Hasil untuk "Special industries and trades"

Mohammadali Ranjbar, Hamid Reza Baghaee, Amin Ramezani

The rapid proliferation of electric vehicles (EVs) has transformed power distribution networks, making the development and optimal management of Electric Vehicle Charging Stations (EVCS) a critical concern. Integrating EVCS with Renewable Energy Sources (RES) not only reduces fossil-fuel dependency but also enhances grid sustainability and lowers operational costs. However, variability in RES generation and unpredictable user charging patterns complicate station management. Strategic siting of EVCS is vital to minimizing network losses, flattening load profiles, and improving power quality. In response, this paper offers a systematic review of recent research on EVCS planning and control, examining optimization techniques for station location and capacity, advanced planning and control models, and grid-interactive charging strategies. We assess intelligent economic charging approaches—including dynamic scheduling, prediction-based control, and real-time grid interaction—and demonstrate how multi-objective optimization frameworks can reconcile cost efficiency, stability, and energy efficiency. Drawing on the IEA's Global EV Outlook 2025 projection of over 40 million EVs by 2030 (20 % CAGR since 2020), we underscore the need for scalable, RES-integrated charging frameworks aligned with techno-economic and policy-driven targets. Finally, we identify emerging trends in machine learning and artificial intelligence applications for predictive control and chart future research directions to enhance EVCS performance and grid integration.

Ruike Lyu, Anna Li, Jianxiao Wang et al.

In many countries, declining demand in energy-intensive industries such as cement, steel, and aluminum is leading to industrial overcapacity. Although industrial overcapacity is traditionally envisioned as problematic and resource-wasteful, it could unlock energy-intensive industries' flexibility in electricity use. Here, using China's aluminum smelting industry as a case study, we evaluate the system-level cost-benefit of retaining energy-intensive industries overcapacity for flexible electricity use in decarbonized energy systems. We find that overcapacity can enable aluminum smelters to adopt a seasonal operation paradigm, ceasing production during winter load peaks that are exacerbated by heating electrification and renewable seasonality. This seasonal operation paradigm could reduce the investment and operational costs of China's decarbonized electricity system by 23-32 billion CNY/year (11-15% of the aluminum smelting industry's product value), sufficient to offset the increased smelter maintenance and product storage costs associated with overcapacity. It may also provide an opportunity for seasonally complementary labor deployment across the aluminum smelting and thermal power generation sectors, offering a potential pathway for mitigating socio-economic disruptions caused by industrial restructuring and energy decarbonization.

Ting-Yen Wang

This study introduces a novel framework for estimating industrial heterogeneity by integrating maximum entropy (ME) estimation of production functions with Zonotope-based measures. Traditional production function estimations often rely on restrictive parametric models, failing to capture firm behavior under uncertainty. This research addresses these limitations by applying Hang K. Ryu's ME method to estimate production functions using World Bank Enterprise Survey (WBES) data from Bangladesh, Colombia, Egypt, and India. The study normalizes entropy values to quantify heterogeneity and compares these measures with a Zonotope-based Gini index. Results demonstrate the ME method's superiority in capturing nuanced, functional heterogeneity often missed by traditional techniques. Furthermore, the study incorporates a "Tangent Against Input Axes" method to dynamically assess technical change within industries. By integrating information theory with production economics, this unified framework quantifies structural and functional differences across industries using firm-level data, advancing both methodological and empirical understanding of heterogeneity. A numerical simulation confirms the ME regression functions can approximate actual industrial heterogeneity. The research also highlights the superior ability of the ME method to provide a precise and economically meaningful measure of industry heterogeneity, particularly for longitudinal analyses.

