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Abstract

Abstract
This paper solves the resource-constrained project scheduling problem (RCPSP) with a satisfiability problem (SAT) solver. This paper builds further on various existing SAT models for this well-known project scheduling problem and extends them with two methods to satisfy the resource constraints. Specifically, we use the wellknown minimal forbidden sets and compare them with the so-called covers that are traditionally used in SAT implementations. Moreover, we also implement an existing binary decision trees approach under various settings and extend the model with networks with adders, so far never used for solving the RCPSP, to guarantee that resource constraints are satisfied. The algorithms are tested under different settings on a set of 13,413 project instances with diverse network and resource structures, and the experiments demonstrate that a combination of these approaches help in finding better solutions within a reasonable time. Moreover, 393 new lower bounds, 62 new upper bounds, and 290 optimally solved instances (including 18 from the PSPLIB) have been discovered, which, to the best of our knowledge, had not been found before. The strong performance of the new algorithm motivated additional experiments, and the preliminary results suggest several promising directions for future research.

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-This study focuses on providing meaningful information to vulnerable road users (VRUs) to support their objectives and perceptions while navigating urban spaces, employing a novel route planning concept. Through three focus group sessions, a comprehensive survey was conducted to identify the needs and concerns of VRUs, leading to the development of an integrated and collaborative mobile application for active mobility. The application encompasses route calculation, prioritizing safety, comfort, civic participation, and empathy. The solution aims to bridge citizen users and city managers, facilitating alerts, historical information on safety and comfort, and collaborative problem-solving and sharing of urban attractions. A prototype of the concept was developed and extensively tested by potential users, and subjective evaluation and feedback demonstrated the usefulness and added value of the integrated and collaborative approach. This study highlights the proposed solution relevance and differentiation from official alerts, user experiences, and civic participation, positioning it as a comprehensive solution for active mobility.

Abstract
Fano resonances emerge from the coherent interference between a discrete resonant state and a continuum of propagating modes, giving rise to a characteristically asymmetric spectral line shape with heightened sensitivity to minute variations in the underlying physical parameters. This study provides a chronological perspective on the development and increasing implementation of Fano-type effects in fiber-optic technology. Their implementation in fiber Bragg gratings (FBGs) through numerical simulations of resonant Fano behavior. Specifically, a Fano-like response in an FBG can be designed by introducing a tailored phase shift into the grating structure; the magnitude of this phase discontinuity constitutes an effective control parameter for tuning the interference condition and, consequently, the resulting spectral asymmetry. The resonance produces a distinctive, asymmetrical spectral response that is attractive for a broad range of photonic functionalities. Key applications include fiber-integrated sensing - where the enhanced spectral sensitivity supports the detection of changes in refractive index, temperature, pressure, or biomolecular interactions - as well as narrowband filtering and spectral shaping for telecommunications and optical signal-processing systems.

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The growing integration of Artificial Intelligence (AI) into interactive systems presents unique challenges and opportunities for Human-Computer Interaction (HCI) and User Experience (UX). While AI can enhance usability and provide novel interaction paradigms, it also raises concerns related to transparency, control, and user trust. This workshop seeks to bring together researchers and practitioners to discuss state-of-the-art engineering methods that support HCI and UX in AI-driven systems. By fostering interdisciplinary collaboration, we aim to identify key challenges, share best practices, and develop a roadmap for future research in this critical area.