Boolean Expressions Research Articles

Neurosyphilis-Induced Psychosis in Europe: A Systematic Review of Case Reports

The tertiary stage of syphilis appears to be the most diverse of the three recognized, with a number of cardiovascular, gummatous, neurological and psychiatric symptoms. This systematic review’s aim is to analyze cases of organic psychoses secondary to tertiary syphilis, inspect the diagnostic procedures and assess the effectiveness of treatment. Case studies from Pubmed and MEDLINE Ultimate were sought out with the Boolean expression ‘((neurosyphilis) OR (syphilis) OR (treponema pallidum)) AND (case report) AND ((psychosis) OR (psychotic))’ to later undergo screening for exclusion criteria (according to the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020 guidelines). Each report was accepted independently by two authors. Case reports were later appraised using the JBI Critical Appraisal Checklist for Case Reports. Finally, 11 case reports were included in the study. The most frequently reported psychiatric symptoms were delusions (91%) and hallucinations (81%), disorientation (about 42%) and sleep disturbances and memory loss (36%). Several inconsistencies in diagnostic processes were found in some of the case reports, while treatment procedures were more in line with the guidelines. A disease as complex as neurosyphilis requires great awareness and cooperation between various medical specialties. Despite its mimicry and variability in presentation, the discussed case reports prove that it can be successfully dealt with.

Conditional Community Search Based on Weight Information

Community search aims to identify cohesive subgraphs containing user-given query nodes in social networks. As information technology develops, user demands for community search have become increasingly sophisticated. The searched communities must not only meet the structural cohesiveness requirements but also adhere to some complex search conditions based on Boolean expressions. For example, certain desired nodes should be contained in the communities, while certain undesired nodes cannot exist in the communities, which is called conditional community search. However, existing solutions for conditional community search often introduce some undesired nodes into the identified communities and exhibit relatively low search efficiency. To overcome these drawbacks, therefore, this paper investigates the problem of conditional community search based on weight information. First, we refine the original problem definition of conditional community search and outline the need for an improved algorithm for calculating the weights of the nodes. Then, we explore two novel algorithms for searching conditional communities based on calculated weight information. Finally, we conduct extensive experiments on several real-world datasets to verify the accuracy and efficiency of our proposed searching algorithms.

Research and Design of Data Encryption Circuit based on D Flip-Flop

Since information technology is developing so quickly, the problem of data security has become increasingly prominent. Data encryption technology, as an important means of securing data transmission, plays a vital role in resisting network attacks and protecting privacy. In this paper, a data encryption circuit based on D flip-flop is studied, aiming to improve the efficiency and security of data encryption. First, this paper introduces the basic principles of data encryption and analyzes the application of D flip-flop in encryption circuit. Subsequently, the design principle of Moore’s state machine is elaborated, and encryption circuit is designed with the use of the state graph and state table. Further, this paper demonstrates the logic circuit design process., including the derivation of D-flip-flop input Circuit construction and Boolean expressions. This research provides a hardware implementation design solution for the design of data encryption circuits, which is expected to play a role in improving encryption speed and enhancing security.

Mixed Thermal and Renewable Energy Generation Optimization in Non-Interconnected Regions via Boolean Mapping

Global efforts aiming to shift towards renewable energy and smart grid configurations require accurate unit commitment schedules to guarantee power balance and ensure feasible operation under different complex constraints. Intelligent systems utilizing hybrid and high-level techniques have arisen as promising solutions to provide optimum exploration–exploitation trade-offs at the expense of computational complexity. To ameliorate this requirement, which is extremely expensive in non-interconnected renewable systems, radically different approaches based on enhanced priority schemes and Boolean encoding/decoding have to take place. This compilation encompasses various mappings that convert multi-valued clausal forms into Boolean expressions with equivalent satisfiability. Avoiding any need to introduce prior parameter settings, the solution utilizes state-of-the-art advancements in the field of artificial intelligence models, namely Boolean mapping. It allows for the efficient identification of the optimal configuration of a non-convex system with binary and discontinuous dynamics in the fewest possible trials, providing impressive performance. In this way, Boolean mapping becomes capable of providing global optimum solutions to unit commitment utilizing fully tractable procedures without deteriorating the computational time. The results, considering a non-interconnected power system, show that the proposed model based on artificial intelligence presents advantageous performance in terms of generating cost and complexity. This is particularly important in isolated networks, where even a-not-so great deviation between production and consumption may reflect as a major disturbance in terms of frequency and voltage.

Depression, Anxiety, and Stress Symptoms Among Students in Croatia During the COVID-19 Pandemic: A Systematic Review.

