Regular Rules Research Articles

Identifying Predictors of Smoking Switching Behaviours Among Adult Smokers in the United States: A Machine Learning Approach.

Completely abstaining from cigarette smoking or fully switching to e-cigarette (EC) use may be beneficial for reducing the global burden of smoking-related diseases. This study aimed to identify and compare the top 10 prospective predictors of smokers switching away from smoking in the United States. Data from adult exclusive cigarette smokers at Wave 4 of the Population Assessment of Tobacco and Health (PATH) study, who were followed up at Wave 6, were analysed. An Xgboost-based machine learning (ML) approach with a nested cross-validation scheme was utilised to develop a multiclass predictive model to classify smokers' behavioural changes from W4 to W6, including smoking cessation, full and partial switching to EC, and cigarette non-switching. The SHapley Additive exPlanations (SHAP) algorithm was deployed to interpret the top 10 predictors of each switching behaviour. A total of 396 variables were selectedto generate the four-class prediction model, which demonstrated a micro- and macro-average area under the receiver operating characteristics curve (ROC-AUC) of 0.91 and 0.81, respectively. The topthreepredictors of smoking cessation were prior regular EC use, age, and household rules about non-combusted tobacco. Forfull switching to EC use, the leading predictorswere age, type of living space, and frequency of social media visits. Forpartial switching to EC use, the key predictorswere dailycigaretteconsumption, the time from waking up to smoking the first cigarette, and living with tobacco users. ML is a promising techniqueforprovidingcomprehensive insights into predicting smokers' behavioural changes. Public health interventions aimed athelping adults switch away from smoking should consider the predictors identified in this study.

Generation and deployment of honeytokens in relational databases for cyber deception

Despite considerable investments in database security, global statistics indicate an exponential increase in data breaches. Organizations are often unaware of data breaches for weeks, months, or even years. Sufficient for adversaries to compromise and ex-filtrate business or mission-critical data. Recent research suggests using honeytokens for early detection of data breaches in organizations. Existing honeytoken generation methods rely on regular expressions, rule mining, constraint satisfaction, or representation learning, which are complex and limited to a few attributes. We created a framework for generating and deploying honeytokens in relational databases that actively monitor sensitive records and quickly detect data breaches and their misuse. To generate the honeytoken we have used the hierarchical machine learning algorithm which uses a recursive technique to model the parent–child relationships of multi-table databases. The proposed method enables the organization to take remedial action to reduce the impact of data breaches and complement existing database security solutions.

Time series clustering using fragmented autocorrelations

We propose and study an autocorrelation procedure designed to characterize and compare large sets of long time series. This time-domain procedure is contrasted with a frequency-domain approach that has recently been introduced and discussed in the literature. In both cases, instead of using all the information available from data, which would be computationally very expensive, adequate regularization rules select and summarize the most relevant information suitable for clustering purposes. Essentially, we suggest to use the autocorrelation coefficients of the time series that are only computed around the lags of greatest interest. Then, we study this method in several ways. We argue theoretically that fragmenting the autocorrelation function can have efficiency advantages when comparing time series. By means of a large simulation study, we show that the suggested procedure can condense the relevant information of the time series. We compare its results with those from the frequency domain counterpart. We further illustrate this procedure in a study of the evolution of several stock markets indices and show the effect of recent financial crises on the behaviour of these indices.

Identifying multidisciplinary problems from scientific publications based on a text generation method

Abstract Purpose A text generation based multidisciplinary problem identification method is proposed, which does not rely on a large amount of data annotation. Design/methodology/approach The proposed method first identifies the research objective types and disciplinary labels of papers using a text classification technique; second, it generates abstractive titles for each paper based on abstract and research objective types using a generative pre-trained language model; third, it extracts problem phrases from generated titles according to regular expression rules; fourth, it creates problem relation networks and identifies the same problems by exploiting a weighted community detection algorithm; finally, it identifies multidisciplinary problems based on the disciplinary labels of papers. Findings Experiments in the “Carbon Peaking and Carbon Neutrality” field show that the proposed method can effectively identify multidisciplinary research problems. The disciplinary distribution of the identified problems is consistent with our understanding of multidisciplinary collaboration in the field. Research limitations It is necessary to use the proposed method in other multidisciplinary fields to validate its effectiveness. Practical implications Multidisciplinary problem identification helps to gather multidisciplinary forces to solve complex real-world problems for the governments, fund valuable multidisciplinary problems for research management authorities, and borrow ideas from other disciplines for researchers. Originality/value This approach proposes a novel multidisciplinary problem identification method based on text generation, which identifies multidisciplinary problems based on generative abstractive titles of papers without data annotation required by standard sequence labeling techniques.

