Logical Inference Research Articles

Information system for assessing reliability a decoder remote piloting station

Under conditions of armed aggression by Russia against Ukraine, which escalated into a full-scale military invasion on February 24, 2022, national security is facing the greatest threat, as it is about destroying Ukrainian statehood. As experience in preparing and conducting intelligence operations by the Armed Forces of Ukraine during the large-scale invasion by the armed forces of the Russian Federation into your country has shown, using class I unmanned aerial vehicles has proven their effectiveness, which has raised issues of improving intelligence gathering with their help. A key role in the operations of a first-class unmanned aerial vehicle is played by a remote pilot station decoder. Quality operation of airborne reconnaissance assets depends on its efficiency. The article considers main reliability indicators of a human-machine system operator. The analysis results of the existing methods for controlling the humanmachine system operator, which shows that indicators of his performance are not measured, but are determined by monitoring his functional state with further evaluation of performance indicators. Based on results of the analysis, it was found that using fuzzy set theory and convolution according to a nonlinear trade-off scheme in tasks of evaluating efficiency of human-machine systems makes it possible to identify reliability status of a remote piloting station decoder in real time, taking into account human performance and equipment efficiency. The article develops an information system for assessing the reliability of a remote piloting station decoder based on a generalized mathematical model in the form of a multilevel hierarchical tree of logical inference that reflects classification of indicators and intermediate estimates. The roots of the trees correspond to evaluation results, and tops correspond to reliability indicators of a remote piloting point. This process is based on mathematical apparatus of fuzzy logic and is carried out using available expert information in form of logical rules "IF - THEN" that link fuzzy terms of remote piloting point reliability indicators and the assessment result. Reliability of the obtained data is achieved by forming a fuzzy knowledge base using fuzzy terms that take into account specifics of process obtaining intelligence information at remote piloting points.

A Study to Understand Miasmatic Evaluation of Bronchial Asthma and Its Homeopathic Management

Asthma as a clinical entity has been known to the medical world since antiquity. Many physicians have described its exact symptomatology with aggravating and ameliorating factors, its epidemiology at that time and some other factors, but they could not know the exact cause of this condition. Many hypotheses were formed for the causes of this condition, some of which include effects of evil eye, wrath of devils, bad effects of some food etc., even till date, there are many etiologies of this condition has been noted, the exact cause of asthma is not determined. There are many influences that act with each other to produce this condition. Modern system of medicine believes in local concept of disease. They believe in materialistic concept of disease. They believe that in any disease condition the organs and tissues affected are affected solely, and they don’t have any correlation with other parts. So they try to manage bronchial asthma with anti-inflammatory and anti-histaminic medicines as maintenance therapy and bronchodilators to curb the acute exacerbation. Steroidal agents which are used are the potent anti-inflammatory agents that suppress the inflammatory reaction, but in a way only suppressing manifestation of disease, weakening the immunity and constitution of the patient and thus making him prone to more life threatening conditions as manifested by airway remodeling, fibrosis of airway basement membrane and decreased responsiveness of the airway smooth muscles leading to emphysematous changes superadded to asthma. Same condition happens with anti-histaminics. These drugs act at local level and do not give permanent relief. These drugs give temporary symptomatic relief to the patient and do not remove the disease, i.e. the cause for hypersensitivity. Among all medical systems for treating diseases, homeopathic system is most efficacious. It is based on natural observations, rational experiments, and logical inferences. It works on the basis of similia similibus curenture, the nature’s law of cure. In homeopathic system of medicine we consider disease as a whole, with its causes, the process, along with constitutional totality of the person and not the local manifestations of disease. Mind, body and spirit, these are called trinity of life. When considered together, they form the constitution of the person. They are in interaction with one another and responsible for maintaining life. Any defect in function of any of them cause dis-ease. Medicine selected on these bases removes the disease permanently and corrects the constitutional deficit/ miasm from the person, as an ideal cure preached by our master, Dr. Hahnemann. Miasms are the fundamental cause of the chronic diseases. There are basically 3 miasms described by Dr. Hahnemann, namely psora, sycosis, and syphilis. Initially, during the early days of practice, Dr. Hahnemann faced some obstacles to the permanent cure in chronic cases. This miasmatic block can be removed with proper anti-miasmatic remedies.Thus, we have undertaken Asthma as our topic of study.

