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In an age of fast-paced technological change, patents have evolved into not only legal mechanisms of intellectual property, but also structured storage containers of knowledge full of metadata, categories, and formal innovation. This chapter proposes to reframe patents in the context of information science, by focusing on patents as knowledge artifacts, and by seeing patents as fundamentally tied to the global movement of scientific and technological knowledge. With a focus on three areas, the inventions of AIs, biotech patents, and international competition with patents, this work considers how new technologies are challenging traditional notions of inventorship, access, and moral accountability.The chapter provides a critical analysis of AI's implications for patent authorship and prior art searches, ownership issues arising from proprietary claims in biotechnology to ethical dilemmas, and the problem of using patents for strategic advantage in a global context of innovation competition. In this analysis, the chapter identified the importance of organizing information, creating metadata standards about originality, implementing retrieval systems to access previous works, and ethi

Modern science is organized around specialization in training and teamwork. Scientists develop deep expertise within a field and combine complementary knowledge through collaboration to solve complex problems. Yet whether specialization is the most effective path to sustained innovation remains unclear. Here we introduce a quantitative framework that distinguishes generalists from specialists based on scaling patterns of disciplinary mobility while remaining independent of career age and productivity. Applying this framework to 49 million publications produced by 3 million scientists between 1900 and 2020, we examine how research style relates to innovation, learning, collaboration, and productivity. We find that scientists who move across fields are more likely to sustain innovative contributions throughout their careers, whereas those who remain within narrow fields exhibit the age-related decline in innovation. Generalists are less anchored to the literature of their training. They are more likely to pursue research independently, and, when they collaborate, they preferentially partner with other generalists. Teams with a greater share of generalists produce more innovative rese

With the largest population of the world and one of the highest enrolments in higher education, India needs efficient and effective means to educate its learners. India started focusing on open and digital education in 1980's and its efforts were escalated in 2009 through the NMEICT program of the Government of India. A study by the Government and FICCI in 2014 noted that India cannot meet its educational needs just by capacity building in brick and mortar institutions. It was decided that ongoing MOOCs projects under the umbrella of NMEICT will be further strengthened over its second (2017-21) and third (2021-26) phases. NMEICT now steers NPTEL or SWAYAM (India's MOOCs) and several digital learning projects including Virtual Labs, e-Yantra, Spoken Tutorial, FOSSEE, and National Digital Library on India - the largest digital education library in the world. Further, India embraced its new National Education Policy in 2020 to strongly foster online education. In this chapter, we take a deep look into the evolution of MOOCs in India, its innovations, its current status and impact, and the roadmap for the next decade to address its challenges and grow. AI-powered MOOCs is an emerging o

This paper studies firms' optimal response to a trade liberalization shock in terms of export and product innovation both theoretically and empirically. We find that trade liberalization, namely China's WTO accession, reduces trade cost and promotes export, which in turn incentivizes firms to innovate as the marginal benefit of innovation for exporting firms is higher than that for non-exporting firms. In addition, as a firm starts to innovate, it predicts to have a higher probability of moving to a better productivity state and can save the entry cost of innovation in the future, resulting in additional dynamic benefits. Such an innovation-promotion effect is an unintended consequence of trade liberalization.

This chapter examines the impact of the geopolitical rivalry between the United States and China on the prospects for inclusive innovation in artificial intelligence (AI) development. We explore three critical aspects of the American and Chinese legal infrastructure that significantly impact AI innovation: data privacy, intellectual property (IP rights), and export restrictions. Through this comparative analysis, we argue that, while China's legal environment may offer certain advantage in terms of access to training data and IP protection, the United States maintains superior resources by enforcing strict export controls on semiconductor chips, AI models, as well as outbound investments in these areas. This nuanced examination helps illuminate how each country's legal framework could influence the ultimate trajectory of AI race and how the technological rivalry has led to exclusionary rulemaking on a global scale.

At a time when the phenomenon of 'AI washing' is quietly spreading, an increasing number of enterprises are using the label of artificial intelligence merely as a cosmetic embellishment in their annual reports, rather than as a genuine engine driving transformation. A test regarding the essence of innovation and the authenticity of information disclosure has arrived. This paper employs large language models to conduct semantic analysis on the text of annual reports from Chinese A-share listed companies from 2006 to 2024, systematically examining the impact of corporate AI washing behaviour on their green innovation. The research reveals that corporate AI washing exerts a significant crowding-out effect on green innovation, with this negative relationship transmitted through dual channels in both product and capital markets. Furthermore, this crowding-out effect exhibits heterogeneity across firms and industries, with private enterprises, small and medium-sized enterprises (SMEs), and firms in highly competitive sectors suffering more severe negative impacts from AI washing. Simulation results indicate that a combination of policy tools can effectively improve market equilibrium. Ba

This paper presents a two-dimensional method-of-moments (MoM) solver for electromagnetic scattering from infinitely long perfectly electrically conducting (PEC) cylinders. Both TMz and TEz polarizations are considered. Starting from the scalar Helmholtz equation, the electric field integral equation (EFIE) is derived for TMz scattering and the magnetic field integral equation (MFIE) is derived for TEz scattering. The induced surface current on the PEC boundary is expanded using pulse basis functions, and the boundary integral equations are discretized using point matching at the segment centers. Circular cylinders with radii $R = λ$ and $R = 2λ$ are used as validation cases because analytical series solutions are available. The MoM-computed surface currents, total near fields, scattered near fields, and field-error distributions are compared against the analytical solutions. After validation, the same solver is applied to a square PEC cylinder, for which no simple closed-form analytical solution is used. The results show strong agreement between the MoM and analytical circular-cylinder solutions and demonstrate the geometry-dependent scattering behavior of the square cylinder.

