Lifecycle Management Research Articles

Abstract. The use of Mobile Mapping Systems (MMS) has revolutionized urban road infrastructure management, offering unprecedented precision and efficiency in data acquisition and analysis. This study focuses on the application of the RIEGL VMY-2 MMS to assess pavement conditions in an urban environment. The RIEGL VMY-2 system, equipped with dual LiDAR sensors and spherical cameras, enabled the collection of high-density point clouds enriched with RGB and intensity values. These attributes were critical for the automated detection and characterization of pavement defects, such as cracks, potholes, and deformations. Advanced algorithms processed the MMS data to classify the point cloud, extract surface features, and attribute semantic information, such as defect severity and location. Additionally, the study integrates Building Information Modeling (BIM) methodologies to enhance urban infrastructure management. By incorporating the processed geospatial data into a BIM environment, municipalities can create comprehensive digital representations of road assets, facilitating improved planning, maintenance, and lifecycle management. The BIM model serves as a dynamic repository that links geometric and semantic data, offering a more structured and interactive approach to infrastructure monitoring. The results demonstrate the potential of MMS technologies in creating actionable geospatial datasets for urban infrastructure management. The geospatial database generated through this workflow includes detailed pavement condition maps and the Pavement Condition Index (PCI), enabling municipalities to prioritize maintenance interventions and optimize resource allocation. This study underscores the critical role of MMS technologies in modernizing urban infrastructure management, bridging the gap between raw geospatial data and actionable insights for sustainable urban planning.

ITA − AITES tunnelling information modelling − A BIM approach for a sustainable life cycle management

Critical assessment of the scope and applicability of circularity indicators for the sustainable life cycle management of wind turbine blades

Advancements in self-healing concrete: Material mechanisms, durability assessment, and implications for infrastructure lifecycle management

For several decades, generating salt forms of the active pharmaceutical ingredients (APIs) has remained the preferred approach for the pharmaceutical industries. The salt form of the APIs provides the advantage of improving the solubility and stability, enhancing the dissolution rate, permeability, manufacturing robustness, taste masking, and life cycle management. The pharmaceutical salts are the ionic compounds of a drug and its counterion. The counterions can be classified as anionic, cationic, organic anions, and aromatic sulfonates. The salt form of the drug does not exhibit pH-dependent solubility. Developing a salt form of the drug will improve the bioavailability of poorly soluble drugs belonging to the biopharmaceutical classification system (BCS) class II and IV. Around 50% of the solid oral dosage forms in the market utilize the salt form of the drug. Identifying the suitable salt form right in the early stages of development will eliminate the need for complex formulation platforms such as amorphous solid dispersions. Despite various advantages, a few limitations must be addressed, such as hygroscopicity, polymorphism, and counterions. The salt screening can be integrated with artificial intelligence (AI) and computational tools to accelerate development. The review article summarizes the principles, benefits, limitations, and future outlook for pharmaceutical salts.

As climate change, urbanization and sustainability pressure intensify, there is a paradigm shift in construction materials for resilient infrastructure. Historically effective materials like concrete and steel are not, however, perfect for the job given their high carbon emissions and susceptibility to environmental degradation as well as increasing lifecycle costs. This review paper examines the development and utilization of next-generation sustainable and ultra-high-performance materials (NG-S&UHPM) to satisfy current requirements for sustainability, durability, and functional performance. The review organizes this diverse range of materials systematically, including cementitious composites (e.g., UHPC, geopolymers, carbon-negative cements), bio-based materials (engineered timber, bamboo and mycelium), recycled and circular economy-based options as well as advanced smart materials options (self-healing concretes, shape memory alloys and phase change materials). Their mechanical performance, resilience to hazards and environmental soundness are the most asymmetrically compared features, whereas weaknesses have rather strong asymmetry indexes in term of costly investment, lack of standardization and low adoption ratio. Case studies and research also highlight huge potential in the use of hybrid and multifunctional solutions, when coupled with digital tools like AI, BIM, or digital twins to enhance design process predictive maintenance, and lifecycle management. Overall, NG-S&UHPM are disruptive technologies that can help mitigate emissions and improve climate adaptation while stimulating sustainable urban development, however its adoption will need to be supported by shared research efforts, policy stimuli and upscaling production.