Emily L. Tucker, Mohammadhossein Mohammadisiahroudi

Quantum computing is rapidly emerging as a new computing paradigm with the potential to improve decision-making, optimization, and simulation across industries. For industrial engineering (IE) and operations research (OR), this shift introduces both unprecedented opportunities and substantial challenges. The learning curve is high, and to help researchers navigate the emerging field of quantum operations research, we provide a road map of the current field of quantum operations research. We introduce the foundational principles of quantum computing, outline the current hardware and software landscape, and survey major algorithmic advances relevant to IE/OR, including quantum approaches to linear algebra, optimization, machine learning, and stochastic simulation. We then highlight applied research directions, including the importance of problem domains for driving long-term value of quantum computers and how existing classical OR models can be reformulated for quantum hardware. Recognizing the steep learning curve, we propose pathways for IE/OR researchers to develop technical fluency and engage in this interdisciplinary domain. By bridging theory with application, and emphasizing the interplay between hardware and research development, we argue that industrial engineers are uniquely positioned to shape the trajectory of quantum computing for practical problem-solving. Ultimately, we aim to lower the barrier to entry into quantum computing, motivate new collaborations, and chart future directions where quantum technologies may deliver tangible impact for industry and academia.

Samiran Sardar, Nasif Morshed, Shezan Ahmed

Although Digital Twin is actively deployed in manufacturing, its human-centric counterpart - Human Digital Twin (HDT) is understudied, especially in job-shop production with high task variability and manual labor. HDT applications like ergonomic posture monitoring, fatigue prediction and health-based task assignment offer benefits to industries in emerging economies. However, poor digital maturity, lack of awareness and doubts about use-case applicability hinder adoption. This study provides a strategic prioritization framework to aid human-centric digital evolution in labor-intensive industries for guiding the selection of HDT applications delivering the highest value with the lowest implementation threshold. An integrated Fuzzy AHP-TOPSIS approach evaluates the use-cases based on criteria like implementation cost, technological maturity, scalability. These criteria and use-cases were identified based on input from a five-member expert panel and verified for consistency (CR < 0.1). Analysis shows posture monitoring and fatigue prediction as most influential and practicable, especially in semi-digital environments. Strengths include compliance with Industry 5.0 principles incorporating technology and human factors. Lack of field validation and subjective knowledge pose drawbacks. Future work should include simulation-based validation and pilot tests on real job-shop settings. Ultimately, the research offers a decision-support system helping industries balance innovativeness and practicability in early stage of HDT adoption.

José Ignacio Rivero-Wildemauwe

Two agents trade an item in a simultaneous offer setting, where the exchange takes place if and only if the buyer's bid price weakly exceeds the seller's ask price. Each agent is randomly assigned the buyer or seller role. Both agents are characterized by a certain degree of Kantian morality, whereby they pick their bidding strategy behind a Veil of Ignorance, taking into account how the outcome would be affected if their trading partner adopted their strategy. I consider two variants with asymmetric information, respectively allowing buyers to have private information about their valuation or sellers to be privately informed about the item's quality. I show that when all trades are socially desirable, even the slightest degree of morality guarantees that the outcome is fully efficient. In turn, when quality is uncertain and some exchanges are socially undesirable, full efficiency is only achieved with sufficiently high moral standards. Moral concerns also ensure equal ex-ante treatment of the two agents in equilibrium. Finally, I show that if agents are altruistic rather than moral, inefficiencies persist even with a substantial degree of altruism.

Anthony William Bennett, Simone Mueller Loose

In recent decades, the German wine market has undergone significant structural changes due to intensifying competition and shifting consumption patterns. Increased imports and declining exports have pressured German wine estates to adapt for survival. The study explores these long-term trends and structural changes in German wine estates, focusing on those marketing bottled wine. It aims to understand how these businesses have adapted to economic pressures in a highly competitive market from 1993 to 2020, using business panel data and regression analysis for 16 key performance indicators (KPIs). At first (until the financial crisis of 2008) estates benefitted from mechanisation and economies of scale, leading to a significant reduction in labour hours per hectare and moderate increases in wine prices, improving labour productivity and profitability. However, yields declined due to a shift towards lower-yield grape varieties in response to market demand. From 2009 onward, rising labour and material costs as well as stagnating yields started eroding profitability gains, leading to an overall stagnation of long-term profitability. When observing differences in developments between size groups, large wine estates experienced a considerably sharper increase in costs per ha than small to medium sized wine estates, from 2009 onward. Nonetheless, this could be counterbalanced by large wine estates also generating significantly higher productivity increases in the same time period, resulting in a significant increase in profitability for large wine estates from 2009 onward, while small to medium sized wine estates stagnated.

