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Abstract The ongoing war in Ukraine has seriously impacted the agricultural sector, yet its exact effects on agricultural production are not well understood. Here we combine satellite imagery, machine learning, and statistical regression approaches to present a spatially detailed assessment of agricultural losses for five high-risk provinces in eastern Ukraine (Crimea, Donets’k, Kherson, Luhans’k, and Zaporizhzhya). Our findings indicated that approximately 18.11 ± 2.47% of croplands were left unplanted following the war. Among the cultivated areas, wheat, sunflower, and rapeseed experienced average production losses of 36.39–37.19% in 2022 compared to pre-war levels during 2019–2021. Economically, the indirect losses resulting from decreased production, corresponding to $520.36 ± 22.52, $427.59 ± 24.62, and $205.02 ± 11.53 million USD for wheat, sunflower, and rapeseed crops, respectively, were 1.31–2.16 times higher than direct losses due to unplanted. We also found that reductions in crop production were primarily affected by war intensity indicated by changes in nighttime lights. This methodology offers a comprehensive framework for quantifying agricultural damages from wars, which can be applicable beyond the Russia-Ukraine context.

Abstract Fever and diarrhea are key causes of malnutrition, growth and development disorders, and death among children. At present, most studies on the associated factors of fever and diarrhea in children are concentrated in African and South Asian countries, but relevant research in China is very limited. This study was aimed to analyze the two-week prevalence of fever, diarrhea, and coexisting fever and diarrhea among children aged 6–23 months in rural areas of Hunan Province and to explore the associated factors. The survey data of the Nutrition Improvement Program for Children in Poor Areas (NIPCPA) from 2016 to 2023 was used here. NIPCPA is a cross-sectional survey completed annually in Hunan to collect children’s nutrition and health indicators. The two-week prevalence rates of fever, diarrhea, and coexisting fever and diarrhea among children aged 6–23 months were 12.2% (2066/16,985), 9.6% (1634/16,985), and 3.2% (542/16,985), respectively. Multivariate logistic regression analysis showed the risks of fever, diarrhea, and coexisting fever and diarrhea were higher among younger children. The high educational level of caregivers, effective consumption of Yingyangbao (a complementary food supplement containing iron, zinc, calcium, vitamins A, D, B1, B2, B12, folic acid, and other micronutrients), and complementary feeding meeting minimum dietary diversity and meeting minimum acceptable diet were protective factors against fever in children, with adjusted odds ratios (aORs) of 0.87 (95%CI: 0.78–0.98), 0.78 (0.69–0.87), 0.73 (0.65–0.82), and 0.74 (0.66–0.84), respectively. Effective consumption of Yingyangbao, and complementary feeding meeting the minimum dietary diversity and meeting minimum acceptable diet were protective factors against diarrhea in children, with aORs of 0.72 (95%CI: 0.63–0.83), 0.79 (0.70–0.91), and 0.80 (0.70–0.92), respectively. Effective consumption of Yingyangbao, and complementary feeding meeting the minimum dietary diversity and meeting minimum acceptable diet were protective factors against coexisting fever and diarrhea among children, with aORs of 0.53 (95%CI: 0.43–0.66), 0.71 (0.58–0.89), and 0.70 (0.56–0.88), respectively. Fever, diarrhea, and the coexisting fever and diarrhea affect one in eight, one in ten, and one in thirty children respectively in rural areas of Hunan. Effective interventions should be actively taken, such as improving the education level of caregivers, enhancing their scientific feeding skills for children, and promoting children’s compliance with Yingyangbao consumption, to further reduce the prevalence of fever and diarrhea in children.

