Hasil untuk "Special industries and trades"

Yi Liu, Wenzhi Zeng, Chang Ao et al.

Soil salinization impairs fertility and reduces crop productivity across more than 6 % of the world’s arable land. Traditional remediation approaches, like chemical amendments, are often costly and involve ecological compromises. This study investigates an innovative nano-bio strategy that integrates multi-walled carbon nanotubes (MWCNTs) with Bacillus subtilis (B. subtilis) under drip irrigation to boost maize tolerance in saline environments. Germination tests and field studies were conducted in soils treated with 50 mM NaCl. The results from four comparative treatments revealed that MWCNTs markedly improved seed germination (achieving 52 % by day two versus 24 % in controls) and enhanced root elongation by 52.36 %. These effects were linked to the upregulation of key ion transporters (ZmSKOR). Furthermore, MWCNTs application enhanced the expression of aquaporin genes ZmPIP1;1 and ZmPIP2;1. Although B. subtilis alone had a minimal impact on germination, its combination with MWCNTs fostered stronger soil-microbe-nanomaterial interactions under drip irrigation. This synergy increased maize yield by 20.6 %, raised the 1000-grain weight by 3.08 %, lowered the leaf Na⁺/K⁺ ratio by 19.93 %, and improved antioxidant defense mechanisms, such as a 10.44 % rise in SOD activity. Importantly, while MWCNTs alone decreased soil nitrogen in non-saline conditions, adding B. subtilis helped rebalance nutrients, an effect that was reinforced by the uniform distribution provided by drip irrigation. The mechanism involves improved nutrient assimilation, better stomatal control, and reduced reactive oxygen species under salt stress. These findings indicate that the MWCNTs and B. subtilis act synergistically with drip irrigation via molecular soil-root interactions to mitigate salt toxicity. This integrated approach, which combines nanotechnology, microbiome engineering, and water-efficient irrigation, offers a sustainable and effective solution for reclaiming saline soils and advancing stress-resistant agriculture.

Syed Ijaz Ul Haq, Guobin Wang, Shahid Nawaz Khan et al.

Early and accurate detection of crop stress is essential for sustainable agriculture and food security, particularly as climate change and environmental degradation intensify agricultural challenges. This comprehensive review examines advanced crop stress monitoring strategies that leverage multi-dimensional optical remote sensing approaches, specifically integrating spectral, angular, and spatial perspectives across diverse observation scales. We systematically analyze how biotic stresses (diseases, pests) and abiotic stresses (drought, nutrient deficiency, temperature extremes) manifest through detectable changes in plant spectral signatures, from chlorophyll degradation in the visible spectrum to water content variations in shortwave infrared regions. Our review encompasses sensing technologies spanning RGB, multispectral, hyperspectral, thermal infrared, and chlorophyll fluorescence sensors deployed across three complementary scales: proximal ground-based systems for detailed physiological assessment, unmanned aerial vehicles (UAVs) for field-scale monitoring, and satellites for regional surveillance. A key innovation of this work is the emphasis on multi-angle remote sensing, which captures bidirectional reflectance distribution function (BRDF) effects that reveal stress-induced changes in canopy structure and leaf orientation invisible to conventional nadir-only observations. We demonstrate how viewing geometry significantly affects vegetation indices (NDVI, PRI) and sun-induced fluorescence (SIF) measurements, requiring sophisticated angular correction methods for accurate stress assessment. Through synthesis of 138 recent studies spanning 12 major crop types, we identify critical research gaps including: (1) inconsistent angular reflectance modeling across stress types, (2) inadequate sensor calibration protocols for variable field conditions, and (3) lack of standardized frameworks for integrating multi-source, multi-scale data streams. Our analysis reveals that advanced machine learning approaches particularly deep learning and transformer networks show exceptional promise for extracting meaningful stress signatures from complex, high-dimensional datasets while maintaining interpretability for agricultural decision-making. We propose a hierarchical monitoring architecture supported by physics-aware artificial intelligence models that address three fundamental challenges: temporal optimization for capturing stress progression dynamics, spatial integration across observation scales, and angular standardization for consistent stress quantification. This framework aims to transform crop stress monitoring from reactive management to predictive intervention, enabling real-time diagnostics suitable for diverse agricultural systems ranging from high-value specialty crops to extensive grain production. The review concludes with a strategic roadmap for operational implementation, addressing economic constraints, technological limitations, and knowledge transfer requirements necessary for widespread adoption. Our findings indicate that successful deployment requires service-based delivery models, simplified decision support interfaces, and staged implementation approaches that demonstrate incremental value while building organizational capacity. The literature selection was conducted using Scopus, Web of Science, and IEEE Xplore databases, covering publications from 2018 to 2024. Search terms included “crop stress monitoring,” “spectral remote sensing,” “multi-angle sensing,” and “UAV agriculture.” A total of 138 peer-reviewed studies meeting relevance and methodological rigor criteria were included. These studies span 12 major crop types: wheat, maize, rice, soybean, cotton, sugarcane, potato, grapevine, tomato, barley, sorghum, and rapeseed, ensuring broad coverage across cereal, legume, fiber, tuber, and horticultural crops.

