Reza Hosseininejad, Steven Recoskie, Patrick Zdunich
et al.
Hybrid and electric aircraft offer a viable solution to reducing the environmental impact of conventional aviation, which aligns with the industry’s intention to reduce its carbon footprint while maintaining safety and operational standards. However, current limitations in battery production technology and the insufficient robustness and accuracy of existing algorithms in Battery Management Systems (BMS) for monitoring and estimating battery states, including State of Charge (SOC) and State of Health (SOH), pose significant challenges in the adoption of electrifying technologies in aviation applications. This paper proposes an approach for SOC estimation by combining advanced model-based concepts into the aviation environment that have not been implemented before. This method is applied to an aircraft’s Energy Storage System (ESS) by integrating merged sets of third-order Equivalent Circuit Models (ECM) representing battery dynamics at the module level. A novel dual-model filtering structure is introduced, in which a Dual Filter (DF) architecture enables concurrent estimation of internal resistance and SOC bias at the module level. The DF structure is deployed based on the Smooth Variable Structure Filter (SVSF), in which one filter provides the estimated internal resistance vector, while the other filter estimates the SOC bias. These filters are embedded within the Interacting Multiple Model (IMM) framework, which lets the algorithm utilize a mixture of several model-filter structures at each time step with respect to the discharge rate. Instead of using off-line and simulated data, the developed methods are implemented and tested in real-time in the laboratory and on a hybrid electric aircraft. This work enables safer and more efficient hybrid-electric aircraft operations by reducing SOC estimation error to below 1%.
Prediction markets provide a unique setting where event-level time series are directly tied to natural-language descriptions, yet discovering robust lead-lag relationships remains challenging due to spurious statistical correlations. We propose a hybrid two-stage causal screener to address this challenge: (i) a statistical stage that uses Granger causality to identify candidate leader-follower pairs from market-implied probability time series, and (ii) an LLM-based semantic stage that re-ranks these candidates by assessing whether the proposed direction admits a plausible economic transmission mechanism based on event descriptions. Because causal ground truth is unobserved, we evaluate the ranked pairs using a fixed, signal-triggered trading protocol that maps relationship quality into realized profit and loss (PnL). On Kalshi Economics markets, our hybrid approach consistently outperforms the statistical baseline. Across rolling evaluations, the win rate increases from 51.4% to 54.5%. Crucially, the average magnitude of losing trades decreases substantially from 649 USD to 347 USD. This reduction is driven by the LLM's ability to filter out statistically fragile links that are prone to large losses, rather than relying on rare gains. These improvements remain stable across different trading configurations, indicating that the gains are not driven by specific parameter choices. Overall, the results suggest that LLMs function as semantic risk managers on top of statistical discovery, prioritizing lead-lag relationships that generalize under changing market conditions.
In many decision processes, a decision maker or planner must review and optionally adjust the recommendations that are generated by a decision support system (DSS). When the DSS is well-tuned to its task, adjustments by a planner can be rare and may even degrade the DSS’s performance. Targeted automation could address these inefficiencies by predicting whether a planner will adjust a recommendation and improve the performance of the system. The remaining recommendations can be automated. However, as more recommendations are automated, fewer will receive planner input. This may starve the prediction model of the observations it needs for retraining. To maintain predictive performance, we must therefore address the loss that automation imposes on the model’s ability to learn from a planner’s decisions over time. Using 4 years of procurement ordering data from our research partner, a large materials handling equipment manufacturer, we develop and train a series of machine learning classifiers that predict individual instances in which a planner will improve a DSS-generated procurement order decision. We mitigate the performance erosion that automation engenders by structuring the selection of the model’s classification threshold similar to a newsvendor problem, accounting for the value of learning and balancing the costs and benefits of under or over automating. In our setting, this approach automates around 84% of all DSS recommendations while retaining three times more planner improvements than random automation. The models maintain their predictive performance over time, despite losing automated outcomes for retraining and substantial dataset shift. Our research contributes to a broader debate on the allocation of decision authority between humans and algorithms, and creates a framework for targeted automation in an operational setting that balances the net benefits of automation versus the long-term benefits of algorithmic learning.