Generic drug entry into the pharmaceutical market typically leads to a substantial decline in originator sales. Understanding the extent and trajectory of this erosion is essential for effective lifecycle management and strategic planning. This study quantified sales erosion after generic entry for originator drugs approved in the United States between 2010 and 2019 and developed a model to predict year-specific sales retention based on key product- and market-level characteristics. A total of 140 originator drugs were analyzed using FDA approval records and sales data from Evaluate Pharma. Five-year retention patterns were modeled using a three-parameter exponential decay function. Subgroup analyses were conducted by year of generic entry, therapeutic class, and product-specific features. A polynomial regression model using 700 product-year observations incorporated three binary market indicators and linear and quadratic time terms. Sales retention declined from 73.1% in the first year after generic entry to 31.7% by year five. The exponential decay model demonstrated a strong goodness-of-fit (root mean squared error [RMSE] = 0.006), capturing the initial steep decline and subsequent stabilization. Subgroup analyses showed faster erosion for blockbuster drugs and in markets with multiple first-generation generics. The regression model explained 96.4% of annual variation in retention (RMSE = 0.033), accounting for product and market heterogeneity. Sales decline after generic entry follows a predictable yet heterogeneous trajectory shaped by product and market factors. Exponential decay and polynomial regression models together offer a robust framework for forecasting sales retention and guiding strategic decisions in the pharmaceutical industry.

As carbon-based nanocomposite materials (CBNCMs) become increasingly integrated into wide range of technological sectors, including energy storage, electronics, biomedicine, and structural engineering, concerns about their environmental, health, and sustainability implications are gaining prominence. This study explores key aspects influencing the sustainable development of CBNCMs, including familiarity with existing policies and risk prevention measures, environmental and health risk assessments, life cycle emissions and exposure risks, end-of-life solutions, and the integration of circular economy principles. The role of standardization and harmonization is also examined as a crucial factor in ensuring consistent and safe practices across the CBNCMs lifecycle. Insights from expert opinions, including findings from a questionnaire survey conducted within the framework of the COST Action CA19118 (EsSENce), are used to support the analysis. The study highlights existing gaps and opportunities for improving risk governance, promoting safe-by-design approaches, and advancing sustainable lifecycle management of CBNCMs.

Large-scale lithium-ion energy storage systems (ESSs) are indispensable for renewable energy integration and grid support, yet ensuring long-term reliability under field conditions remains challenging. This study investigates degradation trends in a 50 MW-class ESS deployed on Jeju Island, South Korea, focusing on two indicators: direct current internal resistance (DCIR) and state-of-health (SOH). Annual round-trip (capacity) and hybrid pulse power characterization (HPPC) tests conducted from 2023 to 2025 quantified capacity fade and resistance growth. A polynomial-regression-based temperature compensation was applied—compensating DCIR to 23 °C and SOH to 30 °C—which reduced environmental scatter and clarified year-to-year degradation trends. Beyond mean shifts, intra-bank variability increased over time, indicating rising internal imbalance. A focused case study (Bank 03-01) revealed concurrent SOH decline and DCIR escalation localized near specific racks; spatial maps linked this hotspot to heating, ventilation, and air conditioning (HVAC)-driven airflow asymmetry and episodic fan operation. These findings underscore the importance of combining temperature compensation, variability-based diagnostics, and spatial visualization in field ESS monitoring. The proposed methodology provides practical insights for the early detection of abnormal degradation and supports lifecycle management of utility-scale ESSs under real-world conditions.

The integration of artificial intelligence technologies into network intelligence systems presents unprecedented opportunities for operational enhancement while simultaneously introducing significant ethical challenges that require comprehensive governance frameworks. Modern organizations face increasing pressure to balance technological innovation with responsible deployment practices as AI-driven network surveillance capabilities become increasingly sophisticated and autonomous. This paper examines critical issues in responsible AI implementation, including bias mitigation measures essential for ensuring equitable treatment across diverse network segments and user populations. Network data contains historical patterns that may perpetuate discriminatory decisions when processed by machine learning algorithms without adequate safeguards. Transparency mechanisms constitute fundamental requirements for establishing stakeholder trust and enabling effective human oversight of automated decision-making processes within complex network environments. Explainable AI methodologies become crucial for empowering network administrators to understand algorithmic rationales behind security alerts, configuration recommendations, and traffic prioritization decisions. Privacy protection represents another critical challenge, requiring technical, procedural, and governance controls that preserve individual privacy while supporting legitimate security objectives. Privacy-preserving technologies such as differential privacy, homomorphic encryption, and federated learning offer significant potential for enabling robust monitoring without exposing sensitive user information. Comprehensive governance structures are essential to address end-to-end lifecycle management from initial development to final system decommissioning, incorporating risk assessment protocols, stakeholder engagement mechanisms, and continuous monitoring systems that track ethical performance alongside technical metrics.

