Complex Asset Research Articles

Proposal of a Machine Learning Predictive Maintenance Solution Architecture

This article proposes an architecture model for a predictive maintenance solution that can be used to detect degradation of equipment in industrial units. This platform is compliant with Industry 4.0 standards and employs machine-learning algorithms alongside with data analytics methodologies to model the kinetics of equipment degradation. This serves the overarching aim of Industry 4.0 by enabling real-time, data-driven decision-making and complex asset management. To model the deterioration of the equipment, advanced data analysis techniques and machine learning were used, thus allowing for the early identification of imminent failures and reducing system downtime. The proposed solution was validated through numerous experiments and a comprehensive analysis of data. The results indicate not only enhanced operational reliability but also a reduction in environmental impact, thereby highlighting the value of intersecting Industry 4.0 and Sustainability paradigms in the field of industrial systems.

Enriching User-Visitor Experiences in Digital Museology: Combining Social and Virtual Interaction within a Metaverse Environment

This study investigates the potential of integrating multilayer animations and sophisticated shader technologies to enhance visitor social interactions within metaverse exhibition spaces. It is part of a broader initiative aimed at developing innovative digital museology strategies that foster social engagement through virtual reality (VR) experiences. The methodology adopted seeks to provide a more immersive and human-centric exploration of 3D digital environments by blending elements of physical spaces with the interactive dynamics common in video games. A virtual exhibition space themed around Mars was created as a testbed to facilitate social interactions among users, who navigate the environment via avatars. This digital space was developed using a specialized Unity template designed by the metaverse platform Spatial.io. Overcoming the programming constraints imposed by Spatial.io, which limits the use of external scripts for security and stability, posed a significant challenge. Nonetheless, by leveraging the ability to modify shader codes used for material creation and employing advanced animation techniques with layered effects, the authors of this work achieved dynamic material responses to lighting changes and initiated complex asset interactions beyond simple linear animations.

Portfolio optimization and risk management through Hierarchical Risk Parity and Logic Learning Machine: a case study applied to the Turkish stock market

This study explores an innovative approach to portfolio optimization, bridging traditional Modern Portfolio Theory (MPT) with advanced Machine Learning techniques. We start by recognizing the significance of Markowitz's model in MPT and quickly proceed to focus on the Hierarchical Risk Parity (HRP) method. HRP overcomes some of the limitations of Markowitz's model, particularly in managing complex asset correlations, by offering a more refined risk management strategy that ensures balanced risk distribution across the portfolio. The paper then introduces an innovative Machine Learning approach that employs the Logic Learning Machine (LLM) method to enhance the explainability of the Hierarchical Risk Parity strategy. Such integration is considered the core research part of the study, given that its application makes the output of the model more accessible and transparent. A case study based on the Turkish stock market has been provided as an example. The combination of traditional financial theories with modern Machine Learning tools marks a significant advancement in investment management and portfolio optimization, emphasizing the importance of clarity and ease of understanding in complex financial portfolio models.

Asset Digitalization Strategy using IoT Platforms and Asset Health Model

Despite technological advances in areas such as digital twins, we still face a significant challenge in asset digitization. This challenge lies in the precise and comprehensive determination of how assets adapt to the new management of complex systems that arise as a result of digitization.This gap in understanding and implementing asset digitization directly impacts the organization of information related to these assets. Digital assets are much more than mere pieces of machinery; they are crucial components in an interconnected digital ecosystem. Without a precise understanding of how these assets integrate and behave in this new digital environment, our decision-making and management capabilities are compromised. This can lead to operational inefficiencies, unnecessary costs, and ultimately a decrease in the quality of service and infrastructure reliability.This article focuses on addressing this challenge from an asset digitization-centered perspective. It provides a practical case of how we can advance in this space through the integration of various models. This includes the interpretation of how we define and categorize assets according to established standards, the assessment of the criticality of these assets, and the combination of Internet of Things (IoT)-based monitoring models with the Asset Health Index (AHI) model. This integration of models offers a holistic view of asset digitization, covering different levels of complex asset system management, and enables a better connection between real-time monitoring and a deeper, long-term understanding of asset condition and performance. The resulting synergy enhances the effectiveness of asset digitization strategies, especially in critical applications such as infrastructure management. A concrete example is presented in the article: the digitization of a bridge. This demonstrates how these practices can have a positive impact on asset management and maintenance in the context of digital transformation, contributing to improving the safety, efficiency, and reliability of our digital infrastructures

