University of Vaasa

Public governance refers to the mechanisms, processes, and institutions through which societies organise and govern themselves. It encompasses a variety of forms, ranging from traditional hierarchies to more decentralised and participatory models. The term governance is broad and has a neutral connotation, which means that if the term is to be used effectively in analytical discussions, it requires further specification. As Peters et al. (2022) note, incorporating descriptive adjectives is necessary to render the term more precise and relevant to specific contexts or frameworks. In recent decades, the concept of governance has become a focal point across a multitude of academic disciplines. As a result, a wealth of literature has emerged, diving into the many dimensions and implications of governance. Among the diverse types of governance discussed in the literature, several distinct models stand out: network governance (e.g. Pierre and Peters 2000) emphasises the decentralised nature of decision-making, relying on a web of interconnected actors and institutions; participatory governance (e.g. Grote and Gbikpi 2002) values the direct involvement of citizens in the decision-making process, promoting a democratic approach; metagovernance (Kooiman 2003) offers an overarching framework, focusing on the governance of governance itself, to ensure efficient coordination among different governance systems; adaptive governance (e.g. Folke et al. 2005) recognises the need for governance systems to be dynamic and responsive to changes in the environment; anticipatory governance (e.g. Quay 2010) stresses the importance of forward-looking policies and practices, aiming to prepare for future challenges proactively; experimentalist governance (e.g. Sabel and Zeitlin 2012) is built on the idea of continuous learning, with policies being treated as experiments that can be adjusted based on outcomes; collaborative governance (e.g. Emerson and Nabatchi 2015) encourages cooperative interactions among various stakeholders, emphasising shared decision-making and common goals; robust governance (Ansell et al. 2023) addresses the interdependence of stability and change.

This book comprises fourteen articles that explore the concept of information resilience from various complementary perspectives. Together, they underscore the concept’s diversity and complexity, presenting information resilience as a multifaceted phenomenon. Information resilience is described as not only the capacity to respond to high-quality warnings but also the development of systems and processes to ensure the continuous flow of accurate information. Information resilience also refers to the ability of individuals, communities, and societies to withstand and recover from misinformation, disinformation, and other forms of information distortion. A state possessing information resilience can anticipate challenges, allocate resources, and take preventative action, thereby enhancing overall preparedness and reducing vulnerability to unexpected events. In addition, information resilience draws attention to adaptive learning, in which good quality information plays a key role and is needed after crises and disruptions.

As the penetration level of renewable energies increases, many inverter‐based resources (IBRs) managing algorithms are proposed to control the impact of this penetration on the system. Some of these algorithms are based on a local control without visualizing the status of the whole network and others are based on a centralized control approach that results in dynamic settings without considering the mutual sensitivity among different IBRs. Hence, flexible management of IBRs to be simultaneously controlled either locally or remotely and satisfy technical and fair utilization at IBRs and systems levels becomes essential. The proposed flexible management algorithm suggested a dynamic setting that estimates the mutual sensitivity among IBRs at the system level and assures fairness among different owners. This mutual sensitivity link provides a quantifying measure of IBRs injected power on the status of the whole network. Furthermore, this measure defines dynamic clusters' boundaries that are continuously updated by including the most sensitive IBRs and updating their dynamic settings. This update takes place as system configuration, loads, and available sun‐power change in order to satisfy the flexible management algorithm without impacting fairness. The algorithm is applied on a sample DN and imposes compliance with system standard limits within a time‐frame of the stand‐alone LoRaWAN communication platform.