Urban expansion often occurs at the expense of cropland loss, posing challenges to sustainable urban growth and food security. However, detailed investigations into urban expansion and cropland loss remain limited, particularly in regions with varying levels of urbanization. Here, we take Guangdong Province, China, as a case study to exemplify how urban expansion affects cropland using remotely sensed land use products. We adopted geospatial analysis, correlation indicators, and landscape metrics to uncover their spatial relationships at 10-m spatial resolutions. Results showed that urban areas increased by 6335 km2 while cropland decreased by 3780 km2 from 2017 to 2022. Notably, 41 % of newly expanded urban areas were from croplands, and 45 % of lost croplands were converted to urban areas. Western Guangdong experienced the largest extent of urban expansion and cropland loss, emerging as a hotspot region in recent years. Additionally, our analysis observed the increasing compactness of urban areas and the growing fragmentation of cropland landscapes over time. These findings shed light on the intricate dynamics between urban expansion and cropland loss in rapidly urbanizing regions, which provide valuable insights for sustainable urban development, agricultural practice, and land management in the future.

Accurate, detailed, and long-term urban land use mapping is crucial for urban planning, environmental assessment, and health evaluation. Despite previous efforts, mapping essential urban land use categories (EULUCs) across multiple periods remains challenging, primarily due to the scarcity of enduring consistent socio-geographical data, such as the widely used Point of Interest (POI) data. Addressing this issue, this study presents an experimental method for mapping the time-series of EULUCs in Dalian city, China, utilizing Local Climate Zone (LCZ) data as a substitute for POI data. Leveraging multi-source geospatial big data and the random forest classifier, we delineate urban land use distributions at the parcel level for the years 2000, 2005, 2010, 2015, 2018, and 2020. The results demonstrate that the generated EULUC maps achieve promising classification performance, with an overall accuracy of 78% for Level 1 and 71% for Level 2 categories. Features derived from nighttime light data, LCZ, Sentinel-2 satellite imagery, and topographic data play leading roles in our land use classification process. The importance of LCZ data is second only to nighttime light data, achieving comparable classification accuracy to that when using POI data. Our subsequent correlation analysis reveals a significant correlation between POI and LCZ data (p = 0.4), which validates the rationale of the proposed framework. These findings offer valuable insights for long-term urban land use mapping, which can facilitate effective urban planning and resource management in the near future.

ObjectiveTo explore the prevalence and influencing factors of school bullying among high school children left behind by both mother and father in Hunan province. MethodsUsing multistage stratified cluster sampling, we recruited 8 439 children from 32 high schools in 4 prefectures of Hunan province for a self-administered anonymous questionnaire survey conducted during April – July 2018. The Olweus Bullying/Victimization Questionnaire (OBVQ) and a self-designed questionnaire were used in the survey. Among all the participants, 1 506 children with both maternal and paternal migration were included in the analysis. ResultsOf the 1506 left-behind children, the overall prevalence of school bullying victimization and perpetration were 20.39% and 5.64%, respectively. Verbal bullying victimization was the most common type of school bullying reported by 17.60% of victimized children. Multivariate logistic regression analysis revealed following significant risk factors for school bullying victimization: male gender (odds ratio [OR] = 2.116, 95% confidence interval [95%CI]: 1.471 – 3.045), playing violent games frequently (OR = 1.624, 95%CI: 1.040 – 2.536), rarely or never communicating with their parents (OR = 1.528, 95%CI: 1.066 – 2.189), frequent quarrels between parents or primary caregivers (OR = 3.127, 95%CI: 1.659 – 5.895), lack of perceived safety in school (OR = 1.876, 95%CI: 1.290 – 2.728), and with a perceived disharmony atmosphere in class (OR = 1.604, 95%CI: 1.138 – 2.261). ConclusionSchool bullying victimization is common among high school children with both maternal and paternal migration in Hunan province and targeted interventions should be developed for comprehensive prevention of the school bullying.