Xiaoqing Wang, Fengzi Lu, Adnan Safi et al.

Determining the influence of climate policy uncertainty (CPU), fossil fuel dynamics, and renewable energy (REE) adoption on carbon market volatility (CTM) is essential for ensuring its stability and sustainable development. Therefore, this study captures the dynamic relationships among CTM, CPU, crude oil (COP), coal (COA) and REE across different time horizons utilizing a Time-Varying Parameter Structural Vector Autoregression with Stochastic Volatility (TVP-SVAR-SV) model. Results reveal that in the short term, shocks from CPU, COP, COA, and REE all significantly intensify carbon price fluctuations. In the medium term, the carbon market exhibits heightened sensitivity particularly to CPU and COA shocks, while the effects of all factors diminish over the long term. Furthermore, the analysis confirms pronounced time-varying characteristics, with the influence of oil prices on carbon price volatility notably strengthening over time. By comparing influence degree, CPU and COP emerge as the more influential and volatile drivers, whereas the impact of COA remains more stable. Finally, all shocks are significantly amplified during periods of major external disruption, especially during the Russia-Ukraine conflict. These findings highlight the importance of maintaining clear climate policy signals and stabilizing energy market dynamics to enhance the resilience and efficiency of carbon markets.

Rituraj Singh, Sachin Pawar, Girish Palshikar

Commonsense knowledge bases (KB) are a source of specialized knowledge that is widely used to improve machine learning applications. However, even for a large KB such as ConceptNet, capturing explicit knowledge from each industry domain is challenging. For example, only a few samples of general {\em tasks} performed by various industries are available in ConceptNet. Here, a task is a well-defined knowledge-based volitional action to achieve a particular goal. In this paper, we aim to fill this gap and present a weakly-supervised framework to augment commonsense KB with tasks carried out by various industry groups (IG). We attempt to {\em match} each task with one or more suitable IGs by training a neural model to learn task-IG affinity and apply clustering to select the top-k tasks per IG. We extract a total of 2339 triples of the form $\langle IG, is~capable~of, task \rangle$ from two publicly available news datasets for 24 IGs with the precision of 0.86. This validates the reliability of the extracted task-IG pairs that can be directly added to existing KBs.

Gian Jaeger, Wang Ngai Yeung, Renaud Lambiotte

Although the automation and digitisation of anti-financial crime investigation has made significant progress in recent years, detecting insider trading remains a unique challenge, partly due to the limited availability of labelled data. To address this challenge, we propose using a data-driven networks approach that flags groups of corporate insiders who report coordinated transactions that are indicative of insider trading. Specifically, we leverage data on 2.9 million trades reported to the U.S. Securities and Exchange Commission (SEC) by company insiders (C-suite executives, board members and major shareholders) between 2014 and 2024. Our proposed algorithm constructs weighted edges between insiders based on the temporal similarity of their trades over the 10-year timeframe. Within this network we then uncover trends that indicate insider trading by focusing on central nodes and anomalous subgraphs. To highlight the validity of our approach we evaluate our findings with reference to two null models, generated by running our algorithm on synthetic empirically calibrated and shuffled datasets. The results indicate that our approach can be used to detect pairs or clusters of insiders whose behaviour suggests insider trading and/or market manipulation.