Hierarchical Flight Control System for Tilt-Rotor Drones

This paper presents a hierarchical flight control system for tilt-rotor drones. The offered approach performs high-level mission goals by gradually confirming them into machine-level instructions. The learned data from numerous sensors is spread backside to the greater levels for sensitive decision making. Each vertical take-off and landing drone is linked through regular wireless communication rules for accessible multi-agent facility. The proposed flight control system has been effectively employed on several small tilt-rotor drones and validated in some applications. Solutions from waypoint navigation, a probabilistic chase-evasion competition and vision-based object chasing show the capability of the recommended method for intelligent flying drones.

Strange Metals: Newly discovered materials bend the regular rules of physics.

Strange Metals: Newly discovered materials bend the regular rules of physics.

A Goddess and a City or How to Read the Hieroglyphic Luwian Sign MANUS+MANUS

This paper argues that the unusual determinative MANUS+MANUS of the goddess Pahalati in Hama that resisted explanation until now can be understood due to its new attestation in the logographic spelling of a Cilician toponym. It will be shown that an earlier attempt that identified MANUS+MANUS as a variant of MAGNUS, the city as Urušša, and the name of the goddess as a Phoenician-Luwian mixed phrase meaning ‘Great Lady’, is palaeographically, linguistically, and geographically impossible. A clue to the decipherment of MANUS+MANUS is provided by the homo(io)phonous settlement in Cilicia, Pahra-, which explains how the same sign could have been used both as a determinative and as a logogram in accordance with the regular rules of the usage of the determinatives.

Knowledge, Attitudes, Practices, and Acceptance of Gynecologic Cancer Patients Toward Covid-19 Vaccine in Thailand: A Multicenter Cross-sectional Study

OBJECTIVE To evaluate the knowledge, attitudes, practices (KAP), and acceptance of gynecologic cancer patients from the 4 regions of Thailand related to the COVID-19 vaccine. METHODS Gynecologic cancer patients from Chiang Mai University Hospital, Khon Kaen University Hospital, Khon Kaen Hospital, Prince of Songkhla University Hospital, and the National Cancer Institute (NCI) in Bangkok were surveyed using a WHO survey instrument. RESULTS Between February and September 2022, 1,263 patients partici-pated in this project of whom 1,084 (85.8%) had received the COVID-19 vaccine. The highest rate of vaccination was the NCI followed by Khon Kaen, Chiang Mai, and Songkhla. Of the participants, 28.2% were infected with COVID-19 and 12.9% of the infected participants were unvaccinated. Regarding KAP, the average scale level of overall participants reported ease in obtaining health literacy, a moderate probability of having severe COVID-19 infection, knowledge and adoption of proper appropriate behavior for the prevention of COVID infection, little stress regarding possible COVID-19 infection, and quite a lot of a significant level of trust in healthcare workers. Most participants generally agreed with the lifting of some regular rules to reduce the risk of infection and often many expressed a feeling of general well-being. The significantly different levels of rating scale by unvaccinated and vaccinated participants in the key areas were as follows: (Patients rated each of the areas investigated on a scale of 0 to 6, with 0 indicating lowest level of agreement/acceptance and 6 indicating the highest level) health ministry recommendations (3.92 vs. 4.16), ease of getting the COVID vaccine (3.6 vs. 3.9), “no need to receive the vaccine due to the disease being rare” (2.6 vs. 2.2), “stress made me not want to get vaccinated” (2.6 vs. 2.1), “if everyone is vaccinated, no need for me to vaccinate” (2.5 vs. 1.9), and the importance of COVID-19 vaccines (3.7 vs. 4.2). CONCLUSIONS Most gynecologic cancer patients from the 4 regions of Thailand had received the COVID-19 vaccine and exhibited good knowledge, attitudes, and practices related to this pandemic. KEYWORDS COVID-19-vaccine, gynecologic cancer patients, attitude, practice

