In this paper we study intermediate logics between the degree preserving companion of Godel fuzzy logic with an involution and classical propositional logic CPL, as well as the intermediate logics of their finite-valued counterparts. Although these degree-preserving Godel logics are explosive with respect to Godel negation, they are paraconsistent with respect to the involutive negation. We introduce the notion of saturated paraconsistency, a weaker notion than ideal paraconsistency, and we fully characterize the ideal and the saturated paraconsistent logics between the degree-preserving n-valued Godel fuzzy logic with an involution and CPL. We also identify a large family of saturated paraconsistent logics in the family of intermediate logics for degree-preserving finite-valued Lukasiewicz logics.
Mohamed Naoui, Mabrouka Romdhane, Abdelmalek Gacem
et al.
Abstract Energy management systems (EMSs) designed for sustainable buildings can also support smart grids by implementing efficient control algorithms. This role is particularly important when integrating renewable energy sources (RES), including photovoltaic (PV) and wind systems, with electric energy storage systems (ESSs). However, advanced EMS strategies for real-time operation and storage management often demand substantial computational resources, which conventional EMSs may lack. This paper presents and validates an improved type-2 fuzzy logic (T2FL)–based EMS for optimizing PV systems with a battery pack, implemented through edge computing technology. This method is improved by adding 3 main inputs, respectively, Price, SOC, and the energy difference to the T2FL Control, and increasing the rule base to 45 rules to reduce the range of uncertainties. The proposed control system was developed on the MATLAB/Simulink platform and compared with a conventional rule-based EMS. Its effectiveness was further confirmed using data collected from a real system. The findings illustrate the viability and enhanced efficacy of the T2FL algorithm in optimizing energy storage management under various operating conditions. Moreover, the study highlights the advantages and limitations of each control approach, emphasizing the importance of selecting an appropriate EMS to enhance storage effectiveness and deliver energy at the lowest possible cost.
Realizability interprets propositions as specifications for computational entities in programming languages. Specifically, syntactic realizability is a powerful machinery that handles realizability as a syntactic translation of propositions into new propositions that describe what it means to realize the input proposition. This paper introduces EffHOL (Effectful Higher-Order Logic), a novel framework that expands syntactic realizability to uniformly support modern programming paradigms with side effects. EffHOL combines higher-kinded polymorphism, enabling typing of realizers for higher-order propositions, with a computational term language that uses monads to represent and reason about effectful computations. We craft a syntactic realizability translation from (intuitionistic) higher-order logic (HOL) to EffHOL, ensuring the extraction of computable realizers through a constructive soundness proof. EffHOL's parameterization by monads allows for the synthesis of effectful realizers for propositions unprovable in pure HOL, bridging the gap between traditional and effectful computational paradigms. Examples, including continuations and memoization, showcase EffHOL's capability to unify diverse computational models, with traditional ones as special cases. For a semantic connection, we show that any instance of EffHOL induces an evidenced frame, which, in turn, yields a tripos and a realizability topos.
Kim C. Brimhall, Kim C. Brimhall, Kayla Kuhfeldt
et al.
IntroductionClinical and Translational Science Award (CTSA) programs seek to improve the quality and impact of clinical and translational science. CTSA evaluation teams implement structured, evidence-based continuous quality improvement (CQI) processes to enhance activities and outcomes, ultimately benefiting public health. The Translational Science Benefits Model (TSBM) provides a framework for assessing translational science’s health and societal impact, yet additional tools are needed to integrate CQI with impact evaluation. Addressing this gap requires combining CQI methodologies with team science approaches. Building on TSBM, CQI theories (e.g., Plan-Do-Study-Act cycles), and team science principles (e.g., inclusive leadership), we propose a theory-driven, evidence-based logic model to enhance CTSA programs. Using our TL1 Regenerative Medicine Training Program (RMTP) as a case study, we demonstrate its practical application for CTSA evaluation teams.MethodsWe conducted a literature review on impact evaluation, CQI, and team science to develop a theory-based approach for CTSA evaluation teams. Using case study methodology, we analyzed RMTP data (2015–2023) through: (a) Interviews with RMTP leaders, mentors, and trainees to explore program implementation and outcomes; (b) Document analysis of program materials, meeting notes, and reports; (c) Bibliometric and policy analysis of publications, citations, and policy documents to assess impact; and (d) Surveys to capture trainees’ perspectives on program quality and leadership. This mixed-methods approach provided a comprehensive assessment of RMTP’s impact and demonstrated the utility of our team science-based approach to CQI and evaluation.ResultsOur sample included RMTP directors (N = 2), mentors (N = 24), and trainees (N = 38). Among trainees, 68% identified as female, and 21% were from underrepresented groups in medicine. Of 34 graduates, 31 continued in regenerative medicine research. Qualitative data highlighted CQI strategies, such as embedding evaluation into advisory meetings to enhance program functioning. Inclusive leadership fostered a climate where diverse perspectives informed improvements. Quantitative and document analysis further demonstrated how RMTP activities led to positive health and societal impacts within the TSBM framework.DiscussionCTSA evaluation teams must integrate CQI and impact evaluation, yet few theory-based approaches exist. Our evaluation and CQI framework merges TSBM, CQI, and team science principles, providing a practical tool for guiding evaluation teams in continuous improvement while maximizing translational science impact.