Cutaneous squamous cell carcinoma (cSCC) is the second most frequent of the keratinocyte-derived malignancies with actinic keratosis (AK) as a precancerous lesion. To comprehensively delineate the underlying mechanisms for the whole progression from normal skin to AK to invasive cSCC, we performed single-cell RNA sequencing (scRNA-seq) to acquire the transcriptomes of 138,982 cells from 13 samples of six patients including AK, squamous cell carcinoma in situ (SCCIS), cSCC, and their matched normal tissues, covering comprehensive clinical courses of cSCC. We identified diverse cell types, including important subtypes with different gene expression profiles and functions in major keratinocytes. In SCCIS, we discovered the malignant subtypes of basal cells with differential proliferative and migration potential. Differentially expressed genes (DEGs) analysis screened out multiple key driver genes including transcription factors along AK to cSCC progression. Immunohistochemistry (IHC)/immunofluorescence (IF) experiments and single-cell ATAC sequencing (scATAC-seq) data verified the expression changes of these genes. The functional experiments confirmed the important roles of these genes in regulating cell proliferation, apoptosis, migration, and invasion in cSCC tumor. Furthermore, we comprehensively described the tumor microenvironment (TME) landscape and potential keratinocyte-TME crosstalk in cSCC providing theoretical basis for immunotherapy. Together, our findings provide a valuable resource for deciphering the progression from AK to cSCC and identifying potential targets for anticancer treatment of cSCC.

Global land cover has undergone extensive and rapid changes as a result of human activities and climate change. These changes have had a significant impact on biodiversity, the surface energy balance, and sustainable development. Global land cover data underpins research on the development of earth system models, resource management, and evaluation of the ecological environment. However, there are limitations in the classification detail, spatial resolution, and rapid change monitoring capability of global land cover change data. Building on the earlier Global Land Cover Mapping (Finer Resolution Observation and Monitoring – Global Land Cover, FROM-GLC), we developed the improved Global Land Cover Change Monitoring Platform (FROM-GLC Plus) using methods such as multi-season sample space-time migration, multi-source data time series reconstruction, and machine learning. The FROM-GLC Plus system provides a capacity for producing global land cover change data set from the 1980s with flexibility in spatio–temporal details. The preliminary results show that FROM-GLC Plus provides a framework for near real-time land cover mapping at multi-temporal (annual to daily) and multi-resolution (30 m to sub-meter) levels.

ObjectiveThe mental health of sexual minorities has received increasing attention, but there are few studies on the risk of psychotic-like experiences (PLEs) among sexual minorities. The purpose of this study is to explore the relationship between different sexual orientations and PLEs among college students and the moderating effect of gender.MethodsA total of 4,460 college students from seven provinces participated in this cross-sectional survey. The χ2 test and logistic regression were used to investigate the relationship between sexual orientation and PLEs.ResultsOf the participants, 4.9% identified as bisexual, 1.1% as lesbian/gay, and 5.6% were questioning/unsure; 60.1% of the sample experienced at least one PLE item, 59.2% reported delusional experiences (DEs), and 20.6% had hallucinatory experiences (HEs). Compared with heterosexual college students, bisexual and questioning students showed a higher risk of PLEs, DEs, and HEs, and lesbian/gay students showed a higher risk of HEs. Stratified analysis indicated that sexual orientation was significantly associated with PLEs only for female college students.ConclusionSexual orientation is a predictive factor of PLEs. In particular, different sexual minority subgroups show the different effects on PLEs between male and female college students. Mental health interventions for PLEs could employ distinct strategies based on different sexual orientations and gender disparity.

