Splitting Criterion Research Articles

Exploring the properties of Zr2CO2/GaS van der Waals heterostructures for optoelectronic applications.

We investigate the structural, electronic, and optical properties of eight possible Zr2CO2/GaS van der Waals (vdW) heterostructures using first-principles calculations based on a hybrid functional. These structures display favorable stability, indicated by matching crystal structures and negative formation energies. In all considered configurations, these heterostructures act as indirect band gap semiconductors with a type-II band alignment, allowing efficient electron-hole separation. Optical studies reveal their suitability for optoelectronic applications. Zr2CO2/GaS under 4% biaxial compressive strain meets the criteria for photocatalytic water splitting, suggesting their potential for electronic and optoelectronic devices in the visible spectrum. Our findings present prospects for advanced photocatalytic materials and optical devices.

Criteria for Efficient Photocatalytic Water Splitting Revealed by Studying Carrier Dynamics in a Model Al‐doped SrTiO3 Photocatalyst

AbstractOverall water splitting (OWS) using semiconductor photocatalysts is a promising method for solar fuel production. Achieving a high quantum efficiency is one of the most important prerequisites for photocatalysts to realize high solar‐to‐fuel efficiency. In a recent study (Nature 2020, 58, 411–414), a quantum efficiency of almost 100 % has been achieved in an aluminum‐doped strontium titanate (SrTiO3 : Al) photocatalyst. Herein, using the SrTiO3 : Al as a model photocatalyst, we reveal the criteria for efficient photocatalytic water splitting by investigating the carrier dynamics through a comprehensive photoluminescence study. It is found that the Al doping suppresses the generation of Ti3+ recombination centers in SrTiO3, the surface band bending facilitates charge separation, and the in situ photo‐deposited Rh/Cr2O3 and CoOOH co‐catalysts render efficient charge extraction. By suppressing photocarrier recombination and establishing a facile charge separation and extraction mechanism, high quantum efficiency can be achieved even on photocatalysts with a very short (sub‐ns) intrinsic photocarrier lifetime, challenging the belief that a long carrier lifetime is a fundamental requirement. Our findings could provide guidance on the design of OWS photocatalysts toward more efficient solar‐to‐fuel conversion.

Criteria for Efficient Photocatalytic Water Splitting Revealed by Studying Carrier Dynamics in a Model Al-doped SrTiO3 Photocatalyst.

Overall water splitting (OWS) using semiconductor photocatalysts is a promising method for solar fuel production. Achieving a high quantum efficiency is one of the most important prerequisites for photocatalysts to realize high solar-to-fuel efficiency. In a recent study (Nature 2020, 58, 411-414), a quantum efficiency of almost 100 % has been achieved in an aluminum-doped strontium titanate (SrTiO3 : Al) photocatalyst. Herein, using the SrTiO3 : Al as a model photocatalyst, we reveal the criteria for efficient photocatalytic water splitting by investigating the carrier dynamics through a comprehensive photoluminescence study. It is found that the Al doping suppresses the generation of Ti3+ recombination centers in SrTiO3 , the surface band bending facilitates charge separation, and the in situ photo-deposited Rh/Cr2 O3 and CoOOH co-catalysts render efficient charge extraction. By suppressing photocarrier recombination and establishing a facile charge separation and extraction mechanism, high quantum efficiency can be achieved even on photocatalysts with a very short (sub-ns) intrinsic photocarrier lifetime, challenging the belief that a long carrier lifetime is a fundamental requirement. Our findings could provide guidance on the design of OWS photocatalysts toward more efficient solar-to-fuel conversion.

Split or whole liver transplantation? Utilization and posttransplant survival.

