Hasil untuk "Special industries and trades"

Owen Nyo Wei Yuan, Victor Tan Jia Xuan, Ong Jun Yao Fabian et al.

The report presents with the development and optimisation of an enhanced algorithmic trading strategy through the use of historical S&P 500 market data and earnings call sentiment analysis. The proposed strategy integrates various technical indicators such as moving averages, momentum, volatility, and FinBERT-based sentiment analysis to improve overall trades being taken. The results show that the enhanced strategy significantly outperforms the baseline model in terms of total return, Sharpe ratio, and drawdown amongst other factors. The findings helped demonstrate the relevance and effectiveness of combining technical indicators, sentiment analysis, and computational optimisation in algorithmic trading systems.

Akhil Gupta Chigullapally, Sharvan Vittala, Razin Farhan Hussian et al.

The fast pace of modern AI is rapidly transforming traditional industrial systems into vast, intelligent and potentially unmanned autonomous operational environments driven by AI-based solutions. These solutions leverage various forms of machine learning, reinforcement learning, and generative AI. The introduction of such smart capabilities has pushed the envelope in multiple industrial domains, enabling predictive maintenance, optimized performance, and streamlined workflows. These solutions are often deployed across the Industrial Internet of Things (IIoT) and supported by the Edge-Fog-Cloud computing continuum to enable urgent (i.e., real-time or near real-time) decision-making. Despite the current trend of aggressively adopting these smart industrial solutions to increase profit, quality, and efficiency, large-scale integration and deployment also bring serious hazards that if ignored can undermine the benefits of smart industries. These hazards include unforeseen interoperability side-effects and heightened vulnerability to cyber threats, particularly in environments operating with a plethora of heterogeneous IIoT systems. The goal of this study is to shed light on the potential consequences of industrial smartness, with a particular focus on security implications, including vulnerabilities, side effects, and cyber threats. We distinguish software-level downsides stemming from both traditional AI solutions and generative AI from those originating in the infrastructure layer, namely IIoT and the Edge-Cloud continuum. At each level, we investigate potential vulnerabilities, cyber threats, and unintended side effects. As industries continue to become smarter, understanding and addressing these downsides will be crucial to ensure secure and sustainable development of smart industrial systems.

Sabrina Sharmin, Md. Tazel Hossan, Mohammad Shorif Uddin

Accurate and timely yield prediction of hydroponically grown lettuce is essential for financial planning, strategic decision-making, and enhancing farmers' profitability. In controlled hydroponic environments, this prediction remains challenging, mainly due to complex factors influencing growth. Machine Learning (ML) offers advanced methods to address these challenges. This review analyzes ML techniques for forecasting lettuce yield in hydroponic systems, starting with an overview of global trends in lettuce production. It then explores core ML methodologies, key model characteristics, and application-specific features that contribute to yield prediction. A comparative analysis of existing ML models also highlights their strengths and limitations. Current challenges, such as data integration and prediction accuracy, are discussed alongside potential improvements through remote sensing, monitoring, and feature optimization. This paper concludes by proposing a framework aimed at efficient yield prediction in hydroponics, offering insights for future research and applications in agricultural technology.

Thomas Rose, Nawab Ali, Younsuk Dong

Real-time assessment of trunk growth is vital for understanding tree growth fluctuation, especially under irrigation application and other environmental factors. Accurate trunk diameter assessment is crucial for optimizing water use and tree health improvement, and its cost-effectiveness is needed for widespread adoption in agriculture. This study focused on the development of an accurate and low-cost IoT-based dendrometer system for real-time trunk diameter measurement of Christmas trees. The dendrometer sensor was calibrated (R2 = 0.99) to ensure the accurate conversion of sensor voltage to trunk diameter fluctuations. This IoT-based dendrometer system consists of a platform that enables wireless data transmission, cloud-based storage and real-time analysis of the trunk diameter. Temperature fluctuation influenced the sensor readings with no impact, which validated the system's reliability in open field conditions. Christmas tree diameter monitoring showed significant trunk expansion and contraction under irrigation application and water stress, respectively, which signifies the system ability to monitor the real-time trunk growth responses. Cost analysis makes this technology economical and reliable for widespread application in precision agriculture. Therefore, this low-cost IoT-based dendrometer system is reliable, accurate, and economically viable for improving irrigation management, tree health monitoring, and supporting farmers through data-driven agricultural practices.

