Rethinking urban wilderness: Status, hotspots, and prospects of ecosystem services.

An urban wilderness (UW) portrays a coupled relationship between natural dominance and human management in urban spaces. Superior ecosystem services support sustainable urban development. Systematic assessments of the status, changes, and trends of urban wilderness ecosystem services (UWESs) are a debated and complex issue in the field of ecology despite their importance as key components for ensuring the sustainable development of human society. We aimed to analyze the scientific literature on UWESs published between 2000 and 2022. Hence, we used bibliometric methods to comprehensively understand the research lineages, hotspots, and trends in UWESs. We found that the research has roughly encompassed two phases: initial exploration (2000-2011)and rapid growth (2012-2022). The number of publications has shown a continuous growth trend; the research hotspots include UWs compared with urban greenfield ecosystems, the spatio-temporal dynamics of UWs, ecosystem services and value assessments, and the coupling and linkage between ecosystem maintenance and human health. We summarized relevant trends for the concept of harmonious coexistence between human beings and nature, focusing on spatio-temporal dynamics and multidisciplinary integration as well as reinforcing the link with human health. This study can serve as a reference for demonstrating the value of UWESs and their practical application in a UW.

Gas Sensors Based on Semiconductor Metal Oxides Fabricated by Electrospinning: A Review.

Electrospinning has revolutionized the field of semiconductor metal oxide (SMO) gas sensors, which are pivotal for gas detection. SMOs are known for their high sensitivity, rapid responsiveness, and exceptional selectivity towards various types of gases. When synthesized via electrospinning, they gain unmatched advantages. These include high porosity, large specific surface areas, adjustable morphologies and compositions, and diverse structural designs, improving gas-sensing performance. This review explores the application of variously structured and composed SMOs prepared by electrospinning in gas sensors. It highlights strategies to augment gas-sensing performance, such as noble metal modification and doping with transition metals, rare earth elements, and metal cations, all contributing to heightened sensitivity and selectivity. We also look at the fabrication of composite SMOs with polymers or carbon nanofibers, which addresses the challenge of high operating temperatures. Furthermore, this review discusses the advantages of hierarchical and core-shell structures. The use of spinel and perovskite structures is also explored for their unique chemical compositions and crystal structure. These structures are useful for high sensitivity and selectivity towards specific gases. These methodologies emphasize the critical role of innovative material integration and structural design in achieving high-performance gas sensors, pointing toward future research directions in this rapidly evolving field.

Through-Holes Design for Ideal LiNbO3 A1 Resonators.

This paper proposes a method to realize ideal lithium niobate (LiNbO3) A1 resonators. By introducing subwavelength through-holes between the interdigital transducer (IDT) electrodes on the LiNbO3 surface, all unfavorable spurious modes of the resonators can be suppressed completely. It is convenient and valid for various IDT electrode parameters and different LiNbO3 thicknesses. Also, this method does not require additional device fabrication steps. At the same time, these through-holes can greatly reduce the suspended area of the LiNbO3 thin film, thus significantly improving the design flexibility, compactness, mechanical stability, temperature stability, and power tolerance of the resonators (and subsequent filters). It is expected to become an important means to promote the practical application of LiNbO3 A1 filters and even all Lamb waves filters.

Driving forces and variation in water footprint before and after the COVID-19 lockdown in Fujian Province of China.

The COVID-19 outbreak has injured the global industrial supply chain, especially China as the world's largest manufacturing base. Since 2020, China has implemented a rigorous lockdown policy, which has sternly damaged sectoral trade in export-oriented coastal areas. Fujian Province, which mainly processes imported materials, has a more profound influence. Although the COVID-19 lockdown has had some detrimental consequences on the world economy, it also had some favorable benefits on the global ecology. Previous studies have shown that the lockdown has altered the physical water quantity and quality, but the lack of total, virtual, and physical water research that combines water quantity and water quality simultaneously to pinpoint the subject and responsibility of water resources consumption and pollution. This research quantified the physical, virtual, and total water consumption and water pollution among 30 sectors in Fujian Province based on the theory of water footprint and the Economic Input-Output Life Cycle Assessment model. SDA model was then used to investigate the socioeconomic elements that underpin variations in the water footprint. The results show that after the lockdown, the physical water quantity and the physical grey WF in Fujian Province decreased by 2.6 Gm3 (-6.7%) and 0.4 Gm3 (-1.3%) respectively. The virtual water quantity decreased by 2.3 Gm3 (-4.5%), whereas the virtual grey WF rose by 1.5 Gm3 (4.3%). The total water quantity dropped by 3.3 Gm3 (-4.9%), while the grey WF increased by 1.2 Gm3 (2.5%), i.e. the COVID-19 lockdown decreases physical water quantity and improves local water quality. More than 50% of the water comes from virtual water trade outside the province (virtual water is highly dependent on external), and around 60% of the grey WF comes from physical sewage in the province. The COVID-19 lockdown reduced water outsourcing across the province (paid nonlocally decrease) but increased pollution outsourcing (paid nonlocally increase). And gross capital formation's contribution to the growth in water footprint will continue to rise. As a result, this study suggested that Fujian should take advantage of sectoral trade network to enhance the transaction of green water-intensive intermediate products, reduce the physical water consumption of blue water-intensive sectors, and reduce the external dependence on water consumption. Achieving the shared responsibility of upstream and downstream water consumption and reducing the external dependence on water in water-rich regions is crucial to solving the world's water problems. This research provides empirical evidence for the long-term effects of COVID-19 lockdown on the physical and virtual water environment.