Split liver transplantation (SLT), where a single donor liver is divided for transplantation to 2 recipients, has the potential to increase the availability of size-matched livers for pediatric candidates and expand the supply of donor organs available for adult candidates. Although SLT is a well-established technique, the number of SLTs has remained flat during the past 2 decades, partly due to concerns about the posttransplant survival of SLT recipients compared with whole liver transplantation (WLT) recipients. Prior work on SLT versus WLT survival analysis had limitations because, for pediatric recipients, it did not consider the correlations between donor age/weight and the allograft type, and for adult recipients, it may have included records where the donor livers did not meet the split liver criteria (splittable). Using the Organ Procurement and Transplantation Network's database (2003-2019), this study analyzes and compares (i) key characteristics of donors and recipients, (ii) donor-recipient match dynamics (organ offers and accept/decline decisions), and (iii) recipient posttransplant survival, for SLT and WLT. The results in this study show that the posttransplant survival of SLT and WLT recipients is similar (controlling for other confounding factors that may impact posttransplant survival), highlighting the importance of SLT for increasing the liver supply and potential benefits for both pediatric and adult candidates.

Content of clinker and other materials in personal thoracic aerosol samples from cement plants estimated by scanning electron microscopy and energy-dispersive X-ray microanalysis.

To estimate the composition and exposure to clinker and other specific components in personal thoracic dust samples of cement production workers. A procedure for the classification of airborne particles in cement production plants was developed based on classification trees. For this purpose, the chemical compositions of 27,217 particles in 29 material samples (clinker, limestone, gypsum, clay, quartz, bauxite, iron source, coal fly ash, and coal) were determined automatically by scanning electron microscopy (SEM) and energy-dispersive X-ray microanalysis (EDX). The concentrations of the major elements in cement (calcium, aluminium, silicon, iron, and sulphur) were used for the classifications. The split criteria of the classification trees obtained in the material samples were used to classify 44,176 particles in 34 personal thoracic aerosol samples. The contents of clinker and other materials were estimated, and the clinker contents were analysed statistically for differences between job types and job tasks. Between 64% and 88% of the particles from material samples were classified as actual materials. The material types with variable composition (clay, coal fly ash, and coal) were classified with the lowest consistency (64% to 67%), while materials with a more limited compositional variation (clinker, gypsum, and quartz) were classified more consistently (76% to 85%). The arithmetic mean (AM) of the clinker content in personal samples was 62.1%, the median was 55.3%, and 95% confidence interval (CI) was 42.6% to 68.1%. No significant differences were observed between job types. However, the clinker content in samples when workers handled materials with high clinker content was significantly higher than when materials with lower clinker content were handled, 85% versus 65% (P = 0.02). The limestone content was AM 14.8%, median 13.2% (95% CI 5.5 to 20.9), whereas the other materials were present with relative abundances of median ≤ 6.4%. Automated particle analysis by SEM-EDX followed by classification tree analysis quantified clinker with fairly high consistency when evaluated together with raw materials that are expected to be airborne in cement production plants. The clinker proportions for job types were similar. Tasks a priori ranked by assumed clinker content were significantly different and according to expectations, which supports the validity of the chosen methodology. The composition of personal samples of mineral aerosols in the cement production industry could be estimated by automated single particle analysis with SEM-EDX and classification by a classification tree procedure. Clinker was the major component in the thoracic aerosol that cement production workers were exposed to. Differences between job types were relatively small and not significant. The clinker content from tasks was in agreement with assumptions.

Simulation analysis of visual perception model based on pulse coupled neural network

Pulse-coupled neural networks perform well in many fields such as information retrieval, depth estimation and object detection. Based on pulse coupled neural network (PCNN) theory, this paper constructs a visual perception model framework and builds a real image reproduction platform. The model firstly analyzes the structure and generalization ability of neural network multi-class classifier, uses the minimax criterion of feature space as the splitting criterion of visual perception decision node, which solves the generalization problem of neural network learning algorithm. In the simulation process, the initial threshold is optimized by the two-dimensional maximum inter-class variance method, and in order to improve the real-time performance of the algorithm, the fast recurrence formula of neural network is derived and given. The PCNN image segmentation method based on genetic algorithm is analyzed. The genetic algorithm improves the loop termination condition and the adaptive setting of model parameters of PCNN image segmentation algorithm, but the PCNN image segmentation algorithm still has the problem of complexity. In order to solve this problem, this paper proposed an IGA-PCNN image segmentation method combining the improved algorithm and PCNN model. Firstly, it used the improved immune genetic algorithm to adaptively obtain the optimal threshold, and then replaced the dynamic threshold in PCNN model with the optimal threshold, and finally used the pulse coupling characteristics of PCNN model to complete the image segmentation. From the coupling characteristics of PCNN, junction close space of image and gray level characteristics, it determined the local gray mean square error of image connection strength coefficient. The feature extraction and object segmentation properties of PCNN come from the spike frequency of neurons, and the number of neurons in PCNN is equal to the number of pixels in the input image. In addition, the spatial and gray value differences of pixels should be considered comprehensively to determine their connection matrix. Digital experiments show that the multi-scale multi-task pulse coupled neural network model can shorten the total training time by 17 h, improve the comprehensive accuracy of the task test data set by 1.04%, and shorten the detection time of each image by 4.8 s compared with the series network model of multiple single tasks. Compared with the traditional PCNN algorithm, it has the advantages of fast visual perception and clear target contour segmentation, and effectively improves the anti-interference performance of the model.

