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Hybrid electric vehicles (HEVs) are set to play a critical role in the future of the automotive industry. To operate efficiently, HEVs require a robust energy management strategy (EMS) that decides whether the vehicle is powered by the engine or electric motors while managing the battery's state of charge. The EMS must rapidly adapt to driver demands and optimize energy usage, ideally predicting battery charge rates and fuel consumption to adjust the powertrain in real time, even under unpredictable driving conditions. As HEVs become more prevalent, EMS technologies will advance to improve predictive capabilities. This analysis provides an overview of current EMS systems, including both rule-based and optimization-based approaches. It explores the evolution of EMS development through a technology roadmap, highlighting the integration of advanced algorithms such as reinforcement learning and deep learning. The analysis addresses the technologies that underly this evolution, including machine learning, cloud computing, computer vision, and swarm technology. Key advances and challenges in these technologies are discussed, along with their implications for the next generation of EMS systems for HEVs. The analysis of these technologies indicates that they will play a key role in the evolution of EMS technology, allowing it to better optimize driver needs and fuel economy. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In the industrial era, production equipment serves as an essential mother machine. In the global manufacturing industry, components such as laptop computers, mobile phones, and automotive parts all strive for aesthetic appearance. Taiwan's machine tool industry plays a significant role globally. Faced with the constantly changing market environment, the development and competitive advantage of CNC machines are crucial topics for manufacturers. Domestic manufacturers of computer numerical control machines should move towards the integration of automated equipment to accommodate various advanced parts processing procedures. Smart manufacturing will become the trend of the industry in the future. This study invited experts from academia, industry, and research institutions to conduct expert interviews. Their opinions were compiled and analyzed, supplemented by fuzzy Delphi analysis to establish the development trends of various modules. The feasibility and demand of the product's functional technology for industrial development were analyzed under three research dimensions and eight technical items. A total of 26 key sub-technical items were identified, achieving an expert consensus level of over 80. Furthermore, the importance ranking was analyzed using the fuzzy analytic hierarchy process, and the consistency tests were passed with C.I. < 0.1 and C.R. < 0.1. Finally, the obtained importance ranking of the hierarchical structure was used to predict the future development of computer numerical control machines through a technology roadmap, helping manufacturers use it as a reference model for future development trends to enhance market competitiveness. [ABSTRACT FROM AUTHOR]

In the era of rapid technological advancement and the ascent of the United Nations' Sustainable Development Goals (SDGs), addressing the persistent gap between education and employment is crucial to ensure "decent work for all" and enhance human well-being. This study initiates its inquiry from the construction of learning indicators, aiming to facilitate the early exposure of learners to relevant industrial technologies and the acquisition of future-oriented competencies. Firstly, through a review of the literature and an analysis of the current situation, the concept of technology mapping in the industrial sector is employed for technology forecasting. This involves the development of a technology roadmap and the identification of key technologies. Subsequently, the Delphi method is utilized to invite expert scholars to assess the suitability and importance of learning indicators. Data processing is carried out using descriptive statistics, qualitative recommendations, and the Kolmogorov–Smirnov single-sample analysis. Using a smart home system practice curriculum as an example, this study's final development includes 9 main constructs and 56 sub-constructs, serving as the foundation for curriculum and instructional material development. Upon receiving relevant instruction, students can swiftly integrate into related industries. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) and its data-driven counterpart, machine learning (ML), are rapidly evolving disciplines with increasing applications in modeling, simulation, control, and optimization within the drying industry. This paper presents a comprehensive overview of progress made in ML from shallow to deep learning and its implications for food drying. Theoretical foundations, advantages, and limitations of various ML approaches employed in this domain are explored. Additionally, advancements in ML models, particularly those enhanced by optimization algorithms, are reviewed. The review underscores the role of intelligent configuration of ML models, which affects their accuracy and ability to solve problems of high energy consumption, nutrient degradation, and uneven drying. Drawing upon research achievements, integrating of AI models with real-time measuring methods is discussed, enabling dynamic determination of optimal drying conditions and parameter adjustments. This integration facilitates automated decision-making, reducing human errors and enhancing operational efficiency in food drying. Moreover, AI models demonstrate proficiency in predicting drying times and analyzing energy usage patterns, thereby enabling optimization to minimize resource consumption while preserving product quality. Finally, this paper identifies current obstacles in technology development and proposes novel research avenues for sustainable drying technologies. The strengths and weaknesses of various AI methodologies are examined Artificial neural networks are extensively used for modeling drying phenomena Machine learning models can simulate complex processes of food drying Deep learning has significant potential for real-time monitoring of drying Intelligent control systems can optimize food drying [ABSTRACT FROM AUTHOR]

