Logical Pattern Research Articles

Exploring the Computational Effects of Advanced Deep Neural Networks on Logical and Activity Learning for Enhanced Thinking Skills

The Logical and Activity Learning for Enhanced Thinking Skills (LAL) method is an educational approach that fosters the development of critical thinking, problem-solving, and decision-making abilities in students using practical, experiential learning activities. Although LAL has demonstrated favorable effects on children’s cognitive growth, it presents various obstacles, including the requirement for tailored instruction and the complexity of tracking advancement. The present study presents a model known as the Deep Neural Networks-based Logical and Activity Learning Model (DNN-LALM) as a potential solution to tackle the challenges above. The DNN-LALM employs sophisticated machine learning methodologies to offer tailored instruction and assessment tracking, and enhanced proficiency in cognitive and task-oriented activities. The model under consideration has been assessed using a dataset comprising cognitive assessments of children. The findings indicate noteworthy enhancements in accuracy, precision, and recall. The model above attained a 93% accuracy rate in detecting logical patterns and an 87% precision rate in forecasting activity outcomes. The findings of this study indicate that the implementation of DNN-LALM can augment the efficacy of LAL in fostering cognitive growth, thereby facilitating improved monitoring of children’s advancement by educators and parents. The model under consideration can transform the approach toward LAL in educational environments, facilitating more individualized and efficacious learning opportunities for children.

The Role of Organization Culture Management of Work Related Musculoskeletal Disorders of Masonry in Construction Workplaces: Case Study, Nairobi County, Kenya

Globally masonry workers are often exposed to work-related risks resulting to development of musculoskeletal disorder and associated diseases affecting their body's movements. Despite the gains made, existing workplace risk management preventive and control methods have not adequately addressed this problem. The investigation was on the role of organizational culture in the management of musculoskeletal disorders of masonry workers in Building Construction Works, Nairobi County. Multiple-case study methods were employed to complete the inquiry. The study was guided by the complexity theory. A review of the existing literature in the field of the study was carried, and the knowledge gap for the study identified. The field data was collected using questionnaires and interview guides. Paired null and alternative hypotheses for the study were defined. The data collected was subjected to descriptive and inferential analysis for logical patterns, relationships and level of statistical significance. The key findings of the study showed involvement of masonry workers in risk management decision-making, work performance, process audits and reviews. The Pearson correlation results (P<= 0.05) showed that organizational culture was positively related to workers musculoskeletal disorder of masonry workers. The regression model summary results of the study indicated that R =.576, implying that the organization's culture, OSH government policies, and challenges correlated at .576. The study results for the coefficient for multiple-regression determination (R- squared) was .332, implying that the changes in independent variables influenced changes in the dependent variable by 33.2%. Other factors beyond the scope of this study explained the remainder 66.8% out of 100% of the changes. However, the figure does not reveal information about the causation relationship between the independent and dependent variables or indicate the correctness of the regression model. Keywords: Role of Organization Culture, management, Work-Related Musculoskeletal Disorders, masons, building construction workplaces.

Logic Patterns in Prime Numbers

Logic Patterns in Prime Numbers

Proof-carrying parameters in certified symbolic execution

Abstract Complex frameworks for defining programming languages aim to generate various tools (e.g. interpreters, symbolic execution engines, deductive verifiers, etc.) using only the formal definition of a language. When used at an industrial scale, these tools are constantly updated, and at the same time, it is required to be trustworthy. Ensuring the correctness of such a framework is practically impossible. A solution is to generate proof objects as correctness artefacts that can be checked by an external trusted checker. A logic suitable for developing such frameworks is matching logic. K framework is a canonical example having matching logic-based foundation. Since the (symbolic) configurations of the programs are represented by matching logic patterns, the algorithms computing the dynamics of these configurations can be seen as pattern transformers and a proof object should be generated for the relationship between these patterns. In this paper, we show that conjunctions and disjunctions of patterns, produced by semantics or analysis rules, can be safely normalized using unification and antiunification algorithms. We also provide a prototype implementation of our proof object generation technique and a checker for certifying the generated objects.

