Classification Perspective Research Articles

Classify patients with Moyamoya disease according to their cognitive performance might be helpful in clinical and practical with support vector machine based on hypergraph.

Moyamoya disease (MMD) patients were now classified according to their cerebrovascular manifestations, with cognition and emotion ignored, which attenuated the therapy. The present study tried to classify them based on their cognitive and emotional performance and explored the neural basis underlying this classification using resting-state fMRI (rs-fMRI). Thirty-nine MMD patients were recruited, assessed mental function and MRI scanned. We adopted hierarchical analysis of their mental performance for new subtypes. Next, a three-step analysis, with each step consisting of 10 random cross validation, was conducted for robust brain regions in classifying the three subtypes of patients in a support vector machine (SVM) model with hypergraph of rs-fMRI. We found three new subtypes including high depression-high anxiety-low cognition (HE-LC, 50%), low depression-low anxiety-high cognition (LE-HC, 14%), and low depression-low anxiety-low cognition (LE-LC, 36%), and no hemorrhagic MMD patients fell into the LE-HC group. The temporal and the bilateral superior frontal cortex, and so forth were included in all 10 randomized SVM modeling. The classification accuracy of the final three-way classification model was 67.5% in average of 10 random cross validation. In addition, the S value between the frontal cortex and the angular cortex was positively correlated with the anxiety score and backward digit span (p<.05). Our results might provide a new perspective for MMD classification concerning patients' mental status, guide timely surgery and suggest angular cortex, and so forth should be protected in surgery for cognitive consideration.

Media Representation: Coverage of the Nepali Army's Rescue and Relief Operations

&#x0D; This paper explores the media representation of the Nepali Army in its efforts of rescue and relief operations at home and abroad. In recent decades, the Nepali Army personnel have been directly involved in multiple public–welfare affairs along with their primary responsibilities of national security and territorial protection. Media representation of the Nepal Army has been examined from the perspective of Stuart Hall's classification of representation of meaning through language works. To unfold the media representation of the Nepali Army in its significant roles and core principles, I have taken a sample of the Nepali mass media content from the coverage of the Sankatmochan rescue and relief operations launched by the Nepal Army in two major national dailies, Kantipur and Nagarik in the aftermath of the 2015 Gorkha earthquake, for a period of a fortnight. The Nepali Army’s effective manoeuvre of the earthquake relief embodies the institution’s relevance in serving the cause of humanity along its core tasks of maintaining security, peace and order at home and abroad. In this paper, the coverage of different activities run by the Nepal Army is characterized as either reflective, intentional or constructionist. Moreover, the content concerning such rescue and relief operations has been contributing to shaping the image of the institution.

Quantifying and Comparing the Cooling Effects of Three Different Morphologies of Urban Parks in Chengdu

Urban parks have significant cooling effects, which can both mitigate the urban heat is-land effect and are crucial to the sustainable development of the human habitat. Although studies have been conducted to explore the influence of urban park morphology on the cooling effect of parks, they are not sufficiently in depth. Therefore, this paper took 117 urban parks in the central city of Chengdu as the research objects based on the perspective of the quantitative classification of urban park morphology. Then, remote sensing interpretation, spatial statistics, and regression analysis were used, and the four indicators of cooling intensity, cooling distance, cooling area, and cooling efficiency of urban parks were integrated to explore the cooling effect of the different morphological types of urban parks. The results show that (1) urban parks in Chengdu could be divided into five categories, among which the cooling effect of round parks was the best, and the cooling efficiency was 0.7. (2) In terms of park cooling area, urban parks’ area and perimeter thresholds were 30 ha and 4000 m, respectively. (3) When the area and perimeter of urban parks reached 70 ha and 3000 m, respectively, the increase in the cooling distance slowed down. (4) The cooling efficiency of the park was best when the shape index (indicating the complexity of the park boundaries) of the urban park was 2.8. The results of the study provide theoretical support for the intensive use of urban park green space and help the construction and promotion of a beautiful and livable park city in Chengdu.

