Formal Model Research Articles

Ostracism in Japan

Abstract Informal social sanctions such as ostracism are the primary means of controlling deviance in communities. Formal legal sanctions are a costly back-up. Yet outside of university laboratories, studies of ostracism barely exist. We examine legal cases brought by targets of ostracism in Japan, encompassing nearly all the non-trivial reported opinions. The cases do not involve villagers who actually offended their community. Instead, most plaintiffs are victims of opportunistic ostracism, where ostracism is used to extort property, hide community-wide malfeasance, or harass rivals. We explore carefully the non-random character of the disputes and provide a formal model in the annex. We conclude that typical plaintiffs in these lawsuits are not seeking to harness the government's coercive power. Instead, they bring suit for the informational role of courts, aiming to have the court publicly certify that they did not misbehave, contrary to what ostracism might be thought to imply. This analysis contributes to the growing body of legal scholarship on social norms and the role of the courts as informational intermediaries.

Ultra-Short Pulses Laser Heating of Dielectrics: A Semi-Classical Analytical Model

Femtosecond laser pulses are currently regarded as an emerging and promising tool for processing wide bandgap dielectric materials across a variety of high-end applications, although the associated physical phenomena are not yet fully understood. To address these challenges, we propose an original, fully analytical model combined with Two Temperatures Model (TTM) formalism. The model is applied to describe the interaction of fs laser pulses with a typical dielectric target (e.g., Sapphire). It describes the heating of dielectrics, such as Sapphire, under irradiation by fs laser pulses in the range of (1012–1014) W/cm2. The proposed formalism was implemented to calculate the free electron density, while numerical simulations of temperature field evolution within the dielectrics were conducted using the TTM. Mathematical models have rarely been used to solve the TTM in the context of laser–dielectric interactions. Unlike the TTM applied to metals, which requires solving two heat equations, for dielectrics the free electron density must first be determined. We propose an analytical model to solve the TTM equations using this parameter. A new simulation model was developed, combining the equations for non-equilibrium electron density determination with the TTM equations. Our analyses revealed the non-linear nature of the physical phenomena involved and the inapplicability of the Beer–Lambert law for fs laser pulse interactions with dielectric targets at incident laser fluences ranging from 6 to 20 J/cm2.

Ensuring the Correctness and Reliability of CBPS System Using Event‐B

ABSTRACTDuring the early phases of software system development, error detection can be challenging due to the complexity of both the requirements and the operating environments. This paper advocates for the utilization of formal modelling and verification throughout the first phases of systems development to promptly detect and correct errors. The formalism employed throughout is Event‐B, which is backed by the Rodin toolset. To conquer requirements complexity, the frameworks of set theory and first‐order logic are employed, which provide the necessary tools for formalizing and analysing the properties and behaviours associated with Event‐B. Also, we detail the way in which modelling may be used to achieve abstraction, as well as the way in which refinement can be used to manage complexity through layering. Furthermore, we emphasize the significance of model validation and verification in improving the precision of formal models and requirements in IoT communication systems. The model is exemplified using a Content‐Based Publish Subscribe System (CBPS), with a special emphasis on a fire alarm system as a motivating example.

Correlative Activation Free Energy (CAFE) Model: Application to Viscous Processes in Inorganic Oxide Glass-Formers

We explore the concept of correlative activation free energy (CAFE) for the analysis and interpretation of viscosity data of a collection of 847 inorganic oxide glass formers that exhibit various degrees of non-Arrhenius behavior. The CAFE model formalism is strictly based on transition state theory and accounts for the variation in the activation barrier for the viscous dissipation due to the structural evolution the system undergoes upon traversing the glass transition regime. Thus, fitting parameters are meaningful in a statistical thermodynamic context. Compared to the VFT and MYEGA equations, fits using the CAFE model are more robust when extrapolating to infinite temperature because the latter encodes non-enthalpic contributions to the rate coefficient more realistically. The CAFE model-based analysis reveals a strong connection between melt fragility and the degree of change in the potential energy landscape with temperature. Accordingly, while the average ground-state potential energy of the glass forming liquid gradually increases with temperature, the energy of the activated state remains relatively invariant. Our analysis also allows one to estimate the number of atoms involved in the viscous relaxation process, which ranges from approximately 10 to 50 atoms for the oxide glass formers studied. We observe that thermally activated dynamic phenomena exhibited by glassy networks, such as the mixed alkali effect, persist into the supercooled liquid state.