The predictive maintenance alert lifecycle is a critical topic in the aviation industry. Stakeholders, including operators, suppliers, and Original Equipment Manufacturers (OEMs), require effective frameworks to support the value proposition of predictive maintenance products and services. However, defining alert effectiveness is challenging due to the lack of industry standards for the end-to-end lifecycle of predictive maintenance alerts. Adding to the challenge, different stakeholders may want to optimize on different objectives. Often, alert performance is measured prematurely or not at all. To ensure high-quality alerts, all alerts should be managed through their entire lifecycle until obsolescence. This whitepaper outlines a clear lifecycle and best practices for predictive maintenance alert lifecycle management.

The transformation of the global automotive industry has entered a new stage characterized by digitalization, low carbonization, and sustainable innovation. Green supply chain management and circular economy have become key strategic pathways for enterprise to achieve green transformation and sustainable competitiveness. This paper explores the theoretical basis, integration mechanism, and practical path of combining the green supply chain with the circular economy in automotive manufacturers. Through literature analysis, system integration theory, and sustainability evaluation perspectives, the paper constructs a conceptual framework that links resource efficiency, waste minimization, and value circulation. The study finds that the deep integration of green supply chain management and circular economy can effectively reduce resource consumption, improve environmental performance, and enhance corporate innovation capability. The research concludes that the automotive industry must establish a systematic integration path through green design, green procurement, clean production, and product life-cycle management to achieve ecological, economic, and social benefits simultaneously.

In this study, a GMA based approach to predict proton exchange membrane fuel cell (PEMFC) stack voltage and remaining useful life (RUL) was proposed, and how different combinations of input and output sizes affect model performance was analyzed. The results show that the GMA model effectively captures the voltage degradation trend of the PEMFC, accurately reproducing the early rapid voltage drop and the later smooth degradation. Model performance is strongly influenced by the input and output configurations. Smaller input sizes lead to larger fluctuations in performance metrics (e.g., RMSE and score), whereas larger input sizes provide more informative features and improve predictive accuracy. In particular, with an input size of 300 and an output size of 40, the model achieves its best performance, yielding the lowest RMSE and a near optimal Score. Overall, the GMA model offers clear advantages for improving the accuracy and reliability of PEMFC prediction, and its predictive effectiveness and stability can be further enhanced through careful selection of input and output sizes. This study provides a practical reference for PEMFC RUL prediction and supports maintenance planning, performance evaluation and life cycle management of fuel cells.

Secure authentication in vehicular ad hoc networks (VANETs) remains a fundamental challenge due to their dynamic topology, susceptibility to attacks, and scalability constraints in multi-hop communication. Existing approaches based on elliptic curve cryptography (ECC), blockchain, and fog computing have achieved partial success but suffer from latency, resource overhead, and limited adaptability, leaving a gap for lightweight and hardware-rooted trust models. To address this, we propose a multi-hop mutual authentication protocol leveraging Physical Unclonable Functions (PUFs), which provide tamper-evident, device-specific responses for cryptographic key generation. Our design introduces a structured sequence of phases, including pre-deployment, registration, login, authentication, key establishment, and session maintenance, with optional multi-hop extension through relay vehicles. Unlike prior schemes, our protocol integrates fuzzy extractors for error tolerance, employs both inductive and game-based proofs for security guarantees, and maps BAN-logic reasoning to specific attack resistances, ensuring robustness against replay, impersonation, and man-in-the-middle attacks. The protocol achieves mutual trust between vehicles and RSUs while preserving anonymity via temporary identifiers and achieving forward secrecy through non-reused CRPs. Conceptual comparison with state-of-the-art PUF-based and non-PUF schemes highlights the potential for reduced latency, lower communication overhead, and improved scalability via cloud-assisted CRP lifecycle management, while pointing to the need for future empirical validation through simulation and prototyping. This work not only provides a secure and efficient solution for VANET authentication but also advances the field by offering the first integrated taxonomy-driven evaluation of PUF-enabled V2X protocols in multi-hop Wi-Fi environments.