Domenico Longo, Serena Guarrera, Delia Ventura et al.

The need for accurate measurement of CO2 emissions from surfaces arises from various fields, particularly in precision agriculture, irrigation water management, wastewater management, volcanology, geothermal exploration, environmental and climate monitoring. This study introduces a novel, cost-effective closed dynamic accumulation chamber system designed to measure CO2 fluxes from soil and water surfaces. A short review of existing measurements techniques is provided, alongside a detailed explanation of key algorithms used for processing field data. The proposed system collects raw CO2 concentration data via an internal data logger. A custom-developed software suite enables real-time first-approximation flux calculation through a user-friendly Javascript web application compatible with smartphones with any type of operating system and web browser. A freely available Matlab® tool allows for post-processing data analysis for a more accurate flux calculation. After calibration against the commercial PP Systems EGM-5, assumed as a reference, some case studies in agriculture, wastewater treatment and volcanic environments demonstrate the instrument's versatility, showcasing its potential for advance in agricultural field and environmental sustainability.

Lucas Lacasa, Abel Pardo, Pablo Arbelo et al.

Methods of Machine and Deep Learning are gradually being integrated into industrial operations, albeit at different speeds for different types of industries. The aerospace and aeronautical industries have recently developed a roadmap for concepts of design assurance and integration of neural network-related technologies in the aeronautical sector. This paper aims to contribute to this paradigm of AI-based certification in the context of supervised learning, by outlining a complete validation pipeline that integrates deep learning, optimization and statistical methods. This pipeline is composed by a directed graphical model of ten steps. Each of these steps is addressed by a merging key concepts from different contributing disciplines (from machine learning or optimization to statistics) and adapting them to an industrial scenario, as well as by developing computationally efficient algorithmic solutions. We illustrate the application of this pipeline in a realistic supervised problem arising in aerostructural design: predicting the likelikood of different stress-related failure modes during different airflight maneuvers based on a (large) set of features characterising the aircraft internal loads and geometric parameters.

Aimira Baitieva, David Hurych, Victor Besnier et al.

Automating visual inspection in industrial production lines is essential for increasing product quality across various industries. Anomaly detection (AD) methods serve as robust tools for this purpose. However, existing public datasets primarily consist of images without anomalies, limiting the practical application of AD methods in production settings. To address this challenge, we present (1) the Valeo Anomaly Dataset (VAD), a novel real-world industrial dataset comprising 5000 images, including 2000 instances of challenging real defects across more than 20 subclasses. Acknowledging that traditional AD methods struggle with this dataset, we introduce (2) Segmentation-based Anomaly Detector (SegAD). First, SegAD leverages anomaly maps as well as segmentation maps to compute local statistics. Next, SegAD uses these statistics and an optional supervised classifier score as input features for a Boosted Random Forest (BRF) classifier, yielding the final anomaly score. Our SegAD achieves state-of-the-art performance on both VAD (+2.1% AUROC) and the VisA dataset (+0.4% AUROC). The code and the models are publicly available.

Martin Detloff

The rapid evolution of software development necessitates enhanced safety measures. Extracting information about software failures from companies is becoming increasingly more available through news articles. This research utilizes the Failure Analysis Investigation with LLMs (FAIL) model to extract industry-specific information. Although the FAIL model's database is rich in information, it could benefit from further categorization and industry-specific insights to further assist software engineers. In previous work news articles were collected from reputable sources and categorized by incidents inside a database. Prompt engineering and Large Language Models (LLMs) were then applied to extract relevant information regarding the software failure. This research extends these methods by categorizing articles into specific domains and types of software failures. The results are visually represented through graphs. The analysis shows that throughout the database some software failures occur significantly more often in specific industries. This categorization provides a valuable resource for software engineers and companies to identify and address common failures. This research highlights the synergy between software engineering and Large Language Models (LLMs) to automate and enhance the analysis of software failures. By transforming data from the database into an industry specific model, we provide a valuable resource that can be used to identify common vulnerabilities, predict potential risks, and implement proactive measures for preventing software failures. Leveraging the power of the current FAIL database and data visualization, we aim to provide an avenue for safer and more secure software in the future.

