Hasil untuk "Special industries and trades"

Toni Seibold, Fabian Neumann, Falko Ueckerdt et al.

Greenhouse gas emissions from the steel, fertiliser and plastic industries can be mitigated by producing their precursors with green hydrogen. In Germany, green production may be economically unviable due to high energy costs. This study quantifies the 'renewables pull' of cheaper production abroad and high-lights trade-offs between cost savings and import dependence. Using a detailed European energy system model coupled to global supply curves for hydrogen and industry precursors (hot briquetted iron, ammonia and methanol), we assess five scenarios with increasing degrees of freedom with respect to imports. We find that precursor import is preferred over hydrogen import because there are significant savings in hydrogen infrastructure. Cost savings in the German industry sector from shifting precursor production to European partners compared to domestic production are at 4.1 bnEUR/a or 11.2 %. This strategy captures 47.7 % of the cost savings achievable by precursor import from non-European countries, which lowers industry costs by 8.6 bnEUR/a (23.3 %). Moving energy-intensive precursor production abroad allows Germany to save costs while still retaining a substantial share of subsequent value-creating industry. However, cost savings must be weighed against the risks of import dependence, which can be mitigated by sourcing exclusively from regional partners.

Eghbal Hosseini, Barzan Saeedpour, Mohsen Banaei et al.

Accurate time-series forecasting of energy consumption and photovoltaic (PV) production is essential for effective energy management and sustainability. Deep Neural Networks (DNNs) are effective tools for learning complex patterns in such data; however, optimizing their architecture remains a significant challenge. This paper introduces a novel hybrid optimization approach that integrates Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) to enhance the DNN architecture for more accurate energy forecasting. The performance of GA-PSO is compared with leading hyperparameter optimization techniques, such as Bayesian Optimization and Evolutionary Strategy, across various optimization benchmarks and DNN hyperparameter tuning tasks. The study evaluates the GA-PSO-enhanced Optimized Deep Neural Network (ODNN) against traditional DNNs and state-of-the-art machine learning methods on multiple real-world energy forecasting tasks. The results demonstrate that ODNN outperforms the average performance of other methods, achieving a 27% improvement in forecasting accuracy and a 22% reduction in error across various metrics. These findings demonstrate the significant potential of GA-PSO as an effective tool to optimize DNN models in energy forecasting applications.

Jiaqi Chen, Yibo Wang, Yuxuan Zhou et al.

DEX, or decentralized exchange, is a prominent class of decentralized finance (DeFi) applications on blockchains, attracting a total locked value worth tens of billions of USD today. This paper presents the first large-scale empirical study that uncovers unfair trades on popular DEX services on Ethereum and Binance Smart Chain (BSC). By joining and analyzing 60 million transactions, we find 671,400 unfair trades on all six measured DEXes, including Uniswap, Balancer, and Curve. Out of these unfair trades, we attribute 55,000 instances, with high confidence, to token thefts that cause a value loss of more than 3.88 million USD. Furthermore, the measurement study uncovers previously unknown causes of extractable value and real-world adaptive strategies to these causes. Finally, we propose countermeasures to redesign secure DEX protocols and to harden deployed services against the discovered security risks.

Yijie Weng, Jianhao Wu, Tara Kelly et al.

Artificial Intelligence (AI) is fundamentally reshaping various industries by enhancing decision-making processes, optimizing operations, and unlocking new opportunities for innovation. This paper explores the applications of AI across four key sectors: healthcare, finance, manufacturing, and retail. Each section delves into the specific challenges faced by these industries, the AI technologies employed to address them, and the measurable impact on business outcomes and societal welfare. We also discuss the implications of AI integration, including ethical considerations, the future trajectory of AI development, and its potential to drive economic growth while posing challenges that need to be managed responsibly.