Causalities-multiplicity oriented joint interval-trend fuzzy information granulation for interval-valued time series multi-step forecasting

Interval-valued time series (ITSs) multi-step forecasting research is still in its infancy. Two cruces here lie in counterintuitive or conservative nature of semantic descriptors for ITSs, and disregard for multiplicity of causalities resulting from uncertainty in causalities between data or between trends within a set of interval-valued data. In this paper, we put forth a type of joint interval-trend fuzzy information granules, which takes non-loss of within-interval information as the main design criterion. A modified fuzzy information granulation method carries originality in portraying intuitive and accurate interval-trends, directly linked with inherent relational constraints such as lower bound data should not be greater than upper bound data. Furthermore, we formulate a legible format of multi-factor fuzzy IF-THEN rules, which exhibits interesting interpretations to causalities between interval-trends at a higher level of multiplicity. The forecasting process is fuzzy rules-based, resulting in wise results by calculating rule firing weights. Thus, we develop a well construct of accuracy and interpretability for multi-step forecasting of ITSs, manifested in: (a) reducing cumulative errors by operating at the granular level, and (b) perceiving interval-trends in an intelligible manner and emphasizing multiple causalities via transparent fuzzy logic inference. Experimental results convincingly confirm the validity of the model.

Adaptive human–machine theorem proving system

This paper presents a novel approach to constructing human–machine theorem proving systems. These systems integrate machine learning capabilities, human expert knowledge, and rigorous logical control for the effective construction and verification of proofs. The innovation of this approach lies in its openness: the user is given a tool for building such systems. Users can create theorem proving systems by selecting existing logical inference strategies or adding new ones in accordance with the provided interfaces or relationship agreements. The systems being built have a significant human--machine adaptive nature. During their operation, a meta-strategy is developed and trained based on the foundational elements of the existing strategies. The system is designed as a universal framework for managing various strategies, with the provision of a basic architecture and a library of strategies with the possibility of adding new ones. During the learning process, a system of structural characteristics is also accumulated and trained, on the basis of which a decision is made on the use of specific strategy elements at the next step. The presentation is conducted for the sequential calculus of minimal positive predicate logic as the most suitable for the deductive synthesis of programs and algorithms, for which it is proposed to use this approach.

A fuzzy-logic approach for longitudinal assessment of patients’ psychophysiological state: an application to upper-limb orthopedic robot-aided rehabilitation

Understanding the psychophysiological state during robot-aided rehabilitation is crucial for assessing the patient experience during treatments. This paper introduces a psychophysiological estimation approach using a Fuzzy Logic inference model to assess patients’ perception of robots during upper-limb robot-aided rehabilitation sessions. The patients were asked to perform nine cycles of 3D point-to-point trajectories toward different targets at varying heights with the assistance of an anthropomorphic robotic arm (i.e. KUKA LWR 4+). Physiological parameters, including galvanic skin response, heart rate, and respiration rate, were monitored across ten out of forty daily sessions. This data enabled the construction of an inference model to estimate patients’ perception states. Results expressed in terms of correlation coefficients between the patient state and the increasing number of sessions. Correlation coefficients showed statistically significant strong associations: a state of heightened engagement (formerly described as “Excited”) had ρ=-0.73\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\rho = -0.73$$\\end{document} (p-value=0.01), and a more calm and resting state (namely “Relaxed” state) had ρ=0.70\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\rho = 0.70$$\\end{document} (p-value=0.02) with the number of sessions completed. All patients had positive interaction with the robot, initially expressing curiosity and interest that gradually shifted to a more “Relaxed” state over time.

