Conditional Logic Research Articles

Facial information, as a highly identifiable biometric trait, possesses triple legal attributes: sensitive information, biometric information, and personality interests. Based on the particularity of facial information, this paper analyzes the operational mechanisms of existing facial recognition technologies and systematically examines the processing logic and regulatory status of facial information throughout its full data lifecycle. The impact of facial recognition technology on personal information protection is primarily reflected in three aspects: legitimacy disputes in the information collection stage, technical risks during information processing, and rights infringements during information use. After analyzing these inherent contradictions, this paper proposes systematic response strategies from both legal regulation and technical governance perspectives.

We consider a family of two-valued ‘fully evaluated left-sequential logics’ (FELs), of which Free FEL (defined by Staudt in 2012) is weakest and immune to atomic side effects. Next is Memorising FEL, in which evaluations of subexpressions are memorised. The following stronger logic is Conditional FEL (inspired by Guzmán and Squier's Conditional logic, 1990). The strongest FEL is static FEL, a sequential version of propositional logic. We use evaluation trees as a simple, intuitive semantics and provide complete axiomatisations for closed terms. For each FEL except Static FEL, we also define its three-valued version, with a constant U for ‘undefinedness’ and again provide complete, independent axiomatisations, each one containing two additional axioms for U on top of the axiomatisations of the two-valued case. In this setting, the strongest FEL is equivalent to Bochvar's logic. Finally, we discuss how the family of FELs is related to the previously defined family of ‘short-circuit logics’.

Sinusitis is a prevalent disease for which nasal endoscopy (NE) is an optimal diagnostic modality. However, NE accuracy is limited by inter-operator variability in landmark identification and localization of mucus that is necessary for sinusitis diagnosis. We sought to develop a novel multi-class machine learning (ML) framework that detects anatomical landmarks and structures for sinusitis assessment as supported by clinical best practices. A total of 3513 NE images from 452 patients were manually annotated by four physicians for three classes: middle turbinate (MT), inferior turbinate (IT), and mucus. A YOLOv11-nano model was trained for multi-class detection and segmentation. We developed a rule-based logic for middle meatus localization, implementing a clinical algorithm that applies anatomy Intersection over Union (IoU) and conditional logic for sinusitis diagnosis. The system was validated on 178 images from 50 patients with chronic rhinosinusitis without polyps (CRSsNP) with benchmarking of real-time performance. The multi-class detection and segmentation model achieved > 75% F1 score for detecting turbinates with mucus. The clinical algorithm achieved 75.0% sensitivity, 76.0% specificity, and 75.2% accuracy for sinusitis classification, with a F1 score of 81.8%, approaching the accuracy of a trained otolaryngologist. The framework achieved near real-time performance at > 20fps on GPU device, demonstrating suitability for integration into live clinical workflows. This novel ML-driven diagnostic framework with a rule-based clinical algorithm enhances decision-making for diagnosing sinusitis with NE. By reducing inter-operator variability, achieving performance comparable to otolaryngologists, and enabling real-time processing for non-specialists, this work holds potential for standardizing care and improving patient outcomes. Future research will focus on expanding to different sinusitis phenotypes and prospective real-time implementation in clinical settings.

ABSTRACT The current study examines the role of social media influencers’ (SMIs) attributes on viewers’ inspiration towards travelling to tourism destinations and their revisit intention, underpinned by both the ‘stimulus to organism response’ (S-O-R) model and ‘persuasion theory’. Firstly, a lay theory approach was used to assess visitors’ beliefs of SMIs’ travel content, via a trinary questionnaire (Yes/Maybe/No). Secondly, PLS-SEM and ‘necessary condition analysis’ (NCA) approaches were applied to assess sufficiency logic and necessary conditions. The trinary responses indicate that travellers believe that SMIs’ attributes are important to them. Moreover, the findings of PLS and NCA reveal that three main persuasive attributes of SMIs, notably expertise, trustworthiness and familiarity, are significant influences as well as the necessary conditions to drive viewers’ inspiration, which further leads to revisit intention. While entertainment and synchronous interactivity are identified as influential factors, they are not necessary conditions. Additionally, informativeness and asynchronous interactivity have neither significant impacts nor necessary conditions. The current study findings extend the persuasion theory by confirming the new attributes of SMIs and the outcome variables of viewers’ inspiration and revisit intention. This study offers practical implications for stakeholders to develop effective online travel promotions through SMIs to increase tourist volume towards destinations.

