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This introduction to the symposium and to this issue of Current Anthropology attempts to provide some sense of the topic, the meeting itself, the participants, and some of the initial results. Our symposium brought together a diverse international group of archaeological scientists to consider a topic of common interest and substantial anthropological import-the origins of agriculture. The group included individuals working in most of the places where farming began. This issue is organized by chronology and geography. Our goal was to consider the most recent data and ideas from these different regions in order to examine larger questions of congruity and disparity among the groups of first farmers. There is much new information from a number of important areas, particularly Asia. Following a review of the history of investigation of agricultural origins, this introduction summarizes the results of the conference. There are at least 10 different places around the world where agriculture was independently developed, and the antiquity of domestication is being pushed back in time with new discoveries. Our symposium has emphasized the importance of a multidisciplinary approach to such large questions in order to assemble as much information as possible. We anticipate that the results and consequences of this symposium will have long-term ripple effects in anthropology and archaeology.

China is facing one of the largest challenges of this century to continue to increase annual cereal production to about 600 Mt by 2030 to ensure food security with shrinking cropland and limited resources, while maintaining or improving soil fertility, and protecting the environment. Rich experiences in integrated and efficient utilization of different strategies of crop rotation, intercropping, and all possible nutrient resources accumulated by Chinese farmers in traditional farming systems have been gradually abandoned and nutrient management shifted to over-reliance on synthetic fertilizers. China is now the world’s largest producer, consumer and importer of chemical fertilizers. Overapplication of nitrogen (N) is common in intensive agricultural regions, and current N-uptake efficiency was reported to be only 28.3, 28.2 and 26.1% for rice, wheat and maize, respectively, and less than 20% in intensive agricultural regions and for fruit trees or vegetable crops. In addition to surface and groundwater pollution and greenhouse gas emissions, over-application of N fertilizers has caused significant soil acidification in major Chinese croplands, decreasing soil pH by 0.13 to 2.20. High yield as a top priority, small-scale farming, lack of temporal synchronization of nutrient supply and crop demand, lack of effective extension systems, and hand application of fertilizers by farmers are possible reasons leading to the over-application problems. There is little doubt that current nutrient management practices are not sustainable and more efficient management systems need to be developed. A review of long-term experiments conducted around the world indicated that chemical fertilizer alone is not enough to improve or maintain soil fertility at high levels and the soil acidification problem caused by overapplication of synthetic N fertilizers can be reduced if more fertilizer N is applied as NO 3 − relative to ammonium- or urea-based N fertilizers. Organic fertilizers can improve soil fertility and quality, but long-term application at high rates can also lead to more nitrate leaching, and accumulation of P, if not managed well. Well-managed combination of chemical and organic fertilizers can overcome the disadvantages of applying single source of fertilizers and sustainably achieve higher crop yields, improve soil fertility, alleviate soil acidification problems, and increase nutrient-use efficiency compared with only using chemical fertilizers. Crop yield can be increased through temporal diversity using crop rotation strategies compared with continuous cropping and legume-based cropping systems can reduce carbon and nitrogen losses. Crop yield responses to N fertilization can vary significantly from year to year due to variation in weather conditions and indigenous N supply, thus the commonly adopted prescriptive approach to N management needs to be replaced by a responsive in-season management approach based on diagnosis of crop growth, N status and demand. A crop sensor-based in-season site-specific N management strategy was able to increase Nuptake efficiency by 368% over farmers’ practices in the North China Plain. Combination of these well-tested nutrient management principles and practices with modern crop management technologies is needed to develop sustainable nutrient management systems in China that can precisely match field-to-field and year-to-year variability in nutrient supply and crop demand for both single crops and crop rotations to not only improve nutrient-use efficiency but also increase crop yield and protect the environment. In addition, innovative and effective extension and service-providing systems to assist farmers in adopting and applying new management systems and technologies are also crucially important for China to meet the grand challenge of food security, nutrient-use efficiency and sustainable development.

With the rapid development of fault prognostics and health management (PHM) technology, more and more deep learning algorithms have been applied to the intelligent fault diagnosis of rolling bearings, and although all of them can achieve over 90% diagnostic accuracy, the generality and robustness of the models cannot be truly verified under complex extreme variable loading conditions. In this study, an end-to-end rolling bearing fault diagnosis model of a hybrid deep neural network with principal component analysis is proposed. Firstly, in order to reduce the complexity of deep learning computation, data pre-processing is performed by principal component analysis (PCA) with feature dimensionality reduction. The preprocessed data is imported into the hybrid deep learning model. The first layer of the model uses a CNN algorithm for denoising and simple feature extraction, the second layer makes use of bi-directional long and short memory (BiLSTM) for greater in-depth extraction of the data with time series features, and the last layer uses an attention mechanism for optimal weight assignment, which can further improve the diagnostic precision. The test accuracy of this model is fully comparable to existing deep learning fault diagnosis models, especially under low load; the test accuracy is 100% at constant load and nearly 90% for variable load, and the test accuracy is 72.8% at extreme variable load (2.205 N·m/s–0.735 N·m/s and 0.735 N·m/s–2.205 N·m/s), which are the worst possible load conditions. The experimental results fully prove that the model has reliable robustness and generality.

