green IoV. We also discuss how to make rational use of resources to realize the sustainable development of IoV. By classifying and comparing the existing researches according to different emphases, the energy consumption can be managed effectively with the above-mentioned technologies. Finally, we analyze the possible research directions and challenges in the future.

Road transport safety is an important part of transport construction in China. China is now the world’s second-largest country for road traffic deaths. Research on the road traffic mortality rate (RTMR) in China is of great significance in promoting sustainable development in global traffic. This study analyzes the RTMR in 31 provinces in China between 2003 and 2018. Research shows that the RTMR of China demonstrated a downward trend after 2004, but it increased slightly after reaching the lowest points in 2015. The RTMR in coastal and western areas was quite high, requiring targeted management and prevention. During the study period, the RTMR in Guangdong and Tibet improved greatly, whereas the RTMR in Hubei and Guangxi deteriorated. Tobit model results show that economic development level, medical assistance level and government expenditure on health are significantly negatively correlated with RTMR, while urbanization level and motorization level significantly promote RTMR. This study provides macro policy support for improving traffic safety in China.

The literature about changes in land use includes many studies of global sustainable development goals, while studies of transport land expansion have been relatively scarce. In this paper, we present an analysis of the spatiotemporal characteristics of transport land expansion in China’s 31 provinces from 2009 to 2017, applying the spatial Dubin model to identify the factors that influenced changes in per capita transport land area (PCTLA). The eastern and western regions have continued to lead the nation in terms of the total area dedicated to transport land. The expansion speed of transport land in the central and western regions, however, has been faster than in the eastern and northeast regions. As for PCTLA, the western region had the greatest amount and the central region the least. Further, PCTLA showed significant spatial autocorrelation. Economic development, government regulations, industrial structure, and the extent of opening up and urbanization had significant positive impacts on PCTLA, while the development of railway freight had a negative impact. This paper concludes with some policy suggestions for optimizing transport investment, accelerating the adjustment of industrial structure and transport structure, and implementing high-quality urbanization. The results should be of interest to those involved in the sustainable development of transport systems.

CO2 emission reduction has become a consensus all around the world. This paper investigates the spatiotemporal characteristics of industrial land carbon emission intensity (ILCEI) in China by spatial autocorrelation analysis, and applies the spatial Durbin model to reveal the influence of driving factors on ILCEI. The results indicate the following: (1) national ILCEI first shows a downward and then an upward trend during the period and presents a low pattern in both Eastern and Northeastern regions and a high pattern in the Northwestern region. (2) From a global perspective, ILCEI shows significant spatial agglomeration characteristics; from a local perspective, ILCEI is dominated by H-H and L-L agglomeration types, showing that spatial heterogeneity and spatial dependence are apparent in ILCEI. (3) ILCEI is significantly negatively affected by both R & D personnel and foreign-trade dependence, while urban population density notably has positive impacts on ILCEI. This paper is a beneficial policy practice for harmonizing the contradiction between industrial land expansion and carbon discharge.

Summary: Four potent native human monoclonal antibodies (mAbs) targeting distinct epitopes on tetanus toxin (TeNT) are isolated with neutralization potency ranging from approximately 17 mg to 6 mg each that are equivalent to 250 IU of human anti-TeNT immunoglobulin. TT0170 binds fragment B, and TT0069 and TT0155 bind fragment AB. mAb TT0067 binds fragment C and blocks the binding of TeNT to gangliosides. The co-crystal structure of TT0067 with fragment C of TeNT at a 2.0-Å resolution demonstrates that mAb TT0067 directly occupies the W pocket of one of the receptor binding sites on TeNT, resulting in blocking the binding of TeNT to ganglioside on the surface of host cells. This study reveals at the atomic level the mechanism of action by the TeNT neutralizing antibody. The key neutralization epitope on the fragment C of TeNT identified in our work provides the critical information for the development of fragment C of TeNT as a better and safer tetanus vaccine.