From Sample to Population Generalization in Qualitative Research

The split of methodology into qualitative and quantitative had all sorts of negative consequences, including the neglecting of some important scientific goals, such as generalizing research results in qualitative studies. Even if they don’t talk about generalizations, many qualitative researchers illegitimately extend their research results to populations much wider than those investigated. The article shows that the generalization of research results (from sample to population) is possible in qualitative research but that it can only be achieved through a sampling procedure that complies with specific rules. The article describes this procedure of internal generalization of results through a logical or scientific inference process. It is a theoretical sampling process in four steps that does not place emphasis on the size of the sample, but on the criteria for choosing the participants and seeks to obtain the generalization of findings following data variations. The procedure offers some advantages such as the transparency of the sampling process, provocative novelties such as the possibility to adjust the research population during the research but has also significant limitations such as the effects generated by the difficulty of anticipating the complexity of the studied phenomena.

An Infrastructural Brussels Effect: The translation of EU Law into the UK's digital borders

This article gives an account of the legal standards and safeguards that guide and constrain the current design of the UK's digital borders. Based on an empirical engagement with the development of Cerberus – an advanced risk-based analytics platform aimed at the detection of previously ‘unknown’ threats – the article presents a dual argument. On the one hand, it provides an account of the remaining salience and extraterritorial reach of EU law in setting standards for the collection, retention, processing and sharing of Passenger Name Records (PNR) data in the UK. This PNR data is a constitutive component of the digital border. Through the EU-UK Trade and Cooperation Agreement (TCA), the UK is now bound to comply with the rather stringent legal safeguards developed by the CJEU (in Opinion 1/15) on the retention and automated processing of PNR data. Our analysis shows the different channels through which EU law obtains this extraterritorial reach, how compliance can be monitored and enforced, and, crucially, how it has influenced and constrained the technical design of the UK's digital borders – a salient and unexplored phenomenon that we describe as an Infrastructural Brussels Effect. Yet, on the other hand, the article empirically shows that this is not merely a process of norm diffusion and extraterritoriality. Once legal standards become infrastructurally embedded in Cerberus, we witness normative translations and sociotechnical shifts with important legal and political consequences. Legal standards on ‘reasonable suspicion’ and the ‘objective evidence’ of ‘risk’, we argue, are given specific meaning through a logic of relational inference and algorithmic pattern detection (leading to forms of ‘concern by association’). By studying the entanglements between legal norms and material infrastructures – an approach we describe as infra-legalities – these normative effects become visible and contestable, providing a productive site for the sociolegal study of law and algorithmic governance.

MOF-on-MOF Growth: Inducing Naturally Nonpreferred MOFs and Atypical MOF Growth.