UnTiCk: Unsupervised Type-Aware Complex Logical Queries Reasoning over Knowledge Graphs

For the task of answering complex logical queries on large-scale incomplete knowledge graphs, the promising approach is to embed the knowledge graph and complex logical queries into a low-dimensional space and perform iterative reasoning to find the final answers. The general problem is that these models do not include entity types as an important feature, which reduces the reasoning potential. However, explicit type information is not always available on large-scale knowledge graphs. We innovatively propose an embedding-based framework for Unsupervised Type-Aware Complex Logical Queries (UnTiCk). Our approach implements unsupervised type constraints on multi-hop complex logical query processing. Moreover, it can capture the different representations of type features when entities are in different locations in the logical path. We designed type compatibility measurement meta-operators combined with popular Existential Positive First-Order (EPFO) neural logical operators to achieve type-aware EPFO complex query embedding. We validated the effectiveness of our framework on popular large-scale knowledge graphs by using the same embedding dimensionality as complex logical embedding methods. The results showed an average relative improvement of 1.9–12.8% on Hit@3 and up to 42.1% on the certain logical pattern.

Psychometric Validation of the ViSIO-PRO and ViSIO-ObsRO in Retinitis Pigmentosa and Leber Congenital Amaurosis.

Retinitis Pigmentosa (RP) and Leber Congenital Amaurosis (LCA) are rare inherited retinal degenerative disorders. The Visual Symptom and Impact Outcomes patient-reported outcome (ViSIO-PRO) and observer-reported outcome (ViSIO-ObsRO) instruments were developed in this population to assess visual function symptoms and impacts on vision-dependent activities of daily living (ADL) and distal health-related quality of life (HRQoL). This study aimed to explore the psychometric properties of the ViSIO-PRO and ViSIO-ObsRO in RP/LCA. The 49-item ViSIO-PRO and 27-item ViSIO-ObsRO instruments were completed by 83 adult and adolescent patients and 22 caregivers of child patients aged 3-11years with RP/LCA, respectively, at baseline and 12-16-day follow-up. Concurrent measures were also administered at baseline. Psychometric analyses assessed item (question) properties, dimensionality, scoring, reliability, validity, and score interpretation. Item responses were mainly evenly distributed across the response scale, and inter-item correlations were mostly moderate to strong (> 0.30) at baseline within hypothesized domains. Item deletion was informed by item properties, qualitative data, and clinical input and supported retention of 35 ViSIO-PRO items and 25 ViSIO-ObsRO items. Confirmatory factor analysis in line with pre-hypothesized domains supported a four-factor model assessing visual function symptoms, mobility, vision-dependent ADL, and distal HRQoL. A bifactor model supported calculation of total scores and four domain scores. Internal consistency was high for domain and total scores (Cronbach's alpha > 0.70) and test-retest reliability for total scores was strong between baseline and 12-16-day follow-up (intraclass correlation coefficients 0.66-0.98). Convergent validity was supported by strong correlations in a logical pattern with concurrent measures. Mean baseline scores differed significantly between severity groups. Distribution-based methods provided initial insights to guide interpretation of scores. Findings supported item reduction and established scoring of the instruments. Evidence of reliability and validity as outcome measures in RP/LCA was also reported. Further research is ongoing to explore responsiveness of the ViSIO-PRO and ViSIO-ObsRO instruments and interpretation of change scores.

Off-policy and on-policy reinforcement learning with the Tsetlin machine

The Tsetlin Machine is a recent supervised learning algorithm that has obtained competitive accuracy- and resource usage results across several benchmarks. It has been used for convolution, classification, and regression, producing interpretable rules in propositional logic. In this paper, we introduce the first framework for reinforcement learning based on the Tsetlin Machine. Our framework integrates the value iteration algorithm with the regression Tsetlin Machine as the value function approximator. To obtain accurate off-policy state-value estimation, we propose a modified Tsetlin Machine feedback mechanism that adapts to the dynamic nature of value iteration. In particular, we show that the Tsetlin Machine is able to unlearn and recover from the misleading experiences that often occur at the beginning of training. A key challenge that we address is mapping the intrinsically continuous nature of state-value learning to the propositional Tsetlin Machine architecture, leveraging probabilistic updates. While accurate off-policy, this mechanism learns significantly slower than neural networks on-policy. However, by introducing multi-step temporal-difference learning in combination with high-frequency propositional logic patterns, we are able to close the performance gap. Several gridworld instances document that our framework can outperform comparable neural network models, despite being based on simple one-level AND-rules in propositional logic. Finally, we propose how the class of models learnt by our Tsetlin Machine for the gridworld problem can be translated into a more understandable graph structure. The graph structure captures the state-value function approximation and the corresponding policy found by the Tsetlin Machine.