Arit Kumar Bishwas, Mousumi Sen

As quantum computing continues to advance, its ability to compromise widely used cryptographic systems projects a significant challenge to modern cybersecurity. This paper outlines a strategic roadmap for industries to anticipate and mitigate the risks posed by quantum attacks. Our study explores the development of a quantum-resistant cryptographic solutioning framework for the industry, offering a practical and strategic approach to mitigating quantum attacks. We, here, propose a novel strategic framework, coined name STL-QCRYPTO, outlines tailored, industry-specific methodologies to implement quantum-safe security systems, ensuring long-term protection against the disruptive potential of quantum computing. The following fourteen high-risk sectors: Financial Services, Banking, Healthcare, Critical Infrastructure, Government & Defence, E-commerce, Energy & Utilities, Automotive & Transportation, Cloud Computing & Data Storage, Insurance, Internet & Telecommunications, Blockchain Applications, Metaverse Applications, and Multiagent AI Systems - are critically assessed for their vulnerability to quantum threats. The evaluation emphasizes practical approaches for the deployment of quantum-safe security systems to safeguard these industries against emerging quantum-enabled cyber risks. Additionally, the paper addresses the technical, operational, and regulatory hurdles associated with adopting quantum-resistant technologies. By presenting a structured timeline and actionable recommendations, this roadmap with proposed framework prepares industries with the essential strategy to safeguard their potential security threats in the quantum computing era.

Mahdi Pourbafrani, Hossein Ghadamian, Mohammad Aminy et al.

Evacuated tube solar collectors (ETSC) are widely utilized in both domestic and industrial solar water heaters (SWH) due to their commendable thermal performance and straightforward installation. However, a significant challenge associated with ETSC lies in the fact that half of the collector remains unexposed to sunlight. To overcome this limitation, parabolic reflectors can be employed as a viable solution. The primary objective of this study is to assess the performance of a compound parabolic concentrator (CPC) in conjunction with ETSC, taking into account a specific ratio between the areas of the CPC and ETSC. To achieve the desired configuration, the CPC was meticulously designed, fabricated, installed, and subsequently tested. Moreover, the energy performance of the absorber tube was scrutinized both with and without the integration of a parabolic trough collector. The experiments and data collection were conducted on two selected days for both the conventional ETSC device and the system incorporating the CPC. Meteorological data and operational conditions were measured and digitally stored for subsequent analysis. A noteworthy outcome of the study is the revelation that the energy efficiency of the system with a concentrator exhibited a notable improvement of 2.8% compared to the conventional system. Offline results further indicated that the performance of a single absorber tube with a concentrator increased by approximately 2.7 times when compared to the standard system. This suggests that the energy performance of the solar water heater, with a capacity of about 200 liters and featuring 7 absorber tubes with a concentrator, is comparable to that of the conventional system equipped with 18 absorber tubes.

Viktor I. Karagodin

When planning the operation of land transport and transport technology facilities, their technical condition is not sufficiently taken into account. This can lead to unplanned failure of the machines and failure of the planned work by the remaining machines. The proposed methods of distributing machines by objects and types of work are based on the theory of aging of machines, but unlike the performance potential of machines, which reflects the technical condition of an average machine, they are focused on a specific machine, the probability of failure of which is determined using technical diagnostic methods. The results of the study of the dependence of the probability of failure of the car on the value of the inter-control period, the patterns of change in the probability of failure during the inter-control period and with an increase in the mileage of the car are presented. The goal is to increase the efficiency of the use of ground transportation and transportation technology facilities. Method and methodology. The theory of aging of machines, mathematical modeling, statistical methods of analysis. Results. New dependences of the probability of a car failure on the value of the inter-control period, the regularity of the change in the probability of failure during the inter-control period and with an increase in the mileage of the car are obtained and justified. The field of application of the results is the operation of ground transportation and transportation technology facilities.

Arti Kumbhar, Amruta Chougule, Priya Lokhande et al.

Utilizing Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Generative Adversarial Networks (GANs), our system introduces an innovative approach to defect detection in manufacturing. This technology excels in precisely identifying faults by extracting intricate details from product photographs, utilizing RNNs to detect evolving errors and generating synthetic defect data to bolster the model's robustness and adaptability across various defect scenarios. The project leverages a deep learning framework to automate real-time flaw detection in the manufacturing process. It harnesses extensive datasets of annotated images to discern complex defect patterns. This integrated system seamlessly fits into production workflows, thereby boosting efficiency and elevating product quality. As a result, it reduces waste and operational costs, ultimately enhancing market competitiveness.

