Rule-based Research Articles

A System Control and Waveform Acquisition Framework for the Kicker System at SHINE

A lumped kicker magnet system is being developed as part of the electron beam switchyard of the Shanghai High Repetition Rate X-Ray Free-Electron Laser (XFEL) and Extreme Light (SHINE) facility at Shanghai Advanced Research Institute (SARI), Chinese Academy of Sciences (CAS). SHINE is a linac-based facility with an energy of 8 GeV and a repetition rate of 1 MHz for multiple users. The kicker system is a critical element of the deflection system and determines the stability performance of the beamlines. To ensure autonomous and reliable operation in a harsh environment, a dedicated control system—consisting of a custom-designed programmable logic controller (PLC) and waveform acquisition system—has been developed based on the Experimental Physics and Industrial Control System (EPICS). The PLCsystem enables status acquisition, control implantations, and alarm handling, allowing for automated operation. The waveform acquisition system provides an intuitive demonstration and enables the real-time diagnosis of kicker waveforms. The results demonstrate a half quasi-sinuous waveform with a peak current of 11.5 A and a full width at half maximum of 218 ns at a repetition rate of 10 kHz. The detailed design, overall concept, achieved performance, and initial test results of two modular controls are provided for further demonstration.

Popper and Lakatos on what is distinctive about empirical science

ABSTRACT This article offers critical analysis of the debate between Karl Popper and Imre Lakatos about distinctive features of empirical science that allow to demarcate it from non-science and especially pseudoscience. Popper's demarcational proposition is explained as involving essentially two requirements, logical (to the theory itself, that it had potential falsifiers) and methodological (to scientists' attitude with respect to the theory, that they were willing to recognise its empirical falsification. Lakatos's criticisms of ‘Popper's basic rule’ that ‘criteria of refutation have to be laid down beforehand’ and Popper's reply to these criticisms, as well as Lakatos's alternative demarcational proposition, are discussed. The argument is made that although Lakatos's criticisms and proposition have considerable merits, he goes too far in claiming that it is OK for scientists to wave away or ignore apparent falsifications of a theory so long as the theory is successful in generating lots of successful unexpectable predictions, and generally understates the importance of the critical attitude for the development of science. On the other hand, Popper's falsificationist approach can be modified so as to accommodate sound points of Lakatos's criticisms and proposition while retaining the basic structure and critical rationalist character of Popper's demarcational proposition.

An AI-Driven Approach to Automatic Code Analysis and Summarization for Enhanced Software Understanding

- The AI-Driven Code Analyzer is an innovative tool designed to address the challenges developers, students, and educators face in understanding complex code. With programming becoming an integral part of various industries, the need for accessible tools that simplify code comprehension has never been greater. This tool allows users to input code and receive detailed, step-by-step explanations of its functionality, helping to demystify programming logic and enhance understanding. Whether it’s a beginner struggling with foundational concepts or an experienced programmer dealing with an unfamiliar codebase, the AI-Driven Code Analyzer serves as a valuable resource for improving productivity and learning efficiency. At the heart of this tool lies its integration with the Gemini API, a sophisticated backend system that processes inputted code and generates human-readable explanations. This integration ensures a seamless and efficient user experience, providing accurate and insightful breakdowns of even the most complex algorithms. The modular design of the platform allows it to handle diverse programming languages and concepts, making it a versatile solution for various user needs. Complementing its technical prowess is a user-friendly interface that simplifies interaction, enabling users to focus solely on understanding the code rather than navigating the tool itself. The applications of the AI-Driven Code Analyzer extend far beyond individual use. In educational settings, it can be leveraged as a teaching aid, helping students grasp difficult programming concepts through real-world examples. In professional environments, it can assist developers in debugging, code reviews, and understanding legacy systems, significantly reducing time spent on manual analysis. By streamlining the process of code explanation, this tool not only fosters a deeper understanding of programming but also addresses the growing demand for accessible and intelligent software solutions in an increasingly tech-driven world. Key Words: Code Analysis, Code Summarization, Code Comprehension, Human-Readable Explanations, Natural Language Processing (NLP), Programming Logic, Collaborative Development, Productivity Enhancement, Educational Applications, System Architecture, Learning Efficiency