Alexandru Ionut Radu, Bogdan Adrian Tolea, Horia Beles
et al.
Rear-end collisions represent a major concern in automotive safety, particularly due to the risk of whiplash injuries among vehicle occupants. The accurate simulation of occupant kinematics during such impacts is critical for the development of advanced safety systems. This paper presents an enhanced multibody simulation model specifically designed for rear-end crash scenarios, incorporating integrated active headrest mechanisms and sensor-based activation logic. The model combines detailed representations of vehicle structures, suspension systems, restraint systems, and occupant biomechanics, allowing for the precise prediction of crash dynamics and occupant responses. The system was developed using Simscape Multibody, with CAD-derived components interconnected through physical joints and validated using controlled experimental crash tests. Special attention was given to modelling contact forces, suspension behaviour, and actuator response times for the active headrest system. The model achieved a root mean square error (RMSE) of 4.19 m/s<sup>2</sup> and a mean absolute percentage error (MAPE) of 0.71% when comparing head acceleration in frontal collision tests, confirming its high accuracy. Validation results demonstrate that the model accurately reproduces occupant kinematics and head acceleration profiles, confirming its reliability and effectiveness as a predictive tool. This research highlights the critical role of integrated sensor-actuator systems in improving occupant safety and provides a flexible platform for future studies on intelligent vehicle safety technologies.
Abstract Background Artificial intelligence (AI) is becoming increasingly relevant in healthcare, necessitating healthcare professionals’ proficiency in its use. Medical students and practitioners require fundamental understanding and skills development to manage data, oversee AI tools and make informed decisions based on AI applications. Integrating AI into medical education is essential to meet this demand. Method This cross-sectional study aimed to evaluate the level of undergraduate medical students’ readiness for AI as they enter their clinical years at Sultan Qaboos University’s College of Medicine and Health Sciences. The students’ readiness was assessed after being exposed to various AI related topics in several courses in the preclinical phases of the medical curriculum. The Medical Artificial Intelligence Readiness Scale For Medical Students (MAIRS-MS) questionnaire was used as the study instrument. Results A total of 84 out of 115 students completed the questionnaire (73.04% response rate). Of these, 45 (53.57%) were female while 39 (46.43%) were male. The cognition section, which evaluated the participants’ cognitive preparedness in terms of knowledge of medical AI terminology, the logic behind AI applications, and data science, received the lowest score (Mean = 3.52). Conversely, the vision section of the questionnaire, which assessed the participants’ capacity to comprehend the limitations and potential of medical AI, and anticipate opportunities and risks displayed the highest level of preparedness, had the highest score (Mean = 3.90). Notably, there were no statistically significant differences in AI competency scores by gender or academic year. Conclusion This study’s findings suggest while medical students demonstrate a moderate level of AI-readiness as they enter their clinical years, significant gaps remain, particularly in cognitive areas such as understanding AI terminology, logic, and data science. The majority of students use ChatGPT as their AI tool, with a notable difference in attitudes between tech-savvy and non-tech-savvy individuals. Further efforts are needed to improve students’ competency in evaluating AI tools. Medical schools should consider integrating AI into their curricula to enhance students’ preparedness for future medical practice. Assessing students’ readiness for AI in healthcare is crucial for identifying knowledge and skills gaps and guiding future training efforts.
