Cause in the Ecclesiastic Tradition

The concept of truth table is based on the content to analyze in teaching and learning logical reasoning and logic gate, which is a visual representation of possible combination of input and output information of Boolean prepositions in logical reasoning and Boolean functions in logic gate plotted into a table. It adopts Boolean algebra for problem solving as a method or science of reasoning, or ability to argue and convince. In the teaching and learning processes of logic, formal and informal reasoning tasks are used in a variety of ways, including using symbols and entirely in plain language without symbols where symbolic logics are commonly referred to as mathematical logic. This paper therefore considers the Use of Truth Table, Logical Reasoning and Logic Gate in Teaching and Learning Process It highlighted the benefits of using the truth table and compared logical reasoning and logic gate connectives and concluded that, there exist similarities, differences and peculiarities in the interactive features of the content of the truth table. The paper suggested that teachers should vividly state the similarities, differences and peculiarities in the interactive features of truth table in logical reasoning and logic gate while incorporating thought in teaching and learning process to avoid confusion.

Pediatric hematology normal ranges derived from pediatric primary care patients.

Problem. The purpose of this study is to explore spiritual beliefs as defined by an interconnectedness with self and others, generated from a relationship with a higher power, and consider its impact on attitude toward life and mental health. Method. This study employed the survey research method to collect data investigating the relationships between spiritual beliefs, attitude toward life, and mental health. A battery of three instruments was selected for this study. The Royal Free Questionnaire on Beliefs and Experiences, developed by King, Speck, and Thomas (1994), was used for measuring spiritual beliefs. The Optimism and Pessimism Questionnaire provided a global perspective of optimism and pessimism on the participants. The Mental Health Inventory (MHI) assessed the psychological distress or psychological well-being of the participants' focusing on the frequency or intensity of a psychological symptom during the past month. Results. The relationship between spiritual belief and overall psychological well-being and the relationship between spiritual belief and depression were significant. As the commitment to a particular spiritual belief system strengthened, depression significantly decreased. The relationship between spiritual beliefs and loss of behavioral/emotional control was significant. Participants with a stronger commitment to a particular spiritual belief system reflected lower levels of uncontrollable behavior or emotion. General positive affect, emotional ties, and psychological distress also demonstrated significant relationships with spiritual belief. No relationships between spiritual beliefs and anxiety or life satisfaction were found. Conclusions. There appeared co be no significant correlation between the spiritual beliefs scores and attitude toward life scores. There was some indication that spiritual beliefs affected mental health. Participants presented with strong general positive affect, suffered significantly less psychological distress in their lives, were less depressed, more in control of their emotions and behaviors, and better able to establish emotional ties with others. Spiritual belief, attitude toward life and mental health, as a set, were significantly related to age. Specific predictors include anxiety, loss of emotional/behavioral control, and psychological distress. Only psychological distress and psychological well-being differentiated the men and women participants.

Causal reasoning and logical reasoning are two important types of reasoning abilities for human intelligence. However, their relationship has not been extensively explored under machine intelligence context. In this paper, we explore how the two reasoning abilities can be jointly modeled to enhance both accuracy and explainability of machine learning models. More specifically, by integrating two important types of reasoning ability--counterfactual reasoning and (neural) logical reasoning--we propose Counterfactual Collaborative Reasoning (CCR), which conducts counterfactual logic reasoning to improve the performance. In particular, we use recommender system as an example to show how CCR alleviate data scarcity, improve accuracy and enhance transparency. Technically, we leverage counterfactual reasoning to generate "difficult" counterfactual training examples for data augmentation, which--together with the original training examples--can enhance the model performance. Since the augmented data is model irrelevant, they can be used to enhance any model, enabling the wide applicability of the technique. Besides, most of the existing data augmentation methods focus on "implicit data augmentation" over users' implicit feedback, while our framework conducts "explicit data augmentation" over users explicit feedback based on counterfactual logic reasoning. Experiments on three real-world datasets show that CCR achieves better performance than non-augmented models and implicitly augmented models, and also improves model transparency by generating counterfactual explanations.

This study examined how Puerto Rican parents living in the United States reason about illnesses and remedies. Special focus is placed on the influence of Puerto Rican cultural, religious, and spiritual beliefs due to the influence of Taino, West African, Spanish, and U.S. beliefs and customs. Parents of 3.5 to 12-year-old children from the Rochester, NY area were asked to complete a questionnaire centered on their beliefs and an open-ended interview that explored how one could get sick and treated for biological illnesses (e.g., the common cold, COVID-19, cancer). Although participants reported religious, spiritual, and mystical beliefs in the questionnaire, they provided scientific explanations for illness and remedies in the interview. This study provides novel findings on how individuals of Puerto Rican descent understand illnesses and remedies and the variation across individuals. This study provides strong evidence for the variation in coexistence of beliefs within this population.

