Single Rule Research Articles

Integrated electrophysiological and genomic profiles of single cells reveal spiking tumor cells in human glioma

Prior studies have described the complex interplay that exists between glioma cells and neurons; however, the electrophysiological properties endogenous to glioma cells remain obscure. To address this, we employed Patch-sequencing (Patch-seq) on human glioma specimens and found that one-third of patched cells in IDH mutant (IDHmut) tumors demonstrate properties of both neurons and glia. To define these hybrid cells (HCs), which fire single, short action potentials, and discern if they are of tumoral origin, we developed the single cell rule association mining (SCRAM) computational tool to annotate each cell individually. SCRAM revealed that HCs possess select features of GABAergic neurons and oligodendrocyte precursor cells, and include both tumor and non-tumor cells. These studies characterize the combined electrophysiological and molecular properties of human glioma cells and describe a cell type in human glioma with unique electrophysiological and transcriptomic properties that may also exist in the non-tumor brain.

A homogeneous multi-vehicle cooperative group decision-making method in complicated mixed traffic scenarios

Connected and Automated Vehicles (CAVs) are expected to reshape the transportation system, and cooperative group intelligence of CAVs has great potential for improving transportation efficiency and safety. One challenge for CAV group driving is the decision-making under scenarios mixed with CAV and human-driven vehicles (HDV). Current studies mainly use methods based on single physical rules such as platoon driving or formation switch control, failing to reach a balanced and homogeneous state of optimal efficiency and risk in mixed traffic environments. In addition, most studies focus only on one specific type of scene, lacking the scene adaptability to various surrounding conditions. This paper proposes a homogeneous multi-vehicle cooperative group decision-making method targeting mixed traffic scenarios. A bi-level framework composed of behavior-level and trajectory-level decision-making is established to achieve balanced optimal cooperation. A region-driven behavioral decision mechanism is designed to decompose vehicle actions into a unified form of sequential target regions. Solutions are derived based on Cooperative Driving Safety Field, a risk assessment module inspired by field energy theory. The trajectory-level decision module takes the target regions as input and generates the control quantities of the CAVs through target point selection, conflict reconciliation, and dynamic constraint consideration. Experimental results on 19 various scenarios and continuous traffic flow scenes indicate that the proposed method significantly increases passing efficiency, reduces driving risk, and improves scene adaptability. In addition, experiments on multiple kinds of scenarios including intersections, ramps, bottlenecks, etc. prove that our method can adapt to various road topology structures. Feasibility is also verified through scaled physical platform validations and real-vehicle road tests.

Emergency department clinical performance of ADVIA Centaur n-terminal prohormone of B-type natriuretic peptide assay

BackgroundNatriuretic peptide testing is guideline recommended as an aid to the diagnosis of heart failure (HF). We sought to evaluate the performance of the ADVIA Centaur (Siemens Healthcare Diagnostics, Tarrytown, NY) NT-proBNPII assay (PBNPII) in emergency department (ED) dyspneic patients. MethodsEligible patients presented to the ED with dyspnea, with their gold standard diagnosis determined by up to 3 cardiologists blinded to the PBNPII results. Patients were stratified into 3 groups based on PBNPII resultsa rule out group of NT-proBNP<300 pg/mL, an age-specific rule in group using cutoffs of 450, 900, and 1800 pg/mL, for <50, 50–75, and > 75 years respectively, and an intermediate cohort for results between the rule out and rule in groups. ResultsOf 3128 eligible patients, 1148 (36.7 %) were adjudicated as acute heart failure (AHF). The gold standard AHF diagnosis rate was 3.7, 24.3, and 67.2 % for patients with NTproBNPII in the negative, indeterminate, and positive groups, respectively. Overall likelihood ratios (LR) were 0.07 (95 % CI: 0.05,0.09), 0.55 (0.45,0.67), and 3.53 (3.26,3.83) for the same groups, respectively. Individual LR+for age dependent cutoffs were 5.01 (4.25,5.91), 3.71 (3.25,4.24), and 2.38 (2.10,2.69), respectively. NTproBNPII increased with increasing severity of HF when stratified by NYHA classification. ConclusionsThe ADVIA Centaur PBNPII assay demonstrates acceptable clinical performance using the recommended single rule out and age dependent rule in cutoffs for an AHF diagnosis in dyspneic ED patients.