Quality evaluation of Cnidii Fructus in commodity grade based on theory of "quality evaluation through morphological identification"

From the perspective of market classification of Cnidii Fructus, this paper revealed the scientific connotation of evaluating the quality grade of Cnidii Fructus by its appearance traits. Thirty batches of Cnidii Fructus in different grades were selected as the research objects. The canonical correlation analysis and principal component analysis(PCA) were used to explore the measurement values of 15 appearance traits and intrinsic content indexes. The results of correlation analysis showed that except the aspect ratio, the 5 appearance trait indexes(length, width, 1 000-grain weight, broken grain weight proportion, and chroma) and 9 internal content indexes(the content of moisture, total ash, acid insoluble ash, osthole, imperatorin, 5-methoxy psoralen, isopimpinellin, xanthotoxin, and xanthotol) showed significant correlation to varying degrees. In addition, there was a significant positive correlation between the first typical variable U_1 composed of appearance traits and the first typical variable V_1 composed of internal content indexes(CR_1=0.963, P&lt;0.01). The results of PCA showed that the classification results of appearance traits for 30 batches of Cnidii Fructus were consistent with the actual information of the samples. Under the same analysis conditions, 30 batches of Cnidii Fructus were reclassified by 9 groups of internal content indexes, and the analysis results were consistent. From the classification standard of the appearance traits of the system study, the statistical results of 6 appearance traits of Cnidii Fructus showed a correlation with grades. There was a good correlation between the appearance and the internal content of Cnidii Fructus, and the appearance quality effectively predicted the level of the internal content. There is a certain scientific basis for the quality classification of Cnidii Fructus by main appearance traits. Appearance classification can replace quality grading to realize the &quot;quality evaluation through morphological identification&quot; of Cnidii Fructus.

Progress on eco-technology classification in ecologically vulnerable regions of China

Ecological technology is the core of ecological environment governance and restoration in ecologically vulnerable regions. A reasonable classification method is the basis for induction and summary of ecological techno-logy, which is of great significance to classify and solve ecological environmental problems and evaluate the effects of ecological technology implementation. However, there is still no standard method for the classification of ecological technology. From the perspective of ecological technology classification, we summarized the concept of eco-technology and related classification methods, in view of current situation and deficiency of ecological technology related classification, we put forward a system suitable for defining and classifying ecological technology in ecologically vulnerable regions of China, and analyzed the practicality and application prospect. Our review would provide reference for the management and promotion of ecological technology classification.

Grid Network: Feature Extraction in Anisotropic Perspective for Hyperspectral Image Classification

Grid Network: Feature Extraction in Anisotropic Perspective for Hyperspectral Image Classification

Local and Long-Range Collaborative Learning for Remote Sensing Scene Classification

With the development of high-resolution satellites, more and more attention has been paid to remote sensing (RS) scene classification. Convolutional neural networks (CNNs), which replace the traditional handcrafted features with a learning-based feature extraction mechanism, are widely used in scene classification. But CNNs are less effective in deriving long-range contextual relations, which limits the further improvement. Visual transformer (VT), an emerging image processing method, provides a new perspective for RS scene classification by directly acquiring long-range features. Although there have been limited works combining CNN and VT through simple concatenation, the collaborations between them are insufficient. To address these issues, we propose a local and long-range collaborative framework (L2RCF). First, we design a dual-stream structure to extract the local and long-range features. Second, a cross-feature calibration (CFC) module is designed for them to improve representation of the fusion features. Then, combining deep supervision (DS) and deep mutual learning (DML), a novel joint loss is proposed to enhance the dual-stream feature extractor and further improve the fused features. Finally, a two-stage semi-supervised training strategy is designed to improve performance with unlabeled samples. To demonstrate the effectiveness of L2RCF, we conducted experiments on three widely used RS scene classification data sets: RSSCN7, AID, and NWPU. The results show that L2RCF performs significantly better compared with some state-of-the-art scene classification methods.

