Technology Life Cycle Research Articles

Microplastics as an Emerging Potential Threat: Toxicity, Life Cycle Assessment, and Management

The pervasiveness of microplastics (MPs) in terrestrial and aquatic ecosystems has become a significant environmental concern in recent years. Because of their slow rate of disposal, MPs are ubiquitous in the environment. As a consequence of indiscriminate use, landfill deposits, and inadequate recycling methods, MP production and environmental accumulation are expanding at an alarming rate, resulting in a range of economic, social, and environmental repercussions. Aquatic organisms, including fish and various crustaceans, consume MPs, which are ultimately consumed by humans at the tertiary level of the food chain. Blocking the digestive tracts, disrupting digestive behavior, and ultimately reducing the reproductive growth of entire living organisms are all consequences of this phenomenon. In order to assess the potential environmental impacts and the resources required for the life of a plastic product, the importance of life cycle assessment (LCA) and circularity is underscored. MPs-related ecosystem degradation has not yet been adequately incorporated into LCA, a tool for evaluating the environmental performance of product and technology life cycles. It is a technique that is designed to quantify the environmental effects of a product from its inception to its demise, and it is frequently employed in the context of plastics. The control of MPs is necessary due to the growing concern that MPs pose as a newly emergent potential threat. This is due to the consequences of their use. This paper provides a critical analysis of the formation, distribution, and methods used for detecting MPs. The effects of MPs on ecosystems and human health are also discussed, which posed a great challenge to conduct an LCA related to MPs. The socio-economic impacts of MPs and their management are also discussed. This paper paves the way for understanding the ecotoxicological impacts of the emerging MP threat and their associated issues to LCA and limits the environmental impact of plastic.

Methodological Guidelines and Publications of Benefitrisk Assessment for Health Technology Assessment: A Scoping Review

Introduction Benefit-risk assessment (BRA) is an important component in decision-making throughout the lifecycle of a health technology. The BRA of comparative technologies was usually carried out informally, without the use of a systematized process, which can lead to inappropriate decisions. In the last two decades, efforts have been observed to use more structured, objective and transparent approaches, aiming at better communication and decision-making by stakeholders. In this sense, some frameworks have been proposed to guide BRA, and are currently available. Therefore, we conducted a scoping review to: (1) map the available methodological guidelines and documents for conducting and reporting BRA in the different phases of the lifecycle of health technologies; and (2) identify methodological guidelines on methods for BRA, which could be used as the basis for the development of a BRA guideline for the Brazilian Health Technology Assessment (HTA) context. Methods Our searches were conducted between Oct 2022 to Mar 2023 in three main sources: (1) electronic databases (EMBASE and MEDLINE); (2) grey literature (48 HTA and regulatory organizations); and (3) manual search and contacting experts in the field. We included methodological guidelines or publications presenting methods for conducting or reporting BRA of any types of health technologies developed in any context of the health technology’s lifecycle. Selection process and data charting were conducted by pairs of independent reviewers. We provided a narrative synthesis of the findings. Results From the 83 eligible documents, only six stated the HTA context, 30 were produced in the regulatory setting and 35 documents involved guidance for BRA throughout the health technology’s lifecycle. Among the 129 methodological approaches for BRA identified, the most frequently cited descriptive frameworks were the Problem, Objectives, Alternatives, Consequences, Trade-offs, Uncertainty, Risk, and Linked decisions (PrOACT-URL) and the Benefit Risk Action Team (BRAT), reported in 29 (34.9%) of the documents. Multicriteria Decision Analysis was the most common quantitative framework, cited in 52 (62.7%) of the documents. Other frequent quantitative frameworks cited were Markov Decision Process (n=18; 21.7%) and Decision Tree (n=16; 19.3%). We also identified the most cited metric indices which could be used to measure benefits and risks: numbers needed to harm (n=38; 45.8%) and to treat (n=37; 44.6%) as threshold indices, quality-adjusted life-year (n=37; 44.6%) as health index, and incremental net health benefit (n=32; 38.6%) as trade-off index. The documents also report probabilistic simulation method (n=32; 38.6%) and indirect treatment comparison (n=18; 21.7%) as the most common estimation techniques, while discrete choice experiment and conjoint analysis, mentioned in 26 (31.3%) documents, were the most mentioned utility survey techniques to elicit preferences for quantitative BRA. Discussion and conclusion The efforts for using formal structure approaches for BRA in HTA and at population level have been more modest than in the regulatory setting. We will test and explore the potential of the two most cited descriptive and quantitative frameworks in case studies in the context of HTA to evaluate their performance. The case studies using the frameworks identified in this review will support the elaboration of the Brazilian guideline of BRA for HTA.