Background: The COVID-19 pandemic has led to an increased fear of infection, social isolation, financial concerns, and feelings of loneliness and uncertainty. Studies reveal that the pandemic has had a significant negative impact on mental health. This systematic review aimed to investigate the prevalence of depression, anxiety, and stress symptoms among students in Croatia during the COVID-19 pandemic. Methods: A systematic review was conducted following PRISMA guidelines. A literature search was performed on August 5, 2024, using the electronic databases Scopus, PubMed, Web of Science, and PsycINFO. The search utilized the Boolean logical operator expression ('depression' AND 'anxiety' AND 'stress' AND 'COVID-19' AND 'Croatia' AND 'student'). Results: Out of one hundred and fifty-four identified studies, five met the inclusion criteria and were included in the review. The studies used the Generalized Anxiety Disorder 7-item scale (GAD-7), the Patient Health Questionnaire-9 (PHQ-9), and the Depression, Anxiety, and Stress Scale-21 (DASS-21) to assess mental health. The results indicate a high prevalence of symptoms of depression, anxiety, and stress among students in Croatia during the pandemic. Conclusion: A high prevalence of negative affective emotions was observed among students in Croatia during the pandemic. This finding underscores the importance of developing effective strategies for the early identification and management of mental disorders among students, irrespective of the pandemic's conclusion.

Optimizing Over Serial Dictatorships

Motivated by the success of the serial dictatorship mechanism in social choice settings, we explore its usefulness in tackling various combinatorial optimization problems. We do so by considering an abstract model, in which a set of agents are asked to act in a particular ordering, called the action sequence. Each agent acts in a way that gives her the maximum possible value, given the actions of the agents who preceded her in the action sequence. Our goal is to compute action sequences that yield approximately optimal total value to the agents (a.k.a., social welfare). We assume query access to the value vi(S)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$v_i(S)$$\\end{document} that the agent i gets when she acts after the agents in the ordered set S. We establish tight bounds on the social welfare that can be achieved using polynomially many queries. Even though these bounds show a marginally sublinear approximation of optimal social welfare in general, excellent approximations can be obtained when the valuations stem from an underlying combinatorial domain. Indicatively, when the valuations are defined using bipartite matchings, arborescences in directed graphs, and satisfiability of Boolean expressions, simple query-efficient algorithms yield 2-approximations. We discuss issues related to truthfulness and show how some of our algorithms can be implemented truthfully using VCG-like payments. Finally, we introduce and study the price of serial dictatorship, a notion that provides an optimistic measure of the quality of combinatorial optimization solutions generated by action sequences.

BDD‐based optimized majority/minority gate using electro‐optic Mach‐Zehnder interferometer

AbstractIn this article, an optical majority/minority circuit has been designed using four electro‐optic Mach‐Zehnder interferometers. The binary decision diagram (BDD) is used to optimize the Boolean logical expression. In this design we get two outputs, one performs majority logic and the other performs minority logic simultaneously. Numerical simulation has been done with the obtained data from the beam propagation method. Pseudo‐eye diagram is also plotted to find amplitude modulation (AM) and extinction ratio (ER). AM and ER are found nearly 0.4 and 13 dB, respectively. A new parameter, the optical circuit complexity factor (OCC) factor has been calculated and from the result, it has been shown that the proposed design is more optimized than other majority/minority gate designs so far.

LogicGep: Boolean networks inference using symbolic regression from time-series transcriptomic profiling data.

Reconstructing the topology of gene regulatory network from gene expression data has been extensively studied. With the abundance functional transcriptomic data available, it is now feasible to systematically decipher regulatory interaction dynamics in a logic form such as a Boolean network (BN) framework, which qualitatively indicates how multiple regulators aggregated to affect a common target gene. However, inferring both the network topology and gene interaction dynamics simultaneously is still a challenging problem since gene expression data are typically noisy and data discretization is prone to information loss. We propose a new method for BN inference from time-series transcriptional profiles, called LogicGep. LogicGep formulates the identification of Boolean functions as a symbolic regression problem that learns the Boolean function expression and solve it efficiently through multi-objective optimization using an improved gene expression programming algorithm. To avoid overly emphasizing dynamic characteristics at the expense of topology structure ones, as traditional methods often do, a set of promising Boolean formulas for each target gene is evolved firstly, and a feed-forward neural network trained with continuous expression data is subsequently employed to pick out the final solution. We validated the efficacy of LogicGep using multiple datasets including both synthetic and real-world experimental data. The results elucidate that LogicGep adeptly infers accurate BN models, outperforming other representative BN inference algorithms in both network topology reconstruction and the identification of Boolean functions. Moreover, the execution of LogicGep is hundreds of times faster than other methods, especially in the case of large network inference.

