Classification Categories Research Articles

A Novel Hybrid Algorithm for Enhanced Re-Ranking Optimal Web Page Classification

World Wide Web (WWW) is a platform that explores a wide range of information used for the development of web applications. Some examples of these applications include social network analysis, personalized item recommendations, and web page classification and ranking. Among these applications, search engines and web page ranking are particularly important as they consistently index and store billions of web pages on the internet. The main objective of this paper is to create an innovative framework for the classification and re-ranking of web pages using intelligent techniques. The framework is structured into two key phases: classification and re-ranking-based retrieval. In the initial classification phase, a series of pre-processing steps are implemented, including the elimination of HTML tags, punctuation, stop words, and the application of stemming. After pre-processing, a word-to-vector conversion is performed, followed by feature extraction utilizing Principal Component Analysis (PCA). This sequence of actions leads to optimal feature selection, which is vital for the precise classification of web pages. Given the multitude of features present in web pages that can compromise classification accuracy, this study employs a novel meta-heuristic algorithm, the Opposition Based-Tunicate Swarm Algorithm (O-TSA), to facilitate optimal feature selection. The refined features are subsequently processed through the Enhanced Convolutional-Recurrent Neural Network (E-CRNN), enhanced by O-TSA, resulting in the effective classification of diverse web page categories. In the second phase, the re-ranking process is executed using O-TSA, which establishes the objective function based on a similarity function (correlation) for URL matching, leading to optimal re-ranking of web.

Zero-Shot Rolling Bearing Fault Diagnosis Based on Attribute Description

Traditional fault diagnosis methods for rolling bearings rely on nemerous labeled samples, which are difficult to obtain in engineering applications. Moreover, when unseen fault categories appear in the test set, these models fail to achieve accurate diagnoses, as the fault categories are not represented in the training data. To address these challenges, a zero-shot fault diagnosis model for rolling bearings is proposed, which realizes knowledge transfer from seen to unseen categories by constructing attribute information, thereby reducing the dependence on labeled samples. First, an attribute method Discrete Label Embedding Method (DLEM) based on word embedding and envelope analysis is designed to generate fault attributes. Fault features are extracted using the Swin Transformer model. Then, the attributes and features are input into the constructed model Distribution Consistency and Multi-modal Cross Alignment Variational Autoencoder (DCMCA-VAE), which is built on Convolutional Residual SE-Attention Variational Autoencoder (CRS-VAE). CRS-VAE replaces fully connected layers with convolutional layers and incorporates residual connections with the Squeeze-and-Excitation Joint Attention Mechanism (SE-JAM) for improved feature extraction. The DCMCA-VAE also incorporates a reconstruction alignment module with the proposed distribution consistency loss LWT and multi-modal cross alignment loss function LMCA. The reconstruction alignment module is used to generate high-quality features with distinguishing information between different categories for classification. In the face of multiple noisy datasets, this model can effectively distinguish unseen categories and has stronger robustness than other models. The model can achieve 100% classification accuracy on the SQ dataset, and more than 85% on the CWRU dataset when unseen and seen categories appear simultaneously with noise interference.

Review On Layout Optimization For Rollingstock Maintenance Depot

This review paper discusses the importance of having an optimal layout for rollingstock maintenance depots to optimize rollingstock maintenance in improving maintenance efficiency and reducing maintenance costs while maintaining the availability and reliability of railway vehicles. The current problems in rollingstock maintenance depots faced by railway industry are limited land availability, limited storage space, material handling issues, and the lack of standards for train maintenance depot layouts. The paper also presents classification criteria and categories on layout planning and maintenance optimization approach for rollingstock maintenance depots based on various recent studies including the methods used and the results obtained. Finally, this review paper proposes guidelines for future research on rollingstock maintenance depots in Indonesia as decision-making that considers the economic factors of layout optimization and the implications for safety and the environment of maintenance activities. This could also help improve the company's reputation as well as prepare for future expansion

