bluuebunny/crossref_metadata_2025 huggingface dataset:
Hubungan Kualitas Kehidupan Kerja Perawat dengan Kepuasan Pasien di RSUD Prof. Dr. H. Aloei Saboe
Authors: Hiola Dewi Suryaningsi , Dulahu Wirda Y , Gobel Hairunnisa
Published: July 2023

Kualitas kehidupan kerja merupakan kondisi yang ditunjukkan sebagai pemikiran dan tindakan pegawai tentang kondisi fisik dan psikologis terhadap pekerjaan yang dilakukan, untuk mencapai rasa aman, puas dan mampu berkembang sebagaimana layaknya manusia. Metode penelitian yang digunakan adalah kuantitatif. Desain penelitian analitik korelasional dengan pendekatan cross sectional. Dengan populasi 30 pasien dan 30 perawat. Penelitian ini menggunakan teknik pengambilan sampel total sampling untuk perawat dan accidental sampling untuk pasien. Hasil penelitian didapatkan kualitas kehidupan kerja perawat dengan kepuasan pasien sebanyak 12 responden (40,0%) memiliki kualitas kehidupan kerja perawat yang baik dengan tingkat kepuasan pasien tinggi sebanyak 9 responden (30.0%), tingkat kepuasan cukup sebanyak 2 responden (6,7%) dan kepuasan kurang sebanyak 1 responden (3,3%). Sedangkan kualitas kehidupan kerja perawat cukup sebanyak 18 responden (60.0%) dengan tingkat kepuasan pasien tinggi sebanyak 5 responden (16,7%), dan kepuasan pasien cukup sebanyak 13 responden (43,3%). Hasil dari uji Fisher test diperoleh p value 0,399 (P value < 0,05). Disimpulkan bahwa tidak terdapat hubungan kualitas kehidupan kerja perawat dengan kepuasan pasien RSUD Prof. Dr. H. Aloei Saboe. Keterbatasan penelitian ini adalah jumlah responden yang masih belum bisa menggambarkan keadaan sebenarnya. Oleh karena itu, saran untuk penelitian selanjutnya harusnya bisa mengambil jumlah sampel yang lebih banyak dan menilai sikap serta perilaku responden saat memberikan informasi melalui kuesioner.
Tingkat Pendidikan Ibu dan Keberhasilan ASI Eksklusif di Perkotaan Indonesia: Analisis Data IFLS 5
Authors: Suja Monica Dara Delia , Puspitaningrum Elisa Murti , Bata Verayanti Albertina
Published: July 2023

Air Susu Ibu (ASI) merupakan nutrisi yang paling baik bagi bayi hingga berusia 6 bulan. Manfaat ASI sudah banyak dibuktikan secara ilmiah. Faktor dari ibu yang turut berperan dalam pemberian ASI adalah karakteristik ibu seperti usia, pendidikan, pekerjaan, paritas dan tempat tinggal. Pendidikan ibu memiliki peran yang sangat penting dalam keberhasilan ASI eksklusif. Penelitian ini merupakan penelitian cross-sectional menggunakan data sekunder dari Indonesian Family Life Survey (IFLS) 5. Survei tersebut digunakan sebagai informasi yang berguna dalam mengamati perilaku atau keadaan saat waktu tertentu yang memerlukan intervensi pemerintah contohnya masalah kesehatan. Kriteria inklusi penelitian adalah ibu berusia 15-49 tahun, memiliki anak termuda berusia 6-24 bulan dan tinggal di perkotaan. Total responden dalam penelitian ini adalah 683 responden. Hasil penelitian ini hanya 24,74% ibu di perkotaan yang memberikan ASI eksklusif. Peluang untuk ASI eksklusif lebih besar 1,47 kali pada responden dengan tingkat pendidikan tinggi dibandingkan dengan pendidikan rendah. Tingkat pendidikan ibu memiliki hubungan yang signifikan dengan keberhasilan pemberian ASI eksklusif di perkotaan Indonesia. Pendidikan ibu memiliki peranan yang penting dalam mendukung keberhasilan pemberian ASI eksklusif di perkotaan Indonesia.
Research on Forecasting Sales of Products Based on Spatiotemporal Graph Neural Network
Authors: Zuo Shao-yan , Huang Hai-fu , Gao Lin , Li Hai-ying , Wang Hai-bo , Zhang Chen , Wu Zhang-sheng
Published: July 2023