Modular asset management framework based on Petri-net formalisations and risk-aware maintenance

Probabilistic risk analysis (PRA) is fundamental in safety assessment. Current PRA tools face notable limitations for complex systems, such as the heavily reliance on historical failure data. Moreover, existing tools cannot replicate complex asset management strategies easily, leading to inefficiency when analysing a multitude of scenarios. This paper addresses these limitations by introducing a Coloured Hybrid Petri Net (CHPN) framework for the PRA of complex systems. The framework integrates a hybrid system to capture the complex nature of degradation. Moreover, unlike other tools, the framework is modular. This provides a flexible approach to scenario modelling and ensures a more accurate understanding of the system. This paper also investigates the effect of maintenance policy on system performance. The paper evaluates condition-based maintenance (CBM) to two-levels of risk-based maintenance (RBM). The paper also presents a risk-aware policy that integrates a system-level RBM and CBM to capture the dynamic between components condition, health and their influence on system performance. This ensures a holistic view of system's safety and reliability. By integrating advanced modelling techniques and maintenance policies, the CHPN framework provides a new dimension to PRA to enable more accurate risk assessments, informed asset management strategies, and enhanced safety assurance for critical infrastructure.

Readiness of Banks in Intellectual Property-Based Financing

The creative economy is an economic resource expected to become a pillar of Indonesia's economic growth. One of the ecosystems developed is intellectual property-based financing that arises from cultural heritage, science, and technology. From a banking perspective, intellectual property as an object of credit/financing guarantees cannot yet be accepted by banks because there are no technical guidelines for both valuation and the profession of intellectual property appraisal. In contrast, banks must adhere to prudential banking principles. This leads to the implementation of intellectual property-based financing practices still needs to be done by the bank. Furthermore, concerning the execution of intellectual property guarantees, the auction office still needs a legal basis for legal certainty in its implementation, particularly for forms of intellectual property guarantees in the form of contracts and collection rights arising from creative economic activities. Based on the research results, the following results are obtained: 1) there are no technical guidelines for Banks to accept the intellectual property as collateral objects; 2) intellectual property is a complex asset, so the Appraiser profession must be submitted to a public appraiser possessing expertise it is crucial in the domain of intellectual property to be officially registered with the Ministry of Tourism and Creative Economy. The auction office is willing to conduct intellectual property executions provided there exists a legal foundation associated with licensing. valuation, and legal protection of intellectual property to attract public interest in executing intellectual property auctions.

Complex Asset Markets

ABSTRACTInvestors' individual arbitrage models introduce idiosyncratic risk into complex asset strategies, driving up average returns and Sharpe ratios. However, despite the attractive risk‐return trade‐off, participation is limited. This is because effective Sharpe ratios in complex asset markets vary with investors' expertise. Investors with higher expertise, better models, and lower resulting idiosyncratic risk exposures realize higher Sharpe ratios. Their demand deters entry by less sophisticated investors. As predicted by our model, market dislocations are characterized by an increase in idiosyncratic risk, investor exit, and persistently elevated alphas and Sharpe ratios. The selection effect from higher expertise agents' more favorable Sharpe ratios is unique to our model and key to our main results.

Complex asset protection assessment of wastewater treatment plants 21-23

Aim: Wastewater is generated in the daily activities of population. In order to ensure the continuous treatment of wastewater, we maintain wastewater network system and operate wastewater treatment plants. The position and condition of wastewater treatment plants plays an important role in maintaining the safety of society, since untreated wastewater may cause significant health and environmental problems if it is released untreated into the environment. The aim of this article is to examine the complex protection situation of wastewater treatment plants.Methodology: This article is based on literature, on-site observation and analysis of images available on Google Maps.Findings: Without the complex protection of the wastewater treatment plants, the continuous provision of wastewater treatment cannot be maintained.Value: The article focuses on the situation of the fences as one element of the outdoor protection, which is part of the complex protection system of wastewater treatment plants.