Optimizing Traveler Behavior Between MADINA and JEDDA Using UPPAAL Stratego: A Stochastic Priced Timed Games Approach

This study looks at how travelers move between MADINA and JEDDA, using the UPPAAL Stratego tool to tackle the complexities of urban mobility. As cities grow, effective transportation planning becomes more challenging. Travelers have three options: car, bus, and train. The choices for car and bus travel are impacted by traffic conditions, which can vary between heavy and light, affecting both travel time and cost. We propose a detailed mathematical model that captures all possible scenarios related to these travel options, incorporating the uncertainties of real life. This allows us to simulate different traffic situations. By using UPPAAL Stratego, we evaluate three strategies: the Safe Strategy, which minimizes risk; the Fast Strategy, which aims to reduce travel time; and the Fast and Safe Strategy, which seeks a balance between speed and safety. This paper starts with an introduction to the Stochastic Priced Timed Games approach, highlighting its relevance in modeling dynamic travel environments. We then provide an overview of UPPAAL Stratego, showcasing its abilities in generating, optimizing, and comparing strategies. Next, we outline our mathematical model, explaining the assumptions, parameters, and data sources we used. Our simulation results illustrate how each strategy performs under different conditions, shedding light on traveler preferences and behaviors. The findings underscore the significance of accounting for traffic variability in travel planning and offer important insights for urban transportation policies aimed at improving the traveler experience and optimizing resource use. Additionally, we emphasize the theoretical contributions of our model by demonstrating its applicability to real-world scenarios and its potential to inform future research in urban mobility optimization. Ultimately, this research adds to the growing knowledge of smart transportation systems, demonstrating how formal mathematical modeling can address complex real-world challenges and inform future urban mobility strategies.

Awareness Revision and Belief Extension

ABSTRACT What norm governs how an agent should change their beliefs when they encounter a completely new possibility? Orthodox Bayesianism has no answer, as it takes all learning to involve updating prior beliefs. A partial proposal is Reverse Bayesianism, which mandates the preservation of ratios of prior probabilities, but it faces counterexamples introduced by Mahtani (2021). I propose to separate awareness growth into two stages: awareness revision and belief extension. I argue that Mahtani’s cases highlight that we need to theorize awareness revision before we can define a proposal for belief extension, such as Reverse Bayesianism. I provide a formal model of awareness revision which makes explicit how propositions are distinguished within awareness states and identified across them. Reformulating Reverse Bayesianism to take input from my model allows it to navigate Mahtani-style cases. My model leaves open how agents choose to identify propositions across awareness states, and I propose that they ought to do so conservatively: preserving undisturbed prior reasoning about the structure of their awareness. I then spell out this proposal in a special case. This is a partial proposal, and I close with a discussion of how to elaborate on it and how to advance research into awareness revision.

New trends in didactic research in university mathematics education

AbstractRecent research in university mathematics education has moved beyond the traditional focus on the transition from secondary to tertiary education and students' understanding of introductory courses such as pre-calculus and calculus. There is growing interest in the challenges students face as they move into more advanced mathematics courses that require a shift toward formal reasoning, proof, modeling, and problem-solving skills. This survey paper explores emerging trends and innovations in the field, focusing on three key areas: innovations in teaching and learning advanced mathematical topics, transitions between different levels and contexts of mathematics education, and the role of proof and proving in advanced university mathematics. The survey reflects the evolving landscape of mathematics education research and addresses the theoretical and practical challenges of teaching and learning advanced mathematics across various contexts.

Modeling collaborative memory with SAM.

While humans often encode and retrieve memories in groups, the bulk of our knowledge of human memory comes from paradigms with individuals in isolation. The primary phenomenon of interest within the relatively new field of collaborative memory is collaborative inhibition: the tendency for collaborative groups to underperform in free recall tasks compared with noncollaborative groups of the same size. This effect has been found in a variety of materials and group compositions. However, most research in this field is led by verbal conceptual theories without guidance from formal computational models. We present a framework to scale the Search of Associative Memory model (SAM) to collaborative free recall paradigms with multiple models working together. Multiple SAM models recalling together naturally produce collaborative inhibition when the group members use recalls by the group as cues to retrieve from memory, strongly supporting the "retrieval disruption" hypothesis. This work shows that SAM can act as a unified theory to explain both individual and collaborative memory effects and offers a framework for future predictions of scaling to increased group sizes, shared knowledge, and factors facilitating the spread of false memories in groups.