ConspectusOverflowing metal-organic frameworks (MOFs) have been synthesized from a wide range of metal and organic components for specific purposes and intellectual curiosity. Each MOF has unique chemical and structural characteristics directed by the incorporated components, metal ions (or clusters), organic linkers, and their intrinsic coordination interactions. These incorporated components and structural characteristics are two pivotal factors influencing MOFs' fundamental properties and subsequent applications. Therefore, selecting the appropriate metal and organic components, considering their innate chemical and structural properties, is crucial to endow the final MOFs with the desired properties. Ultimately, producing MOFs with a desired structure using ideal components is the best approach to achieving the best MOFs tailored for specific purposes with desired properties. However, achieving MOFs with the intended structure from chosen components remains underdeveloped. In many cases, the resulting MOF structure is governed by the thermodynamically and/or kinetically preferred configuration (refers to a naturally preferred structure) of the chosen components and given reaction conditions. Additionally, producing hybrid MOFs with complex components, structures, and morphologies presents a great opportunity to obtain special MOFs with advanced properties and functions. In this Account, we outline our group's efforts over the past few years to develop naturally nonpreferred MOFs through the induced MOF-on-MOF growth process and atypical hybrid MOFs via nonstandard MOF-on-MOF growth. First, we highlight the prime strategy for producing naturally nonpreferred MOFs based on template-induced MOF-on-MOF growth. In this section, we discuss the two basic growth behaviors, isotropic and anisotropic growth of naturally nonpreferred MOFs, determined by the degree of matching between the cell lattices of the two MOFs. Second, we introduce the MOF farming concept for the productive cultivation and effective harvesting of naturally nonpreferred MOFs made by MOF-on-MOF growth. Here we discuss the importance of selecting the ideal MOF template for productive growth and developing an efficient method for harvesting cultivated MOFs. Next, we describe atypical anisotropic MOF-on-MOF growths between two MOFs with mismatched cell lattices. In this section, we introduce tip-to-middle MOF-on-MOF growth involving self-structural adjustment of the secondary MOF, logical inference of unidentified MOF structures based on MOF-on-MOF growth behavior and morphological features, and MOF-on-MOF growth accompanied by etching and transformation of the template. Finally, we discuss the perspectives and challenges of MOF-on-MOF growth and the synthesis of naturally nonpreferred MOFs. We hope that this Account offers valuable insights into the rational design and development of MOFs with desired structural and compositional characteristics, leading to the creation of ideal MOFs.

Location and Size Planning of Charging Parking Lots Based on EV Charging Demand Prediction and Fuzzy Bi-Objective Optimization

The market share of electric vehicles (EVs) is growing rapidly. However, given the huge demand for parking and charging of electric vehicles, supporting facilities generally have problems such as insufficient quantity, low utilization efficiency, and mismatch between supply and demand. In this study, based on the actual EV operation data, we propose a driver travel-charging demand prediction method and a fuzzy bi-objective optimization method for location and size planning of charging parking lots (CPLs) based on existing parking facilities, aiming to reduce the charging waiting time of EV users while ensuring the maximal profit of CPL operators. First, the Monte Carlo method is used to construct a driver travel-charging behavior chain and a user spatiotemporal activity transfer model. Then, a user charging decision-making method based on fuzzy logic inference is proposed, which uses the fuzzy membership degree of influencing factors to calculate the charging probability of users at each road node. The travel and charging behavior of large-scale users are then simulated to predict the spatiotemporal distribution of charging demand. Finally, taking the predicted charging demand distribution as an input and the number of CPLs and charging parking spaces as constraints, a bi-objective optimization model for simultaneous location and size planning of CPLs is constructed, and solved using the fuzzy genetic algorithm. The results from a case study indicate that the planning scheme generated from the proposed methods not only reduces the travelling and waiting time of EV users for charging in most of the time, but also controls the upper limit of the number of charging piles to save construction costs and increase the total profit. The research results can provide theoretical support and decision-making reference for the planning of electric vehicle charging facilities and the intelligent management of charging parking lots.

Making Formulog Fast: An Argument for Unconventional Datalog Evaluation

With its combination of Datalog, SMT solving, and functional programming, the language Formulog provides an appealing mix of features for implementing SMT-based static analyses (e.g., refinement type checking, symbolic execution) in a natural, declarative way. At the same time, the performance of its custom Datalog solver can be an impediment to using Formulog beyond prototyping—a common problem for Datalog variants that aspire to solve large problem instances. In this work we speed up Formulog evaluation, with some surprising results: while 2.2× speedups can be obtained by using the conventional techniques for high-performance Datalog (e.g., compilation, specialized data structures), the big wins come by abandoning the central assumption in modern performant Datalog engines, semi-naive Datalog evaluation. In the place of semi-naive evaluation, we develop eager evaluation, a concurrent Datalog evaluation algorithm that explores the logical inference space via a depth-first traversal order. In practice, eager evaluation leads to an advantageous distribution of Formulog’s SMT workload to external SMT solvers and improved SMT solving times: our eager evaluation extensions to the Formulog interpreter and Soufflé’s code generator achieve mean 5.2× and 7.6× speedups, respectively, over the optimized code generated by off-the-shelf Soufflé on SMT-heavy Formulog benchmarks. All in all, using compilation and eager evaluation (as appropriate), Formulog implementations of refinement type checking, bottom-up pointer analysis, and symbolic execution achieve speedups on 20 out of 23 benchmarks over previously published, hand-tuned analyses written in F ♯ , Java, and C++, providing strong evidence that Formulog can be the basis of a realistic platform for SMT-based static analysis. Moreover, our experience adds nuance to the conventional wisdom that traditional semi-naive evaluation is the one-size-fits-all best Datalog evaluation algorithm for static analysis workloads.

