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The total variation (TV)-seminorm is considered for piecewise polynomial, globally discontinuous (DG) and continuous (CG) finite element functions on simplicial meshes. A novel, discrete variant (DTV) based on a nodal quadrature formula is defined. DTV has favorable properties, compared to the original TV-seminorm for finite element functions. These include a convenient dual representation in terms of the supremum over the space of Raviart–Thomas finite element functions, subject to a set of simple constraints. It can therefore be shown that a variety of algorithms for classical image reconstruction problems, including TV- L 2 denoising and inpainting, can be implemented in low- and higher-order finite element spaces with the same efficiency as their counterparts originally developed for images on Cartesian grids.

In the light-front formulation of quantum field theory, one finds that the vacuum of an interacting theory has a very simple relationship to the vacuum of a free theory. This relationship is not present in the equal-time formulation. In order to further explore this property, we can use the framework of algebraic quantum field theory. In this framework there are two central ingredients. First, there is a C*-algebra corresponding to the collection of possible observables. Second, there is a positive linear functional acting on the algebra which assigns to each element of the algebra, the vacuum expectation value of that element. This framework is applied to a simplified model of two massive scalar fields each with a different mass. In this context, the triviality of the light-front vacuum can be studied by considering the equivalence between representations of the canonical commutation relations associated with each free field. Using the original description of the light-front C*-algebra, first proposed by Leutwyler, Klauder, and Streit, these representations can be shown to be unitar- ily equivalent. However, in order to ensure that the resulting representations satisfy the properties of a local quantum theory, the light-front algebra needs to be modified in such a way that the description of the algebra is dependent on the mass of the field. As a result, the representations associated with each free field are no longer unitarily equivalent. They are instead quasi-equivalent. This property is shared by the equal-time representations as well. In this way we are able to restore some degree of similarity between the light-front and equal-time descriptions of the vacuum, and lay the groundwork for developing a deeper understanding the light-front vacuum.

Software modelling and model-driven engineering (MDE) is traditionally studied from a technical perspective. However, one of the core motivations behind the use of software models is inherently human-centred. Models aim to enable practitioners to communicate about software designs, make software understandable, or make software easier to write through domain-specific modelling languages. Several recent studies challenge the idea that these aims can always be reached and indicate that human factors play a role in the success of MDE. However, there is an under-representation of research focusing on human factors in modelling. During a GI-Dagstuhl seminar, topics related to human factors in modelling were discussed by 26 expert participants from research and industry. In breakout groups, five topics were covered in depth, namely modelling human aspects, factors of modeller experience, diversity and inclusion in MDE, collaboration and MDE, and teaching human-aware MDE. We summarise our insights gained during the discussions on the five topics. We formulate research goals, questions, and propositions that support directing future initiatives towards an MDE community that is aware of and supportive of human factors and values.

We discuss the relation between the trivial light-front vacuum and the non-trivial Heisenberg vacuum.

Software development is a collaborative task involving diverse development teams, where toxic communication can negatively impact team mood and project success. Mood surveys enable the early detection of underlying tensions or dissatisfaction within development teams, allowing communication issues to be addressed before they escalate, fostering a positive and productive work environment. The mood can be surveyed indirectly by analyzing the text-based communication of the team. However, emotional subjectivity leads to varying sentiment interpretations across team members; a statement perceived neutrally by one developer might be seen as problematic by another developer with a different conversational culture. Early identification of perception volatility can help prevent misunderstandings and enhance team morale while safeguarding the project. This paper analyzes the diversity of perceptions within arbitrary development teams and determines how many team members should report their sentiment to accurately reflect the team's mood. Through a Monte Carlo experiment involving 45 developers, we present a preliminary mathematical model to calculate the minimum agreement among a subset of developers based on the whole team's agreement. This model can guide leadership in mood assessment, demonstrating that omitting even a single member in an average-sized 7-member team can misrepresent the overall mood. Therefore, including all developers in mood surveying is recommended to ensure a reliable evaluation of the team's mood.

Sentiment analysis gets increasing attention in software engineering with new tools emerging from new insights provided by researchers. Existing use cases and tools are meant to be used for textual communication such as comments on collaborative version control systems. While this can already provide useful feedback for development teams, a lot of communication takes place in meetings and is not suited for present tool designs and concepts. In this paper, we present a concept that is capable of processing live meeting audio and classifying transcribed statements into sentiment polarity classes. We combine the latest advances in open source speech recognition with previous research in sentiment analysis. We tested our approach on a student software project meeting to gain proof of concept, showing moderate agreement between the classifications of our tool and a human observer on the meeting audio. Despite the preliminary character of our study, we see promising results motivating future research in sentiment analysis on meetings. For example, the polarity classification can be extended to detect destructive behaviour that can endanger project success.