Jędrzej Maskiewicz, Paweł Sakowski

The paper explores the use of Deep Reinforcement Learning (DRL) in stock market trading, focusing on two algorithms: Double Deep Q-Network (DDQN) and Proximal Policy Optimization (PPO) and compares them with Buy and Hold benchmark. It evaluates these algorithms across three currency pairs, the S&P 500 index and Bitcoin, on the daily data in the period of 2019-2023. The results demonstrate DRL's effectiveness in trading and its ability to manage risk by strategically avoiding trades in unfavorable conditions, providing a substantial edge over classical approaches, based on supervised learning in terms of risk-adjusted returns.

Yunqi Liu

This study investigates the impact of artificial intelligence (AI) adoption on job loss rates using the Global AI Content Impact Dataset (2020--2025). The panel comprises 200 industry-country-year observations across Australia, China, France, Japan, and the United Kingdom in ten industries. A three-stage ordinary least squares (OLS) framework is applied. First, a full-sample regression finds no significant linear association between AI adoption rate and job loss rate ($β\approx -0.0026$, $p = 0.949$). Second, industry-specific regressions identify the marketing and retail sectors as closest to significance. Third, interaction-term models quantify marginal effects in those two sectors, revealing a significant retail interaction effect ($-0.138$, $p < 0.05$), showing that higher AI adoption is linked to lower job loss in retail. These findings extend empirical evidence on AI's labor market impact, emphasize AI's productivity-enhancing role in retail, and support targeted policy measures such as intelligent replenishment systems and cashierless checkout implementations.

Leonardo Berti, Bardh Prenkaj, Paola Velardi

Financial markets are complex systems characterized by high statistical noise, nonlinearity, volatility, and constant evolution. Thus, modeling them is extremely hard. Here, we address the task of generating realistic and responsive Limit Order Book (LOB) market simulations, which are fundamental for calibrating and testing trading strategies, performing market impact experiments, and generating synthetic market data. We propose a novel TRAnsformer-based Denoising Diffusion Probabilistic Engine for LOB Simulations (TRADES). TRADES generates realistic order flows as time series conditioned on the state of the market, leveraging a transformer-based architecture that captures the temporal and spatial characteristics of high-frequency market data. There is a notable absence of quantitative metrics for evaluating generative market simulation models in the literature. To tackle this problem, we adapt the predictive score, a metric measured as an MAE, to market data by training a stock price predictive model on synthetic data and testing it on real data. We compare TRADES with previous works on two stocks, reporting a 3.27 and 3.48 improvement over SoTA according to the predictive score, demonstrating that we generate useful synthetic market data for financial downstream tasks. Furthermore, we assess TRADES's market simulation realism and responsiveness, showing that it effectively learns the conditional data distribution and successfully reacts to an experimental agent, giving sprout to possible calibrations and evaluations of trading strategies and market impact experiments. To perform the experiments, we developed DeepMarket, the first open-source Python framework for LOB market simulation with deep learning. In our repository, we include a synthetic LOB dataset composed of TRADES's generated simulations.

Fouad Bousetouane

The evolution of agentic systems represents a significant milestone in artificial intelligence and modern software systems, driven by the demand for vertical intelligence tailored to diverse industries. These systems enhance business outcomes through adaptability, learning, and interaction with dynamic environments. At the forefront of this revolution are Large Language Model (LLM) agents, which serve as the cognitive backbone of these intelligent systems. In response to the need for consistency and scalability, this work attempts to define a level of standardization for Vertical AI agent design patterns by identifying core building blocks and proposing a \textbf{Cognitive Skills } Module, which incorporates domain-specific, purpose-built inference capabilities. Building on these foundational concepts, this paper offers a comprehensive introduction to agentic systems, detailing their core components, operational patterns, and implementation strategies. It further explores practical use cases and examples across various industries, highlighting the transformative potential of LLM agents in driving industry-specific applications.

Oleksandr Diachenko, Maksym Delembovskyi, Kateryna Levchuk et al.

