Hasil untuk "Special industries and trades"

Yong Wang, Doudou Wu, Hongbo Kang et al.

Zhiqiang Li, Kun Luo, Liang Tao et al.

Electric fertilizer applicators significantly improve the uniformity, stability, and efficiency of summer maize fertilization. However, the complex soil environment in farmlands introduces uncertainties such as parameter variations, load disturbances, frictional resistance, and positioning errors, which degrade the control accuracy and robustness of the driving motor. To address these challenges, this study proposes a disturbance observer (DO)-based adaptive sliding mode control (ASMC) method. First, a control model for the soil-straw coupling system of the electric fertilizer applicator (EFA) was established, accounting for parameter variations and external load disturbances, thereby simplifying controller design. Second, a convergence rate mechanism was introduced to accelerate convergence time, ensuring the system reaches the sliding surface within a finite time, with the convergence rate being adjustable through parameter design. Additionally, a disturbance observer was designed to estimate both mismatched and matched disturbances, enabling feedforward compensation to improve tracking accuracy and reduce system chattering. Experimental results demonstrate that the proposed method achieves high control accuracy and robustness, ensuring rapid and stable state regulation for the EFA. This work provides new ideas for the design of smart agricultural machinery controllers and effectively promotes the control upgrade of agricultural electromechanical systems.

Yumeng Yang, Qi Liu, Xiaodong Gao et al.

In the past decades, extreme precipitation and drought have increased in both intensity and frequency in global drylands, threatening the sustainability of agricultural systems. To address this challenge, this study refined the process-oriented STEMMUS (simultaneous transfer of energy, mass, and momentum in unsaturated soil) model by introducing a dynamic leaf area index (LAI) development sub-module, and defined scenarios incorporating variations in precipitation amount, precipitation intensity, and temperature. These scenarios elucidate the response patterns of shallow and deep soil moisture and apple orchard evapotranspiration to climatic fluctuations. Key findings reveal that, at the interannual scale, increased growing-season precipitation significantly enhanced soil water storage in both shallow (0–200 cm) and deep (200–450 cm) layers. High-intensity precipitation partially increased soil water storage, particularly under reduced precipitation scenarios, though its contribution to deep soil remained limited. Compared to ambient temperature conditions, 2°C warming resulted in maximum reductions of 30.6 mm and 29.7 mm in shallow and deep soil water storage, respectively. Growing-season cumulative canopy transpiration (T), soil evaporation (E), and T/ET all increased significantly with greater precipitation. Conversely, high-intensity precipitation and 2°C warming reduced cumulative transpiration by 9.7–16.4 % and 7.2–18.3 %, respectively, while T/ET decreased by 4.0–9.4 % and 8.9–15.5 %. Notably, 2°C warming markedly amplified cumulative soil evaporation by 11.5–15.7 %, whereas high-intensity precipitation had no significant effect on soil evaporation. These findings provide a theoretical foundation for developing sustainable water management and climate adaptation strategies in dryland agroecosystems.

Zhang Wenbo, Zhang Ziyang, Xi Chengyu et al.

Accurate identification of wheat varieties at the seedling stage is crucial for maintaining seed purity and optimizing field management. However, the subtle phenotypic variations among seedlings present a significant challenge for visual recognition. To address this, we propose SeedlingNet, a novel deep learning model specifically designed for fine-grained wheat seedling variety classification. The core innovations of SeedlingNet include: The Kolmogorov-Arnold-based Convolutional Attention (KCA) mechanism, which dynamically enhances feature representation by replacing static activation functions with learnable, adaptive ones; A multi-scale feature fusion architecture that integrates hierarchical features to capture both global and local characteristics. We established a comprehensive image dataset of 13,600 images representing 17 wheat varieties at the early growth stage. Experimental results demonstrate that SeedlingNet achieves a remarkable classification accuracy of 99.26 %, outperforming traditional machine learning methods and mainstream deep learning models. Ablation studies confirm the significant impact of the KCA module and the multi-scale fusion structure on the model's performance. This research provides an effective, non-destructive tool for early-stage variety identification, with strong potential for precision agriculture applications.The dataset is licensed in Zhang, Wenbo (2025), ''Seedings'', Mendeley Data, V1, doi: 10.17632/f8ykx4sz6w. 1.

Gong Cheng, Zhanling Wu, Xiaonan Guo et al.