The servicing of crane systems has been a major problem in the crane business value chain. The crane industry has been exploring well-defined solutions to solve problems on the customer’s site. The service-oriented concept, empowered by artificial intelligence (AI) technology, has shown advantages in addressing issues in manufacturing. In this study, the authors propose a novel crane business model for the crane industry: a service-oriented autonomous crane system, which integrates advanced management concepts and AI-powered technology into the traditional industry-crane system. With the help of the novel business model, the crane industry and customers/users can benefit from an improvement in crane performance, and the model also offers further potential to promote problem-solving in the crane industry on the customer’s site. From the perspective of knowledge development, this study gives a clear description of an intelligent service-oriented crane system. Crane stakeholders can develop their own customized autonomous crane systems based on the novel model with its technical structure. From the perspective of strengthening the crane business, the proposed model provides the foundation for developing smart solutions for the crane industry according to the specific requirements. From the perspective of scenarios applied to large construction machinery, the case study in this article provides a valuable reference for augmenting the servicing of large scale construction machinery.

This chapter is one of the first studies to specifically analyze the lack of insurance for agricultural microentrepreneurs in Pakistan’s mountainous peripheral Gilgit-Baltistan region. Based on field interviews with 20 fresh fruit farmers and retailers, we found out that even though they are knowledgeable about micro-health insurance, and most have access to banking services, none of the respondents use business or agricultural insurance. The knowledge of agriculture and business insurance (including the benefits and access possibilities) could have been better for microentrepreneurs operating in these sectors. Currently, the sample microentrepreneurs rely on social networks, including family, in case of difficulties, as informal insurance mechanisms. Due to the perishable nature of produce, lack of proper storage facilities, and supply chain problems, the vulnerability of such businesses is high, and lack of insurance further complicates this situation. Based on respondents’ feedback and literature review, our chapter offers several policy implications to further financial inclusion, particularly on the insurance aspect in this region.

Accurate forecasts of renewable and nonrenewable energy output are essential for meeting global energy needs and resolving environmental issues. Energy sources like the sun and wind are variable, making forecasting difficult. Changes in weather, demand, and energy policy exacerbate this unpredictability. These challenges will be addressed by the bidirectional gated recurrent unit (Bi‐GRU) model, which forecasts power‐generating outcomes more efficiently. The investigation is done over a health data set from 2000 to 2023, including the energy states of the United Kingdom, Finland, Germany, and Switzerland. The comparison of our model (Bi‐GRU) performance with other popular models, including bidirectional long short‐term memory (Bi‐LSTM), ensemble techniques combining convolutional neural networks (CNN) and Bi‐LSTM, and CNNs, make the study more interesting. The performance remains better with a mean absolute percentage error (MAPE) of 2.75%, root mean square error (RMSE) of 0.0414, mean squared error (MSE) of 0.0017, and authentify that Bi‐GRU performs much better than others. This model's superior prediction accuracy significantly enhances our ability to forecast renewable and nonrenewable energy outputs in European states, contributing to more effective energy management strategies.

Femvertising, a progressive advertising approach, combines product promotion with empowering messages for women. Recent trends, especially in feminine hygiene, have shifted towards such empowering narratives, moving away from traditional stigmatized portrayals of women. This research investigates how femvertising impacts consumer perceptions in feminine hygiene advertising. Focusing on transformational versus informational advertising, we examine femvertising's effects on purchase behavior together with the role of perceived authenticity, and the impact of consumers’ construal level mindset. The findings from four experimental studies reveal that transformational messages significantly boost purchase behavior more than informational ones. Key to this effect is the alignment of message framing with the consumer's construal level and the mediating role of perceived authenticity. These results provide critical insights for brands using femvertising strategies, emphasizing the importance of authentic, resonant messages aligned with the target audience's mindset.

The objective of this study is to address existing study gaps by defining what materials are demand-driven material requirements planning (DDMRP) suitable and building a tool that helps to identify such materials. The research problems are approached with three different questions about suitability, features of the identification tool, and financial impact. The research methodology consists of a mixed method case study approach, where semi-structured interviews were conducted with supply chain professionals from the case company, and quantitative data related to the case company’s operations was analyzed. The data consisted of the relevant data of over 10,000 purchased materials. The findings of this study suggest that there are no certain characteristics that materials suitable for DDMRP have, but the potential must be defined individually in the case of every purchased material. The tool initially developed in this study helps to identify materials that meet the requirement of providing potential positive financial impact if brought into DDMRP scope by analyzing historical demand data, lead times, and inventory carrying costs.