Carbon emission structure decomposition analysis of manufacturing industry from the perspective of input-output subsystem: a case study of China.

In China, manufacturing is the industry that consumes the most energy and emits the most carbon, and the effect of emission reduction on the process of reaching carbon peaking and carbon neutrality is decisive. The existing research on the driving factors of manufacturing carbon emissions has not analyzed the specific structural characteristics of manufacturing carbon emissions from the perspective of industrial relevance, and little attention has been paid to the discussion of carbon emission reduction paths of different manufacturing sectors from the perspective of final demand. This study examines the direct carbon emissions and carbon emissions from final demand in China's manufacturing sector, and decomposes the carbon emissions from final demand into six distinct components using input-output analysis. In addition, this study examines the carbon emission path in manufacturing production activities, as well as the carbon emission reduction potential and scenario prediction of the factors influencing manufacturing carbon emissions. In 2018, the direct carbon emissions and carbon emissions from final demand were approximately 4.61 billion tons and 3.50 billion tons, respectively. Meanwhile, direct and indirect spillovers accounted for 62.1% and 23.1% of carbon emissions from final demand, respectively. Using the carbon emission transfer route map of the manufacturing industry, the direction and amount of carbon emission transfer from various energy sources can be accurately determined. The CR scenario predicts that the manufacturing industry will reach its carbon peak between 2025 and 2030, with a corresponding peak between 4.02 and 4.06 billion tons, and that carbon emissions in 2060 will be 40% lower than in 2018.

Extended topological valley-locked surface acoustic waves.

Stable and efficient guided waves are essential for information transmission and processing. Recently, topological valley-contrasting materials in condensed matter systems have been revealed as promising infrastructures for guiding classical waves, for they can provide broadband, non-dispersive and reflection-free electromagnetic/mechanical wave transport with a high degree of freedom. In this work, by designing and manufacturing miniaturized phononic crystals on a semi-infinite substrate, we experimentally realized a valley-locked edge transport for surface acoustic waves (SAWs). Critically, original one-dimensional edge transports could be extended to quasi-two-dimensional ones by doping SAW Dirac "semimetal" layers at the boundaries. We demonstrate that SAWs in the extended topological valley-locked edges are robust against bending and wavelength-scaled defects. Also, this mechanism is configurable and robust depending on the doping, offering various on-chip acoustic manipulation, e.g., SAW routing, focusing, splitting, and converging, all flexible and high-flow. This work may promote future hybrid phononic circuits for acoustic information processing, sensing, and manipulation.

Seed viability testing for research and conservation of epiphytic and terrestrial orchids.

BACKGROUND: Seed viability testing is essential in plant conservation and research. Seed viability testing determines the success of ex-situ conservation efforts, such as seed banking but commonly testing protocols of orchids lack consistency and accuracy, therefore, there is a need to select an appropriate and reliable viability test, especially when conducting comparative studies. Here, we evaluated the suitability of three seed viability tests, Evans blue test (EB), Fluorescein diacetate test (FDA) and Tetrazolium test (TTC), with and without sterilization, on seeds of 20 orchid species, which included five epiphytes and fifteen terrestrials, using both fresh seeds and seeds stored at - 18 ºC for 6 to 8 years. RESULTS: We found that sterilization and lifeform of seeds affected seed viability across all tests but the storage time was not an influential factor. Sterilization negatively affected seed viability under EB and FDA test conditions but increased the detection of viable seeds in the TTC test in both epiphytic and terrestrial species. The EB test, when administered without sterilization provided the highest viability results. Being non-enzymatic unlike TTC and FDA tests, as expected, the EB test was the most reliable with similar results between sterilized and not sterilized seeds for most epiphytic and terrestrial species as well as when compared between groups. CONCLUSIONS: The lifeform of the species and seed sterilization prior to testing are important influential factors in orchid seed viability testing. Since EB test was found to be reliable we recommend the EB test for seed viability assessment in orchids rather than the less reliable but commonly used TTC test, or the FDA test, which require more expensive and sophisticated instrumentation. Since storage time was not an influential factor in orchid seed viability testing, the recommendations of this study can be used for both fresh as well as long-term stored orchid seeds. This is helpful for research and especially for conservation measures such as seed banking. However, due to the species specificity of the bio-physiology of orchids, we call for comprehensive viability test assessment in the hyper diverse orchid family to be extended to a greater number of species to facilitate efficient conservation and research.

