Hasil untuk "Special industries and trades"

Ameera Yacoob, Shaeden Gokool, Alistair Clulow et al.

Water stress significantly threatens sugarcane production, particularly among smallholder farmers in South Africa, where spatially explicit assessments remain limited. This study aimed to improve the quantification of crop water stress by developing a machine learning (ML) model to predict the Normalised Difference Water Index (NDWI), a proxy for vegetation water content. An ML approach was adopted to capture complex, non-linear relationships between structural vegetation indices (SVIs) and NDWI. Sentinel-2 satellite data and UAV-acquired multispectral imagery were integrated, with the model trained using satellite-derived SVIs and NDWI, and then applied to UAV-derived SVIs to predict NDWI. The model achieved high predictive accuracy (R² = 0.95, RMSE = 0.03, MAE = 0.02) and effectively captured temporal variations in sugarcane water status, including post-rainfall stress recovery and increased water retention during early maturation—aligning with changes in leaf area index (LAI), chlorophyll content (CC), and Total Soil Water Profile (TSWP). NDWI also showed a positive correlation with actual evapotranspiration (ETa; R² = 0.60) and a negative correlation with the Water Deficit Index (WDI; R² = 0.62), suggesting its potential to reflect crop water status under certain conditions. When interpreted in conjunction with in situ measurements of precipitation, TSWP, and WDI, the predicted NDWI provides valuable insights into crop water dynamics. This approach demonstrates the potential of ML-driven NDWI estimation to support site-specific irrigation scheduling, enhance resource use efficiency, and promote sustainable sugarcane cultivation. The findings contribute to climate-resilient water management practices tailored to the needs of smallholder systems in water-scarce regions.

SoYoung Park, Hyewon Lee, Mingyu Choi et al.

Anomaly segmentation is essential for industrial quality, maintenance, and stability. Existing text-guided zero-shot anomaly segmentation models are effective but rely on fixed prompts, limiting adaptability in diverse industrial scenarios. This highlights the need for flexible, context-aware prompting strategies. We propose Image-Aware Prompt Anomaly Segmentation (IAP-AS), which enhances anomaly segmentation by generating dynamic, context-aware prompts using an image tagging model and a large language model (LLM). IAP-AS extracts object attributes from images to generate context-aware prompts, improving adaptability and generalization in dynamic and unstructured industrial environments. In our experiments, IAP-AS improves the F1-max metric by up to 10%, demonstrating superior adaptability and generalization. It provides a scalable solution for anomaly segmentation across industries

P. Vijaya Bharati, J. S. V. Siva Kumar, Sathish K Anumula et al.

Fourth Industrial Revolution has brought in a new era of smart manufacturing, wherein, application of Internet of Things , and data-driven methodologies is revolutionizing the conventional maintenance. With the help of real-time data from the IoT and machine learning algorithms, predictive maintenance allows industrial systems to predict failures and optimize machines life. This paper presents the synergy between the Internet of Things and predictive maintenance in industrial engineering with an emphasis on the technologies, methodologies, as well as data analytics techniques, that constitute the integration. A systematic collection, processing, and predictive modeling of data is discussed. The outcomes emphasize greater operational efficiency, decreased downtime, and cost-saving, which makes a good argument as to why predictive maintenance should be implemented in contemporary industries.

Katharina Ledebur. Ladislav Bartuska, Klaus Friesenbichler, Peter Klimek

The automotive industry is undergoing transformation, driven by the electrification of powertrains, the rise of software-defined vehicles, and the adoption of circular economy concepts. These trends blur the boundaries between the automotive sector and other industries. Unlike internal combustion engine (ICE) production, where mechanical capabilities dominated, competitiveness in electric vehicle (EV) production increasingly depends on expertise in electronics, batteries, and software. This study investigates whether and how firms' ability to leverage cross-industry diversification contributes to competitive advantage. We develop a country-level product space covering all industries and an industry-specific product space covering over 900 automotive components. This allows us to identify clusters of parts that are exported together, revealing shared manufacturing capabilities. Closeness centrality in the country-level product space, rather than simple proximity, is a strong predictor of where new comparative advantages are likely to emerge. We examine this relationship across industrial sectors to establish patterns of path dependency, diversification and capability formation, and then focus on the EV transition. New strengths in vehicles and aluminium products in the EU are expected to generate 5 and 4.6 times more EV-specific strengths, respectively, than other EV-relevant sectors over the next decade, compared to only 1.6 and 4.5 new strengths in already diversified China. Countries such as South Korea, China, the US and Canada show strong potential for diversification into EV-related products, while established producers in the EU are likely to come under pressure. These findings suggest that the success of the automotive transformation depends on regions' ability to mobilize existing industrial capabilities, particularly in sectors such as machinery and electronic equipment.