With the rapid adoption of artificial intelligence (AI) in domains such as power, transportation, and finance, the number of machine learning and deep learning models has grown exponentially. However, challenges such as delayed retraining, inconsistent version management, insufficient drift monitoring, and limited data security still hinder efficient and reliable model operations. To address these issues, this paper proposes the Intelligent Model Lifecycle Management Algorithm (IMLMA). The algorithm employs a dual-trigger mechanism based on both data volume thresholds and time intervals to automate retraining, and applies Bayesian optimization for adaptive hyperparameter tuning to improve performance. A multi-metric replacement strategy, incorporating MSE, MAE, and R2, ensures that new models replace existing ones only when performance improvements are guaranteed. A versioning and traceability database supports comparison and visualization, while real-time monitoring with stability analysis enables early warnings of latency and drift. Finally, hash-based integrity checks secure both model files and datasets. Experimental validation in a power metering operation scenario demonstrates that IMLMA reduces model update delays, enhances predictive accuracy and stability, and maintains low latency under high concurrency. This work provides a practical, reusable, and scalable solution for intelligent model lifecycle management, with broad applicability to complex systems such as smart grids.

Crisis management in smart cities demands coherent, interoperable, and reusable semantic models to represent complex systems, their risks, crisis situations, interdependencies, and decision-making processes across all lifecycle phases, i.e., prevention, preparedness, response, and recovery. This paper presents the TERMINUS (TERritorial Management and INfrastructures ontology for institutional and industrial USage) ontology, a BFO (Basic Formal Ontology)-aligned conceptual model based on semantic patterns for the crisis management lifecycle operationalized as both ontology design patterns (ODPs) to structure the ontology and ontology query patterns (OQPs) to use it in specific contexts. ODPs capture reusable conceptual structures for modeling domains, while OQPs provide SPARQL (SPARQL Protocol and RDF Query Language)-based templates to retrieve and reason over knowledge graph instances derived from these model chunks. The approach ensures semantic continuity from conceptual modeling to operational applications, enabling automated scenario generation, cascading risk analysis, and participatory decision-making. We position the patterns within the crisis management lifecycle and demonstrate their use through real-world case studies, covering semantic spatio-temporal risk assessment, interdependent infrastructure risk cascades, creative emergency scenario design, and recovery planning. Evaluation results highlight the ontology’s ability to support domain experts in generating plausible context-specific models, fostering collaborative validation, and enhancing preparedness and resilience. TERMINUS thus provides a versatile and interoperable semantic infrastructure for integrating ontologies and knowledge graphs into urban crisis management workflows.

The 2020s represent both the digital decade and the pivotal period in the fulfillment of long-standing commitments made by public, private, and institutional actors in favor of sustainable development. In the manufacturing context, Product Lifecycle Management (PLM) systems are used during the design phase to reduce product environmental footprint. However, only a few studies have thoroughly identified the environmental impacts associated with these technological solutions. This study proposes a sensitivity analysis of five environmental impact categories associated with two PLM system architectures and three mitigation scenarios. To this end, we use an engineering school as a representative PLM system case study, relying on the Life Cycle Assessment (LCA) methodology and leveraging specialized tools that enable the execution and comparative analysis of multiple LCA scenarios. Our results consistently identify the manufacturing and usage phases of PLM system users’ equipment as the main contributors of the PLM system to climate change, acidification, and the depletion of abiotic mineral and metal resources. End-of-life contributes significantly to particulate matter impact, and usage phase, in a nuclear mix country, to ionizing radiation. The policy of purchasing and reselling reconditioned users’ equipment is clearly identified as a key lever for reducing the magnitude of these five environmental impacts.

Against the backdrop of global energy shortages and increasing ecological pressures, green buildings have become an important direction for the development of the construction industry. As a core link in construction activities, engineering management plays a crucial supporting role in promoting sustainable development. Based on the core concepts and evaluation system of green buildings, this paper analyzes the connotations and development trends of engineering management in terms of resource utilization, environmental optimization, and informatization application, and further explores the logic of integration between the two in concepts, methods, and objectives. On this basis, the study examines the key elements of sustainable development in engineering management, including resource utilization efficiency and life-cycle management, environmentally friendly construction technologies and material selection, as well as intelligent monitoring and informatized collaborative management. Finally, the paper proposes optimization strategies for engineering management oriented toward sustainable development, emphasizing the reconstruction of organizational models through systems thinking, dynamic control of quality and risk throughout the entire process, and innovation-driven continuous improvement under multidimensional collaboration, in order to promote the efficient and sustainable operation of green building projects in complex environments. The research results provide theoretical support and methodological reference for the deep integration of green buildings and engineering management.

This paper explores whole-process engineering consulting, including its application models in public buildings and elderly-friendly projects, such as service integration and whole lifecycle management. It also addresses the construction of multi-dimensional collaborative theoretical models, public space streamline organization, and other aspects, emphasizing the importance of multi-dimensional collaboration. Additionally, it highlights the role of talent cultivation and digital transformation in enhancing project efficiency.

An industry perspective on clinical development and regulatory strategies for subcutaneously administered high-dose biologics.