Intelligent marketing and recommendation are a core business of commercial companies, and accurate prediction of sales is the premise and foundation for greater efficiency of smart marketing and recommendation. In order to predict product sales, deep neural network (DNN), convolutional neural network (CNN), time series analysis and other methods have been put forward, but most of which only focus on the temporal or spatial characteristics of data. According to modeling and analyzing sales of products, they are closely related to the spatial location and time of the corresponding merchants. The goal is to predict the sales of products accurately at a given time and place, we advance a hybrid model of CNN-LSTM to forecast sales. Firstly, a large-scale knowledge graph system based on merchants is constructed, which describes the sales data and the relevant interaction scenarios of the corresponding business, merchants and users through the data model of a graph, and add the spatial and data characteristics of the business data on the graph model to describe the temporal and spatial characteristics of the merchants. Based on the constructed business knowledge graph, graph convolutional neural network (GCN) is used to aggregate information and obtain spatial features. Correspondingly, long short-term memory (LSTM) is used to extract time features. Researchers combine the two characteristics to make the sales forecast. In this study, neural network and GCN-LSTM algorithm are respectively used to carry out experiments on two kinds of product regulations. The result shows that the sales predicted by hybrid model of GCN-LSTM is almost as equal as the actual sales. The average accuracy of the proposed model is 89%.
Undergraduates’ Knowledge Attitude and Behavior (KAB) Towards the Disclosure of Personal Data Online in China
Authors: Li Xiaoyu , An Qin , Hong Wilson Cheong Hin , Zhang Yunfeng , Kolletar-Zhu Kimberly , Xu Xiaoshu
Published: July 2023

In the midst of the COVID-19 pandemic, the employment and education sectors have shifted significantly toward online platforms. However, the increased reliance on these digital spaces has raised concerns about personal security information. Scholars have taken note of this issue and have explored its implications, with some employing the extended knowledge, attitude, and behavior (KAB) model to investigate the moderating effects of societal education level on the relationship between knowledge and attitude. Hong et al. [1] conducted a study to examine undergraduates’ KAB regarding personal data sharing in Chinese higher education institutions during the pandemic. Using a questionnaire, the study recruited 156 participants from three universities in West and East China. Using SPSS 23.0, data analysis revealed a widespread lack of awareness, a positive attitude, and proper behavior among college students regarding online personal information leakage during the pandemic. Notably, disparities were observed in KAB among students of different grades, majors, and genders. Students in their sophomore, junior, and senior years were found to be more concerned than freshmen about the availability of their personal information online; what’s more, science majors were more concerned than students of other majors. There appear to be significant gender differences in personal information sharing, ie., males are more concerned about the security of personal information online than females. Through this study, we aim to emphasize that college students’ awareness of personal information protection needs to be improved and suggest that university administrators and policymakers increase information security training. The findings of this study contribute to the theoretical and practical efforts to improve information security in higher education. Future studies should broaden the survey sample and examine the primary factors that influence college students’ KAB of personal information security to ensure the generalization of findings.
The History and Current Situation of China’s Undergraduate Major Evaluation and the Prospect of Intelligent Education Evaluation Based on Big Data Technology
Authors: Gu Shiqian , Li Wei , Liu Xiaoli
Published: July 2023

The accreditation and evaluation of undergraduate majors is one of the most important elements in the monitoring of China’s higher education quality. with the combination of quantitative and qualitative research methods, this paper illustrates the stages of development, problems of undergraduate majors operation in China, and proposes an evaluation framework for undergraduate majors from the fourth paradigm perspective. The development of undergraduate major evaluation in China are divided into four stages: the budding period (1985–1998), the rising period (1998–2009), and the booming period (2010–2022). The forms of evaluation are divided into major accreditation, major ranking and major assessment. Major evaluation mode includes independent evaluation mode, comprehensive evaluation mode and appraisal mode. Continuous collection of sample data, customized indicators, multiple fusion calculation analysis, visual feedback are the typical features of big- data-based intelligent education evaluation.
Research on the Evaluation of Economic Responsibility Audit of State-Owned Enterprise Leaders from the Perspective of Green and Low Carbon
Authors: Liu Jing , Ma Li , Wang Cuilin
Published: July 2023