A technology roadmap is fundamental in corporate and industrial strategic planning, outlining goals and technological alternatives for products and processes. This tool forecasts technological evolution, identifying opportunities for competitive advantage and sustainability. Companies use it to integrate medium- and long-term strategies, linking product development and technological innovation with research and development. In water treatment, roadmaps are vital for designing and optimizing water management systems, driven by global and regional examples that align water treatment strategies with sustainability objectives. The application of the roadmap at a non-biodegradable wastewater treatment company improved Chemical Oxygen Demand (COD) removal, validating the effectiveness of the process and allowing the technology to be scaled up while maintaining high-efficiency rates. This study highlights the importance of technology roadmaps in strategic decision-making and fostering innovation and sustainability in critical sectors such as water treatment. [ABSTRACT FROM AUTHOR]

ABSTRACTThis paper addresses the problem of integrating sustainability and business issues in manufacturing through the implementation of Industry 4.0 and process management. The objective of this work is to propose a novel methodology to guide companies in benefitting the so-called Twin Transition (digital and sustainable) drivers at the operation level for reaching business and strategic objectives. The proposed methodology results in a unique tool to address both strategic and business objectives and sustainability through practical managerial actions and technological implementations. The roadmapping methodology is divided into five phases that, starting from the definition of corporate objective and strategy, provide a comprehensive, multi-dimensional, cross-functional plan of Industry 4.0 technology implementation and related changes in terms of processes and Information & Communication Technologies (ICT) architectures. The methodology is presented through an application case performed on an Aerospace company site devoted to the assembly, integration, and test of satellites.

This paper discusses the application of the Technology Development Envelope (TDE) as a road mapping tool in combination with the Delphi method and the Analytic Hierarchy Process (AHP). The method helps policy and decision-makers in organizations strategically manage emerging technologies by mapping their anticipated development path. The paper focused on creating a Technology Development Envelope (TDE) for artificial intelligence (AI) technologies in the food industry. In the first step, an expert panel identified a list of AI technologies applicable to the food industry. A hierarchical decision-making model, comprising 5 criteria and 24 sub-criteria for technology evaluation, was extracted from the literature and validated using the Delphi method. The Analytic Hierarchy Process (AHP) was used to determine the relative importance of criteria and subcriteria for evaluating the identified technologies. At this stage, the most important criteria considered were food safety and social and political acceptance of the technology. The expected value of each technology was calculated over different periods, resulting in the development of the Technology Development Evaluation (TDE) for AI technologies in the food industry within a ten-year horizon. The analysis of the future indicates that technologies such as computer vision, robotics, industrial automation, and machine learning will undergo continuous improvements over time and eventually become dominant technologies in the food industry. These advancements can lead to an increase in quality and efficiency within the food industry. Finally, sensitivity analysis was conducted to test the model’s sensitivity and explore how policymakers’ extreme strategies affect the model’s outputs. [ABSTRACT FROM AUTHOR]

Technology roadmaps are essential for technology management, but current roadmaps that use text mining to build new processes are time-consuming and laborious. In particular, the identification of relationships between different layers of roadmaps mainly relies on experts. To remedy this problem, this study proposes a systematic and objective process for constructing a technology roadmap. First, by integrating the Stanford parser and a co-word analysis method, we develop a modified Stanford parser-based method to select two-word technological terms, which is suitably comprehensive and complete for building a technology roadmap. Second, we innovatively integrate patents, products, and keywords into the same matrix, thus generating an incidence matrix of products and technologies. Based on this matrix, products and technologies can be directly linked to construct a two-layer product technology roadmap without the assistance of experts. We recommend applying a technology lifecycle to estimate the development time of technologies on the technology roadmap timeline, which is an important but often ignored issue. Finally, we provide an example of cloud computing-related products to demonstrate how the proposed technology roadmap approach can provide managers with a competitive technology and product development strategy. [ABSTRACT FROM AUTHOR]