World Trends in Dental Ergonomics Research: A Bibliometric Analysis.

Dental ergonomics provides an overview of dentists' work efficiency. The objective of this study was to obtain quantitative information and produce a visualization of the network of scientific publications on the topic of ergonomics and dentistry using bibliometric analysis. Data mining was conducted using the Scopus database and Boolean expressions (ergonom* AND dentist*) on 14 April 2023. Data extraction and analysis were performed using Open Refine version 3.5.2., VOSviewer version 1.6.17., VOSviewer thesaurus, Microsoft Excel, and Tableau Professional version 2020.1.2. A total of 682 documents were identified, with the United States having the largest number of documents and citations (89 documents, 1321 citations). Work, Dentistry Today, and the International Journal of Environmental Research and Public Health were the top three sources. Ergonomics and musculoskeletal disorders (MSDs) are two of the very prominent keywords, with research topics covering prevalence, causes, factors related to causes, prevention, assessment, rehabilitation, evaluation, and intervention. There was no research on ergonomic interventions that collaborated with human factors and ergonomics (HFE). We conclude that the trending topic of dental ergonomics research topics around the world is centered on MSDs. The future research challenge is to apply HFE science to improve the health, safety, efficiency, and quality of dentists' work.

Implementing policies and predictive stochastic models to restrict borderline personality disorder’s access to restricted medications: comorbidity with factitious disorder, functional neurological disorder and medically unexplained symptoms

IntroductionWe are facing increased access to hospital beds and increased use of restricted medications by people with borderline personality disorder (BPD). Our former research shows BPD comorbidity with factitious conditions, functional neurological disorder and medically unexplained symptoms. We also registered that persons with BPD might craft or exaggerate symptoms to access restricted medications. In the worst cases, they might share these medications (benzodiazepines, hypnotics, and anxiolytics) with street values for profit or other recreational purposes.ObjectivesTo generate forecasting models and preventive policies to deal with BPD factitious disorders and improve the effectiveness of the UK National Healthcare Service (NHS) in reducing unnecessary admissions to general and psychiatric hospitals. More selective policies will capture and discourage BPD’s feigning and exaggerating symptoms for accessing restricted medications.MethodsThe underlying analysis framework is stochastic forecasting. We used current knowledge and data to complete systematic future predictions extracted from recent trends. A logical-mathematical model generated the required expressions. We identify four major model components to be introduced in the model: BPD (A), factious disorders (B), prescribing restricted medications (C), antisocial behaviours (D), and access to hospital beds (E).ResultsThe Boolean expression becomes [A then B then C then D then E], or [A ⇒ (B ⇒ (C ⇒ (D ⇒ E))] with a truth density of 96.875% (Figure 1).ConclusionsBPD should alert healthcare of the risks of symptom exaggeration and factitious mental diseases. These conditions are used to access often restricted medications, such as benzodiazepines, sleep tablets, and anxiolytics, for personal and communal use. Street sharing of these last increases local criminality. In worst cases, a hospital bed is granted without preventive triage. The risk is the indoor access to these medications. We advocate policies for the discontinuation of community prescription of these drugs.Disclosure of InterestNone Declared

Composing Biases by Using CP to Decompose Minimal Functional Dependencies for Acquiring Complex Formulae

Given a table with a minimal set of input columns that functionally determines an output column, we introduce a method that tries to gradually decompose the corresponding minimal functional dependency (mfd) to acquire a formula expressing the output column in terms of the input columns. A first key element of the method is to create sub-problems that are easier to solve than the original formula acquisition problem, either because it learns formulae with fewer inputs parameters, or as it focuses on formulae of a particular class, such as Boolean formulae; as a result, the acquired formulae can mix different learning biases such as polynomials, conditionals or Boolean expressions. A second key feature of the method is that it can be applied recursively to find formulae that combine polynomial, conditional or Boolean sub-terms in a nested manner. The method was tested on data for eight families of combinatorial objects; new conjectures were found that were previously unattainable. The method often creates conjectures that combine several formulae into one with a limited number of automatically found Boolean terms.