Refining the Scrum Paradigm: A Comprehensive Study of Software Development Practices (2020-2023)

This article presents a complex vision on software production practices aimed at enhancing the Scrum methodology within software project management. The recommended best practices are closely aligned with contemporary trends in the IT sector, including the total digitalization and virtualization of production processes, the transition to fully remote software development models, the incorporation of artificial intelligence technologies, and the implementation of cost-effective models in team organization. The modifications to software production processes discussed herein are grounded in research conducted between 2020 and 2023, encompassing insights from leading Russian, European, and global IT companies. This research underscores a significant transformation in IT business practices, reflecting a shift towards new benchmarks for Scrum team efficiency. These benchmarks emphasize the data-driven formalization of production models, automation facilitated by AI tools, and noticeable cost optimization within engineering teams, which collectively align with the evolving demands of the modern IT landscape. The article details a series of management strategies designed to adapt to these shifts, including the formalization of sprint goals and processes within Scrum, management of technical debt, and the cost-efficient organization of development teams.

Legislative communication and power: measuring leadership in the U.S. House of Representatives from social media data

Abstract Control over the legislative messaging agenda has important political, electoral and policy consequences. Existing models of congressional agenda-setting suggest that national polarization drives the agenda. At the same time, models of home style and formal models of leadership hypothesize that legislators shift their messaging as they balance coordination and information problems. We say the coordination problem dominates when conditions incentivize legislators to agree on the same message rather than fail to reach consensus. Conversely, the information problem is said to dominate in circumstances where legislators prefer to say nothing at all rather than reach consensus on the wrong political message. Formal theories predict that when coordination problems are pressing, legislative members follow the policy positions of party leaders. When their party’s information problem is acute, party members instead rely on the wisdom of the caucus to set the party’s agenda. To test these theories, we analyze the Twitter accounts of U.S. House members with a Joint Sentiment Topic model, generating a new understanding of House leadership power. Our analyses reveal complex leader-follower relationships. Party leaders possess the power to substantially affect the propensity of rank-and-file members to discuss topics, especially when the coordination problem dominates; these effects are pronounced even when coordination problems are pressing. That said, when the underlying politics are unclear, rank-and-file members exert influence on the discussion of a topic because the information problem is more acute. At the same time and for these uncertain topics, leadership influence decreases, consistent with theory. We show these results are robust to the underlying dynamics of contemporary political discussion and context, including leading explanations for party leadership power, such as national polarization.

On formal modeling, analysis and optimization of reconfigurable manufacturing systems

ABSTRACT This paper deals with the formal specification and multi-objective optimization of reconfigurable manufacturing systems (RMSs). RMSs are characterized by their dynamic structure, crucial for responding to functional variations, adjusting production capacity and overcoming operational dysfuntions promptly and cos-effectively. The effective utilization of this dynamic capability depends on thorough study and optimization of RMS configurations. To tackle this challenge, a novel formalism called genetic reconfigurable object nets (Gen-RONs) is introduced. Gen-RONs formalism integrates the modeling, verification and reconfiguration capabilities of reconfigurable object nets with advanced multi-objective techniques from genetic algorithms. Within Gen-RONs, Petri nets serve to: (i) encode RMS configurations, (ii) manage RMS scheduling and (iii) employ graph transformation theory rules as genetic algorithm operators for real-time system reconfiguration. Thus, Gen-RONs formalism provides a robust framework for optimizing RMSs using genetic algorithms. To ensure deadlock-free RMS configurations, Gen-RONs incorporate a binary tree-based technique. Furthermore, Taguchi’s method is employed to fine-tune genetic algorithm parameters. The contribution of this paper is demonstrated through a case study involving an RMS and the widely used genetic algorithm NSGA-II. Significant gains in terms of the number of solutions (i.e. configurations) and the criteria values are achieved.

"We Want to be There to Support … Do We Have the Capacity to Do That?" Current Priorities and Challenges in School Social Work.

While adolescent mental health needs have continued to rise, schools have become a central location for adolescents seeking support for mental health needs, and approximately 96% of public schools in the United States report providing some form of mental health care to students. This study aims to explore school social workers' current challenges and priorities in their roles. Qualitative focus groups with current school social workers in Virginia (N = 16) were conducted for this study. Participants worked in middle and high schools for a minimum of 3 years across the state. Braun and Clarke's steps for thematic analysis were used to guide coding and themes. The current study found 3 primary themes: (1) role confusion due to vast scope of responsibilities within their schools; (2) a disconnect between desired roles and assigned roles and responsibilities; and (3) lack of formal model of service provision. Focus should be placed on the integration of current models of school social work practice to ensure the effective utilization of their services. The study reveals important information regarding the current and future state of school social work practice.