This study evaluates the performance of a haulage system in a South Korean limestone open-pit mine using the standardized time classification framework proposed by the Global Mining Guidelines Group (GMG). Additionally, a new integrated key performance indicator (KPI) that consolidates nine individual KPIs proposed by GMG is introduced. The aim is to assess the availability, utilization, and effectiveness of the haulage system comprising trucks and loaders, while developing a more intuitive and comprehensive KPI for performance assessment. Data were collected from various sources, including work orders, work logs, the haulage management system, and time studies, then classified according to the GMG standardized time categories. This study applied three methods—equal weighting, GMG group-based weighted aggregation, and correlation analysis with clustering—to calculate the integrated KPI and compare the results. The results showed high physical and mechanical availability (over 97%), but relatively low asset utilization (average 35.9%) and production effectiveness (average 59.8%). Using the GMG group-based weighted aggregation method, integrated KPI scores ranged from 69.8% (Truck 33) to 79.2% (Trucks 37 and 38), indicating performance differences related to uptime and utilization. This research suggests that integrated KPI is an effective tool for efficiently evaluating system performance and quickly identifying issues, thereby significantly improving the efficiency and sustainability of mining operations.
Risk management often plays an important role in decision making under uncertainty. In quantitative risk management, assessing and optimizing risk metrics requires efficient computing techniques and reliable theoretical guarantees. In this paper, we introduce several topics on quantitative risk management and review some of the recent studies and advancements on the topics. We consider several risk metrics and study decision models that involve the metrics, with a main focus on the related computing techniques and theoretical properties. We show that stochastic optimization, as a powerful tool, can be leveraged to effectively address these problems.
William Webb, Arturas Medeisis, Leo Fulvio Minervini
This article discusses the key principles of radio spectrum management with a focus on spectrum allocation and access. We show the current regime's inherent rigidity and constrained possibilities for introducing new radiocommunication services and applications. The article proposes how governments and spectrum users could cooperate in taking spectrum management to a qualitatively new level, characterized by light touch regulation and flexible use. This could be achieved through the broader introduction of emerging practices such as Spectrum Usage Rights, liberalized spectrum trading, and full shared spectrum access. We conclude by presenting a vision for a 'perfect' spectrum management arrangement and future research directions.
In this paper, we explore the effectiveness of late penalties in reducing patient tardiness. Specifically, we evaluate two types of penalties: the last-place penalty (i.e., assigning late-arriving patients to the end of the queue) and the fixed-place penalty (i.e., moving late patients back by a fixed number of positions), under two conditions—absence of a time recommendation and presence of an explicit time recommendation. We conducted a randomized controlled trial involving 9,573 patient visits at a partner hospital in Asia. Our findings indicate that the fixed-place penalty has no significant impact on patient tardiness. In contrast, the last-place penalty significantly reduces patients’ late rate and encourages earlier arrival, particularly when no time recommendation is provided. When combined with a time recommendation, the last-place penalty continues to lower the late rate but does not further shift arrival times earlier. We also find that the last-place penalty is effective in increasing the patient’s compliance with the time recommendation. We further implemented the last-place penalty in the partner hospital across a broader dataset involving 61,829 visits, yielding consistent results.