Abstract Background Forest succession is an important ecological process and has been studied for more than a century. However, changes in nitrogen (N) availability during succession remain unclear as they may lead to either N saturation or N limitation. Here, we propose a conceptual model to illustrate changes in N availability during four stages of secondary succession using the natural abundance of 15N in plant leaves (foliar δ15N). We predicted that N availability would decline in the early stages of succession and then increase in late stages, coinciding with the changes in foliar δ15N, with the inflection point varying in different climate zones. Data on foliar δ15N from 16 succession sequences were synthesized to explore changes in N availability during forest succession. Results The compiled data were consistent with the proposed conceptual model. Foliar δ15N in boreal and temperate forests decreased significantly in the first two stages of succession (estimated to last at least 66 years in temperate forests), at a rate of 0.18‰ and 0.38‰ per decade, respectively, and decreased slightly in tropical forests in the first 23 years. Foliar δ15N is projected to increase in later stages in all forests, which is supported by observations in both temperate and tropical forests. The inflection points of N availability when N limitation peaked during succession were different in different climate zones, implying different ecosystem N turnovers. Conclusions Our study reconciles the controversies regarding changes in N availability during forest secondary succession. Our findings are also useful for predicting the recovery of N and carbon accumulation during succession. Nonetheless, studies on forest secondary succession using foliar δ15N have thus far been limited, and more research should be conducted to further verify the conceptual model proposed here.

Cinnamaldehyde (CA) is a promising antimicrobial agent for the preservation of fruits and vegetables due to its excellent antibacterial activity. The application is however, limited by its unstable and volatile properties. A biocompatible carbon dots hybrid γ-cyclodextrin-based metal organic framework (CD/MOF) was developed by the seed-mediated method to improve the encapsulation and sustained continuous release of CA. CD/MOF-0.5 exhibited a CA loading efficiency of 28.42% and a sustained release duration time of more than 15 days at 8 oC. The release kinetics results showed that the release behavior of CD/MOF-0.5 fitted well with the Korsmeyer–Peppas release kinetics model, indicating that its sustained release is mainly controlled by diffusion. Both the Fourier-transform infrared spectroscopy and X-ray photoelectron spectroscopy analyses revealed that CD/MOF-0.5 and CA molecules were linked by hydrogen bonds. Due to the high sustained release performance, CA-loaded CD/MOF-0.5 considerably inhibited the growth of Escherichia coli, hence preventing the spoilage of fresh-cut cantaloupes. CD/MOF-0.5/CA treatment also maintained the qualities of the fresh-cut cantaloupes, prolonging their edibility to five days. This work provides a promising strategy for the prevention of spoilage in food industry.

BackgroundBullying is a serious public health concern affecting the physical and mental health of children. Migrant children are at higher risk of developing health problems. We conducted this study to investigate the prevalence of school bullying and its possible influencing factors of migrant children.MethodsA cross-sectional study was carried out in Hunan Province, China from April to July 2018. Multi-stage cluster sampling was adopted to achieve a representative sample covering both urban and rural areas. Migrant children are defined as those who migrate with one or both parents to other places and who do not have a hukou in their city of residence. The Chinese version of Olweus Bully/Victim Questionnaire was applied to measure children’s involvement in school bullying.ResultsA total of 7,607 students were surveyed, including 995 migrant children and 6,612 non-migrant children. The prevalence of school bullying was significantly higher in migrant children than in non-migrant children (χ2 = 22.740; p

Greenspace exposure metrics can allow for comparisons of green space supply across time, space, and population groups, and for inferring patterns of variation in opportunities for people to enjoy the health and recreational benefits of nearby green environments. A better understanding of greenspace exposure differences across various spatial scales is a critical requirement for lessening environmental health disparities. However, existing studies are typically limited to a single city or across selected cities, which severely limits the use of results in measuring systemic national and regional scale differences that might need policy at above individual city planning level. To close this knowledge gap, our study aims to provide a holistic assessment of multi-scale greenspace exposure across provinces, cities, counties, towns, and land parcels for the whole of China. We mapped the nationwide fractional greenspace coverage at 10 m with Sentinel-2 satellite imagery, and then modeled population-weighted greenspace exposure to examine variation of greenspace exposure across scales. Our results show a prominent scaling effect of greenspace exposure across multi-scale administrative divisions in China, suggesting, as expected, an increase in heterogeneity with finer spatial scales. We also identify an asymmetric pattern of the difference between greenspace exposure and greenspace coverage, across a geo-demographic demarcation boundary (i.e., along the Heihe–Tengchong Line). In general, the greenspace coverage rate will overestimate more realistic human exposure to greenspace in East China while underestimating in West China. We further found that, in China, more recently urbanized areas have much better greenspace exposure than older urban areas. Our study provides a spatially explicit greenspace exposure metric for discovering multi-scale greenspace exposure difference, which will enhance governments’ capacity to quantify environmental justice, detect vulnerable greenspace exposure risk hotspots, prioritize greenspace management at the supra-city scale, and monitor the balance between greenspace supply and demand.