Is calendar time the true clock of innovation? By combining complexity science with innovation economics and using vaccine datasets containing over three million citations and eight regulatory authorisations, we discover that calendar time and network order describe innovation progress at varying accuracy. First, we present a method to establish a mathematical link between technological evolution and complex networks. The result is a path of events that narrates innovation bottlenecks. Next, we quantify the position and proximity of documents to these innovation paths and find that research, by and large, proceed from basic research, applied research, development, to commercialisation. By extension, we are able to causally quantify the participation of innovation funders. When it comes to vaccine innovation, diffusion-oriented entities are preoccupied with basic, later-stage research; biopharmaceuticals tend to participate in applied development activities and clinical trials at the later-stage; while mission-oriented entities tend to initiate early-stage research. Future innovation programs and funding allocations would benefit from better understanding innovation orders.

A long-standing discussion is to what extent patents can be used to monitor trends in innovation activity. This study quantifies the amount and quality of information about actual innovation contained in the patent system, based on 4,460 Swedish innovations (1970-2015) that have been matched to international patents. The results show that most innovations were not patented and that among those that were, 43.9% of all innovations, only a fraction can be identified with patent quality data. The best-performing models identify 17% of all information about innovations, equivalent to an information loss of at least 83%. Econometric tests also show that the fraction of innovations responding to strengthened patent laws during the period were on average 8% percent. The overlap between the patent and innovation systems is hence more modest than often assumed. This accentuates the need to, alongside patents, develop versatile approaches in order to induce and monitor various aspects of innovation.

This study addresses the challenges composers and sound designers face in creating and refining tools to achieve their musical goals. Using evolutionary processes to promote diversity and foster serendipitous discoveries, we automate the search through uncharted sonic spaces for sound discovery, arguing that diversity-promoting algorithms can bridge the gap between the theoretical realisation and practical accessibility of sounds. We describe a system for generative sound synthesis combining Quality Diversity (QD) algorithms with a supervised discriminative model, inspired by the Innovation Engine algorithm, and explore different configurations and the interplay between the chosen synthesis approach and the discriminative model. We examine the interaction between Compositional Pattern Producing Networks (CPPNs) and Digital Signal Processing (DSP) graphs, introducing a novel approach that uses multiple specialised CPPNs for different frequency ranges; this yields simpler networks while maintaining performance comparable to single-CPPN setups. We also investigate evolutionary stepping stones by analysing goal switches between musical and non-musical contexts, revealing how lineages t

This paper presents a two-dimensional TMz finite-difference time-domain (FDTD) solver based on Yee's scheme for modeling radiation from an infinitely long z-directed line current, with the open region truncated by a Berenger split-field perfectly matched layer (PML). After validating cylindrical-wave propagation and negligible late-time reflections in free space, the solver is applied to three inhomogeneous configurations: (i) diffraction through a one-cell-thick perfectly electrically conducting (PEC) sheet with single and double slits; (ii) scattering from infinitely long PEC cylinders of circular and rectangular cross section; and (iii) scattering from infinitely long dielectric cylinders of varying cross section and permittivity. Beyond qualitative field maps, the diffraction case is characterized quantitatively: a steady-state phasor extracted by a running discrete Fourier transform yields the transmitted intensity, from which the fringe visibility and the far-field pattern are computed and compared against the closed-form Fraunhofer prediction. The single- and double-slit cases are cleanly separated by a visibility that rises from near zero to near unity, and the double-slit

To address the high risks associated with improper use of safety gear in complex power line environments, where target occlusion and large variance are prevalent, this paper proposes an enhanced PEC-YOLO object detection algorithm. The method integrates deep perception with multi-scale feature fusion, utilizing PConv and EMA attention mechanisms to enhance feature extraction efficiency and minimize model complexity. The CPCA attention mechanism is incorporated into the SPPF module, improving the model's ability to focus on critical information and enhance detection accuracy, particularly in challenging conditions. Furthermore, the introduction of the BiFPN neck architecture optimizes the utilization of low-level and high-level features, enhancing feature representation through adaptive fusion and context-aware mechanism. Experimental results demonstrate that the proposed PEC-YOLO achieves a 2.7% improvement in detection accuracy compared to YOLOv8s, while reducing model parameters by 42.58%. Under identical conditions, PEC-YOLO outperforms other models in detection speed, meeting the stringent accuracy requirements for safety gear detection in construction sites. This study contrib