Large language models (LLMs) have achieved remarkable success in natural language processing tasks but still struggle with complex causal and logical reasoning. Previous neuro-symbolic methods can be summarized into a two-stage framework: first translating natural language (NL) problems into symbolic language (SL) representation, and then performing the symbolic reasoning process. To facilitate this direction, we provide a comprehensive survey, summarizing two main challenges including complex logical question-answering (QA) and cross-question logical consistency, and further propose a new taxonomy. To achieve precise symbolic representation and enhance the accuracy of LLMs’ logical reasoning, we propose several effective and efficient approaches, including adaptively selecting the most suitable SL for each QA problem, a data-driven approach to determine the fine-tuning samples order, and an efficient multi-agent debate framework with sparse communication. Our future research will focus on theoretical analysis for optimal SL selection, translation refinement and robust neuro-symbolic approach to improve LLMs' reasoning.

Background: Empirical legal research methods have gained increasing significance in legal scholarship, offering insights into the real-world application and outcomes of laws. In Indonesia, the use of these methods remains relatively underexplored despite the growing need for evidence-based policy and legal reforms. Legal research in Indonesia has traditionally relied on doctrinal methods, but empirical approaches have the potential to enhance the understanding of how legal systems function in practice and how laws impact society. Objective: This study aims to explore the applications of empirical legal research methods in legal research in Indonesia. The research focuses on examining how these methods can be effectively implemented in the context of Indonesian legal research to address pressing legal issues and improve the legal system's overall effectiveness. Method: A qualitative research design was employed, combining a review of existing literature, case studies, and interviews with legal scholars, practitioners, and policymakers. The study also analyzed existing empirical research on legal issues in Indonesia to identify current trends and gaps in research. Results: The findings suggest that empirical methods, such as surveys, interviews, and case studies, are increasingly being adopted in Indonesian legal research, though challenges remain in terms of resources, training, and institutional support. Conclusion: Empirical legal research holds significant potential for advancing legal scholarship in Indonesia. There is a need for greater integration of these methods to enhance the quality and relevance of legal research and inform legal reforms.

Mathematical reasoning ability objectively reflects a language model's understanding of implicit knowledge in contexts, with logic being a prerequisite for exploring, articulating and establishing effective reasoning. Large language models (LLMs) have shown great potential in complex reasoning tasks represented by mathematical reasoning. However, existing mathematical datasets either focus on commonsense reasoning, assessing the model's knowledge application ability, or arithmetic problems with fixed calculation rules, evaluating the model's rapid learning capability. There is a lack of datasets that require solving problems solely through logical reasoning. As a result, the performance of LLMs in accurately understanding the implicit logical relationships in problems and deriving conclusions based solely on given conditions is hindered. To address this challenge, we construct a dataset specifically for multiple step reasoning tasks: Reasoning-Math (RMath). This dataset focuses on evaluating logical reasoning abilities with mathematical reasoning problems, covering typical problem types, including direct reasoning problems, hypothetical reasoning problems, and nested reasoning problems. Additionally, we design a standardized annotation scheme that transforms natural language descriptions of conditions into formal propositions. Other annotation contents include problem categories, proposition truth values, and proposition relationship types. This not only reduces biases caused by semantic misunderstandings during problem-solving, but also facilitates the incorporation of theoretically grounded logical reasoning methods to enhance reasoning abilities. Furthermore, we propose a normalization problem-solving framework based on propositional logic for RMath and design the problem-solving process for prompt tuning to guide LLMs to absorb mathematical logical theories and improving reasoning abilities. Finally, we evaluate RMath on several popular LLMs and present the corresponding results.

In light of mining large amounts of data, artificial intelligence (AI) has learned a very strong correlation between objects. However, its limitations lie in that it can’t summarize the causality between objects like human beings and it forms the blind box association mechanism. In this paper, we address these limitations and make the contributions: We propose an implementation of causal reasoning based on spiking neural network (SNN), which simulates causality using information processing with spiking activities. And the spike-timing-dependent plastic rules (STDP) is utilized as a method of causal reasoning, which is based on the topological structure of causal graph and can make the process visible. Through experiments, our model completes the inference of causal ladder proposed by Judea Pearl, and experiments prove that it can complete more complex causal reasoning under the condition of integrating multiple causality.