Ionospheric tomography offers three-dimensional (3D) description of the electron density distribution, enabling the direct incorporation of electron density data into the slant total electron content (STEC) computation. As a result, STEC derived from tomography helps mitigate the ionospheric delay experienced in the line of sight between global navigation satellite systems (GNSS) and satellites positioned in low Earth orbits (LEO). Tomography can therefore be effectively employed to correct single-frequency GNSS observations and allow enhanced positioning of spaceborne platforms. We demonstrate the accuracy and performance of a global-scale ionospheric tomography method for determining satellite orbits, utilizing single-frequency GNSS measurements combined with a precise point positioning (PPP) algorithm. We compare the tomographic outcomes against orbit determination derived from the GRoup and PHase ionospheric correction (GRAPHIC) observable and based on an ionospheric climatological model. Near the peak of solar cycle 24, the overall accuracy achieved with tomography was around 3.8 m. notably, compared to the background climatological model, tomography demonstrated improvements ranging from 15 to 20%. The GRAPHIC method outperformed tomography, achieving an accuracy of 0.7 m, whereas we obtained around 7 m accuracy when no ionospheric model is employed. Although the developed ionospheric tomography has yet to match the precision of GRAPHIC, our results bring us relatively closer to this objective.

Digitalization and virtualization are integral parts of today’s competitive and dynamic business environments. Yet very little is known about the impact of digitalization and virtualization on technology transfer in strategic collaborative partnerships. Therefore, examining the impact of digitalization and virtualization on technology transfer in strategic collaborative partnerships holds much potential for contributing to the ongoing discussions in the technology transfer literature. This introductory article to the Special Issue reflects on the contributions of the Special Issue articles to the research on technology transfer and reveals three central themes through which the articles as a whole contribute to research in technology transfer: Theme 1 describes the role of digitalization in technology transfer outcomes, Theme 2 focuses on extending the understanding of knowledge transfer capabilities to include digital and virtual capabilities, and Theme 3 illustrates how technology transfer facilitators and intermediaries continue to play an important role in technology transfer in the digital world. We conclude the introductory article by proposing four promising avenues for future research on technology transfer in the digital age. These include Avenue 1: Understanding context specificity and temporality, Avenue 2: Focusing on capabilities and government policy, Avenue 3: Bridging distance, and Avenue 4: Protecting against threats.

Electricity is essential for keeping power networks balanced between supply and demand, especially since it costs a lot to store. The article talks about different deep learning methods that are used to guess how much green energy different Asian countries will produce. The main goal is to make reliable and accurate predictions that can help with the planning of new power plants to meet rising demand. There is a new deep learning model called the Green-electrical Production Ensemble (GP-Ensemble). It combines three types of neural networks: convolutional neural networks (CNNs), gated recurrent units (GRUs), and feedforward neural networks (FNNs). The model promises to improve prediction accuracy. The 1965–2023 dataset covers green energy generation statistics from ten Asian countries. Due to the rising energy supply-demand mismatch, the primary goal is to develop the best model for predicting future power production. The GP-Ensemble deep learning model outperforms individual models (GRU, FNN, and CNN) and alternative approaches such as fully convolutional networks (FCN) and other ensemble models in mean squared error (MSE), mean absolute error (MAE) and root mean squared error (RMSE) metrics. This study enhances our ability to predict green electricity production over time, with MSE of 0.0631, MAE of 0.1754, and RMSE of 0.2383. It may influence laws and enhance energy management.