ARTIFICIAL INTELLIGENCE DRIVEN DRONE OBSERVATION AND PEST CONTROL IN BANANA CROP: A SYSTEMATIC REVIEW

Bananas are the most commonly eaten and significant fruit in global trade. Bananas are produced using a variety of methods and environments. In addition to regularly updating farmers on problems in banana plant leaves, this system aims to discover, diagnose, and treat banana leaf diseases. Customers' wants and lifestyles have changed significantly during the past few decades. These modifications provide additional difficulties for farmers whose output must satisfy consumer needs. Both the farmer and the consumer will benefit from the capacity to categorize agricultural products according to size and quality. In this case, the system gets its input in the form of standard photos of banana leaves taken using various image capture devices. It will then process those photos to identify any diseases and alert the farmer. Additionally, the system will advise the farmer on what to do next, including which fertilizers, herbicides, and agricultural practices to employ in order to prevent illnesses from harming neighboring crops. In this systematic review, useful and efficient methods for identification are presented in works that fall under the categories of image classification, AI/ML, deep learning, and mobile applications.

Classification of Subdivision Land Use and Land Cover Using Deep Learning Models

Land cover provides crucial information related to biological geography, ecological climatology, and human activities. In the past, land cover mapping was performed based on visual interpretation, but it had limitations in terms of time and cost. Recently, it has become possible to create land cover maps with higher temporal resolution over wider areas using artificial intelligence-based models. The accuracy and reliability of AI model-based land cover maps increase with the amount of training data, but it is difficult to acquire large amounts of data due to the time required for label data annotation. In South Korea, the Environmental Geographic Information Service provides self-learning data consisting of aerial orthoimages and subdivision land cover classification level label data, making it possible to collect high-quality data. Therefore, this study examined the feasibility of self-learning data by building and evaluating a U-Net-based land cover classification model for waterfront areas using self-learning data. The trained model showed relatively low performance with an F-1 score of 0.61 for training data and 0.31 for test data. The model’s low performance is thought to be due to insufficient training caused by the large number of classification categories (34) and data imbalance between categories. Although the model performance using self-learning data was low, it is believed that model performance can be improved by grouping classification categories according to research purposes or resolving data imbalance through data augmentation techniques. Therefore, self-learning data is expected to be utilized in various studies using land cover.

PATHOGENETIC APPROACH TO THE TREATMENT OF WOUNDS AFTER AMPUTATION OF THE FOOT AGAINST THE BACKGROUND OF CHRONIC LIMB-THREATENING ISCHEMIA (CLTI)

SUMMARY. The strategy of treatment of chronic limb-threatening ischemia (CLTI) should be its timely revascularization and prioritization of performing "small" amputations on the foot. The aim – to improve the healing of wounds after amputation of a part of the foot with the help of a biological membrane enriched with growth factors for their subsequent autodermoplasty Material and Methods. 81 patients with IV grade CLTI were under observation according to the Fontaine classification (category 6 according to the Rutherford classification), with open wounds after amputation of a part of the foot due to dry gangrene. The average age of the patients was 56.7±9.3 years, all men. Patients underwent vasoballoon angioplasty of the arteries of the lower extremities. After that, amputation of the foot was performed using various methods. In connection with the shortage of skin flaps of the wound, 41 patients were treated by the open method, according to the traditional method, followed by autodermoplasty. In other patients (40 patients), to stimulate the reparative process, the entire area of the wound surface on the foot was covered with a plasma membrane (PRGF®-Endoret® supernatant), enriched with growth factors. To control the regeneration process, the tyrosine kinase index was calculated at different times of treatment (6-10 and 15 days after amputation). Also, the content of interleukins IL-1β, IL-4, tumor necrosis factor (TNF-α), interferon (IFN-γ) was determined. After that, the intercytokine coefficient (ICC) was calculated. Result. In patients who underwent endovascular angioplasty and PRP therapy before limb amputation and closed the wound with a plasma membrane, ICC decreased below baseline. This can be explained by a significant increase in the level of anti-inflammatory cytokines against the background of a stable amount of pro-inflammatory factors. The reaction of IL-4 and IFN-γ can be regarded as preparation for the transition of the wound process to the proliferation stage, and the decrease in the levels of IL-1β and TNF-α reflects a decrease in the manifestations of the inflammatory reaction in the wound. Conclusions: 1. Wound healing after foot amputation in patients with CLTI depends not only on restoration of blood supply to the limb. The healing process in an acute wound is largely controlled by the spatio-temporal action of growth factors, cytokines, and chemokines. The use of plasma membrane as a biological membrane enriched with growth factors (PRGF)-Endoret can be a new therapeutic strategy to accelerate the healing process of wounds after amputation of the foot on the background of CLTI with the purpose of subsequent autodermoplasty.