The forecasted 9.1 billion population in 2050 will require an increase in food production for an additional two billion people. There is thus an active debate on new farming practices that could produce more food in a sustainable way. Here, we list agroecological cropping practices in temperate areas. We classify practices according to efficiency, substitution, and redesign. We analyse their advantages and drawbacks with emphasis on diversification. We evaluate the potential use of the practices for future agriculture. Our major findings are: (1) we distinguish 15 categories of agroecological practices (7 practices involve increasing efficiency or substitution, and 8 practices need a redesign often based on diversification). (2) The following agroecological practices are so far poorly integrated in actual agriculture: biofertilisers; natural pesticides; crop choice and rotations; intercropping and relay intercropping; agroforestry with timber, fruit, or nut trees; allelopathic plants; direct seeding into living cover crops or mulch; and integration of semi-natural landscape elements at field and farm or their management at landscape scale. These agroecological practices have only a moderate potential to be broadly implemented in the next decade. (3) By contrast, the following practices are already well integrated: organic fertilisation, split fertilisation, reduced tillage, drip irrigation, biological pest control, and cultivar choice.

Enhanced plant biomass accumulation in response to elevated atmospheric CO2 concentration could dampen the future rate of increase in CO2 levels and associated climate warming. However, it is unknown whether CO2-induced stimulation of plant growth and biomass accumulation will be sustained or whether limited nitrogen (N) availability constrains greater plant growth in a CO2-enriched world. Here we show, after a six-year field study of perennial grassland species grown under ambient and elevated levels of CO2 and N, that low availability of N progressively suppresses the positive response of plant biomass to elevated CO2. Initially, the stimulation of total plant biomass by elevated CO2 was no greater at enriched than at ambient N supply. After four to six years, however, elevated CO2 stimulated plant biomass much less under ambient than enriched N supply. This response was consistent with the temporally divergent effects of elevated CO2 on soil and plant N dynamics at differing levels of N supply. Our results indicate that variability in availability of soil N and deposition of atmospheric N are both likely to influence the response of plant biomass accumulation to elevated atmospheric CO2. Given that limitations to productivity resulting from the insufficient availability of N are widespread in both unmanaged and managed vegetation, soil N supply is probably an important constraint on global terrestrial responses to elevated CO2.

The Amazon rainforest is the Earth’s largest reservoir of plant and animal diversity, and it has been subjected to especially high rates of land use change, primarily to cattle pasture. This conversion has had a strongly negative effect on biological diversity, reducing the number of plant and animal species and homogenizing communities. We report here that microbial biodiversity also responds strongly to conversion of the Amazon rainforest, but in a manner different from plants and animals. Local taxonomic and phylogenetic diversity of soil bacteria increases after conversion, but communities become more similar across space. This homogenization is driven by the loss of forest soil bacteria with restricted ranges (endemics) and results in a net loss of diversity. This study shows homogenization of microbial communities in response to human activities. Given that soil microbes represent the majority of biodiversity in terrestrial ecosystems and are intimately involved in ecosystem functions, we argue that microbial biodiversity loss should be taken into account when assessing the impact of land use change in tropical forests.

Following Kant, Frege took the idea that there is such a thing as knowledge of a large range of necessary propositions for granted. In particular he assumed that such is the character of our knowledge of basic logic and arithmetic. This view is no longer orthodoxy. The idea that pure (for Frege, logical) intellection can provide for substantial knowledge of necessary features of the world is widely regarded with suspicion. However it is fair to say that most recent scepticism about it has been driven either by abstract background theoretical commitments—for instance, by a thoroughgoing empiricism, as in Mill and Quine, or by epistemological externalism1—or by the conviction that the concept of justification allows of no stable, theoretically interesting characterisation.2 The present discussion focuses on the example of elementary arithmetic to develop and explore a different, relatively neglected and, it may be suggested, more fundamental kind of sceptical challenge, one prefigured in the writings on mathematics of the later Wittgenstein but independent of the discussion of following a rule to which his generally deflationary or conventionalist cast of thinking about mathematical knowledge is, after Kripkenstein, nowadays usually attributed. Elementary arithmetical truths are normally taken to be justifiable if any truths are. They are also normally taken to be both necessary and substantial—essentially applicable to our dealings with the world and empirically predictive, yet good for counterfactual reasoning about however far-fetched and exotic scenarios. Yet, I will argue, scrutiny of the kind of methods—simple informal cognitive routines involving counting and pictures—whereby such judgements are initially apt to win our confidence serves to make it puzzling how they can what they are supposed to at all: how such procedures can merit either the very high levels of confidence we standardly place in the judgements they lead to, or the modal (counterfactual) significance we standardly attach to those judgements. The present discussion elaborates these apparent shortfalls (§§2–4) and reviews four proposals (§§5–8) for redressing them, arguing that each is ineffective. The sceptical challenge accordingly stands unanswered. §9 elaborates its relation to one strand in the . §10 summarises the resulting dialectical position. The upshot, I believe, is a deepened understanding of an important aspect of Wittgenstein’s later philosophy and a major intellectual challenge to those—possibly still a majority—who incline to side with Frege’s view of the epistemological status of arithmetic.