Estimation of quadrature errors for layer potentials evaluated near surfaces with spherical topology

Numerical simulations with rigid particles, drops, or vesicles constitute some examples that involve 3D objects with spherical topology. When the numerical method is based on boundary integral equations, the error in using a regular quadrature rule to approximate the layer potentials that appear in the formulation will increase rapidly as the evaluation point approaches the surface and the integrand becomes sharply peaked. To determine when the accuracy becomes insufficient, and a more costly special quadrature method should be used, error estimates are needed. In this paper, we present quadrature error estimates for layer potentials evaluated near surfaces of genus 0, parametrized using a polar and an azimuthal angle, discretized by a combination of the Gauss-Legendre and the trapezoidal quadrature rules. The error estimates involve no unknown coefficients, but complex-valued roots of a specified distance function. The evaluation of the error estimates in general requires a one-dimensional local root-finding procedure, but for specific geometries, we obtain analytical results. Based on these explicit solutions, we derive simplified error estimates for layer potentials evaluated near spheres; these simple formulas depend only on the distance from the surface, the radius of the sphere, and the number of discretization points. The usefulness of these error estimates is illustrated with numerical examples.

Application of Regular Grammar in the Syntactic Analysis of Email Addresses

Context: This article proposes the use of regular grammar as a strategy to validate the textual structures of emails. It focuses on the RFC 5321 standard and its syntax, formalizing regular grammars to apply production rules with the aim of validating the syntactic context of each structure of an email address. Method: This article presents a literature review and the development of an email validation model. Related texts focus on the Internet Protocol, along with building automata that apply IPV4 protocol. There are three phases: the development of the model from syntax and regular grammar rules and its construction and application. Results: The result is a functional application that validates email addresses based on regular grammars and existing regulations. When running efficiency tests, our application obtained a higher email validation margin in comparison with JFLAP. The library can work as a great analyzer of grammatical or lexical structures. Conclusions: The email validation tool based on GR regular grammars contributes to the practical use of specialized algorithms in the field of computer science, since it is possible to apply it to the recognition of search patterns such as the analysis of lexical structures (e.g., NITs, alphanumeric codes, and valid URLs).

Exploring optimal joint operating rules for large-scale inter-basin water transfer projects with multiple water sources, diversion routes, and water demand areas

Study region: The Jiangsu Province section of the South-to-North Water Diversion Project (JS-SNWDP) is an essential section of the SNWDP, which includes multiple water sources, diversion routes, and water demand areas. Study focus: Exploring effective operating rules for the JS-SNWDP is critical to improving its long-term operational performance. However, deriving the operating rule for the JS-SNWDP remains a pressing challenge due to the complexity of its water allocation, which requires trade-offs between multiple water sources and diversion routes when supplying and transferring water. Here, we design a new form of the joint operating rules for the JS-SNWDP that includes water-supply rules and water-transfer rules, and establish a multi-objective optimization operation model by simulation-optimization approach to identify the optimal joint operating rules that contribute to the improvement of water supply reliability and the reduction of operation cost. New hydrological insights for the region: The proposed joint operating rule shows obvious advantages over the regular operating rule, with an increase in the water supply rate (2.0%) while the strongly reduced pumping water (24.7%). Meanwhile, compared with the regular operating rule, the joint operating rule enables a more rational use of the runoff regulation capacity of the lakes, which obviously reducing the pumping water from the Yangtze River and the surplus water in the two lakes obviously. The findings of this study are applicable to improve the operational performance of the JS-SNWDP, and provide a reference for exploring the joint operating rules for other IBWTPs with similar structures.

Knowledge, Attitudes, and Practices of Gynecologic Cancer Patients at Maharaj Nakorn Chiang Mai Hospital Toward COVID-19: A Cross-Sectional Survey

