Classification Rules Research Articles

Calculus and Fine Properties of Functions of Bounded Variation on RCD Spaces

We generalize the classical calculus rules satisfied by functions of bounded variation to the framework of {textrm{RCD}} spaces. In the infinite dimensional setting, we are able to define an analogue of the distributional differential and, on finite dimensional spaces, we prove fine properties and suitable calculus rules, such as the Vol’pert chain rule for vector valued functions.

CasPEDIA Database: a functional classification system for class 2 CRISPR-Cas enzymes.

CRISPR-Cas enzymes enable RNA-guided bacterial immunity and are widely used for biotechnological applications including genome editing. In particular, the Class 2 CRISPR-associated enzymes (Cas9, Cas12 and Cas13 families), have been deployed for numerous research, clinical and agricultural applications. However, the immense genetic and biochemical diversity of these proteins in the public domain poses a barrier for researchers seeking to leverage their activities. We present CasPEDIA (http://caspedia.org), the Cas Protein Effector Database of Information and Assessment, a curated encyclopedia that integrates enzymatic classification for hundreds of different Cas enzymes across 27 phylogenetic groups spanning the Cas9, Cas12 and Cas13 families, as well as evolutionarily related IscB and TnpB proteins. All enzymes in CasPEDIA were annotated with a standard workflow based on their primary nuclease activity, target requirements and guide-RNA design constraints. Our functional classification scheme, CasID, is described alongside current phylogenetic classification, allowing users to search related orthologs by enzymatic function and sequence similarity. CasPEDIA is a comprehensive data portal that summarizes and contextualizes enzymatic properties of widely used Cas enzymes, equipping users with valuable resources to foster biotechnological development. CasPEDIA complements phylogenetic Cas nomenclature and enables researchers to leverage the multi-faceted nucleic-acid targeting rules of diverse Class 2 Cas enzymes.

Criterion Validation of Tau PET Staging Schemes in Relation to Cognitive Outcomes.

Utilization of NIA-AA Research Framework requires dichotomization of tau pathology. However, due to the novelty of tau-PET imaging, there is no consensus on methods to categorize scans into "positive" or "negative" (T+ or T-). In response, some tau topographical pathologic staging schemes have been developed. The aim of the current study is to establish criterion validity to support these recently-developed staging schemes. Tau-PET data from 465 participants from the Alzheimer's Disease Neuroimaging Initiative (aged 55 to 90) were classified as T+ or T- using decision rules for the Temporal-Occipital Classification (TOC), Simplified TOC (STOC), and Lobar Classification (LC) tau pathologic schemes of Schwarz, and Chen staging scheme. Subsequent dichotomization was analyzed in comparison to memory and learning slope performances, and diagnostic accuracy using actuarial diagnostic methods. Tau positivity was associated with worse cognitive performance across all staging schemes. Cognitive measures were nearly all categorized as having "fair" sensitivity at classifying tau status using TOC, STOC, and LC schemes. Results were comparable between Schwarz schemes, though ease of use and better data fit preferred the STOC and LC schemes. While some evidence was supportive for Chen's scheme, validity lagged behind others-likely due to elevated false positive rates. Tau-PET staging schemes appear to be valuable for Alzheimer's disease diagnosis, tracking, and screening for clinical trials. Their validation provides support as options for tau pathologic dichotomization, as necessary for use of NIA-AA Research Framework. Future research should consider other staging schemes and validation with other outcome benchmarks.