A new machine learning based computational framework identifies therapeutic targets and unveils influential genes in pancreatic islet cells

Pancreatic islets comprise a group of cells that produce hormones regulating blood glucose levels. Particularly, the alpha and beta islet cells produce glucagon and insulin to stabilize blood glucose. When beta islet cells are dysfunctional, insulin is not secreted, inducing a glucose metabolic disorder. Identifying effective therapeutic targets against the disease is a complicated task and is not yet conclusive. To close the wide gap between understanding the molecular mechanism of pancreatic islet cells and providing effective therapeutic targets, we present a computational framework to identify potential therapeutic targets against pancreatic disorders. First, we downloaded three transcriptome expression profiling datasets pertaining to pancreatic islet cells (GSE87375, GSE79457, GSE110154) from the Gene Expression Omnibus database. For each dataset, we extracted expression profiles for two cell types. We then provided these expression profiles along with the cell types to our proposed constrained optimization problem of a support vector machine and to other existing methods, selecting important genes from the expression profiles. Finally, we performed (1) an evaluation from a classification perspective which showed the superiority of our methods against the baseline; and (2) an enrichment analysis which indicated that our methods achieved better outcomes. Results for the three datasets included 44 unique genes and 10 unique transcription factors (SP1, HDAC1, EGR1, E2F1, AR, STAT6, RELA, SP3, NFKB1, and ESR1) which are reportedly related to pancreatic islet functions, diseases, and therapeutic targets.

Application of nanoarchitectonics in moist-electric generation.

The consumption of energy is an important resource that cannot be ignored in modern society. Non-renewable forms of energy, such as coal, natural gas, and oil, have always been important strategic resources and are always facing a crisis of shortage. Therefore, there is an urgent need for green renewable forms of energy. As an emerging green energy source, the moist-electric generator (MEG) has been studied in recent years and may become an energy source that can be utilized in daily life. Along with the advancement of technological means, nanoarchitectonics play an important role in MEG devices. This review aims to provide a comprehensive summary of the fundamentals of the MEG from the perspective of different material classifications and to provide guidance for future work in the field of MEGs. The effects of various parameters and structural designs on the output power, recent important literature and works, the mechanism of liquid-solid interactions at the nanoscale, and the application status and further potential of MEG devices are discussed in this review. It is expected that this review may provide valuable knowledge for future MEG research.

Symmetry methods for a hyperbolic model for a class of populations

We investigate a hyperbolic system that describe the dispersal dynamics of a population, introduced by Méndez and Camacho (1997)[1], in Lie symmetry classification perspective. A Lie group classification is provided for different forms of two constitutive functions: the propagation coefficient D(u) and the reaction term r(u). We establish the Lie symmetry determining equations by utilizing an equivalence generator and the projection theorem. Several extensions of principal Lie algebra are found for different forms of D(u) and r(u). By performing classical reductions we obtain several exact solutions.

Recent advances in interface engineering strategy for highly‐efficient electrocatalytic water splitting

AbstractThe hydrogen energy generated by the electrocatalytic water splitting reaction has been established as a renewable and clean energy carrier with ultra‐high energy density, which can well make up for shortcomings of conventional renewable energy sources, such as geographical limitations, climatic dependence, and energy wastage. Notably, the introduction of electrocatalysts can enhance the efficiency of the water splitting process to generate hydrogen. Particularly, the heterostructure electrocatalysts constructed by coupling multiple components (or phases) have emerged as the most promising option for water splitting due to the well‐known electronic and synergistic effects. The existing reviews on interface engineering for electrocatalyst design mostly focus on the relationship between the heterostructures and specific electrocatalytic reactions. However, a comprehensive overview of the integration of model building, directional synthesis, and electrocatalytic mechanism has been rarely reported. To this end, in this review, the development of heterostructure catalysts is systematically introduced from the perspective of interface classification, interface growth and synthesis, and regulation of electrocatalytic performance based on the interfacial microenvironment (bonding, electronic configuration, lattice strain, etc.), thereby offering useful insights on the design and construction of interfacial models. Besides, combined with the current development and applications of interface engineering strategies, the challenges of future heterostructure catalysts are discussed and relevant solutions are proposed. Overall, this review can serve as a useful theoretical reference for the integration of interfacial model building, directional synthesis, and electrocatalytic mechanism, which can further promote the development of hydrogen production technologies with low energy consumption and high yield.image

Contrasting EfficientNet, ViT, and gMLP for COVID-19 Detection in Ultrasound Imagery