Reducing the Length of Dynamic and Relevant Slices by Pruning Boolean Expressions

Dynamic and relevant (backward) slicing helps programmers in the debugging process by reducing the number of statements in an execution trace. In this paper, we propose an approach called pruned slicing, which can further reduce the size of slices by reasoning over Boolean expressions. It adds only those parts of a Boolean expression that are responsible for the evaluation outcome of the Boolean expression to the set of relevant variables. We empirically evaluate our approach and compare it to dynamic and relevant slicing using three small benchmarks: the traffic collision avoidance system (TCAS), the Refactory dataset, and QuixBugs. Pruned slicing reduces the size of the TCAS slices on average by 10.2%, but it does not reduce the slice sizes of the Refactory and QuixBugs programs. The times required for computing pruned dynamic and relevant slices are comparable to the computation times of non-pruned dynamic and relevant slices. Thus, pruned slicing is an extension of dynamic and relevant slicing that can reduce the size of slices while having a negligible computational overhead.

The Role of Regulatory T cells (Tregs) in Tumorigenesis: A Comprehensive Literature Review

Introduction: Regulatory T cells (Tregs) are a subpopulation of CD4+ T lymphocytes that contribute to immune homeostasis by suppressing excessive immune activation. However, these immunosuppressive properties can lead to the suppression of anti-tumor immune responses. Depletion or blocking of Tregs through therapeutics has emerged as a possible method for enhancing anti-tumor immunity. However, the lack of selective targeting of Tregs in the tumor microenvironment is a significant limitation to the effectiveness of Treg therapies. Therefore, this investigation aims to review current literature on how Tregs suppress the antitumor immune response and how they can be targeted to promote anti-tumor immunity. Methods: This review examines recent literature on Tregs in the tumor microenvironment, focusing on both cell-contact dependent and independent mechanisms. Clinical trial studies were also included to assess therapeutic targeting of Tregs. The PubMed database was systematically searched for English articles from 2010 to present, supplemented by manual searches without date restrictions. Boolean expressions ensured comprehensive study retrieval. Results: The involvement of Tregs in the development of multiple cancer types is evident, and targeting these cells could potentially enhance the efficacy of antitumor immunity. In addition, we compiled a list of the novel approaches currently being used for Treg targeting in the context of cancer. Discussion: This review has identified the most promising targets for Treg-based therapies, opening avenues for accelerating the development of innovative cancer treatments. Conclusion: Our literature review offers insights into the complex interplay between the immune system and cancer. The understanding of this interaction is not just an endpoint but could potentially act as a steppingstone towards new scientific discoveries.

Information Needs among Parents of Cancer Children: A Systematic Review

Background: When a child is diagnosed with cancer, parents are affected, especially if they are unprepared and do not understand the illness. Parents' informational needs are frequently expressed and vary, so identifying parent needs is critical before providing health or nursing interventions. Aims: This review aimed to identify the information needs of parents of children with cancer. Method: PubMed, Medline, and CINAHL were applied to conduct electronic searches. The terms “information needs”, “parent”, “cancer”, and “child” were combined using Boolean expressions “OR” and “AND”. The inclusion criteria were non-experimental studies published in English within the last 10 years (2011-2021). A risk of bias assessment was conducted for each included study using the Joanna Bridge Institute critical appraisal tools. After identification and screening, the articles were filtered according to the criteria with a total of 15 articles included in this study. Furthermore, data extraction and synthesis were carried out. Narrative synthesis is used by collecting information on the findings of the article to be grouped into themes of finding problems. Results: Four themes emerged based on the results of the analysis: types of information needed; information formats; provision of information; and sources of information. Conclusion: This study concluded that parents require accurate information that addresses their specific information needs and is appropriate for their child's cancer stage. The provision of balanced written and verbal information, as well as information that is complete, accurate, and reliable, makes it easier for parents to increase their knowledge about caring for their children.

Conjecturing-Based Discovery of Patterns in Data

We propose the use of a conjecturing machine that suggests feature relationships in the form of bounds involving nonlinear terms for numerical features and Boolean expressions for categorical features. The proposed Conjecturing framework recovers known nonlinear and Boolean relationships among features from data. In both settings, true underlying relationships are revealed. We then compare the method to a previously proposed framework for symbolic regression on the ability to recover equations that are satisfied among features in a data set. The framework is then applied to patient-level data regarding COVID-19 outcomes to suggest possible risk factors that are confirmed in the medical literature. Discovering patterns in data is a first step toward establishing causal relationships, which can be the basis for effective decision making. Data Ethics & Reproducibility Note: Code and data to reproduce results are available here: https://github.com/jpbrooks/conjecturing . COVID-19 synthetic patient data were obtained as part of the Veterans Health Administration (VHA) Innovation Ecosystem and precisionFDA COVID-19 Risk Factor Modeling Challenge and are used here with permission from the Food and Drug Administration (FDA). The e-companion is available at https://doi.org/10.1287/ijds.2021.0043 . History: Olivia Sheng served as the senior editor for this article.