Tommaso Cesari, Roberto Colomboni

We explore brokerage between traders in an online learning framework. At any round $t$, two traders meet to exchange an asset, provided the exchange is mutually beneficial. The broker proposes a trading price, and each trader tries to sell their asset or buy the asset from the other party, depending on whether the price is higher or lower than their private valuations. A trade happens if one trader is willing to sell and the other is willing to buy at the proposed price. Previous work provided guidance to a broker aiming at enhancing traders' total earnings by maximizing the gain from trade, defined as the sum of the traders' net utilities after each interaction. In contrast, we investigate how the broker should behave to maximize the trading volume, i.e., the total number of trades. We model the traders' valuations as an i.i.d. process with an unknown distribution. If the traders' valuations are revealed after each interaction (full-feedback), and the traders' valuations cumulative distribution function (cdf) is continuous, we provide an algorithm achieving logarithmic regret and show its optimality up to constant factors. If only their willingness to sell or buy at the proposed price is revealed after each interaction ($2$-bit feedback), we provide an algorithm achieving poly-logarithmic regret when the traders' valuations cdf is Lipschitz and show that this rate is near-optimal. We complement our results by analyzing the implications of dropping the regularity assumptions on the unknown traders' valuations cdf. If we drop the continuous cdf assumption, the regret rate degrades to $Θ(\sqrt{T})$ in the full-feedback case, where $T$ is the time horizon. If we drop the Lipschitz cdf assumption, learning becomes impossible in the $2$-bit feedback case.

Tita Alissa Bach, Aleksandar Babic, Narae Park et al.

The maritime industry requires effective communication among diverse stakeholders to address complex, safety-critical challenges. Industrial AI, including Large Language Models (LLMs), has the potential to augment human experts' workflows in this specialized domain. Our case study investigated the utility of LLMs in drafting replies to stakeholder inquiries and supporting case handlers. We conducted a preliminary study (observations and interviews), a survey, and a text similarity analysis (LLM-as-a-judge and Semantic Embedding Similarity). We discover that while LLM drafts can streamline workflows, they often require significant modifications to meet the specific demands of maritime communications. Though LLMs are not yet mature enough for safety-critical applications without human oversight, they can serve as valuable augmentative tools. Final decision-making thus must remain with human experts. However, by leveraging the strengths of both humans and LLMs, fostering human-AI collaboration, industries can increase efficiency while maintaining high standards of quality and precision tailored to each case.

Baozhen Hao, Jingli Ma, Shihua Si et al.

Adopting drought-tolerant (DT) cultivars is an effective strategy to sustain maize (Zea mays L.) production under water shortage. Optimizing plant density is an important management practice for improving maize yield. In a two-year field trial, the response of yield, actual evapotranspiration (ETc act), and water productivity (WP) to plant density (6, 7.5, 9 plants m−2) was assessed under irrigated and rainfed conditions using a DT (ZD958) and a drought-susceptible (DS, ZY309) maize cultivar, and additionally, the comparison of soil water depletion will be conducted among soils growing different DT maize varieties. Under rainfed, average yield, ETc act, and WP were 24.7%, 8.6% and 14.8% greater in ZD958 than ZY309, respectively. When density increased from 6 to 9 plants m−2, for ZD958 and ZY309 ETc act remained relatively constant, whereas their yield and WP first increased and then decreased and ultimately reached their maximum at 7.5 plants m−2. Under irrigation, increasing density (6–9 plants m−2) significantly increased yield and WP for ZD958, but for ZY309, yield and WP were not significantly impacted. Yield across seasons did not differ between cultivars at 6 and 7.5 plants m−2, and ZD958 had a 10.2% yield advantage over ZY309 at 9 plants m−2. The findings imply that DT cultivar showed greater high density tolerance than DS cultivar and thus higher optimal density under irrigation. Under rainfed, both cultivars had similar density tolerance and optimum density, whereas DT cultivar had stronger drought tolerance than DS cultivar, which could explain DT cultivar’s greater yield and WP. This study indicate that DT cultivar showed higher and more stable yields than DS cultivar across rainfed and irrigated conditions when grown at optimal densities. Thus, sustainable maize production could be achieved by adopting DT cultivars and optimizing density for different conditions in the study region.

