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The reduction of the agricultural workforce due to the rapid development of technology has resulted in labor shortages. Agricultural mechanization, such as drone use for pesticide spraying, can solve this problem. However, the terrain, culture, and operational limitations in mountainous orchards in Taiwan make pesticide spraying challenging. By combining reinforcement learning with deep neural networks, we propose to train drones to avoid obstacles and find optimal paths for pesticide spraying that reduce operational difficulties, pesticide costs, and battery consumption. We experimented with different reward mechanisms, neural network depths, flight direction granularities, and environments to devise a plan suitable for sloping orchards. Reinforcement learning is more effective than traditional algorithms for solving path planning in complex environments. &nbsp

Sodium bismuth titanate-based solid solutions are important lead-free piezoelectrics with potential applications. Large electromechanical strain is of particular interest, which can be realized via the structural change in the relaxor states under an electric field. In this work, a polycrystalline ceramic 0.85Na1/2Bi1/2TiO3–0.15BaZr0.2Ti0.8O3, in which a long-range ordered ferroelectric phase and a relaxor state coexist, has been investigated to unveil the origin of its electromechanical strain using various experimental techniques. An ergodic-relaxor to nonergodic-relaxor transition is first observed under a relatively weak electric field, and a more stable long-range ferroelectric phase is induced under a larger electric field. This two-step phase transformation is accompanied with the process of local polarization freezing as well as ferroelectric domain growth. The domain formation during the reversible phase transition is found to be the main contribution to the macroscopic strain. Our investigation provides an in-depth understanding of the origin of reversible electromechanical strain in the NBT-based relaxor-ferroelectric system.

The concentration and sources of organic nitrogen (ON) in lake sediment significantly affect the lake nitrogen cycle. However, the influencing factors and contributors to the ON accumulation rate (ONAR) are unclear. In this study, tree sediment cores from northern, eastern, and southern Dianchi Lake (DC-N, DC-E, and DC-S, respectively), sampled in July 2014, were used to study the effects of autochthonous and allochthonous sources on ON. The results showed that ON and the ONAR increased 2.4–5.1 and 2.6–4.8 times, respectively, from1900 to2000, especially since the 1980s, at which point algal blooms occurred more frequently. The ON contents decreased in the order: DC-S > DC-N > DC-E, whereas the ONAR values followed the order: DC-N > DC-S > DC-E, suggesting that the ONAR was influenced by ON content as well as depositional environmental conditions. The total concentrations of n-alkanes (n-C12 to n-C34) ranged from 4719.4 ng g− 1 to 61,959.6 ng g− 1 in the three sediment cores, each of which exhibited different n-alkanes characteristic variation with vertical depth. The sources of ON were mainly allochthonous (soil erosion and terrestrial plants) and autochthonous (algal and aquatic plants) in DC-S and DC-N, respectively, whereas they were primarily mixed planktonic and terrestrial sources in DC-E. Using the stochastic impacts by regression on population, affluence, and technology model to further examine the ONAR values revealed that 1% increase in temperature and nitrogen fertilizer can increase the ONAR by 73.8–86.2% and 73.2–151.3% in all sediments, especially in DC-S and DC-E. However, a 1% increase in construction area could reduce the ONAR by 2.4–14.2%, especially in DC-N. Overall, climate change and human activities determine the spatial and temporal ONAR variation in Dianchi Lake.

Thin-walled cylindrical shells are key load-carrying components for aerospace structures. Axial compression is the most common load for cylindrical shells; however, critical buckling loads obtained experimentally are significantly lower than the theoretical values, owing to the presence of initial geometric imperfections (GIs). Previous studies often determined only the knockdown factor through the statistics of experimental data; it is an approximate lower limit of the load-carrying capacity and cannot accurately predict the buckling mode in real time. In this paper, an image-driven framework for the intelligent prediction of buckling load and mode based on measured GIs is proposed. First, through measured samples and the random field method, the GI data pool is obtained. Subsequently, two convolutional neural network (CNN) models are trained to predict the buckling load and mode for cylindrical shells, respectively. The active learning strategy is adopted to discern beneficial sample sets and enhance the prediction accuracy and training speed of the CNN models. A numerical example demonstrates that the proposed framework can effectively predict the buckling behavior of cylindrical shells with GIs. Furthermore, using the proposed prediction framework, it was observed that a common feature of unfavorable GIs is a large fluctuation in magnitude along the axial direction.

