Planting patterns have significant effects on rice growth. Nonetheless, little is known about differences in annual crop yield and resource utilization among mechanized rice planting patterns in a rice–wheat cropping system. Field experiments were conducted from 2014 to 2017 using three treatments: pot seedling transplanting for rice and row sowing for wheat (PST-RS), carpet seedling transplanting for rice and row sowing for wheat (CST-RS) and row sowing for both crops (RS-RS). The results showed that, compared with RS-RS, PST-RS and CST-RS prolonged annual crop growth duration by 25–26 and 13–15 days, increased effective accumulated temperature by 399 and 212°C days and increased cumulative solar radiation by 454 and 228 MJ/m2 because of the earlier sowing of rice by 28 and 16 days in PST-RS and CST-RS, respectively. Compared with RS-RS, the annual crop yield of PST-RS and CST-RS increased by 3.1–3.8 and 2.0–2.6 t/ha, respectively, because of the increase in the number of spikelets/kernels per hectare, aboveground biomass, mean leaf area index and grain–leaf ratio. In addition, temperature production efficiency, solar radiation production efficiency and solar radiation use efficiency were higher in PST-RS, followed by CST-RS and RS-RS. These results suggest that mechanized rice planting patterns such as PST-RS increase annual crop production in rice–wheat cropping systems by increasing yield and solar energy utilization.

The past 50 yr of advances in weed recognition technologies have poised site-specific weed control (SSWC) on the cusp of requisite performance for large-scale production systems. The technology offers improved management of diverse weed morphology over highly variable background environments. SSWC enables the use of nonselective weed control options, such as lasers and electrical weeding, as feasible in-crop selective alternatives to herbicides by targeting individual weeds. This review looks at the progress made over this half-century of research and its implications for future weed recognition and control efforts; summarizing advances in computer vision techniques and the most recent deep convolutional neural network (CNN) approaches to weed recognition. The first use of CNNs for plant identification in 2015 began an era of rapid improvement in algorithm performance on larger and more diverse datasets. These performance gains and subsequent research have shown that the variability of large-scale cropping systems is best managed by deep learning for in-crop weed recognition. The benefits of deep learning and improved accessibility to open-source software and hardware tools has been evident in the adoption of these tools by weed researchers and the increased popularity of CNN-based weed recognition research. The field of machine learning holds substantial promise for weed control, especially the implementation of truly integrated weed management strategies. Whereas previous approaches sought to reduce environmental variability or manage it with advanced algorithms, research in deep learning architectures suggests that large-scale, multi-modal approaches are the future for weed recognition.

Patent data have been utilized for engineering design research for long because it contains massive amount of design information. Recent advances in artificial intelligence and data science present unprecedented opportunities to mine, analyse and make sense of patent data to develop design theory and methodology. Herein, we survey the patent-for-design literature by their contributions to design theories, methods, tools, and strategies, as well as different forms of patent data and various methods. Our review sheds light on promising future research directions for the field.

The early identification and prediction of hand-foot-and-mouth disease (HFMD) play an important role in the disease prevention and control. However, suitable models are different in regions due to the differences in geography, social economy factors. We collected data associated with daily reported HFMD cases and weather factors of Zibo city in 2010~2019 and used the generalised additive model (GAM) to evaluate the effects of weather factors on HFMD cases. Then, GAM, support vectors regression (SVR) and random forest regression (RFR) models are used to compare predictive results. The annual average incidence was 129.72/100 000 from 2010 to 2019. Its distribution showed a unimodal trend, with incidence increasing from March, peaking from May to September. Our study revealed the nonlinear relationship between temperature, rainfall and relative humidity and HFMD cases and based on the predictive result, the performances of three models constructed ranked in descending order are: SVR > GAM> RFR, and SVR has the smallest prediction errors. These findings provide quantitative evidence for the prediction of HFMD for special high-risk regions and can help public health agencies implement prevention and control measures in advance.

Mars exploration motivates the search for extraterrestrial life, the development of space technologies, and the design of human missions and habitations. Here, we seek new insights and pose unresolved questions relating to the natural history of Mars, habitability, robotic and human exploration, planetary protection, and the impacts on human society. Key observations and findings include:

1. – high escape rates of early Mars' atmosphere, including loss of water, impact present-day habitability;

2. – putative fossils on Mars will likely be ambiguous biomarkers for life;

3. – microbial contamination resulting from human habitation is unavoidable; and

4. – based on Mars' current planetary protection category, robotic payload(s) should characterize the local martian environment for any life-forms prior to human habitation.