Hong Chang, Qingyi Ding, Wanzheng Zhao et al.

The digital economy plays a pivotal role in assisting the world in tackling climate change. This paper explores the intrinsic mechanism of the digital economy on carbon emissions intensity. Initially, it scrutinizes the suppressive effect of the digital economy on carbon emissions intensity, as well as the mediating mechanism of industrial structure upgrading, on a theoretical level. Subsequently, it utilizes provincial panel data from China between 2010 and 2019 to investigate the quantitative relationship between the digital economy and carbon emissions intensity empirically. The results revealed that, firstly, the digital economy significantly diminishes carbon emissions intensity; secondly, it confirms the significant mediating role of industrial structure upgrading; thirdly, increased levels of economic development, market openness, human capital, technological advancement, and urbanization all have constructive moderating effects on the carbon emission reduction facilitated by the digital economy; fourthly, the influence of the digital economy on carbon emission intensity has spatial spill-overs. This paper contributes an integrated analytical framework and method for studying the digital economy, industrial structure upgrading, and carbon emissions intensity. Furthermore, it offers valuable insight and suggestions for policy-making concerning the digital economy's contribution to carbon emissions reduction.

Boban Pavlović, Dejan Ivezić, Marija Živković

U radu su prikazani rezultati istraživanja i ankete koja je sprovedena 2020. godine u domaćinstvima sa individualnim sistemima grejanja u Srbiji. Cilj ankete je bio da se sagleda stanje individualnih sistema grejanja, ali i stavovi vlasnika sistema vezano za energetsku tranziciju. Rezultati ukazuju na zastarelost sistema grejanja, nisku efikasnost, nedovoljna ulaganja u primenu mera energetske efikasnosti i nedostatak finansijskih sredstava za ulaganje u održivo grejanje. Osnovni prioriteti kod izbora načina grejanja jesu troškovi kupovine sistema i troškovi energenta, dok su uticaj ekološkog faktora i svest o potrebi energetske tranzicije relativno slabo izraženi. Troškovi nabavke modernih sistema su identifikovani i kao najveća prepreke za zamenu postojećih sistema, a oko polovine domaćinstava ima pozitivan stav prema potencijalnom subvencionisanju troškova za zamenu postojećih sistema za nove i efikasnije.

Yuriy Abrashkevich, Mykola Prystaylo, Andriy Polishchuk

Собівартість абразивної різки в основному визначається зносостійкості абразивного круга, що складається з абразивного зерна, наповнювача, фенольного сполучного і склосітки. У зв'язку з тим, що в процесі різання в результаті підсумовування теплових імпульсів від ріжучих зерен, які перебувають на ріжучій кромці круга, виділяють значну кількість тепла, в зоні різання досягаються великі значення температури. Тим часом добре відомо, що фенольна сполучна володіє низькою теплостійкістю, вона руйнується при температурі 520-570 °К, тому характер теплових процесів, що протікають при абразивному різанні, визначає і температуру в крузі і, відповідно, швидкість його зносу. В ідеалі, звичайно, швидкість теплового руйнування зв'язки повинна корелювати зі швидкістю механічного руйнування абразивних зерен з тим, щоб різання здійснювалося лише гострими, неспрацьовану зернами, при цьому усуватися з ріжучої кромки повинні лише тупі зерна. Оскільки швидкість стирання абразивних зерен різна для різних оброблюваних матеріалів, то й характеристики зв'язуючих повинні бути в залежності від виду оброблюваного матеріалу, тобто необхідно створювати абразивні круги для різки різних матеріалів. На практиці ж випускаються абразивні круги без особливого урахування особливостей розрізає мого матеріалу, що в значній мірі пояснюється неясністю характеру теплових процесів в абразивних армованих кругах і технологічними складнощами, пов'язаними зі зміною теплофізичних властивостей кругів.

Yufeng He, Deepak Jaiswal, Xin‐Zhong Liang et al.