The production of concrete mixes, along with their use in the production of building materials and structures, is one of the key processes in the construction industry during the construction, restoration and repair of buildings and structures. Because of this, the need to create modern concrete mixing plants that will meet the requirements of minimum energy consumption and maximum productivity of concrete mixture production is an urgent task. Not only the main operations, which include the dosing of the components of the mixture and their mixing, but also the maintenance operations, namely operations that ensure the timely movement of the components of the concrete mixture from warehouses to the main technological equipment, affect the set rhythm of the concrete mixture production. Conveyors of various types and designs are used to move bulk materials, such as crushed stone and sand. For the rational selection of such equipment in accordance with the characteristics of the cargo to be transported, knowledge of the types of conveyors, their structures and parameters, understanding of operation issues and methods of parameter calculation are required. In addition, it is worth paying attention to the following parameters: maximum cargo transportation productivity, low energy consumption per unit of moved products, low metal content of the structure. The work reviewed the most common designs of conveyors used to move bulk materials in concrete mixing plants, analyzed the disadvantages and advantages of conveyors, as well as technical parameters. As a result, the predominant directions for the use of belt and plate conveyors at construction enterprises were determined. The advantages of belt conveyors, which contribute to their widespread distribution, are high productivity, simplicity of design, reliability, quiet operation, low specific power consumption. When choosing a conveyor, it is recommended to choose the equipment with the highest productivity and the lowest power of the drive motors, however, the performance should be clearly related to other technological equipment.

Pau Pujolas, Jack Rossbach

Trade imbalances significantly alter the welfare implications of tariffs. Using an illustrative model, we show that trade deficits enhance a country's ability to alter its terms of trade, and thereby benefit from tariffs. Greater trade deficits imply higher optimal, or welfare maximizing, tariffs. We compute optimal unilateral and Nash equilibrium tariffs between the United States and China $\unicode{x2014}$ the countries with the largest bilateral trade imbalance $\unicode{x2014}$ using a multi-region, multi-sector applied general equilibrium model with service sectors and input-output linkages, a computationally complex task. Free trade benefits both countries compared to a trade war. Relative to existing tariff rates, however, the United States gains from a trade war with China $\unicode{x2014}$ a result that hinges on their bilateral trade imbalance.

Davoud Sarani, Parviz Rashidi-Khazaee

In today's forex market traders increasingly turn to algorithmic trading, leveraging computers to seek more profits. Deep learning techniques as cutting-edge advancements in machine learning, capable of identifying patterns in financial data. Traders utilize these patterns to execute more effective trades, adhering to algorithmic trading rules. Deep reinforcement learning methods (DRL), by directly executing trades based on identified patterns and assessing their profitability, offer advantages over traditional DL approaches. This research pioneers the application of a multi-agent (MA) RL framework with the state-of-the-art Asynchronous Advantage Actor-Critic (A3C) algorithm. The proposed method employs parallel learning across multiple asynchronous workers, each specialized in trading across multiple currency pairs to explore the potential for nuanced strategies tailored to different market conditions and currency pairs. Two different A3C with lock and without lock MA model was proposed and trained on single currency and multi-currency. The results indicate that both model outperform on Proximal Policy Optimization model. A3C with lock outperforms other in single currency training scenario and A3C without Lock outperforms other in multi-currency scenario. The findings demonstrate that this approach facilitates broader and faster exploration of different currency pairs, significantly enhancing trading returns. Additionally, the agent can learn a more profitable trading strategy in a shorter time.

Nesreen Mufid

Nowadays, electric robots play big role in many fields as they can replace humans and/or decrease the amount of load on humans. There are several types of robots that are present in the daily life, some of them are fully controlled by humans while others are programmed to be self-controlled. In addition there are self-control robots with partial human control. Robots can be classified into three major kinds: industry robots, autonomous robots and mobile robots. Industry robots are used in industries and factories to perform mankind tasks in the easier and faster way which will help in developing products. Typically industrial robots perform difficult and dangerous tasks, as they lift heavy objects, handle chemicals, paint and assembly work and so on. They are working all the time hour after hour, day by day with the same precision and they do not get tired which means that they do not make errors due to fatigue. Indeed, they are ideally suited to complete repetitive tasks.