Analytic inclined flow rule for determining granular rheology under strong non-local effects

This study theoretically establishes a flow rule for a granular flow down a rough inclined plane, capable of determining granular rheology in the presence of strong non-local effects resulting from grain cooperativity. To describe the non-local rheology, a Landau–Ginzburg model is formulated in terms of the fluidisation parameter represented by the granular inertial number. The exact solutions of the inertial-number field are solved and provide physical insights into the evolution of the internal rheology and the flow arrest process controlled by the flow height. Through asymptotic analysis in the regime dominated by strong non-locality, the exact solutions are further reduced to yield an analytical flow rule for the mean flow velocity. A comparison between the prediction of the flow rule and experimental data from the literature for sand grains determines the underlying rheology law and the relevant rheological parameters. Thus, the proposed flow rule serves as an effective tool for inferring granular rheology from strongly non-local inclined flow data, surpassing the limitations of the classical flow rule deduced from the local rheology framework.

An atypical anatomy of permanent mandibular second molar with five roots.

Especially in molars that may have sometimes aberrant additional root canals, the complexity of tooth internal morphology in individual cases sometimes does not match to admitted classification rules and underlines the risk of missed anatomy during the endodontic management. To our knowledge, a permanent mandibular second molar with independent five roots, three mesial and two distal, each of them harboring a single canal, was not yet reported. Despite the treatment difficulties this tooth could be successfully approached by using dental operative microscope and cone-beam computed tomography (CBCT) with small field of view. Though CBCT is not a routine imagistic examination, in case of atypical tooth anatomy aiming to establish adequate diagnosis and treatment plan, the successful clinical outcome prevails over the irradiation dose.

Hybrid coupling rules for leaderless heterogeneous oscillators: Uniform global asymptotic and finite-time synchronization

We investigate the engineering scenario where the objective is to synchronize heterogeneous oscillators in a distributed fashion. The internal dynamics of each oscillator are general enough to capture their time-varying natural frequency as well as physical couplings and unknown bounded terms. A communication layer is set in place to allow the oscillators to exchange synchronizing coupling actions through a tree-like leaderless network. In particular, we present a class of hybrid coupling rules depending only on local information to ensure uniform global practical or asymptotic synchronization, which is impossible to obtain by using the Kuramoto model customarily used in the literature. We further show that the synchronization set can be made uniformly globally prescribed finite-time stable by selecting the coupling function to be discontinuous at the origin. Novel mathematical tools on non-pathological functions and set-valued Lie derivatives are developed to carry out the stability analysis. The effectiveness of the approach is illustrated in simulations where we apply our synchronizing hybrid coupling rules to models of power grids previously used in the literature.

Automatic Identification of the Working State of High-Rise Building Machine Based on Machine Learning

High-rise building machines (HBMs) play a crucial role in the construction of super-tall buildings, with their working states directly impacting safety, quality, and progress. Given their extensive floor coverage and complex internal structures, monitoring priorities should shift according to specific workflows. However, existing research has primarily focused on monitoring key HBM components during specific stages, neglecting the automated recognition of HBM workflows, which hinders adaptive monitoring strategies. This study investigates the critical states of HBM construction across various structural layers and proposes a method rooted in vibration signal analysis to determine the HBM’s working state. The method involves collecting vibration signals with a triaxial accelerometer, extracting five distinct vibration signal features, classifying these signals using a k-Nearest Neighbors (kNN) classifier, and finally, outputting the results through a classification rule that aligns with the actual workflow of the HBM. The method was implemented in super-high-rise buildings exceeding 350 m, achieving a measured accuracy of 97.4% in HBM working state recognition. This demonstrates its proficiency in accurately determining the construction state and facilitating timely feedback. Utilizing vibration signal analysis can enhance the efficiency and safety, with potential applications in monitoring large-scale formwork equipment construction processes. This approach provides a versatile solution for a wide range of climbing equipment used in the construction of super-tall buildings and towering structures.

Disclosure and pricing of attributes

AbstractA monopolist sells an object characterized by multiple attributes. A buyer can be one of many types, differing in their willingness to pay for each attribute. The seller can provide arbitrary attribute information in the form of a statistical experiment. To screen different types, the seller offers a menu of options that specify information prices, experiments, and object prices.I characterize revenue‐maximizing menus. All experiments belong to a class of linear disclosure rules. An optimal menu may be nondiscriminatory. The analysis highlights the importance of demand microstructure and the benefits of information control in trade settings.