Pore structure, especially pore shape and size, is a key factor affecting reservoir permeability. There is a poor correlation between permeability and porosity when the pore structure is complex, which leads to the low accuracy of permeability prediction by using porosity. In view of the preceding problems, six factors related to pore structure are summarized from rock physical models and entitled seismic pore structure factors (SPSFs), and the sensitivity of SPSF to permeability is analyzed with the data of two groups of clastic rock cores. The results find that the shear Lee factor is most sensitive to permeability, followed by the shear flexibility factor. In addition, there are good linear correlations between the shear Lee factor and pore size and between the shear flexibility factor and pore aspect ratio. Therefore, the combination of the shear Lee factor and pore aspect ratio can better characterize pore structure, and thus reservoir permeability. A seismic permeability prediction method based on pore aspect ratio, the shear Lee factor, and lithofacies is developed and applied to the Jurassic tight sandstone reservoirs of the Sichuan Basin. The results find that the permeability prediction accuracy is within half an order of magnitude, which is five times higher than the single-factor method of the shear flexibility factor. Compared with porosity, permeability distribution is more consistent with the measured productivity, which confirms the effectiveness of the method. It also provides a new way to predict the spatial distribution of reservoir permeability.

Plant biodiversity-productivity relationships become stronger over time in grasslands, forests, and agroecosystems. Plant shoot and root litter is important in mediating these positive relationships, yet the functional role of plant litter remains overlooked in long-term experiments. We propose that plant litter strengthens biodiversity-ecosystem functioning relationships over time in four ways by providing decomposing detritus that releases nitrogen (N) over time for uptake by existing and succeeding plants, enhancing overall soil fertility, changing soil community composition, and reducing the impact of residue-borne pathogens and pests. We bring new insights into how diversity-productivity relationships may change over time and suggest that the diversification of crop residue retention through increased residue diversity from plant mixtures will improve the sustainability of food production systems.

BACKGROUND: With the exponential increase in the volume of biomedical literature, text mining tasks are becoming increasingly important in the medical domain. Named entities are the primary identification tasks in text mining, prerequisites and critical parts for building medical domain knowledge graphs, medical question and answer systems, medical text classification. OBJECTIVE: The study goal is to recognize biomedical entities effectively by fusing multi-feature embedding. Multiple features provide more comprehensive information so that better predictions can be obtained. METHODS: Firstly, three different kinds of features are generated, including deep contextual word-level features, local char-level features, and part-of-speech features at the word representation layer. The word representation vectors are inputs into BiLSTM as features to obtain the dependency information. Finally, the CRF algorithm is used to learn the features of the state sequences to obtain the global optimal tagging sequences. RESULTS: The experimental results showed that the model outperformed other state-of-the-art methods for all-around performance in six datasets among eight of four biomedical entity types. CONCLUSION: The proposed method has a positive effect on the prediction results. It comprehensively considers the relevant factors of named entity recognition because the semantic information is enhanced by fusing multi-features embedding.

This was a daily diary study using intensive longitudinal methods to evaluate the dynamic structure of relationships among employees’ self-reported health, psychological capital, daily workplace incivility, and daily emotional exhaustion. We recruited 200 participants working in health and fitness clubs for experience sampling and had them file the same questionnaires for 10 consecutive working days. A total of 179 participants (men = 57, 31.85%; women = 122, 68.15%) completed the questionnaires, with a response rate of 89.5%, leading to 1790 data points at the within-person level. We used a multilevel location-scale model of dynamic structural equation modeling (DSEM) for data analysis. We ran the model in Mplus software (version 8.4) and found an autoregressive ‘carry over’ relationship between the previous day’s emotional exhaustion changes on current emotional exhaustion changes ([Formula: see text] = 0.067, CI = [0.003, 0.132]). Daily workplace incivility significantly and positively predicted daily emotional exhaustion at the within-person level ([Formula: see text] = 0.953, CI = [0.465, 1.489]). Self-reported health was negatively associated with a person’s mean ratings of daily emotional exhaustion ([Formula: see text] = −0.256, CI = [0.465, 1.489]) and moderated the strength of the workplace incivility effect on emotional exhaustion ([Formula: see text] = −0.201, CI = [−0.292, −0.121]). Finally, psychological capital was negatively related to a person’s mean ratings of daily emotional exhaustion ([Formula: see text] = −0.240, CI = [−0.377, −0.102]).