Bashang Plateau in China serves as an ecological barrier against wind-driven sand invasion and is a vital water conservation area in the Beijing-Tianjin-Hebei region. Since the 1990s, the expansion of the vegetable industry has increased irrigation demand and actual groundwater extraction, threatening regional water security and ecological stability. This study aims to quantify crop-specific water consumption and explore sustainable planting structures that reduce agricultural water use while maintaining economic and ecological viability. We analyzed the temporal dynamics of dominant crop planting areas (corn, beans, naked oats, oilseeds, coarse cereals, sugar crop, potatoes, and vegetables), and the spatial-temporal characteristics of regional precipitation, temperature, and soil moisture distribution from 2000 to 2020. Crop-specific evapotranspiration (ET) was measured through field experiments (2021–2022), and the nondominated sorting genetic algorithm II (NSGA-II) was employed to generate sustainable planting structures under 10 %, 20 %, and 30 % regional water-saving targets. Over two decades, planting structure shifted toward water-intensive crops, peaking during 2013–2016 before declining due to water scarcity and market dynamics. The 10 % water reduction scenario (S1) proved feasible by reducing the planting area of potatoes and vegetables and increasing coarse cereals (particularly in Shangyi and Kangbao, with lower precipitation), maintaining economic benefits and ecosystem service value. However, 20 % and 30 % reduction (S2, S3) caused economic losses of 6 % and 12.7 %, respectively, due to coarse cereals could not fully offset losses from reduced potato and vegetable production. Balancing groundwater sustainability with agricultural productivity requires optimizing planting structures, supported by improved irrigation technologies and policy incentives. The findings emphasize the need for a balanced crop restructuring strategy, prioritizing high-value crops while limiting water-intensive crops to ensure a sustainable agricultural system in this ecologically sensitive region.

Alice Di Bella, Toni Seibold, Tom Brown et al.

This study analyzes how Europe can decarbonize its industrial sector while remaining competitive. Using the open-source model PyPSA-Eur, it examines key energy- and emission-intensive industries, including steel, cement, methanol, ammonia, and high-value chemicals. Two development paths are explored: a continued decline in industrial activity and a reindustrialization driven by competitiveness policies. The analysis assesses cost gaps between European green products and lower-cost imports, and evaluates strategies such as intra-European relocation, selective imports of green intermediates, and targeted subsidies. Results show that deep industrial decarbonization is technically feasible, led by electrification, but competitiveness depends strongly on policy choices. Imports of green intermediates can lower costs while preserving jobs and production, whereas broad subsidies are economically unsustainable. Effective policy should focus support on sectors like ammonia and steel finishing while maintaining current production levels.

Samuel Hardwick

Trade agreements are often understood as shielding commerce from fluctuations in political relations. This paper provides evidence that World Trade Organization membership reduces the penalty of political distance on trade at the extensive margin. Using a structural gravity framework covering 1948 to 2023 and two measures of political distance, based on high-frequency events data and UN General Assembly votes, GATT/WTO status is consistently associated with a wider range of products traded between politically distant partners. The association is strongest in the early WTO years (1995 to 2008). Events-based estimates also suggest attenuation at the intensive margin, while UN vote-based estimates do not. Across all specifications, GATT/WTO membership increases aggregate trade volumes. The results indicate that a function of the multilateral trading system has been to foster new trade links across political divides, while raising trade volumes among both close and distant partners.

Tobi Ramella, Nicholas P. Warner

The special locus plays an important role in the construction of the non-BPS microstate geometries known as microstrata. These supergravity solutions are dual to combinations of left-moving and right-moving momentum states in the D1-D5 CFT and because supersymmetry is broken the anomalous dimensions of these states are not protected. This means even the simplest combinations of excitations can create a cascade of frequency dependences through the non-linearities of the supergravity interactions. Solutions on the special locus manage to lock some of these anomalous dimensions together and allow one to construct complete solutions using gauged supergravity in three dimensions. In the dual holographic CFT, the special locus has been shown to correspond to creating a "pure" gas of single particle states, however, in supergravity the special locus remains mysterious especially because it does not seem to be defined by a geometric symmetry. In this paper we reveal the supergravity structure of the special locus, first in three-dimensional supergravity and then in the uplift to six dimensions and IIB supergravity. The key insight is that, in three dimensions, a family of dual vector fields must vanish, and this implies that there are algebraic relations between tensor gauge fields in six and ten dimensions. These insights show how one can generalize the special locus Ansatz to more general mode excitations of six-dimensional supergravity. We also construct the full six-dimensional uplift of the simplest special locus.