AGBOKPONTO Engelbert, DAOUDA Mohamed M. Arêmou, POKOU Aurelle et al.

L’eau est un produit vital pour l’Homme, mais également une source de nombreuses maladies hydriques. Au Bénin, avec les changements de mode de vie, le conditionnement de l'eau s'est rapidement répandu, avec de nombreuses unités de production qui échappent au contrôle sanitaire des autorités mettant en danger la santé des consommateurs. Au total 70 unités de production dont 64 d’ensachage d’eau et 6 de mise en bouteilles d’eau dans 4 départements du sud du Bénin ont été inspectées et des échantillons de lot issu de production récente ont été prélevés pour des tests physicochimiques et microbiologiques. De l’analyse des résultats, il ressort des risques de contamination notés à l'inspection sanitaire notamment : l’absence de filtres à charbon obligatoires (64,1%) et de lampe UV (42,2%), la présence de fuite d’eau (14,1%) sur le circuit de production d’eau en sachet ainsi que le manque d’hygiène (34,4%). Les analyses de laboratoire quant à elles ont rapporté que le pH des échantillons d’eau variait entre 3,94 et 7,74 avec 48,57% de non-conformité (norme pH : 6,5-8,5). Les tests microbiologiques ont mis en évidence une contamination des eaux conditionnées par des germes banals (64,1% ; n=47), des présumés coliformes (7,1% ; n=5) et Escherichia coli (1,4% ; n=1). Cette mauvaise qualité microbiologique concerne aussi bien les échantillons d’eau conditionnée en sachet (n=44 sur 64) que les échantillons d’eau en bouteille (n=3 sur 6). Ces résultats montrent la nécessité de renforcer le suivi des installations de conditionnement d'eau afin d'assurer la sécurité sanitaire des consommateurs.

Huiping Wu, Zhaohan Shen, Wei Yu et al.

Lithium–oxygen (Li–O2) batteries with ultra-high theoretical specific energy (3500 Wh kg−1) have attracted significant attention, but the sluggish electrochemical processes of discharge product Li2O2 lead to poor cycling stability. Redox mediators (RMs) as soluble catalysts are widely used to assist with the electrochemical formation/decomposition of Li2O2. However, the shuttle effect of RMs causes severe deterioration of both RMs and Li metal anodes. Herein, for the first time we synthesize a lithiated zeolite-based protective layer on Li anodes to mitigate the shuttle effect of 2,2,6,6-tetramethylpiperidinyloxy (TEMPO) in Li–O2 batteries. The protective layer successfully blocks the migration of TEMPO toward the Li anode owing to the angstrom-level aperture size of lithiated zeolite. Due to the excellent redox-mediator-sieving capability of the protective layer, the cycle life of the Li−O2 batteries is significantly prolonged more than ten times at a current density of 250 mA g−1 and a limited capacity of 500 mA h g−1. This work demonstrates that the lithiated zeolite-based protective layer capable of molecular sieving is a facile and scalable way to mitigate the shuttle effect of RMs in Li–O2 batteries.

Changjian Ma, Cuiling Jiang, Yan Li et al.