Spatial variation in the crustal deformation pattern in the Koyna-Warna region, western India using shear wave splitting

SUMMARY This study investigates crustal anisotropy beneath the Koyna-Warna region (KWR) using waveforms of local earthquakes of magnitude ML ≥ 1.0, recorded at 24 surface and 6 borehole seismic stations during September 2005–February 2020. Waveforms of 1402 local earthquakes that fulfil the splitting criteria (ic < 45°) are used for shear wave splitting analysis, yielding 738 measurements. The study examines the spatial, temporal and depth variations in the fast polarization azimuths (FPAs) and delay times (δt). The average delay times in the Koyna and Warna regions are 0.035 and 0.050 s, respectively, which did not show any significant correlation with depth. The FPAs are predominantly oriented in the NNW–SSE direction in the KWR, commensurate with the in situ maximum horizontal stress (SHmax) obtained using data from a 3-km-deep scientific borehole and individual focal mechanism solutions. The anisotropy beneath KWR is due to the alignment of fluid-filled cracks and pore throats along the SHmax, and is attributed to stress-induced anisotropy. A considerable variation in the orientation of FPAs from NW to NNW–SSE beneath KWR could be associated with heterogeneity in the lithological properties due to variation in the fracture orientation under regional stress. The non-parallel orientation of FPAs with SHmax or faults at some stations is attributed to structure-induced anisotropy, which seems to be associated with surface topography. Measurements at stations, even near the reservoirs, did not show any significant temporal variation in the splitting parameters due to the fluctuation in the water levels.

Experimental investigation of cell generation in an expanding spherical hydrogen-air flame front

This paper reports on the cellular structure formation on the front of a spherically expanding hydrogen-air flame. The hydrogen-air mixture was considered with hydrogen concentration in experiments from 10 to 50 vol%. This paper aims to analyze cell cascade formation, which occurs due to diffusional-thermal and hydrodynamics instabilities. Using experimentally obtained schlieren images, the flame front radius as the function of an angle was obtained. The cell amplitude dependencies on the normalized time were also analyzed. The values corresponding to cell splitting were obtained by the discrete Fourier transform method. The cell split criterion, which allows taking into account the known instability mechanisms, was formulated.

Certain issues of substituting the life imprisonment punishment with more lenient one

The article deals with the problematic issues of substituting the life imprisonment punishment with more lenient one. It is stated that before being recently amended, the Criminal Code of Ukraine did not foresee the possibility of such substitution, so that the life convicts could be exempted from the further serving the punishment because of the illness or being pardoned. According to new Part 5 of Article 82 of the CC of Ukraine, a punishment of life imprisonment may be substituted by a punishment of imprisonment for a term of fifteen to twenty years, if the convict has served at least fifteen years of the punishment imposed by the court. If one analyzes this provision from the point of view of the arguments set forth in the decision of the European Court of Human Rights in the case "Petukhov v. Ukraine (No. 2)" and draw parallels between the procedures of pardoning and substituting the punishment of life imprisonment with more lenient one, then a similar question will arise: are at least fifteen years of the sentence imposed by the court and served by the convict included in the term of the punishment in the form of imprisonment, assigned in the order of substitution for a term of fifteen to twenty years? The article presents a number of arguments in favor of a negative answer to this question. In particular, it is stated that the term during which the punishment in the form of life imprisonment was actually served is taken into account as one continuous term, and is taken into account only once as a condition for substituting the punishment. As well, the author comes to the conclusion about the impossibility of partial counting of the term during which the person served the punishment of life imprisonment (that is, in the part that exceeds the established in Part 5 of Article 82 of the CC of Ukraine minimum required for the substitution the fifteen-year term) into the term of imprisonment appointed in the order of substitution. Firstly, the criminal law does not establish the criteria for such splitting of terms, and secondly, then the procedure of life imprisonment substituting can create significant prerequisites for abuses during its implementation. The court cannot retrospectively assess which term of actual serving the punishment is sufficient, but shall assess it at the very moment of considering the motion for substituting the punishment.