Borehole thermal energy storage (BTES) is an innovative renewable energy technology for building heating and cooling. The lack of studies about BTES in unsaturated soils acts as a barrier to further implementation. In this study, the research obstacles, progress, hotspots, and differences between countries of BTES systems in unsaturated soils were described in detail based on a literature review, bibliometric analysis, technology roadmap, and data visualizations. The results show that the process of coupled heat and moisture transfer has been a research hotspot in recent years, and the thermo‐hydraulic properties increase the complexity of analysis. There are few studies on experimental data and engineering practices of BTES systems in unsaturated soils. China is leading in publications number, but European and North American countries perform better in international cooperation and policy incentives. Technology development will be directly driven by policy momentum, followed by the gradual establishment of a complete research, design, and evaluation system, which helps accelerate the replacement of traditional fossil energy with renewable energy. In an attempt to improve society's awareness and move forward with scientific foresight of BTES systems in unsaturated soils, this review has revealed the evolutionary process, identified its current status, and depicted the future development from political, economic, social, and other dimensions. [ABSTRACT FROM AUTHOR]

This paper discusses the application of the technology development envelop (TDE) as a road mapping tool in combination with Delphi method and Analytic Hierarchy Process (AHP). The method aids policy and decision makers in organizations to strategically manage emerging technologies by mapping their anticipated development path. The paper focused on creating a Technology Development Envelope (TDE) for artificial intelligence (AI) technologies in the food industry. At first step a list of AI technologies applicable to the food industry was identified through an expert panel. A hierarchical decision-making model, consisting of 5 criteria and 24 sub-criteria for technology evaluation, was extracted from literature and verified through the Delphi method. The AHP was used to determine the relative importance of criteria and sub-criteria for evaluating the identified technologies. At this step, food safety and social and political acceptance of technology were considered as the most important criteria. Then the expected value of each technology was then calculated over different time periods, resulting in the development of the TDE for AI technologies in the food industry within a ten-year horizon. The analysis of the future indicates that technologies such as computer vision, robotics, industrial automation, and machine learning will undergo improvements over time and eventually transform into dominant technologies in the food industry. These advancements can lead to an increase in quality and efficiency within the food industry. Finally sensitivity analysis was conducted to test the model's sensitivity and explore how policy makers' extreme strategies affects the model's outputs.

Hybrid electric vehicles (HEVs) are set to play a critical role in the future of the automotive industry. To operate efficiently, HEVs require a robust energy management strategy (EMS) that decides whether the vehicle is powered by the engine or electric motors while managing the battery’s state of charge. The EMS must rapidly adapt to driver demands and optimize energy usage, ideally predicting battery charge rates and fuel consumption to adjust the powertrain in real time, even under unpredictable driving conditions. As HEVs become more prevalent, EMS technologies will advance to improve predictive capabilities. This analysis provides an overview of current EMS systems, including both rule-based and optimization-based approaches. It explores the evolution of EMS development through a technology roadmap, highlighting the integration of advanced algorithms such as reinforcement learning and deep learning. The analysis addresses the technologies that underly this evolution, including machine learning, cloud computing, computer vision, and swarm technology. Key advances and challenges in these technologies are discussed, along with their implications for the next generation of EMS systems for HEVs. The analysis of these technologies indicates that they will play a key role in the evolution of EMS technology, allowing it to better optimize driver needs and fuel economy.

The use of the technology roadmap has become a key tool for forecasting future market directions and technological developments, and it is a basis for making strategic decisions. The literature highlights its usefulness, especially in key technological areas, to guide and prioritize company investments in Research & Development (R&D). Our work is based on the application of this approach to a key technological area, one that is prioritized at the country level - renewable energies. The research focused on data from the case study of a company in the hotel sector located in a city in the Colombian Caribbean region. The development of the study facilitated the definition of technological objectives, the selection of strategic alternatives and the planning of innovation actions in a company in the hotel sector that seeks to incorporate photovoltaic energy as part of its sustainability strategy. [ABSTRACT FROM AUTHOR]