Temporal analysis of multi-spectral instrument level and surface reflectance data sets for seasonal variation in land cover dynamics by using Google Earth Engine

By rapid growth in programming tools, accessibility to end consumer computing power, and the availability of free satellite data, the data science and remote sensing fields have begun to converge in recent years. Before this major processing time is wasted in collection of data. Google Earth Engine easily overcomes above problem; it contains data from different satellites and has power of processing and computation also. Well known data provider satellites are present in the library of GEE and users can easily process and track real time data from these satellites over GEE. “Sentinel”, a mission of the European Space Agency and “Landsat”, an American Earth observation satellite have been used in a variety of remote sensing applications. GEE makes these data sets available to the general public. These datasets are utilised for computing and analysis purposes. The objective of this study is to find change in study area by using above discussed two satellite data, over each season of year on different category of classification (Random Forest, CART, GTB and SVM). This work focuses on improving the classification accuracy of different classification algorithm by reviewing training samples and analyzing post-classification with image differencing in the algebraic technique. Because Landsat data have a medium spatial resolution, therefore point-wise computation was used. Lastly, we also detect which data sets are working better on an appropriate machine learning algorithm, so after final calculation we estimate accuracy of each algorithm by using confusion matrix and kappa.

Exploring Thematic Diversity in Classical Chinese Poetry: A Novel Dataset and a BERT-Enhanced Ensemble Learning Approach

Classical Chinese poetry, as an essential aspect of cultural heritage, exhibits rich theme diversity often overlooked in natural language processing research. To address this gap, we aim to explore the classification of thematic categories within this literary domain. We curate a dataset of 2,918 annotated poems spanning 7 common themes and propose a BERT-based ensemble learning approach for effective classification. Although this method integrates existing models, it achieves an accuracy and F1 score of over 72% in the 7-class task, surpassing established baselines, and providing a baseline for future research. The experimental findings reveal the effectiveness of ensemble strategies in improving individual base model performance and highlight the potential of the MLP-based ensemble technique. The study contributes to a deeper understanding of thematic categories and textual features in classical Chinese poetry and offers an automated classification system for classical Chinese poems.

Three-Dimensional Assessment of the Condylar Position in Different Malocclusions Using Cone-Beam Computed Tomography: A Cross-Sectional Study.

Introduction The role of the condylar position in the correct functioning of the stomatognathic system has been the center of the study. Using cone-beam computed tomography (CBCT), this study looked at the three-dimensional (3D) position of the condylar bone in patients from Class I, Class II, Division 1, and Division 2. Materials and methods This cross-sectional, retrospective study was conducted using 102 CBCT records, with 34 records allocated to each category of malocclusion classification, such as dentoskeletal Class I, skeletal Class II, and dental Class II, Division 1 and 2. CBCT scans were conducted utilizing a Carestream New Generation CBCT apparatus (Carestream Dental, Atlanta, Georgia) in accordance with a standardized protocol (operating at a voltage of 120 kV, a current of 80 mA, a seven-second scan time, a field of view (FOV) measuring 10 x 10 cm, and a resolution of 0.2 voxels, and 1-mm slice thickness). The condylar position was assessed as the superior, inferior, and medial distance of the condyle from the glenoid fossa, along with the condylar angle. The distance from the most anterior point on the anterior surface of the condyle to the articular eminence was taken as anterior condylar distance; the distance of the superior surface of the condyle from the deepest point of the glenoid fossa was taken as superior condylar distance; the distance of the posterior surface of the condyle from the glenoid fossa was taken as posterior condylar distance; the condylar angle was measured as an angle between the XY line and the FH' line passing through X, where X is the center of the condyle; and the distance of the medial surface of the condyle from the glenoid fossa was taken as medial distance. The data were then subjected to statistical analyses. Results For anterior distance, the highest distance was noted in Class II Division 1 (3.32 ± 0.4 mm), and the lowest was seen in Class I (2.43 ± 0.26 mm). In the posterior distance, Class I exhibited the highest mean distance of 2.05 ± 0.14 mm, while Class II Division 1 showed the lowest distance of 1.83 ± 0.18 mm. For superior distance, the highest mean value was noticed in Class I patients at 2.92 ± 0.22 mm, and the lowest value was seen in Class II Division 1 at 2.61 ± 0.35 mm (p=0.001). For the condylar angle, the highest mean value was observed in Class I (30.96 ± 1.910) and the lowest in Class II Division 1 (26.71 ± 1.480), with p=0.001. Confirmatory factor analysis revealed that the most substantial loading was attributed to the condylar angle at -2.28, signifying its significant contribution to Fc1. Conclusion The condyle was placed anteriorly, superiorly, and medially in Class II Division 1 and posteriorly in Class II Division 2, compared to Class I patients.