A timely diagnosis of coronavirus is critical in order to control the spread of the virus. To aid in this, we propose in this paper a deep learning-based approach for detecting coronavirus patients using ultrasound imagery. We propose to exploit the transfer learning of a EfficientNet model pre-trained on the ImageNet dataset for the classification of ultrasound images of suspected patients. In particular, we contrast the results of EfficentNet-B2 with the results of ViT and gMLP. Then, we show the results of the three models by learning from scratch, i.e., without transfer learning. We view the detection problem from a multiclass classification perspective by classifying images as COVID-19, pneumonia, and normal. In the experiments, we evaluated the models on a publically available ultrasound dataset. This dataset consists of 261 recordings (202 videos + 59 images) belonging to 216 distinct patients. The best results were obtained using EfficientNet-B2 with transfer learning. In particular, we obtained precision, recall, and F1 scores of 95.84%, 99.88%, and 24 97.41%, respectively, for detecting the COVID-19 class. EfficientNet-B2 with transfer learning presented an overall accuracy of 96.79%, outperforming gMLP and ViT, which achieved accuracies of 93.03% and 92.82%, respectively.

Three-way multi-attribute decision making under incomplete mixed environments using probabilistic similarity

In recent years, many scholars have explored a variety of methods integrating three-way decision (3WD) and multi-attribute decision making (MADM), which enables the classification and priority ranking of alternatives possible and fully reflects the effectiveness and advantages of 3WD in solving MADM problems. However, few of these methods can effectively deal with the MADM problems with incomplete mixed information that are frequently encountered in real-world situations. This study proposes a three-way MADM method for an incomplete mixed information system (IMIS), where the objective determination of conditional probabilities and utility functions in IMIS without decision label is the pivotal issue. To overcome this issue, we define a probabilistic similarity measure for incomplete mixed information. The probabilistic similarity measure is used to replace the distance measure of classical TOPSIS for estimating the conditional probabilities objectively. The probabilistic similarity class is introduced with arithmetic average method to design a conversion mechanism and obtain the objective relative utility functions of incomplete evaluation values. We then construct a novel 3WD model in IMIS and combine it with two customized ranking principles, to solve the incomplete mixed MADM problems from the perspective of classification and ranking in a more thoughtful and interpretable manner. Our study provides a new perspective for the research on MADM in incomplete mixed information environment. Several examples and experimental comparisons verify the effectiveness and stability of the proposed method. The experiments demonstrate that our method can meet more decision-making requirements and is more accurate and rational in some decision-making scenarios than several prior similar methods.

The Influence of Trunk Impairment Level on the Kinematic Characteristics of Alpine Sit-Skiing: A Case Study of Paralympic Medalists

This study aimed to examine the relationship between the trunk impairment level and the trunk kinematic characteristics during alpine sit-skiing from a classification perspective. Three Paralympic medalists in sitting classes (LW10-2, LW11, and LW12-2) participated in the present study. To simulate the racing conditions, giant slalom gates were set. To measure the kinematics of the skier and sit-ski during skiing, a motion capture method with inertial measurement units was used. The muscle activities of the trunk muscles were evaluated using electromyography. Chest lateral flexion, chest flexion, and hip flexion/extension angle during sit-skiing were reduced due to impairment. Additionally, the insufficient lateral flexion (angulation) caused a decrease in edging angle, and that the insufficient chest and hip flexion/extension caused a lower loading in the latter half of the turn through smaller vertical movement. Since edging angle and loading are key factors in ski control, the three joint motions could be measures of sport-specific activity limitation in sit-skiing classification. Between the LW10-2 and LW11 skiers, no distinct differences in trunk kinematics were found. Assuming the scaling factor of race time as a measure of skiing performance, one possible reason is that the difference in skiing performance the LW10-2 and LW11 skiers is considerably smaller relative to differences between the LW11 and LW12-2 skiers. There were no distinct differences among classes in the results of muscle activity, and therefore, this information appears to play a minimal role for classification.