Sleep Spindle Characteristics and Relationship with Memory Ability in Patients with Obstructive Sleep Apnea-Hypopnea Syndrome.

Obstructive sleep apnea syndrome (OSAS) causes intermittent hypoxia and sleep disruption in the brain, resulting in cognitive dysfunction, but its pathogenesis is unclear. The sleep spindle wave is a transient neural event involved in sleep memory consolidation and synaptic plasticity. This study aimed to investigate the characteristics of sleep spindle activity and its relationship with memory ability in patients with OSAS. A total of 119 patients, who were divided into the OSAS group (n = 59, AHI ≥ 15) and control group (n = 60, AHI < 15) according to the Apnea Hypopnea Index (AHI), were enrolled and underwent polysomnography. Power spectral density (PSD) and omega complexity were used to analyze the characteristics of single and different brain regions of sleep spindles. Memory-related cognitive functions were assessed in all subjects, including logical memory, digit ordering, pattern recognition, spatial recognition and spatial working memory. The spindle PSD of the OSAS group was significantly slower than the control group, regardless of the slow, fast, or total spindle. The complexity of the spindles in the prefrontal and central region decreased significantly, whereas it increased in the occipital region. Sleep spindle PSD was positively correlated with logical memory and working memory. Spindle complexity was positively correlated with immediate logical and visual memory in the prefrontal region and positively correlated with immediate/delayed logical and working memory in the central region. In contrast, spindle complexity in the occipital region negatively correlated with delayed logical memory. Spindle hyperconnectivity in the prefrontal and central regions underlies declines in logical, visual and working memory and weak connections in the occipital spindles underlie the decline in delayed logical memory.

Process bottlenecks identification and its root cause analysis using fusion-based clustering and knowledge graph

Drawing the strengths of data science and machine learning, process mining has recently emerged as an effective research approach for process management and its decision support. Bottleneck identification and analysis is a key problem in process mining which is considered a critical component for process improvement. While previous studies focusing on bottlenecks have been reported, visible gaps remain. Most of these studies considered bottleneck identification from local perspectives by quantitative metrics, such as machine operation and resource requirement, which can not be applied to knowledge-intensive processes. Moreover, the root cause of such bottlenecks has not been given enough attention, which limits the impact of process optimisation. This paper proposes an approach that utilises fusion-based clustering and hyperbolic neural network-based knowledge graph embedding for bottleneck identification and root cause analysis. Firstly, a fusion-based clustering is proposed to identify bottlenecks automatically from a global perspective, where the execution frequency of each stage at different periods is calculated to reveal the abnormal stage. Secondly, a process knowledge graph representing tasks, organisations, workforce and relation features as hierarchical and logical patterns is established. Finally, a hyperbolic cluster-based community detection mechanism is researched, based on the process knowledge graph embedding trained by a hyperbolic neural network, to analyse the root cause from a process perspective. Experimental studies using real-world data collected from a multidisciplinary design project revealed the merits of the proposed approach. The execution of the proposed approach is not limited to event logs; it can automatically identify bottlenecks without local quantitative metrics and analyse the causes from a process perspective.

Network Leadership is a Promising Direction in Management Science

The social features of leadership and its influence on the development of organizations have long been an important and relevant research topic by scientists in effective management. This trend is related to the provision of research on practical tools managers use to achieve high economic results in a managed system. The humanization of processes in the organization and the manager’s focus on employee development contribute to the formation of the organization’s competitiveness and financial stability. The bibliometric analyses helped investigate the bibliographic characteristics of scientific works that reveal the content of the networking process and the practicality of its use in the social phenomenon of leadership. The bibliometric analyses proposed in the article show the quantitative indicators of the document flow in management to establish logical patterns between the broad management concept of «leadership» and the social features of the idea of «network leadership». The proposed study contributes to forming an informational scientific flow in the field of studying the development of various topical areas of leadership to increase organizations’ financial results by targeting the manager to improve the conditions for the social resource in the managed system. Studying the development of network leadership allows you to consider all the positive aspects and correct certain shortcomings in this modern leadership direction. The proposed work highlights and analyses general concepts that connect the Networking category with the phenomenon of Network Leadership in organizations where managers focus on the social component of the managed organizational system – Social capital. The identified general definitions are the basis for effective manager activity. The study is helpful for managers who strive to achieve high results in management activities through the development of each organization member. The development of the professional potential of employees is an indicator of an effective manager and the basis for achieving the organization’s goals.