In the industrial era, production equipment serves as an essential mother machine. In the global manufacturing industry, components such as laptop computers, mobile phones, and automotive parts all strive for aesthetic appearance. Taiwan’s machine tool industry plays a significant role globally. Faced with the constantly changing market environment, the development and competitive advantage of CNC machines are crucial topics for manufacturers. Domestic manufacturers of computer numerical control machines should move towards the integration of automated equipment to accommodate various advanced parts processing procedures. Smart manufacturing will become the trend of the industry in the future. This study invited experts from academia, industry, and research institutions to conduct expert interviews. Their opinions were compiled and analyzed, supplemented by fuzzy Delphi analysis to establish the development trends of various modules. The feasibility and demand of the product’s functional technology for industrial development were analyzed under three research dimensions and eight technical items. A total of 26 key sub-technical items were identified, achieving an expert consensus level of over 80. Furthermore, the importance ranking was analyzed using the fuzzy analytic hierarchy process, and the consistency tests were passed with C.I. < 0.1 and C.R. < 0.1. Finally, the obtained importance ranking of the hierarchical structure was used to predict the future development of computer numerical control machines through a technology roadmap, helping manufacturers use it as a reference model for future development trends to enhance market competitiveness.

The semi-automatic construction and analysis of technology roadmaps are at the forefront of applying artificial intelligence techniques. To clarify the development path of the core technologies in the field of agricultural machinery, we propose a core technology evolution path analysis method based on patent text semantic mining. First, the key sentences in the text were extracted, and the BERT model was used to represent the topic's key sentences in semantic vectorization. Then, the technology roadmap was constructed through the unsupervised LDA topic clustering method, and the main fields of activity, blank fields, and fields of agricultural machinery were visually analyzed. Next, investment in research fields was strengthened. Finally, we mapped the technology roadmap and patent IPC codes and found that the evolution of core technologies in the field of agricultural machinery could be divided into the technology development stage, technology focus stage, and technology transformation stage; this allows us to analyze the evolution and integration of these core technologies. The internal laws of the technology evolution provide a reference for future research plans of governments, enterprises, and institutions, aiding in the patent portfolio planning by revealing the microlevel process of technology integration and the technological trends in agricultural machinery. [ABSTRACT FROM AUTHOR]

Additive manufacturing technology has a broad development prospect in the field of aviation equipments. As an important metal additive manufacturing process, the electron beam additive manufacturing is in a rapid development stage. The wire-feed electron beam additive manufacturing can meet the requirements of rapid and low-cost manufacturing of aviation large size structural parts and can be used to repair high-value damaged parts. Electron beam selective melting has obvious advantages in the manufacturing of complex structures and refractory alloy parts. Based on the analysis of the state-of-arts of the electron beam additive manufacturing technology, the roadmap of the electron beam additive manufacturing for aviation equipments in 2035 is comprehensively analyzed and drawn from the five aspects of development needs, objectives, common key technologies, applications, strategic support and guarantee in order to provide reference for the development of the electron beam additive manufacturing technology of aviation equipments. In 2035, a perfect additive manufacturing standard system is to be built; and electron beam additive manufactured key load bearing components for airplane andaeroengine realize mass production and application.

Additive Manufacturing (AM) demonstrates significant potential with rapid growth and widespread industrial adoption. To support the integration and innovation of AM technologies, the development of guidance tools and support methods are crucial, and a technological roadmap can assist in this effort. Despite its widespread use in production processes, the need for further research on the potential impact of AM remains significant. The full impact of AM is still uncertain and lacks consensus, highlighting the need for increased knowledge and investment from the scientific community and organizations. While the benefits of AM are recognized, the challenges of its adoption are not entirely known. AM will bring changes in the way organizations create, distribute, and derive value. Thus, in this article, a roadmap for AM is proposed and presented as a tool to map technological knowledge on the implementation and evolution of AM and serve as a strategic guide for organizations. The methodology for its elaboration involves three phases: planning and preparation, roadmap development, and review and update. Through a literature review, database and project consultation, and questionnaires to Portuguese companies that use AM in their production process it was possible to characterize the AM technology and through the visual format, based on a time horizon, summarize in a common framework all the information about the current and future state of AM in Portugal. The results of this study show that research and development initiatives are essential to promote the evolution of knowledge of the AM technology. Throughout this study and with the development of the roadmap it is anticipated that in the near future the AM will be widely used for prototyping and manufacturing of components and may be used for direct production in the short to medium term. It was also found that the main obstacles to the implementation of AM are the economic/productivity factors and the shortage of professionals with knowledge and skills in the area.