In the context of global industrial chain decarbonization, the perpetuation of corporate green innovation has emerged as a linchpin for sustaining a competitive advantage in the pursuit of environmental sustainability. Employing a panel data framework, this investigation analyzes A-share listed firms in China from 2011 to 2023. In terms of supply chain perspectives, this study utilizes fixed effects models, mediation analysis, and moderation analysis to empirically examine how downstream enterprises’ digital transformation affects the sustainability of upstream enterprises’ green innovation, while deconstructing the “capability–motivation” dual pathway underlying such sustainability. The key findings are as follows: (1) downstream digital transformation significantly strengthens upstream green innovation persistence through both capability reinforcement and motivation amplification, with a notably stronger impact on the latter; (2) mechanism tests show that capability improvement primarily arises from knowledge spillovers and enhanced supply–demand coordination efficiency, while motivation enhancement stems from intensified market competition and greater responsiveness to tax incentives; and (3) supply chain structural characteristics exert critical moderating effects. This research elucidates the operational logic and boundary conditions of supply chain digital coordination in driving green innovation persistence, contributing to theoretical frameworks while offering actionable insights for policymaking and corporate strategic optimization in sustainable supply chain management.

602 Background: Informed consent forms (ICF) are essential decision-making aids for clinical trial participants. ICFs must be written in plain language while preserving legal and scientific accuracy yet can take weeks to develop, even with templates. Study teams face significant challenges including the lack of simple alternatives for complex terminology, tendency to overestimate participants' health literacy, and inherent difficulty achieving 6th grade reading levels. These obstacles often result in ineffective communication with excessive jargon and complexity. When properly engineered, large language models (LLMs) excel at transforming complex information into accessible explanations while maintaining accuracy. This feasibility pilot aimed to develop and evaluate an LLM-based system for generating first-draft ICFs from research protocols to reduce development time while maintaining appropriate reading levels and regulatory compliance. Methods: We utilized Amazon Bedrock with Anthropic's Claude 3.7 for systematic prompt engineering. From the research protocols we extracted key clinical trial information step-by-step and structured the information into simplified sentences formatted as JavaScript Object Notation (JSON). We inserted the outputs into a predetermined template using placeholders. The system incorporated conditional logic for study-type-specific content requirements. Principal investigators and IRB leaders reviewed drafts and provided feedback. Results: Through iterative prompt optimization and one-to-two shot examples we achieved 8 th grade reading level outputs from the LLM. Reviewer feedback was consistently positive regarding content accuracy and readability. However, human oversight remained essential for content validation and formatting. A significant challenge emerged: mandatory verbatim language sections comprised approximately half of the consent forms and substantially elevated reading levels, with legal sections showing highest complexity. We provided simplified, concise alternatives to IRB and legal teams for review. Conclusions: This LLM implementation represents a transformative innovation for study teams and IRB through dramatic improvement in efficiency by reducing ICF development time and providing high-quality initial drafts for further human refinement. This approach addresses a critical pain point by ensuring consistency between research protocols and ICFs, eliminating common discrepancies that frequently delay IRB approval. However, achieving true reading level targets requires institutional commitment to revising mandatory language sections. This technology enables teams to focus on higher-level content review, potentially transforming clinical trial recruitment and participant trust.