Who Is Most Burdened in Health Care? An Analysis of Responses to the ICAN Discussion Aid.

To create a model based on patients' characteristics that can predict the number of burdens reported using the ICAN Discussion Aid, to target use of this tool to patients likeliest to benefit. Six hundred thirty-five patients (aged ≥18 years) completed the ICAN Discussion Aid at a Scottsdale, Arizona, family medicine clinic. Patient characteristics were gathered from their health records. Regression trees with Poisson splitting criteria were used to model the data. Our model suggests the patients with the most burdens had major depressive disorder, with twice as many overall burdens (personal plus health care burdens) than patients without depression. Patients with depression who were younger than 38 years had the highest number of personal burdens. A body mass index (BMI) of 26 or greater was associated with increased health care burden versus a BMI below 26. The number of burdens a patient will report on the ICAN Discussion Aid can be approximated based on certain patient characteristics. Adults with major depression, a BMI of 26 or greater, and younger age may have greater reported burdens on ICAN, but this finding needs to be validated in independent samples.

Statistical analysis of various splitting criteria for decision trees

Decision trees are frequently used to overcome classification problems in the fields of data mining and machine learning, owing to their many perks, including their clear and simple architecture, excellent quality, and resilience. Various decision tree algorithms are developed using a variety of attribute selection criteria, following the top-down partitioning strategy. However, their effectiveness is influenced by the choice of the splitting method. Therefore, in this work, six decision tree algorithms that are based on six different attribute evaluation metrics are gathered in order to compare their performances. The choice of the decision trees that will be compared is done based on four different categories of the splitting criteria that are criteria based on information theory, criteria based on distance, statistical-based criteria, and other splitting criteria. These approaches include iterative dichotomizer 3 (first category), C[Formula: see text] (first category), classification and regression trees (second category), Pearson’s correlation coefficient based decision tree (third category), dispersion ratio (third category), and feature weight based decision tree algorithm (last category). On eleven data sets, the six procedures are assessed in terms of classification accuracy, tree depth, leaf nodes, and tree construction time. Furthermore, the Friedman and post hoc Nemenyi tests are used to examine the results that were obtained. The results of these two tests indicate that the iterative dichotomizer 3 and classification and regression trees decision tree methods perform better than the other decision tree methodologies.

Decision Rules Induced From Sets of Decision Trees

Decision rules belong to known forms of knowledge representation. Among popular measures of their quality length and support can be distinguished. Shorter rules are easier to understand and interpret. Support allows to present patterns hidden in the data. Nowadays, data mining tasks are oriented toward extracting knowledge from data in both distributed and centralized forms. Learning decision rules from a decision tree is a relatively simple task. However, the challenge arises when decision rules are induced from a set of decision trees. Moreover, in the case of distributed data, the decision trees may be constructed independently on different sources, and merging them into a unified set requires resolving conflicts and inconsistencies. In this paper, decision rules are constructed from distributed data based on decision trees induced using the randomly chosen attributes as the splitting criterion. The aim of the study is to compare the quality of two algorithms for constructing rules which are true for a maximum number of trees. The comparison was made based on three factors: the number of trees for which the rule is true, their length and support. Based on performed experiments it was possible to see that the number of true rules for the maximum number of decision trees from the set is greater for algorithm A than for heuristics H. This algorithm allows the induction of shorter rules with greater support compared to heuristic H. However, it should be also noted that the rules induced by heuristic H are often true for a larger number of trees than the rules constructed by algorithm A. Thus, both algorithms can be applied to distributed data.