As an innovative economic and clean source of energy, now nuclear power is the most worthy of large-scale development in the world. Especially in China, the development of nuclear power is not only related to China's energy security, but also of great significance to economic development and international status. Mature and stable carbon emission market is also very important for the implementation of new energy technology. Therefore whenever it comes to carbon trading, as a derivative means for countries in the world to participate in global carbon emission control, it has developed rapidly in China in recent years. Through theoretical analysis and sample data analysis, this paper studies current policies and historical sources, finds out the possible mechanism for nuclear power to enter the carbon trading market, and describes the economic benefits of the combination of nuclear power and carbon trading market. The results show that there is a correlation and causality between nuclear power and other power generation methods and carbon market, and there is a large profit space when carbon trading is included. Nuclear power has potential value for inclusion in the carbon market.

The Carpathian-Pannonian Region (CPR) hosted some of the largest silicic volcanic eruptions in Europe during the Early and Middle Miocene, contemporaneously with major lithospheric thinning of the Pannonian Basin. This was recorded as an ignimbrite flare-up event from approximately 18.1–14.4 Ma. To gain in-depth perspectives on the eruption chronology, tephrostratigraphy, and petrogenesis at the onset of CPR silicic volcanism, we applied a multi-proxy approach to Lower Miocene rhyolitic ignimbrites and pyroclastic fall deposits from the northern CPR to the Dinaride Lake System. High-precision zircon U-Pb geochronology distinguished two Lower Miocene groups of volcaniclastic rocks at ∼ 18.1 Ma and ∼ 17.3 Ma. Based on combined tephrostratigraphic signatures we propose that the ∼ 18.1 Ma Kalnik and ∼ 17.3 Ma Eger eruptions produced widespread (intermediate to) large caldera-forming massive rhyolitic ignimbrites, deposited across northern and southwestern regions of the CPR. Due to easterly winds that carried volcanic ash hundreds of kilometers to the southwest, Eger eruption products also reached distal intra-montane Dinaride lacustrine basins, recorded as pyroclastic fall deposits. Heterogeneous major and trace elemental compositions of ∼ 18.1 Ma volcanic glass shards suggest that the Kalnik eruption was sourced from complex silicic magmatic systems, with simultaneous tapping of two discrete melt bodies during the eruption. The homogeneous geochemical composition of ∼ 17.3 Ma glasses is distinct from the older glasses. Integrated zircon and bulk glass Nd-Hf isotope compositions have a positive correlation, defining a regional mantle array, and are more radiogenic in the younger phase of volcanism. The recorded systematic isotopic change, moving from older more crustal signatures to younger more juvenile compositions, imply that during the period of lithospheric thinning of the Pannonian Basin the region underwent more complex variations in the interaction between metasomatized lithospheric mantle-derived magmas and various crustal components than previously recognized.

Development of a closed fly wheel based energy system is a concept for generating energy free that is emission free like the renewable energy sources. It comprises a fly wheel 315kg, 2.5Hp 220volts DC motor,10Hp 250volt Alternator, locally developed hub powered with pump for cooling and hydrodynamic lubrication of the flywheel shaft, charge controller, and smart system. The concept of the design and development of a fossil fuel free generator is driven from the high energy stored in fly wheels. An ecofriendly power generation is needed to reduce emissions that have led to the well-discussed global warming and depletion of ecosystem. The momentary motion of any fly wheel is a function of its mass and its velocity as it affect the duration of its rotation before coming to rest. The interconnectivity among the flywheel, generator, a battery and a DC motor can lead production of a reasonable amount of Electrical Energy. Inertia laws and momentum were used in the design of the flywheel. The use of charging controllers and battery management system (BMS) as well as rectifier are used to maintain steady supply and battery life Generation principles, identification of areas of loses, power stability, energy bank, electronic energy booster (fondly known as electronic gear) and smart system for energy management and sustainability.

The objective of this study was to develop a novel resin composite containing yttrium aluminum garnet (Y