Abstract
Methane (CH4) monitoring is vital for climate goals and industrial safety, yet high-pressure and fluctuating temperature environments often compromise sensor accuracy. This paper presents a numerical investigation into a 3D-MA-WMS technique, which expands Multiple Amplitude Wavelength Modulation Spectroscopy by integrating two-line thermometry. Using the HITRAN database, 27,000 absorption spectra were simulated to create a 3D parameter space of P,C, and T. Two retrieval methods-a geometric crossing algorithm and a Artifical Neural Network (ANN)-were evaluated. Results: The ANN outperformed the geometric approach, achieving average errors of 0.15% for concentration, 4.5 mbar for pressure, and 0.78 K for temperature. Significance: These results demonstrate the theoretical feasibility of a self-correcting, autonomous optical sensor capable of decoupling P, C, and T without auxiliary electronic sensors.

Abstract
The need to improve the flexibility and dynamism of the Electric Power Systems (EPS) has driven the development and integration of new devices. Among these, FACTS controllers are particularly notable for their ability to regulate multiple system variables. Incorporating FACTS into the grid requires integrating their electrical models into the analysis and operational tools of the EPS, ensuring precise monitoring and effective system control. This article presents novel steady-state models for FACTS controllers, specifically designed for decoupled state estimation methods. The framework updates the algorithm decoupled and model decoupled state estimators and introduces the modified algorithm decoupled estimator, which offers enhanced robustness and convergence. These improvements are validated through theoretical analysis and simulations. The methodology introduces new state variables and decoupled nonlinear functions to represent FACTS controllers, enabling seamless integration into decoupled estimation frameworks. The study assesses the effectiveness of bad data processing using the Largest Normalized Residual Test (LNR-Test), ensuring robustness under decoupled FACTS modeling. Simulations on the IEEE 30-bus and IEEE 118-bus test systems include shunt, series, series-shunt, and multiple FACTS controllers, as well as single and multiple bad data. Results demonstrate the accuracy and effectiveness of the decoupled estimators with FACTS controllers and confirm the practical applicability of LNR-Test within the proposed approaches.

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Abstract
PurposeThis study aims to explore how identity is formed and evolves within entrepreneurial teams operating in dynamic and uncertain environments. It examines how individual and shared identities interact with knowledge processes and influence collaboration, trust and decision-making, shaping the formation and development of entrepreneurial teams.Design/methodology/approachThis research adopts a multiple-case study approach to explore the entrepreneurial team identity formation and development. It uses one-to-one semi-structured interviews with active entrepreneurial team members who have completed the Central European University iLab Incubation Program. First, an inductive coding strategy was applied. In addition, secondary data were collected through manual web scraping.FindingsThis study shows that knowledge sharing, resource exchange and mentorship through proactive networking shape teams' entrepreneurial identity. Networking and social interactions reinforce each other, while incubation increases legitimacy and identity strength. Trust, open communication and feedback foster adaptability, agility and performance, further consolidating entrepreneurial identity.Originality/valueThis study shifts the focus from individual to collective identity in entrepreneurship, examining how team identity forms and evolves in incubation settings. It highlights the dynamic interplay of networking, knowledge sharing, legitimacy, team dynamics and feedback in shaping shared identity. By emphasizing collaboration and context, it advances understanding of entrepreneurial identity as a collective process.

Abstract
PurposeThis study aims to investigate the relationship between fun at work (FW) and organizational cohesion (OC), using an integrative model where FW acts as a precursor to cohesion. Psychological empowerment (PE) and intrinsic motivation (IM) are examined as potential mediating mechanisms.Design/methodology/approachData was collected through a questionnaire distributed via Google Forms between April and July 2025 to small and medium-sized enterprises (SMEs) identified through Informa D&B. Of the 315 responses, 288 valid cases were retained for analysis. Structural equation modeling was applied to test the proposed integrative model linking FW, PE, IM and OC.FindingsThe results show that FW positively influences PE and OC, both directly and indirectly. PE also significantly enhances cohesion, reinforcing its mediating role. In contrast, IM does not significantly mediate the relationship between FW and cohesion, suggesting that enjoyable workplace practices may strengthen empowerment and collective bonds without necessarily fostering deeper intrinsic drivers.Originality/valueThis study advances knowledge by integrating two previously disconnected domains (i.e. FW and OC) into a single empirical model. The research offers practical value for managers seeking to enhance team dynamics through targeted workplace interventions, highlighting the strategic role of fun in fostering empowerment and collective bonds. In addition, it identifies the nuanced, limited role of IM, opening avenues for further exploration of contextual and cultural factors shaping these relationships.

Abstract
When directional data fall in the positive orthant of the unit hypersphere, a folded directional distribution is preferred over a simple directional distribution for modeling the data. Since directional data, especially axial data, can be modeled using a Watson distribution, this paper considers a folded Watson distribution for such cases. We first address the parameter estimation of this distribution using maximum likelihood, which requires a numerical algorithm to solve the likelihood equations. We use the Expectation-Maximization (EM) algorithm to obtain these estimates and to analyze the properties of the concentration estimator through simulation. Next, we propose the Bayes rule for a folded Watson distribution and evaluate its performance through simulation in various scenarios, comparing it with the Bayes rule for the Watson distribution. Finally, we present examples using both simulated and real data available in the literature.