With the implementation of the “carbon peaking and carbon neutrality” target strategy, green, low-carbon development has become the main way of economic development, and the low-carbon economic operation and healthy development of state-owned enterprises not only play a leading role in the operation of the national economy as a whole, but also are economic entities that bear greater pressure for green and low-carbon development. This paper focuses on the construction of an evaluation system for the economic responsibility audit of the leaders of state-owned enterprises from a green and low-carbon perspective, using the hierarchical analysis method to determine the weights of each evaluation index, and selecting three commercial state-owned enterprises of Gansu province for verification. The evaluation system is feasible and contributes to the balanced development between the economic development of state-owned enterprises and green and low-carbon.
Research on the Influence of Agricultural Products Brand Story on Consumers’ Purchase Intention Under the Background of Social New Retail
Authors: Wei Jia
Published: July 2023

Under the background of the trend of social new retail, based on narrative transmission theory, this study collects 208 samples by questionnaire, integrating social media interaction and consumers’ purchase intention, and analyzes the conditional process of brand stories affecting how to affect consumers’ purchase intention by hierarchical regression method. The result shows that brand story has a positive impact on consumers’ purchase intention, and this process is realized through the intermediary of narrative transmission. Social media interaction negatively regulates the relationship between brand story and consumers’ purchase intentions. Finally, according to the research conclusion, it is suggested that enterprises and marketers pay attention to the role of brand story, make full use of various social media platforms, tell excellent brand stories, increase emotional resonance, and promote consumers’ purchase intention.
Advantages and Strategies of Ideological Education in University Physics Course
Authors: Zou Ailing , Lv Huanlin , Zhao Lulu , Wang Lin , Shi Ying
Published: July 2023

Curriculum Ideology and politics plays an important role in promoting the development of higher education and training talents. This paper reported the unique advantages of the ideological education in university physics curriculum. Based on the practice of teaching physics, some strategies for the development of the concealed ideological education are proposed. Taking physics learning as the entry point to carry out ideological and political education, we can cultivate student’s moral cultivation and patriotism. Therefore, it is not only beneficial to improve the interesting and ideological nature of the class, but also to enrich student’s political spirit and build a complete and noble personality.
A Corpus and Computer-Assisted Translation-Based Study on English Translation of Intangible Cultural Heritage Terms
Authors: Lv Menglei , Jiang Liping
Published: July 2023

The translation of intangible cultural heritage (ICH) texts presents significant challenges due to their rich cultural connotations and unique regional characteristics. To address this, the author of this study utilized Corpus and Computer-assisted Translation (CCAT) to assist in the English translation of ICH during the creation of a parallel corpus. Three patterns of CCAT-based English translation of ICH were proposed: machine translation, human-machine interactive translation (combining human and machine translation), and human translation. The implementation of these patterns can improve the efficiency and quality of ICH translation, promote terminology standardization, and facilitate the exportation and transmission of ICH.
Influence of the Targeted Poverty Alleviation Policy on the Livelihood of Poor Farmers in Western China
Authors: Ma Jin’e , Hou Qingfeng
Published: July 2023

This paper surveyed 589 farmer families from three different areas in Western China to analyze the influence of the targeted poverty alleviation policy on livelihoods of low-income families. We can conclude that the policy has led to an obvious increase in the total income of low-income families, as well as positive effects on those poor families who rely on agriculture and government subsidies. Yet the policy showed little impact on low-income families that rely on non-farm income. The program has also encouraged farm families to increase their various sources of capital stock. Hence, we suggest that the Chinese government institute policies that provide more balanced growth and support a more sustainable development path for the rural economy and society. This might involve increasing human capital, increasing rural employment, and promoting a more efficient allocation of rural resources.
The Influence of Tax Incentive Policy on the Development of Alternative Fuel Vehicle Enterprises—Taking Chinese Companies as an Example
Authors: Ma Jun , Mayburov Igor
Published: July 2023