INTRODUCTIONDigital technologies can increase the accessibility of evidence‐based caregiver programs.METHODSA 6‐month, phase I, exploratory, randomized‐controlled trial of two dementia caregiver support platforms, GamePlan4Care (GP4C) and Resources4Care (R4C), each enrolling 120 community‐based family caregivers. Outcome measures included burden, positive aspects of caregiving, social support, and depression.RESULTSCaregivers showed significant follow‐up improvements in burden (GP4C: effect size [ES] = 0.50, p < 0.001; R4C: ES = 0.47, p < 0.001), positive aspects of caregiving (GP4C: ES = 0.26, p = 0.022; R4C: ES = 0.23, p = 0.030), social support (GP4C: ES = 0.21, p = 0.035), and distress (GP4C: ES = 0.30, p = 0.010). Caregivers engaged more in GP4C (GP4C: mean 5.5 h, SD = 0.61; R4C: mean 1.9 h, SD = 0.20) and set more goals for the safety domain (mean 8.9 goals, SD = 7.60).DISCUSSIONGP4C was not superior to R4C; however, both platforms demonstrated improved outcomes. Findings highlight a health system's successful development and implementation of online dementia caregiver platforms. Improving digital technology for caregivers requires studies with larger populations and longitudinal outcomes.TRIAL REGISTRATION NUMBERClinicalTrials.gov Identifier: NCT04540198HighlightsOnline platforms can be useful in the goal of supporting family caregivers with educational and skills‐training material to reduce the negative consequences of caregiving and to improve positive feelings of caregiving.Rules‐based conditional logic was successfully integrated into a Web‐based platform to tailor evidence‐based strategies to an individual's unique caregiving context and needs.Health systems are in an ideal position to adopt online technologies that provide education, skills training, and support for family caregivers of persons living with dementia.

The topic of environmental protection has attained highest importance in this global era but the basic practices of waste disposal are often seems neglected. In a state like Kerala there is a greater need for systematic waste management as the density is much higher compared to other states in India. As a densely populated state, the issues with regard to proper waste management are to be made in an intensive mode in Kerala. This study aims to investigate the impact of Haritha Karma Sena on waste management among households in Ernakulam. The study investigates the role of the Haritha Karma Sena in improving the waste management practices in Kerala, with an emphasis on the environmental, economic, social, and operational implications. Haritha Karma Sena is a community-driven initiative, has successfully decentralized waste management by engaging local communities in garbage segregation, collection, recycling, and public education. The study also examined the factors which influences waste management factors among households. A statistical analysis was carried out based on data collected, using a structured questionnaire instrument from 100 respondents selected through convenience sampling. The data collected from the respondents are tabulated and analyzed into logical statements mainly using Regression, Mean Comparison, Correlation and ANOVA. The results shows that the study instrument is reliable, as well as the study values are considered highly valid and acceptable. The study revealed that Haritha Karma Sena volunteers have a positive impact on waste management. The results also shows that the activities of Haritha Karma Sena significantly plays an important role in reducing improper treatment of waste and there by improve environmental quality.

Three-valued conditional logic (CL), defined by Guzm ́an and Squier (1990) and based on McCarthy’s noncommutative connectives, axiomatises a short-circuit logic (SCL), that is, a logic that prescribes shortcircuit evaluation of conjunction and disjunction. CL defines more identities than three-valued MSCL (Memorising SCL, which also has a two-valued variant). This follows from the fact that the definable connective that prescribes full left-sequential conjunction is commutative in CL. We observe that CL also has a two-valued variant of which the full left-sequential connectives and negation define a commutative logic that is weaker than propositional logic because the absorption laws do not hold. Next, we show that the original, equational axiomatisation of CL is not independent and give several alternative, independent axiomatisations. Finally, we show that in CL, the full left-sequential connectives and negation define Bochvar’s three-valued logic. The paper ends with an appendix on the use of Prover9 and Mace4.