Dongyi Yi, Guibo Zhu, Chenglin Ding et al.

With the rapid advancement of Multimodal Large Language Models (MLLMs), numerous evaluation benchmarks have emerged. However, comprehensive assessments of their performance across diverse industrial applications remain limited. In this paper, we introduce MME-Industry, a novel benchmark designed specifically for evaluating MLLMs in industrial settings.The benchmark encompasses 21 distinct domain, comprising 1050 question-answer pairs with 50 questions per domain. To ensure data integrity and prevent potential leakage from public datasets, all question-answer pairs were manually crafted and validated by domain experts. Besides, the benchmark's complexity is effectively enhanced by incorporating non-OCR questions that can be answered directly, along with tasks requiring specialized domain knowledge. Moreover, we provide both Chinese and English versions of the benchmark, enabling comparative analysis of MLLMs' capabilities across these languages. Our findings contribute valuable insights into MLLMs' practical industrial applications and illuminate promising directions for future model optimization research.

Varsha N. Behrunani, Philipp Heer, Roy S. Smith et al.

Peer-to-peer(P2P) energy trading may increase efficiency and reduce costs, but introduces significant challenges for network operators such as maintaining grid reliability, accounting for network losses, and redistributing costs equitably. We propose a novel loss-aware pricing strategy for P2P energy markets that addresses these challenges while incentivizing participation in the cooperative energy trading market. The problem is formulated as a hierarchical Stackelberg game, where a grid operator determines network tariffs while prosumers optimize their trades based on these tariffs while guaranteeing that network constraints are satisfied. The algorithm is designed to minimize and recover their cost from the trading parties, while also minimizing the total cost of the hubs. The mechanism dynamically adjusts tariffs based on location and network topology, discouraging loss-intensive trades. Finally, the complete framework includes the computation of fair trading prices, ensuring all market participants benefit equitably. An ADMM-based hyper-gradient descent method is proposed for solving this problem. Extensive numerical simulations using the benchmark IEEE 33-bus system demonstrate significant cost reductions and improved network efficiency through reduction in network losses compared to constant tariff schemes. Results highlight the adaptability and scalability of the proposed mechanism to varying network configurations and size, demand profiles, and seasonal conditions.

Th. K. Mavrogordatos

We operationally uncover aspects of wave/particle duality for the open driven Jaynes-Cummings (JC) model in its strong-coupling limit. We lay special emphasis on the vacuum Rabi resonance, and determine the corresponding normalized intensity-field correlation function via mapping to ordinary resonance fluorescence. We demonstrate that temporal wave-particle fluctuations of light emanating from an established vacuum Rabi resonance are explicitly non-classical, while the limit of vanishing spontaneous emission restores detailed balance. When photon blockade sets in, individual realizations show a rapidly increasing frequency of fluctuations towards the two-photon resonance, arising as a direct consequence of a resolved JC spectrum. About the two-photon resonance peak, spontaneous emissions are more likely to revive a high-frequency quantum beat in the conditioned electromagnetic field amplitude than photons escaping out of the cavity mode. The beat originates from a coherent superposition of the first excited JC couplet states, and sets the background against which nonclassical phase shifts are observed in the conditioned quadrature amplitudes. We also find that the onset of steady-state bimodality reduces the variation of the normalized intensity-field correlation, at the expense of its temporal symmetry.

Sathish Krishna Anumula, Sivaramkumar Ponnarangan, Faizal Nujumudeen et al.

A mix of intelligent systems and robotics is making engineering industries much more efficient, precise and able to adapt. How artificial intelligence (AI), machine learning (ML) and autonomous robotic technologies are changing manufacturing, civil, electrical and mechanical engineering is discussed in this paper. Based on recent findings and a suggested way to evaluate intelligent robotic systems in industry, we give an overview of how their use impacts productivity, safety and operational costs. Experience and case studies confirm the benefits this area brings and the problems that have yet to be solved. The findings indicate that intelligent robotics involves more than a technology change; it introduces important new methods in engineering.

Pengfei Qin, Jinli Wang, Aiping Xu et al.