Relating the Ramsay Quotient Model to the Classical D-Scoring Rule

In a series of papers, Dimitrov suggested the classical D-scoring rule for scoring items that give difficult items a higher weight while easier items receive a lower weight. The latent D-scoring model has been proposed to serve as a latent mirror of the classical D-scoring model. However, the item weights implied by this latent D-scoring model are typically only weakly related to the weights in the classical D-scoring model. To this end, this article proposes an alternative item response model, the modified Ramsay quotient model, that is better-suited as a latent mirror of the classical D-scoring model. The reasoning is based on analytical arguments and numerical illustrations.

Leukemia Blood Cells Detection using Neural Network Classifier

Image segmentation is an image processing operation performed on the image in order to partition the image into some images based on the information contained in the original image. Image segmentation plays an important role in many medical imaging applications, image segmentation facilitates the anatomy process in a particular body of human body. Classification and clustering are the methods used un data mining for analyzing the data sets and divide them on the basis of some particular classification rules. There are many image segmentation tools that used for medical purpose, so it is necessary to define and/or to improve the image segmentation methods in order to get the best method. In this study, the image of leukemia and red blood cells will be used as samples to determine the best algorithm in image segmentation. The procedure for doing segmentation itself is clustering image, edge detection on image, and image classification. The clustering is to extract important information from an image. The edge detection is to determine the existence of edges of lines in image in order to investigate and localize the desired edge features. Moreover, the classification analyzes the properties of some images and organizes the information into certain categories. In this study, the Neural Network and K-Nearest Neighbor are used for image classification by paired with Local Binary Pattern and Principal Component Analysis. The results revealed that the best method of proven in classifying images is from Local Binary Pattern feature extraction with the average accuracy of 94%.

Structural optimization of composite corrugated cores with variable stiffness

Outstanding progress in manufacturing technology of fiber-reinforced composite materials, allows designers to go beyond classical design rules. Composites with variable stiffness are an innovative division of composite materials that provide more efficient designs for engineers due to their diverse design parameters. In this study, a straightforward methodology is presented for the design of sandwich panels with corrugated variable stiffness core. Based on the homogenization method, a unit-cell of the sandwich panel is used as a representative volume element for the sandwich panel with corrugated core. Due to multiple design variables, conventional numerical and analytical methods cannot be applied to variable stiffness composites, thus PYTHON/ABAQUS framework is developed to determine the extensional and bending behavior of unit-cells. Details of this framework including PYTHON scripting and ABAQUS finite element solver are given along with their corresponding functions. Furthermore, the elitist genetic algorithm is incorporated into a framework for the optimum design of variable stiffness cores. Two optimization problems, namely composites with variable thickness and variable angle are considered for unit-cells, and associated constraints, boundary conditions, and fitness functions are discussed in detail. In addition to the benefits of the proposed framework, the obtained results reveal notable enhancement in the extensional and bending behavior of unit-cell. Results reveal that corresponding displacements are significantly improved (up to 80%) when using the optimum variable thickness case. For variable angle cases, the improvement of displacements is up to about 28%.

Modeling of modulus and strength in void-containing clay platelet/cellulose nanocomposites by unit cell approach

Clay platelets/cellulose nanofibril nanocomposites are recyclable engineering materials of interest for sustainable development. There is substantial experimental data for mechanical properties, but modeling efforts are scarce. Here, a conceptual unit cell with voids was used in finite element modeling. Predictions for the in-plane modulus taking voids into account were more accurate than the classical rule of mixtures. Simulations also reveal that the cellulosic matrix undergoes shear and tensile deformation with inclined fracture located near the end of platelets for low clay content while predicting brittle tensile failure for high clay content. The results from the unit cell approach provide improved understanding of experimental observations, supporting the strive to better understand mechanisms of deformation and fracture.