A Classification Model Based on Interval Rule Inference Network with Interpretability

Interpretability requirements, complex uncertain data processing, and limited training data are characteristics of classification in some real industry applications. The interval belief rule base (IBRB) can deal with various types of uncertainty and provides high interpretability. However, there is a large number of parameters in IBRB, which makes it difficult for experts to accurately set them manually, limiting its application scope. To address this issue, this paper proposes an interval rule inference network (IRIN) with interpretability for classification models to automatically generate IBRB through integrating the ideas of the IBRB and the neural network. Firstly, hybrid data with different types are transformed into an interval belief distribution for automatic generation processing. Secondly, the interval evidence reasoning method is utilized as the inference engine to transfer information ensuring the process’s interpretability. Finally, a reasonable IBRB is generated automatically by updating the parameters by employing the learning engine in the neural network. Moreover, the differentiability of the interval evidence reasoning method in the IRIN is proved as a theoretical foundation of the IRIN, and an interpretability analysis of the IRIN’s structures is discussed. Experimental results demonstrate that the proposed method possesses high interpretability, enhancing the reliability of classification and maintaining the accuracy. Its application in an actual engineering case illustrates that it is particularly suitable for engineering problems where the explanation of results is a critical requirement.

Identifying Needs and Support Services for Family Caregivers of Older Community-Based Family Members: Mixed-Method Research Findings.

A recent Canadian study conducted in one province identified family caregiver support needs and essential support services when caring for older community-based family members requiring assistance with activities of daily living. Weekly interviews of 150 volunteer caregivers over 6 months identified 11 support needs and 5 essential support services. Scoping literature reviews of the 11 needs found they had all been identified before. Program logic investigations of the 5 support services identified a patch-work of temporarily available support services in existence across the province. Two governmental policies are recommended: (a) provincial policy assuring access to the five support services, and (b) federal policy for federal-provincial funding transfers to address the provincial cost of assured community-based support services. Family caregivers require this support to maintain their own and their family member's well-being, particularly as this caregiving prevents or delays older family member hospitalizations and nursing home entry.

Transcript-specific enrichment enables profiling of rare cell states via single-cell RNA sequencing.

Single-cell genomics technologies have accelerated our understanding of cell-state heterogeneity in diverse contexts. Although single-cell RNA sequencing identifies rare populations that express specific marker transcript combinations, traditional flow sorting requires cell surface markers with high-fidelity antibodies, limiting our ability to interrogate these populations. In addition, many single-cell studies require the isolation of nuclei from tissue, eliminating the ability to enrich learned rare cell states based on extranuclear protein markers. In the present report, we addressed these limitations by developing Programmable Enrichment via RNA FlowFISH by sequencing (PERFF-seq), a scalable assay that enables scRNA-seq profiling of subpopulations defined by the abundance of specific RNA transcripts. Across immune populations (n = 184,126 cells) and fresh-frozen and formalin-fixed, paraffin-embedded brain tissue (n = 33,145 nuclei), we demonstrated that programmable sorting logic via RNA-based cytometry can isolate rare cell populations and uncover phenotypic heterogeneity via downstream, high-throughput, single-cell genomics analyses.

Program Logics à la Carte

Program logics have proven a successful strategy for verification of complex programs. By providing local reasoning and means of abstraction and composition, they allow reasoning principles for individual components of a program to be combined to prove guarantees about a whole program. Crucially, these components and their proofs can be reused. However, this reuse is only available once the program logic has been defined. It is a frustrating fact of the status quo that whoever defines a new program logic must establish every part, both semantics and proof rules, from scratch. In spite of programming languages and program logics typically sharing many core features, reuse is generally not available across languages. Even inside one language, if the same underlying operation appears in multiple language primitives, reuse is typically not possible when establishing proof rules for the program logic. To enable reuse across and inside languages when defining complex program logics (and proving them sound), we serve program logics à la carte by combining program logic fragments for the various effects of the language. Among other language features, the menu includes shared state, concurrency, and non-determinism as reusable, composable blocks that can be combined to define a program logic modularly. Our theory builds on ITrees as a framework to express language semantics and Iris as the underlying separation logic; the work has been mechanized in the Coq proof assistant.