China has become a major global power due to its rapid economic growth, which is being driven by resource-intensive industries but at a considerable environmental cost. This study investigates the intricate relationships that exist between China's mining regulations, resource utilization, and environmentally friendly policies, as well as the overall impacts of these relationships on economic growth. The research uses a rigorous regression analysis approach and data from the Ministry of National Resources and the China Statistical Yearbook from 1986 to 2022. Integrating a Vector Error Correction Model (VECM) enables an investigation of the relationships among debt levels, GDP, and natural financing. Wald test estimates also reveal exact Corporate Social Responsibility (CSR) relationships. The results indicate that while China's economy has significantly benefited from mineral-intensive sectors, green measures are urgently needed to slow environmental deterioration. The focus should be redirected toward encouraging sustainable and ongoing development. These results highlight the critical role that green projects play in fostering economic growth. How China strikes a careful balance between its goals for ecological sustainability and economic growth is crucial for both China and the rest of the world in an era characterized by worries about climate change and resource shortages. The objective of this study is to analyze the complex relationship among China's mining regulations, resource utilization, and environmental policies. It will evaluate the effects of these factors on economic growth, with a particular focus on the necessity of adopting sustainable development practices in the face of environmental challenges and economic expansion. The study's goal is to examine the intricate relationships between China's resource use, environmental policies, and mining restrictions. It focuses on the importance of sustainable development methods in the face of environmental issues and economic growth. This research highlights the essential relationship between environmental stewardship and sustainable economic development, and policymakers, businesses, and environmental activists can all benefit from it.

刘坚, 王建光, 王晶 et al.

新型储能技术的快速进步为提升电力系统灵活性提供了更多技术选择，也为构建竞争性灵活资源市场提供了条件。国外部分独立系统运营商为适应储能物理特性，在能量市场、辅助服务市场、容量市场、输电资产方面做了机制探索；国内一系列电力体制改革文件强调探索容量补偿机制以鼓励新型储能投资建设，但国家层面尚未出台针对新型储能的容量补偿政策。结合主流新型储能技术经济参数与国内典型地区现货市场价格曲线，测算了现货市场环境下各类新型储能经济性差异与未来趋势，并基于国内外容量补偿机制经验与研究成果，研究不同容量补偿机制情景下容量电价对提升新型储能经济性的作用。研究发现，以锂电池为代表的新型储能已开始显现相对于抽水蓄能的经济性，赋予新型储能独立市场地位，鼓励其参与电力现货市场，有助于降低新能源发电系统消纳成本。然而，目前新型储能参与电力现货市场仍面临阻碍，为此在容量补偿、现货与辅助服务市场建设等方面给出了政策建议。

Philipp J. di Dio

These are the lecture notes based on [dD23] for the (upcoming) lecture "T-systems with a special emphasis on sparse moment problems and sparse Positivstellensätze" in the summer semester 2024 at the University of Konstanz. The main purpose of this lecture is to prove the sparse Positiv- and Nichtnegativstellensätze of Samuel Karlin (1963) and to apply them to the algebraic setting. That means given finitely many monomials, e.g. $1, x^2, x^3, x^6, x^7, x^9,$ how do all linear combinations of these look like which are strictly positive or non-negative on some interval $[a,b]$ or $[0,\infty)$, e.g. describe and even write down all $f(x) = a_0 + a_1 x^2 + a_2 x^3 + a_3 x^6 + a_4 x^7 + a_5 x^9$ with $f(x)>0$ or $f(x)\geq 0$ on $[a,b]$ or $[0,\infty)$, respectively. To do this we introduce the theoretical framework in which this question can be answered: T-systems. We study these T-systems to arrive at Karlin's Positiv- and Nichtnegativstellensatz but we also do not hide the limitations of the T-systems approach. The main limitation is the Curtis$-$Mairhuber$-$Sieklucki Theorem which essentially states that every T-system is only one-dimensional and hence we can only apply these results to the univariate polynomial case. This can also be understood as a lesson or even a warning that this approach has been investigated and found to fail, i.e., learning about these results and limitations shall save students and researchers from following old footpaths which lead to a dead end. We took great care finding the correct historical references where the results appeared first but are perfectly aware that like people before we not always succeed.

e Souza Amarílis Severino, de Souza Pinto Thiago César de, Sarkis Alfredo Moisés et al.