Asystemic Application of the System of Law

In the prism of a specific act of law enforcement, an urgent problem of systemic relations between norms and principles of various branches of law that are outside the traditional institution of bluntness has been identified. The theses were substantiated that the application of the rule of law should be based on a systematic approach to the entire legal space, that compliance or enforcement of a prescription within one branch of law should not be a tort-forming factor in another branch of law, the norms of one branch of law objectively cannot prohibit what is permitted by the norms of another branch of law; if this is the case, then this indicates a systemic problem of the legal space. And this question is addressed not so much to the legislator as to the law enforcement officer, the answer to it is in the plane of professional legal understanding. Only the formation of a systematic approach to the application not of a single rule of law, but of normative legal prescriptions in a system of corresponding links with the provisions of other norms of various branches of law can serve as a guarantee of proper law enforcement.

Out-of-order execution enabled deep reinforcement learning for dynamic additive manufacturing scheduling

Additive Manufacturing (AM) has revolutionized the production landscape by enabling on-demand customized manufacturing. However, the efficient management of dynamic AM orders poses significant challenges for production planning and scheduling. This paper addresses the dynamic scheduling problem considering batch processing, random order arrival and machine eligibility constraints, aiming to minimize total tardiness in a parallel non-identical AM machine environment. To tackle this problem, we propose the out-of-order enabled dueling deep Q network (O3-DDQN) approach. In the proposed approach, the problem is formulated as a Markov decision process (MDP). Three-dimensional features, encompassing dynamic orders, AM machines, and delays, are extracted using a ‘look around’ method to represent the production status at a rescheduling point. Additionally, five novel composite scheduling rules based on the out-of-order principle are introduced for selection when an AM machine completes processing or a new order arrives. Moreover, we design a reward function that is strongly correlated with the objective to evaluate the agent’s chosen action. Experimental results demonstrate the superiority of the O3-DDQN approach over single scheduling rules, randomly selected rules, and the classic DQN method. The average improvement rate of performance reaches 13.09% compared to composite scheduling rules and random rules. Additionally, the O3-DDQN outperforms the classic DQN agent with a 6.54% improvement rate. The O3-DDQN algorithm improves scheduling in dynamic AM environments, enhancing productivity and on-time delivery. This research contributes to advancing AM production and offers insights into efficient resource allocation.

Application of Fuzzy Logic to Classify Community Welfare Levels

Information regarding family welfare does not only affect family members, but also influences the success of government, including village government. Therefore, information regarding the level of family welfare is needed to monitor the progress of development programs that have been carried out. The fuzzy logic of the Tahani model is one method that can be applied to classify things. The aim of this research is to classify the level of welfare of families as potential recipients of assistance based on population data held by the Mentawai Social Service &amp; P3A. This research was processed using Fuzzy Tahani logic. Fuzzy Tahani is an optimization algorithm that can be used to support decisions by utilizing relational databases. Based on the research results obtained, fuzzy logic with the Tahani model can be used to process family data in accordance with indicators of family welfare levels by providing output in the form of family classification. It's just that the application of the Tahani model should be done on a single rule search function, not to process all the rules using a Tahani query to produce a family classification

The Regulations of the Supervisory Officer Personal Data Protection-Based Accountability Principle

&lt;div&gt;&lt;table cellspacing="0" cellpadding="0" align="left"&gt;&lt;tbody&gt;&lt;tr&gt;&lt;td align="left" valign="top"&gt;&lt;p&gt;This study examines The Model of Regulations Supervisory Officer Personal Data Protection-Based Accountability Principle: Lessons from Sweden. This study is a qualitative approach by reviewing and analyzing legal aspects and comparing laws. Even though Indonesia has a personal data protection law, misuse of personal data is still excessive. Owing to accountability basis PDP supervision arrangements have not been properly implemented. Meanwhile, Sweden was chosen as a comparison country since it was one of the first countries to have a personal data protection law. The result shows that in Sweden, personal data is regulated in the General Data Protection Regulation and the personal data supervisor well implemented the principle of accountability therefore it worked effectively. Meanwhile in Indonesia, given that a data protection supervisor has not been established, Protection of personal data is still carried out by each agency appointed by law on a sectoral basis and it deemed less effective in providing personal data protection. Therefore, the personal data supervisor with a single rule and direct responsibility to the president is the perfect model for Indonesia.&lt;/p&gt;&lt;/td&gt;&lt;/tr&gt;&lt;/tbody&gt;&lt;/table&gt;&lt;/div&gt;

