Termination Rule Research Articles

Polysomatic intergrowths between amphiboles and non-classical pyriboles in magnetite: Smallest-scale features recording a protracted geological history

Abstract Non-classical pyriboles (NCPs) have tetrahedral silicate chains (Ibeam) of multiplicity higher than single (pyroxene) or double (amphibole) Ibeams and are known from amphiboles in altered mafic-ultramafic complexes. NCPs, their polysomatic sequences, and inherent chain-width disorder are petrogenetic tools for interpreting igneous and metamorphic processes. Magnetite, a refractory mineral that can trap and preserve NCPs is a major constituent of iron oxide-copper-gold (IOCG) deposits. We undertook a nanoscale study to show that NCPs and amphiboles are hosted within magnetite cores from the Jatobá Ni-bearing IOCG deposit, Carajás Mineral Province, Brazil. Monoclinic amphiboles and NCPs form polysomatic intergrowths or occur as sparse inclusions along {111}magnetite. There are two chemical populations of amphiboles: Mg-Fe- and Ca-(Al)-amphiboles, the latter including Ce-bearing Mg-hornblende and (ferro)tschermakite. The occurrence contains one of the widest ranges of chain silicates ever recorded, from simple intergrowths of single to triple Ibeam zippers, including pyroxene slabs, to longer NCP polysomes up to 15-Ibeam chains. Clinojimthompsonite (Cjim) is observed for the first time within magnetite. Although no discrete polysomes could be defined, the NCP-amphibole intergrowths have compositions between Mg-Fe amphiboles and Cjim based on Ibeam averages of 2.5–2.7. Relationships between increase in the number of C and A cations from amphibole (2-Ibeam) to n chain silicates (nIbeam) are formulated as nIbeam = T(2 + n) = C(5 + 3n) = A(1 + n), n = integer. Empirical models of crystal structures, validated by STEM simulation, are shown for 4- and 5-Ibeam chain silicates. Co-crystallization of double- and triple-chain silicate structures with rhythmic intergrowths as larger blocks along b is often accompanied by rhythmic Ca-Fe zonation along a, supporting primary NCP crystallization via self-patterning during amphibole growth within magnetite in a close-to-equilibrium system. Chain-width disorder is documented from defects including planar faults, derailments, jogs, and swells. Violations of zipper termination rules indicate primary growth rather than replacement. Amphibole-NCPs inclusions support a multi-stage evolution for Jatobá magnetite. They formed during the first cycle of magnetite overprinting within a mafic/ultramafic lithology that records syn-shearing events. Subsequent formation of calcicamphiboles, including Ce-bearing species, indicate IOCG-related fluids at the onset of mineralization. (Ferro) tschermakite formed at ~7.5 kbar during high-pressure shearing is preserved during main ore deposition. The multi-stage amphibole-NCPs generations in magnetite revealed by our nanoscale study emphasize the interpretive value of magnetite for overprinting events in terranes with protracted geological histories. Analogous NCPs are likely to be abundant in magnetite from magmatic-hydrothermal deposits hosted by greenstone belts and altered mafic/ultramafic complexes. Likewise, discovery of Ce-rich hornblende provides new avenues to understand the early, alkali-calcic alteration stages of IOCG systems and models for REE incorporation into, and subsequent release from chain silicates.

Derivation of a clinical decision rule for termination of resuscitation in non-traumatic pediatric out-of-hospital cardiac arrest

AimPrehospital termination of resuscitation (ToR) rules are used to predict medical futility in adult out-of-hospital cardiac arrest (OHCA), however, the available evidence for pediatric patients is limited. The primary aim of this study is to derive a Pediatric Termination of Resuscitation (PToR) prediction rule for use in pediatric non-traumatic OHCA patients. MethodsWe analyzed a retrospective cohort of pediatric OHCA patients within the CARES database over a 10-year period (2013–2022). We split the dataset into training and test datasets and fit logistic regressions with Least Absolute Shrinkage and Selection Operator (LASSO) to select predictor variables and estimate predictive test characteristics for the primary outcome of death and a secondary composite outcome of death or survival to hospital discharge with unfavorable neurologic status. ResultsWe analyzed a sample of 21,240 children where 2,326 (11.0%) survived to hospital discharge, and 1,894 (8.9%) survived to hospital discharge with favorable neurologic status. We derived a PToR rule for death demonstrating a specificity of 99.1% and a positive predictive value (PPV) of 99.8% and a PToR rule for death or survival with poor neurologic status with a specificity of 99.7% and PPV of 99.9% within the test dataset. ConclusionWe derived a clinical prediction rule with high specificity and positive predictive value in prehospital settings utilizing Advanced Life Support (ALS) providers which may inform termination of resuscitation considerations in pediatric patients. Further prospective and validation studies will be necessary to define the appropriateness and applicability of these PToR criteria for routine use.