BackgroundQuality indicators (QIs) can help assess intensive care quality, identify potential for improvement, and ultimately enhance patient outcomes. Therefore, the German Interdisciplinary Association of Critical Care and Emergency Medicine (DIVI) has developed QIs for intensive care medicine. However, variability in how these are technically implemented across health care facilities currently limits their comparability.ObjectiveThe aim of the study is to develop unambiguous computer-interpretable representations of the DIVI QIs for intensive care medicine using Fast Healthcare Interoperability Resources (FHIR) and to establish a replicable process for translating narrative QIs into standardized digital formats.MethodsWe first decomposed the narrative DIVI intensive care medicine QIs into two sets of semantic concepts that characterize (1) the targeted patient population and (2) the care aspect specified by each indicator. We mapped the concepts to international vocabularies, defining a supplementary code system for concepts not appropriately represented in existing vocabularies. The decomposed and semantically mapped QIs were then implemented in FHIR using an implementation guide we previously developed to represent clinical practice guideline recommendations. As the translation process holds risks of inducing logical and semantic deviations, the final FHIR representations were back-translated into a narrative form and reviewed with clinical experts, including the authors of the original QIs. The decomposition and semantic mapping were iteratively adjusted based on the experts’ feedback until the results accurately reflected the original intent of the QIs.ResultsThe 10 DIVI QIs were decomposed into 31 separately measurable indicators, including 9 structural indicators, 17 process indicators, and 5 outcome indicators. All process and outcome indicators were successfully specified as computer-interpretable representations in FHIR. In total, 58 unique medical concepts were used, of which 52 (90%) could be mapped to concepts from international vocabularies. The remaining 6 concepts—mostly intensive care unit–specific scores or roles—were defined in a supplementary code system. Nested Boolean logic and temporal conditions were fully supported using standard FHIR mechanisms. After iterative adjustments, the final representations were approved as accurate representations of the DIVI QIs by the clinical expert panel.ConclusionsOur work demonstrates that the structured process developed here enables the unambiguous, computer-interpretable representation of QIs for intensive care. These representations can be used in automated quality management systems to standardize quality assessments across health care facilities. Our newly defined structured process can serve as a blueprint for similar efforts in other specialties. The here-developed computer-interpretable QIs are openly available for reuse and ongoing maintenance. Future work will focus on piloting these indicators in real-world clinical systems and extending the framework to include structural indicators.

This paper introduces the surveydown survey platform. With surveydown, researchers can create surveys that are programmable and reproducible using markdown and R code, leveraging the Quarto publication system and R Shiny web framework. While most survey platforms rely on graphical interfaces or spreadsheets to define survey content, surveydown uses plain text, enabling version control and collaboration via tools like GitHub. The package renders surveys as interactive Shiny web applications, allowing for complex features like conditional skip logic, dynamic question display, and complex randomization. The package supports a diverse set of question types and formatting options and users can leverage Shiny’s powerful reactive programming model to create a wide variety of interactive features. As an open-source platform, surveydown provides researchers full control over their survey implementation, including the survey application as well as where and how the resulting response data are stored. Workflows are entirely reproducible and integrate seamlessly with existing workflows for data collection and analysis in R.

This paper introduces the surveydown survey platform. With surveydown, researchers can create surveys that are programmable and reproducible using markdown and R code, leveraging the Quarto publication system and R Shiny web framework. While most survey platforms rely on graphical interfaces or spreadsheets to define survey content, surveydown uses plain text, enabling version control and collaboration via tools like GitHub. The package renders surveys as interactive Shiny web applications, allowing for complex features like conditional skip logic, dynamic question display, and complex randomization. The package supports a diverse set of question types and formatting options and users can leverage Shiny’s powerful reactive programming model to create a wide variety of interactive features. As an open-source platform, surveydown provides researchers full control over their survey implementation, including the survey application as well as where and how the resulting response data are stored. Workflows are entirely reproducible and integrate seamlessly with existing workflows for data collection and analysis in R.