Universal enzymatic numerical P systems with small number of enzymatic rules

Enzymatic Numerical P Systems (ENPSs) are a model of membrane computing that is well-suited for the simulation of physical processes and that has been used for the design and the implementation of motion controllers for wheeled robots and flying drones. The ENPSs model has been proven to be Turing universal and the theoretical effort was focused on minimizing various descriptional complexity parameters. In this paper, we explore the minimum number of enzymatic rules needed to achieve universality in ENPSs, specifically focusing on the all-parallel derivation mode where all applicable rules are applied at the same time. We show that in the case of a linear restriction for production functions, the universality can be obtained using 21 enzymatic rules, substantially improving previously known results. If production functions are allowed to be polynomials of degree 2, we show that a single enzymatic rule is sufficient to achieve universality. To obtain these results, a new proof method is introduced based on the translation of ENPSs to systems of conditional recurrences.

Accurate and Efficient Simulation Models for Power Electronics Transformers Based on Series-Connected Active Unity Power Factor Rectifier Modules

This paper contributes an efficient simulation technique for input-series output-parallel multilevel power electronics transformers based on active unity-power-factor rectifiers as building blocks. The technical motivation of the work comes from the fact that the computational burden for the simulation of these technologies tends to become unaffordable as the number of modules grows when using commercial software blocksets. This paper presents a new method to drastically reduce the simulation complexity by simplifying the switching rules: in a three-phase circuit, each phase zero-crossing condition is implemented by a single and simple algebraic rule, so the complex calculations for determining the conduction state of each single diode are removed from the model. The computational enhancement and accuracy of the proposed technique are proved by experimental verification using a single-phase single-cell prototype together with multiple simulations of full-scale systems of different sizes.

Northern Ireland and the United Kingdom Internal Market

Post-Brexit Northern Ireland occupies a unique position in the internal market of the United Kingdom due, primarily, to the Protocol on Ireland / Northern Ireland, or Windsor Framework. Agreed as part of the UK-EU Withdrawal Agreement, the Protocol/Windsor Framework provides that EU Single Market rules concerning the free movement of goods, customs, VAT, state aid and energy markets, continue to apply in Northern Ireland, despite it having formally left the EU along with the rest of the UK. To allow for the domestic implementation of the novel arrangements for post-Brexit Northern Ireland, set out in the Protocol/Windsor Framework, the UK Internal Market (UKIM) Act 2020 includes a series of specific provisions that except goods entering and leaving Northern Ireland from the ‘market access principles’ established by the UKIM Act 2020 in certain circumstances. This commentary introduces the UKIM Act 2020 then presents a review of its provisions that are specifically dedicated to post-Brexit Northern Ireland. Written in September 2023 the analysis then provides an assessment of the implications of measures agreed between the UK and EU laid down in the Windsor Framework texts which were published in February of this year. Based on the analysis of the UKIM Act 2020 set against the backdrop of the Protocol, then Windsor Framework, the commentary argues that the position of Northern Ireland post-Brexit is not only newly unique but also newly consequential for those both inside and outside its borders.

Low-latency Virtual Network function Scheduling Algorithm Based on Deep Reinforcement Learning

This paper addresses the problem of mapping, scheduling, and routing of virtual network functions (VNF) on a service function chain (SFC) that is sensitive to latency in a virtual network. A scheduling algorithm for VNF is proposed, which aims to minimize the SFC rejection rate while taking into account VNF mapping, scheduling, and traffic routing during the scheduling process. To achieve this goal, a Markov decision process (MDP)-based VNF scheduling model is established that guarantees SFC resource requirements are met. The model uses the D3QN (Dueling Double DQN) algorithm based on composite rules to select the SFC at each scheduling time point, and selects virtual nodes and routes using a routing optimization algorithm to minimize the SFC rejection rate. We compare our algorithm with the single rule, DQN and genetic algorithm, and the simulation results show that the proposed algorithm can reduce the rejection rate of SFC by approximately 8% compared to genetic algorithms.