Power electronic converter-based microgrids generally suffer from poor power delivery/handling capability during sudden load changes, especially during islanded operations. This is due to the lack of transient energy support to compensate for sudden load changes. The literature has suggested the use of adaptive droop control to provide compensation during transient conditions, thereby improving the power delivery capability. In this context, fuzzy logic-based adaptive droop control is a state-of-the-art technique that was developed based on empirical knowledge of the system. However, this way of designing the droop coefficient values without considering the mathematical knowledge of the system leads to instability during transient conditions. This problem further aggravates when dominant inductive load changes occur in the system. To address this limitation, this paper proposes an improved fuzzy logic-based adaptive droop control method. In the proposed methodology, the values of droop coefficients that are assigned for different membership functions are designed based on the stability analysis of the microgrid. In this analysis, the feasible range of active power–frequency droop values that could avoid instability during large inductive load changes is identified. Accordingly, the infeasible values are avoided in the design of the fuzzy controller. The performance of the proposed and the conventional fuzzy logic methods is verified through simulation in MATLAB/Simulink. From the results, it is identified that the proposed method has improved the power delivery capability of the microgrid by 14% compared to the conventional method.
We present a uniform characterisation of three-valued logics by means of the bisequent calculus (BSC). It is a generalised form of a sequent calculus (SC) where rules operate on the ordered pairs of ordinary sequents. BSC may be treated as the weakest kind of system in the rich family of generalised SC operating on items being some collections of ordinary sequents, like hypersequent and nested sequent calculi. It seems that for many non-classical logics, including some many-valued, paraconsistent and modal logics, the reasonably modest generalisation of standard SC offered by BSC is sufficient. In this paper we examine a variety of three-valued logics and show how they can be formalised in the framework of BSC. We present a constructive syntactic proof provided that these systems are cut-free, satisfy the subformula property, and allow one to prove the interpolation theorem in many cases.
With distributed energy sources connected to the distribution grid on a large scale for distributed photovoltaic power randomness, this paper proposes a flexible interconnection system optimization operation strategy. First, the virtual synchronous control technology is improved to improve the DC bus voltage stability; second, it analyzes the system operation mode to judge the output logic of PV and storage units, takes DC bus power balance as the underlying logic, and puts forward the power coordination optimization strategy and fault power supply restoration strategy with full consideration of factors such as the load balance degree of the distribution station area, the economic operation of the main transformer, and the amount of power lost in the faulty station area. It also establishes a multi-objective optimization model to obtain the power commands of each port and achieves the power flexibility mutualization of the flexible interconnected system through the accurate regulation of the soft normally open point (SNOP). Finally, a simulation model of the flexible interconnection system is built using MATLAB/Simulink to verify the effectiveness of the proposed optimization strategy.
Leroux has proved that unreachability in Petri nets can be witnessed by a
Presburger separator, i.e. if a marking $\vec{m}_\text{src}$ cannot reach a
marking $\vec{m}_\text{tgt}$, then there is a formula $\varphi$ of Presburger
arithmetic such that: $\varphi(\vec{m}_\text{src})$ holds; $\varphi$ is forward
invariant, i.e., $\varphi(\vec{m})$ and $\vec{m} \rightarrow \vec{m}'$ imply
$\varphi(\vec{m}'$); and $\neg \varphi(\vec{m}_\text{tgt})$ holds. While these
separators could be used as explanations and as formal certificates of
unreachability, this has not yet been the case due to their worst-case size,
which is at least Ackermannian, and the complexity of checking that a formula
is a separator, which is at least exponential (in the formula size).
We show that, in continuous Petri nets, these two problems can be overcome.
We introduce locally closed separators, and prove that: (a) unreachability can
be witnessed by a locally closed separator computable in polynomial time; (b)
checking whether a formula is a locally closed separator is in NC (so, simpler
than unreachability, which is P-complete).
We further consider the more general problem of (existential) set-to-set
reachability, where two sets of markings are given as convex polytopes. We show
that, while our approach does not extend directly, we can efficiently certify
unreachability via an altered Petri net.
Artificial vision is crucial for most artificial intelligence applications. Conventional artificial visual systems have been facing challenges in terms of real-time information processing due to the physical separation of sensors, memories, and processors, which results in the production of a large amount of redundant data as well as the data conversion and transfer between these three components consuming most of the time and energy. Emergent optoelectronic memristors with the ability to realize integrated sensing-computing-memory (ISCM) are key candidates for solving such challenges and therefore attract increasing attention. At present, the memristive ISCM devices can only perform primary-level computing with external light signals due to the fact that only monotonic increase of memconductance upon light irradiation is achieved in most of these devices. Here, we propose an all-optically controlled memristive ISCM device based on a simple structure of Au/ZnO/Pt with the ZnO thin film sputtered at pure Ar atmosphere. This device can perform advanced computing tasks such as nonvolatile neuromorphic computing and complete Boolean logic functions only by light irradiation, owing to its ability to reversibly tune the memconductance with light. Moreover, the device shows excellent operation stability ascribed to a purely electronic memconductance tuning mechanism. Hence, this study is an important step towards the next generation of artificial visual systems.