We consider a dynamic resource provisioning problem for a supplier in which the availability of the provisioned resource is subject to random disruptions whose distribution is only indirectly observable through samples. To signal its commitment to service quality, the supplier adopts a service level agreement contract that specifies both the target service level and the associated penalty for violation over a finite contract period. The supplier needs to dynamically determine resource provisioning decisions with the objective of minimizing operational costs and penalties incurred due to service-level agreement violations. We construct a Wasserstein-based distributionally robust dynamic programming framework to model and solve the dynamic resource provisioning problem under a service-level agreement. In particular, we provide a convexification algorithm that enables us to solve the nonconvex robust dynamic programming problem in a backward manner. We further examine a special case where service shortages depend linearly on the provisioned resources, enabling the problem to be reformulated into a sequence of linear programs. This linear shortage model naturally connects to residual-based robust formulations, which facilitate us to accommodate nonlinear relationships between resource provisioning and service shortages. We propose several approximation algorithms to improve computational efficiency. To mitigate the possibly over-conservativeness, we explore radius adjustment strategies based on sample size, state, stage, and cumulative cost information, which yield consistent out-of-sample performance. We perform a case study of a cloud computing example to demonstrate the effectiveness of the proposed solution approach and elicit managerial insights. The results suggest that suppliers should provide fewer backup servers when cumulative downtime is low or when approaching the end of the planning horizon. The dynamic resource provisioning policy significantly reduces the total cost compared to the best static policy. Furthermore, applying appropriate radius adjustments can further enhance the out-of-sample performance.
Clarissa Maria Rodrigues de Oliveira, Paula Cristina de Amorim Andrade, Maria Socorro Ferreira dos Santos
O intenso aumento da geração de resíduos sólido associado as práticas inadequadas de descartes corroboram com a necessidade do desenvolvimento de alternativas para o reaproveitamento de materiais de maneira a mitigar, sobretudo, os danos ao meio ambiente. Dentre esses materiais, destaca-se o óleo vegetal residual (OVR), o qual possui um elevado potencial poluidor e é amplamente utilizado em estabelecimentos comerciais e usuários domésticos. Nesse sentido, o presente estudo apresenta a avaliação da cadeia reversa do OVR destinado à fabricação de biodiesel mediante a realização de uma revisão sistemática na literatura. Dessa maneira, foi possível investigar e levantar informações acerca dos fatores relativos à articulação e instituição dessa cadeia, bem como contribuir para mitigar as lacunas nas discussões científicas presente na literatura sobre a temática estudada.
Production management. Operations management, Production capacity. Manufacturing capacity
Pedro Luis Solorzano, Carlos Emilio Giosa, Carol Stephanny Rojas
et al.
This paper presents the application of injection in cycles, assisted by the first smart selective completion system with remote valve operation. The completion system was installed in the Chichimene Field unit T2 (Fm. San Fernando). The results were compared with conventional water injection, technology that utilizes a simple selective completion system, in the aspects of recovery factor, water consumption, operation style, and others.
The target unit has been subjected to selective water injection since 2016, this process consisted of injection of a controlled amount of water within 3-4 zones in equilibrium with the extractive system on producers wells to find a better crude oil movement and a commercial recovery factor. Despite this, the surveillance evidence suggests that some zones consume too much water due its high permeability (heterogeneity) and this impacts on high water cut on producers due to the (adverse mobility ratio), but also suggested that there is a better way to improve even more the recovery factor. This enhancement was enabled with a smart selective completion system by promoting a scheduled temporal closure of zones while production continues, achieving pressure-production restoration effects on the closed zones, allowing an efficiency increase of the vertical and areal sweep.
To determine if this could work, a smart completion system was installed in one well in August 2019. During more than one year, the operation and interpretation of the DAS measuring tool (Continuous sound recording) was understood, from which injected water per zone is daily known, with this, the optimization of the injection rate per zone allowed enhancement of the production pattern behaviour. In February 2021, the injection cycles started on the well by zone, and to the current date, more than 25 monthly cycles have been done, increasing the recovery factor, with a considerable reduction in water and energy consumption, maintaining a continuous measurement of zone injection, this level of information has never been reached in the industry.
The case study establishes a new frontier in the field of selective injection and in cycles as well, allowing an evolution step in water injection technology. This pilot combined in one design: selective injection, intelligent remote operate completions, continuous layer injection measuring by DAS, extra heavy oil WF, management of high permeability variations and it is aligned with the latest regulations in decarbonization (water and energy saving). From a reservoir point of view, it has never been there, a surveillance in injectors with this level of quality to enhance performance, and on the surface, this technology has allowed an important reduction in the use of wirelines for well operations and improve some well cleaning interventions with coiled tubing.