Effect of Differential Item Functioning on Computer Adaptive Testing Under Different Conditions.

This study provides an overview of the effect of differential item functioning (DIF) on measurement precision, test information function (TIF), and test effectiveness in computer adaptive tests (CATs). Simulated data for the study was produced and analyzed with the Rstudio. During the data generation process, item pool size, DIF type, DIF percentage, item selection method for CAT, and the test termination rules were considered changed conditions. Sample size and ability parameter distribution, Item Response Theory (IRT) model, DIF size, ability estimation method, test starting rule, and item usage frequency method regarding CAT conditions were considered fixed conditions. To examine the effect of DIF, measurement precision, TIF and test effectiveness were calculated. Results show DIF has negative effects on measurement precision, TIF, and test effectiveness. In particular, statistically significant effects of the percentage DIF items and DIF type are observed on measurement precision.

Group efficiency based on the termination rule in the multiple-targets visual search task.

In practical visual search fields, observers often encounter errors that result from an unknown number of targets, which may induce reduced accuracy and speed. Our current study addresses the potential enhancement of collaborative search efficiency as a dyad to mitigate such incurred search costs. Utilizing the capacity coefficient, we evaluated search efficiency and explored the interplay of task difficulty and termination rule in collaborative visual search. Our prediction that collaborative benefits increased with elevated task difficulty was not supported in Experiment 1, where participants were tasked with judging the presence of any target. In contrast, Experiment 2 demonstrated that dyads exhibited greater search efficiency during exhaustive searches for multiple targets with elevated task difficulty. Notably, our findings indicated an advantage in dyad searches compared to baseline predictions from individual searches. Our results underscored the significance of task difficulty and termination rules in leveraging human resources for improved collaborative visual search performance.

MDEFC: Automatic recognition of human activities using modified differential evolution based fuzzy clustering method

In the present scenario, automatic Human Activity Recognition (HAR) is an emerging research topic, particularly in the applications of healthcare, Human Computer Interaction (HCI), and smart homes. By reviewing existing literature, the majority of the HAR methods achieved limited performance, while trained and tested utilizing unseen Internet of Things (IoT) data. In order to achieve higher recognition performance in the context of HAR, a new clustering method named Modified Differential Evolution based Fuzzy Clustering (MDEFC) is proposed in this article. The proposed MDEFC method incorporates an asymptotic termination rule and a new differential weight for enhancing the termination condition and improving this method’s ability in exploring the solution space of the objective function. The extensive empirical analysis states that the proposed MDEFC method achieved impressive recognition results with minimal training time by using both spatial and temporal features of the individual. The proposed MDEFC method’s effectiveness is tested on a real time dataset and an online Wireless Sensor Data Mining (WISDM) v1.1 dataset. The result findings demonstrate that the proposed MDEFC method averagely obtained 99.73 % of precision and 99.86 % of recall on the WISDM v1.1 dataset. Similarly, the proposed MDEFC method averagely obtained 93.46 % of f1-measure, 94.60 % of recall, and 93.88 % of precision on the real time dataset. These obtained experimental results are significantly higher in comparison to the traditional HAR methods.

Additive dynamic Bayesian networks for enhanced feature learning in soft sensor modeling

Due to the advantages of indicating variable structure and efficient reasoning, Bayesian Networks (BN) have been widely used in data-driven soft sensor applications. However, restricted to linear and conditional Gaussian property, BN-based soft sensors rarely achieve high prediction accuracy. In this paper, an ensemble learning framework – additive dynamic Bayesian networks (ADBN) is proposed for enhanced feature learning, in which Dynamic Bayesian networks are used to learn the conditional independencies among variables and construct feature sets for the following base learners. Additional DBNs are constructed upon the residual information from the past model, to carry out feature learning to fit the residuals. The procedure is repeated and a termination rule from the perspective of feature learning is proposed to end this process, and thus the model complexity can be well restricted. The proposed method is validated on two actual industrial cases. It reveals that the ADBN feature learning method has obtained great improvements. Compared to the single DBN feature engineering method, the root mean square error (RMSE) performance has been improved by 20% and 13% on the two cases, respectively.