Background and Aim: During the Covid-19 pandemic, the knowledge, attitudes, and practices about COVID-19 infection in gynecologic cancer patients were still limited. This study aimed to explore these issues.Methods: Gynecologic cancer patients who attended our gynecologic oncology unit between February and June 2022 were invited to a cross-sectional survey using a WHO survey tool. Results: 363 patients participated with 20.9% undergoing treatment and receiving the COVID-19 vaccine significantly less than the surveillance participants (85.4% vs.57.9%, P < 0.01). Forty-eight participants developed a COVID-19 infection. The most common vaccine used for the two first doses was AstraZeneca. Regarding attitudes issues, most participants felt health literacy was obtained easily. They considered that they had a moderate probability to have severe symptoms of COVID-19 infection and almost all knew the proper behavior for prevention. The participant reported little stress and most trusted healthcare workers and family doctors. They agreed with lifting regular rules to control the COVID-19 pandemic. The other opinions that were significantly higher in participants who did not receive the vaccine versus those who received the vaccine, were as follows: “If everyone is vaccinated, no need for me to vaccinate”, “the stress made me not want to vaccinate”, “no need to vaccine due to rare disease” and “the vaccine is unsafe”. Conclusion: Gynecologic cancer patients who were under treatment received the COVID-19 vaccine less than those under surveillance. Most participants revealed a good attitude toward this pandemic. However, unvaccinated participants were concerned about the severe side effects of the vaccine.

An fMRI study of inflectional encoding in spoken word production: Role of domain-general inhibition

A major issue concerning inflectional encoding in spoken word production is whether or not regular forms (e.g., past tense walked) are encoded by rule application and irregular forms (e.g., swam) by retrieval from associative memory and inhibition of the regular rule. We used functional magnetic resonance imaging (fMRI) to examine the involvement of domain-general inhibition, thought to be underpinned by right inferior frontal gyrus (IFG), right pre-supplementary motor area (SMA), and right basal ganglia. Participants were presented with infinitive verbs that take either regular or irregular past tense. They switched between producing the past tense of these regular and irregular verbs in one block, and between inflecting or reading these infinitive verbs aloud in another block. As concerns corticobasal areas, compared to reading, inflecting activated left IFG and left preSMA/SMA. Regulars yielded higher activation than irregulars in these frontal areas, both on switch and repeat trials, which did not differ in activation. Switching between inflecting and reading activated left preSMA/SMA. These results indicate that inflectional encoding, and switching between inflecting and reading, engage frontal areas in the left hemisphere, including left preSMA/SMA for both and left IFG for inflecting, without recruiting the domain-general inhibition circuitry in the right hemisphere. We advance an account of inflectional encoding in spoken word production that assumes a distinction between regulars and irregulars, but without engaging domain-general inhibition.

The use of magnets for the nonvisual construction of syntax-based tangible object assemblies by individuals who are blind or visually impaired

ABSTRACT Many students have difficulty in mastering syntax when programming. To alleviate this problem, the Scratch programming language was developed to have a visual block-based drag and drop code editor, where the shapes of the blocks and animation are used to make the syntax intuitive. To similarly make syntax intuitive to individuals who are blind or visually impaired (BVI) using tangible code blocks, a method needs to take into account perceptual processing differences between the senses and implementation differences between the physical and virtual world. The studies in this paper examined the effectiveness of using localized reciprocal shape information in combination with magnetic attraction for providing intuitive feedback about syntax. Supplementing the use of localized reciprocal shape constraints with magnetic attraction increased the accuracy and perceived ease of use by blindfolded BVI users to detect whether blocks successfully connected and, therefore, represented a code segment with valid syntax. This did not appear to change significantly with changes in the size and weight of the blocks, although stronger magnetic connections appeared preferred. We also introduced a “stopper” design to allow for code blocks to have exceptions to the regular syntax rules, while not adding complexity to the majority of code blocks.

Policy Forum: Canada's Proposed Cryptoasset Legislation

Cryptoasset miners verify and record transactions, maintaining the integrity and security of the blockchain network. The Department of Finance ("Finance") has recently proposed new Excise Tax Act (ETA) provisions regarding the goods and services tax (GST)/harmonized sales tax (HST) treatment of crypto mining. Under these proposed provisions, crypto mining activities provided to anonymous recipients will not be subject to GST/HST, but the crypto miners performing these activities will also not be eligible to recover any GST/HST paid on their business inputs (and thus will be forced to bear the brunt of the tax themselves). We believe that Finance's decision to tax what it can identify—the business inputs of Canadian crypto miners—is a roughly balanced but reasonable approach. Although Finance might be legitimately criticized as departing from Canada's decision to eliminate the cascading of tax found in the former origin-based federal sales tax, it seems impossible to administer a destination-based transactional tax such as the GST/HST when faced with "anonymous" recipients (the users of the crypto miner's services). Finance appears to have minimized the cascading of tax by including a carve-out for identifiable recipients of a crypto miner's services, allowing the regular zero-rating rules in the ETA to apply in limited circumstances. In the face of utter uncertainty, Finance's reactive approach is likely the best that it can do. Given the rapid evolution and inherent decentralization of the crypto space, a more broadly based proactive approach would seem imprudent at this time.