Gisèle KOUAKOU SIRANSY

Contexte : Le développement de phytomédicaments ou médicaments traditionnels améliorés en Afrique sub-saharienne connait un succès grandissant. En Côte d’Ivoire, diverses unités artisanales de fabrication de phytomédicaments se développent mais restent peu évalués pour leur efficacité, innocuité et qualité. Justificatif : Parmi toutes les pathologies affectant la population subsaharienne, le paludisme occupe une place importante étant la première cause de maladie infectieuse parasitaire, et la 3ème cause de maladies infectieuses. Les produits de santé des tradithérapeutes restent peu évalués pour leur efficacité, innocuité et qualité. Les chercheurs et enseignants chercheurs au sein des universités ont emboîté le pas dans plusieurs pays. En Côte d’Ivoire aucune université n’a franchi le pas de la production à l’échelle d’unité industrielle pilote. Objectif : L’objectif de ce travail visait à sélectionner des plantes pour la mise au point de phytomédicaments antipaludiques de qualité de catégorie 2 OMS. Méthodologie : La sélection des plantes à l’essai a concerné celles ayant fait l’objet de travaux de recherche des Universités en Côte d’Ivoire. Parmi ces derniers, ceux évaluant l’effet sur des extraits aqueux de parties aériennes des plantes. De ces extraits ceux présentant les meilleures inhibitions de croissance du Plasmodium selon les critères de Wilcox, ont été retenues pour la mise au point de phytomédicaments de catégorie 2 OMS. Les essais de pré formulation er formulation galénique à l’échelle de laboratoire ont permis de mettre en œuvre le procédé de fabrication adéquat. Une transposition à l’échelle pilote a été ensuite réalisée pour démontrer la reproductibilité de la fabrication industrielle de la forme galénique mise au point.   Résultats : Ces résultats issus des travaux de chercheurs des universités ivoiriennes ont permis de recenser 58 plantes médicinales étudiées pour leur activité antiplasmodiale depuis 1996. Parmi ces plantes 38 ont fait l’objet d’extraits aqueux, décoctés ou infusés. Sept extraits aqueux présentant de CI50 <5µg/ml ont été retenues entre autres. Cependant la majorité des études scientifiques portant sur les plantes médicinales potentiellement antipaludiques ont été réalisées dans des modèles in vitro, rare sont celle réalisés in vivo, dans des modèles murins. Les résultats des essais pharmacologiques, de formulation et de transposition à l’échelle pilote ont permis de disposer de gélules à base de granulés de plantes issus d’une granulation humide. Conclusion : Les travaux scientifiques des Universités de Côte d’Ivoire offre un large éventail de plantes médicinales à potentiel antimalarial pour la conception de phytomédicaments de qualité de catégorie 2 OMS. Des essais préliminaires réalisés in vivo ont permis d’obtenir un brevet d’invention.

Hasan Mehedi, Xiaobin Wang, Shilong Ye et al.

Decarbonization of the power sector in China is an essential aspect of the energy transition process to achieve carbon neutrality. The power sector accounts for approximately 40% of China’s total CO2 emissions. Accordingly, collaborative optimization in power generation expansion planning (GEP) simultaneously considering economic, environmental, and technological concerns as carbon emissions is necessary. This paper proposes a collaborative mixed- integer linear programming optimization approach for GEP. This minimizes the power system’s operating cost to resolve emission concerns considering energy development strategies, flexible generation, and resource limitations constraints. This research further analyzes the advantages and disadvantages of current GEP techniques. Results show that the main determinants of new investment decisions are carbon emissions, reserve margins, resource availability, fuel consumption, and fuel price. The proposed optimization method is simulated and validated based on China’s power system data. Finally, this study provides policy recommendations on the flexible management of traditional power sources, the market-oriented mechanism of new energy sources, and the integration of new technology to support the attainment of carbon-neutral targets in the current energy transition process.

Ben-Hur Francisco Cardoso, Eva Yamila da Silva Catela, Guilherme Viegas et al.

Research on productive structures has shown that economic complexity conditions economic growth. However, little is known about which type of complexity, e.g., export or industrial complexity, matters more for regional economic growth in a large emerging country like Brazil. Brazil exports natural resources and agricultural goods, but a large share of the employment derives from services, non-tradables, and within-country manufacturing trade. Here, we use a large dataset on Brazil's formal labor market, including approximately 100 million workers and 581 industries, to reveal the patterns of export complexity, industrial complexity, and economic growth of 558 micro-regions between 2003 and 2019. Our results show that export complexity is more evenly spread than industrial complexity. Only a few -- mainly developed urban places -- have comparative advantages in sophisticated services. Regressions show that a region's industrial complexity is a significant predictor for 3-year growth prospects, but export complexity is not. Moreover, economic complexity in neighboring regions is significantly associated with economic growth. The results show export complexity does not appropriately depict Brazil's knowledge base and growth opportunities. Instead, promoting the sophistication of the heterogeneous regional industrial structures and development spillovers is a key to growth.