Predictive factors for walking in acute stroke patients: a multicenter study using classification and regression tree analysis.

[Purpose] Walking ability should be predicted as early as possible in acute stroke patients. The purpose is to construct a prediction model for independent walking from bedside assessments using classification and regression tree analysis. [Participants and Methods] We conducted a multicenter case-control study with 240 stroke patients. Survey items included age, gender, injured hemisphere, the National Institute of Health Stroke Scale, the Brunnstrom Recovery Stage for lower extremities, and "turn over from a supine position" from the Ability for Basic Movement Scale. The National Institute of Health Stroke Scale items, such as language, extinction, and inattention, were grouped under higher brain dysfunction. We used the Functional Ambulation Categories to classify patients into independent (four or more the Functional Ambulation Categories; n=120) and dependent (three or fewer the Functional Ambulation Categories; n=120) walking groups. A classification and regression tree analysis was used to create a model to predict independent walking. [Results] The Brunnstrom Recovery Stage for lower extremities, "turn over from a supine position" from the Ability for Basic Movement Scale, and higher brain dysfunction were the splitting criteria for classifying patients into four categories: Category 1 (0%), severe motor paresis; Category 2 (10.0%), mild motor paresis and could not turn over; Category 3 (52.5%), with mild motor paresis, could turn over, and had higher brain dysfunction; and Category 4 (82.5%), with mild motor paresis, could turn over, and no higher brain dysfunction. [Conclusion] We constructed a useful prediction model for independent walking based on the three criteria.

Asset Pricing with Panel Tree Under Global Split Criteria

Asset Pricing with Panel Tree Under Global Split Criteria

Asymptotic properties of high-dimensional random forests

As a flexible nonparametric learning tool, the random forests algorithm has been widely applied to various real applications with appealing empirical performance, even in the presence of high-dimensional feature space. Unveiling the underlying mechanisms has led to some important recent theoretical results on the consistency of the random forests algorithm and its variants. However, to our knowledge, almost all existing works concerning random forests consistency in a high-dimensional setting were established for various modified random forests models where the splitting rules are independent of the response; a few exceptions assume simple data generating models with binary features. In light of this, in this paper we derive the consistency rates for the random forests algorithm associated with the sample CART splitting criterion, which is the one used in the original version of the algorithm (Mach. Learn. 45 (2001) 5–32), in a general high-dimensional nonparametric regression setting through a bias-variance decomposition analysis. Our new theoretical results show that random forests can indeed adapt to high dimensionality and allow for discontinuous regression function. Our bias analysis characterizes explicitly how the random forests bias depends on the sample size, tree height and column subsampling parameter. Some limitations on our current results are also discussed.

Predicting Students’ Mobility using Different Statistical Tools : Basis for Students’ Success

This paper investigates students’ success at Pangasinan State University by identifying patterns and models that might be used to correctly classify and predict if a student will transfer or finish their studies. In this study, three categorical variables or attributes and one continuous variable were considered independent variables due to the availability of the data. The results from the binary logistic regression model with the high school general average and course as independent variables (Model 3), and the decision tree model with transition gain as a splitting criterion were fitted to the dataset to generate a model that possibly best describes the students’ mobility in Pangasinan State University Urdaneta City Campus. The decision tree model is better than the binary logistic regression model based on accuracy, AUC, and sensitivity values. This implies that the decision tree model is better at correctly classifying observations as "transferred" than Model 3. Thus, it was concluded that the decision tree model with information gain as the splitting criterion best describes the mobility of PSU students. The results of this paper can be used for school administration involving students’ mobility/success, particularly in classifying whether a student will transfer based on other.

A numerical study of the mechanical and fracture properties of ZTA (zirconia toughened alumina)p/40Cr steel spatial spherical dot matrix composite

In this work, the geometrical characteristics of ZTA reinforcement particles were obtained, and a three-dimensional numerical model of ZTAp/40Cr steel spatial spherical dot matrix composite, similar to the actual structure, was established. In addition, the matrix toughness and shear fracture criterion, the brittle failure criterion of reinforcement particles, and the interfacial traction splitting criterion were all established. Under uniaxial compression, the influence of constitutive factors (volume fraction of spatial spherical dot matrix composite zone - VFB, and particle size - RPS) on the mechanical characteristics and fracture behavior of spatial spherical dot matrix composites was investigated. The elastic modulus, the compressive strength, and elongation in the stress–strain curves were calculated to assess the mechanical properties of the composites, and the particle average Mises stress and particle stress concentration coefficient were introduced to quantify the load carrying capacity of the composites. Furthermore, the fracture behavior of the secondary interface damage, the internal isotropic strain, and the macroscopic failure behavior were used to study the composites' fracture behavior. A new approach and theoretical foundation for following composites conformal design were established.