In the process of promoting the development of China’s alternative fuel vehicle industry, the government has issued preferential tax policies that are beneficial to its industrial development, and actively guided its industrialization process. The purpose of this paper is to research and analyze the preferential tax policy of the alternative fuel vehicle industry, optimize the allocation of resources, and promote the rapid realization of energy saving, emission reduction and environmental protection. This paper adopts the case analysis method, selects X, a listed company of alternative fuel vehicles in China, as a case, and analyzes the company’s development in the past ten years, including its operation, financial situation, and tax burden. The research hypothesis is that preferential tax policies have a positive effect on the development of Alternative Fuel Vehicle enterprises. Through financial indicators and tax burden, the impact of tax preferential policies on X is analyzed, and the current problems are pointed out. Research has confirmed that preferential tax policies have a certain role in promoting the profitability of enterprises, but enterprises are too dependent on policies, and there are problems such as poor policy effects and weak pertinence.
Research on Storage Life Prediction of Fuze Based on Accelerated Life Tests
Authors: Guo Junling , Zhang Hongtao , Ma Yuepeng , Li Donghua , Yu Weiguo , Zhao Dongzhi
Published: July 2023

Fuze is an intelligent system for detection, identification and determination of targets. It is a core component of ammunition equipment and its life prediction is of great practical significance to improvement of the reliability of ammunition equipment. Based on the analysis of failure modes and failure mechanisms of fuze in natural storage environment, a storage reliability model was established for fuze. The acceleration model and the accelerated life test plan were determined. Tests were conducted in an isothermal and humidity-constant incubator to test and testing data of a fuze was processed. The results demonstrated that the storage life of this fuze was 10.05 years (prediction by Weibull distribution and maximum likelihood estimation). The proposed method improves cost effectiveness of fuze logistics and provides an important way to predict fuze life.
Government Thoughts on Environmental Cost Control
Authors: Fa Lina
Published: July 2023

The Japanese government arbitrarily releases nuclear pollution into the Pacific Ocean. It reduces the time and economic costs of enterprises, but also causes the loss of environmental costs. From the perspective of international law, this is even more illegal. This also reflects that Japanese enterprises and the government do not pay enough attention to environmental cost control. They fail to realize that environmental damage will inevitably lead to enterprises and governments needing to spend more costs to make up for it, which is not conducive to ecological environment protection and human long-term development. Therefore, from the long-term interests, the government and enterprises should bear the responsibility of controlling environmental costs. The government should play the role of macrocontrol and replace the end of governance with the source of governance. It plays a role in improving legal norms, strengthening supervision and guidance, establishing and improving the emission trading system, and increasing environmental information disclosure.
Research on Innovative Strategies for Online Education Management in Universities in the Era of Big Data
Authors: Huang Wen , Guo Yonghui
Published: July 2023

In the era of the Internet, the continuous development of information technology represented by 5G, big data, artificial intelligence, etc. has driven the transformation of university education from traditional offline education mode to digital, networked, and intelligent online education mode. Online education in universities has become an important way to promote educational reform and innovation. However, in the process of Digital transformation of education, there are still many problems such as solidification of teaching management mode, insufficient integration of online resources, lack of intelligent education practice platform, etc., so it is urgent to improve the online education management ability of colleges and universities. In the future, university education management needs to use big data tools to improve management level, explore educational resources, unify teaching and learning, and achieve the transformation and upgrading of online education management mode.
A Higher Whole-Body Extracellular to Intracellular Water Ratio Is Associated with Increased Odds of Cognitive Impairment in Hemodialysis Patients
Authors: Zhou Chaomin , Zhan Lin , He Pinghong , Yuan Jing , Zha Yan
Published: July 2023

Background:

Cognitive impairment (CI) is highly prevalent in patients undergoing hemodialysis. Whether fluid overload with malnutrition as assessed by the ratio of extracellular water to intracellular water (ECW/ICW) is associated with CI in patients on maintenance hemodialysis (MHD) has yet to be studied.

Objective:

This study aimed to investigate the association between ECW/ICW and CI in patients with MHD.

Methods:

We conducted a multicenter, cross-sectional study that enrolled 3,025 adult patients with MHD. Cognitive function was assessed through the Mini-Mental State Examination. The ECW/ICW was derived from a portable body composition monitor and analyzed by quartiles.