Development of core sets for deafblindness using the International Classification of Functioning, Disability, and Health: the perspectives of individuals with lived experience.

The needs of people with deafblindness remain poorly understood and addressed globally. This study is part of a larger body of work to develop Core Sets for deafblindness using the International Classification of Functioning, Disability, and Health (ICF). To determine the perspectives on functioning of individuals with lived experience of deafblindness as they relate to the ICF. Cross-sectional interview study. Global, representing all six regions of the World Health Organization. A diverse cohort of 72 individuals living with deafblindness or as close family members, ranging across the spectrum of severity. Qualitative interviews and focus groups were used to explore six open-ended questions about perceived barriers and facilitators to functioning of body functions and structures, activities and participation, and environmental and personal factors. Data were synthesized using content analysis, and the resulting topics were linked to the ICF codes using established linking methodology prescribed by the World Health Organization's ICF Research Branch. Descriptive statistics summarized all demographic data. The analyses of the transcripts uncovered 2534 meaning units, leading to the identification of 492 corresponding distinct codes from the ICF framework, and spanning across 93.33% of coding categories available. Sensory (b2) and Mental Functions (b1) emerged as the most frequent Body Functions (b) codes. Most categories referred to Activities & Participation, with Mobility (d4) being the most frequently mentioned topic. Services, Systems and Policies (e5) was the most frequently used Environmental factor. Over 93% of the ICF classification categories were accessed to code the data provided by participants, demonstrating the complexity of functioning with deafblindness. This study provides valuable information to shape policy and research by providing representation of lived experience towards the consensus conference for the comprehensive and abbreviated Core Sets for deafblindness. The inclusion of lived experience provides a holistic understanding of the daily challenges faced by individuals living with deafblindness. By being part of this process, they have a voice in shaping the classification system that will be used to describe their experiences, fostering a sense of ownership and empowerment.

Geochemical studies of potential acid-forming classification materials to reduce acid mine drainage formation

Abstract Acid mine drainage (AMD) is acidic wastewater produced in coal mining operations due to sulfide mineral oxidation. In compliance with the regulations of good mining practice procedures, each mining operation must conduct a geochemical study of the distribution of material classification categories to identify the potential acid-forming (PAF) or not acid-forming (NAF) materials to mitigate the AMD. This research conducted a field observation at one of the post-mining lakes of a coal mining operation in South Kalimantan. It determined materials or rocks that have the potential for acid formation using static tests with the Net Total Acid Producing Potential (NTAPP) method. The results of coring samples were processed to obtain rock lithology data for four drilling points, demonstrating one layer of low-capacity PAF material in borehole point A, one layer of low-capacity PAF material, and one layer of PAF material in borehole point B. Only one layer of PAF material was in borehole point C and borehole point D. These PAF/NAF data highlighted essential information about PAF/NAF, underscoring the significant role of our work in improving the strength and accuracy of mitigation plans and reducing acid mine drainage.