The construction and application of event logic graph for pedestrian flow evacuation in typical scenarios

An event logic graph is a kind of knowledge mapping technology for knowledge inference and simulation analysis, which takes events as the core and portrays the hierarchical system and logical evolution pattern between events. In order to apply it to further improve the accuracy of related studies, such as pedestrian flow evacuation, simulation model optimization and risk prediction. In this paper, we use social network resources, media resources and journal database resources to build our corpus and adopt the explicit event relationship extraction method based on syntactic dependency and the implicit event relationship extraction method based on BERT+Bi-LSTM+Attention+Softmax for the characteristics of explicit event relationship and implicit event relationship, respectively. This paper constructs a pedestrian flow evacuation matter mapping for three typical scenarios and discusses its application path. It is found that once a sound knowledge base of logical reasoning and event logic graph is established, both research on optimization of pedestrian flow evacuation simulation models and research on identification and assessment of pedestrian flow evacuation safety risks will receive excellent support.

Control of Free Piston Stirling Linear Generator system connected with dc/dc converter for energy storage applications based on SVPWM Rectification Method

Recently, the world has been facing several challenges in terms of environmental change, which is expected to increase in the coming periods. Further challenging is how to reduce the emissions of greenhouse gasses into the atmosphere. Such emissions pollute our atmosphere, resulting in global warming. However, the wide use of renewable energies can both help to fight against this phenomenon and reach long-term goals. Considering the growing global concern over the use of fossil fuels and environmental pollution. In this case, a Stirling engine with diversified heat sources and low pollution was developed, and the Free Piston Stirling Linear Generator (FPSLG), which can be directly connected to the Stirling engine and thus eliminate the need for an intermediate mechanical transmission mechanism, was used to create a power generation system with the Stirling engine and has been employed in a range of applications, including space power supply, solar energy generation, and others. This article models two sets of power converters, one of which is connected to the FPSLG and the other to the storage battery system. The Space Vector Pulse Width Modulation (SVPWM) control methods adaption to the AC/DC rectifier and the two-loop Proportional Integral (PI) control method adaption to the DC/DC converter used for renewable energy conversion is proposed. Furthermore, the traditional PI in the position loop is replaced by a Proportional Resonance (PR) controller to increase the frequency and displacement tracking performance. The linear generator’s control system is simulated, and the efficiency of the proposed control strategy is proven. The system was initially simulated in MATLAB/Simulink using the dq-PI current controller to produce the logic pattern for the Voltage Source Rectifier (VSR), and the resulting logic pattern was examined.

Polymorph Separation by Ordered Patterning.

We herein address the problem of polymorph selection by introducing a general and straightforward concept based on their ordering. We demonstrated the concept by the ordered patterning of four compounds capable of forming different polymorphs when deposited on technologically relevant surfaces. Our approach exploits the fact that, when the growth of a crystalline material is confined within sufficiently small cavities, only one of the possible polymorphs is generated. We verify our method by utilizing several model compounds to fabricate micrometric "logic patterns" in which each of the printed pixels is easily identifiable as comprising only one polymorph and can be individually accessed for further operations.

