John Deere (United States)

Agricultural mechanization has a key role in the growth of agricultural productivity in a country with a larger population like India. Investments in agricultural mechanization are increasing day by day due to the rising wage of labor. Therefore, boosting farm productivity has become increasingly reliant on the use of mechanization, especially in states like Punjab, Haryana, Western Uttar Pradesh, and some southern states as well. Machinery, which serves as an extension of human labor, is undoubtedly a costly investment. Thus, to achieve the maximum output from the artisan’s capital and labor investments, it is essential to plan for its maintenance properly.

Until the end of the 19th century, agriculture in industrialized countries relied on men and animals. It was only after the introduction of fossil fuel-based mechanical energy sources that the first simple machines emerged, thereby increasing the productivity of agriculture. These machines, such as seeders and plows, were constructed for a long time with general specificity, without a particular adaptation to each culture. Only with the increase in work productivity and the improvement of the work process, did the cultivated species influence the design of the machines. The movement of mechanization in agriculture began in America and spread throughout the world. After the Second World War, many agrarian countries were encouraged to develop agricultural mechanization programs, allowing the import of equipment and the installation of local factories. Although the sector is now well-developed, with multinationals and local machines, the trend is still towards greater specialization of the equipment.

The accelerated development of science and technology has raised people's living standards, but the rise in human population and corresponding increase in demand for food continues to be a major challenge. Agriculture is not only able to meet the food needs of the population, it is also important for economic development, especially in developing countries. However, ordinary farm production is limited in many aspects, such as measurement of production, varieties, cultivation techniques and management deficiency. Therefore, farm yield is often uncoupled from resource input, or even worsened. Massive amounts of agricultural data have emerged due to advanced technology and instrumentation, which present an excellent solution for improving farm productivity. Big data can be described with three characteristics: volume, velocity and variety. Data volume is growing at such rapid rates that it is becoming increasingly difficult to manage within traditional database environments (Kamilaris & Prenafeta-Boldú, 2018; Liakos et al., 2018; Klerkx et al., 2019). Data of many types is being generated at an increasing velocity. Various cloud services and databases have emerged and been utilized to address a plethora of typical, individual problems. Data contains variety, which reflects in its heterogeneous nature, such as open data, remote sensing data, and cloud data. Analysis of Big Data offers the potential to explore hidden patterns in data in revealing new knowledge and using new technology to improve applications. Big Data generally describes data that moves quickly, comes in deep volume, and is available in a wide variety of forms, from raw data to curated data products, and can provide novel insights when correctly analyzed and non-trivially processed. Understanding or optimizing such systems is now one of the grand challenges that science and industry face, yet are circumventing specialized tools or statistical induction. Data science, at the intersection of mathematics, statistics, computer science, and domain area expertise, has emerged as a new field. Big Data are characterized by great volume, velocity, variety, and low value density.

This study presents an application of fractal mathematics to financial markets through the utilization of the Multifractal Model of Asset Returns (MMAR). The model is employed to analyse a 30-year dataset encompassing three distinct financial markets: the USD/NOK currency pair, the OMXS30 stock index, and the 12-month LIBOR rate. The MMAR model successfully captures essential stylized facts about financial markets, including high kurtosis, non-independent price movements, and clustered volatile days. Simulations generated by the model demonstrate realistic behaviour and multifractal characteristics. However, the model lacks a statistical method for assessing its accuracy and may underestimate kurtosis in high-kurtosis markets. Notably, the model is capable of producing simulations across a wide range of kurtosis values and can be instrumental in stress-testing portfolios. Despite its potential, the model is subject to limitations, such as the need for human judgment, ambiguous predictions, and interpretability issues. Furthermore, the model may become unreliable following a high-kurtosis event.

This paper presents an advanced pulse-width modulation (PWM) technique for a Si/SiC hybrid five-level (5-L) inner-paralleled ANPC traction inverters used in heavy-duty electric vehicle (EV) applications. The proposed novel modulation algorithm can produce interleaved switching states by leveraging an order-reduced space-vector (SV) hexagon, i.e., three-level (3-L) counterpart, thereby not only capacitor voltages are perfectly maintained at a balanced state, but also the heavy computational cost is overcome because of the carrier-based (CB) sextant coordinate implementation. Attributed to these salient features, the presented next-generation electric powertrains would be able to operate more efficiently and reliably. The effectiveness and feasibility of the proposed modulation technique are verified through MATLAB/Simulink and a 3.3-kV Si IGBT and 1.7-kV SiC H-bridge module-based 5-L hybrid ANPC prototype.

This study aims to explore methods for automating the replication of correlated portfolios across shares using various algorithms. The goal is to select one algorithm for implementation in a visualization tool that will facilitate portfolio building. The dynamics of the stock market provide a comprehensive view of the global economy, acting as a catalyst for stakeholders to participate and benefit various sectors financially, primarily through investments in companies that yield returns and contribute to the economy. The evaluation and comparison of these algorithms are crucial for making well-informed decisions regarding the core objectives of the study. Ultimately, a determination will be made as to whether the program can effectively analyse data from Yahoo finance to provide a quick overview of the financial market state of companies in the S&P 500 US stock prices.

Abstract Equidistant plant arrangements have shown positive impacts, in the United States over the last century, on soybean (Glycine max L.) yield and seed quality by reducing intraspecific plant competition and enhancing early canopy cover. This study aims to (i) assess the effects of equidistant versus non‐equidistant plant arrangements on soybean yield and seed quality across different regions in the United States; (ii) explore the effect of modified soybean plant canopy resulting from different plant arrangements on yield and seed quality. In 2021 and 2022, 13 trials were conducted in Kansas, Mississippi, and South Dakota. The treatments consisted of three equidistant plant arrangements of 13 cm × 13 cm, 15 cm × 15 cm, and 20 cm × 20 cm (620,000, 430,000, 242,363 seeds ha−1, respectively), and a non‐equidistant with 38 cm of row space (321,236 seeds ha−1). Soybean yield and yield components were collected, and canopy coverage (%) was monitored throughout the growing season. Overall, there is no evidence of any advantage of equidistant over non‐equidistant arrangements in soybeans. Yield differences related to spatial arrangement were observed in only three trials: the non‐equidistant increased yield compared to the 20 cm × 20 cm equidistant arrangement (lowest density) in two trials, while the 13 × 13 and 15 × 15 patterns (highest density) showed higher yield compared to the 20 cm × 20 cm arrangement in one trial. Furthermore, seed quality remained constant across spatial arrangements in the 13 trials. In contrast to prior research, our study found no correlation between canopy coverage development and yield improvement. The tested equidistant arrangement while promising did not provide substantial evidence of yield improvement relative to the non‐equidistant.

This paper presents a multi-objective model predictive control-based energy management system. The proposed scheme design aims to perform the power sharing among available sources, minimizing the diesel engine fuel consumption, and regulating the battery state-of-charge, while considering power source constraints. The proposed MPC-based EMS is designed as a high-level controller whose control actions are the references for the low-level controllers for the diesel generator set. A Pareto front analysis is appealed to get a better understanding of the conflicts between the MPC’s objectives. The MPC scheme is designed following a weighting sum scalarization, where Pareto front analysis is crucial for the tuning of the MPC weights, resulting in the proposal of a weight schedule to achieve the desired trade-off performance. Moreover, six typical agricultural working cycles have been considered, where, as expected, a characteristic Pareto front is obtained for each cycle power profile.