Energy industries. Energy policy. Fuel trade, Chemical engineering
We discuss averaging for dispersion-managed nonlinear Schrödinger equations in the fast dispersion management regime, with an application to the problem of constructing soliton-like solutions to dispersion-managed nonlinear Schrödinger equations.
This document explores the advantages of integrating open source software and practices in managing a scientific lab, emphasizing reproducibility and the avoidance of pitfalls. It details practical applications from website management using GitHub Pages to organizing datasets in compliance with BIDS standards, highlights the importance of continuous testing for data integrity, IT management through Ansible for efficient system configuration, open source software development. The broader goal is to promote transparent, reproducible science by adopting open source tools. This approach not only saves time but exposes students to best practices, enhancing the transparency and reproducibility of scientific research.
Mohamed Younis, Sookyoung Lee, Wassila Lalouani
et al.
Traffic management and on-road safety have been a concern for the transportation authorities and the engineering communities for many years. Most of the implemented technologies for intelligent highways focus on safety measures and increased driver awareness, and expect a centralized management for the vehicular traffic flow. Leveraging recent advances in wireless communication, researchers have proposed solutions based on vehicle-to-vehicle (V2V) and vehicle-to-Infrastructure (V2I) communication in order to detect traffic jams and better disseminate data from on-road and on-vehicle sensors. Moreover, the development of connected autonomous vehicles (CAV) have motivated a paradigm shift in how traffic will be managed. Overall, these major technological advances have motivated the notion of dynamic traffic management (DTM), where smart road reconfiguration capabilities, e.g., dynamic lane reversal, adaptive traffic light timing, etc. will be exploited in real-time to improve traffic flow and adapt to unexpected incidents. This chapter discusses what the challenges in realizing DTM are and covers how CAV has revolutionized traffic management. Moreover, we highlight the issues for handling human-driven vehicles while roads are transitioning to CAV only traffic. Particularly, we articulate a new vision for inter-vehicle communication and assessment of road conditions, and promote a novel system for traffic management. Vehicle to on-road sensors as well as inter-vehicle connectivity will be enabled through the use of handheld devices such as smartphones. This not only enables real-time data sharing but also expedites the adoption of DTM without awaiting the dominant presence of autonomous vehicle on the road. ...
Gabriel G. Zimmermann, Samir P. Jasper, Mariane C. da Costa
et al.
ABSTRACT Automatic production management (APM) is a tool that assists in the operations of agricultural tractors, increasing yield and energy efficiency. The objective of the experiment was to compare the energy and operational performance of a 373-kW articulated tractor equipped with APM and manual mode of engine transmission and rotation, across different real-world speeds. The experiment was conducted in a randomized block design with five replicates, using a split-plot arrangement with two system modes (manual and automatic) in the plots and four real-world speeds (4, 6, 8, and 10 km h-1) in the subplots, totaling 40 experimental units. The evaluated variables were: wheel slippage; engine rotation; hourly and specific fuel consumptions; drawbar force, power, and yield; operating speed; and engine thermal efficiency. The variance of the data was analyzed using Tukey’s test for the first factor, and regression analysis for the second factor and interactions. The automatic mode showed lower engine rotation and wheel slippage without compromising the other variables. The use of this mode showed energy advantages at 4 and 6 km h-1 by resulting in less fuel consumption per hour. In addition, the manual mode presented higher thermal efficiency at lower speeds than the automatic mode, which showed a linear increase.
Ehsan Khodarezaie, Korous Khoshbakht, Hadi Veisi
et al.