Mechanics-based classification rule for plants

The height of thick and solid plants, such as woody plants, is proportional to two-thirds of the power of their diameter at breast height. However, this rule cannot be applied to herbaceous plants that are thin and soft because the mechanisms supporting their bodies are fundamentally different. This study aims to clarify the rigidity control mechanism resulting from turgor pressure caused by internal water in herbaceous plants to formulate the corresponding scaling law. We modeled a herbaceous plant as a cantilever with the ground side as a fixed end, and the greatest height was formulated considering the axial tension force from the turgor pressure. The scaling law describing the relationship between the height and diameter in terms of the turgor pressure was theoretically derived. Moreover, we proposed a plant classification rule based on stress distribution.

Cost-sensitive probabilistic predictions for support vector machines

Support vector machines (SVMs) are widely used and constitute one of the best examined and used machine learning models for two-class classification. Classification in SVM is based on a score procedure, yielding a deterministic classification rule, which can be transformed into a probabilistic rule (as implemented in off-the-shelf SVM libraries), but is not probabilistic in nature. On the other hand, the tuning of the regularization parameters in SVM is known to imply a high computational effort and generates pieces of information that are not fully exploited, not being used to build a probabilistic classification rule.In this paper we propose a novel approach to generate probabilistic outputs for the SVM. The new method has the following three properties. First, it is designed to be cost-sensitive, and thus the different importance of sensitivity (or true positive rate, TPR) and specificity (true negative rate, TNR) is readily accommodated in the model. As a result, the model can deal with imbalanced datasets which are common in operational business problems as churn prediction or credit scoring. Second, the SVM is embedded in an ensemble method to improve its performance, making use of the valuable information generated in the parameters tuning process. Finally, the probabilities estimation is done via bootstrap estimates, avoiding the use of parametric models as competing approaches. Numerical tests on a wide range of datasets show the advantages of our approach over benchmark procedures.

Rules of Appositive Classification in the Grammatical Heritage

This paper deals with similarities in pronunciation and meaningamong appositives which were behind devoting an independentsection in all grammatical books to appositives, althougheach type of them has its own rules and signs. However, Thisdistinction did not prevent them from being classified under onesection with one basis that is agreement between main andsubordinate appositives as well as the existence of agreement insemantics related to definite and indefinite noun, plural, dual, singularand gender adjacent with little exception as in adjectives of cause .This paper, is therefore divided according to types of appositives intofour sections: Adjective, Affirmation, Apposition and Coupling.Each section explains aspects of agreement between the mainand subordinate appositives especially the semantic featuresspecifically related to each subordinate appositive. Due to thedomination of the structural aspect and the clarity of accord in pronunciation, this becomes the basis of classification of appositives in general or the sub-division of the types of each subordinate appositive in particular. This distinction of each partdid not prevent appositivesfrom being classified under one label namely the agreement betweenmain and subordinate appositives.Due to the dominance of the structural aspect and clarity ofaccord in pronunciation, it was the most clear basis and inthe classification of appositives.

The Hull of Two Classical Propagation Rules and Their Applications

In this work, we study and determine the dimensions of Euclidean and Hermitian hulls of two classical propagation rules, namely, the (u, u + v)-construction and the direct sum construction. Some new criteria for the resulting codes derived from these two propagation rules being self-dual, self-orthogonal, or linear complementary dual (LCD) codes are given. As applications, we employ the (u, u + v)-construction to obtain (almost) self-orthogonal codes; employ the direct sum construction to provide lower bounds on the minimum distance of FSD (LCD) codes; and employ both these two constructions to derive linear codes with prescribed hull dimensions. Many (almost) optimal codes are presented. In particular, a family of binary almost Euclidean self-orthogonal Griesmer codes is constructed. We also obtain many binary, ternary Euclidean and quaternary Hermitian FSD LCD codes of larger lengths and improve some lower bounds on the minimum distance of known ternary Euclidean LCD codes.