Interest-bearing capital and monetary policy: a heterogeneous agent Marxian model

PurposeIn terms of understanding the new issues emerging in the practice of monetary policies and how to evaluate the latest theories of monetary policy, this paper proposes referring to Das Kapital and developing a monetary policy theory grounded in Marxist political economy.Design/methodology/approachBased on the discussion of interest-bearing capital in Das Kapital and using a heterogeneous agent model, this paper tries to explain the determining mechanism of interest rate, leverage ratio, and asset price.FindingsThe research finds that if there are differences in the techniques possessed by capital, the resulting disparities in production efficiency will lead to differences in profit rates and further influence the functional choices of capital in the movement of social total capital. Thus, with the formation of lending relationships, interest rates, leverage ratios, and asset prices will be endogenously determined simultaneously. Moreover, as the degree of technological diffusion influences the industrial capitalists’ willingness to take loans as well as the level of profit rates, there may be counter-cyclical changes in the returns on productive investment and financial investment at different stages of the technology life cycle, contributing to diverting funds out of the real economy. Besides, this paper discusses the challenges, tools, and goals of monetary policy within the credit money system.Originality/valueClarify the intrinsic mechanism of the functional differentiation of capital determined by heterogeneous technologies and exogenous capital-labor relation and analyze the impact of capital differentiation on the economy.

The Human Side of Generative AI: Creating a Path to Productivity

The rapid rise of generative artificial intelligence (AI) presents both opportunities and challenges for organizations. While generative AI offers transformative potential to boost productivity through new forms of data creation, its implementation also brings significant human impacts that require thoughtful leadership. This article explores the human side of generative AI from both theoretical and practical perspectives, focusing on change management, people-centered design, and responsible implementation. Key areas include managing cognitive, emotional, and behavioral responses to AI-driven change, as well as fostering cultures of transparency, learning, and accountability. The article highlights best practices such as early stakeholder involvement, iterative evaluation, and robust governance to ensure generative AI aligns with human needs and ethical standards. Case studies, including Anthropic’s AI safety efforts, illustrate how leaders can promote responsible innovation by integrating human-centered practices throughout the technology lifecycle. By proactively addressing the human dimensions of AI adoption, organizations can achieve sustainable productivity gains while maintaining trust, well-being, and ethical integrity.

Mapping the technological landscape of green smart buildings: A patent analytics of key topics, leading companies, and technology gaps

In this study, we investigate the technological landscape of green smart buildings, using patent data from the European Patent Office (EPO) from 2007 onwards. Latent Dirichlet Allocation identified five key technology topics, with the HVAC system (Topic 4) emerging as particularly promising. Technology life cycle analysis revealed a growth phase for leading companies' patents after the Kyoto Protocol's second commitment period, with a predicted shift towards maturity after the Paris Agreement. Self-Organizing Maps and Social Network Analysis further pinpointed eight vacant technology nodes and 39 high-centrality patents within them, predominantly held by Japanese companies. This research provides valuable insights into the current state and future trajectory of green smart building technologies, facilitating informed decision-making for industry stakeholders and contributing to a more sustainable built environment. The novelty lies in the combined application of topic modeling, technology life cycle analysis, and network analysis to comprehensively explore the green smart building patent landscape, offering a unique perspective on its evolution and potential future directions.