Given Canada's child care deficit, economic migration remains contingent on the unpaid care work of grandparent migrants, particularly grandmothers or ‘flying grannies’, who arrive through temporary pathways such as the super visa and often juggle multiple transnational caring obligations. However, routine pauses to the parent and grandparent sponsorship program render humanitarian and compassionate applications one of the few options available for grandparents seeking permanent residence. Yet this discretionary tool and grandparents’ multiple caregiving roles continue to be understudied. This socio-legal study, therefore, unpacks narratives of care in 171 humanitarian and compassionate grounds cases involving grandparents who applied to, considered applying, or were referred by judges and immigration officers to apply for the Super Visa. Drawing on Ellermann , we argue that the types of care that are valued and, subsequently, which ‘exceptional’ cases are granted permanent residence, reflect a human-capital citizenship logic and membership status. The subjective criteria used by judges and other ‘gatekeepers’, especially when determining the best interest of any child and hardship, reveal multiple tensions, inconsistencies and a limited notion of care that entrench stereotypes based on race, gender, culture, class and other vectors of social location. Ultimately, family reunification is deemed conditional, and grandparents are rendered temporary.

This article examines the cognitive structures of secondary school mathematics pre-service teachers (PSTs) in Mayotte regarding fundamental mathematical concepts such as theorem and proof. A deep understanding of these concepts is essential for effective teaching practice. To explore this, we employed the free word association test, a methodological tool designed to elicit spontaneous cognitive associations with specific concepts. The research is framed within the theoretical framework of concept image and concept definition as articulated by Vinner in 1991. Participants responded to the concept-stimulus theorem, which elicited nine distinct response categories, while the concept-stimulus proof yielded eight categories. The findings suggest that although PSTs demonstrate a basic familiarity with the notions of theorem and proof, significant gaps in their cognitive understanding persist. For example, there is a notable absence of association between theorem and its logical status as a statement, assertion, or proposition. Moreover, essential terms such as truth, deduction, and validity are not commonly linked to the concept of proof. In light of these findings, we recommend the integration of targeted training on the nature of mathematical statements and proofs within teacher education programs. Such training would aim to strengthen PSTs’ conceptual understanding, equipping them to better support students in developing rigorous mathematical reasoning.

This article explores the evolution of residential districts in the city of Kharkiv during its transformation into a gubernial center in the second half of the 18th and the first half of the 19th century. The research focuses on how administrative, political, and infrastructural shifts affected urban morphology, spatial planning, and the structure of housing development. The study uses a multidisciplinary approach, combining historical cartography, architectural analysis, and urban morphology to examine the transformation of Kharkiv from a fortified border settlement into a planned administrative city. Special attention is given to the implementation of imperial urban planning standards, the transition from irregular to regular city layouts, and the formation of key urban axes. These transformations led to the emergence of three major housing typologies: suburban homestead structures, dense central townhouses, and elite administrative buildings. The article demonstrates how the implementation of regular planning principles — including block zoning, alignment of facades, and standardized typologies — helped structure urban housing space, enabling greater functional clarity and social stratification. A significant part of the analysis is dedicated to the role of professional architectural activity. The rise of state-controlled architectural institutions and the appointment of gubernial architects contributed to the systematization of planning practices. The adaptation of standardized projects to local conditions fostered a balance between imperial strategy and regional specificity. These processes are illustrated through archival cartographic comparisons and morphological mapping of residential areas. The introduction of railway infrastructure in the late gubernial period is discussed as a catalyst for spatial decentralization and the expansion of working-class housing districts. New residential zones developed around stations and industrial sites, marking the transition from administrative to industrial urban logic. Ultimately, the article concludes that Kharkiv’s gubernial period established a fundamental morphological framework for the city's modern development. It set the stage for the emergence of spatially organized, typologically diverse, and socially stratified residential areas, deeply rooted in both imperial planning logic and local urban conditions.