Theory of Multiple Intelligence among Middle School Students

The purpose of the case study on “ Theory of Multiple Intelligence” was to map the dominant intelligences among the students attending school in the centre of the country, in order to enhance school teaching. First, we decided to examine the population of learners among seventh graders. Students’ age was selected based on the school needs . A case study was conducted in a middle school in Israel and included 158 students of different achievement levels and different backgrounds.The theoretical components of MI theory as proposed by Gardner (1983, 1999, 2004) and their implications for curriculum in the classroom were used to inform this study. Gardner is a professor of education at Harvard University whose groundbreaking research in psychology and human cognition helped him develop a theory that we each have distinct intellectual abilities that we demonstrate with unique aptitudes.The nine multiple intelligences:Verbal-linguistic: demonstrates strong verbal communication, advanced vocabulary, etc.Logical-mathematical: understands logical and numerical patterns as well as abstract concepts.Spatial-visual: visualizes accurately and uses abstract information to picture the information in one’s head.Bodily-kinaesthetic: skilfully uses and controls the body and objects. Musical: produces rhythm and pitch and appreciates musical values.Interpersonal: responds appropriately to others by understanding social cues, motivations and moods.Intrapersonal: aware of one’s own feelings, values and thinking processes.Naturalist: identifies and categorizes animals, plants and other aspects of nature.Existential: contemplates deep questions about human existence, including the purpose of life, how we came to be, etc. How to cite this article: Gayatri V, Kumar YSS. Theory of Multiple Intelligence among Middle School Students. JAdv Res Eng & Edu 2022; 7(1): 8-10. DOI: https://doi.org/10.24321/2456.4370.202212

“Can I go into the artwork?” Material–relational situations with abstract art

The purpose of this article is to study museum educational situations, where 5–7-year-old children encounter abstract art, from a new materialist perspective. The children visit an exhibition curated by me, where abstract modernist art is mediated, with an emphasis on multisensory experiences and experimentation. The visits were recorded with stationary and action cameras. By focusing on material–relational situations, I investigate how learning takes place when the children engage with the museum educational setting. A girl asks a surprising question that challenges the takenfor-granted beliefs about what art can be. The children break rational and logical patterns by creating abstract art, and “aesthetic-intuitive order” takes place in the compositions. A child and an adult relate differently to the agential “teaching matter.” Embodied and material pedagogy with abstract art indicates how “making sense” of the world is not only done in verbal and logical ways but also by experimenting with bodies and senses with teaching matter.

THE RELATIONSHIP OF SELF-CONCEPT AND LOGICAL THINKING PATTERNS WITH DIGITAL LITERACY ABILITY

This study discusses the relationship between self-concept, logical thinking patterns, and digital literacy ability. The research respondents were elementary school teacher education students at the Se University of West Java from initial to final levels without distinguishing gender. Respondents were selected randomly using the proportional random sampling technique. The research data was obtained through a questionnaire on a scale of 0-1 for logical thinking patterns and 1-5 for self-concept and digital literacy ability. The results of this study indicate: that first, there is a positive relationship between self-concept and digital literacy ability of 0.563. Second, a positive relationship exists between logical thinking patterns and digital literacy ability of 0.234. Third, a positive relationship exists between self-concept and logical thinking patterns and digital literacy ability of 0.595. This study put forward some suggestions as follows: (1) prospective teacher students can develop self-concept by focusing on themselves, loving themselves, being responsible, having goals, and being able to interact with the social environment to improve digital literacy ability, (2 ) student teacher candidates must have logical thinking patterns by continuing to train themselves through being able to think with logical rules, be able to determine valid conclusions, think systematically in digital literacy ability, (3) prospective teacher students must be able to control self-concept and logical thinking patterns to improve digital literacy ability as a support in the learning process and for improving academic achievement.

Development of Method for Optimization of Diagnostic Algorithms for Car Engines

In modern practice of diagnosing complex technical devices, along with developing built-in diagnostic systems, a wide niche is still occupied by troubleshooting based on logical patterns. Logical algorithms for troubleshooting are the basis for diagnostic maps of car power plants. They do not always have an optimal structure in terms of achieving the lowest cost and time indicators of the diagnostic process. Thus, the problem of structural optimization of logical diagnostic algorithms is relevant, as well as the formation of a simple and understandable standardized procedure for optimizing diagnostic algorithms. The paper substantiates the criterion for evaluating the effectiveness of a diagnostic algorithm and the structure of the initial data for calculating the criterion. Variants of visual formalization of a diagnostic map are proposed in order to increase visibility and carry out further actions to optimize its structure, as well as options for formalizing visual information in digital form for its processing by computing means. The procedure for optimizing the structure in the JavaScript programming language has been developed and implemented. The reverse transition from a digital form to graphic one is described, which will immediately introduce the results into the practice of car service organizations. An analysis of a sample of 22 diagnostic maps has shown that the proposed optimization method will reduce the average cost of diagnostics up to 17 %.