Soumyadip Sarkar

Reinforcement learning (RL) is a subfield of machine learning that has been used in many fields, such as robotics, gaming, and autonomous systems. There has been growing interest in using RL for quantitative trading, where the goal is to make trades that generate profits in financial markets. This paper presents the use of RL for quantitative trading and reports a case study based on an RL-based trading algorithm. The results show that RL can be a useful tool for quantitative trading and can perform better than traditional trading algorithms. The use of reinforcement learning for quantitative trading is a promising area of research that can help develop more sophisticated and efficient trading systems. Future research can explore the use of other reinforcement learning techniques, the use of other data sources, and the testing of the system on a range of asset classes. Together, our work shows the potential in the use of reinforcement learning for quantitative trading and the need for further research and development in this area. By developing the sophistication and efficiency of trading systems, it may be possible to make financial markets more efficient and generate higher returns for investors.

Haaroon Yousaf, Naomi A. Arnold, Renaud Lambiotte et al.

Recent years have witnessed the availability of richer and richer datasets in a variety of domains, where signals often have a multi-modal nature, blending temporal, relational and semantic information. Within this context, several works have shown that standard network models are sometimes not sufficient to properly capture the complexity of real-world interacting systems. For this reason, different attempts have been made to enrich the network language, leading to the emerging field of higher-order networks. In this work, we investigate the possibility of applying methods from higher-order networks to extract information from the online trade of Non-fungible tokens (NFTs), leveraging on their intrinsic temporal and non-Markovian nature. While NFTs as a technology open up the realms for many exciting applications, its future is marred by challenges of proof of ownership, scams, wash trading and possible money laundering. We demonstrate that by investigating time-respecting non-Markovian paths exhibited by NFT trades, we provide a practical path-based approach to fraud detection.

Samidh Pal

This paper investigates the inter-regional intra-industry disparity within selected Indian manufacturing industries and industrial states. The study uses three measures - the Output-Capital Ratio, the Capital-Labor Ratio, and the Output-Labor Ratio - to critically evaluate the level of disparity in average efficiency of labor and capital, as well as capital intensity. Additionally, the paper compares the rate of disparity of per capita income between six major industrial states. The study finds that underutilization of capacity is driven by an unequal distribution of high-skilled labor supply and upgraded technologies. To address these disparities, the paper suggests that policymakers campaign for labor training and technology promotion schemes throughout all regions of India. By doing so, the study argues, the country can reduce regional inequality and improve economic outcomes for all.

Aaron Baughman, Daniel Bohm, Micah Forster et al.

Even skilled fantasy football managers can be disappointed by their mid-season rosters as some players inevitably fall short of draft day expectations. Team managers can quickly discover that their team has a low score ceiling even if they start their best active players. A novel and diverse combinatorial optimization system proposes high volume and unique player trades between complementary teams to balance trade fairness. Several algorithms create the valuation of each fantasy football player with an ensemble of computing models: Quantum Support Vector Classifier with Permutation Importance (QSVC-PI), Quantum Support Vector Classifier with Accumulated Local Effects (QSVC-ALE), Variational Quantum Circuit with Permutation Importance (VQC-PI), Hybrid Quantum Neural Network with Permutation Importance (HQNN-PI), eXtreme Gradient Boosting Classifier (XGB), and Subject Matter Expert (SME) rules. The valuation of each player is personalized based on league rules, roster, and selections. The cost of trading away a player is related to a team's roster, such as the depth at a position, slot count, and position importance. Teams are paired together for trading based on a cosine dissimilarity score so that teams can offset their strengths and weaknesses. A knapsack 0-1 algorithm computes outgoing players for each team. Postprocessors apply analytics and deep learning models to measure 6 different objective measures about each trade. Over the 2020 and 2021 National Football League (NFL) seasons, a group of 24 experts from IBM and ESPN evaluated trade quality through 10 Football Error Analysis Tool (FEAT) sessions. Our system started with 76.9% of high-quality trades and was deployed for the 2021 season with 97.3% of high-quality trades. To increase trade quantity, our quantum, classical, and rules-based computing have 100% trade uniqueness. We use Qiskit's quantum simulators throughout our work.