Some of the outstanding questions are:

1. – which interpretation of the hemispheric dichotomy of the planet is correct;

2. – to what degree did deep-penetrating faults transport subsurface liquids to Mars' surface;

3. – in what abundance are carbonates formed by atmospheric processes;

4. – what properties of martian meteorites could be used to constrain their source locations;

5. – the origin(s) of organic macromolecules;

6. – was/is Mars inhabited;

7. – how can missions designed to uncover microbial activity in the subsurface eliminate potential false positives caused by microbial contaminants from Earth;

8. – how can we ensure that humans and microbes form a stable and benign biosphere; and

9. – should humans relate to putative extraterrestrial life from a biocentric viewpoint (preservation of all biology), or anthropocentric viewpoint of expanding habitation of space?

Studies of Mars' evolution can shed light on the habitability of extrasolar planets. In addition, Mars exploration can drive future policy developments and confirm (or put into question) the feasibility and/or extent of human habitability of space.

Schistosomiasis has been subjected to extensive control efforts in the People's Republic of China (China) which aims to eliminate the disease by 2030. We describe baseline results of a longitudinal cohort study undertaken in the Dongting and Poyang lakes areas of central China designed to determine the prevalence of Schistosoma japonicum in humans, animals (goats and bovines) and Oncomelania snails utilizing molecular diagnostics procedures. Data from the Chinese National Schistosomiasis Control Programme (CNSCP) were compared with the molecular results obtained.

Sixteen villages from Hunan and Jiangxi provinces were surveyed; animals were only found in Hunan. The prevalence of schistosomiasis in humans was 1.8% in Jiangxi and 8.0% in Hunan determined by real-time polymerase chain reaction (PCR), while 18.3% of animals were positive by digital droplet PCR. The CNSCP data indicated that all villages harboured S. japonicum-infected individuals, detected serologically by indirect haemagglutination assay (IHA), but very few, if any, of these were subsequently positive by Kato-Katz (KK).

Based on the outcome of the IHA and KK results, the CNSCP incorporates targeted human praziquantel chemotherapy but this approach can miss some infections as evidenced by the results reported here. Sensitive molecular diagnostics can play a key role in the elimination of schistosomiasis in China and inform control measures allowing for a more systematic approach to treatment.

The long-term inflammatory impact of diet could potentially elevate the risk of periodontal disease through modification of systemic inflammation. The aim of the present study was to prospectively investigate the associations between a food-based, reduced rank regression (RRR)-derived, empirical dietary inflammatory pattern (EDIP) and incidence of periodontitis. The study population was composed of 34 940 men from the Health Professionals Follow-Up Study, who were free of periodontal disease and major illnesses at baseline (1986). Participants provided medical and dental history through mailed questionnaires every 2 years and dietary data through validated semi-quantitative FFQ every 4 years. We used Cox proportional hazard models to examine the associations between EDIP scores and validated self-reported incidence of periodontal disease over a 24-year follow-up period. No overall association between EDIP and the risk of periodontitis was observed; the hazard ratio comparing the highest EDIP quintile (most proinflammatory diet) with the lowest quintile was 0·99 (95 % CI 0·89, 1·10, P-value for trend = 0·97). A secondary analysis showed that among obese non-smokers (i.e. never and former smokers at baseline), the hazard ratio for periodontitis comparing the highest EDIP quintile with the lowest was 1·39 (95 % CI 0·98, 1·96, P-value for trend = 0·03). In conclusion, no overall association was detected between EDIP and incidence of self-reported periodontitis in the study population. From the subgroups evaluated, EDIP was significantly associated with increased risk of periodontitis only among non-smokers who were obese. Hence, this association must be interpreted with caution.

Previous studies have revealed associations of meteorological factors with tuberculosis (TB) cases. However, few studies have examined their lag effects on TB cases. This study was aimed to analyse nonlinear lag effects of meteorological factors on the number of TB notifications in Hong Kong. Using a 22-year consecutive surveillance data in Hong Kong, we examined the association of monthly average temperature and relative humidity with temporal dynamics of the monthly number of TB notifications using a distributed lag nonlinear models combined with a Poisson regression. The relative risks (RRs) of TB notifications were >1.15 as monthly average temperatures were between 16.3 and 17.3 °C at lagged 13–15 months, reaching the peak risk of 1.18 (95% confidence interval (CI) 1.02–1.35) when it was 16.8 °C at lagged 14 months. The RRs of TB notifications were >1.05 as relative humidities of 60.0–63.6% at lagged 9–11 months expanded to 68.0–71.0% at lagged 12–17 months, reaching the highest risk of 1.06 (95% CI 1.01–1.11) when it was 69.0% at lagged 13 months. The nonlinear and delayed effects of average temperature and relative humidity on TB epidemic were identified, which may provide a practical reference for improving the TB warning system.