Abstract Perennial grasses can reduce soil erosion, restore carbon stocks, and provide feedstocks for biofuels and bioproducts. Here, we show an additional benefit, amelioration of regional climate warming, and drying. Growing Miscanthus × giganteus, an example of perennial biomass crops, on US marginal land cools the Midwest Heartland summer by up to 1°C as predicted by a new coupled climate‐crop modeling system. This cooling is mainly caused by the increased duration and size of the Miscanthus × giganteus leaf canopy when compared with the existing vegetations on marginal land, resulting in larger solar reflection, more evapotranspiration, and decreased sensible heat transfer. Summer rainfall is increased through mesoscale circulation responses by 23–29 mm (14%–15%) and water vapor pressure deficit reduced by 5%–13%, lowering potential transpiration for all Midwest crops. Similar but weaker effects are simulated in the Southern Heartland. This positive feedback through the climate–crop interaction and teleconnection leads to 4%–8% more biomass production and potentially 12% higher corn and soybean yields, with greater yield stability. Growing perennials on marginal land could be a feasible solution to climate change mitigation and adaptation by strengthening food security and providing sustainable alternatives to fossil‐based products.

Jozef Barunik, Mattia Bevilacqua, Robert Faff

We argue that uncertainty network structures extracted from option prices contain valuable information for business cycles. Classifying U.S. industries according to their contribution to system-related uncertainty across business cycles, we uncover an uncertainty hub role for the communications, industrials and information technology sectors, while shocks to materials, real estate and utilities do not create strong linkages in the network. Moreover, we find that this ex-ante network of uncertainty is a useful predictor of business cycles, especially when it is based on uncertainty hubs. The industry uncertainty network behaves counter-cyclically in that a tighter network tends to associate with future business cycle contractions.

Marcela G. dos Santos, Fabio Petrillo

Industrial robots are important machines applied in numerous modern industries that execute repetitive tasks with high accuracy, replacing or supporting dangerous jobs. In this kind of system, with increased complexity in which cost is related to the time the system keeps working, the system must operate with a minimum number of failures. In other words, a quality aspect important in industry is reliability. We hypothesize that Automated Acceptance Testing improves reliability for industrial robot program. We present the research question, the motivation for this study, our hypothesis and future research efforts.

Arth Patel, Won Suk Lee, Natalia A. Peres et al.

Botrytis fruit rot and anthracnose are fungal diseases of strawberry. These diseases are a significant contributor to yield losses, requiring farmers to use fungicides frequently to prevent them. The proliferation of botrytis and anthracnose is directly linked to the duration of the presence of free water on the plant canopy, which is generally defined as leaf wetness duration (LWD). LWD is an important measure in determining the risk for these diseases to develop in the strawberry crop. By accurately measuring LWD, the risk of disease can be calculated more accurately, and specific fungicide application recommendations can be given to the farmers. This reduces the frequency with which fungicide is applied and ultimately reduces costs for farmers. There is no standard method to detect leaf wetness, but leaf wetness sensors are widely used for that purpose. These wetness sensors are difficult to calibrate and not very accurate, which reduces their reliability. The objective of this study was to find a better alternative to the commonly used leaf wetness sensors. This study implemented color and thermal imaging-based approaches as a solution to the problem of leaf wetness detection in strawberry plants. The proposed method used deep learning and computer vision techniques to detect leaf wetness from color and thermal images. The deep learning model was highly accurate in detecting wetness when compared with the visual observation of the images. It was also found that leaf wetness could be detected with a high degree of accuracy using deep learning with color images. In the future, using the findings of this study, a portable device can be developed to replace the commonly used wetness sensor with a more reliable imaging-based device.