Evaluation and simulation of landscape evolution and its ecological effects under vegetation restoration in the northern sand prevention belt, China

Vegetation restoration has brought about remarkable landscape evolution in arid regions, and it is of great significance to evaluate its ecological benefits. However, the landscape evolution and ecological effects of different vegetation restoration measures have yet to be distinguished, and their future trends remain to be revealed, especially from the perspective of fine vegetation classification. In this study, we evaluated the ecosystem service value (ESV) of the northern sand prevention belt (NSPB) based on the fine land use/cover classification and benefit transfer method. Then, we analyzed changes in landscape and ESV induced by vegetation restoration from 2000 to 2015 and designed 9 future vegetation restoration scenarios to improve ESV. The results showed that the built-up area and dry farmland expanded by 35.37% and 3.48%, respectively, and paddy field and bush decreased by 19.00% and 6.80% from 2000 to 2015, respectively. Total ESV decreased by 0.62% (1.76 billion USD) during 2000–2015. The reduction in bush led to a loss of 0.85% (2.41 billion USD) in ESV. Vegetation restoration involved grassland restoration (1.99 million ha), bush restoration (0.26 million ha), and forestland restoration (0.18 million ha), which increased ESV by 1.21, 1.17, and 4.29 billion USD, respectively. Anthropogenic disturbance resulted in the loss of 2.97 million ha of vegetation and 9.23 billion USD in ESV. Current vegetation restoration in the NSPB is insufficient to counteract the effects of anthropogenic disturbance on ESV. Riparian vegetation buffer and grain for green scenarios have limited ecological benefits. Desertification control scenarios have the potential to increase ESV by at least 8.94% (25.12 billion USD) and to reverse ESV losses. Bush and grassland can be used instead of forestland for restoration in arid regions. This study can provide important support for the formulation and adjustment of landscape restoration in arid regions.

An environmental synoptic analysis of tropical transitions in the central and Eastern North Atlantic

A tropical transition (TT) is the process whereby a baroclinic, high-to-moderate vertical wind shear, extratropical or subtropical cyclone is transformed into a warm-core, low vertical wind shear, tropical cyclone. Thirty TT events were identified over the central and eastern North Atlantic basin during the period 1979–2019. The TT process is here studied from a synoptic storm-centered composite climatology and an environmental classification perspective. The aim is to study their common features and highlight their differences. The storm-centered composite analysis reveals that a westerlies meridional trough with quasigeostrophic forcig acts as precursor. TT environments are characterized by a trough at 300 hPa geopotential and the increase of the 1000–500 hPa thickness, i.e., the system evolves into a warm-core, and a strong anticyclone is located north of the surface cyclone. The transition is accompanied by a large latent heat release which promotes the vertical redistribution of potential vorticity and a reduction of the 850–300 hPa vertical wind shear. The identified TTs in the central North Atlantic predominantly developed in environments with warm sea surface temperatures (> 25 °C) and low-to-moderate wind shear (10–15 m s−1). In contrast, the eastern North Atlantic TTs transitioned in low sea surface temperature values (< 25 °C) and high wind shear (> 15 m s−1). Finally, the statistically significant differences in the environmental classification encouraged further analysis of their environments via storm-centered composites, revealing that eastern North Atlantic cyclones have a more defined extratropical structure, while central North Atlantic cyclones show more tropical characteristics.

Supervised machine learning application of lithofacies classification for a hydrodynamically complex gas - condensate reservoir in Nam Con Son basin

Conventional integration of rock physics and seismic inversion can quantitatively evaluate and contrast reservoir properties. However, the available output attributes are occasionally not a perfect indicator for specific information such as lithology or fluid saturation due to technology constraints. Each attribute commonly exhibits a combination of geological characteristics that could lead to subjective interpretations and provides only qualitative results. Meanwhile, machine learning (ML) is emerging as an independent interpreter to synthesise all parameters simultaneously, mitigate the uncertainty of biased cut-off, and objectively classify lithofacies on the accuracy scale.&#x0D; In this paper, multiple classification algorithms including support vector machine (SVM), random forest (RF), decision tree (DT), K-nearest neighbours (KNN), logistic regression, Gaussian, Bernoulli, multinomial Naïve Bayes, and linear discriminant analysis were executed on the seismic attributes for lithofacies prediction. Initially, all data points of five seismic attributes of acoustic impedance, Lambda-Rho, Mu-Rho, density (ρ), and compressional wave to shear wave velocity (VpVs) within 25-metre radius and 25-metre interval offset top and base of reservoir were orbitally extracted on 4 wells to create the datasets. Cross-validation and grid search were also implemented on the best four algorithms to optimise the hyper-parameters for each algorithm and avoid overfitting during training. Finally, confusion matrix and accuracy scores were exploited to determine the ultimate model for discrete lithofacies prediction. The machine learning models were applied to predict lithofacies for a complex reservoir in an area of 163 km2.&#x0D; From the perspective of classification, the random forest method achieved the highest accuracy score of 0.907 compared to support vector machine (0.896), K-nearest neighbours (0.895), and decision tree (0.892). At well locations, the correlation factor was excellent with 0.88 for random forest results versus sand thickness. In terms of sand and shale distribution, the machine learning outputs demonstrated geologically reasonable results, even in undrilled regions and reservoir boundary areas.