Examining determinants of loan default: An empirical analysis on credit factors in Thai savings and credit cooperatives

Savings and credit cooperatives (SACCOs) are crucial institutions in promoting financial accessibility. SACCOs provide financial loans to individuals who may not have access to traditional banking. SACCOs take their own risk to get loan defaults from the offerings because member loans are approved without checking the members’ credit background by SACCO committees. This study aims to investigate factors influencing loan defaults of savings and credit cooperatives in Thailand. Based on the savings and credits cooperative database in November 2023, the cooperative has emergency loans, regular loans, and special loans totaling 11,441 contracts. In this study, all loan contracts of this cooperative were used to analyze. The data were divided into two categories of debt classification, including (1) non-default status and (2) default status. The data were analyzed using logistics regression to select the highest accuracy model. Furthermore, the finding reveals that the highest accuracy model, at 99.78%, contains five variables, including interest rate, collateral value, remaining contract duration, outstanding debt, and installment amount. The savings and credit cooperatives institution should adjust the loan interest rates according to economic conditions. Moreover, closely monitoring members with high remaining debt would help the institution prevent loan defaults, and the institution should also create a conservative loan approval policy to reduce its loan default. AcknowledgmentsThe research for the work featured in this article is funded by the Prince of Songkla Savings and Credit Cooperatives, Limited.

Labskanīgs mašīntulkojums – ieguvums un risks

Today, translation cannot be imagined without various language technologies, including opportunities provided by machine translation. Owing to the neural machine translation, MT has become a revolution, and from 2016, it is continuing its triumphal march around the world, i. e., MT used to be one of many tools used by translators, while now it is one of the leading tools. Neural MT undoubtedly offers several advantages compared to rule-based and statistical MT. More and more systems are also adaptive and learn in real time, thus ensuring consistency and speed. The systems also provide fluent translations. Although translation fluency is one of the greatest achievements of MT, it makes it difficult to detect the errors made by MT engines during the post-editing. The article discusses the concept of a fluent translation and analyses 20 MT examples containing different errors in a fluent translation, such as grammar errors, untranslated text elements, unusable MT, wrong meaning, omissions, and hallucinations. At the same time, it should be noted that any classification of error categories depends on the selected MT system, languages, and texts and that the examples for analysis were selected in one year when translating from English to Latvian by selecting the most notable examples dictated by specific features of a translator’s job, as well as terms and confidentiality provisions.

White-Light Imaging and Image-Enhanced Endoscopy With Magnifying Endoscopy for the Optical Diagnosis of Superficial Nonampullary Duodenal Epithelial Tumors: A Systematic Review and Meta-Analysis.

Optical diagnosis of superficial nonampullary duodenal epithelial tumors using white-light imaging (WLI) and/or narrow-band imaging with magnifying endoscopy (NBI-ME) is used to guide the treatment strategy and avoid biopsy-induced fibrosis. However, the effectiveness of this approach has not been elucidated. We conducted a systematic review and meta-analysis aiming to investigate the diagnostic yield between Vienna classification category 3 (VCL C3) and categories 4 or 5 (VCL C4/C5) using biopsy, WLI, NBI-ME, and WLI + NBI-ME. A literature search identified studies on the diagnosis of superficial nonampullary duodenal epithelial tumors using biopsy, WLI, or NBI-ME. A bivariate random-effects model was utilized to analyze the summary estimates of sensitivity and specificity, as well as the area under the summary receiver operating characteristic curves for diagnosing VCL C4/C5. Ultimately, 13 studies were included in the meta-analysis. For the diagnosis of VCL C4/C5, summary estimates of sensitivity, specificity, and area under the curve were for biopsy 47% (95% confidence interval: 37-58), 86% (79-91), and 0.745; for WLI 80% (65-89), 80% (70-87), and 0.859; for NBI-ME were 72% (61-81), 76% (68-85), and 0.811; and for WLI + NBI-ME 88% (67-96), 87% (51-98), and 0.929, respectively. WLI, NBI-ME, and WLI + NBI-ME showed high values for sensitivity and area under the curve. Biopsies can be replaced by WLI, NBI-ME, and WLI + NBI-ME for the preoperative diagnosis of superficial nonampullary duodenal epithelial tumors. However, further accumulation of research findings is needed.

Does type II diabetes mellitus increase the morbidity of patients with diverticulitis?