Drying operations in iron ore processing plants have a particularly high energy demand due to the massive solid flow rates employed in this industry. A 33 full-factorial design was applied to investigate the effects of air temperature, airflow velocity, and solids load on the drying time and the specific energy consumption (SEC) of the convective drying of iron ore fines in a fixed bed. The results demonstrated that each drying air condition was associated with an optimal solids load that minimized the SEC. A load of 73 g (bed height of about 0.8 cm) was identified and validated as the optimal condition in terms of energy consumption for the configuration with the highest air temperature (90°C) and airflow velocity (4.5 m/s). This condition resulted in a drying time of 29.0 s and a corresponding SEC of 12.8 MJ/kg to reduce the moisture from 0.11 kg water/kg dry solids to a target of 0.05 kg water/kg dry solids. Identifying the optimum values for the process variables should assist in designing and operating energy-efficient convective dryers for iron ore fines.

Muhammad Waseem, Sania Batool

This study analyzes the influence of adequate electricity supply on the industrial sector in developing nations, utilizing panel data from 2000 to 2022. Contrary to original beliefs, the study examines industry output as the dependent variable, with renewable energy as the main explanatory factor. The study incorporated control variables such as CO2 emissions, government expenditure, GDP per capita, labor force participation, and gross capital formation. The investigation included panel Autoregressive Distributed Lag (ARDL) models, unit root tests, and causality tests. In emerging countries, industrial growth is positively impacted by government spending, labor force involvement, CO2 emissions, and GDP per capita. Developed countries demonstrate favorable impacts on industrial growth through gross fixed capital formation, renewable energy, and other factors, as indicated by the long-term outcomes of the ARDL method. Policymakers in developing nations may contemplate raising government spending in pertinent sectors, encouraging worker engagement, and enacting laws to decrease CO2 emissions based on these findings. Developed countries' authorities should prioritize improving gross fixed capital creation, integrating more renewable energy sources, and sustaining factors boosting industry growth.

Faten Hameed Kamil, Suondos K. A. Barno, Firas Shems et al.

Some medications in aquatic media pose a serious environmental risk. Sulfamethoxazole (SMX) is a member of the sulfonamide group. Photocatalysis offers a promising technique to degrade organic pollutants into environmentally friendly substances. This study examined the effect of operating conditions (pH, time, and temperature) of the ultraviolet (UV)/TiO2 photocatalytic process on the degradation of SMX in an aqueous solution. Decreasing the pH value positively affects SMX degradation, and better removal values were obtained at a pH equal to 4. The optimum operating conditions for complete degradation in a solution containing 500 mg/L of SMX, TiO2 0.5 mg/L irradiation time of 420 min, and pH 4. Under these conditions, Chemical Oxygen Demand (COD) removal was 62.6% at a temperature of 25 ℃. The effect of temperature was studied at three temperatures (25, 40, and 60 ℃) with pH 4. The elevation of temperature increased the COD removal rate to 99.62% at 60 ℃. Finally, the results of the reaction kinetics study showed that a first-order kinetics model described organic contamination removal data over time, and the obtained activation energy was 42.195 kJ/mol.

Xi Yuan, Chi-Wei Su, Adelina Dumitrescu Peculea

With the control of the cryptocurrency market in environmental protection, investors pay attention to the risk conduction mechanism between energy consumption and the Bitcoin market. This paper applies quantile connectedness to analyse the overall situation and dynamic evolution of information spillover in the system of the Bitcoin market. The results show that the hashrate and electricity demand are the primary sources of risk in the information network, and their fluctuations have intensified the risk spillover effects in the system. In addition, the spillover level is more prominent in extreme cases, which means the information linkage in the system is integrated. The spillover effect of each variable fluctuates and is uncertain with time. This helps in the sustainable development of Bitcoin and guides the government's policy development and supervision of cryptocurrencies. The risk infection path helps prevent the risk of infection in the Bitcoin market and improves the sustainability of the encrypted market.

Ivanchina Emilia, Chernyakova Ekaterina, Pchelintseva Inna et al.

Catalytic naphtha reforming is extensively applied in petroleum refineries and petrochemical industries to convert low-octane naphtha into high-octane gasoline. Besides, this process is an important source of hydrogen and aromatics obtained as side products. The bifunctional Pt-catalysts for reforming are deactivated by coke formation during an industrial operation. This results to a reduction in the yield and octane number. In this paper modeling and optimization of a semi-egenerative catalytic reforming of naphtha is carried out considering catalyst deactivation and a complex multicomponent composition of a hydrocarbon mixture. The mathematical model of semi-egenerative catalytic reforming considering coke formation process was proposed. The operating parameters (yield, octane number, activity) for different catalysts were predicted and optimized. It was found that a decrease in the pressure range from 1.5 to 1.2 MPa at the temperature 478–481 °C and feedstock space velocity equal to 1.4–1 h induces an increase in the yield for 1–2 wt.% due to an increase in the aromatization reactions rate and a decrease in the hydrocracking reactions rate depending on the feedstock composition and catalyst type. It is shown that the decrease in pressure is limited by the requirements for the catalyst stability due to the increase in the coke formation rate. The criterion of optimality is the yield, expressed in octanes per tons.