Giovanni De Gasperis, Sante Dino Facchini

Industrial monitoring systems, especially when deployed in Industry 4.0 environments, are experiencing a shift in paradigm from traditional rule-based architectures to data-driven approaches leveraging machine learning and artificial intelligence. This study presents a comparison between these two methodologies, analyzing their respective strengths, limitations, and application scenarios, and proposes a basic framework to evaluate their key properties. Rule-based systems offer high interpretability, deterministic behavior, and ease of implementation in stable environments, making them ideal for regulated industries and safety-critical applications. However, they face challenges with scalability, adaptability, and performance in complex or evolving contexts. Conversely, data-driven systems excel in detecting hidden anomalies, enabling predictive maintenance and dynamic adaptation to new conditions. Despite their high accuracy, these models face challenges related to data availability, explainability, and integration complexity. The paper suggests hybrid solutions as a possible promising direction, combining the transparency of rule-based logic with the analytical power of machine learning. Our hypothesis is that the future of industrial monitoring lies in intelligent, synergic systems that leverage both expert knowledge and data-driven insights. This dual approach enhances resilience, operational efficiency, and trust, paving the way for smarter and more flexible industrial environments.

Sinan Erdogan, Ugur Korkut Pata, Andrew Adewale Alola

Cutting the global economy's dependence on coal has always been seen as one of the most effective measures to reduce carbon emissions and ensure environmental sustainability. However, the demand for coal can vary greatly from country to country. Therefore, the primary objective of this study is to identify where the countries with the highest coal consumption stand in reducing coal dependence in energy supply from 1997 to 2021 by utilizing panel data methods and accounting for the possible occurrence of cross-sectional dependence. The empirical results denote that (i) the models used are cross-sectionally independent, (ii) there is a long-run relationship between the variables (iii) rising economic growth upsurges coal demand, while its square reduces coal consumption; therefore, the coal-Kuznets curve is valid; (iv) population density and industrialization boost coal consumption, while demand for natural gas and renewable energy reduces it. Based on the empirical outcomes, the study suggests that natural gas should be promoted alongside renewables to displace coal consumption globally, and that countries should consider the coal-Kuznets curve to channel the increased revenues into clean energy investments.

Kržanović Daniel, Gomilanović Miljan, Jovanović Milenko et al.

The disposal of mining waste is an indispensable technological operation during the exploitation of mineral raw materials and mining activity. A new technological approach to the disposal of mining waste, which is presented in this paper, predicts that all mining waste generated during the excavation and processing of ore in open pit mines and in flotation is disposed of in integrated tailings dumps. In this way, the construction of a flotation tailings pond is eliminated, i.e., the permanent risk that may arise due to an accident situation for the environment, facilities and population located downstream of the originally planned location of the flotation tailings pond is eliminated

Luca D'Amico-Wong, Yannai A. Gonczarowski, Gary Qiurui Ma et al.

We model the role of an online platform disrupting a market with unit-demand buyers and unit-supply sellers. Each seller can transact with a subset of the buyers whom she already knows, as well as with any additional buyers to whom she is introduced by the platform. Given these constraints on trade, prices and transactions are induced by a competitive equilibrium. The platform's revenue is proportional to the total price of all trades between platform-introduced buyers and sellers. In general, we show that the platform's revenue-maximization problem is computationally intractable. We provide structural results for revenue-optimal matchings and isolate special cases in which the platform can efficiently compute them. Furthermore, in a market where the maximum increase in social welfare that the platform can create is $ΔW$, we prove that the platform can attain revenue $Ω(ΔW/\log(\min\{n,m\}))$, where $n$ and $m$ are the numbers of buyers and sellers, respectively. When $ΔW$ is large compared to welfare without the platform, this gives a polynomial-time algorithm that guarantees a logarithmic approximation of the optimal welfare as revenue. We also show that even when the platform optimizes for revenue, the social welfare is at least an $O(\log(\min\{n,m\}))$-approximation to the optimal welfare. Finally, we prove significantly stronger bounds for revenue and social welfare in homogeneous-goods markets.

Wenming Yang, Changqing Hui, Zhiquan Chen et al.

Yashar Aryanfar, Jorge Luis García Alcaraz, Julio Blanco Fernández et al.