Results:

23.04% of participants had CI in our study. The prevalence of CI tended to increase as the quartiles of the ECW/ICW ratio increased. Unadjusted analysis showed that participants in quartile 4 were 3.02 times more likely to have a CI compared to those in quartile 1. After adjusting for age, sex, education, smoking status, body mass index, dialysis vintage, history of hypertension, and history of stroke, the adjusted OR (95% CI) for the highest quartile of ECW/ICW ratio was 1.36 (1.01, 1.83) for CI compared with the lowest quartile. Interestingly, the association between ECW/ICW ratio and CI persisted across all subgroups stratified by age, gender, history of diabetes, and stroke. (p for interaction > 0.05 for all).

Conclusion:

An increased ECW/ICW ratio is associated with higher odds of cognitive impairment in patients undergoing hemodialysis.
Neuropathologic Changes of Alzheimer’s Disease and Related Dementias: Relevance to Future Prevention
Authors: White Lon R. , Corrada Maria M. , Kawas Claudia H. , Cholerton Brenna A. , Edland Steve E. , Flanagan Margaret E. , Montine Thomas J.
Published: July 2023

Background:

Decedents with late-life dementia are often found at autopsy to have vascular pathology, cortical Lewy bodies, hippocampal sclerosis, and/or TDP-43 encephalopathy alone or with concurrent Alzheimer’s disease (AD) lesions. Nonetheless, it is commonly believed that AD neuropathologic changes (NC) are the dominant or exclusive drivers of late-life dementia.

Objective:

Assess associations of end-of-life cognitive impairment with any one or any combination of five distinct NC. Assess impairment prevalence among subjects having natural resistance to each type of NC.

Methods:

Brains from 1,040 autopsied participants of the Honolulu-Asia Study, the Nun Study, and the 90 + Study were examined for NC of AD, Lewy body dementia, microvascular brain injury, hippocampal sclerosis, and limbic predominate TDP-43 encephalopathy. Associations with impairment were assessed for each NC and for NC polymorbidity (variable combinations of 2-5 concurrent NC).

Results:

Among 387 autopsied decedents with severe cognitive impairment, 20.4% had only AD lesions (ADNC), 25.3% had ADNC plus 1 other NC, 11.1% had ADNC plus 2 or more other NC, 28.7% had no ADNC but 1-4 other NC, and 14.5% had no/negligible NC. Combinations of any two, three, or four NC were highly frequent among the impaired. Natural resistance to ADNC or any other single NC had a modest impact on overall cohort impairment levels.

Conclusion:

Polymorbidity involving 1-5 types of concurrent NC is a dominant neuropathologic feature of AD and related dementias. This represents a daunting challenge to future prevention and could explain failures of prior preventive intervention trials and of efforts to identify risk factors.
Cross feature enhanced graph convolutional network for aspect-based sentiment analysis
Authors: Zhang Longji , zhao Hui
Published: July 2023

Traditional graph convolutional neural networks (GCN) utilizing linear feature combination methods have limited capacity to capture the interaction between complex features. While current research has extensively investigated various syntactic dependency tree structures, the optimization of GCN algorithms has often been overlooked, leading to suboptimal efficiency in practical applications. To address this issue, this paper proposes a cross-feature method that utilizes feature vector multiplication to construct non-linear combinations of GCN features and enhance the model’s capability to extract complex feature correlations. Experimental results demonstrate the superiority of the proposed method, with our models outperforming state-of-the-art methods and achieving significant improvements on three standard benchmark datasets. These results suggest that the cross-feature method can effectively extract potential connections between features, highlighting its potential for improving the performance of GCN-based models in real-world applications.
Picture fuzzy prospect theory-MAIRCA method considering optimal reference point for emergency decision-making
Authors: Chen Liuxin , Wang Yutai , Liu Jinyuan
Published: July 2023