X-RMTV: An Integrated Approach for Requirement Modeling, Traceability Management, and Verification in MBSE

Formal requirements modeling and traceability management are essential for effectively implementing Model-Based Systems Engineering (MBSE). However, few studies have explored the integration of requirement modeling, traceability management, and verification within MBSE-based systems engineering methodologies. Moreover, the predominant modeling language for MBSE, SysML, lacks sufficient capabilities for requirement description and traceability management and for depicting physical attributes and executable capabilities, making it challenging to verify functional and non-functional requirements collaboratively. This paper proposes an integrated approach for requirement modeling, traceability management, and verification, building on the previously proposed integrated modeling and the simulation language called X language. Our contributions primarily include defining the ReqXL specification for MBSE-oriented requirement modeling based on X language, proposing an algorithm for automatically generating requirement traces, and an integrated framework for requirements modeling, traceability management, and verification was developed by combining the X language with ReqXL. These functionalities were customized on the self-developed integrated modeling and simulation platform, XLab, which is specifically tailored for the X language. Furthermore, we showcase the efficacy and promise of our approach through a case study involving the design of an aircraft electrical system.

Mathematical Model of Horizontal Track Conflict Prevention Algorithm in Detect-and-Avoid Framework

With the proliferation of Unmanned Aerial Vehicle (UAV) technology, the demand for effective collision avoidance technology has intensified. The DAIDALUS algorithm, devised by NASA Langley Research Center under the Detect-and-Avoid (DAA) framework, provides conflict prevention bands for remotely piloted UAVs navigating in intricate airspace. The algorithm computes the bands in two distinct phases: Conflict and Recovery. The formal model for both phases has been established and implemented through iterative programming approaches. However, the mathematical model remains incomplete. Therefore, based on the model, this paper proposes the mathematical model for the two phases of the horizontal track conflict prevention algorithm. Firstly, Cauchy’s inequality is proposed to formulate the model that addresses trajectory conflicts considering the UAV non-instantaneous maneuvering dynamics model, and then a prudent maneuvering strategy is designed to optimize the model for the recovery phase. Finally, the execution procedure of the algorithm within the two-stage mathematical model is also detailed. The results demonstrate that the proposed model achieves a higher precision in the preventive bands, implements an effective collision avoidance strategy, and consistently aligns with the DAIDALUS model while offering a larger buffer time or distance. This work theoretically validates the formal model of the DAIDLAUS algorithm and provides insights for further refinement.

Ketogenesis protects against MASLD-MASH progression through fat oxidation-independent mechanisms.

The progression of metabolic-dysfunction-associated steatotic liver disease (MASLD) to metabolic-dysfunction-associated steatohepatitis (MASH) involves complex alterations in both liver-autonomous and systemic metabolism that influence the liver's balance of fat accretion and disposal. Here, we quantify the relative contribution of hepatic oxidative pathways to liver injury in MASLD-MASH. Using NMR spectroscopy, UHPLC-MS, and GC-MS, we performed stable-isotope tracing and formal flux modeling to quantify hepatic oxidative fluxes in humans across the spectrum of MASLD-MASH, and in mouse models of impaired ketogenesis. We found in humans with MASH, that liver injury correlated positively with ketogenesis and total fat oxidation, but not with turnover of the tricarboxylic acid cycle. The use of loss-of-function mouse models demonstrated that disruption of mitochondrial HMG-CoA synthase (HMGCS2), the rate-limiting step of ketogenesis, impairs overall hepatic fat oxidation and induces a MASLD-MASH-like phenotype. Furthermore, disruption of mitochondrial β-hydroxybutyrate dehydrogenase (BDH1), the terminal step of ketogenesis, also impaired fat oxidation, but surprisingly did not exacerbate steatotic liver injury. Taken together, these findings suggest that quantifiable variations in overall hepatic fat oxidation may not be a primary determinant of MASLD-to-MASH progression, but rather, that maintenance of hepatic ketogenesis could serve a protective role through alternative fat oxidation-independent mechanisms.

Formal models and justifications of democracy

Recently, some epistemic democrats have taken a result from Hong and Page (Proc Natl Acad Sci 101(46):16385–16389, 2004) to help justify the virtues of deliberative democracy. In this paper, I present novel computational models and show that the result fails to be robust in ways that undermine it as evidence for justifying the virtues of deliberative democracy. In particular, the same model lauded by epistemic deliberative democrats can also be used to support certain kinds of epistocracy.