Random vector functional link forests and extreme learning forests applied to UAV automatic target recognition

This paper proposes two novel machine learning algorithms, namely Random Vector Functional Link Forests and Extreme Learning Forests, to develop an improved unmanned aerial vehicles automatic target recognition system. Such models take advantage of the stochastic procedure followed by Random Forests, where random subsets of instances and features are selected to build diverse Decision Trees. However, different from the usual uni-variate split criterion from Decision Tree algorithms, we propose and employ the novel Random Vector Functional Link Tree or Extreme Learning Tree, where each decision split is performed using the fast non-linear mapping of multiple features provided by either Random Vector Functional Link or Extreme Learning Machines. To prove the efficacy of the novel algorithm, experiments are performed using 90 binary classification problems to compare the performance of the proposed algorithm against other state-of-the-art ensemble learning techniques. Statistical analysis indicates the success of the proposed algorithms in terms of both predictive performance and computational complexity. While the model with deeper trees outperforms classical ensembles in terms of predictive performance (1.41% error reduction) and has similar results to state-of-the-art ensemble models, the model with shallow trees outperforms all ensembles in terms of computational burden (at least 36% faster). Finally, the novel methods are applied to develop an automatic target recognition system for unmanned aerial vehicles, achieving a valuable trade-off in terms of area under receiver operating characteristic curve (0.9309), F1 score (0.8190), accuracy (0.8646), and computational time (4.14 s).

Research on splitting criterion of the power system with wind-storage system integrated

As the last defense line of the power system, splitting control is of great significance to the safety and stability of power grid. However, with the increasing penetration of wind power, photovoltaic, energy storage and other power electronic power sources in the grid, the transient stability of the power system is seriously affected. Meanwhile, the operation characteristics of power electronic power source are very different from that of traditional synchronous generator. The integration of power electronic power source affects the characteristics about out-of-step oscillation, and ultimately affects the safe implementation of splitting control. In order to split the power system more adaptably, a new splitting control method is proposed in this paper. Firstly, the equivalent external characteristic model of the wind-storage system is derived, and the influence of the wind-storage system on the out-of-step center is analyzed. Secondly, according to the out-of-step oscillation characteristics when the wind-storage system is integrated, line weak-connection index(LWcI) is proposed to evaluate the stability margin of the branch. Thirdly, according to the trajectory difference about LWcI-Δθ in all branches when it is unstable and stable, we propose the splitting control criterion based on LWcI-Δθ and the phase angle of voltage about the branch. Finally, the effectiveness and adaptability of the splitting criterion proposed in this paper are verified by simulation analysis of the improved IEEE-39 system.

Out-of-step Splitting Control based on Network Wide Area Response Information

Influenced by the continuous growth of renewable energy installation scale and the increasingly prominent characteristics of AC/DC hybrid structure, the disturbance behavior of large power grid is becoming more and more complex. To ensure the safety of power grid, higher requirements are put forward for the reliability, accuracy and orderliness of the out-of-step splitting control which is belonged to the third line of defense. Firstly, this paper analyzes the principle and application limitation of out-of-step splitting control of ucosφ based on local measurement information, studies the relationship and error between ucosφ and oscillation center, and the location coefficient ζ of oscillation center is defined. On this basis, the criteria of branch splitting and the control strategy of multi-branch wide area cooperative out-of-step splitting are proposed. The simulation results of AC and HVDC hybrid power grid show that the proposed strategy can adapt to the complex dynamic behavior of large power grid in which multiple generator clusters lose synchronization one after another, accurately identify the cutset corresponding to the out-of-step generator cluster, and realize the unified and orderly splitting of each branch in the cutset.