In the emergency decision-making process, decision-makers usually cannot give rational evaluations, and existing decision-making methods do not adequately consider the risk attitude of decision-makers either. To solve these problems, a combined method based on the prospect theory and the multi-attributive ideal-real comparative analysis (MAIRCA) method is put forward in the picture fuzzy environment. Firstly, the optimal aggregation (OA) model is proposed to obtain the ideal evaluations with the least disagreement among decision-makers. Regarding the evaluations as reference systems, the OA-based prospect theory is put forward, which could calculate the prospect matrix more reasonably. Secondly, considering the prospect matrix and alternative preference, the improved MAIRCA method is proposed, which overcomes the defects of theory and has the better ranking ability. Then, the OA-based prospect theory-MAIRCA method is further put forward to effectively complete the decision-making process with risk attitudes. Finally, an illustrative example of earthquake emergency assessment and a series of comparative experiments are presented. The analyses of results show that the proposed method has great guiding significance in the field of emergency decision-making management.
A generalized approach to compute neutrosophic k-factor analysis of variance
Authors: Ullah Sami , Kashif Muhammad , Aslam Muhammad , Haider Gulfam , AlAita Abdulrahman , Saleem Muhammad
Published: July 2023

The application of classical statistical methods is not feasible given the presence of imprecise, fuzzy, uncertain, or undetermined observations in the underlying dataset. This is due to the existence of uncertainties pervading every aspect of real-life situations, which cannot always be accurately addressed by classical statistical approaches. In order to tackle this problem, a new methodology known as neutrosophic analysis of variance (NANOVA) has been developed as an extension of classical approaches to analyze datasets with uncertainty. The proposed approach can be applied regardless of the number of factors and replications. Moreover, NANOVA introduces a novel matrix-based approach to derive the F_N-test in an uncertain environment. To assess the effectiveness of NANOVA, various real datasets have been employed, and research findings on single- and two-factor NANOVAs with measures of indeterminacy have been presented. According to our comparisons, NANOVA provides a more informative, efficient, flexible, and reliable approach to deal with uncertainties than classical statistical methods. Therefore, there is a need to go beyond conventional statistical techniques and adopt advanced methodologies that can effectively handle uncertainties.
HPRE: Leveraging hierarchy-aware paired relation vectors for knowledge graph embedding
Authors: Zhang Dong , Liu Jinzhu , Liu Duo , Li Guanyu
Published: July 2023

Knowledge graphs exhibit a typical hierarchical structure and find extensive applications in various artificial intelligence domains. However, large-scale knowledge graphs need to be completed, which limits the performance of knowledge graphs in downstream tasks. Knowledge graph embedding methods have emerged as a primary solution to enhance knowledge graph completeness. These methods aim to represent entities and relations as low-dimensional vectors, focusing on handling relation patterns and multi-relation types. Researchers need to pay more attention to the crucial feature of hierarchical relationships in real-world knowledge graphs. We propose a novel knowledge graph embedding model called Hierarchy-Aware Paired Relation Vectors Knowledge Graph Embedding (HPRE) to bridge this gap. By leveraging the power of 2D coordinates, HPRE adeptly model relation patterns, multi-relation types, and hierarchical features in the knowledge graph. Specifically, HPRE employs paired relation vectors to capture the distinct characteristics of head and tail entities, facilitating a better fit for relational patterns and multi-relation scenarios. Additionally, HPRE employs angular coordinates to differentiate entities at various levels of the hierarchy, effectively representing the hierarchical nature of the knowledge graph. The experimental results show that the HPRE model can effectively learn the hierarchical features of the knowledge graph and achieve state-of-the-art experimental results on multiple real-world datasets for the link prediction task.
Robust hyperparameter tuned deep Elman neural network for the diagnosis of osteosarcoma on histology images
Authors: Prabakaran S. , Mary Praveena S.
Published: July 2023