Heth Sethia, Abhishek Priyam

The use of hydrogen fuel cells has greatly increased in recent years. Advanced fuel cells are efficiently addressing the needs of portable power, backup power, and even modular power fuel cells. It has also been used to power cars and other vehicles. Hydrogen fuel cells are now specialized under the name portable power modules to highlight their newly discovered vehicle-mountable outboard engines. This review also targets the other issues of handling and encasing hydrogen fuel in specialized containers. All these gaps that revolve around the modern world are intertwined with one advancing vehicle engine to fix the ever-increasing global warming levels. Challenges faced by cost, storage, and infrastructure barriers are addressed, in addition to technological advancements in catalyst effectiveness, membrane technology, and hydrogen supply logistics. The report ends with a visionary outlook, outlining research avenues to drive the shift to a hydrogen economy.

Chinedu C Nsude, Rebecca Loraamm, Natalie Letsa

Fuel subsidies have been a central topic of discussion for decades in the Global South, including Nigeria, often implemented to enhance energy affordability for the population. However, on 29 May 2023, the President of Nigeria announced the elimination of the fuel subsidy, resulting in an increase in energy and electricity costs exceeding 300%. This resulted in widespread protests nationwide, significantly affecting all sectors, particularly enterprises. Thus, this study examines the impact of fuel subsidy removal on micro, small, and medium enterprises (MSMEs), as well as their level of awareness and correlation with willingness to transition to renewable energy technologies (RETs), utilizing original survey data from 1461 MSMEs across Nigeria. Results indicate that the removal of fuel subsidies impacted 90% of MSMEs surveyed. Regarding the willingness to transition to RETs, 77.2% of MSMEs expressed a positive inclination, whereas 11.7% were unwilling to undertake this transition. The willingness of MSMEs to transition is influenced by several factors, including state of residence, geographical area (settlement), level of education, enterprise category, the role of the respondents, energy type utilized by the enterprise, and the level of awareness of various RETs. The study’s findings enhance understanding of the factors influencing the adoption of RETs among MSMEs in Nigeria and the potential to inform strategies for sustainable energy development. Furthermore, the identification of specific factors influencing the transition decision provides valuable insights for targeted interventions and policymaking.

Zijie Pan, Stepan Gordeev, Jiahui Zhao et al.

This paper presents a novel methodology for predicting international bilateral trade flows, emphasizing the growing importance of Preferential Trade Agreements (PTAs) in the global trade landscape. Acknowledging the limitations of traditional models like the Gravity Model of Trade, this study introduces a two-stage approach combining explainable machine learning and factorization models. The first stage employs SHAP Explainer for effective variable selection, identifying key provisions in PTAs, while the second stage utilizes Factorization Machine models to analyze the pairwise interaction effects of these provisions on trade flows. By analyzing comprehensive datasets, the paper demonstrates the efficacy of this approach. The findings not only enhance the predictive accuracy of trade flow models but also offer deeper insights into the complex dynamics of international trade, influenced by specific bilateral trade provisions.

Hafiz Mughees Ahmad, Afshin Rahimi

Workplace accidents continue to pose significant risks for human safety, particularly in industries such as construction and manufacturing, and the necessity for effective Personal Protective Equipment (PPE) compliance has become increasingly paramount. Our research focuses on the development of non-invasive techniques based on the Object Detection (OD) and Convolutional Neural Network (CNN) to detect and verify the proper use of various types of PPE such as helmets, safety glasses, masks, and protective clothing. This study proposes the SH17 Dataset, consisting of 8,099 annotated images containing 75,994 instances of 17 classes collected from diverse industrial environments, to train and validate the OD models. We have trained state-of-the-art OD models for benchmarking, and initial results demonstrate promising accuracy levels with You Only Look Once (YOLO)v9-e model variant exceeding 70.9% in PPE detection. The performance of the model validation on cross-domain datasets suggests that integrating these technologies can significantly improve safety management systems, providing a scalable and efficient solution for industries striving to meet human safety regulations and protect their workforce. The dataset is available at https://github.com/ahmadmughees/sh17dataset.

Xiupeng Chen, Lu Wang, Yuning Jiang et al.