Requirements for the application of the Digital Twin Paradigm to offshore wind turbine structures for uncertain fatigue analysis

The Digital Twin (DT) paradigm offers an extension of simulation model utility into the operational phase of an engineering asset. The goal is a simulation “twinned” with observed data that reflects the actual performance of the asset. However, exploring sources of uncertainty for both the physical asset and the simulation model are a challenge. For example, random metocean conditions, and uncertainty on model parameters and response behaviour of offshore wind turbine (OWT) structures, contribute to uncertainty for predicted life under fatigue. In-service assessment of OWT structures will benefit from twinning simulations and observed data, where a framework to treat this uncertainty is defined. The DT needs to capture state, condition, and behaviour to a level that allows quantification and propagation of uncertainty for reliability analysis. Using a DT, built for fatigue assessment of bolted ring-flanges on OWT support structures, this paper explores the challenges and opportunities in defining uncertainties of interest. We propagate these uncertainties through the DT in a coherent manner using a Gaussian Process (GP) surrogate modelling approach, efficiently emulating a computationally expensive numerical simulator. The GP is an attractive surrogate model method given this computational efficiency, in addition to providing an estimate of prediction uncertainty at unobserved points in the output space. The use of the GP surrogate model is included within a definition of the Surrogate DT, a framework including “fast” and “slow” twinning processes. We define six requirements to apply DTs to OWT structures which provide practical guidelines for modelling this complex asset under uncertainty.

Two-stage nested simulation of tail risk measurement: A likelihood ratio approach

Estimating tail risk measures is an important task in many financial and actuarial applications and often requires nested simulations, with the outer simulations representing real world scenarios, and the inner simulations typically used for risk neutral pricing or conditional risk measurement. The standard nested simulation method is highly flexible, able to incorporate complex asset models and exotic payoff structures, but it is also computationally highly burdensome, particularly in a multi-period setting, when inner simulation paths are required at each time step of each outer level scenario. In this study, we propose and analyze a two-stage simulation procedure that efficiently estimates the conditional tail expectation of cost of a dynamic hedging program for a Variable Annuity Guaranteed Minimum Withdrawal Benefit (GMWB), under a multi-period nested simulation. In each of the two stages, the method re-uses the same set of inner level simulation paths for each outer scenario at each time point, using a likelihood ratio method to re-weight the probabilities of each individual path for the different outer scenarios. Our numerical study shows that our two-stage, likelihood ratio weighted procedure can offer a very significant improvement in efficiency, of the order of 95% as measured by the RMSE, compared with a standard nested simulation with the same computational cost.

Strategic vendor due diligence in real estate transactions

PurposeIn light of the ever-growing complexity of real estate transactions, the need for vendors and buyers to better understand the role of vendor due diligence (VDD) is imperative. The purpose of this paper is twofold: firstly, it provides a detailed literature review regarding the role of VDD from both the vendor's and buyers' perspectives. Secondly, it analyses the value of VDD over and above the buyer's due diligence (BDD) in real estate transactions by proposing a theoretical model involving two-stage auctions.Design/methodology/approachReal-world examples from the industry are used as a motivation behind listing a set of practical questions. A theoretical construct is built to approximate the real estate environment under study. The construct is then studied from a game-theoretic perspective to obtain theoretical answers to the questions. These answers are then used to shape recommendations for the relevant industry and beyond.FindingsThe model suggested accommodates the feature that even though the VDD is broadly increasing informational efficiency in the market, its value is limited and sometimes harmful when the vendors have a sound prior understanding of their assets and the buyers' pre-transaction information about the asset is already high.Originality/valueThough the real estate market is considered here, the theoretical model we propose is applicable to any other complex asset transaction decision that supports endogenous information disclosure considerations using VDD.

Health Index Framework for Condition Monitoring and Health Prediction

In the field of Maintenance, Repair and Overhaul (MRO), stakeholders such as operators or service providers have to keep track of the health status of fleets of complex systems. The ability to estimate the future health status of these systems and their components becomes more pivotal when seeking to efficiently operate and maintain these systems. Today, these stakeholders have access to a lot of different data sources regarding fleet, operation schedule, ambient condition, system and component information. Many different prognostic methods from different disciplines are available and will further improve henceforward. In many cases these data sources and methods function as isolated methods in their own field. This fragmentation makes a holistic prognosis very challenging in many cases. Therefore, stakeholders need information integrating methods and tools to gain an exhaustive insight into the health status development of the complex assets they are operating or maintaining, in order to make well-founded decisions regarding operation or maintenance planning. In this paper, a Python-based health index framework is presented. It enables users to integrate operation schedules of different detail levels with enriching data sources such as ambient condition data. Furthermore, it provides methods to design complex asset systems which are linked via their construction, function or degradation mechanisms/ health indices via transfer relations. It allows to monitor the asset’s condition based on operation data and to simulate different operation scenarios regarding the health index development.