Quantum logics are non-classical logics defined from the mathematical formalism of quantum mechanics. While they are conventionally used to model inferential processes in physics, their scope of application is potentially much broader. We argue that quantum logics can serve as a framework to model human cognition, as their semantics seem able to capture not only how people make inferences about quantum mechanics, but also how they reason in general. We begin by defining quantum logics from an algebraic perspective in a classical first-order setting. Next, we present findings from cognitive science that suggest these logics are apt to characterize human reasoning. We then consider how such a connection between quantum logics and cognition contributes to longstanding philosophical debates about the epistemological status of logic and the problem of adoption. Finally, we discuss how cognitive applications of quantum logics could advance our understanding of human psychology and even quantum foundations.

Equivalence of Lewis’ Two Kinds of Conditional Logic Systems

Large language models (LLMs) have revolutionized natural language processing (NLP), yet their potential in Internet of Things (IoT) and embedded systems (ESys) applications remains largely unexplored. Traditional IoT interfaces often require specialized knowledge, creating barriers for non-technical users. We present Vega, a modular system that leverages LLMs to enable intuitive, natural language control and interrogation of IoT devices, specifically, a Raspberry Pi (RPi) connected to various sensors, actuators, and devices. Our solution comprises three key components: a physical circuit with input and output devices used to showcase the LLM's ability to interact with hardware, an RPi integrating a control server, and a web application integrating LLM logic. Users interact with the system through natural language, which the LLM interprets to remotely call appropriate commands for the RPi. The RPi executes these instructions on the physically connected circuit, with outcomes communicated back to the user via LLM-generated responses. The system's performance is empirically evaluated using a range of task complexities and user scenarios, demonstrating its ability to handle complex and conditional logic without additional coding on the RPi, reducing the need for extensive programming on IoT devices. We showcase the system's real-world applicability through physical circuit implementation while providing insights into its limitations and potential scalability. Our findings reveal that LLM-driven IoT control can effectively bridge the gap between complex device functionality and user-friendly interaction, and also opens new avenues for creative and intelligent IoT applications. This research offers insights into the design and implementation of LLM-integrated IoT interfaces.

In modern precision agriculture, real-time detection of plant diseases is vital to prevent yield losses, reduce pesticide usage, and enhance crop productivity. While deep learning and edge AI have shown promising results in leaf disease classification, their deployment requires costly and computationally intensive hardware such as Raspberry Pi or Jetson Nano. These limitations make them impractical for large-scale use in economically constrained or rural areas. To bridge this gap, this paper presents an IoT-based, rule-driven diagnostic framework utilizing the NodeMCU ESP8266 microcontroller and ThingSpeak cloud platform. The proposed system leverages low-power sensors (DHT11 and soil moisture) to monitor key environmental parameters indicative of plant disease risk. Disease inference is conducted using logical threshold-based conditions executed through ThingSpeak's MATLAB analytics, enabling real-time alerts via email. The implementation has been evaluated across semi-arid farming plots for tomato and chili crops, showing an alert accuracy of 87%, uptime of 98.9%, and total cost below $15. This low-power system demonstrates high reliability and affordability for real-time field deployment in small-scale farms, thereby supporting the sustainable intensification of agriculture.

We introduce a new inner model [Formula: see text] arising from stationary logic. We show that assuming a proper class of Woodin cardinals, or alternatively PFA, the regular uncountable cardinals of [Formula: see text] are measurable in the inner model [Formula: see text] and [Formula: see text] satisfies CH. Moreover, assuming a proper class of Woodin cardinals, the theory of [Formula: see text] is (set) forcing absolute. We introduce an auxiliary concept that we call Club Determinacy, which simplifies the construction of [Formula: see text] greatly but may have also independent interest. Based on Club Determinacy, we introduce the concept of aa-mouse which we use to prove CH and other properties of the inner model [Formula: see text].