OPTIMIZING PLANT DISEASE PREDICTION: A NEURO-FUZZY-GENETIC ALGORITHM APPROACH

In this essay, the idea of improving plant disease prediction using a Neuro-Fuzzy-Genetic algorithm (NFGA) technique is explored. The concept of a Neuro-Fuzzy-Genetic algorithm is first described. The advantages of the NFGA technique for predicting plant disorders are next addressed. An example is then given to demonstrate how this technique has been successfully used and the advantages it offers you. A hybrid artificial intelligence technique known as a Neuro-Fuzzy-Genetic set of rules (NFGA) combines the genetic algorithms, fuzzy logic, and neural network algorithms. It entails using a method of organizing fuzzy rules for statistics type, developing a network of neurons to anticipate the level of the group of data points to positive fuzzy training, adjusting the weights of fuzzy classes using a genetic algorithm-based totally optimization method to better fit the data factors, and ultimately identifying and predicting patterns of statistics points. The main benefits of this method for predicting plant diseases are its abilities to analyze various plant characteristics, extract complex relationships between statistics points, identify correlations between various environmental factors and illnesses, choose the best combinations of fuzzy rules for accurate classification, and finally adapt to changing data over time.

Explicit and implicit category learning in categorical visual search.

Categorical search has been heavily investigated over the past decade, mostly using natural categories that leave the underlying category mental representation unknown. The categorization literature offers several theoretical accounts of category mental representations. One prominent account is that separate learning systems account for classification: an explicit learning system that relies on easily verbalized rules and an implicit learning system that relies on an associatively learned (nonverbalizable) information integration strategy. The current study assessed the contributions of these separate category learning systems in the context of categorical search using simple stimuli. Participants learned to classify sinusoidal grating stimuli according to explicit or implicit categorization strategies, followed by a categorical search task using these same stimulus categories. Computational modeling determined which participants used the appropriate classification strategy during training and search, and eye movements collected during categorical search were assessed. We found that the trained categorization strategies overwhelmingly transferred to the verification (classification response) phase of search. Implicit category learning led to faster search response and shorter target dwell times relative to explicit category learning, consistent with the notion that explicit rule classification relies on a more deliberative response strategy. Participants who transferred the correct category learning strategy to the search guidance phase produced stronger search guidance (defined as the proportion of trials on which the target was the first item fixated) with evidence of greater guidance in implicit-strategy learners. This demonstrates that both implicit and explicit categorization systems contribute to categorical search and produce dissociable patterns of data.