Building an Explainable Diagnostic Classification Model for Brain Tumor using Discharge Summaries

A brain tumor is a mass of cells growing abnormally in the brain. The lesions formed in the suprasellar region of the brain, called suprasellar lesions, affect common anatomical locations causing an array of symptoms, including headache and blurred or low vision. These symptoms lead to a misdiagnosis of the tumor as common issues like refractive index problems, and the tumor gets diagnosed very late. This study focuses on these suprasellar lesions (namely Pituitary adenoma, Craniopharyngioma, and Meningioma), which have not been explored much using machine learning. We have collected 422 discharge summaries of patients admitted to the neurosurgery department of the National Institute of Mental Health and Neuroscience (NIMHANS), Bangalore, India, during 2014-2019. This work aims to build a model for classifying lesions into three categories. Features are the clinical concepts identified from the discharge summary using Natural Language Processing (NLP) and regular expression-based rules. The features and corresponding values thus extracted are represented as Analytical Base Table and fed to the classification model after the processing. The model utilizes XGBoost, Local Cascade Ensemble, Histogram-based gradient boosting, LightGBM, and CatBoost classifiers, which have the ability to inherently handle the missing data. Though the machine learning models perform well in classification, the interpretability and generalizability is often questioned especially in critical domains such as medical and healthcare. Hence model performance has been analyzed using the ELI5 tool, a python package for explainable AI. This tool identifies the critical features in the data on a patient basis, providing a more interpretable model for clinicians.

Analysis and Evaluation of Fully TCP-Compatible Backpressure-Driven Traffic Engineering

Throughput optimality is a core objective for the operation of modern networks. The backpressure routing and scheduling scheme is well-known for being analytically-optimal in terms of throughput maximization. Its operation is driven by packet queue build-ups at the network nodes, and constant status updates among neighbors in order to deduce the best next packet forwarding step. However, this operating principle is also incompatible to TCP, which uses congestion control to avoid queue build-ups. These factors have so far limited the use of backpressure at the edge only. The present work presents a novel combination of TCP and backpressure at the network layer, overcoming any compatibility issues and allowing for backpressure to be active at all network nodes without exceptions. The key idea is to apply the Lyapunov framework cumulatively, over a large set of router hardware clock ticks. This approach allows for the throughput-optimal backpressure decisions to be expressed via regular traffic engineering routing rules, instead of explicit per-packet forwarding directives. Simulation results validate the analytical findings, and demonstrate significant throughput gains over state-of-the-art related approaches. Proof of concept emulations showcase the simplicity in the implementation of the proposed scheme.

РЕКОНФИГУРАЦИОННЫЕ МЕТАГРАММАТИКИ ДЛЯ ОПИСАНИЯ И МОДЕЛИРОВАНИЯ МНОГОЭТАПНЫХ КОМПЛЕКСНЫХ АТАК

The purpose of the study is determined by a significant expansion of the classes of threats tomodern automated systems, the dynamic development of tactics and techniques for attacking theirinformation resources. The available methods and hardware and software tools effectively resistsingle-stage attacks that have a fixed scheme of destructive impact and time-limited activity. Moderntypes of destructive influences are understood as multi-stage complex attacks, for which it isimportant to create an adequate and effective apparatus for describing, modeling and repellingnew types of attacks. Research methods are based on the development of a structural-algebraicapproach, primarily on the apparatus of formal grammars and metagrammars. It has been establishedthat the well-known formal models for describing and modeling multi-stage complex attacksare cumbersome, and their modification is difficult. Most attack descriptors are not equipped witha representative set of methods for structural and algebraic analysis of such complexly structuredobjects. To describe, model and repel such attacks, a class of reconfiguration metagrammars hasbeen developed. These metagrammars contain a set of regular and reconfiguration rules formatching between grammar elements within the grammar. These rules allow you to select specificbranches of the search graph depending on the achieved parsing states. This property significantlyreduces the search space and thus increases the specific efficiency of the search. The developedapparatus of reconfiguration metagrammars creates the necessary theoretical basis for their effectiveuse in modeling and reflecting existing and prospective ICAs that have a structural-linguisticdescription. The resulting qualimetric five-dimensional diagram, built on a set of practically significantindicators (homogeneity, connectivity, compactness, adaptability, directionality) showedthe advantage of reconfiguration metagrammars over general metagrammars. Methods of parsingin reconfiguration metagrammars differ in structural rules of reconfiguration (structural adaptation)and selection criteria for their adaptation. These procedural features make it possible toexpand the possibilities of attack modeling and improve the efficiency of procedures for repellingmulti-stage complex attacks.