Non-Parametric CD-CAT Item Selection Strategy and Termination Rules Based on Binary Search Algorithm

CD-CAT plays a significant role in diagnosing and assessing students, contributing significantly to improving teaching effectiveness. However, in classroom teaching scenarios, unlike large-scale assessments where a large number of samples can be used to accurately estimate item parameters, non-parametric CD-CAT becomes the only feasible choice. Compared to parametric CD-CAT, non-parametric CD-CAT started later, and research mainly focuses on non-parametric item selection strategies. However, the existing non-parametric item selection strategies have the disadvantage of low efficiency, and there is little research on non-parametric termination rules. Therefore, this study proposes two more efficient item selection strategies: Non-Parametric Dynamic Binary Search (NDBS) and General Non-Parametric Dynamic Binary Search (GNDBS), as well as a non-parametric termination rule：Non-parametric Dynamic Binary Searching Index (NDBI). Simulation results show: (1) Under all conditions, the pattern classification accuracy rate of NDBS is higher than that of NPS, so NDBS can be used as the item selection strategy when there are no samples available. (2) In most cases, the performance of GNDBS is better than other item selection strategies, so GNDBS can be chosen as the item selection strategy when there are few samples available. (3) In variable-length tests, when the research objective is to obtain more accurate classification results, the critical value of the NDBI rule can be reduced; conversely, the critical values of the NDBI and GNDBI rules can be appropriately increased.

Investigation of reinforcement curtailment in tall lightly reinforced structural concrete walls

SummaryRecent studies have investigated the impact of minimum longitudinal reinforcement limits on the ductility of the plastic hinge region of reinforced concrete (RC) walls, resulting in revised minimum reinforcement limits in design standards. The objective of this research was to investigate the influence of the longitudinal reinforcement distribution or termination rules for the additional reinforcement at the end regions of the wall up the wall height on the response of taller lightly RC walls. A model utilizing a displacement‐based fiber element was developed and validated against tested walls with a range of longitudinal reinforcement contents. Pushover analyses were then conducted on a 20‐story wall prototype to investigate the sensitivity of key design parameters, including axial load pattern, curtailment heights of the longitudinal reinforcement in the plastic hinge region, reinforcement content at wall base, reinforcement strain‐hardening ratio, and the length over which the reinforcement curtailment occurred. The results indicate that the current minimum reinforcement provisions in ACI 318–19 are insufficient to prevent reinforcement yielding outside the plastic hinge region at the wall base.

Letter of PTAAARMIGAN (Patent and Trademark Attorneys, Agents, and Applicants for Restoration and Maintenance of Integrity in Government), U.S. Inventor, and 73 Patent Professionals to Patent and Trademark Office re Terminal Disclaimer Rule (July 9, 2024)

Letter of PTAAARMIGAN (Patent and Trademark Attorneys, Agents, and Applicants for Restoration and Maintenance of Integrity in Government), U.S. Inventor, and 73 Patent Professionals to Patent and Trademark Office re Terminal Disclaimer Rule (July 9, 2024)

On the Establishment of General Rules for Termination for Compelling Reasons in the Civil Code

On the Establishment of General Rules for Termination for Compelling Reasons in the Civil Code

Identifying appropriate scenario termination rules for squad-level simulations of warfighter lethality

Small arms combat simulations are emerging as a method to convert raw human performance metrics into quantifiable lethality outcomes. Specifically, speed and accuracy measures can be directly converted into a percentage chance of winning a gunfight using small arms combat modeling. Although the technique provides an opportunity to clearly communicate implications to a military audience, there are numerous assumptions to this modeling technique that require further exploration. One critical assumption involves the termination rule, or criterion to identify when the combat simulation should end. The termination rule must be clearly identified because it will impact the casualties inflicted and suffered by each side during simulation. Here, we discuss the implications of three broad categories of termination rules: (1) total victory, (2) retreat criteria, and (3) resource limitations. Each approach has different scenarios that might be more or less advantageous to use as a termination rule. Taken together, this information should help improve lethality modeling procedures using different simulation techniques to convert human performance metrics into quantifiable chances of winning a combat engagement.