Regeneration and Endogenous Phytohormone Responses to High-Temperature Stress Drive Recruitment Success in Hemiepiphytic Fig Species.

Exposure to high-temperature stress (HTS) during early regeneration in plants can profoundly shape seed germination, seedling growth, and development, thereby providing stress resilience. In this study, we assessed how the timing of HTS, which was implemented as 8 h in 40°C, could affect the early regeneration stages and phytohormone concentration of four hemiepiphytic (Hs) and four non-hemiepiphytic (NHs) Ficus species. Their seed germination, seedling emergence, and seedling survival probabilities and the concentrations of three endogenous phytohormones, abscisic acid (ABA), indole-3-acetic acid (IAA), and salicylic acid (SA) were assessed after HTS imposed during imbibition, germination, and emergence. In both groups, seeds were more sensitive to HTS in the early regeneration process; stress experienced during imbibition affected emergence and survival, and stress experienced during germination affected subsequent emergence. There was no effect from HTS when received after emergence. Survival was highest in hemiepiphytes regardless of the HTS treatment. The phytohormones showed growth form- and regeneration stage-specific responses to HTS. Due to the HTS treatment, both SA and ABA levels decreased in non-hemiepiphytes during imbibition and germination; during germination, IAA increased in hemiepiphytes but was reduced in non-hemiepiphytes. Due to the HTS treatment experienced during emergence ABA and IAA concentrations were greater for hemiepiphytes but an opposite effect was seen in the two growth forms for the SA concentration. Our study showed that the two growth forms have different strategies for regulating their growth and development in the early regeneration stages in order to respond to HTS. The ability to respond to HTS is an ecologically important functional trait that allows plant species to appropriately time their seed germination and seedling development. Flexibility in modulating species regeneration in response to HTS in these subtropical and tropical Ficus species could provide greater community resilience under climate change.

RING-finger protein 5 promotes hepatocellular carcinoma progression and predicts poor prognosis.

Hepatocellular carcinoma (HCC) is one of the most common cause of malignancy-related deaths. Recently, RING-finger protein 5 (RNF5), an E3 ubiquitin ligase, was revealed to be associated with the development of several human cancers. However, the clinical implication and functional role of RNF5 in HCC are poorly understood. We analysed RNF5 expression in HCC samples and observed that both the mRNA and protein levels of RNF5 were significantly increased in HCC tissues. RNF5 upregulation was markedly associated with larger tumour size, more satellite foci, and higher alpha fetoprotein (AFP) level, indicating poor prognosis in patients with HCC. Knockdown and overexpression experiments demonstrated that RNF5 promoted the proliferation, migration, and invasion of HCC cells in vitro. Moreover, RNF5 facilitated HCC growth in vivo. Our findings indicated that RNF5 was an oncogene of HCC progression and could be used as a novel prognostic biomarker and therapeutic target for patients with HCC.

Regeneration responses to water and temperature stress drive recruitment success in hemiepiphytic fig species.

Mechanisms for surviving water and temperature stress in epiphytes are essential adaptations for successful regeneration in forest canopies. Hemiepiphytes start their life cycle as epiphytes, eventually establishing aerial root connections to the ground. This strategy allows for greater light capture, while benefitting from minimized risk of fire, flooding and damage by terrestrial herbivores, but exposes the vulnerable seedling stage to heat and drought stress. However, the response to temperature and water stress during early regeneration in hemiepiphytes is not known. In this study, we tested the effect of temperature (15/5, 25/15 and 35/25 °C; day/night diurnal variation) and water availability, as substrate moisture (0.00, -0.20 and -0.35 MPa) and water vapor (18.5-99.5% relative humidity), on seed germination, seedling emergence and survival in six hemiepiphytic and nine non-hemiepiphytic Ficus species. Under high-temperature conditions (35/25 °C), hemiepiphytes had higher gemination and seedling survival, achieved peak germination slower and extended germination. Greater water stress (-0.35 MPa) in the growth substrate resulted in higher germination of non-hemiepiphytes; hemiepiphytes, in contrast, took a shorter time to complete germination, but had higher seedling emergence and survival. Hemiepiphytes germinated at 99.5% relative humidity more readily compared with non-hemiepiphytes. These findings provide the first comprehensive evidence that hemiepiphytic Ficus species are better adapted to drier and warmer conditions during the critical transition from seed to seedling. Through greater flexibility in achieving peak germination and duration of regeneration activity, hemiepiphytes modulate their recruitment process to be more resilient under abiotic stressors. This may allow them to be more successful in regenerating in forest canopies under ambient conditions that are transient. These results support previous work showing greater drought tolerance of hemiepiphytic Ficus species in larger size classes and extend this finding to show that physiological adaptations for drought and heat tolerance start from the early seedling emergence stage.