Background and aim: "Technology roadmap" and "technology portfolio" are among the most widely used and popular tools in the field of management research, technology and innovation policy. The aim of the current study was to identify and evaluate the coordinates of these two concepts based on scientometric indices. Materials and methods: The present study was conducted using scientometric indicators based on scientific documents extracted from the Web of Science and some scientometric software such as the VOSviewer, without affecting the data. Findings: The results showed that despite the differences in history and application, the two aforementioned concepts followed a similar path for some indicators, but their differences for other indicators were due to different factors such as the type of research (quantitative or qualitative), the object of study, the environment, and the expected objectives. The findings of the current study indicated that despite the larger number of published documents on the technology portfolio concept (4073 documents) compared to the technology roadmap concept (3475 documents) in the last 20 years, the volume of international communication and the number of active countries under the technology roadmap concept were much higher than under the technology portfolio concept. Conclusion: The most commonly used topics of the two concepts differ from each other in other areas despite their similarities in areas such as energy sources, business, environmental and technological sciences, and green sustainable sciences. Although the trend of using both concepts had exceptions in some years, it is generally increasing over time.

Today, Industry 4.0 has led to increased productivity in manufacturing organizations with technological advances in the form of intelligence and digitalization. From a strategic and technological point of view, Industry 4.0 technology roadmap, in addition to aligning the main strategies of the organization with technology programs, has led manufacturing organizations to intelligent production systems and the possibility of using intelligent and digital technologies such as: big data, IoT, Cloud computing, and provides robots for them. The purpose of this study is to present a model of industry 4.0 technology roadmap in power plant equipment and energy supply industries. In this research, by studying the subject literature, interviews and using structural equation modeling tools, 46 index in the form of 14 components in 6 dimensions (drivers, market, product, technology, research

In order to make full use of the return air waste heat in the coal mine production process and provide a “0” carbon emission heat source selection technology path. The heat source of the wind well site in Baijiaqu was reconstructured, from the coal-fired boiler to the heat pump system to the heat pipe system. The advantages and disadvantages were analyzed, cost and applicability of these three heat source systems were analyzed. The carbon emission of the heat source system of the reconstructed Baijiaqu mine is reduced from 9 576 t/a to 1 180 t/a to the final “0”. This indicates that the use of heat pipe heat exchange technology can meet the requirements of energy conservation and emission reduction, a technology that can achieve nearly “0” carbon emissions. Therefore, the use of heat pipe heat exchange technology in the coal mine wind well site to recover the return air waste heat is the best choice.

Objective: The Fourth Industrial Revolution affected all industries and transformed the digital, cyber, and real worlds in the supply chains of corporations. Internet of Things (IoT) is one of the emerging technologies that mostly manifests the fourth industrial revolution. As the future of the food industry is tied to the design and management of supply chains enabled by technologies such as the IoT, this paper is to provide a model for developing an IoT-based intelligent transportation technology roadmap based on alternative IoT scenarios in the food-producing cold supply chain.Methods: In this study, scenarios were developed using qualitative methods such as the PESTEL framework and content analysis, based on the critical uncertainty method or GBN, through open semi-structured interviews with experts in the food industry and IoT. After identifying the best IoT technology stock for each selected scenario, a roadmap was developed using the T-plan quick-start method during a tow-day-interactive workshop.Results: Communication infrastructure and time horizon of technology development were recognized as the most important uncertainties for applying IoT technology in refrigerated transportation of food cold supply chain in producing companies. Finally, “Mutation Alone” and “Ascent Slow” scenarios were selected and technology roadmaps were developed for each scenario in three layers.Conclusion: Food companies with cold supply chains can use one of the roadmaps presented in this paper as guidelines to equip their transport fleet with IoT technology based on their current situation and choose one of the two selected scenarios. This would enable them to plan, control, and manage the cold transportation chain process by digitally monitoring, tracing, and information sharing in an efficient and effective way.