Seeded Classification of Satellite Image Time Series with Lower-Bounded Dynamic Time Warping

Satellite Image Time Series (SITS) record the continuous temporal behavior of land cover types and thus provide a new perspective for finer-grained land cover classification compared with the usual spectral and spatial information contained in a static image. In addition, SITS data is becoming more accessible in recent years due to newly launched satellites and accumulated historical data. However, the lack of labeled training samples limits the exploration of SITS data, especially with sophisticated methods. Even with a straightforward classifier, such as k-nearest neighbor, the accuracy and efficiency of the SITS similarity measure is also a pending problem. In this paper, we propose SKNN-LB-DTW, a seeded SITS classification method based on lower-bounded Dynamic Time Warping (DTW). The word “seeded” indicates that only a few labeled samples are required, and this is not only because of the lack of labeled samples but also because of our aim to explore the rich information contained in SITS, rather than letting training samples dominate the classification results. We use a combination of cascading lower bounds and early abandoning of DTW as an accurate yet efficient similarity measure for large scale tasks. The experimental results on two real SITS datasets demonstrate the utility of the proposed SKNN-LB-DTW, which could become an effective solution for SITS classification when the amount of unlabeled SITS data far exceeds the labeled data.

Health Care Data Analytics – Comparative Study of Supervised Model

In the present pandemic situation, health care data is generated voluminously in an unstructured format posing challenge to technology in perspective of analysis, classification and prediction. The data generated is converted to structured format. Suitability of methodology keeping in mind low computational complexity and high accuracy is a major concern which has emerged as a problem in data science. In this research work real time heart disease data set is considered to evaluate the accuracy of six supervised methods –SVM (Support Vector Machine), KNN (K-Nearest Neighbor), GNB (Gaussian Naïve Bayes), LR (Logistic Regression), DT (Decision Tree) and RF (Random Forest). Analysis through ROC curve and confusion matrix predominantly justify RF classifier and LR gives efficient results compared to other methods. This is a preprocessing stage; every researcher has to perform before deciding the methodology to be considered for further processing.

DIAGNOSTIC Classification Analysis of Problem-Solving Competence using Process Data: An Item Expansion Method.

Process data refer to data recorded in computer-based assessments (CBAs) that reflect respondents' problem-solving processes and provide greater insight into how respondents solve problems, in addition to how well they solve them. Using the rich information contained in process data, this study proposed an item expansion method to analyze action-level process data from the perspective of diagnostic classification in order to comprehensively understand respondents' problem-solving competence. The proposed method cannot only estimate respondents' problem-solving ability along a continuum, but also classify respondents according to their problem-solving skills. To illustrate the application and advantages of the proposed method, a Programme for International Student Assessment (PISA) problem-solving item was used. The results indicated that (a) the estimated latent classes provided more detailed diagnoses of respondents' problem-solving skills than the observed score categories; (b) although only one item was used, the estimated higher-order latent ability reflected the respondents' problem-solving ability more accurately than the unidimensional latent ability estimated from the outcome data; and (c) interactions among problem-solving skills followed the conjunctive condensation rule, which indicated that the specific action sequence appeared only when a respondent mastered all required problem solving skills. In conclusion, the proposed diagnostic classification approach is feasible and promising analyzing process data.