Osteosarcomas are a type of bone tumour that can develop anywhere in the bone but most typically do so around the metaphyseal growth plates at the ends of long bones. Death rates can be lowered by early detection. Manual osteosarcoma identification can be difficult and requires specialised knowledge. With the aid of contemporary technology, medical photographs may now be automatically analysed and categorised, enabling quicker and more effective data processing. This paper proposes a novel hyperparameter-tuned deep learning (DL) approach for predicting osteosarcoma on histology images with effective feature selection mechanism which aims to improve the prediction accuracy of the classification system for bone tumor detection. The proposed system mainly consists of ‘6’ phases: data collection, preprocessing, segmentation, feature extraction, feature selection, and classification. Firstly, the dataset of histology images is gathered from openly available sources. Then Median Filtering (MEF) is utilized as the preprocessing step that enhances the quality of the input images for accurate prediction by eliminating unwanted information from them. Afterwards, the pre-processed image was segmented using Harmonic Mean-based Otsu Thresholding (HMOTH) approach to obtain the tumor-affected regions from the pre-processed data. Then the features from the segmented tumor portions are extracted using the Self-Attention Mechanism-based MobileNet (SAMMNet) model. A Van der Corput sequence and Adaptive Inertia Weight included Reptile Search Optimization Algorithm (VARSOA) is used to select the more relevant features from the extracted features. Finally, a Hyperparameter-Tuned Deep Elman Neural Network (HTDENN) is utilized to diagnose and classify osteosarcoma, in which the hyperparameters of the neural network are obtained optimally using the VARSOA. The proposed HTDENN attains the higher accuracy of 0.9531 for the maximum of 200 epochs, whereas the existing DENN, MLP, RF, and SVM attains the accuracies of 0.9492, 0.9427, 0.9413, and 0.9387. Likewise, the proposed model attains the better results for precision (0.9511), f-measure (0.9423), sensitivity (0.9345) and specificity (0.9711) than the existing approaches for the maximum of 200 epochs. Simulation outcomes proved that the proposed model outperforms existing research frameworks for osteosarcoma prediction and classification.
Clinical value of CT imaging features in the diagnosis of acute and chronic pancreatitis: A retrospective study
Authors: Bing Wanchun , Zhang Xiaoxiao , Wang Dawei , Gu Xiaoyan
Published: July 2023

BACKGROUND: Recurrent acute pancreatitis is a common acute abdominal disease in surgery. OBJECTIVE: To evaluate the radiographic features of pancreatic computed tomography (CT) imaging in the diagnosis of acute and chronic pancreatitis. METHODS: 48 pancreatitis patients who met the criteria were selected in this retrospective study from 2010 to 2019. Each diagnosis was evaluated as functional abdominal pain, recurrent acute pancreatitis, or chronic pancreatitis. All clinical data were collected from the patient’s medical records. 54 radiological features were extracted from each region of interest in outline the pancreas and divided into five categories: first order statistics, the gray level co-occurrence matrix (GLCM), the gray level run-length matrix (GLRLM), the neighborhood gray level difference matrix (NGTDM), and morphological features by the MATLAB program. RESULTS: Of the 48 patients, 16 had functional abdominal pain (33.3%), 18 had recurrent acute pancreatitis (37.5%), and 14 had chronic pancreatitis (29.2%). In the univariate analysis, nine radiological features, eight GLCM features and one NGTDM feature were significantly different between groups. Nine radiological characteristics had important reference values with AUC values ranging from 0.73–0.91. CONCLUSION: Nine radiographic features of CT imaging demonstrate good evaluation efficiency in the diagnosis of pancreatitis and can distinguish patients with functional abdominal pain, recurrent acute pancreatitis, and chronic pancreatitis.
Prediction of fluid responsiveness for patients in shock using a ventilator disconnection test combined with the pulse contour-derived cardiac index
Authors: Yu Zhan-Biao , Cui Na , Zhang Nan , Su Dan , Wu Jia-Qian , Xi Xiu-Ming
Published: July 2023