Finite-Choice Logic Programming

Logic programming, as exemplified by datalog, defines the meaning of a program as its unique smallest model: the deductive closure of its inference rules. However, many problems call for an enumeration of models that vary along some set of choices while maintaining structural and logical constraints—there is no single canonical model. The notion of stable models for logic programs with negation has successfully captured programmer intuition about the set of valid solutions for such problems, giving rise to a family of programming languages and associated solvers known as answer set programming. Unfortunately, the definition of a stable model is frustratingly indirect, especially in the presence of rules containing free variables. We propose a new formalism, finite-choice logic programming, that uses choice, not negation, to admit multiple solutions. Finite-choice logic programming contains all the expressive power of the stable model semantics, gives meaning to a new and useful class of programs, and enjoys a least-fixed-point interpretation over a novel domain. We present an algorithm for exploring the solution space and prove it correct with respect to our semantics. Our implementation, the Dusa logic programming language, has performance that compares favorably with state-of-the-art answer set solvers and exhibits more predictable scaling with problem size.

A Quantitative Probabilistic Relational Hoare Logic

We introduce eRHL, a program logic for reasoning about relational expectation properties of pairs of probabilistic programs. eRHL is quantitative, i.e., its pre- and post-conditions take values in the extended non-negative reals. Thanks to its quantitative assertions, eRHL overcomes randomness alignment restrictions from prior logics, including pRHL, a popular relational program logic used to reason about security of cryptographic constructions, and apRHL, a variant of pRHL for differential privacy. As a result, eRHL is the first relational probabilistic program logic to be supported by non-trivial soundness and completeness results for all almost surely terminating programs. We show that eRHL is sound and complete with respect to program equivalence, statistical distance, and differential privacy. We also show that every pRHL judgment is valid iff it is provable in eRHL. We showcase the practical benefits of eRHL with examples that are beyond reach of pRHL and apRHL.

Bluebell: An Alliance of Relational Lifting and Independence for Probabilistic Reasoning

We present Bluebell, a program logic for reasoning about probabilistic programs where unary and relational styles of reasoning come together to create new reasoning tools. Unary-style reasoning is very expressive and is powered by foundational mechanisms to reason about probabilistic behavior like independence and conditioning . The relational style of reasoning, on the other hand, naturally shines when the properties of interest compare the behavior of similar programs (e.g. when proving differential privacy) managing to avoid having to characterize the output distributions of the individual programs. So far, the two styles of reasoning have largely remained separate in the many program logics designed for the deductive verification of probabilistic programs. In Bluebell, we unify these styles of reasoning through the introduction of a new modality called “joint conditioning” that can encode and illuminate the rich interaction between conditional independence and relational liftings ; the two powerhouses from the two styles of reasoning.

Formal Foundations for Translational Separation Logic Verifiers

Program verification tools are often implemented as front-end translations of an input program into an intermediate verification language (IVL) such as Boogie, GIL, Viper, or Why3. The resulting IVL program is then verified using an existing back-end verifier. A soundness proof for such a translational verifier needs to relate the input program and verification logic to the semantics of the IVL, which in turn needs to be connected with the verification logic implemented in the back-end verifiers. Performing such proofs is challenging due to the large semantic gap between the input and output programs and logics, especially for complex verification logics such as separation logic. This paper presents a formal framework for reasoning about translational separation logic verifiers. At its center is a generic core IVL that captures the essence of different separation logics. We define its operational semantics and formally connect it to two different back-end verifiers, which use symbolic execution and verification condition generation, resp. Crucially, this semantics uses angelic non-determinism to enable the application of different proof search algorithms and heuristics in the back-end verifiers. An axiomatic semantics for the core IVL simplifies reasoning about the front-end translation by performing essential proof steps once and for all in the equivalence proof with the operational semantics rather than for each concrete front-end translation. We illustrate the usefulness of our formal framework by instantiating our core IVL with elements of Viper and connecting it to two Viper back-ends as well as a front-end for concurrent separation logic. All our technical results have been formalized in Isabelle/HOL, including the core IVL and its semantics, the semantics of two back-ends for a subset of Viper, and all proofs.