Companies facing transformation in the automotive industry will need to adapt to new trends, technologies and functions, in order to remain competitive. The challenge is to anticipate such trends and to forecast their development over time. The aim of this paper is to develop a methodology that allows us to analyze the temporal development of technologies, taking automated driving as an example. The framework consists of a technological and a functional roadmap. The technology roadmap provides information on the temporal development of 59 technologies based on expert elicitation using a multi-stage Delphi survey and patent analyses. The functional roadmap is derived from a meta-analysis of studies including 209 predictions of the maturity of automated driving functions. The technological and functional roadmaps are merged into a consolidated roadmap, linking the temporal development of technologies and functions. Based on the publication analysis, SAE level 5 is predicted to be market-ready by 2030. Contrasted to the results from the Delphi survey in the technological roadmap, 2030 seems to be too optimistic, however, as some key technologies would not have reached market readiness by this time. As with all forecasts, the proposed framework is not able to accurately predict the future. However, the combination of different forecast approaches enables users to have a more holistic view of future developments than with single forecasting methods.

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Purpose – The rapid development of Intelligent and Connected Vehicles (ICVs) has boomed a new round of global technological and industrial revolution in recent decades. The Technology Roadmap of Intelligent and Connected Vehicles (2020) comprehensively analyzes the technical architecture, research status and future trends of ICVs. The methodology that supports the roadmap should get studied. Design/methodology/approach – This paper interprets the roadmap from the aspects of strategic significance, technical content and characteristics of the roadmap, and evaluates the impact of the roadmap on researchers, industries and international strategies. Findings – The technical architecture of ICVs as the “three rows and two columns” structure is studied, the methodology that supported the roadmap is explained with a case study and the influence of key technologies with proposed development routes is analyzed. Originality/value – This paper could help researchers understand both thoughts and methodologies behind the technology roadmap of ICVs.

The importance of this paper is its discovery of the unused synergetic potential of integration between several AI techniques into an orchestrated effort to improve service. Special emphasis is given to the conversational capabilities of AI systems. The paper shows that the literature related to the use of AI in service is divided into independent knowledge domains (silos) that are either related to the technology under consideration, or to a small group of technologies related to a certain application; it then discusses the reasons for the isolation of these silos, and reveals the barriers and the traps for their integration. Two case studies of service systems are presented to illustrate the importance of synergy. A special focus is given to the conversation part of these service systems: the first case presents an application with high potential for integrating new AI technologies into its AI portfolio, while the second case illustrates the advantages of a mature application that has already integrated many technologies into its AI portfolio. Finally, the paper discusses the two case studies and presents inclusion relationships between AI capabilities to facilitate generating a roadmap for extending AI capabilities with synergetic opportunities. [ABSTRACT FROM AUTHOR]

In order to make full use of the return air waste heat in the coal mine production process and provide a "0" carbon emission heat source selection technology path. The heat source of the wind well site in Baijiaqu was reconstructured, from the coal-fired boiler to the heat pump system to the heat pipe system. The advantages and disadvantages were analyzed, cost and applicability of these three heat source systems were analyzed. The carbon emission of the heat source system of the reconstructed Baijiaqu mine is reduced from 9 576 t/a to 1 180 t/a to the final "0". This indicates that the use of heat pipe heat exchange technology can meet the requirements of energy conservation and emission reduction, a technology that can achieve nearly "0" carbon emissions. Therefore, the use of heat pipe heat exchange technology in the coal mine wind well site to recover the return air waste heat is the best choice. [ABSTRACT FROM AUTHOR]

This paper discloses an innovative methodology for the generation and update of technology roadmaps to support strategic decisions for future hypersonic transportation systems, specifically targeting non-profit oriented R&D. The methodology is fully integrated into up-to-date conceptual design activity flows. It consists of five main steps that through mathematical and logical models moves from stakeholders' analysis up to planning definition and results evaluation. Complementary to the traditional experts-based methodologies, the rational process here presented allows for a well-structured logical definition of activities and/or missions required to enhance the readiness level of technologies, including a more accurate and reliable budget and time resources estimation to support the technology development plan. This methodology is exploited in the framework of the H2020 STRATOFLY Project to assess the potential of hypersonic civil vehicles to reach Technology Readiness Level 6 by 2035 with respect to key technological, societal and economical aspects. The paper discloses a unique assessment of the readiness level of the European air-breathing propulsive technologies. The final results confirm the crucial role of air-breathing propulsive technologies in the development of future hypersonic transportation system and highlight the urgent need to invest in in-flight demonstration missions with increasing functionalities, to target 2050 as entry in to service of the first Mach 8 civil transport. • Innovative Technology Roadmap Methodology for Hypersonic Transportation Systems. • Accurate time and budget resources estimation for each TRL transit. • Guidelines for the definition of activities, demonstrative missions or in-flight missions. • Technology Assessment of air-breathing propulsive technologies in Europe. • High-speed air-breathing technologies: TRL6 by 2035 and TRL9 by 2050 are confirmed. [ABSTRACT FROM AUTHOR]