Critical thinking pedagogical practices in medical education: a systematic review.

The development of critical thinking (CT) has been a universal goal in higher education. A systematic review of the literature was conducted to evaluate the effectiveness of currently used pedagogical practices to foster CT/ clinical reasoning (CR)/ clinical judgment (CJ) skills and/or dispositions in undergraduate medical students. PubMed, Web of Science and Scopus databases were searched from January 2010 to April 2021 with a predefined Boolean expression. Of the 3221 articles originally identified, 33 articles were included by using PICOS methodology. From these, 21 (64%) reported CR pedagogical practices and 12 (36%) CT pedagogical practices. Overall, pedagogical practices such as cognitive/visual representation, simulation, literature exposure, test-enhancing and team-based learning, clinical case discussion, error-based learning, game-based learning seem to enhance CT/CR skills and/or dispositions. Further research is required to identify the optimal timing, duration and modality of pedagogical interventions for effectively foster CT/CR in medical education.

Investigating the effects of software tool based learning model for minimizing the given boolean expression in computer science/digital electronics

Investigating the effects of software tool based learning model for minimizing the given boolean expression in computer science/digital electronics

Do we need high-order mutation in fault-based Boolean-specification testing?

Fault-based Boolean-specification testing is an essential weak mutation testing technique since the executing paths of the programs are usually dependent on those Boolean expressions in predicate statements. In mutation testing, the coupling effect hypothesis assumes that a test set that detects all the simple faults in the program can detect a high percentage of complex faults. Such a hypothesis suggests that we do not need to perform high-order mutation testing in practice. Many empirical results have shown the existence of the coupling effect. However, as fault-based Boolean-specification testing is mainly used in safety-critical scenarios, the question of whether high-order mutation testing is necessary for Boolean-specifications needs to be answered more strictly. To answer whether we need high-order mutation testing in fault-based Boolean-specification testing, we conduct an empirical study on general-form Boolean expressions. Experimental results show us that: (1) Generally, it is slightly easier to kill high-order mutants (HOMs) than first-order mutants (FOMs) since fault detection probabilities for complex faults (HOMs) are marginally higher than that for simple faults (FOMs) for almost all the cases. (2) However, for any fault types in Boolean-specifications, the coupling effect hypothesis is not guaranteed to hold with absolute certainty. (3) When HOMs involve the expression negation fault (ENF) or the operator reference fault (ORF), the percent of killed HOMs is lower. Such results suggest that, if the testing resource is sufficient, high-order mutation testing could be carried out on fault types ENF and ORF in fault-based Boolean-specification testing.

Recent Advances and Emerging Directions in Fire Detection Systems Based on Machine Learning Algorithms

Fire detection is a critical safety issue due to the major and irreversible consequences of fire, from economic prejudices to loss of life. It is therefore of utmost importance to design reliable, automated systems that can issue early alarms. The objective of this review is to present the state of the art in the area of fire detection, prevention and propagation modeling with machine learning algorithms. In order to understand how an artificial intelligence application penetrates an area of fire detection, a quantitative scientometric analysis was first performed. A literature search process was conducted on the SCOPUS database using terms and Boolean expressions related to fire detection techniques and machine learning areas. A number of 2332 documents were returned upon the bibliometric analysis. Fourteen datasets used in the training of deep learning models were examined, discussing critically the quality parameters, such as the dataset volume, class imbalance, and sample diversity. A separate discussion was dedicated to identifying issues that require further research in order to provide further insights, and faster and more accurate models.. The literature survey identified the main issues the current research should address: class imbalance in datasets, misclassification, and datasets currently used in model training. Recent advances in deep learning models such as transfer learning and (vision) transformers were discussed.

IEC61131-3 Instruction List Language Processor for FPGAs

This paper presents the architecture and field-programmable gate array (FPGA) implementation of a 32-bit central processing unit (CPU) dedicated to programmable logic controllers (PLCs). The CPU instruction set directly matches the instructions of the IEC 61131-3 standard Instruction List (IL) language. The designed IL processor is capable of handling 1-bit, 8-bit, 16-bit, and 32-bit data types. Apart from integer arithmetic operations, the IL processor also performs single precision floating-point operations included in the IEC 61131-3 IL language specification. It also directly performs instructions with a parenthesis modifier for all supported data types and facilitates the fast execution of a code containing Boolean expressions. This paper also presents the performance evaluation results of the developed IL processor. Although the IL processor executed the test algorithms substantially faster than industrial PLCs (e.g., Siemens S7-1200 and Fanux VersaMax), it turned out to be significantly slower than high-performance programmable automation controllers (PACs).