Dunja Mirjanić, Tihomir Dabović, Željko Marković

Tržišta električne energije u zemljama zapadnog Balkana i dalje nisu u potpunosti liberalizovana, pa se stoga mogu uočiti različiti stepeni otvorenosti tržišta električne energije od zemlje do zemlje, pa čak i unutar zemlje, za šta je Bosna i Hercegovina očigledan primjer. U Republici Srpskoj, formalno-pravni uslovi za otpočinjanje procesa otvaranja tržišta električne energije su se stekli stupanjem na snagu Zakona o električnoj energiji, krajem 2007. godine i Pravilnikom o snabdijevanju kvalifikovanih kupaca i postupku promjene snabdjevača, koji je stupio na snagu krajem 2014. godine. Ipak do otpočinjanja stvarnog procesa otvaranja tržišta električne energije nije došlo sve do stupanja na snagu Pravilnika o izmjenama i dopunama Pravilnika o snabdijevanju kvalifikovanih kupaca i postupku promjene snabdjevača, koji je stupio na snagu u martu 2019. godine. U radu se najprije ispituju i analiziraju do sada sprovedene aktivnosti na liberalizaciji tržišta električnom energijom, i daje ocjena u pogledu dosadašnjih rezultata. Dalje se analiziraju potrebni uslovi i pitanja koja se nameću pred sprovođenje daljeg otvaranja tržišta električne energije u Republici Srpskoj. Na kraju, u tekstu se analiziraju najvažnije aktivnosti koje očekuju sve relevantne činioce, u prvom redu Vladu RS, potom resorno ministarstvo i RERS, snabdjevače kao i privredne subjekte koji aktivno učestvuju u oblikovanju tržišta električne energije u cilju pripreme tržišta za dalje otvaranje i ostvarenja uslova za njeno uspješno okončanje

Saeedeh Rashidi, Nasrin Soltankhah

A 3-way $(v,k,t)$ trade $T$ of volume $m$ consists of three pairwise disjoint collections $T_1$, $T_2$ and $T_3$, each of $m$ blocks of size $k$, such that for every $t$-subset of $v$-set $V$, the number of blocks containing this $t$-subset is the same in each $T_i$ for $1\leq i\leq 3$. If any $t$-subset of found($T$) occurs at most once in each $T_i$ for $1\leq i\leq 3$, then $T$ is called 3-way $(v,k,t)$ Steiner trade. We attempt to complete the spectrum $S_{3s}(v,k)$, the set of all possible volume sizes, for 3-way $(v,k,2)$ Steiner trades, by applying some block designs, such as BIBDs, RBs, GDDs, RGDDs, and $r\times s$ packing grid blocks. Previously, we obtained some results about the existence some 3-way $(v,k,2)$ Steiner trades. In particular, we proved that there exists a 3-way $(v,k,2)$ Steiner trade of volume $m$ when $12(k-1)\leq m$ for $15\leq k$ (Rashidi and Soltankhah, 2016). Now, we show that the claim is correct also for $k\leq 14$.

Dalton Cézane Gomes Valadares, Joseana Macêdo Fechine Régis de Araújo, Marco Aurélio Spohn et al.

The advances in wireless network technologies and Industrial Internet of Things (IIoT) devices are easing the establishment of what is called Industry 4.0. For the industrial environments, the wireless networks are more suitable mainly due to their great flexibility, low deployment cost and for being less invasive. Although new wireless protocols are emerging or being updated, changes in existing industries generally can lead to large expenditures. As the well known and accepted IEEE 802.11g standard, mostly used in residential and commercial applications, has a low deployment and maintenance cost, many industries also decide to adopt it. In this scenario, there is a need to evaluate the signal quality to better design the network infrastructure in order to obtain good communication coverage. In this work, we present a practical study about the 802.11g signal strength in a thermoelectric power plant. We collected signal strength values in different points along the engine room and compared our measured values with the estimated ones through the Log-Distance Path Loss model. We concluded that it is possible to use this model in an industrial environment to estimate signal strength with a low error by choosing the right propagation (path loss) exponent.

M. Carolina Rodríguez-Donate, Margarita E. Romero-Rodríguez, Víctor J. Cano-Fernández et al.

The general decline in per capita consumption of wine worldwide over recent decades reveals the need to apply effective marketing strategies to capture segments of the population, such as young people or women, who tend to consume wine sporadically and in small amounts, even among traditional wine-producing countries. However, until now these strategies have been designed considering these segments as homogeneous groups, when in fact they are not. In this paper, several discrete choice models are used to incorporate the unobserved heterogeneity present in individuals’ decisions, such as mixed or latent class models, with the aim of identify the socio-demographics profiles of individuals who consume a certain amount of wine per week. The results highlights the superiority of these models and the variability individuals׳ characteristics due to heterogeneity. Keywords: Preferences, Wine, Heterogeneity, Latent class, Mixed logit