Logical Relation Inference and Multiview Information Interaction for Domain Adaptation Person Re-Identification.

Domain adaptation person re-identification (Re-ID) is a challenging task, which aims to transfer the knowledge learned from the labeled source domain to the unlabeled target domain. Recently, some clustering-based domain adaptation Re-ID methods have achieved great success. However, these methods ignore the inferior influence on pseudo-label prediction due to the different camera styles. The reliability of the pseudo-label plays a key role in domain adaptation Re-ID, while the different camera styles bring great challenges for pseudo-label prediction. To this end, a novel method is proposed, which bridges the gap of different cameras and extracts more discriminative features from an image. Specifically, an intra-to-intermechanism is introduced, in which samples from their own cameras are first grouped and then aligned at the class level across different cameras followed by our logical relation inference (LRI). Thanks to these strategies, the logical relationship between simple classes and hard classes is justified, preventing sample loss caused by discarding the hard samples. Furthermore, we also present a multiview information interaction (MvII) module that takes features of different images from the same pedestrian as patch tokens, obtaining the global consistency of a pedestrian that contributes to the discriminative feature extraction. Unlike the existing clustering-based methods, our method employs a two-stage framework that generates reliable pseudo-labels from the views of the intracamera and intercamera, respectively, to differentiate the camera styles, subsequently increasing its robustness. Extensive experiments on several benchmark datasets show that the proposed method outperforms a wide range of state-of-the-art methods. The source code has been released at https://github.com/lhf12278/LRIMV.

Neuro-Logocentric Representation of Brain Activity as a Conceptual Basis for Artificial Neural Networks: a Critical Analysis

The aim of this article is a critical analysis of the logocentric representation of brain as a conceptual basis for artificial neural networks (ANNs). Neuro-logocentrism turns possible mostly because of some similarities of brain activity to logical reasoning. They are discrete mode of action, semblance of realization of logical connectives and inferences, presence of two “values” (“all-or-none” principle). It is demonstrated that they are only loose analogies. The idea that neurons are net nodes that realize logical and computational operations as quite simple automata is based on the concept of connectionism that appears a new special type of hylemorphism. On the contrary, brain activity demonstrates the primacy of the whole and its irreducibility to the aggregative sum of elements and parts. It appears continual totality, where everything is reciprocally interconnected, so that it is impossible to establish any unified formalism for it. Brain essentially is a part of the living, not “bio-logic” but fully biological reality, which has an emergent mode of existence with its unique properties. Such processes as neuroplasticity and synaptic pruning are responsible for adaptive and teleonomic re‑shaping of the brain. Usual conventional logic is suitable for computing machines and their nets, but it is incapable of reproducing such autopoietic phenomena. As a result, there is a fatal ontological gap between natural networks of neurons and ANNs. The second ones are not able to implement complex cognitive functions as they are product of the logocentric hypertrophy of algorithms which is fully devoid of flexibility of the living brain.