Combinatorial innovation and lifecycle analysis of low-carbon energy technologies in China

Measuring the lifecycle of low-carbon energy technologies is critical to better understanding the innovation pattern. However, previous studies on lifecycle either focus on technical details or just provide a general overview, due to the lack of connection with innovation theories. This article attempts to fill this gap by analyzing the lifecycle from a combinatorial innovation perspective, based on patent data of ten low-carbon energy technologies in China from 1999 to 2018. The problem of estimating lifecycle stages can be transformed into analyzing the rise of fall of knowledge combinations. By building the IPC co-occurrence matrix, this paper demonstrates the lifecycle evolution of technologies graphically constructs an efficient quantitative index to divide lifecycle stages. The mathematical measurement can effectively reflect the evolutionary pattern of technologies. Additionally, this article relates the macro evolution of lifecycle to the micro dynamic mechanism of technology paradigms. The sign of technology maturity is that new inventions tend to follow the patterns established by prior ones. Following this logic, this paper identifies different trends of paradigms in each technology field and analyze their transition. Furthermore, catching-up literature shows that drastic transformation of technology paradigms may open “windows of opportunity” for laggard regions. From the results of this paper, it is clear to see that latecomers can catch up with pioneers especially when there is a radical change in paradigms. Therefore, it is important for policy makers to capture such opportunities during the technology lifecycle and coordinate regional innovation resources.

Promoting sustainability goals: innovation trajectories of Fintech through patent analysis

AbstractPatents are important sources of technical knowledge because they offer a wealth of information on innovations and technological developments. This paper focuses on Fintech-related patent certificates in order to examine the technical trends in this particular sector and discuss the factors influencing technological advancement. The data were retrieved through the World Intellectual Property Organization database, considering selected keywords. Through the network analysis, we identify five relevant technological clusters that allow us to detect the technological substrate underlying the corresponding cluster. The Bass diffusion model permits the observation of the technological trajectories of each cluster, outlining the diffusion patterns. Furthermore, particular attention is given to the developing technologies defined as "green", and we describe how these trends have changed over time and predict their future technological trajectory. Our findings provide an in-depth analysis of the Fintech patent landscape, highlighting the connections between the different technologies. It also allows us to assess the technological leadership of companies and the technological life cycle that describes the diffusion patterns.

Small and medium enterprises meet the metaverse. A study of the wine industry

This study aims to enrich the knowledge of the technology adoption by small and medium enterprises, by focusing on the metaverse phenomenon and the implementation of metaverse projects by small and medium-sized enterprises through the lens of the technology life cycle theory. It adopts a qualitative approach based on multiple-case analysis to examine and categorize 28 metaverse experiences created by wine businesses. The work provides a framework for the metaverse experimentations by firms which is structured around three different stages of metaverse, that are (1) the isolated metaverse, (2) the integrated metaverse, and (3) the augmented metaverse, corresponding to different degrees of integration of the technology in the user experience according to the level of interaction and immersiveness that is generated. This study also identifies the boundaries of metaverse settings by determining, from an experiential perspective, which experiences cannot be defined as belonging to the metaverse sphere. The analysis showcases the patterns toward the metaverse technology adoption by businesses and provides an interpretation of the different types of metaverse experiences that can be integrated into the business strategies of small and medium-sized enterprises. It also highlights the obstacles that preside over the wide adoption of the metaverse technology by firms.

Investigating the Role of Artificial Intelligence in Developing Eco-Friendly Assistive Technologies for People with Disabilities

Current research indicates that artificial intelligence has immense scope to further the cause of assistive technology in improving the quality of life for persons with disabilities by rendering customized support to mobility aids, visual aids, hearing aids, and smart homes. AI-driven devices make communication, adaptive learning, and independence easier for all, with innovations in prosthetics, wheel chairs, and satellite navigation apps such as Google Maps and Moovit. Voice- activated AI-powered smart devices, like Amazon Echo and Google Home, facilitate independent living with voice activation of light and appliances. AI is also in OrCam to further autonomous living. The study examines that artificial intelligence has immense scope to further the cause of assistive technology in enhancing the quality of life for people with disabilities by providing customized support to mobility aids, visual aids, hearing aids, and smart homes. AI makes environmental sustainability a part of the life cycle of assistive technologies—from design to the use of materials, energy efficiency, and e-waste recycling. Efficient waste management is made possible through AI-based sorting systems and smart recycling bins. Blockchain brings transparency into these processes. It is in social integration and economic efficiency that the following devices and services related to sustainable assistive technology can create environmental sustainability, empowering persons with disabilities, reducing healthcare expenditure, and infusing green practices toward an all-inclusive and sustainable world. UN News, Assistive Ware.