Communication is a crucial social factor in the success of software projects, as positively or negatively perceived statements can influence how recipients feel and affect team collaboration through emotional contagion. Whether a developer perceives a written message as positive, negative, or neutral is likely shaped by multiple factors. In this paper, we investigate how mood traits and states, life circumstances, project phases, and group dynamics relate to the perception of text-based messages in software development. We conducted a four-round survey study with 81 students in team-based software projects. Across rounds, participants reported these factors and labeled 30 decontextualized statements for sentiment, including meta-data on labeling rationale and uncertainty. Our results show: (1) Sentiment perception is only moderately stable within individuals, and label changes concentrate on ambiguity-prone statements; (2) Correlation-level signals are small and do not survive global multiple-testing correction; (3) In statement-level repeated-measures models (GEE), higher mood trait and reactivity are associated with more positive (and less neutral) labeling, while predictors of negative labeling are weaker and at most trend-level (e.g., task conflict); (4) We find no clear evidence of systematic project-phase effects. Overall, sentiment perception varies within persons and is strongly statement-dependent. Although our study was conducted in an academic setting, the observed variability and ambiguity effects suggest caution when interpreting sentiment analysis outputs and motivate future work with contextualized, in-project communication.

Software development relies heavily on text-based communication, making sentiment analysis a valuable tool for understanding team dynamics and supporting trustworthy AI-driven analytics in requirements engineering. However, existing sentiment analysis tools often perform inconsistently across datasets from different platforms, due to variations in communication style and content. In this study, we analyze linguistic and statistical features of 10 developer communication datasets from five platforms and evaluate the performance of 14 sentiment analysis tools. Based on these results, we propose a mapping approach and questionnaire that recommends suitable sentiment analysis tools for new datasets, using their characteristic features as input. Our results show that dataset characteristics can be leveraged to improve tool selection, as platforms differ substantially in both linguistic and statistical properties. While transformer-based models such as SetFit and RoBERTa consistently achieve strong results, tool effectiveness remains context-dependent. Our approach supports researchers and practitioners in selecting trustworthy tools for sentiment analysis in software engineering, while highlighting the need for ongoing evaluation as communication contexts evolve.

We discuss the relation between the trivial light-front vacuum and the non-trivial Heisenberg vacuum.

The sensitive and specific detection of proteins is at the center of many routine analyses in fundamental research, medical diagnosis, food quality control and environmental safety. The current gold standard for these applications remains the laborious and costly microwell plate ELISA. Over the last decade, new miniaturized devices have emerged: microfluidic systems that can drastically reduce the costs and the time of analysis. Many approaches and designs have been proposed. However, some recurrent difficulties remain that prevent the achievement of a system with the necessary balance between scientific performance, cost-effectiveness and user friendliness. These limitations include the complexity to maintain a constant flow rate in a simple and repeatable fashion, to mix solutions in a laminar flow regime, to control undesired surface effects, and to connect the chip to external pumping instruments. This thesis describes a novel microfluidic immunoassay platform that addresses the aforementioned issues while also achieving highly sensitive parallel measurements for the rapid quantification of protein biomarkers. The development of this platform followed three consecutive stages: (i) the establishment of an initial design for the simple manipulation of solutions in stop-flow mode, and the elaboration of strategies for mixing and for the simultaneous detection of parallel reactions, (ii) the introduction of the concept of Dual Network system, which removes the need for channel passivation against the non-specific adsorption of proteins, and (iii) the optimization of the critical assay parameters for the quantification of the cytokine TNF-alpha. The main attributes of the developed platform are also presented: the straightforward fabrication process, the simplified flow control, the enzymatically generated fluorescent signal, and the multi-purpose use of magnetic beads. These microbeads were utilized as functionalized substrate to capture the analyte, but also to induce mixing during incubation phases and to transfer the immune-complexes into the clean channels before the immunodetection step. Finally, a standard curve for the quantification of TNF-alpha in serum within the low-picomolar concentration range was obtained in less than 1 hour, confirming the potential of the platform for diagnostic purposes.