Renewable energy, particularly geothermal energy, is on the rise globally. It has been demonstrated that recovering heat lost during geothermal cycles is essential due to the inefficiency of these cycles. This paper pproposes a combined power generation cycle using EES software to model a single-flash geothermal cycle, and a trans-critical carbon dioxide cycle. The study compares the system's performance during its "Without Economizer" and "With Economizer" operational stages. The impact of the economizer on the system's output metrics, including the net power output, energy efficiency, and exergy efficiency, was examined. The results show that the "With Economizer" system's net power output increased from 451.3 kW to 454 kW. The energy efficiency difference between the two systems is based on the first law of thermodynamics, where the value ofthe "Without Economizer" system is 6.036%, and the "With Economizer" system is 6.075%. The system without an economizer had an exergy efficiency value of 26.26%, whereas the system with an economizer reached 26.43%, based on the second law of thermodynamics. Installing the economizer increased the total economic cost rate of the system from 0.225M$/Year to 0.2294M$/Year, which increased the product cost rate from 15.82$/GJ to 16.02$/GJ.

Aichata B A Mariko, Mahamane Haidara, Rasmané Semdé et al.

Introduction : L’albinisme entraine une sensibilité au soleil et accroit les risques de cancer cutané d’où la nécessité de se protéger avec des crèmes solaires importées qui ne sont pas toujours accessibles. Pour apporter des solutions alternatives, une étude a été initiée afin de mettre au point une crème de protection à base de ressources locales recensées et dotées d’un fort indice de protection à travers une bonne activité radicalaire et photo-protectrice. L’objectif de cette étude est de déterminer les caractéristiques physicochimiques, les constituants chimiques et anti radicalaires des échantillons de 10 plantes médicinales pour la mise au point d’une crème de protection solaire à fort indice. Méthodologie : Des plantes ont été recensées, des échantillons ont été collectés, séchés et pulvérisés. Les caractéristiques botaniques et physicochimiques, ont été déterminées selon les procédures classiques du DMT du Département de médecine Traditionnelle du Mali. Des extraits ont été préparés puis les constituants chimiques et anti radicalaires ont été caractérisés par les réactions colorés en tube et par la chromatographie sur couche mince. Les concentrations des constituants anti-radicalaires ont été déterminées. Résultats : Les plantes retenues : Bixa orellana, Carica papaya, Hibiscus sabdariffa, Lawsonia inermis, Mangifera indica, Portulaca oleracea, Punica granatum, Solanum lycopersicum, Spondias mombin, Zea mays. Les teneurs en eau ont été ≤ 10 et des cendres totales étaient de 0.94 à 23 %. Les rendements des extractions de 4.1 à 54.63 %. Plusieurs phyto-constituants ont été caractérisés dans tous les échantillons dont spécifiquement les quinones dans les feuilles de Punica granatum et de Lawsonia inermis; les caroténoïdes dans les graines de Bixa orelana. L’activité anti radicalaire des extraits suivant ont été jugés satisfaisants pour: les infusés des feuilles (Mangifera indica 6,81±1,04, Punica granatum 2,8±0,8, Spondias mombin 4,2±0,6) et les hydro-ethanoliques des feuilles (Lawsonia inermis 6,8±0,09, Mangifera indica 4,2±0,6, Punica granatum 1,83±0,5 et Spondias mombin de 2,6±0,4). Conclusion : Les extraits contenant des phyto-constituants à forte activité anti-radicalaire serviront pour l’élaboration des crèmes de protection pour les personnes atteintes d’albinisme.

Brett Hemenway Falk, Gerry Tsoukalas, Niuniu Zhang

Existing studies on crypto wash trading often use indirect statistical methods or leaked private data, both with inherent limitations. This paper leverages public on-chain NFT data for a more direct and granular estimation. Analyzing three major exchanges, we find that ~38% (30-40%) of trades and ~60% (25-95%) of traded value likely involve manipulation, with significant variation across exchanges. This direct evidence enables a critical reassessment of existing indirect methods, identifying roundedness-based regressions à la Cong et al. (2023) as most promising, though still error-prone in the NFT setting. To address this, we develop an AI-based estimator that integrates these regressions in a machine learning framework, significantly reducing both exchange- and trade-level estimation errors in NFT markets (and beyond).