Diverticular disease is a common condition that has numerous complications. Understanding the impact of diabetes mellitus (DM) on these complications, especially diverticulitis, is crucial for optimizing patient care. This study aimed to determine the relationship between type II DM and the complications of colonic diverticulitis. A retrospective cohort study was conducted on 158 patients complaining of diverticulitis at Asir Central Hospital, Abha, Saudi Arabia, between January 2013 and December 2023. Data on gender, age, and chronic diseases, especially DM, were collected. Data retrieved regarding diverticulitis included the involved segment, complications, Hinchey classification, and management. We classified the patients into groups A for nondiabetics and B for diabetics. We analyzed the data using descriptive statistics, chi-square tests, t tests, and analysis of variance. Diabetic patients were significantly older than their nondiabetic counterparts. Diabetic patients showed a significantly higher complication rate (62.5%) and a higher degree of Hinchey classification compared to nondiabetic patients (43.7%). Furthermore, in comparison to individuals without diabetes, they were hospitalized for a considerably extended period (8.06 ± 7.38 days vs 5.26 ± 5.90 days, respectively). In addition, surgical intervention was observed to be considerably more common in patients with diabetes (46.9%) than in those without diabetes (16.5%). The study showed that DM adversely affected patients with diverticulitis. A greater incidence of complications and a higher category of Hinchey classification were associated with DM compared to nondiabetics. Additionally, diabetics underwent more surgical interventions and had longer hospital stays. Diabetics with diverticulitis require particular care to prevent severe complications.

TC-Sniffer: A Transformer-CNN Bibranch Framework Leveraging Auxiliary VOCs for Few-Shot UBC Diagnosis via Electronic Noses.

Utilizing electronic noses (e-noses) with pattern recognition algorithms offers a promising noninvasive method for the early detection of urinary bladder cancer (UBC). However, limited clinical samples often hinder existing artificial intelligence (AI)-assisted diagnosis. This paper proposes TC-Sniffer, a novel bibranch framework for few-shot UBC diagnosis, leveraging easily obtainable UBC-related volatile organic components (VOCs) as auxiliary classification categories. These VOCs are biomarkers of UBC, helping the model learn more UBC-specific features, reducing overfitting in small sample scenarios, and reflecting the imbalanced distribution of clinical samples. TC-Sniffer employs intensity-based augmentation to address small sample size issues and focal loss to alleviate model bias due to the class imbalance caused by auxiliary VOCs. The architecture combines transformers and temporal convolutional neural networks to capture long- and short-range dependencies, achieving comprehensive representation learning. Additionally, feature-level constraints further enhance the learning of distinctive features for each class. Experimental results using e-nose data collected from a custom-designed sensor array show that TC-Sniffer significantly surpasses existing approaches, achieving a mean accuracy of 92.95% with only five UBC training samples. Moreover, the fine-grained classification results show that the model can distinguish between nonmuscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC), both of which are subtypes of UBC. The superior performance of TC-Sniffer highlights its potential for timely and accurate cancer diagnosis in challenging clinical settings.

Statin Use Not Linked to Rotator Cuff Retear After Arthroscopic Rotator Cuff Repair

PurposeThe purpose of this study was to evaluate the risk factors, including hyperlipidemia and statin use, on rotator cuff healing after arthroscopic repair. MethodsA retrospective review of prospectively collected cases who underwent arthroscopic rotator cuff repair was conducted. Total cholesterol (TC), low-density lipoprotein (LDL), triglycerides (TG) levels, and the presence or absence of statin use and type of statins used (type 1 naturally-derived statins and type 2 synthetic statins) were reviewed. Repair integrity was determined according to the Sugaya classification assessed by MRI (types 4 and 5 were considered retear). Including serum lipid levels and statin use, potential risk factors for retear were tested using multivariate logistic regression. ResultsSix hundred and twenty cases were evaluated. The mean age was 66.9 years; 348 were males, and 272 were females. The overall retear rate was 16.1%. There was no statistically significant difference in serum lipid levels among the categories of the Sugaya classification. Multivariate logistic regression analysis showed no association between serum lipid levels and odds ratio for retear: TC (OR, 1.00; p = 0.72), LDL (OR, 0.99; p = 0.44), and TG (OR, 1.00; p = 0.88). Statins did not have a statistically significant increase or decrease in odds: type 1 statin (OR, 0.3; p = 0.061) and type 2 statin (OR, 1.4; p = 0.26). Older age (OR, 1.1; p < .0001), male gender (OR, 1.8; p = 0.021), and large to massive cuff tear size (OR, 3.4; p < .0001) were significant risk factors. ConclusionNo association was found between serum lipid levels and retear after arthroscopic rotator cuff repair. Statin use was not a statistically significant factor for retears, but different trends were seen for type 1 and type 2 statins. Older age, male gender, and large to massive tears were significant risks for retears. Level of evidenceIII; Retrospective Cohort Design; Prognosis Study