Companies facing transformation in the automotive industry will need to adapt to new trends, technologies and functions, in order to remain competitive. The challenge is to anticipate such trends and to forecast their development over time. The aim of this paper is to develop a methodology that allows us to analyze the temporal development of technologies, taking automated driving as an example. The framework consists of a technological and a functional roadmap. The technology roadmap provides information on the temporal development of 59 technologies based on expert elicitation using a multi-stage Delphi survey and patent analyses. The functional roadmap is derived from a meta-analysis of studies including 209 predictions of the maturity of automated driving functions. The technological and functional roadmaps are merged into a consolidated roadmap, linking the temporal development of technologies and functions. Based on the publication analysis, SAE level 5 is predicted to be market-ready by 2030. Contrasted to the results from the Delphi survey in the technological roadmap, 2030 seems to be too optimistic, however, as some key technologies would not have reached market readiness by this time. As with all forecasts, the proposed framework is not able to accurately predict the future. However, the combination of different forecast approaches enables users to have a more holistic view of future developments than with single forecasting methods. [ABSTRACT FROM AUTHOR]

This article presents the results of the third cycle of an Action Research (AR) study conducted in an offshore exploration and production (E&P) operator on the Norwegian Continental Shelf (NCS) that investigated a digital transformation (DT) in offshore E&P supply chain operations, with a focus on drilling operations. This study provides the results of a brief investigation of the main factors that contribute the success of DT and demonstrates the value of using technology roadmapping alongside systems engineering (SE) practices to create a path between identified "AS‐IS" and "TO‐BE" operational states. Applying SE methods to the adoption of the "T‐Plan" roadmapping process resulted in a strategic communication tool that can be used among stakeholders to support them with the integration of business planning and technology adoption, and to help assess the impact that new technologies may have on their organizations in their journey toward a successful DT. In addition to the roadmap itself, which benefits the case company, this study contributes to building and enriching academic literature by providing insight from the oil and gas industry and demonstrating the use of technology roadmapping to create a strategic plan for DT in a well‐established industrial domain. [ABSTRACT FROM AUTHOR]

The rapid advancement of digital technologies has fundamentally changed the competitive dynamics of the logistics industry. For players in the logistics industry, digitization has become an unavoidable situation to achieve survival and sustainable competitiveness. A technology strategy is essential for digitization, and identifying opportunities and threats of technology development through technology trend exploration is important for technology strategy. In addition, to enable the implementation of the technology strategy, it is necessary to detect the change in technology and search for the technology that is expected to have a practical development effect. The purpose of this study is to identify opportunities and areas for technology development through patent data in establishing technology strategies. Previous research mainly relied on the expert interview method, and there was also a patent analysis study based on topic modeling, but only to grasp technology trends. This paper aims to propose a new framework for the extension to the stage for establishing a technology roadmap. By using the Word2Vec algorithm, we will investigate the patent search formula that reflects the trend, the prediction of changes in logistics technology through LDA (Latent Dirichlet Allocation) clustering of patent data, and the derivation of vacant technology by experimental methods. The proposed framework is expected to be utilized for predicting technological change and deriving promising technologies for establishing technology roadmaps in logistics companies. [ABSTRACT FROM AUTHOR]

Roadmap and especially technology roadmap is a management tool and technique to achieve future goals in order to link business to technology. With the increasing trend and the need to use different types of roadmaps, new tools are needed to analyze the complex relationships between layers and roadmap elements. The purpose of this study is to focus on a new tool for analyzing the relationships between roadmap layers with a combined method with a proposed framework. Previous research has addressed relationships between layers only with tools such as quality function deployment (QFD) and the linking grid. Although DSM and TRM have been extensively studied independently so far, this study, therefore, proposes an integrated six-step framework combining a multi-domain matrix and design structure matrix and fuzzy set theory in designing, creating, and developing technology roadmaps for Suggest systems to support decision making and case studies. In this study, multi-domain MDM matrix and fuzzy inference, and network theory were used in designing the technology roadmap. The advantage of using a multi-domain matrix is ​​the simultaneous analysis of each domain specifically in the DSM format as well as the entire domain in the MDM format. The results of the present study indicate the provision of detailed instructions for managers to prepare a suitable roadmap.