A game-theoretical approach to Islamic law disputes

Purpose This study aims to analyze notable distribution dispute cases from Islamic law history. The authors will assess these alongside resolutions proposed by historical authorities, some of which evolved into established Islamic case law. In addition, the authors intend to apply classic fair division rules to these cases, providing alternative solutions. Using a game-theoretical approach, the authors plan to compare Islamic solutions with traditional division rules through axiomatic analysis. The goal of this study is to systematically explore the unique principles underpinning Islamic distributions. Design/methodology/approach In this study, the authors collate Islamic inheritance law disputes involving conflicting claims, unresolvable by primary Islamic law sources, from historical and modern texts. The authors formally model these as claims problems, surplus-sharing problems and adapted claims problems. Concurrently, the authors gather the proposed solutions and historical backgrounds offered by the era’s authorities and jurists. These solutions are axiomatically generalized into rules, while the axioms characterizing distribution rules are checked if they are aligned with Islamic norms and values. This approach facilitates a comparison between Islamic distributions and classic division rules. Findings The 'Awl and Radd doctrines, used in Islamic inheritance law, are axiomatically equivalent to the Proportional Rule, a prevalent non-Jewish division rule. These doctrines present solutions impervious to manipulation by legal heirs through rights transfer, unlike other possible distributions. Ibn 'Abbas' solution for Awliyya cases uses sequential priorities and diverges uniquely from classic fair division rules in the literature. In addition, it is established that Abu Yusuf's (b. 729) distribution for a legal dispute is axiomatically identical to Abraham ibn Ezra's (b. 1089) division rule. Research limitations/implications There is a noticeable dearth of comprehensive studies investigating contentious disputes concerning resource claims within Islamic law. Many of these studies are lacking in-depth analyses of diverse cases, casting doubts on their reliability. As a result, a robust focus is needed on case collection prior to any analytical process. Future research should concentrate on collating instances of fair division problems throughout Islamic history, as well as separately collecting methods of Islamic sharing. This procedure may lead to the characterization of various Islamic regulations, thereby emphasizing distinct Islamic principles. In forthcoming studies, conducting an exhaustive axiomatic evaluation of the cases and proposed resolutions is imperative. Practical implications This research illuminates existing knowledge gaps, setting a course for novel research trajectories. It underlines the fair division literature’s oversight of disputes within Islamic law, despite the plentiful existence of contentious cases. The research underscores the relevance of cooperative game theory as a tool for dissecting Islamic legal disputes. By accounting for unique Islamic norms and principles, this study lays a foundation for a nuanced comprehension of the dynamics and outcomes of legal disputes. By integrating an interdisciplinary approach, this research strives to bridge the gap between game theory and Islamic law. Social implications Beyond addressing a significant research lacuna, this study carries extensive societal implications. By shedding light on enduring debates within Islamic law, it encourages a rejuvenated understanding of the evolution and interpretation of legal disputes. The axiomatic disparities between rulers’ and jurists’ methods provide invaluable insights within the Islamic context, bolstering the understanding of sociocultural dynamics that influence legal decision-making. This research has the potential to shape legal discourse, guide policymaking and spur scholarly, juristic and societal dialogue. Consequently, it may foster a more comprehensive and enlightened approach toward the resolution of legal disputes in Islamic law. Originality/value To the best of the authors’ knowledge, this study is the first to examine Islamic law’s historical legal disputes from a game-theoretical standpoint. Existing studies rarely collect distribution disputes systematically, and none scrutinize the axiomatic rationales underlying authorities’ and jurists’ distributions, opting instead to focus on historical backgrounds. While the fair division literature extensively examines disputes, it often overlooks those originating from Islamic law, which presents a rich source of disputes that can be modeled as fair division problems. This research makes a distinct contribution by incorporating disputes from Islamic law into the existing body of cooperative game theory literature.

Fusing logic rule-based hybrid variable graph neural network approaches to fault diagnosis of industrial processes

It is practical that industrial processes are regarded as hybrid systems involving both continuous and discrete variables. Particularly, process discrete variables are usually rather crucial to process normal operations. However, existing data-driven fault diagnosis methods have been rarely applied for industrial processes concerning hybrid system characteristics. In response to this issue, this paper proposes a novel industrial process fault diagnosis approach based on fusing logic rule-based hybrid variable graph neural networks (GNNs) able to learn hybrid variable spatial structures. Therein, continuous variables samples are regarded as learning objects, while logic rule information is utilized to guide the learning process. Specifically, a mixed 0–1 integer linear programming algorithm is used to extract logic rules with fault labels from discrete variable data. Subsequently, a rule classification method is used to classify the logic rules with fault labels into independent and interdependent rule classes which are filed in a rule class library using as the basis for topological connections among data samples. In addition, an edge pruning-based graph optimization is employed to reduce redundant connections of the nodes. It is found that the established hybrid variable GNNs is able to learn the spatial topological structure of hybrid industrial process variables, providing rich heuristic knowledge for process fault classifications so as to effectively improve the fault diagnose performance. Simulation experiments are carried out in an industrial coal gasification process platform and a storage tank systems simulation platform, achieving satisfactory results.