Dibutyl phthalate (DBP) and Benzo(a)pyrene (BaP) are ubiquitous contaminants in environment and foodstuffs, which increase the chance of their combined exposure to humans in daily life. However, the combined effects of DBP and BaP on liver and the underlying mechanisms are still unclear. In this study, we explored the combined effects of DBP and BaP on liver and the potential mechanisms in a rat model. We found that DBP and BaP co-exposure activated the MyD88/NF-κB pathway through increasing TLR4 acetylation (TLR4ac) level, leading to the imbalance of pro-inflammatory factors (CXCL-13, IL-6 and TNF-α) and anti-inflammatory factors (IL-10), ultimately resulting in liver tissue damage and functional changes. Sporoderm-broken spores of Ganoderma lucidum (SSGL) had strong alleviating effects on liver injury induced by DBP and BaP co-exposure. Our study found that SSGL suppressed TLR4ac-regulated MyD88/NF-κB signaling to reduce the release of pro-inflammatory factors, and promote the secretion of IL-10, thus alleviating liver injury caused by DBP and BaP co-exposure. In conclusion, SSGL contributed to liver protection against DBP and BaP-induced liver injury in rats via suppressing the TLR4ac-regulated MyD88/NF-κB signaling.

Land cover information depicting the complex interactions between human activities and surface change is critically essential for nature conservation, social management, and sustainable development. Recent advances have shown great potentials of remote sensing data in generating high-resolution land cover maps, but it remains unclear how different models, data sources, and inclusive features affect the classification results, which hinders its applications in regional studies requiring more accurate land cover data. Informing these issues, here we developed a robust framework to improve the mapping results of 10 m resolution land cover classification in Guangdong Province, China using thousands of manually collected samples, multisource remote sensing data (Sentinel-1, Sentinel-2, and Luojia-1), machine learning algorithms, and a free cloud-based platform of Google Earth Engine. Results showed that an overall accuracy of 86.12% and a Kappa coefficient of 0.84 could be achieved for land cover classification in Guangdong for 2019. We found that random forest models achieved better performance than classification and regression trees, minimum distance, and support vector machine models. We also found that features derived from Sentinel-1 data and Sentinel-2 spectral indices greatly contributed to the classification process, while the feature of Luojia-1 data was not as much important as other configurations. A comparison between our results and several existing land cover products in terms of classification accuracy and visual interpretation further validated the effectiveness and robustness of the proposed framework. Our experiments and findings not only systematically elucidate the role of classification methods and data sources in deriving more accurate and reliable land cover maps but also provide certain guidelines for future land cover mapping from regional to global scales.

This paper reports the first discovery of sandstone type industrial uranium mineralization in the Duolun Intermountain Basin in the Mesozoic volcanic rock area of Southeast Inner Mongolia by No.208 Geological Party, CNNC.In order to understand the type of uranium mineralization, uranium metallogenic conditions, mineralization and prospecting prospects, the field geological phenomena, microscopic characteristics, petrology, mineralogy, geochemistry and mineralization patterns were studied in the basin.This paper discusses the characteristics and metallogenic conditions of the newly discovered sandstone type uranium mineralization.The strong Mesozoic volcanic activity and basement subsidence in Duolun Basin provided a good tectonic environment for the formation of uranium bearing formations.The widely distributed acidic volcanic rocks provide abundant uranium sources.The sand body of the target layer is loose and permeable, with moderate thickness (about 40 m), which is conducive to the continuous infiltration of oxygen-containing and uranium-containing water, and has complete oxidation-reduction zoning.All these are favorable for uranium mineralization.According to the existing exploration results, combined with the basin evolution and the wide distribution of regional Intermountain Basins, it is considered that the Duolun Intermountain Basin has a good prospecting prospect for uranium, and there may be rich and thick ore bodies in the deep.In the next step of uranium exploration, attention should be paid to the lacustrine deposits and beach deposits on both sides of the Duolun ancient river.