Implementation of high sensitivity troponin into routine clinical practice - results of the extended CARdiac MArkers guideline uptake in Europe group (CARMAGUE) survey

BackgroundMeasurement of cardiac troponin (cTn) by a high sensitivity method is now the recommended strategy for the detection of myocardial injury. An international survey was undertaken to assess how this has been implemented. MethodsA questionnaire based around 14 domains on cardiac biomarkers was distributed electronically with the aid of professional societies accessed by a web link within the invitation. Results were returned electronically then extracted into a relational database for analysis. ResultsResponses were obtained from 663 laboratories across 76 countries ranging from 1 to 69 largest country. The majority of responses (79.6%) came from the European area. Responses were grouped into broad geographic areas for analysis. Most responses came from hospitals providing a local and regional service of which the majority provided angioplasty. cTn measurement was the dominant biomarker. The majority of laboratories include creatine kinase (CK) in their cardiac profile and approximately 50% also offer the MB isoenzyme of CK. The majority of laboratories (91.9%) measure cTn by a high sensitivity method. Sex specific reference ranges were typically implemented for cardiac troponin I but not for cardiac troponin T. The preferred unit of measurement was nanograms/L. A structured decision-making pathway utilising high sensitivity cTn measurement was used by 83.3% of laboratories who responded. Single sample rule out is common but the majority used serial sampling strategy based on measurement on admission and three hours. ConclusionsMeasurement of cTn by a high sensitivity method is now well established internationally, the use of rapid diagnostic protocols lags behind.

On the application of the calculus of positively constructed formulas for the study of controlled discrete-event systems

The article is devoted to the development of an approach to solving the main problems of the theory of supervisory control of logical discrete-event systems (DES), based on their representation in the form of positively constructed formulas (PCF). We consider logical DESs in automata form, understood as generators of some regular languages. The PCF language is a complete first-order language, the formulas of which have a regular structure of alternating type quantifiers and do not contain a negation operator in the syntax. It was previously proven that any formula of the classical first-order predicate calculus can be represented as a PCF. PCFs have a visual tree representation and a natural question-and-answer procedure for searching for an inference using a single inference rule. It is shown how the PCF calculus, developed in the 1990s to solve some problems of control of dynamic systems, makes it possible to solve basic problems of the theory of supervisory control, such as checking the criteria for the existence of supervisory control, automatically modifying restrictions on the behavior of the controlled system, and implementing a supervisor. Due to some features of the PCF calculus, it is possible to use a non-monotonic inference. It is demonstrated how the presented PCF-based method allows for additional event processing during inference. The Bootfrost software system, or the so-called prover, designed to refute the obtained PCFs is also presented, and the features of its implementation are briefly described. As an illustrative example, we consider the problem of controlling an autonomous mobile robot.

Enhancing Transboundary Freshwater Security: From Online Learning to Global Knowledge Exchange Platform

The management of transboundary water resources presents a complex challenge involving multiple stakeholders and countries. Negotiating a single rule for managing these resources can take years due to various factors, including political, socioeconomic, cultural, and historical backgrounds. To assist transboundary water practitioners worldwide, the GWP and GEF IW:LEARN have developed a free-access educational platform, the Massive Open Online Course (MOOC) on Governance for Transboundary Freshwater Security. The MOOC attracted 3855 learners from 166 countries and included 14 interactive online sessions to facilitate discussions among practitioners. In response to learners’ desire for increased interaction and networking opportunities, the Transboundary Water Knowledge Exchange Hub was established. This global online community enables members to share knowledge and engage in direct peer-to-peer learning through an online knowledge management platform. The effort evolved from online course and interactive sessions into an online community, promoting knowledge sharing and peer-to-peer learning through an online knowledge management platform. Through examining this evolution and analysing key survey results, this paper analyses the role of MOOC and knowledge sharing and peer-to-peer learning for building capacity for improved transboundary water governance.