Foreign Language Processing of English Regular and Irregular Past Tense Verbs by Arabic-Speaking EFL Children

Purpose. This paper examines whether irregular past verbs are acquired earlier than regular past verbs by Arabic-speaking EFL children.
 Methods and procedure. Ninety fifth graders were tested using pictures representing 20 regular and 20 irregular past tense verbs through a sentence completion task. An introspective session was conducted with 70 children following the administration of the tests in order to determine the areas of difficulty. 
 Results. The results mainly revealed that there is a statistically significant difference between regular and irregular verbs in favour of regular verbs. This suggests that the children learn regular forms prior to irregular ones contradicting the Natural Order Hypothesis and providing credence to the two Interlanguage strategies, namely, the L2 Overgeneralization of Alternative L2 Category Strategy and L1 Transfer Strategy. The children’s failure to produce the irregular form of the verb can be ascribed to the default system that they rely on, which is the computation system rather than the storage system. Thus, Arabic-speaking EFL children apply regular inflection of -ed whenever their memory fails to produce an irregular verb form.
 Conclusions. This study has demonstrated that Arabic-speaking EFL children do not follow the same sequence of acquisition as that exhibited by first language learners in acquiring the regular and irregular simple past forms. These learners acquire the regular past tense forms prior to the irregular ones contradicting the Natural Order Hypothesis. Due to the lack of irregularities in their first language, these learners apply the regular inflection rule to novel or unfamiliar verbs without sometimes considering the possibility of an existent irregular form that needs to be retrieved from memory. This also provides credence to two Interlanguage strategies, namely, the L2 Overgeneralization of Alternative L2 Category Strategy and L1 Transfer Strategy. More studies that investigate the sequence of acquisition of other types of morphemes by Arabic-speaking EFL children are needed to explore the effect of L1 and other factors such as the learning situation on the acquisition of English morphemes.

Multidimensional analysis of players’ responses in basketball small-sided games: The impact of changing game rules

This study followed a cross-sectional design and aimed to compare the offensive and defensive tactical-technical actions and the physical and physiological responses between four 3vs.3 basketball small-sided games (SSGs) played on the half-court: with regular rules (3vs.3REGULAR), with defensive pressure (3vs.3DEFPRESS), with the close-shot rule (3vs.3CLOSESHOT), and with offensive numerical superiority (4vs.3). Fifty-one U-14 and U-15 male athletes participated in the study. They were divided into 3-player teams and played one 4-min bout of each SSG type. Heart rate and the time spent in four acceleration zones (0.0–0.5, 0.5–1.0, 1.0–1.5, and 1.5–2.0 g) were recorded using heart rate monitors and triaxial accelerometers. SSGs were filmed for the analysis of Space Creation Dynamics and defensive tactical-technical actions. Results showed a higher frequency of space creation without the ball and a mean number of passes per offense in the 4vs.3, with this SSG showing higher offensive performance than the 3vs.3CLOSESHOT (large effect sizes). Defensive behavior was significantly impacted by all rules: in general, 4vs.3 presented the highest frequency of closeouts, 3vs.3DEFPRESS increased the frequency of close off-ball marking and the 3vs.3CLOSESHOT increased the frequency of double-teaming, all differences presenting large effect sizes. Heart rate and the time spent in moderate and high accelerations (zones 2 and 3) were the highest with defensive pressure and the lowest with numerical superiority; heart rate and accelerations in the close-shot rule are higher than in numerical superiority but similar to the 3vs.3REGULAR. We concluded that the SSGs investigated in this study can stimulate different offensive and defensive actions and be used to develop the performance of basketball athletes.