Selected peer-reviewed full text papers from the 4th International Conference on Science and Technology Applications (ICoSTA)Selected peer-reviewed full text papers from the 4th International Conference on Science and Technology Applications (ICoSTA), November 01-02, 2022, Medan, Indonesia.

Summary Pollination is a major ecosystem service in which insects, particularly bees, play an important role for the reproduction of most angiosperms. Currently, this service is considered under threat due to reported bee declines. Moderately urbanised areas could be important for pollinators and pollination; however, compared to agricultural and natural systems, they are poorly studied. Here, we investigated the relative effects of local habitat quality and anthropogenic land use across an agricultural to urban gradient for local plant and flying insect communities. We quantified local flower visitor networks and related network architecture to these local and landscape factors using structural equation modelling. Flower visitor network architecture is often assumed to act as a surrogate for the ecosystem service of pollination. To test this idea, we related network metrics to pollination of four experimental, insect pollinator‐dependent plant species. Overall, local land use markedly influenced plant and flying insect communities. Flower richness and bee richness were higher in urban compared to agricultural areas. Flower visitor network metrics (e.g. linkage density) increased with the proportion of urban area surrounding a site. Also, relative to agricultural areas, urban flower visitors were more generalised and foraged from a higher number of plant species, likely a consequence of higher urban flowering plant richness. However, urban bees also visited a lower proportion of the available flowering plants (higher specialisation). Surprisingly, linkage density, network specialisation and flower visitor generality were not related to pollination of our four experimental plants per se. Rather, it was the proportion of urban cover, flying insect abundance and bee richness that were positively related to pollination. Our findings show strong effects of local land use on plant and flying insect communities and flower visitor interaction networks. We observed increased overall visitation rates and pollination services to our experimental plants in urban compared to agricultural areas, despite increased urban flower visitor generality. Indeed, flower visitor network metrics were a poor proxy of provision of the ecosystem service of pollination. Nevertheless, our results point to potential facilitating effects of diverse urban floral and bee communities for pollination. A lay summary is available for this article. Lay Summary

Aim: We conducted the most extensive quantitative analysis yet undertaken of the form taken by the island species-area relationship (ISAR), among 20 models, to determine: (1) the best-fit model, (2) the best-fit model family, (3) the best-fit ISAR shape (and presence of an asymptote), (4) system properties that may explain ISAR form, and (5) parameter values and interpretation of the logarithmic implementation of the power model. Location: World-wide. Methods: We amassed 601 data sets from terrestrial islands and employed an information-theoretic framework to test for the best-fit ISAR model, family, and shape, and for the presence/absence of an asymptote. Two main criteria were applied: generality (the proportion of cases for which the model provided an adequate fit) and efficiency (the overall probability of a model, when adequate, being the best at explaining ISARs; evaluated using the mean overall AIC c weight). Multivariate analyses were used to explore the potential of island system properties to explain trends in ISAR form, and to describe variation in the parameters of the logarithmic power model. Results: Adequate fits were obtained for 465 data sets. The simpler models performed best, with the power model ranked first. Similar results were obtained at model family level. The ISAR form is most commonly convex upwards, without an asymptote. Island system traits had low descriptive power in relation to variation in ISAR form. However, the æ and c parameters of the logarithmic power model show significant pattern in relation to island system type and taxon. Main conclusions: Over most scales of space, ISARs are best represented by the power model and other simple models. More complex, sigmoid models may be applicable when the spatial range exceeds three orders of magnitude. With respect to the log power model, z-values are indicative of the process(es) establishing species richness and composition patterns, while c-values are indicative of the realized carrying capacity of the system per unit area. Variation in ISAR form is biologically meaningful, but the signal is noisy, as multiple processes constrain the ecological space available within island systems and the relative importance of these processes varies with the spatial scale of the system.