Finalization and Validation of Questionnaire and Algorithm of SPUR, a New Adherence Profiling Tool.

PurposeThe SPUR (Social, Psychological, Usage and Rational) Adherence Profiling Tool is a recently developed adaptive instrument for assessing key patient-level drivers for non-adherence. This study describes the SPUR questionnaire’s finalization and psychometric evaluation.Patients and MethodsData were collected through an online survey among patients with type 2 diabetes included by general practitioners and diabetologists in France. The survey included four questionnaires, SPUR and three validated adherence measures: BMQ, MARS and ACCEPT. Item-level analysis and a partial credit model (PCM) were performed to refine the response option coding of SPUR items. The final item selection of SPUR was defined using a PCM and a principal component analysis (PCA). Construct validity, concurrent validity and known-groups validity were assessed on the final SPUR questionnaire.ResultsA total of 245 patients (55% men, mean age of 63 years) completed the survey remotely and were included in this analysis. Refining response option coding allowed a better discrimination of patients on the latent trait. After item selection, a short, an intermediate, and a long form composed the final SPUR questionnaire. The short form will be used to screen patients for risk and then the other forms will allow the collection of further information to refine the risk assessment and decide the best levers for action. Results obtained were supportive of the construct validity of the forms. Their concurrent validity was demonstrated: moderate to high significant correlations were obtained with BMQ, MARS and ACCEPT scores. Their known-groups validity were shown with a logical pattern of higher scores obtained for patients considered non-adherent and significant differences between the scores obtained for patients considered adherent versus non-adherent.ConclusionSPUR is a valid tool to evaluate the risk of non-adherence of patients, allowing effective intervention by providing insights into the respective individual reasons for lack of adherence.

The Logical Pattern of Argument: A Case Study of National University Debating Championship

In academic field, argument is an essential factor of understanding statements. In this regard, the present paper aims to analyse National University Debate Contest (NUDC) 2016 using Toulmin model argumentative structure and its relation with the implied meaning from National University Debate Contest. This paper employs descriptive qualitative analysis to examine the logical pattern of arguments through linguistic expressions that occurred in the debate. The data were taken from NUDC in Mercu Buana University focusing on (part 1) the statement of the government proponent (Bina Nusantara University) and the opposite speaker (Brawijaya University). The data were analysed using the Toulmin model of logical argument. The findings showed that the government opposite speaker very often made the rebuttal claim by showing the weaknesses from the government side; while the government proponent speaker has claimed almost in every pattern of argument proposed by Toulmin model. The paper concludes that both speakers’ patterns of argument have utilized Toulmin model such as claims, data, qualifiers, rebutting conditions, and warrants. Even though there are so many grammatical mistake and unwell-organized structure, but the logical structure can be analysed using Toulmin model.

The Analysis of Problem Posing Skills about Integers of Prospective Primary School Mathematics Teachers Who Have Experienced in Problem Posing

This study aims to analyze the problem-posing skills of primary school mathematics teacher candidates with problem-posing experience and their views on problem posing. The case study, one of the qualitative studies, was used in the study. The study group is ten prospective elementary mathematics teachers who were selected using the purposive sampling method and are in their third year in the academic years 2020-2021 Teaching Mathematics in Primary Schools Major at a private university in Istanbul.The data collection process consists of two stages: 9 problem-posing drafts in total for performance evaluation prepared by examining the literature and obtaining an expert opinion and semi-structured interview forms with three questions. The participants were asked to pose problems on the subject of integers. The problems were evaluated and analyzed by the researchers and two mathematics teachers according to the problem-posing evaluation criteria developed by the researchers. As a result of the findings obtained in the performance determination phase of the study, it was determined that the participants posed more successful problems in the case of structured problem posing. It was determined that some of the problems were not problematic; they were prepared without paying attention to grammar rules, the sentences were not in a clear and logical framework that the middle school students could understand. The numbers were not used according to a certain logic pattern to make the problem solvable. Some problem situations were left unanswered. In the second stage of the study, the data obtained through semi-structured interviews with the primary school mathematics prospective teachers were analyzed using content analysis. In examining the research findings, the main issues are what the participants pay attention to in problem setting, whether they emphasize problem setting in the instructional process, whether problem setting is necessary for each student, and whether anxiety occurs in problem setting.