Married to Monet: Alternatives for complex asset division in the modern age of matrimony

AbstractDivorce proceedings can be arduous and emotional actions when it comes time to divide unique, complex marital property. The difficulty often lies in parties’ inability to compromise on value and distribution methods which leave the court with no recourse other than liquidation. However, liquidation rarely maintains equity between parties due to inconsistent valuation techniques and procedures. This Note proposes alternative considerations for drafting prenuptial agreements and arbitration clauses which will allow parties to address and anticipate potential conflicts regarding complex assets prior to the matter arising in court. Thus, parties may be better equipped with the legal tools necessary to dispel ambiguity in the division process and, ultimately, attempt to approach equity.

Have scale effects on cost margins of pension fund investment portfolios disappeared?

ABSTRACT Investment costs of pension funds are crucial for their returns. Consolidation in the pension fund market proceeds continuously, often with cost savings as the main argument. Unused economies of scale in pension fund investment costs, however, have declined over the years to values close to zero, except for very small pension funds. This paper investigates investment economies of scale in the Netherlands and pays special attention to the non-linear relationship between investment costs and sizes of pension funds. Furthermore, investment cost margins are disaggregated into three cost types and into six asset categories. Performance fees are in particular paid for complex asset categories held by large pension funds. They reduce the traditional-scale economy results for the entire portfolio. Cost savings by consolidation is still possible but is very limited.

A Graphics Processing Unit–Based, Industrial Grade Compositional Reservoir Simulator

SummaryRecently, graphics processing units (GPUs) have been demonstrated to provide a significant performance benefit for black-oil reservoir simulation, as well as flash calculations that serve an important role in compositional simulation. A comprehensive approach to compositional simulation based on GPUs has yet to emerge, and the question remains as to whether the benefits observed in black-oil simulation persist with a more complex fluid description. We present a positive answer to this question through the extension of a commercial GPU-based black-oil simulator to include a compositional description based on standard cubic equations of state (EOSs). We describe the motivations for the selected nonlinear formulation, including the choice of primary variables and iteration scheme, and support for both fully implicit methods (FIMs) and adaptive implicit methods (AIMs). We then present performance results on an example sector model and simplified synthetic case designed to allow a detailed examination of runtime and memory scaling with respect to the number of hydrocarbon components and model size, as well as the number of processors. We finally show results from two complex asset models (synthetic and real) and examine performance scaling with respect to GPU generation, demonstrating that performance correlates strongly with GPU memory bandwidth.NOTE: This paper is also published as part of the 2021 SPE Reservoir Simulation Conference Special Issue.

Integrating complex asset health modelling techniques with continuous time simulation modelling: A practical tool for maintenance and capital investments analysis

An Asset Health Index (AHI) is a tool that processes data about asset’s condition. That index is intended to explore if alterations can be generated in the health of the asset along its life cycle. These data can be obtained during the asset’s operation, but they can also come from other information sources such as geographical information systems, supplier’s reliability records, relevant external agent’s records, etc. The tool (AHI) provides an objective point of view to justify, for instance, the extension of an asset useful life, or to identify which assets from a fleet are candidates for an early replacement, or renovation, as a consequence of a premature aging.This paper describes how to build the AHI model as a continuous time simulation model, which is then implemented using Vensim simulation environment. This is done in order to: 1) improve model formulating robustness, 2) benefit of the outstanding software optimization features for AHI model parameters calibration; and 2) easy the provision of predictions for asset degradation, operational and capital investments risk under different possible exogenous scenarios and endogenous managerial options.The process of model building, and parameterization is applied to an industrial case of a regasification terminal. Several strategies involving major maintenance scheduling are compared in terms of total expenditure in assets over their life cycle.

Have scale effects on cost margins of pension fund investment portfolios disappeared?

Investment costs of pension funds are crucial for their returns. Consolidation in the pension fund market proceeds continuously, often with cost savings as the main argument. Unused economies of scale in the pension fund investment costs, however, have declined over the years to values close to zero, except for the very small pension funds. This paper investigates investment economies of scale in the Netherlands and pays special attention to the non-linear relationship between investment costs and sizes of pension funds. Furthermore, investment cost margins are disaggregated into three cost types and into six asset categories. Performance fees are in particular paid for complex asset categories held by large pension funds. They reduce the traditional scale economy results for the entire portfolio. Cost savings by consolidation are still possible but are very limited.