An Efficient Non-parametric Item Selection Method for Polytomous Scoring CD-CAT

In educational evaluations at home and abroad, polytomous scoring items are becoming increasingly important. They can provide richer and more valuable information, with unmatched advantages compared to binary (0-1 scoring) items. If used as a tool for teachers to diagnose and assess students in the classroom, CD-CAT (Cognitive Diagnostic Computerized Adaptive Testing) has significant implications for improving teaching effectiveness. However, in classroom teaching situations, it is not feasible to estimate item parameters accurately with a large sample, as in large-scale assessments. In such cases, non-parametric CD-CAT becomes the only viable option. Compared to parametric CD-CAT, non-parametric CD-CAT started later and is particularly lacking in research related to polytomous scoring. Item selection method is at the core of CD-CAT, so it is essential to develop a non-parametric item selection method suitable for polytomous scoring CD-CAT. This study proposes a non-parametric item selection method for polytomous scoring cognitive diagnostic computerized adaptive testing (PCD-CAT): the Manhattan Distance Non-parametric Difference index item selection method (MD-NDI). The results of simulation studies indicate: (1) MD-NDI item selection method is suitable for PCD-CAT scenarios and exhibits better performance when the item bank quality is poor or the sample size for estimating item parameters is limited. (2) MD-NDI does not require pre-testing of items and distributes item usage more evenly, effectively ensuring the security of the item bank. (3) Even in cases of incorrectly specified item bank of Qc-matrix, MD-NDI still shows higher pattern correct classification rates. (4) In the study of variable-length PCD-CAT, MD-NDI not only reduces the test length in most conditions but also has a higher pattern correct classification rates when reaching the test termination rule.

A relaxed iterated Tikhonov regularization for linear ill-posed inverse problems

Iterated Tikhonov regularization methods are prevalent regularization methods that can make the solution of ill-posed problems less sensitive to noise. As the iteration number grows, their solution gradually converges to the optimum approximation of the desired solution but then changes to the worse, causing a semiconvergence. This difficulty can be reduced by a reliable early termination rule or a strategy for choosing the appropriate regularization parameter at each iteration. Different from the existing methods, this paper describes a novel iterative Tikhonov regularization based on introducing a non-decreasing sequence of relaxation parameters into the stationary iterated Tikhonov regularization, providing a fast and roughly stable convergence. After investigating theoretically the convergence rate of the novel iterative Tikhonov regularization, we propose a series of numerical experiments for the evaluation of its accuracy and finally describe a real-world application in the field of 3-dimensional image restoration problems to verify its efficiency.

On Lexicographic Proof Rules for Probabilistic Termination

We consider the almost-sure (a.s.) termination problem for probabilistic programs, which are a stochastic extension of classical imperative programs. Lexicographic ranking functions provide a sound and practical approach for termination of non-probabilistic programs, and their extension to probabilistic programs is achieved via lexicographic ranking supermartingales (LexRSMs). However, LexRSMs introduced in the previous work have a limitation that impedes their automation: all of their components have to be non-negative in all reachable states. This might result in a LexRSM not existing even for simple terminating programs. Our contributions are twofold. First, we introduce a generalization of LexRSMs that allows for some components to be negative. This standard feature of non-probabilistic termination proofs was hitherto not known to be sound in the probabilistic setting, as the soundness proof requires a careful analysis of the underlying stochastic process. Second, we present polynomial-time algorithms using our generalized LexRSMs for proving a.s. termination in broad classes of linear-arithmetic programs.

Diagnosing Primary Students’ Reading Progression: Is Cognitive Diagnostic Computerized Adaptive Testing the Way Forward?

Cognitive diagnostic computerized adaptive testing (CD-CAT) is a cutting-edge technology in educational measurement that targets at providing feedback on examinees’ strengths and weaknesses while increasing test accuracy and efficiency. To date, most CD-CAT studies have made methodological progress under simulated conditions, but little has applied CD-CAT to real educational assessment. The present study developed a Chinese reading comprehension item bank tapping into six validated reading attributes, with 195 items calibrated using data of 28,485 second to sixth graders and the item-level cognitive diagnostic models (CDMs). The measurement precision and efficiency of the reading CD-CAT system were compared and optimized in terms of crucial CD-CAT settings, including the CDMs for calibration, item selection methods, and termination rules. The study identified seven dominant reading attribute mastery profiles that stably exist across grades. These major clusters of readers and their variety with grade indicated some sort of reading developmental mechanisms that advance and deepen step by step at the primary school level. Results also suggested that compared to traditional linear tests, CD-CAT significantly improved the classification accuracy without imposing much testing burden. These findings may elucidate the multifaceted nature and possible learning paths of reading and raise the question of whether CD-CAT is applicable to other educational domains where there is a need to provide formative and fine-grained feedback but where there is a limited amount of test time.