A Self-Adaptive Neighborhood Search Differential Evolution Algorithm for Planning Sustainable Sequential Cyber–Physical Production Systems

Although Cyber–Physical Systems (CPSs) provide a flexible architecture for enterprises to deal with changing demand, an effective method to organize and allocate resources while considering sustainability factors is required to meet customers’ order requirements and mitigate negative impacts on the environment. The planning of processes to achieve sustainable CPSs becomes an important issue to meet demand timely in a dynamic environment. The problem with planning processes in sustainable CPSs is the determination of the configuration of workflows/resources to compose processes with desirable properties, taking into account time and energy consumption factors. The planning problem in sustainable CPSs can be formulated as an integer programming problem with constraints, and this poses a challenge due to computational complexity. Furthermore, the ever-shrinking life cycle of technologies leads to frequent changes in processes and makes the planning of processes a challenging task. To plan processes in a changing environment, an effective planning method must be developed to automate the planning task. To tackle computational complexity, evolutionary computation approaches such as bio-inspired computing and metaheuristics have been adopted extensively in solving complex optimization problems. This paper aims to propose a solution methodology and an effective evolutionary algorithm with a local search mechanism to support the planning of processes in sustainable CPSs based on an auction mechanism. To achieve this goal, we focus on developing a self-adaptive neighborhood search-based Differential Evolution method. An effective planning method should be robust in terms of performance with respect to algorithmic parameters. We assess the performance and robustness of this approach by performing experiments for several cases. By comparing the results of these experiments, it shows that the proposed method outperforms several other algorithms in the literature. To illustrate the robustness of the proposed self-adaptive algorithm, experiments with different settings of algorithmic parameters were conducted. The results show that the proposed self-adaptive algorithm is robust with respect to algorithmic parameters.

Exploring Electric Vehicle Patent Trends through Technology Life Cycle and Social Network Analysis

In response to environmental and energy challenges, electric vehicles (EVs) have re-emerged as a viable alternative to internal combustion engines. However, existing research lacks a comprehensive analysis of the technology life cycle of EVs in both global and South Korean contexts and offers limited strategic guidance. This study introduces a novel approach to address these gaps by integrating the S-curve model with social network analysis (SNA), time series analysis, and core applicant layouts. The study specifically utilizes the logistic curve to model technology growth. It applies SNA methods, including International Patent Classification (IPC) co-occurrence analysis and the betweenness centrality metric, to identify the stages of technological development and sustainable research directions for EVs. By analyzing patent data from 2004 to 2023, the study reveals that EV technologies have reached the saturation phase globally and in South Korea, with South Korea maintaining a two-year technological advantage. The research identifies sustainable research directions, including fast charging technology and charging infrastructure, battery monitoring and management, and artificial intelligence (AI) applications. Additionally, the study also determined the sustainability of these research directions by examining the sustainability challenges faced by EVs. These insights offer a clear view of EV technology trends and future directions, guiding stakeholders.

Global Mobile Inventors

The number of Global Mobile Inventors (GMIs), inventors moving across borders during their career, has increased more than tenfold over the past two decades, and the corridors of mobility have shifted towards a growing presence of emerging markets. We document that GMIs that have patented in a given technology before moving are 70% more likely to be among the pioneering inventors in that technology once they arrive at destination, which we interpret as evidence of knowledge diffusion across borders. Returnees, which are typically inventors from emerging markets that go back after having spent some time in the US and other advanced economies, are twice as likely to file pioneering patents once returned than migrants when arriving abroad. Finally, we find that the more central the GMIs in the network of inventors during the early stages of the technology life-cycle at destination, the faster the technology-specific knowledge is absorbed by local inventors.