Near-Optimal Resilient Aggregation Rules for Distributed Learning Using 1-Center and 1-Mean Clustering with Outliers

Byzantine machine learning has garnered considerable attention in light of the unpredictable faults that can occur in large-scale distributed learning systems. The key to secure resilience against Byzantine machines in distributed learning is resilient aggregation mechanisms. Although abundant resilient aggregation rules have been proposed, they are designed in ad-hoc manners, imposing extra barriers on comparing, analyzing, and improving the rules across performance criteria. This paper studies near-optimal aggregation rules using clustering in the presence of outliers. Our outlier-robust clustering approach utilizes geometric properties of the update vectors provided by workers. Our analysis show that constant approximations to the 1-center and 1-mean clustering problems with outliers provide near-optimal resilient aggregators for metric-based criteria, which have been proven to be crucial in the homogeneous and heterogeneous cases respectively. In addition, we discuss two contradicting types of attacks under which no single aggregation rule is guaranteed to improve upon the naive average. Based on the discussion, we propose a two-phase resilient aggregation framework. We run experiments for image classification using a non-convex loss function. The proposed algorithms outperform previously known aggregation rules by a large margin with both homogeneous and heterogeneous data distributions among non-faulty workers. Code and appendix are available at https://github.com/jerry907/AAAI24-RASHB.

Novel Method on Mixing Degree Quantification of Mine Water Sources: A Case Study

After a mine water inrush occurs, it is crucial to quickly identify the source of the water inrush and the key control area, and to formulate accurately efficient water control measures. According to the differences in water chemical characteristics of four aquifers in the Fenyuan coal mine, the concentrations of K+~Na+, Ca2+, Mg2+, Cl−, SO42−, and HCO3− were taken as water source identification indexes. A decision tree classification model based on the C4.5 algorithm was adopted to visualize the chemical characteristics of a single water source and extract rules, and intuitively obtained the discrimination conditions of a single water source with Mg2+, Ca2+, and Cl− as important variables in the decision tree: Mg2+ &lt; 39.585 mg/L, Cl− &lt; 516.338 mg/L and Mg2+ ≥ 39.585 mg/L, Ca2+ &lt; 160.860 mg/L. Factor analysis and Fisher discriminant theory were used to eliminate the redundant ion variables, and the discriminant function equations of the two, three, and four types of mixed water sources were obtained successively in turn. This paper puts forward MSE, RMSE, and MAE as the evaluation indexes of the water source mixing degree calculation models and obtains the ranking of the pros and cons of the mixed water source mixing degree calculation models. The results show that the minimum inscribed circle analytical method is the optimal model for the calculation of the mixing degree of two types of water sources, and the MSE, RMSE, and MAE are 0.17%, 4.13%, and 4.13%, respectively. The minimum inscribed circle clustering method is the optimal model for the calculation of the mixing degree of three types of water sources, and the minimum distance method is the optimal model for the calculation of the mixing degree of four types of water sources. The method of mine water source identification based on the decision tree C4.5 algorithm and mixing degree calculation has the characteristics of a simple calculation process, high efficiency, objective accuracy, and low cost, which can provide a scientific basis for the development of stope water control measures.

A novel extended rule-based system based on K-Nearest Neighbor graph

The Belief Rule-Based (BRB) system faces the rule combination explosion issue, making it challenging to construct the rule base efficiently. The Extended Belief Rule-Based (EBRB) system offers a solution to this problem by using data-driven methods. However, using EBRB system requires the traversal of the entire rule base, which can be time-consuming and result in the activation of many irrelevant rules, leading to an incorrect decision. Existing search optimization methods can somewhat solve this issue, but they have limitations. Moreover, the calculation of the rule activation weight only considers the similarity between input data and a single rule, ignoring the influence of the rule linkage. To address these problems, we propose a new EBRB system based on the K-Nearest Neighbor graph index (Graph-EBRB). We introduce the Hierarchical Navigable Small World (HNSW) algorithm to create the K-Nearest Neighbor graph index of the EBRB system. This index allows us to efficiently search and activate a set of key rules. We also propose a new activation weight calculation method based on the Graph Convolution Neural Network (GCN), and we optimize the system performance using a parameter learning strategy. We conduct a comprehensive experiment on 14 commonly used public data sets, and the results show that Graph-EBRB system significantly improves the reasoning efficiency and accuracy of the EBRB system. Finally, we apply the Graph-EBRB system to tree disease identification and achieve excellent classification performance, identifying over 90% of the diseased trees on the complete dataset.