Geochemical, Geological and Groundwater Quality Characterization of a Complex Geological Framework: The Case Study of the Coreca Area (Calabria, South Italy)

Hydrogeochemical characterization and statistical methods were used to investigate the groundwater quality and the origin of constituents (anthropic or natural) in groundwater of the Coreca area (Calabria, South Italy). Coreca is characterized by an articulated geological setting where the three main geological complexes that distinguish the Northern Calabria Peloritan Orogen (CPO) outcrop. This complex asset affects the quality of groundwater mainly exploited for irrigation use. In particular, the presence of ultramafic rocks (e.g., serpentinite and metabasite) promotes the release of harmful elements such as Cr and Ni. In the studied area, two groups of waters were identified: Ca-HCO3 waters strongly controlled by the interaction with Ca-rich phases (e.g., limestone), and Mg-HCO3 waters related to the interaction of meteoric water with the metamorphic units. Statistical elaboration allowed to detect, in the Mg-HCO3 group, a good correlation between Cr and Ni (not observed in Ca waters) and a negative correlation between Cr, Ca and Al, in agreement with direct interaction with ultramafic rocks characterized by low concentrations of CaO and Al2O3. The concentration of major and trace elements has been compared with the Italian law limit values and the drinking water guidelines provided by the World Health Organization (WHO). Only three samples showed Mn and Ni concentration higher than the Italian law threshold. Furthermore, the assessment of groundwater quality was carried out using salinity and metal indexes. The groundwater quality assessment for irrigation allowed to classify the resource as “excellent to good” and “good to permissible”; nevertheless, a salinity problem and a magnesium hazard were found. Lastly, a metal index (MI) calculation revealed values <1 for almost all samples, pointing to good overall quality. Only a few samples showed a value extremely higher than 1, attributable to prolonged interaction with ultramafic rocks and/or localized anthropogenic pollution. From a general point of view, groundwater showed a generally good quality except for limited areas (and limited to the set of constituents analyzed) and a mild exceedance of the maximum salinity thresholds that must be monitored over time. Through a multidisciplinary approach, it was possible to ascertain the main anomalies attributable to the interaction with the hosting rocks and not (with few exceptions) to anthropic processes.

Planetary Analog Field Operations as a Learning Tool

Mars and Moon analog field missions are established tools to investigate the potential of instruments, workflows, materials and human factors for characterizing the astrobiological potential and geoscientific context of planetary surfaces. Historically, there is a broad spectrum on both the scientific focus and the performance parameters for analog missions. This applies specifically where mission performance parameters of a coordinated deployment of mission assets (e.g. rovers, human crewmembers or scientific instruments) is studied. We argue that scientific priorities and workflows shall be consolidated at an early planning stage of deep space missions while they can still impact the mission architecture design process. It is to be expected that a human-robotic mission to Mars or the Moon will include multiple field assets such as human explorers, robotic vehicles including aerial reconnaissance, mobility assets, habitat modules, stationary instruments and engineering elements for power, communication and in-situ resource utilization. These require more complex asset coordination compared to single-rover planetary rover missions. Therefore, we advocate an “Exploration Cascade” which helps to manage these multiple assets to optimize the scientific return of planetary surface missions, to search for extinct and-/or for extant traces of life and to characterize the geoscientific context of the sites of interest.

The pricing of European non-performing real estate loan portfolios: evidence on stock market evaluation of complex asset sales

Recent empirical evidence raises doubt about the ability of financial market participants to generate information efficient valuations for capital market instruments whose cash flows are related to residual claims and dependent on real estate income. We contribute to this literature with the examination of value implications of non-performing loan (NPL) divestitures in the banking industry during the period 2012–2018. In a first step, we provide descriptive statistics of the European NPL market, which lacks transparency and publicly available basic information on portfolio size and components. We then analyze wealth effects of distressed loan sale announcements for a uniquely large transaction database with 317 NPL deals, which is largely driven by real estate collateral. Our results show positive stock market reactions for vendor banks following NPL divestitures that tend to be driven by real estate collateral and a size effect.