László Á. Kóczy, Dávid Csercsik, Balázs R. Sziklai

To understand the impact of keeping Nord Stream 2 off the map, we model the European natural gas market from the point of view of supply security. Focusing on the network aspects, we propose a novel framework to measure supply security, combining a linear programming approach with a risk assessment technique, expected shortfall (ES) borrowed from finance, particularly suited to measure extreme risk, such as the risk of failing pipelines.Shifting Russian gas exports from Ukraine to Nord Stream 2 increases risks for South-Eastern Europe, and the Trans-Anatolian and Trans-Adriatic Pipelines can only partially alleviate these changes.

Mantiwee Nimworapan, Sinwisuth Sutheechai, Wanwarang Wongcharoen et al.

Edoxaban is available in 60 mg, 30 mg, and 15 mg unscored film-coated tablets. Tablet splitting may be an option to reduce medication costs and reduce the country’s budget. The objectives of this study were to determine the weight variation and the dissolution profile of the 60 mg edoxaban in split half-tablets using a pill splitter. Thirty edoxaban 60 mg tablets were cut into halves by a right-handed pharmacist. The weight variation of the whole and half tablets were compared. For the dissolution test, 6 whole tablets and 12 half-tablets were separately dissolved in three dissolution media. Sixty half-tablets of edoxaban had the expected half-tablet weight within the 75% to 125% range that fell within the proxy United States Pharmacopeia (USP). The mean total weight of the 1st and 2nd halves were not significantly different from the mean weight of the intact tablets (p-value=0.216). The amount of drug release from the whole and half tablets in 0.1 N HCl medium was greater than 85% in 15 minutes which met the acceptance criteria for the dissolution test. Edoxaban tablets splitting had low variations in weight. Therefore, edoxaban tablets can be split into halves by a tablet cutter.

Ivan Letteri, Giuseppe Della Penna, Giovanni De Gasperis et al.

In general, traders test their trading strategies by applying them on the historical market data (backtesting), and then apply to the future trades the strategy that achieved the maximum profit on such past data. In this paper, we propose a new trading strategy, called DNN-forwardtesting, that determines the strategy to apply by testing it on the possible future predicted by a deep neural network that has been designed to perform stock price forecasts and trained with the market historical data. In order to generate such an historical dataset, we first perform an exploratory data analysis on a set of ten securities and, in particular, analize their volatility through a novel k-means-based procedure. Then, we restrict the dataset to a small number of assets with the same volatility coefficient and use such data to train a deep feed-forward neural network that forecasts the prices for the next 30 days of open stocks market. Finally, our trading system calculates the most effective technical indicator by applying it to the DNNs predictions and uses such indicator to guide its trades. The results confirm that neural networks outperform classical statistical techniques when performing such forecasts, and their predictions allow to select a trading strategy that, when applied to the real future, increases Expectancy, Sharpe, Sortino, and Calmar ratios with respect to the strategy selected through traditional backtesting.

Irina Pedroso Rodríguez, Lourdes Yamén González Sáez, Jesús Luis Orozco

Introducción: El ácido húmico es un fertilizante de origen orgánico que mejora las condiciones del suelo y favorece el desarrollo de las plantas. La cachaza es un residuo de la producción de azúcar, una fuente rica y renovable de materia orgánica. Objetivo: Determinar las condiciones operacionales del proceso de extracción de ácido húmico a partir de la cachaza. Materiales y Métodos: La extracción se realiza en dos etapas, un tratamiento básico con hidróxido de sodio y luego con ácido sulfúrico. Se planifica un diseño experimental compuesto central, con los factores relación sólido-líquido y los tiempos de cada tratamiento. Se optimiza el proceso a partir gráficos de superficie de respuesta y se aplica un análisis de regresión múltiple para la obtención de los modelos matemáticos que permitan predecir las condiciones óptimas. Resultados y Discusión: La cachaza presentó un contenido de materia orgánica de 74,57% lo que indica un alto grado de maduración de la fuente orgánica, además presenta un pH igual a 6,2 y 65,02% de humedad. Las condiciones óptimas obtenidas fueron relación sólido-líquido igual a 0,12 g/ml y 10 h de extracción en cada etapa, con un porcentaje de extracción de ácido húmico igual a 15,37% y un beneficio bruto de 694,23 $/h. Conclusiones: En el proceso de extracción de ácido húmico a partir de la cachaza influyen para un 10% de nivel de significación la relación sólido-líquido y los tiempos de cada extracción. Se obtiene un 15,37% de extracción de materia orgánica en el ácido húmico.