IUCN&amp;apos;s Application of Cluster Analysis to Usage Classification Areas in Korean protected areas: Based on Gangwon Province

Background and objective: Protected areas in South Korea are currently designated by different ministries for similar purposes, causing overlapping management and conflicts of interest. Moreover, protected areas after designation have been lacking sufficient management. Thus, it is necessary to classify the types of protected areas in South Korea considering variables related to designation, management, and the natural environment, and to verify the applicability of IUCN categories.Methods: We selected variables that are easy to apply to spatial data, easy to interpret, and can solve problems within protected areas based on previous studies and literature review. The optimal number of clusters was derived by conducting hierarchical and non-hierarchical cluster analysis on 174 protected areas using the selected variables. The applicability of IUCN categories was verified based on the results.Results: Cluster centroids represent the characteristics of the variables in each cluster, which were applied to IUCN categories for classification. Based on this standard, we formed clusters of similar protected areas through cluster analysis and classified them according to IUCN categories. The clusters were compared with the protected areas classified by the existing IUCN categories and were further subdivided according to the characteristics of the use districts.Conclusion: This study classified protected areas located in Gangwon Province as well as overlapping protected areas using criteria such as the natural environment, activity restrictions, protected target and area as set in the research method, and derived the characteristics of each cluster. This study has significance in that it verified the protected areas classified as IUCN categories and suggested appropriate management measures for unclear overlapping protected areas.

Conceptualizations of ‘race’: surveys of Polish academics on the race concept

AbstractRecent studies suggest that race is no longer viewed as a biological category by most anthropologists in the United States, but less empirical work has been carried out in other countries. In this study, we engaged the Polish academic community in anthropology (biological and cultural) and biology by conducting surveys to assess how its members approach and conceptualize race in these disciplines. We surveyed participants (a total of 270 respondents) on their views on the existence of races (i.e. whether humans may be subdivided into biological races), whether race is a concept that is needed in science, what the term ‘race’ should symbolize, and whether the respondents were familiar with the term ‘social race’. The results demonstrate that although the view on the reality of races is still generally shared by the majority of the studied academic community, an awareness of the non‐existence of races in our species has emerged among a group of biological anthropologists. Both biologists and cultural anthropologists in Poland associated race mostly with a category of biological classification, while for biological anthropologists, race primarily symbolized a relic of the past. The article emphasizes the importance of becoming acquainted with the debate over race in academic discourse as well as the role of education in shaping attitudes towards race.

Multimodal Representation Learning via Graph Isomorphism Network for Toxicity Multitask Learning.

Toxicity is paramount for comprehending compound properties, particularly in the early stages of drug design. Due to the diversity and complexity of toxic effects, it became a challenge to compute compound toxicity tasks. To address this issue, we propose a multimodal representation learning model, termed multimodal graph isomorphism network (MMGIN), to address this challenge for compound toxicity multitask learning. Based on fingerprints and molecular graphs of compounds, our MMGIN model incorporates a multimodal representation learning model to acquire a comprehensive compound representation. This model adopts a two-channel structure to independently learn fingerprint representation and molecular graph representation. Subsequently, two feedforward neural networks utilize the learned multimodal compound representation to perform multitask learning, encompassing compound toxicity classification and multiple compound category classification simultaneously. To test the effectiveness of our model, we constructed a novel data set, termed the compound toxicity multitask learning (CTMTL) data set, derived from the TOXRIC data set. We compare our MMGIN model with other representative machine learning and deep learning models on the CTMTL and Tox21 data sets. The experimental results demonstrate significant advancements achieved by our MMGIN model. Furthermore, the ablation study underscores the effectiveness of the introduced fingerprints, molecular graphs, the multimodal representation learning model, and the multitask learning model, showcasing the model's superior predictive capability and robustness.