Introduction: Energy use in agriculture has grown faster than other sectors of the global economy. In developing countries, most agricultural systems consume significant amounts of energy to increase production and food security. Energy consumption leads to the emission of greenhouse gases and environmental pollutions in the agriculture sector. Besides, the use of fossil fuels in the production process and transfer of inputs emits greenhouse gases, which in turn cause global warming and climate change. Analyzing and good understanding of energy flow and Greenhouse Gas (GHG) emissions in agricultural production systems can help to optimize crop management practices thereby reducing environmental problems.Qazvin Plain is one of the most important agricultural plains in Iran, which along with the use of groundwater, has the largest irrigation canal network in the country. Differences in agricultural water supply sources can lead to differences in energy consumption and GHG emissions as electricity and other inputs may be affected. Alfalfa and maize silage are major forage crops in Qazvin Plain. Alfalfa and maize silage need a relatively high irrigation water requirement. This paper evaluates the energy flow and Global Warming Potential (GWP) of alfalfa and maize silage farms with two different water supply sources (well and canal) in Qazvin Plain.Material and methods: The data were collected through face-to-face interviews with farmers in the years 2018-2019. Energy indices were estimated based on the analysis of farm inputs and outputs. GWP was calculated using the Life Cycle Assessment (LCA) method and SimaPro 8.2 software. GHGs were calculated using the conversion coefficients presented by the IPCC GWP 100 method. Results and discussion: The output energy values of maize silage and alfalfa were calculated as 232726, 191812 MJ ha-1 for well water irrigation system and 234167 and 248060 MJ ha-1 for the canal water irrigation, respectively. Results showed higher net energy values for alfalfa (176218 MJ ha-1) and maize silage (169192 MJ ha-1) in canal water irrigation system compared to well water irrigation (63115 MJ ha-1 and 132956 MJ ha-1 for alfalfa and maize silage, respectively) mainly because of the relatively lower input energy. The results showed that the highest and lowest values of input energy were related to alfalfa production with well water irrigation (128697 MJ ha-1) and maize silage with canal water irrigation (64975 MJ ha-1), respectively. Also, the energy use efficiency of maize silage (3.6) and alfalfa (3.4) were higher in canal water irrigation systems compared to well water irrigation systems (2.3 for maize silage and 1.49 for alfalfa). In the well water irrigation systems, GWP was calculated to be 7466.9 kg CO2-eq ha−1 and 7995.7 kg CO2-eq ha−1 for maize silage and alfalfa, respectively. These values were 5533.3 kg CO2-eq ha−1 and 4947.6 kg CO2-eq ha−1 for maize silage and alfalfa in the canal water irrigation systems, respectively. Electricity and direct emission showed the highest share of total energy consumption and GHG emission.Conclusion: Generally, our results showed that energy consumption and GWP were lower in the canal irrigation systems than well irrigation systems mainly as a result of electricity used for water pumping in well irrigation operations. It can be inferred from the present study that for efficient use of resources and decreasing environmental problems in the study area, practices such as optimal management of irrigation water, conservation tillage, and optimal management of chemical fertilizers can help to achieve these goals.
Unsustainable human management has negative effects on cropland soil organic carbon (SOC), causing a decrease in soil health and the emission of greenhouse gas. Due to contiguous fields, large-scale mechanized operations are widely used in the Northeast China Plain, which greatly improves production efficiency while decreasing the soil quality, especially for SOC. Therefore, an up-to-date SOC map is needed to estimate soil health after long-term cultivation to inform better land management. Using Quantile Regression Forest, a total of 396 soil samples from 132 sampling sites at three soil depth intervals and 40 environmental covariates (e.g., Landsat 8 spectral indices, and WorldClim 2 and MODIS products) selected by the Boruta feature selection algorithm were used to map the spatial distribution of SOC in the cropland of the Northeast Plain at a 90 m spatial resolution. The results showed that SOC increased overall from the southern area to the northern area, with an average of 17.34 g kg<sup>−1</sup> in the plough layer (PL) and 13.92 g kg<sup>−1</sup> in the compacted layer (CL). At the vertical scale, SOC decreased, with depths getting deeper. The average decrease in SOC from PL to CL was 3.41 g kg<sup>−1</sup>. Climate (i.e., average temperature, daytime and nighttime land surface temperature, and mean temperature of driest quarter) was the dominant controlling factor, followed by position (i.e., oblique geographic coordinate at 105°), and organism (i.e., the average and variance of net primary productivity in the non-crop period). The average uncertainty was 1.04 in the PL and 1.07 in the CL. The high uncertainty appeared in the area with relatively scattered fields, high altitudes, and complex landforms. This study updated the 90 m resolution cropland SOC maps at spatial and vertical scales, which clarifies the influence of mechanized operations and provides a reference for soil conservation policy-making.