Abstract Peer‐to‐peer energy trading enhances distribution network resilience by reducing energy demand from central power plants and enabling distributed energy resources to support critical loads after extreme events. However, adequate reserves from main grids are still required to ensure real‐time energy balance in distribution networks due to the uncertainty in renewable generation. This paper introduces a novel two‐stage joint energy and reserve market for prosumers, wherein local flexible resources are fully utilized to manage renewable generation uncertainty. In contrast to cooperative optimization methods, the interactions between prosumers are modelled as a generalized Nash game, considering that prosumers are self‐interested and should follow distribution network constraints. Then, linear decision rules are employed to ensure a feasible market equilibrium and develop a privacy‐preserving algorithm to guide prosumers the market equilibrium with a proven convergence. Finally, the numerical study on a modified IEEE 33‐power system demonstrates that the designed market effectively manages renewable generation uncertainty, and that the algorithm converges to the market equilibrium.

Wenhao Li, Shaozhong Kang, Taisheng Du et al.

Secondary salinization poses a significant threat to the sustainable development of water-saving irrigation districts. This study aims to explore the spatial and temporal variations in soil salinity and the factors influencing these changes in water-saving irrigation areas in the inland arid regions of Northwest China. The Manas River Irrigation District was selected as the study area. A grid measuring 10 km × 14 km grid was designed to determine the latitude and longitude coordinates of the grid centers, resulting in 66 sample points. Soil samples were collected from these points in 2013, 2014, 2020, and 2021 from the 0100 cm layer to obtain salinity data. Based on existing research and practical conditions in water-saving irrigation areas, 11 factors influencing soil salinity changes were identified, including irrigation area and irrigation amount. Classical statistical methods and interpretable machine learning techniques were employed to analyze the distribution characteristics of soil salinity and the influencing factors. This analysis proposes effective solutions to mitigate potential secondary salinization in irrigation areas. The results revealed that soil salinity in the irrigation area belonged to moderate variation (Cv = 46.74 %51.80 %). The horizontal direction of the irrigation area shows higher salt content in the upstream and downstream areas, and a gradual decrease in variability with increasing depth characterizes the vertical direction. From 2013–2021, soil salinization in the irrigation area gradually decreased. In 2013 and 2014, the area was predominantly covered by mild saline-alkali soil, accounting for 75.1 % and 76.6 % of the total area, respectively. However, in 2020 and 2021, non-saline soils became dominant, covering 60.9 % and 66.5 % of the total irrigation area, respectively. In order of importance, the factors affecting the spatial and temporal evolution of soil salinity are groundwater depth, annual water surface evaporation, water-saving irrigation area, underground water diversion amount, mineralization of groundwater, irrigation amount, surface water diversion amount, and annual rainfall. In the oasis irrigation area, maintaining a groundwater depth of 4.06.0 m and an irrigation amount of 55006000 m3 ha−1 can alleviate the problem of secondary salinization that may result from large-scale development of water-saving irrigation. The findings of this study provide a basis for the prevention and control of soil salinization in water-saving irrigation areas and the development and management of saline land in oasis areas.

Tat-Dat Bui, Hien Minh Ha, Thi Phuong Thuy Tran et al.

This study is to build a causality model to implement energy security strategies (ESSs) in approaching a world-regions comparison. This study contributes to ESSs by indicating a set of valid attributes and those attributes are interrelationships in nature. There is major global interest in ESSs due to the pressure to ensure sustainable energy supply sources. An adequate energy source is decisive for ensuring stable economic growth, enhancing social development, and protecting the environment. Nonetheless, in reviewing the energy literature, generating strategic attributes is still lacking, which leads to difficulties for policymakers in building, executing, and assessing energy policies. This study utilizes a hybrid method: text mining, cluster analysis, fuzzy Delphi method, fuzzy decision-making trial and evaluation laboratory, and entropy weight method. As a result, five aspects and 22 criteria from the data pool are validated. The causal model shows that the energy control system, strategic collaboration and technological capability are the priority. In practice, the effect aspects are waste-to-energy and energy resilience. Although the research trends on ESSs in different regions are quite similar, each continent still has unique concerns such as European countries with distributed energy resources, Asia and Oceania with decarbonization, African countries with new technologies, and Americas with energy planning.

Lihua Dai, Ben Wang, Xuemin Cheng et al.