A neural network model for online one-shot storage of pattern sequences.

Based on the CRISP theory (Content Representation, Intrinsic Sequences, and Pattern completion), we present a computational model of the hippocampus that allows for online one-shot storage of pattern sequences without the need for a consolidation process. In our model, CA3 provides a pre-trained sequence that is hetero-associated with the input sequence, rather than storing a sequence in CA3. That is, plasticity on a short timescale only occurs in the incoming and outgoing connections of CA3, not in its recurrent connections. We use a single learning rule named Hebbian descent to train all plastic synapses in the network. A forgetting mechanism in the learning rule allows the network to continuously store new patterns while forgetting those stored earlier. We find that a single cue pattern can reliably trigger the retrieval of sequences, even when cues are noisy or missing information. Furthermore, pattern separation in subregion DG is necessary when sequences contain correlated patterns. Besides artificially generated input sequences, the model works with sequences of handwritten digits and natural images. Notably, our model is capable of improving itself without external input, in a process that can be referred to as 'replay' or 'offline-learning', which helps in improving the associations and consolidating the learned patterns.

Recognition and rejection of foreign eggs of different colors in Barn Swallows

Brood parasitic birds lay eggs in the nests of other birds, and the parasitized hosts can reduce the cost of raising unrelated offspring through the recognition of parasitic eggs. Hosts can adopt vision-based cognitive mechanisms to recognize foreign eggs by comparing the colors of foreign and host eggs. However, there is currently no uniform conclusion as to whether this comparison involves the single or multiple threshold decision rules. In this study, we tested both hypotheses by adding model eggs of different colors to the nests of Barn Swallows (Hirundo rustica) of two geographical populations breeding in Hainan and Heilongjiang Provinces in China. Results showed that Barn Swallows rejected more white model eggs (moderate mimetic to their own eggs) and blue model eggs (highly non-mimetic eggs with shorter reflectance spectrum) than red model eggs (highly non-mimetic eggs with longer reflectance spectrum). There was no difference in the rejection rate of model eggs between the two populations of Barn Swallows, and clutch size was not a factor affecting egg recognition. Our results are consistent with the single rejection threshold model. This study provides strong experimental evidence that the color of model eggs can has an important effect on egg recognition in Barn Swallows, opening up new avenues to uncover the evolution of cuckoo egg mimicry and explore the cognitive mechanisms underlying the visual recognition of foreign eggs by hosts.

Diagnostic Utility of Cerebrospinal Fluid White Blood Cell Components for the Identification of Bacterial Meningitis in Infants.

To evaluate the diagnostic and predictive utility of cerebrospinal fluid (CSF) white blood cell (WBC) components in the diagnosis of bacterial meningitis in infants discharged from the neonatal intensive care unit (NICU). We identified a cohort of infants discharged from a Pediatrix NICU between 1997 and 2020 who did not have an immunodeficiency, had at least 1 CSF culture collected within the first 120 days of life, and at least 1 CSF laboratory specimen obtained on the day of culture collection. We only included an infant's first CSF culture and excluded cultures from CSF reservoirs and those growing contaminants or nonbacterial organisms. We examined the utility of CSF WBC components to diagnose or predict bacterial meningitis by calculating sensitivity, specificity, positive and negative predictive values, likelihood ratios, and area under the receiver operating curve (AUC) at different cutoff values for each parameter. We performed subgroup analysis excluding infants treated with antibiotics the day before CSF culture collection. Of the 20 756 infants that met the study inclusion criteria, 320 (2%) were diagnosed with bacterial meningitis. We found (AUC [95% CI]) CSF WBC count (0.76 [0.73-0.79]), CSF neutrophil count (0.74 [0.70-0.78]), and CSF neutrophil percent (0.71 [0.67-0.75]) had the highest predictive values for bacterial meningitis, even when excluding infants with early antibiotic administration. No single clinical prediction rule had the optimal discriminatory power for predicting culture-proven bacterial meningitis, and clinicians should be cautious when interpreting CSF WBC parameters in infants with suspected meningitis.