High sediment content in irrigation water is a common challenge in agricultural regions, leading to increased clogging of emitters and reduced system efficiency. Lateral flushing (LF) is an effective measure to reduce emitter clogging for drip irrigation systems. However, the effect of LF on the clogging with high sediment water in different types of emitters remains largely unknown. Thus the effects of LF on the clogging of 8 flat emitters (FE), 3 cylindrical emitters (CE), 3 single wing labyrinth emitters (SL) and 2 inlaid strip emitters (SE) were evaluated. The degree of emitter clogging fluctuation values was 5.4–25.6% higher in the non-flushed drip lines compared to the flushing treatments. Additionally, the average discharge ratio variation (Dra) and coefficient of uniformity (CU) were improved by 7.7–21.9% and 11.6–67.4%, respectively, in the flushed drip lines compared to the non-flushed ones. The CE, FE, SL and SE drip lines that underwent flushing treatment operated for an additional 240, 180, 120 and 60 hourse, respectively, compared to those without flushing treatment. The results indicate that the flushing effect is better for FE and CE due to the accumulation of clogging material at varying rates and locations within the emitters, as well as differences in emitter structure.

Stein Tore Johansen, Bjørn Tore Løvfall, Tamara Rodriguez Duran et al.

A methodology for building pragmatic physics based models (Zoric et al., 2015b) is here adapted to a use-case in the steel industry. The challenge is to predict the erosion of steel ladle linings, such that the model can support operators to decide if the lade lining can be used one more time or not. If the ladle has too thin lining 140 tons of hot liquid steel may escape out of the ladle, with huge consequences for workers and plant. The development was done with a very small core team (two developers), which is typical for many industrial developments. The adopted workflow for the development, challenges that were faced, and some model results are presented. One key learning is that development of models should allow time for maturing the process understanding, and time should be given for many iterations by "questions-responses and actions" at the various levels in the model development. The good interactions between development team and industry case owner is an important success factor. In this case the results of using the PPBM (Pragmatism in physics-based modelling) were good thanks to very successful interaction between development team and industry case owner. Combining or extending the model with use of ML methods and cognition-related methods, such as knowledge graphs and self-adaptive algorithms is discussed.

Yutong Lu, Gesine Reinert, Mihai Cucuringu

The time proximity of trades across stocks reveals interesting topological structures of the equity market in the United States. In this article, we investigate how such concurrent cross-stock trading behaviors, which we denote as co-trading, shape the market structures and affect stock price co-movements. By leveraging a co-trading-based pairwise similarity measure, we propose a novel method to construct dynamic networks of stocks. Our empirical studies employ high-frequency limit order book data from 2017-01-03 to 2019-12-09. By applying spectral clustering on co-trading networks, we uncover economically meaningful clusters of stocks. Beyond the static Global Industry Classification Standard (GICS) sectors, our data-driven clusters capture the time evolution of the dependency among stocks. Furthermore, we demonstrate statistically significant positive relations between low-latency co-trading and return covariance. With the aid of co-trading networks, we develop a robust estimator for high-dimensional covariance matrix, which yields superior economic value on portfolio allocation. The mean-variance portfolios based on our covariance estimates achieve both lower volatility and higher Sharpe ratios than standard benchmarks.

Federico Rollo, Gennaro Raiola, Nikolaos Tsagarakis et al.

Robots with a high level of autonomy are increasingly requested by smart industries. A way to reduce the workers' stress and effort is to optimize the working environment by taking advantage of autonomous collaborative robots. A typical task for Human-Robot Collaboration (HRC) which improves the working setup in an industrial environment is the \textit{"bring me an object please"} where the user asks the collaborator to search for an object while he/she is focused on something else. As often happens, science fiction is ahead of the times, indeed, in the \textit{Iron Man} movie, the robot \textit{Dum-E} helps its creator, \textit{Tony Stark}, to create its famous armours. The ability of the robot to comprehend the semantics of the environment and engage with it is valuable for the human execution of more intricate tasks. In this work, we reproduce this operation to enable a mobile robot with manipulation and grasping capabilities to leverage its geometric and semantic understanding of the environment for the execution of the \textit{Bring Me} action, thereby assisting a worker autonomously. Results are provided to validate the proposed workflow in a simulated environment populated with objects and people. This framework aims to take a step forward in assistive robotics autonomy for industries and domestic environments.