Abstract This study addresses the intricate challenge of circuit layout optimization central to integrated circuit (IC) design, where the primary goals involve attaining an optimal balance among power consumption, performance metrics, and chip area (collectively known as PPA optimization). The complexity of this task, evolving into a multidimensional problem under multiple constraints, necessitates the exploration of advanced methodologies. In response to these challenges, our research introduces deep learning technology as an innovative strategy to revolutionize circuit layout optimization. Specifically, we employ Convolutional Neural Networks (CNNs) in developing an optimized layout strategy, a performance prediction model, and a system for fault detection and real-time monitoring. These methodologies leverage the capacity of deep learning models to learn from high-dimensional data representations and handle multiple constraints effectively. Extensive case studies and rigorous experimental validations demonstrate the efficacy of our proposed deep learning-driven approaches. The results highlight significant enhancements in optimization efficiency, with an average power consumption reduction of 120% and latency decrease by 1.5%. Furthermore, the predictive capabilities are markedly improved, evidenced by a reduction in the average absolute error for power predictions to 3%. Comparative analyses conclusively illustrate the superiority of deep learning methodologies over conventional techniques across several dimensions. Our findings underscore the potential of deep learning in achieving higher accuracy in predictions, demonstrating stronger generalization abilities, facilitating superior design quality, and ultimately enhancing user satisfaction. These advancements not only validate the applicability of deep learning in IC design optimization but also pave the way for future advancements in addressing the multidimensional challenges inherent to circuit layout optimization.

Lochana Telugu Rajesh, Tapadhir Das, Raj Mani Shukla et al.

The rapid growth in Internet of Things (IoT) technology has become an integral part of today's industries forming the Industrial IoT (IIoT) initiative, where industries are leveraging IoT to improve communication and connectivity via emerging solutions like data analytics and cloud computing. Unfortunately, the rapid use of IoT has made it an attractive target for cybercriminals. Therefore, protecting these systems is of utmost importance. In this paper, we propose a federated transfer learning (FTL) approach to perform IIoT network intrusion detection. As part of the research, we also propose a combinational neural network as the centerpiece for performing FTL. The proposed technique splits IoT data between the client and server devices to generate corresponding models, and the weights of the client models are combined to update the server model. Results showcase high performance for the FTL setup between iterations on both the IIoT clients and the server. Additionally, the proposed FTL setup achieves better overall performance than contemporary machine learning algorithms at performing network intrusion detection.

Vikram Duvvur, Aashay Mehta, Edward Sun et al.

The use of machine learning in algorithmic trading systems is increasingly common. In a typical set-up, supervised learning is used to predict the future prices of assets, and those predictions drive a simple trading and execution strategy. This is quite effective when the predictions have sufficient signal, markets are liquid, and transaction costs are low. However, those conditions often do not hold in thinly traded financial markets and markets for differentiated assets such as real estate or vehicles. In these markets, the trading strategy must consider the long-term effects of taking positions that are relatively more difficult to change. In this work, we propose a Reinforcement Learning (RL) algorithm that trades based on signals from a learned predictive model and addresses these challenges. We test our algorithm on 20+ years of equity data from Bursa Malaysia.

Jason Mc Guire, Fionn Rogan, Olexandr Balyk et al.

Decarbonising and improving the energy efficiency of dwellings is a priority for climate and energy policy. However, integrated energy systems modelling, commonly used to inform decarbonisation pathways, typically does not capture the heterogeneity of the dwelling stock, particularly internal dwelling temperatures, a key determinant of the cost of switching to low-carbon heating and upgrading building fabric. Addressing this gap, this paper presents a new model of the residential sector within an energy systems optimisation model (ESOM), which is developed using an Energy Performance Certificate (EPC) database and measured energy consumption data, allowing for the heterogeneous depiction of the housing stock.The analysis is carried out for Ireland, which has committed to ambitious legally binding climate targets. The Irish residential sector, which represented 22% of energy-related CO2 emissions in 2020, is characterised by poor thermal efficiency and high reliance on fossil fuels. The TIMES Ireland Model (TIM) is developed within a framework demonstrating cost-optimal pathways for energy supply, transformation, and demand sectors. The model is open-source, and the methodology outlined in this study is replicable for any country with an EPC database.The results presented are indicative of the type of insights that such modelling can provide and demonstrate that to meet climate targets, a rapid reduction in residential fossil fuel heating is necessary. The results show that deeper decarbonisation pathways require more thermal retrofits and electrical heat pumps, increasing the total cost of the energy system. The results explore the effect of internal temperatures, highlighting the importance of using empirical data for accounting for the rebound effect. Exemplifying the value of an integrated system modelling approach, deeper decarbonisation pathways place further costs on the electrical system.

Hidayatul Fitri, Gürkan A. K. Gürdil, Bahadır Demirel et al.