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The emission of CO2 from major sectors and key industries are the predominant sources of regional CO2 emissions. It is the prerequisite to promote sectoral carbon emissions reduction, to cla-rify their influencing factors and investigate their relationship with regional economic growth. It is also of great significance for the implementation of regional total carbon emissions control. Using the Logarithmic mean Divisia index method (LMDI) and the Tapio decoupling model, we analyzed the driving factors, and decoupling status with economic growth of 13 major carbon emissions industries in Fujian Province from 1997 to 2017. The results showed that the electricity and heat production and supply industry was the major source of CO2 emissions in Fujian Province, with an increase of 101.74 Mt (from 18.89 Mt to 120.63 Mt) during the period 1997 to 2017. The top three industries with the fastest annual growth rate in CO2 emissions were non-ferrous metal smelting and rolling processing industry (18.1%), textile industry (12.1%), and ferrous metal smelting and rolling processing industry (12.1%). Among the influence factors for the changes in carbon emissions in 13 major industries, economic growth effect and population scale effect were the main positive driving factors, while the restraining effects of energy structure, energy intensity, and industrial structure were continuously increasing. In terms of decoupling relationship, the decoupling index between economic growth and industry-related CO2 emissions showed a downward trend on the whole. Since the 11th Five-Year-Plan period, some industries had begun to show strong decoupling to some extent. The farming, forestry, animal husbandry, fishery and water conservancy industry exhibited expansive negative decoupling, whereas the electricity and heat production and supply industry exhibited weak negative decoupling during 13th Five-Year Plan period. The effects of energy structure and energy intensity had substantial impacts on the decoupling with economic growth for various industries. The industrial structure effect had a smaller impact on the decoupling with economic growth, while the population scale effect had almost no impact.

Demonstration of Three-Dimensional Indoor Visible Light Positioning with Multiple Photodiodes and Reinforcement Learning.

To provide high-quality location-based services in the era of the Internet of Things, visible light positioning (VLP) is considered a promising technology for indoor positioning. In this paper, we study a multi-photodiodes (multi-PDs) three-dimensional (3D) indoor VLP system enhanced by reinforcement learning (RL), which can realize accurate positioning in the 3D space without any off-line training. The basic 3D positioning model is introduced, where without height information of the receiver, the initial height value is first estimated by exploring its relationship with the received signal strength (RSS), and then, the coordinates of the other two dimensions (i.e., X and Y in the horizontal plane) are calculated via trilateration based on the RSS. Two different RL processes, namely RL1 and RL2, are devised to form two methods that further improve horizontal and vertical positioning accuracy, respectively. A combination of RL1 and RL2 as the third proposed method enhances the overall 3D positioning accuracy. The positioning performance of the four presented 3D positioning methods, including the basic model without RL (i.e., Benchmark) and three RL based methods that run on top of the basic model, is evaluated experimentally. Experimental results verify that obviously higher 3D positioning accuracy is achieved by implementing any proposed RL based methods compared with the benchmark. The best performance is obtained when using the third RL based method that runs RL2 and RL1 sequentially. For the testbed that emulates a typical office environment with a height difference between the receiver and the transmitter ranging from 140 cm to 200 cm, an average 3D positioning error of 2.6 cm is reached by the best RL method, demonstrating at least 20% improvement compared to the basic model without performing RL.

Iterative point-wise reinforcement learning for highly accurate indoor visible light positioning.

Iterative point-wise reinforcement learning (IPWRL) is proposed for highly accurate indoor visible light positioning (VLP). By properly updating the height information in an iterative fashion, the IPWRL not only effectively mitigates the impact of non-deterministic noise but also exhibits excellent tolerance to deterministic errors caused by the inaccurate a priori height information. The principle of the IPWRL is explained, and the performance of the IPWRL is experimentally evaluated in a received signal strength (RSS) based VLP system and compared with other positioning algorithms, including the conventional RSS algorithm, the k-nearest neighbors (KNN) algorithm and the PWRL algorithm where iterations exclude. Unlike the supervised machine learning method, e.g., the KNN, whose performance is highly dependent on the training process, the proposed IPWRL does not require training and demonstrates robust positioning performance for the entire tested area. Experimental results also show that when a large height information mismatch occurs, the IPWRL is able to first correct the height information and then offers robust positioning results with a rather low positioning error, while the positioning errors caused by the other algorithms are significantly higher.