Theoretical basics of the self-learning system of intelligent locomotive decision support systems

Analysis of works in the field of artificial intelligence allows to make an assumption that today there is a sufficiently developed theoretical basis for the development of intelligent control systems for locomotive control. This will minimize the risks associated with the human factor on the railways. The paper presents the theoretical rationale for the development of a knowledge base for intelligent locomotive control systems. The approach and structure of the self-learning system of intelligent DSS is proposed, the advantage of which is the presence of a fuzzy classifier that works according to the set criteria and determines a fuzzy image of the current train situation. Learning a fuzzy classifier consists in finding a vector K that minimizes the distance between the results of logical inference and experimental data from the sample. The knowledge base is implemented using linguistic variables formalized by methods of fuzzy logic. The use of linguistic values makes it possible to design the base using the usual language of communication, which greatly simplifies both the design process itself and the analysis of the system's performance. Also, the knowledge base has the possibility of constant self-improvement. This happens in two ways. The first is by adding new rules to the knowledge base in case the current situation does not match the existing ones in the base, in which case an additional rule is created and checked for adequacy. The second way is a mechanism for ranking rules in the knowledge base. If the control action of the locomotive driver coincided with the recommendation of DSS in the current situation, then the rating of this recommendation (rule) increases, and in the future the rule selection algorithm will choose one or another control action for the current situation that has the highest rating (that is, it has already been verified several times person). The experiment has shown that the use of intelligent DSS has positive results. On average, the DSS made the correct train control decisions faster than the locomotive driver.

TabMoE: A General Framework for Diverse Table-Based Reasoning with Mixture-of-Experts

Tables serve as a widely adopted data format, attracting considerable academic interest concerning semantic understanding and logical inference of tables. In recent years, the prevailing paradigm of pre-training and fine-tuning on tabular data has become increasingly prominent in research on table understanding. However, existing table-based pre-training methods frequently exhibit constraints, supporting only single tasks while requiring substantial computational resources, which hinders their efficiency and applicability. In this paper, we introduce the TabMoE, a novel framework based on mixture-of-experts, designed to handle a wide range of tasks involving logical reasoning over tabular data. Each expert within the model specializes in a distinct logical function and is trained through the utilization of a hard Expectation–Maximization algorithm. Remarkably, this framework eliminates the necessity of dependency on tabular pre-training, instead exclusively employing limited task-specific data to significantly enhance models’ inferential capabilities. We conduct empirical experiments across three typical tasks related to tabular data: table-based question answering, table-based fact verification, and table-to-text generation. The experimental results underscore the innovation and feasibility of our framework.

Scheduling as a Constraint Satisfaction Problem (Using the Example of Open-Pit Minе Production Scheduling Problem)

The research described in the work is aimed at developing methods for Scheduling. The fundamental disadvantage of the existing methods of Mixed-Integer Linear Programming in application to the problems under consideration is the fact that they are too demanding on the amount of RAM. The difficulty of applying local search procedures to such high-dimensional problems is to develop an effective way to find an acceptable initial approximation and determine the neighboring state transition function, which would allow achieving the optimum fast enough. In the Operations Research Theory, adding additional conditions to a problem can lead to a fundamental change in the problem-solving scheme. The methods proposed in the study are implemented within the framework of the Constraint Programming Paradigm which makes it possible to represent the subject domain dependencies saving RAM, as well as to provide the ability to step-by-step take into account heterogeneous problem conditions without essentially changing the scheme of finding solutions. A significant part of the research deals with methods of logical inference on constraints to reduce the search space and speed up the computational process. The approach to scheduling is illustrated by the Open-Pit Mine Production Scheduling Problem, which was first proposed to be solved as a Constraint Satisfaction Problem. In order to find the first feasible solution, a «greedy» search method is proposed, the result of which can be improved using the developed hybrid method. Both methods rely on original procedures of inference on constraints. The proposed approach has proven its efficiency for block models with sizes of tens and hundreds of thousands of blocks.