Power grids are transitioning toward the smart grid paradigm to facilitate the incorporation of renewable energy sources (RESs) at an advanced stage. This transition is driven by environmental concerns, the imperative for decarbonization within contemporary power infrastructures, and the necessity to enhance energy security. Renewable energy resources, particularly those reliant on inverters such as photovoltaic panels and wind turbines, present novel challenges and technical complexities regarding the security of power systems. These energy sources substantially diminish the aggregate system inertia, thereby jeopardizing system stability. Similarly, the unpredictable nature of renewable resources engenders fluctuations in power generation, resulting in imbalances between supply and demand. Consequently, the security assessment of modern power grids is expected to become increasingly complex due to the significant integration of RESs and the widespread utilization of power electronic devices. Moreover, the emergence of various load types, such as electric vehicles, underscores the imperative for the advancement and deployment of robust and adaptable security assessment tools, such as AI-based algorithms.

The popularity of mobile robots in factories, warehouses, and hospitals has raised safety concerns about human-machine collisions, particularly in non-line-of-sight (NLoS) scenarios such as corners. Developing a robot capable of locating and tracking humans behind the corners will greatly mitigate risk. However, most of them cannot work in complex environments or require a costly infrastructure. This paper introduces a solution that uses the reflected and diffracted Millimeter Wave (mmWave) radio signals to detect and locate targets behind the corner. Central to this solution is a localization convolutional neural network (L-CNN), which takes the angle-delay heatmap of the mmWave sensor as input and infers the potential target position. Furthermore, a Kalman filter is applied after L-CNN to improve the accuracy and robustness of estimated locations. A red-green-blue-depth (RGB-D) camera is attached to themmWave sensor as the annotation system to provide accurate position labels. The results of the experimental evaluation demonstrate that our data-driven approach can achieve remarkable positioning accuracy at the 10-centimeter level without extensive infrastructure. In particular, the approach effectively mitigates the adverse effects of diffraction and multi-bounce phenomena, making the system more resilient.

Wireless sensor network (WSN) cluster‐based architecture is a system designed to control and monitor specific events or phenomena remotely, and one of the important concerns that need quick attention is security risks such as an intrusion in WSN traffic. At the same time, a high‐level security method may refer to an intrusion detection system|intrusion detection systems (IDS), which may be employed effectively to achieve a higher level of security in detecting an intruder attack or any attack initiated within a WSN system. The significance of the detection of network intrusions on heterogeneous cluster‐based sensor networks with wireless connections, as well as the approaches to machine learning utilised in IDS model development, were discussed. In addition, this research conducted several comparative studies of feature selection techniques and machine learning methodologies in the development of intrusion detection systems. The authors used a bibliometric indicator to identify the leading trends when it comes to IDS, and the VOS viewer was used to create a spatial mapping of co‐authorship, co‐occurrence, and citation types of analysis with their respective units of study. The purpose of this research paper is to generate relevant findings and a research problem formulation that can lead to a research gap in the research topic's domain area.

This study investigates the forecasting power of three well‐established financial predictors during the prolonged era of unconventional monetary policy: the term spread, the short‐term interest rate, and stock returns. The focus is on predicting GDP growth in both the United States and the Euro area. Our out‐of‐sample forecasting analysis specifically targets the period characterized by the short‐term interest rate effectively bounded at or near the zero lower bound. We recognize that the information content of the term spread is likely to change under such circumstances. Similarly, the dynamics of the short‐term interest rate could be altered due to unconventional monetary policy measures. To address this, we modify the short rate calculation by incorporating the shadow interest. This shadow interest rate can go much lower on the negative side than normal interest rates, making it a potentially more accurate rate to describe the monetary policy stance of central banks. The forecasting analysis covers the period from 2009:1 to 2022:3. Our results unambiguously reveal that the predictive power of the term spread completely vanishes during the zero lower bound era. Although the shadow rate has minor predictive content, the strongest predictor consistently lies in real stock returns during unconventional monetary policy. Our findings challenge the conventional wisdom and the stylized fact of the term spread as the most reliable financial predictor for economic activity. According to our results, this does not hold true under unconventional monetary policy, and using the shadow interest rate does not make a major difference in that respect. By shedding light on the changing dynamics during unconventional monetary policy, our study contributes novel insights to the existing literature.