This article presents the results of a prospective cost analysis of future regional aviation solutions, specifically focusing on hybrid-electric regional aircraft for 50 passengers. The study spans three time horizons (short term, medium term, long term) and employs a validated workflow to estimate costs for various solutions, including conventional, hybrid-electric, and hydrogen-based architectures. A specialized cost model is introduced to assess the electric components of the powertrain, emphasizing maintenance expenses and energy costs. Despite the environmental promise of hybrid and electric solutions, the results reveal significantly higher direct operating costs (up to 69.8%) compared to conventional options. The study highlights the importance of targeted policies to incentivize sustainable alternatives, suggesting potential solutions such as discouraging kerosene-based aircraft and promoting sustainable aviation fuel. These insights provide valuable guidance for industry stakeholders navigating the path toward a more sustainable and economically viable aviation future. • Hybrid-electric aircraft cost analysis: time horizons, energy sources. • Validated workflow for estimating operating costs in aircraft preliminary design. • Higher direct operating costs for Hybrid-electric configuration (up to 69.8%). • Sustainable fuel advantage: Aviation fuel vs. kerosene • Ticket price increase for hybrid-electric aircraft (up to 36%). [ABSTRACT FROM AUTHOR]

Objective: The Fourth Industrial Revolution affected all industries and transformed the digital, cyber, and real worlds in the supply chains of corporations. Internet of Things (IoT) is one of the emerging technologies that mostly manifests the fourth industrial revolution. As the future of the food industry is tied to the design and management of supply chains enabled by technologies such as the IoT, this paper is to provide a model for developing an IoT-based intelligent transportation technology roadmap based on alternative IoT scenarios in the food-producing cold supply chain. Methods: In this study, scenarios were developed using qualitative methods such as the PESTEL framework and content analysis, based on the critical uncertainty method or GBN, through open semi-structured interviews with experts in the food industry and IoT. After identifying the best IoT technology stock for each selected scenario, a roadmap was developed using the T-plan quick-start method during a tow-day-interactive workshop. Results: Communication infrastructure and time horizon of technology development were recognized as the most important uncertainties for applying IoT technology in refrigerated transportation of food cold supply chain in producing companies. Finally, “Mutation Alone” and “Ascent Slow” scenarios were selected and technology roadmaps were developed for each scenario in three layers. Conclusion: Food companies with cold supply chains can use one of the roadmaps presented in this paper as guidelines to equip their transport fleet with IoT technology based on their current situation and choose one of the two selected scenarios. This would enable them to plan, control, and manage the cold transportation chain process by digitally monitoring, tracing, and information sharing in an efficient and effective way. [ABSTRACT FROM AUTHOR]

How to detect potential useful technical information hidden in patents and scientific papers is crucial for technology trend forecasting and potential research opportunities. Using the text mining method to extract hidden technical information is helpful in assisting strategic decision-making and predicting the tendency of technological development. This study proposes a framework to combine patent analysis and scientific paper analysis to predict technology trends and discover technology opportunities. By mining the hidden technical information, we compared an analysis of patents and scientific papers to reveal the technological development trajectory and future development trends and discover potential technological innovation opportunities. First, we extracted the knowledge contained in patents and scientific papers by text mining. Then, we cleaned and coded the data through natural language processing. We used the Latent Dirichlet Allocation topic model to cluster topics and list the multi-dimensional hierarchical structure diagram. We compared and analyzed results, combined them with expert knowledge, and drew a technology roadmap to predict future development trends and discover technological opportunities. Finally, we used refrigerated container technology as an example for validation. Results showed that the framework proposed in this study can directly and comprehensively display the development trend of technology and discover potential technological innovation opportunities to verify the effectiveness of the framework. The proposed method also provides an effective reference and inspiration for future research. [ABSTRACT FROM AUTHOR]