Not Everyone Will Be Taken into the Future? The Role of Forecasting in International Relations Theory

The 2010s witnessed intensified debates in international academia on the relationship between theory and forecast, but Russian scholarship remained detached from these discussions. This article intends to examine the contribution of theory to forecasting in IR, identifying prospects for combining the prescriptive, explanatory, interpretive and predictive functions of science. It reveals the origins of both theoretical skepticism about prediction and the countercriticism of futile theorizing. The article reveals the limitations of both the hypothetico-deductive model of scholarship, which dominates the discipline, and the statistical inductivism that opposes it. The analysis begins by identifying the different classes of theories in international relations studies. It demonstrates that the basic assumptions of normative, deconstructive, and interpretive theorizing are poorly compatible with prediction. The article further scrutinizes the ambivalent attitude of explanatory theories towards the matter, claiming the fundamental unpredictability of social interactions. It explores the growth in expectations since the late 2000s that the accumulation of big data and the advancement of methods for processing them will provide predictions without relying on explanatory inferences. Nevertheless, the value of scholarship for foreign policy practice requires a combination of theoretical explanations and predictive studies. This article suggests that for theory to play a major role in forecasting, forecasts must play a major role in theorizing. In this sense, reluctance to test their logical inferences with predictions exemplified by scholars poses a barrier to the development of IR studies.

Error is a number, parameter or probability distribution

The problem of the format (the form of representation by difference, scattering parameter, distribution) of the characteristics of the accuracy of the results in solving measurement problems in terms of the theory of errors and the concept of uncertainty is considered. It is shown that for the error as a difference, the key question is the uncertainty of the so-called true value. For measurement uncertainty, when switching to estimating the scattering parameter based on a set of repeated measurements and getting rid of the adjective true, a number of key issues of a different plan arise – the lack of logic of statistical inference, the inconsistency of the term “confidence level” with the term “confidence probability” in state verification schemes and the practical uselessness of measurement uncertainty characteristics in risk analysis tasks. However, the endless improvement of measurement methods and tools in the theory of errors and an unlimited amount of information for a complete description of the measured value in the concept of uncertainty is a common defect of both approaches, only in other words. Conceptually, the transition from error to uncertainty is an intermediate stage to the representation of accuracy characteristics by probability distributions. A brief overview of the transformation of the point of view of international metrology on this issue is given. The evaluation of the accuracy of the results is presented as a structural-parametric identification of the drift of metrological characteristics of measuring instruments and standards, as well as metrological certification of methods for solving measurement problems in the cross-observation scheme in terms of probability distributions. This allows you to get rid of the problems of incompleteness of other formats for representing accuracy characteristics. The most complete characterization of accuracy for the theory of errors and the concept of uncertainty is the format of the probability distribution. However, the endless improvement of measurement methods and tools in the theory of errors and an unlimited amount of information for a complete description of the measured value in the concept of uncertainty is a common defect of both approaches, only in other words. Conceptually, the transition from error to uncertainty is an intermediate stage to the representation of accuracy characteristics by probability distributions. A brief overview of the transformation of the point of view of international metrology on this issue is given. The evaluation of the accuracy of the results is presented as a structural-parametric identification of the drift of metrological characteristics of measuring instruments and standards, as well as metrological certification of methods for solving measurement problems in the cross-observation scheme in terms of probability distributions. This allows you to get rid of the problems of incompleteness of other formats for representing accuracy characteristics. The most complete characteristic of accuracy for the theory of errors and the concept of uncertainty is the format of the probability distribution.