A Taxonomy of Hoare-Like Logics: Towards a Holistic View using Predicate Transformers and Kleene Algebras with Top and Tests

We study Hoare-like logics, including partial and total correctness Hoare logic, incorrectness logic, Lisbon logic, and many others through the lens of predicate transformers à la Dijkstra and through the lens of Kleene algebra with top and tests (TopKAT). Our main goal is to give an overview – a taxonomy – of how these program logics relate, in particular under different assumptions like for example program termination, determinism, and reversibility. As a byproduct, we obtain a TopKAT characterization of Lisbon logic, which – to the best of our knowledge – is a novel result.

The Best of Abstract Interpretations

We study “the best of abstract interpretations” , that is, the best possible abstract interpretations of programs. Abstract interpretations are inductively defined by composing abstract transfer functions for the basic commands, such as assignments and Boolean guards. However, abstract interpretation is not compositional: even if the abstract transfer functions of the basic commands are the best possible ones on a given abstract domain A this does not imply that the whole inductive abstract interpretation of a program p is still the best in A . When this happens we are in the optimal scenario where the abstract interpretation of p coincides with the abstraction of the concrete interpretation of p . Our main contributions are threefold. Firstly, we investigate the computability properties of the class of programs having the best possible abstract interpretation on a fixed abstract domain A . We show that this class is, in general, not straightforward and not recursive. Secondly, we prove the impossibility of achieving the best possible abstract interpretation of any program p either by an effective compilation of p or by minimally refining or simplifying the abstract domain A . These results show that the program property of having the best possible abstract interpretation is not trivial and, in general, hard to achieve. We then show how to prove that the abstract interpretation of a program is indeed the best possible one. To this aim, we put forward a program logic parameterized on an abstract domain A which infers triples [ pre ] A p [ post ] A . These triples encode that the inductive abstract interpretation of p on A with abstract input pre ∈ A gives post ∈ A as abstract output and this is the best possible in A .

Data Race Freedom à la Mode

We present DRFcaml, an extension of OCaml's type system that guarantees data race freedom for multi-threaded OCaml programs while retaining backward compatibility with existing sequential OCaml code. We build on recent work of Lorenzen et al., who extend OCaml with modes that keep track of locality, uniqueness, and affinity. We introduce two new mode axes, contention and portability, which record whether data has been shared or can be shared between multiple threads. Although this basic type-and-mode system has limited expressive power by itself, it does let us express APIs for capsules, regions of memory whose access is controlled by a unique ghost key, and reader-writer locks, which allow a thread to safely acquire partial or full ownership of a key. We show that this allows complex data structures (which may involve aliasing and mutable state) to be safely shared between threads. We formalize the complete system and establish its soundness by building a semantic model of it in the Iris program logic on top of the Rocq proof assistant.

The Role of the Family in the System Of Spiritual and Intellectual Upbringing of the Personality