Developing A Computerized Adaptive Test Form of the Occupational Field Interest Inventory

In this research, the aim was to apply the Occupational Field Interest Inventory (OFII), which was developed in paper-pencil format, as a Computerized Adaptive Test (CAT). For this purpose, the paper and pencil form of the OFII was applied to 1425 high school students and post-hoc simulations were carried out with the obtained data. According to results obtained from the simulations, it was decided that the most ideal criteria for the CAT application were GPCM as the IRT model, .40 standard error value as the test termination rule, and MFI as the item selection method. The OFII ended with an average of 59 items, and the correlations between scores obtained from the paper-pencil form and thetas (θ) estimated by simulation ranged between .91-.97. According to post-hoc simulation results, the CAT application was applied to 150 students. It was observed that the correlations between the scores of students from the online application of the paper-pencil form and θ levels estimated by the CAT form varied between .73 and .91.

Applying Item Response Theory to the Student Adaptation to College Questionnaire: Examining Psychometric Characteristics and Developing Computerized Adaptive Testing Version

Incoming students have many difficulties adjusting to college, and selecting appropriate measures to effectively screen them is indispensable, especially in China, where there is insufficient research in this area. To enrich domestic research, this study seeks to examine psychometric characteristics and develop a computerized adaptive version of the Student Adaptation to College Questionnaire (SACQ-CAT) based on a sample of Chinese students. Under the framework of item response theory, the item bank of student adaptation to college was formulated after uni-dimensionality testing, model comparison, item fit testing, and local independence testing. Subsequently, a CAT simulation, including three termination rules, was performed using real data to evaluate and verify the SACQ-CAT. The results showed reliability values exceeding 0.90 when participants’ latent traits were between −4 and 3, covering majority of the subjects. The SACQ-CAT administered an average of fewer than 10 items to participants compared to 67 items on the original scale. The correlation coefficient between latency estimated by the SACQ-CAT and the SACQ is greater than .85, whereas the correlation coefficient with the Symptom Checklist 90 (SCL-90) scores ranges from −.33 to −.55 (p < .001). The SACQ-CAT largely reduced the number of items administered to the participants and avoided losing measurement precision.

A Parallel Implementation of the Differential Evolution Method

Global optimization is a widely used technique that finds application in many sciences such as physics, economics, medicine, etc., and with many extensions, for example, in the area of machine learning. However, in many cases, global minimization techniques require a high computational time and, for this reason, parallel computational approaches should be used. In this paper, a new parallel global optimization technique based on the differential evolutionary method is proposed. This new technique uses a series of independent parallel computing units that periodically exchange the best solutions they have found. Additionally, a new termination rule is proposed here that exploits parallelism to accelerate process termination in a timely and valid manner. The new method is applied to a number of problems in the established literature and the results are quite promising.

Optimal Feedback in Contests

Abstract We obtain optimal dynamic contests for environments where the designer monitors effort through coarse, binary signals—Poisson successes—and aims to elicit maximum effort, ideally in the least amount of time possible, given a fixed prize. The designer has a vast set of contests to choose from, featuring termination and prize-allocation rules together with real-time feedback for the contestants. Every effort-maximizing contest (which also maximizes total expected successes) has a history-dependent termination rule, a feedback policy that keeps agents fully apprised of their own success, and a prize-allocation rule that grants them, in expectation, a time-invariant share of the prize if they succeed. Any contest that achieves this effort in the shortest possible time must in addition be what we call second chance: once a pre-specified number of successes arrive, the contest enters a countdown phase where contestants are given one last chance to succeed.

Hybrid Fine-Tuning Strategy for Few-Shot Classification.

Few-shot classification aims to enable the network to acquire the ability of feature extraction and label prediction for the target categories given a few numbers of labeled samples. Current few-shot classification methods focus on the pretraining stage while fine-tuning by experience or not at all. No fine-tuning or insufficient fine-tuning may get low accuracy for the given tasks, while excessive fine-tuning will lead to poor generalization for unseen samples. To solve the above problems, this study proposes a hybrid fine-tuning strategy (HFT), including a few-shot linear discriminant analysis module (FSLDA) and an adaptive fine-tuning module (AFT). FSLDA constructs the optimal linear classification function under the few-shot conditions to initialize the last fully connected layer parameters, which fully excavates the professional knowledge of the given tasks and guarantees the lower bound of the model accuracy. AFT adopts an adaptive fine-tuning termination rule to obtain the optimal training epochs to prevent the model from overfitting. AFT is also built on FSLDA and outputs the final optimum hybrid fine-tuning strategy for a given sample size and layer frozen policy. We conducted extensive experiments on mini-ImageNet and tiered-ImageNet to prove the effectiveness of our proposed method. It achieves consistent performance improvements compared to existing fine-tuning methods under different sample sizes, layer frozen policies, and few-shot classification frameworks.