The Two Word Test as a semantic benchmark for large language models

Large language models (LLMs) have shown remarkable abilities recently, including passing advanced professional exams and demanding benchmark tests. This performance has led many to suggest that they are close to achieving humanlike or “true” understanding of language, and even artificial general intelligence (AGI). Here, we provide a new open-source benchmark, the Two Word Test (TWT), that can assess semantic abilities of LLMs using two-word phrases in a task that can be performed relatively easily by humans without advanced training. Combining multiple words into a single concept is a fundamental linguistic and conceptual operation routinely performed by people. The test requires meaningfulness judgments of 1768 noun-noun combinations that have been rated as meaningful (e.g., baby boy) or as having low meaningfulness (e.g., goat sky) by human raters. This novel test differs from existing benchmarks that rely on logical reasoning, inference, puzzle-solving, or domain expertise. We provide versions of the task that probe meaningfulness ratings on a 0–4 scale as well as binary judgments. With both versions, we conducted a series of experiments using the TWT on GPT-4, GPT-3.5, Claude-3-Optus, and Gemini-1-Pro-001. Results demonstrated that, compared to humans, all models performed relatively poorly at rating meaningfulness of these phrases. GPT-3.5-turbo, Gemini-1.0-Pro-001 and GPT-4-turbo were also unable to make binary discriminations between sensible and nonsense phrases, with these models consistently judging nonsensical phrases as making sense. Claude-3-Opus made a substantial improvement in binary discrimination of combinatorial phrases but was still significantly worse than human performance. The TWT can be used to understand and assess the limitations of current LLMs, and potentially improve them. The test also reminds us that caution is warranted in attributing “true” or human-level understanding to LLMs based only on tests that are challenging for humans.

A Predictive Model of Pressure Injury in Children Undergoing Living Donor Liver Transplantation Based on Machine Learning Algorithm.

The aim of our study was to formulate and validate a prediction model using machine learning algorithms to forecast the risk of pressure injuries (PIs) in children undergoing living donor liver transplantation (LDLT). A retrospective cohort study. The research was carried out at China's largest paediatric liver transplantation centre. A total of 438 children who underwent LDLT between June 2021 and December 2022 constituted the study cohort. The dataset was partitioned randomly into 70% for training datasets (306 cases) and 30% for testing datasets (132 cases). Utilising four machine learning algorithms-Decision Tree, Random Forest, Gradient Boosting Decision Tree and eXtreme Gradient Boosting-we identified risk factors and constructed predictive models. Out of 438 children, 42 developed PIs, yielding an incidence rate of 9.6%. Notably, 94% of these cases were categorised as Stage 1, and 54% were localised on the occiput. Upon evaluating the four prediction models, the Decision Tree model emerged as the most effective. The primary contributors to pressure injury in the Decision Tree model were identified as operation time, intraoperative corticosteroid administration, preoperative skin protection measures and preoperative skin conditions. A visualisation elucidating the logical inference process for the 10 variables within the Decision Tree model was presented. Ultimately, based on the Decision Tree model, a predictive system was developed. Machine learning algorithms facilitate the identification of crucial factors, enabling the creation of an effective Decision Tree model to forecast pressure injury development in children undergoing LDLT. With this predictive model at their disposal, nurses can assess the pressure injury risk level in children more intuitively. Subsequently, they can implement tailored preventive strategies to mitigate the occurrence of PIs. Paediatric patients contributed electronic health records datasets.

Fuzzy logic for the management of vaccination during pandemics: A spread-rate-based approach

<p>Pandemics, such as coronavirus disease COVID-19 are known to cause massive damage to the world's economic growth and their impacts are serious and influence across every aspect of social structure. The most inevitable factor in responding to the disaster of pandemics is the right management in terms of allocating a limited vaccine supply. The focus of this research work is to utilize a fuzzy logic inference system in the allocation of vaccine doses to the regional authorities by a central authority. The objective is obtained by designing a system based on fuzzy logic that considers the spread rate as the input to infer the vaccination rate of the local population. This system makes it possible for sufficient doses of vaccines to be allotted to the prioritized regions where the severity of the spread rate is a concern and vaccines are not held up in regions where the severity of the spread rate is lesser. The designed system is verified using MATLAB software, which shows that this method can ensure an effective and efficient allocation of vaccination in the local regions and aid the fight against the disastrous spread of the disease.</p>