Usability Checklists for Health Technology: Case Study and Experts' Opinions.

Application of usability evaluations throughout the health technology lifecycle is necessary to improve the efficiency, safety, and effectiveness of health service delivery. Unfortunately, technology vendors and healthcare organizations may not have funding, time or expertise to conduct usability studies. In this paper, we describe how usability checklists can potentially fill this gap. First, we introduce a case study using a checklist to identify usability issues with a primary care dashboard. Then we provide an expert summary of the strengths and limitations of usability checklists. Findings suggest that checklists are efficient to identify important usability issues. They can be used effectively by project team members - including clinicians - without formal usability training. However, checklists should complement rather than replace usability evaluations with representative users.

A Data Analytics and Machine Learning Approach to Develop a Technology Roadmap for Next-Generation Logistics Utilizing Underground Systems

The increasing density of urban populations has spurred interest in utilizing underground space. Underground logistics systems (ULS) are gaining traction due to their effective utilization of this space to enhance urban spatial efficiency. However, research on technological advancements in related fields remains limited. To address this gap, we applied a data-driven approach using patent data related to the ULS to develop a technology roadmap for the field. We employed Latent Dirichlet Allocation (LDA), a machine learning-based topic modeling technique, to categorize and identify six specific technology areas within the ULS domain. Subsequently, we conducted portfolio analytics to pinpoint technology areas with high technological value and to identify the major patent applicants in these areas. Finally, we assessed the technology market potential by mapping the technology life cycle for the identified high-value areas. Among the six technology areas identified, Topic 1 (Underground Material Handling System) and Topic 4 (Underground Transportation System) showed significant patent activity from companies and research institutions in China, the United States, South Korea, and Germany compared to other countries. These areas have the top 10 patent applicants, accounting for 20.8% and 13.6% of all patent applications, respectively. Additionally, technology life cycle analytics revealed a growth trajectory for these identified areas, indicating their rapid expansion and high innovation potential. This study provides a data-driven methodology to develop a technology roadmap that offers valuable insights for researchers, engineers, and policymakers in the ULS industry and supports informed decision-making regarding the field’s future direction.

Big Tech, Small Tech, and the Dynamics of Technology Life Cycle: The Case of AI’s Evolution

Big Tech, Small Tech, and the Dynamics of Technology Life Cycle: The Case of AI’s Evolution

The Technology Innovation of Hybrid Electric Vehicles: A Patent-Based Study

A hybrid electric vehicle (HEV) is a relatively practical technology that has emerged as electric vehicle technology has gradually matured. The analysis of the HEV patent lifecycle is crucial for understanding its impact on the development of this technology. This lifecycle tracks the progress of HEV technologies from their inception and patenting, through their market adoption, and to the expiration of their patent protection. In this study, we aimed to evaluate the technology lifecycle of the HEV industry using the growth S-curve method. The purpose of this study is to describe the technological lifecycle trajectory and current stage of the HEV industry, as well as the technical stages of each sub-technology, to facilitate better decision making. As part of this study, we used patent family data collected from the Derwent Innovation Index database from 1975 to 2022 and established an S-curve model for HEVs and their sub-technologies using logistic regression. In 2022, the technological maturity of HEVs reached 44%. The sub-technologies with the most substantial diffusion capabilities are energy management, propulsion systems, and cooling circuits. According to predictions, the saturation period for the patent family quantity related to HEVs is estimated to be around 53 years.

Nasal Drug Delivery and Nose-to-Brain Delivery Technology Development Status and Trend Analysis: Based on Questionnaire Survey and Patent Analysis.