Nitrogen dioxide (NO2) is one of the most important atmospheric pollutants and the precursors of acid rain, tropospheric ozone, and atmospheric aerosols. However, due to the poor quality of source data and the computing power of the models, current ground-level NO2 concentration data lack either high-resolution coverage or full nation-wide coverage. This study estimates the ground-level NO2 concentration in China with national coverage at relatively high spatiotemporal resolution (0.25°; daily intervals) over the newest past 6 years (2013–2018). We developed an advanced model, named Random Forest model integrated K-means (RF-K), for the estimates with multi-source parameters. Besides meteorological parameters, satellite retrievals parameters, and anthropogenic emission inventories parameters, we also innovatively introduce socioeconomic parameters to assess the impact of human activities. Our results show that: (1) the RF-K model developed by us shows better prediction performance than others. (2) the annual average NO2 concentration of China showed a weak declining trend (-0.013±0.217 μgm−3yr−1) from 2013 to 2018, indicating that pollutant controlling targets had been achieved in China overall. By mapping daily nationwide ground-level NO2 concentrations, this study provides high-quality timely, and detailed data for air quality management and epidemiological analyses for China. The RF-K model can be used easily for other pollutants (e.g. SO2 and O3) considering that their ground-level concentrations can be estimated depending on the similar emitting sources and influence factors, and our model's input data sources also cover information on other pollutants.

The increasingly frequent flooding imposes tremendous and long-lasting damages to lives and properties in impoverished rural areas. Rapid, accurate, and large-scale flood mapping is urgently needed for flood management, and to date has been successfully implemented benefiting from the advancement in remote sensing and cloud computing technology. Yet, the effects of agricultural emergency response to floods have been limitedly evaluated by satellite-based remote sensing, resulting in biased post-flood loss assessments. Addressing this challenge, this study presents a method for monitoring post-flood agricultural recovery using Sentinel-1/2 imagery, tested in three flood-affected main grain production areas, in the middle and lower Yangtze and Huai River, China. Our results indicated that 33~72% of the affected croplands were replanted and avoided total crop failures in summer 2020. Elevation, flood duration, crop rotation scheme, and flooding emergency management affect the post-flood recovery performance. The findings also demonstrate rapid intervention measures adjusted to local conditions could reduce the agricultural failure cost from flood disasters to a great extent. This study provides a new alternative for comprehensive disaster loss assessment in flood-prone agricultural regions, which will be insightful for worldwide flood control and management.

Detailed information on urban land uses has been an essential requirement for urban land management and policymaking. Recent advances in remote sensing and machine learning technologies have contributed to the mapping and monitoring of multi-scale urban land uses, yet there lacks a holistic mapping framework that is compatible with different end users’ demands. Moreover, land use mix has evolved to be a key component in modern urban settings, but few have explicitly measured the spatial complexity of land use or quantitively uncovered its driving forces. Addressing these challenges, here we developed a novel two-stage bottom-up scheme for mapping essential urban land use categories. In the first stage, we conducted object-based land use classification using crowdsourcing features derived from multi-source open big data and an automated ensemble learning approach. In the second stage, we identified parcel-based land use attributes, including the dominant type and mixture mode, by spatially correlating land parcels with the object-based results. Furthermore, we investigated the potential influencing factors of land use mix using principal components analysis and multiple linear regression. Experimental results in Ningbo, a coastal city in China, showed that the proposed framework could accurately depict the distribution and composition of urban land uses. At the object scale, the highest classification accuracy was as high as 86% and 78% for the major (Level I) and minor (Level II) categories, respectively. At the parcel scale, the generated land use maps were spatially consistent with the object-based maps. We found larger parcels were more likely to be mixed in land use, and industrial lands were characterized as the most complicated category. We also identified multiple factors that had a collective impact on land use mix, including geography, socioeconomy, accessibility, and landscape metrics. Altogether, our proposed framework offered an alternative to investigating urban land use composition, which could be applied in a broad range of implications in future urban studies.