Enterprise Systems purport to bring innovation to organizations. Yet, no past studies, neither from innovation nor from ES disciplines have merged their knowledge to understand how ES could facilitate lifecycle-wide innovation. Therefore, this study forms conceptual bridge between the two disciplines. In this research, we seek to understand how ES could facilitate innovation across its lifecycle phases. We associate classifications of innovation such as radical vs. incremental, administrative vs. technical innovation with the three phases of ES lifecycle. We introduce Continuous Restrained Innovation (CRI) as a new type of innovation specific to ES, considering restraints of technology, business processes and organization. Our empirical data collection at the implementation phase, using data from both the client and implementation partner, shows preliminary evidence of CRI. In addition, we state that both parties consider the implementation of ES as a radical innovation yet, are less interest in seeking further innovations through the system.

With the increasing significance of Research, Technology, and Innovation (RTI) policies in recent years, the demand for detailed information about the performance of these sectors has surged. Many of the current tools are limited in their application purpose. To address these issues, we introduce a requirements engineering process to identify stakeholders and elicitate requirements to derive a system architecture, for a web-based interactive and open-access RTI system monitoring tool. Based on several core modules, we introduce a multi-tier software architecture of how such a tool is generally implemented from the perspective of software engineers. A cornerstone of this architecture is the user-facing dashboard module. We describe in detail the requirements for this module and additionally illustrate these requirements with the real example of the Austrian RTI Monitor.

Cities and metropolitan areas are major drivers of creativity and innovation in all possible sectors: scientific, technological, social, artistic, etc. The critical concentration and proximity of diverse mindsets and opportunities, supported by efficient infrastructures, enable new technologies and ideas to emerge, thrive, and trigger further innovation. Though this pattern seems well established, geography's role in the emergence and diffusion of new technologies still needs to be clarified. An additional important question concerns the identification of the innovation pathways of metropolitan areas. Here, we explore the factors that influence the spread of technology among metropolitan areas worldwide and how geography and political borders impact this process. Our evidence suggests that political geography has been highly important for the diffusion of innovation till around two decades ago, slowly declining afterwards in favour of a more global innovation ecosystem. Further, the visualisation of the evolution of countries and metropolitan areas in a 2d space of competitiveness and diversification reveals the existence of two main innovation pathways, discriminating between diff

The future of innovation processes is anticipated to be more data-driven and empowered by the ubiquitous digitalization, increasing data accessibility and rapid advances in machine learning, artificial intelligence, and computing technologies. While the data-driven innovation (DDI) paradigm is emerging, it has yet been formally defined and theorized and often confused with several other data-related phenomena. This paper defines and crystalizes "data-driven innovation" as a formal innovation process paradigm, dissects its value creation, and distinguishes it from data-driven optimization (DDO), data-based innovation (DBI), and the traditional innovation processes that purely rely on human intelligence. With real-world examples and theoretical framing, I elucidate what DDI entails and how it addresses uncertainty and enhance creativity in the innovation process and present a process-based taxonomy of different data-driven innovation approaches. On this basis, I recommend the strategies and actions for innovators, companies, R&D organizations, and governments to enact data-driven innovation.

There is a resurging interest in automation because of rapid progress of machine learning and AI. In our perspective, innovation is not an exemption from their expansion. This situation gives us an opportunity to reflect on a direction of future innovation studies. In this conceptual paper, we propose a framework of innovation process by exploiting the concept of unit process. Deploying it in the context of automation, we indicate the important aspects of innovation process, i.e. human, organizational, and social factors. We also highlight the cognitive and interactive underpinnings at micro- and macro-levels of the process. We propose to embrace all those factors in what we call Innovation-Automation-Strategy cycle (IAS). Implications of IAS for future research are also put forward. Keywords: innovation, automation of innovation, unit process, innovation-automation-strategy cycle

Accurate modeling of scattering from three-dimensional (3D) perfectly electrically conducting (PEC) targets at microwave frequencies constitutes a fundamental objective in computational electromagnetics, particularly for radar cross section (RCS) prediction and microwave scattering analysis. Classical solvers, such as the method of moments and the Multilevel Fast Multipole Algorithm (MLFMA), although provide high physical fidelity, they become costly under scenarios of repeated queries involving many incidence configurations or frequencies, whereas purely data-driven surrogates often lack accuracy on geometrically complex targets. This paper proposes a U-shaped physics-informed artificial neural network (U-PINet) for 3D microwave scattering analysis. Inspired by the near-far field decomposition of MLFMA, U-PINet combines a near-field graph encoder, parameterized by learnable univariate basis functions, with a hierarchical multi-scale fusion module organized on an octree partition. The proposed network is trained against a discretized residual of the electric-field integral equation at surface collocation points, without requiring reference current labels. Experiments on canonical a

We provide a model of investment in innovation that is dynamic, features multiple heterogeneous research projects of which only one potentially leads to success, and in each period, the researcher chooses the set of projects to invest in. We show that if a search for innovation starts, it optimally does not end until the innovation is found -- which will be never with a strictly positive probability.