Saiedreza Radpour, Md Alam Hossain Mondal, Deepak Paramashivan et al.

The aim of this paper is to review and evaluate models used to assess the market penetration of energy technologies. While there are different models and tools, choosing the appropriate approach for a particular application is very challenging. In this paper, each model is reviewed and discussed extensively and a hierarchy diagram is developed to help choose a model. Market penetration models based on subjective estimation and market survey could be individual-dependent and not reliable for long-term forecasts. Cost estimation, diffusion, and econometric models offer more reliable results both for short- and long-term forecasts. Based on the review, a new combined model was developed and applied to a case study. The combined use of different market penetration models offers more accurate and robust results, as demonstrated in the case study.

Nahid Tavassoli, Hamid Noghanibehambari, Farzaneh Noghani et al.

This paper aims to assess the effects of industrial pollution on infant mortality between the years 1850-1940 using full count decennial censuses. In this period, US economy experienced a tremendous rise in industrial activity with significant variation among different counties in absorbing manufacturing industries. Since manufacturing industries are shown to be the main source of pollution, we use the share of employment at the county level in this industry to proxy for space-time variation in industrial pollution. Since male embryos are more vulnerable to external stressors like pollution during prenatal development, they will face higher likelihood of fetal death. Therefore, we proxy infant mortality with different measures of gender ratio. We show that the upswing in industrial pollution during late nineteenth century and early twentieth century has led to an increase in infant mortality. The results are consistent and robust across different scenarios, measures for our proxies, and aggregation levels. We find that infants and more specifically male infants had paid the price of pollution during upswing in industrial growth at the dawn of the 20th century. Contemporary datasets are used to verify the validity of the proxies. Some policy implications are discussed.

Ilaria Malanchini, Patrick Agostini, Khurshid Alam et al.

Two main trends characterize today's communication landscape and are finding their way into industrial facilities: the rollout of 5G with its distinct support for vertical industries and the increasing success of machine learning (ML). The combination of those two technologies open the doors to many exciting industrial applications and its impact is expected to rapidly increase in the coming years, given the abundant data growth and the availability of powerful edge computers in production facilities. Unlike most previous work that has considered the application of 5G and ML in industrial environment separately, this paper highlights the potential and synergies that result from combining them. The overall vision presented here generates from the KICK project, a collaboration of several partners from the manufacturing and communication industry as well as research institutes. This unprecedented blend of 5G and ML expertise creates a unique perspective on ML-supported industrial communications and their role in facilitating industrial automation. The paper identifies key open industrial challenges that are grouped into four use cases: wireless connectivity and edge-cloud integration, flexibility in network reconfiguration, dynamicity of heterogeneous network services, and mobility of robots and vehicles. Moreover, the paper provides insights into the advantages of ML-based industrial communications and discusses current challenges of data acquisition in real systems.

Simon Clinet, Jean-François Perreton, Serge Reydellet

We develop a dynamic trading strategy in the Linear Quadratic Regulator (LQR) framework. By including a price mean-reversion signal into the optimization program, in a trading environment where market impact is linear and stage costs are quadratic, we obtain an optimal trading curve that reacts opportunistically to price changes while retaining its ability to satisfy smooth or hard completion constraints. The optimal allocation is affine in the spot price and in the number of outstanding shares at any time, and it can be fully derived iteratively. It is also aggressive in the money, meaning that it accelerates whenever the price is favorable, with an intensity that can be calibrated by the practitioner. Since the LQR may yield locally negative participation rates (i.e round trip trades) which are often undesirable, we show that the aforementioned optimization problem can be improved and solved under positivity constraints following a Model Predictive Control (MPC) approach. In particular, it is smoother and more consistent with the completion constraint than putting a hard floor on the participation rate. We finally examine how the LQR can be simplified in the continuous trading context, which allows us to derive a closed formula for the trading curve under further assumptions, and we document a two-step strategy for the case where trades can also occur in an additional dark pool.