Performance Evaluation in Multimedia Retrieval

Performance evaluation in multimedia retrieval, as in the information retrieval domain at large, relies heavily on retrieval experiments, employing a broad range of techniques and metrics. These can involve human-in-the-loop and machine-only settings for the retrieval process itself and the subsequent verification of results. Such experiments can be elaborate and use-case-specific, which can make them difficult to compare or replicate. In this paper, we present a formal model to express all relevant aspects of such retrieval experiments, as well as a flexible open-source evaluation infrastructure that implements the model. These contributions intend to make a step towards lowering the hurdles for conducting retrieval experiments and improving their reproducibility.

Transit-guided radiation therapy: a novel patient monitoring approach

Background and purposeTransit-Guided Radiation Therapy (TGRT) is a novel technique that uses the transit portal images (TPIs) acquired with Electronic Portal Image Devices (EPID) to quantify patient position errors during the treatment. It has been validated using anthropomorphic phantoms but a validation in a clinical setting was lacking. A pilot clinical study is presented to confirm our previous results. Materials and methodsA prospective study was conducted between June and December 2022 with patients who received whole-brain or breast radiotherapy treatments. The selected treatments were composed of radiation fields using skin-flash, where the body contour projected a sharp edge on the EPID which has been used as a surrogate of the true patient position. Daily imaging procedures were applied as scheduled before running the one- and two-parameter model (1PM and 2PM) of the TGRT formalism on the acquired TPIs to independently estimate the patient position errors. Results43 patients and 1015 TPIs have been assessed. The 2PM showed a better correlation with the true position errors (R2 = 0.76 vs. 0.73), a lower detection threshold (0.77 mm vs. 1.24 mm), and a lower overcorrection risk above the detection threshold (7.0 % vs. 11.1 %) than the 1PM. Overall, the 2PM would have significantly reduced the true position errors by a factor of 0.58 (0.49 – 1.27) (p < 0.0001). ConclusionThe TGRT technique has confirmed the ability to reduce the position errors in a clinical setting, demonstrating the potential to enhance the patient position monitoring without increasing treatment time or patient dose.

Design of Provably Secure and Lightweight Authentication Protocol for Unmanned Aerial Vehicle systems

Drones also called Unmanned Aerial Vehicles (UAVs) have become more prominent in several applications such as package delivery, real-time object detection, tracking, traffic monitoring, security surveillance systems, and many others. As a key member of IoT, the group of Radio Frequency IDentification (RFID) technologies is referred to as Automatic Identification and Data Capturing (AIDC). In particular, RFID technology is becoming a contactless and wireless technique used to automatically identify and track the tagged objects via radio frequency signals. It also has drawn a lot of attention among researchers, scientists, industries, and practitioners due to its broad range of real-world applications in various fields. However, RFID systems face two key concerns related to security and privacy, where an adversary performs eavesdropping, tampering, modification, and even interception of the secret information of the RFID tags, which may cause forgery and privacy problems. In contrast to security and privacy, RFID tags have very limited computational power capability. To deal with these issues, this paper puts forward an RFID-based Lightweight and Provably Secure Authentication Protocol (LPSAP) for Unmanned Aerial Vehicle Systems. The proposed protocol uses secure Physically Unclonable Functions (PUFs), Elliptic-Curve Cryptography (ECC), secure one-way hash, bitwise XOR, and concatenation operations. We use Ouafi and Phan’s formal security model for analyzing security and privacy features such as traceability and mutual authentication. The rigorous informal analysis is carried out which ensures that our proposed protocol achieves various security and privacy features as well as resists various known security attacks. The performance analysis demonstrates that our proposed protocol outperforms other existing protocols. In addition, Scyther and Automated Validation of Internet Security Protocols and Applications (AVISPA) tool simulation results demonstrates that there is no security attack possible within bounds. Therefore, our proposed LPSAP protocol achieves an acceptable high level of security with the least computational, communication, and storage costs on passive RFID tags.

LibLISA: Instruction Discovery and Analysis on x86-64

Even though heavily researched, a full formal model of the x86-64 instruction set is still not available. We present libLISA, a tool for automated discovery and analysis of the ISA of a CPU. This produces the most extensive formal x86-64 model to date, with over 118000 different instruction groups. The process requires as little human specification as possible: specifically, we do not rely on a human-written (dis)assembler to dictate which instructions are executable on a given CPU, or what their in- and outputs are. The generated model is CPU-specific: behavior that is "undefined" is synthesized for the current machine. Producing models for five different x86-64 machines, we mutually compare them, discover undocumented instructions, and generate instruction sequences that are CPU-specific. Experimental evaluation shows that we enumerate virtually all instructions within scope, that the instructions' semantics are correct w.r.t. existing work, and that we improve existing work by exposing bugs in their handwritten models.