Systems-thinking for environmental policy coherence: Stakeholder knowledge, fuzzy logic, and causal reasoning

Accurate estimation of the barometric coefficient () is important for correcting pressure effects in soil moisture data from cosmic-ray neutron sensing (CRNS) due to the barometric effect. To evaluate estimation strategies for , we compared analytical and empirical approaches using 71 CRNS and 46 neutron monitor (NM) stations across the United States, Europe, and globally. Our results show spatio-temporal variation in the barometric effect, with ranging from 0.66 to 0.82 %hPa for NM and from 0.63 to 0.80 %hPa for CRNS. These coefficients exhibit higher variability than previously published semi-analytical models. In addition, we found that the analytically determined values were systematically lower compared with empirical estimates, with stronger agreement between the two empirical methods () than between empirical and analytical approaches. Furthermore, NM stations produced higher values than CRNS, indicating that differences in detector energy sensitivity affected the values of . Principal Component Analysis (PCA) further showed that the analytical and empirical estimates clustered together, reflecting shared sensitivity to elevation. In contrast, soil moisture and atmospheric humidity projected nearly orthogonally to the vectors, indicating negligible influence, while cut-off rigidity contributed to a separate, inverse gradient. Analytical estimates were fully orthogonal to AH, while empirical methods showed only slight deviations beyond orthogonality. The barometric coefficient (), therefore, varies with location, altitude, atmospheric conditions, and sensor type, highlighting the necessity of station-specific values for precise correction. Overall, our study emphasizes the need for atmospheric correction in CRNS measurements and introduces a method for deriving site- and sensor-specific values for accurate soil moisture estimation.

This study aims to explain the forms of social change and shifts in belief systems that occur within the Dayak Ma’amp community, as well as their relationship to the implementation of the Babukokng ritual and the continuity of the Katipak Dance as one of the sacred cultural elements. The focus of the research is directed toward identifying the forms of social and belief changes within the Dayak Ma’amp community, particularly those related to the practice of Babukokng and the preservation of the Katipak Dance. The study employs a qualitative approach with technique triangulation, which involves comparing data derived from interviews, observations, and document studies. Anthropological and historical approaches are used to understand how social and belief changes unfold within the Ma’amp community. The findings indicate that social change in the Ma’amp community can be seen through the shift from traditional customary leadership to a dual system involving village governance, the transition of livelihoods toward a monetary economy, and the simplification of the duration and procedures of the Babukokng ritual. The community’s belief system has also shifted due to the influence of formal religions, which have weakened traditional beliefs in spirits and spiritual elements, resulting in the modification or removal of certain ritual stages. The Katipak Dance, which was once strictly sacred, has undergone desanctification and can now be performed in public cultural events as an effort toward preservation. Overall, these changes demonstrate that the Ma’amp community is able to adapt to modern developments while simultaneously facing challenges in cultural regeneration if deliberate preservation efforts are not implemented.

Fast screening of COVID-19 inpatient samples by integrating machine learning and label-free SERS methods.

If one travels across time, continents and cultures, one needs to attend to the interests and attitudes of one’s audience. If one does not do so, one risks being thought strange. Of course, the same may hold true if one travels just from New York City to Texas (a mere 1600 miles or so). Many years ago I traveled from New York City, where I was teaching at the time, to Houston, Texas, to give a talk at one of the law schools in Houston. At the time I was much preoccupied with the work that had been done by people in artificial intelligence. I was interested in such stuff because I wondered about the possible applications of that research to the study of evidentiary processes in litigation and adjudication. In my talk in Houston, I tried to explain my tentative conclusion that artificial reasoning methods (including statistical methods) could not supplant ordinary methods of reasoning. After the talk, it soon became apparent that some or much of the audience viewed me as closely akin a man from Mars.1 In this paper, I will use some words—words such as ‘ontology’—that may seem strange or repellent to some or many readers. So I thought I would begin by talking briefly about some of my motivations for talking in ways that may seem strange or unpleasant to some or many readers. Several decades ago I revised the first volume of John Henry Wigmore’s multivolume treatise on the law of evidence.2 Being a reviser is both harder and easier than being an author of one’s own book. It is easier because one can, if one wishes, take the position of a critic and commentator rather than that of author. And in dealing with some or many of the theoretical portions of Wigmore’s magnum opus, I often did exactly that. However, after finishing my revision, I agreed to write a successor to my revision. Now, after a delay of many years, I am doing that. The job of this successor volume, like part of its precursor, is to examine ‘theoretical considerations’ that bear on the law of evidence and proof. This means that I can no longer just be a commentator. Now I must present my own ‘theory of proof’. In looking at what others had done in trying to develop a theory of the law of evidence and proof, I saw many impressive accomplishments. I also saw a variety of approaches. On the one hand, some of the authors seemed to focus on the abstract logic of uncertain reasoning; and their theory of the law of evidence and proof (a theory that they often called a theory of ‘relevance’) effectively amounted to their view of the nature of logical thinking about uncertain factual propositions.3 It seemed to me then—and it seems to me now—that there is something wrong with this approach. Above all, I wondered and I wonder still how it can be confidently said that the methods of reasoning and demonstration in a particular legal system (such as the American one) rest on and express such logic.

API Standard 521 new alternative method to evaluate fire relief for pressure relief device sizing and depressuring system design