Abstract The West Nusa Tenggara (WNT) province is one of the regions that contribute the most to the production of rice, corn, and cacao. The residues of these crops increase as production increases. The potential availability of the residue was calculated on the basis of the amount of agricultural product and the availability of unutilized residues. The estimated potential energy and collected data were processed and combined with converted factors, such as the yield per hectare and the calorific value, taking into account another purpose, the use of domestic residues for animal feed. Paddy straw, corn straw, and corn cobs had the highest percentage of residue availabilities, 85.91%, 82.26%, and 88.25%, respectively. In addition, the WNT regency has a rich diversity of agricultural residues from superior commodities such as rice, corn, coffee, coconut and cacao. The calculation of the total heating value (THV) of the agricultural residue available reached up to 42.4 PJ. Furthermore, the use of biomass for bioenergy resources is promising, particularly for the WNT region, with the potential for unused agricultural residues. The dependence on unsustainable energy, such as coal and fossil fuel, can be reduced by deploying and developing energy production from biomass use. Therefore, the potential for bioenergy generation and the availability of biomass can be developed for sustainable agriculture and energy management.

Konstantinos Pelechrinis, Xin Liu, Prashant Krishnamurthy et al.

Non-Fungible Token (NFT) markets are one of the fastest growing digital markets today, with the sales during the third quarter of 2021 exceeding $10 billions! Nevertheless, these emerging markets - similar to traditional emerging marketplaces - can be seen as a great opportunity for illegal activities (e.g., money laundering, sale of illegal goods etc.). In this study we focus on a specific marketplace, namely NBA TopShot, that facilitates the purchase and (peer-to-peer) trading of sports collectibles. Our objective is to build a framework that is able to label peer-to-peer transactions on the platform as anomalous or not. To achieve our objective we begin by building a model for the profit to be made by selling a specific collectible on the platform. We then use RFCDE - a random forest model for the conditional density of the dependent variable - to model the errors from the profit models. This step allows us to estimate the probability of a transaction being anomalous. We finally label as anomalous any transaction whose aforementioned probability is less than 1%. Given the absence of ground truth for evaluating the model in terms of its classification of transactions, we analyze the trade networks formed from these anomalous transactions and compare it with the full trade network of the platform. Our results indicate that these two networks are statistically different when it comes to network metrics such as, edge density, closure, node centrality and node degree distribution. This network analysis provides additional evidence that these transactions do not follow the same patterns that the rest of the trades on the platform follow. However, we would like to emphasize here that this does not mean that these transactions are also illegal. These transactions will need to be further audited from the appropriate entities to verify whether or not they are illicit.

Shen Yuan, Shaobing Peng

As the world's largest rice-producing country, China's energy and environmental performance related to rice production have attracted widespread attention. To achieve a sustainable rice production with low energy expenditure and environmental impacts, systematic assessments are required to examine the life cycle food-energy-emission nexus of rice production system on a national scale. This study employed energy analysis and systematic indicators to explore the interplay among productivity, energy inputs, and greenhouse gas emissions in China's rice production from 1998 to 2018. We found that energy inputs decreased by 11% during the study period, mainly due to the reduction of energy inputs from fertilizer, labor, and animal power. However, energy structure has become increasingly dependent on nonrenewable energy. Generally, net energy and energy use efficiency increased during the period from 1998 to 2013, implying a substantial increase in production efficiency from energy input. Meanwhile, global warming potential from rice production increased by 20% from 1998 to 2018, primarily due to increased methane emissions. Nevertheless, environmental loading intensity including yield- and net energy-scaled global warming potential of rice production showed a decreasing trend, accompanied by dramatic improvements in food production and energy efficiency, especially since 2013. Strategies for achieving energy-efficient and eco-friendly agriculture for China and other countries around the world with similar bio-physical background are discussed.

Bindiya Aribam, Boynaoton Konthoujam, Pardeep Singh et al.

Ashwin Misra, Ankit Mittal, Vihaan Misra et al.

The latest Industrial revolution has helped industries in achieving very high rates of productivity and efficiency. It has introduced data aggregation and cyber-physical systems to optimize planning and scheduling. Although, uncertainty in the environment and the imprecise nature of human operators are not accurately considered for into the decision making process. This leads to delays in consignments and imprecise budget estimations. This widespread practice in the industrial models is flawed and requires rectification. Various other articles have approached to solve this problem through stochastic or fuzzy set model methods. This paper presents a comprehensive method to logically and realistically quantify the non-deterministic uncertainty through probabilistic uncertainty modelling. This method is applicable on virtually all Industrial data sets, as the model is self adjusting and uses epsilon-contamination to cater to limited or incomplete data sets. The results are numerically validated through an Industrial data set in Flanders, Belgium. The data driven results achieved through this robust scheduling method illustrate the improvement in performance.