Cell adhesive force, exerting on the local matrix or neighboring cells, plays a critical role in regulating many cell functions and physiological processes. In the past four decades, significant efforts have been dedicated to cell adhesive force detection, visualization and quantification. A recent important methodological advancement in cell adhesive force visualization is to adopt force-to-fluorescence conversion instead of force-to-substrate strain conversion, thus greatly improving the sensitivity and resolution of force imaging. This review summarizes the recent development of force imaging techniques (collectively termed as cell adhesive force microscopy or CAFM here), with a particular focus on the improvement of CAFM’s spatial resolution and the biomaterial choices for constructing the tension sensors used in force visualization. This review also highlights the importance of DNA-based tension sensors in cell adhesive force imaging and the recent breakthrough in the development of super-resolution CAFM.

Understanding distributions of urban land use is of great importance for urban planning, decision support, and resource allocation. The first mapping results of essential urban land use categories (EULUC) in China for 2018 have been recently released. However, such kind of national maps may not sufficiently meet the growing demand for regional analysis. To address this shortcoming, here we proposed a segmentation-based framework named EULUC-seg to improve the mapping results of EULUC at the city scale. An object-based segmentation approach was first applied to generate the basic mapping units within urban parcels. Multiple features derived from high-resolution remotely sensed and social sensing data were updated and then recalculated within each unit. Random forest was adopted as the machine learning algorithm for classifying urban land use into five Level I classes and twelve Level II classes. Finally, an accuracy assessment was carried out based on a collection of manually interpreted samples. Results showed that our derived map achieved an overall accuracy of 87.58% for Level I, and 73.53% for Level II. The accurate and refined map of EULUC-seg is expected to better support various applications in the future.

Light-addressable potentiometric sensors (LAPS) measure ac photocurrent at electrolyte-insulator-semiconductor (EIS) and, more recently, electrolyte-semiconductor structures to produce spatiotemporal images of chemical or biological analytes, electrical potentials and impedance. One of the most important properties for LAPS is spatial resolution, which determines the smallest features that can be resolved in LAPS images. In this work, the use of nanostructured ZnO for LAPS was explored. The effect of ZnO morphology on the spatial resolution was studied with a LAPS setup. The best resolution of 2 µm was achieved in ZnO films produced by aerosol-assisted chemical vapour deposition (AACVD).

To explain how individuals' self-perceived long-term mate value influences their mate preference and mate choice, two hypotheses have been presented, which are "potentials-attract" and "likes-attract", respectively. The potentials-attract means that people choose mates matched with their sex-specific traits indicating reproductive potentials; and the likes-attract means that people choose mates matched with their own conditions. However, the debate about these two hypotheses still remains unsolved. In this paper, we tested these two hypotheses using a human's actual mate choice data from a Chinese online dating system (called the Baihe website), where 27,183 users of Baihe website are included, in which there are 590 paired couples (1180 individuals) who met each other via the website. Our main results show that not only the relationship between individuals' own attributes and their self-stated mate preference but also that between individuals' own attributes and their actual mate choice are more consistent with the likes-attract hypothesis, i.e., people tend to choose mates who are similar to themselves in a variety of attributes.