Data plays a fundamental role in training Large Language Models (LLMs). Efficient data management, particularly in formulating a well-suited training dataset, is significant for enhancing model performance and improving training efficiency during pretraining and supervised fine-tuning stages. Despite the considerable importance of data management, the underlying mechanism of current prominent practices are still unknown. Consequently, the exploration of data management has attracted more and more attention among the research community. This survey aims to provide a comprehensive overview of current research in data management within both the pretraining and supervised fine-tuning stages of LLMs, covering various aspects of data management strategy design. Looking into the future, we extrapolate existing challenges and outline promising directions for development in this field. Therefore, this survey serves as a guiding resource for practitioners aspiring to construct powerful LLMs through efficient data management practices. The collection of the latest papers is available at https://github.com/ZigeW/data_management_LLM.
The emergence of vehicle-to-everything (V2X) technology offers new insights into intersection management. This, however, has also presented new challenges, such as the need to understand and model the interactions of traffic participants, including their competition and cooperation behaviors. Game theory has been widely adopted to study rationally selfish or cooperative behaviors during interactions and has been applied to advanced intersection management. In this paper, we review the application of game theory to intersection management and sort out relevant studies under various levels of intelligence and connectivity. First, the problem of urban intersection management and its challenges are briefly introduced. The basic elements of game theory specifically for intersection applications are then summarized. Next, we present the game-theoretic models and solutions that have been applied to intersection management. Finally, the limitations and potential opportunities for subsequent studies within the game-theoretic application to intersection management are discussed.
Traffic management systems play a vital role in ensuring safe and efficient transportation on roads. However, the use of advanced technologies in traffic management systems has introduced new safety challenges. Therefore, it is important to ensure the safety of these systems to prevent accidents and minimize their impact on road users. In this survey, we provide a comprehensive review of the literature on safety in traffic management systems. Specifically, we discuss the different safety issues that arise in traffic management systems, the current state of research on safety in these systems, and the techniques and methods proposed to ensure the safety of these systems. We also identify the limitations of the existing research and suggest future research directions.
Increasing applications and markets for essential oils could bring new opportunities for cost-effective and sustainable management of unused forestry biomass; however, better knowledge of the production and application of such essential oils is necessary. The objective of this work is to contribute to greater knowledge of the essential oil production on a pilot scale from foliage biomass of wild shrubs and tree residues produced in some forestry enhancement operations and to study their antioxidant capacity (ORAC—oxygen radical absorbance capacity). Fresh biomass (twigs) of seven species (<i>E. globulus</i>, <i>E. nitens</i>, <i>P. pinaster</i>, <i>P. sylvestris</i>, <i>R. officinalis</i>, <i>C. ladanifer</i>, and <i>J. communis</i>) was manually collected in Spain in two different periods and was ground at 30 mm and distilled in a 30 L stainless steel still with saturated steam. The essential oil components were identified by GC–MS and quantified by GC–FID, and their antioxidant activity was determined with the ORAC method. Promising results on essential oil yield were obtained with <i>E. globulus</i>, <i>E. nitens</i>, <i>R. officinalis,</i> and <i>J. communis</i>. All essential oils studied exhibited antioxidant capacity by the ORAC assay, particularly that from <i>C. ladanifer.</i> Moreover, oxygenated sesquiterpenes contents, one of the minor components of oils, were significantly correlated with ORAC values.