Based on the analysis of the essence of the family, family values and relationships, the principles of upbringing of children by parents and formation of a strong personality are proposed. The relevance of the topic is due to the general recognition of the importance of raising children in the family and the insufficient elaboration of methodological developments on the purposeful formation of the personality in children by parents, the spiritual core in them, which would allow them to confidently follow their life path. It is in the family that a child receives the foundations that shape him or her as an individual, the foundation on which the entire personality structure is formed in the future. Mostly, this happens subconsciously, usually through copying parents: how they communicate, treat themselves, each other, others, what they consider right and wrong, what they like/dislike, even their manners and intonations. In this way, a system of values is laid down - what is considered important, what to value, what one cannot live without. These values then form a person's worldview, a certain civic position, behavior in society, social activity, and much more, which makes him or her (or does not make him or her) a socially responsible person capable of effective work for the benefit of themselves, their family, and society as a whole. The author analyzes the composition of the main spiritual values of the family and the principles of building a happy family: responsibility, feelings, honesty, sincerity, openness, communication, generosity, forgiveness, cohesion, traditions, flexibility. The features of relationships in the family and appropriate traditions that enhance the educational effect, allow to unite different generations in one event, and ensure the continuity of generations are investigated. The author presents the historical development of views on family education and the current state of the problem. The issues of interaction between family and school in the upbringing of younger generations, preparing them for independent life and work are considered. Recommendations on the basic rules of parenting are developed, aimed at forming a personality, a person strong in spirit and body, with a spiritual core. The results of the study can serve as a basis for the development of methodological guides for parents as a guide in the difficult task of upbringing children in a family to be self-confident and confident in the future they are ready to build as individuals. Additional research can provide the basis for creating a holistic system as a social institution for preparing parents for upbringing children.

Biocatalytic programming of protocell-embodied logic gates and circuits

Biocatalytic programming of protocell-embodied logic gates and circuits

Industrial Design of Type-1 and Interval Type-2 Fuzzy Logic Control

This paper focuses on the design of type-1 and interval type-2 (IT2) PID fuzzy logic controllers (FLC) for ensuring - by a programmable logic controller (PLC) - a high-performance real-time liquid level control in a carbonization column (CCl) for soda production. Firstly, Takagi-Sugeno-Kang models - derived via genetic algorithms parameter optimizations, experimental data and simulations for the basic and the worst CCl loads - are studied at different operation points, and the worst Ziegler-Nichols (ZN) model is assessed. Next, two-input fuzzy units are designed - assuming various membership functions (MF) and uncertainties - and the greatest linearization gain is computed. Based on it and the ZN worst plant model, the parameters of the FLC input pre-processing differentiator and the PI post-processing are empirically tuned. Finally, a PLC oriented analytical description of the IT2 MF, fuzzy rules and type-reduction is suggested. The designed FLC systems are studied via simulation to determine the factors that have the greatest impact on the system performance improvement. The obtained results unveil that the tuned FLC outperforms the tuned linear PI. Better system performance is achieved by a small number of MF with large support ensuring economical PLC presentation.

Industrial robot control system research based on the direction of programmable logic controllers

Industrial robot control system research based on the direction of programmable logic controllers

Implementation of an MPPT Technique for Solar PV Systems Based on a Programmable Logic Controller

Implementation of an MPPT Technique for Solar PV Systems Based on a Programmable Logic Controller

An Evolutionary Federated Learning Approach to Diagnose Alzheimer's Disease Under Uncertainty.

Background: Alzheimer's disease (AD) leads to severe cognitive impairment and functional decline in patients, and its exact cause remains unknown. Early diagnosis of AD is imperative to enable timely interventions that can slow the progression of the disease. This research tackles the complexity and uncertainty of AD by employing a multimodal approach that integrates medical imaging and demographic data. Methods: To scale this system to larger environments, such as hospital settings, and to ensure the sustainability, security, and privacy of sensitive data, this research employs both deep learning and federated learning frameworks. MRI images are pre-processed and fed into a convolutional neural network (CNN), which generates a prediction file. This prediction file is then combined with demographic data and distributed among clients for local training. Training is conducted both locally and globally using a belief rule base (BRB), which effectively integrates various data sources into a comprehensive diagnostic model. Results: The aggregated data values from local training are collected on a central server. Various aggregation methods are evaluated to assess the performance of the federated learning model, with results indicating that FedAvg outperforms other methods, achieving a global accuracy of 99.9%. Conclusions: The BRB effectively manages the uncertainty associated with AD data, providing a robust framework for integrating and analyzing diverse information. This research not only advances AD diagnostics by integrating multimodal data but also underscores the potential of federated learning for scalable, privacy-preserving healthcare solutions.