Abstract
Happiness at work has been increasingly attracting the attention of academics and human resources managers. Literature on the topic provides clear evidence of the benefits for companies resulting from the adoption of strategies that promote happiness among employees. Despite its growing popularity, companies that define and implement a happiness strategy within their internal marketing are still scarce, particularly small and medium companies (SMEs). This paper illustrates the impact of happiness at work perceived by employees of SMEs at three levels: in themselves, in customers, and in the business's success, in the particular case of companies that do not implement such strategies. The research question was: what is the perception of employees on happiness at work outcomes when the company has no explicit strategy to promote it? This article includes a qualitative study comprising twelve semi-structured interviews with employees who directly deal with customers while working in various B2C companies that do not have a defined strategy to stimulate happiness at work. The study shows employees' acknowledgment of the multidimensional impacts of happiness at work, which makes them more motivated, productive, and more able to influence their relationships with customers positively. Based on these findings, even when lacking clear corporate strategies to improve happiness at work, the company is still expected to benefit in terms of customer loyalty and overall profitability, as well as in terms of employees' affective commitment.

Abstract
The manufacturing industry faces a new revolution, grounded on the intense digitalization of assets and industrial processes and the increasing computation capabilities imposed by the new data-driven digital architectures. This reality has been promoting the Digital Twin concept and its importance in the industrial companies' business models. However, with these new opportunities, also new threads may rise, mainly related to industrial data protection and sovereignty. Therefore, this research paper will demonstrate the International Data Spaces reference model's application to overcome these limitations. Following a pilot study with a Portuguese machine producer/maintainer enterprise, this paper will demonstrate the development of a cutting and bending machine Digital Twin, leveraged on an International Data Spaces infrastructure for interoperability, for the plastic and metal industry and its importance to introduce this machine manufacturing company in a new business-to-business marketplace from the EU project Market 4.0.

Abstract
This paper presents the goals and components of a quantitative energy balance assessment framework to define Positive Energy Districts (PEDs) flexibly in three important contexts: the context of the district's density and local renewable energy supply (RES) potential, the context of a district's location and induced mobility, and the context of the district's future environment and its decarbonized energy demand or supply. It starts by introducing the practical goals of this definition approach: achievable, yet sufficiently ambitious, to be inline with Paris 2050 for most urban and rural Austrian district typologies. It goes on to identify the main design parts of the definition-system boundaries, balancing weights, and balance targets-and argues how they can be linked to the definition goals in detail. In particular, we specify three levels of system boundaries and argue their individual necessity: operation, mobility, and embodied energy and emissions. It argues that all three pillars of PEDs, energy efficiency, onsite renewables, and energy flexibility, can be assessed with the single metric of a primary energy balance when using carefully designed, time-dependent conversion factors. Finally, it is discussed how balance targets can be interpreted as information and requirements from the surrounding energy system, which we identify as a context factor. Three examples of such context factors, each corresponding to the balance target of one of the previously defined system boundaries, operation, mobility, and embodied emissions, are presented: density (as a context for operation), sectoral energy balances and location (as a context for mobility), and an outlook on personal emission budgets (as a context for embodied emissions). Finally, the proposed definition framework is applied to seven distinct district typologies in Austria and discussed in terms of its design goals.

Abstract
In a photovoltaic power plant (PVPP), the DC-AC converter (inverter) is one of the components most prone to faults. Even though they are key equipment in such installations, their fault detection techniques are not as much explored as PV module-related issues, for instance. In that sense, this paper is motivated to find novel tools for detection focused on the inverter, employing machine learning (ML) algorithms trained using a hybrid dataset. The hybrid dataset is composed of real and synthetic data for fault-free and faulty conditions. A dataset is built based on fault-free data from the PVPP and faulty data generated by a digital twin (DT). The combination DT and ML is employed using a Clarke/space vector representation of the inverter electrical variables, thus resulting in a novel feature engineering method to extract the most relevant features that can properly represent the operating condition of the PVPP. The solution that was developed can classify multiple operation conditions of the inverter with high accuracy.

Abstract
Influence Analysis is one of the well-known areas of Social Network Analysis. However, discovering influencers from micro-blog networks based on topics has gained recent popularity due to its specificity. Besides, these data networks are massive, continuous and evolving. Therefore, to address the above challenges we propose a dynamic framework for topic modelling and identifying influencers in the same process. It incorporates dynamic sampling, community detection and network statistics over graph data stream from a social media activity management application. Further, we compare the graph measures against each other empirically and observe that there is no evidence of correlation between the sets of users having large number of friends and the users whose posts achieve high acceptance (i.e., highly liked, commented and shared posts). Therefore, we propose a novel approach that incorporates a user's reachability and also acceptability by other users. Consequently, we improve on graph metrics by including a dynamic acceptance score (integrating content quality with network structure) for ranking influencers in micro-blogs. Additionally, we analysed the topic clusters' structure and quality with empirical experiments and visualization.

Abstract
Over time, distribution systems have progressed from small-scale systems to complex networks, requiring modernization to adapt to these increasing levels of active loads and devices. It is essential to manage the capacity of distribution networks to support all these new technologies. This work, therefore, presents a method for evaluating the impact of optimal allocation and sizing of DGs and load shedding for response demand programs on distribution networks to improve the reliability and financial performance of electric power systems. The proposed optimization tool uses the Greedy Randomized Adaptive Search Procedure and Tabu Search algorithms. The combined optimization of DG allocation simultaneously with load shedding, reliability indices, load transference, and the possibility of islanded operation significantly improves the quality of the planning proposals obtained by the developed method. The results demonstrate the efficiency and robustness of the proposed method, improving the voltage profile by up to 2.02%, relieving the network capacity, and increasing the load restoration capability and reliability. Statistical analysis is also carried out to highlight the performance of the proposed methodology.