Nasal administration is a non-invasive method of drug delivery that offers several advantages, including rapid onset of action, ease of use, no first-pass effect, and fewer side effects. On this basis, nose-to-brain delivery technology offers a new method for drug delivery to the brain and central nervous system, which has attracted widespread attention. In this paper, the development status and trends of nasal drug delivery and nose-to-brain delivery technology are deeply analyzed through multiple dimensions: literature research, questionnaire surveys, and patent analysis. First, FDA-approved nasal formulations for nose-to-brain delivery were combed. Second, we collected a large amount of relevant information about nasal drug delivery through a questionnaire survey of 165 pharmaceutical industry practitioners in 28 provinces and 161 different organizations in China. Third, and most importantly, we conducted a patent analysis of approximately 700+ patents related to nose-to-brain delivery, both domestically and internationally. This analysis was conducted in terms of patent application trends, technology life cycle, technology composition, and technology evolution. The LDA topic model was employed to identify technological topics in each time window (1990-2023), and the five key major evolution paths were extracted. The research results in this paper will provide useful references for relevant researchers and enterprises in the pharmaceutical industry, promoting the further development and application of nasal drug delivery and nose-to-brain delivery technology.

PHANTOM CHAINS TONTOR OF BIONIC OBJECT'S MOVEMENTS OF AUTOMATED SYSTEMS. Part 1

This presented work results provides an understanding of how phantom life cycle diagrams of abstract objects can be formed based on the conditions of their existence. Thus, an essential feature of the action of these objects is their possibility of application in automated systems, which is an important aspect of the development of technical bionic means and systems. The problem of using a phantom for modeling actions, movements of objects in space, features of the obtained results is quite widely used in various fields of scientific research. However, there remain tasks related to understanding how the main actions of robotic working bodies of automated systems take place. Thus, it becomes possible to create formalized models of the processes of performing actions of experimental objects in medicine, industry, astronomy, based on phantom constituents of chain and step geometric elements. Thus, it is possible to present similar bionic objects in medical automated systems, which refers to the design of bionic prostheses, exoskeleton systems, as well as systems for scientific research of biomechanical properties of objects. At the same time, we analyze the peculiarities of motor movements, for which we create and analyze phantom models of TONTOR steps and their combination into phantom chains. Prospects for further research, which will be presented in the next part of this work, are the creation of analytical models of phantom features of the existence of various bionic technical means in view of their connection between the volume of the abstract object and the phantom. Similar analytical approaches make it possible to determine the phantom matrix model of the life cycle of a bionic abstract object in automated systems.

RECYCLING OF PHOTOVOLTAIC CELLS – A REVIEW

The growth of solar energy has been remarkable between 2013 and 2022, with a doubling of capacity from 80 GW to 197 GW. To recover valuable materials and mitigate environmental impact, it is imperative to have effective waste management practices in place. While recycling technologies are still in the experimental phase, the EU has put in place legal frameworks for manufacturer liability. There are continuous efforts being made to develop commercially viable technologies for the management of end-of-life PV module waste. An important part of the system of renewable sources is the establishment of a high-quality and efficient waste management system, which is contained within the concept of the circular economy. PV modules will at some point reach the end of their life cycle, which is estimated to be around 25 years, and become waste, therefore a lot of effort is currently being put into research and development of different waste treatment technologies of PV modules. They are still in the experimental phase, and few technologies become commercially available. Also, the management of waste generated at the end of the life cycle of photovoltaic technology is not yet fully regulated. However, at the level of the European Union, there is a legal framework for the liability of PV module manufacturers according to Directive on electric waste and electronic equipment. This paper is a detailed review of recycling technologies based on existing literature.

A circular economy roadmap for solar photovoltaics

Solar photovoltaics (PV) and other clean energy technologies are increasingly being deployed as an environmentally responsible and economic approach to energy system decarbonization. The shift from fossil fuel-centric to material-centric equipment has also shifted the consideration and management of environmental and societal impacts across the technology life cycle. Societal desire to reduce raw material needs, mitigate supply chain social and governance challenges, and take advantage of the reuse and recycling capabilities of materials has enhanced interest in regenerative economic models, called circular economies (CEs). A recent critical review documented that solar PV is on a “path towards increased circularity,” but the need remains to expand activities beyond recycling to a broader set of environmental and policy activities to fully realize the benefits [1,2]. Thus, the goal of this research roadmap is to facilitate and accelerate the transition to a solar PV CE by 1) highlighting current opportunities for PV value chain stakeholders to adopt circular strategies and 2) assessing research and development (R&D) needs that can be addressed in the short term to advance a CE for the solar industry.