Recent reports of silent data corruption because of hardware faults in large data centers bring to the forefront the importance of in-field testing. In-field testing, enabled by logic built-in self-test (<inline-formula> <tex-math notation="LaTeX">$LBIST$ </tex-math></inline-formula>), addresses defects that occur during the lifetime of a chip and ones that escaped manufacturing tests. A class of <inline-formula> <tex-math notation="LaTeX">$LBIST$ </tex-math></inline-formula> approaches for scan circuits store partitioned deterministic test data on-chip and produce tests by combining stored test data entries in one of two ways: 1) pseudo-random combinations are selected by linear-feedback shift-registers (<inline-formula> <tex-math notation="LaTeX">$LFSR\text{s}$ </tex-math></inline-formula>); or 2) deterministic combinations are stored on-chip as sets of indices of stored test data entries. This article introduces a third option where counters are used for forming combinations of stored test data entries. Counters do not require additional storage, and ensure complete fault coverage with a limited number of tests. Experimental results for benchmark circuits demonstrate the advantages of counters in the context where test data entries for on-chip storage are obtained by partitioning compressed deterministic tests, and the universally available on-chip decompression logic is used as part of the test application process.
José Rubio-León, José Rubio-Cienfuegos, Cristian Vidal-Silva
et al.
Managing the energy produced to support industries and various human activities is highly relevant nowadays. Companies in the electricity markets of each country analyze the generation, transmission, and distribution of energy to meet the energy needs of various sectors and industries. Electrical markets emerge to economically analyze everything related to energy generation, transmission, and distribution. The demand for electric energy is crucial in determining the amount of energy needed to meet the requirements of an individual or a group of consumers. But energy consumption often exhibits random behavior, making it challenging to develop accurate prediction models. The analysis and understanding of energy consumption are essential for energy generation. Developing models to forecast energy demand is necessary for improving generation and consumption management. Given the energy variable’s stochastic nature, this work’s main objective is to explore different configurations and parameters using specialized libraries in Python and Google Collaboratory. The aim is to develop a model for forecasting electric power demand using fuzzy logic. This study compares the proposed solution with previously developed machine learning systems to create a highly accurate forecast model for demand values. The data used in this work was collected by the European Network of Transmission System Operators of Electricity (ENTSO-E) from 2015 to 2019. As a significant outcome, this research presents a model surpassing previous solutions’ predictive performance. Using Mean Absolute Percentage Error (MAPE), the results demonstrate the significance of set weighting for achieving excellent performance in fuzzy models. This is because having more relevant fuzzy sets allows for inference rules and, subsequently, more accurate demand forecasts. The results also allow applying the solution model to other forecast scenarios with similar contexts.
This paper proposes a three-terminals soft open point (SOP) operation control mode switching technology under the multi-feeder faults. Firstly, combined with the inner and outer loop structures of control mode of three-terminals SOP, a control mode switching strategy with improved control logic is proposed. Secondly, in order to realize the smooth transition between SOP working modes under multiple feeder faults, a control mode switching process suitable for three-terminals SOP under multiple feeder faults is proposed. Then, by adopting the phase angle pre-synchronization strategy, the smoothness of the phase angle when the power-loss feeder is connected to the grid is ensured. Thirdly, the phase angle pre-synchronization strategy is adopted to ensure the smoothness of the phase angle when the power-loss feeder is connected to the grid. Finally, A power distribution system model with three-terminals SOP is built for simulation. The simulation results show that the proposed operation control mode switching technology can reduce the maximum voltage fluctuation on the DC side, and the voltage and phase angle of each port can transition smoothly.
Electricity, Production of electric energy or power. Powerplants. Central stations
In this paper we show how prescritive type checking and constraint solving can be combined to increase automation during software verification. We do so by defining a type system and implementing a typechecker for {log} (read `setlog'), a Constraint Logic Programming (CLP) language and satisfiability solver based on set theory. Hence, we proceed as follows: a) a type system for {log} is defined; b) the constraint solver is proved to be safe w.r.t. the type system; c) the implementation of a concrete typechecker is presented; d) the integration of type checking and set constraint solving to increase automation during software verification is discussed; and f) two industrial-strength case studies are presented where this combination is used with very good results. Under consideration in Theory and Practice of Logic Programming (TPLP)