BACKGROUND: Finding a simple and reliable method to predict and assess fluid responsiveness has long been of clinical interest. OBJECTIVE: To investigate the predictive value of a ventilator disconnection (DV) test combined with the pulse contour-derived cardiac output (PiCCO) index on fluid responsiveness for patients in shock. METHODS: Thirty-two patients were chosen for the study. Patients who were in shock, received mechanical ventilation, and met the inclusion criteria were selected. Patients were divided into a fluid-responsive group (14 patients) and fluid-unresponsive group (18 patients) based on whether the increase in cardiac index (Δ CI) was > 10% or not, respectively, following the fluid challenge test. Changes in heart rate, pulse oximeter-measured oxygen saturation, mean arterial pressure (MAP), and CI before and after passive leg raising (PLR), DV, and fluid challenge tests were observed. We used Pearson’s correlation coefficient to analyze an increase in the MAP (Δ MAP) and Δ CI before and after the PLR, DV, and fluid challenge tests; the sensitivity and specificity of the Δ MAP and Δ CI in the PLR and DV tests for predicting fluid response were also analyzed by plotting the receiver operating characteristic (ROC) curves. RESULTS: CI results in the PLR and DV tests, as well as the fluid challenge test, were significantly higher in the fluid-responsive group compared with before the test (P< 0.05). The Δ CI before and after the PLR, DV, and fluid challenge tests were positively correlated among patients in the fluid-responsive group. The area under the ROC curve for the post-PLR test CI and the post-DV CI for predicting fluid responsiveness was 0.869 (95% confidence interval (CI) [0.735–1.000, P= 0.000]) and 0.937 (95% CI [0.829–1.000, P= 0.000]), respectively, in patients in the fluid-responsive group. The sensitivity and specificity of the post-DV CI for predicting fluid responsiveness in all patients was 100.0% and 88.9%, respectively, using a 5% increase as the cut-off value. CONCLUSION: Application of DV, combined with PiCCO, has a high predictive value for fluid responsiveness among patients in shock.
The development and application of pediatric complicated appendicitis prediction model
Authors: Tang Hui-Wen , Wang Zha-Gen , Huang Jia-Hu , Zhang Guo-Qin , Xu Yun-Feng , Zheng Lu-Lu , Li Ting-Jun
Published: July 2023

BACKGROUND: Acute appendicitis in children refers to the acute inflammation of the appendix, which accounts for 20% ∼ 30% of cases of acute abdomen in pediatric surgery. OBJECTIVE: This study aimed to establish a decision tree model of complicated appendicitis in children using appendiceal ultrasound combined with an inflammatory index and evaluated its clinical efficacy in pediatric patients. METHODS: A total of 395 children admitted to the Emergency Department of the Shanghai Children’s Hospital from January 2018 to December 2021 and diagnosed with appendicitis by postoperative pathology were retrospectively analyzed. According to the postoperative pathology, the children were divided into a complicated and non-complicated appendicitis group, respectively. Routine laboratory inflammatory indicators, including white blood cell count, N(%), neutrophil (Neu) count, Neu/lymphocyte ratio (NLR), C-reactive protein (CRP), and procalcitonin were collected from the two groups. Collecting data on ultrasound examination of the appendix includes whether the appendix diameter is thickened, whether the echogenicity of the mesenteric rim surrounding the appendix is enhanced, whether there is rich blood supply in the appendix, and whether there are fecaliths in the appendix lumen. The risk factors for complicated appendicitis were screened out by univariate and multivariate logistic regression analyses, the binary logistic regression prediction and decision tree models were established, respectively, and the receiver operating characteristic (ROC) curve was used to verify the accuracy of the two prediction models. RESULTS: Binary logistic regression analysis showed that CRP, NLR, the presence of an appendicolith, and peripheral retina echo enhancement were independent risk factors for complicated appendicitis in children (P< 0.05). The decision tree model had an overall accuracy of 79%, an area under the ROC curve (AUC) of 0.809 (95% confidence interval [CI] 0.780–0.865), and sensitivity and specificity of 71.3% and 77.7%, respectively. The logistic regression model had an overall accuracy of 74.9%, an AUC value of 0.823 (95% CI, 0.765–0.853), a sensitivity value of 80.3%, and a specificity of 71.8%. CONCLUSION: This predictive model, based on ultrasound of the appendix combined with inflammatory markers, provides a useful method to assist pediatric emergency physicians in diagnosing childhood appendicitis. The decision tree model reflected the interaction of various indexes, and the model was simple, intuitive, and effective.
Why Should Urbanites be Earth/Geosciences Literate?
Authors: Bohle Martin
Published: July 2023

Urbanites, i.e., people living in urban environments, should be Earth/Geosciences literate. The urban realm is a social-ecological system on a planetary scale. Its complex-adaptive dynamics couple human practices and the geosphere (e.g., through buildings, mines, shipping), causing massive fluxes (e.g., of energy, water, materials), implies extensive civil-engineering works (e.g., housing, transport, infrastructure). Earth/Geosciences expertise combined with engineering expertise makes the coupling happen. Hence, designing, building, operating, and governing the urban realm requires professionals using Earth/Geosciences expertise. Urban environments emphasise socio-economic interactions of people sheltered from everyday geosphere phenomena (e.g., weather) and many disasters (e.g., floods, storms, heatwaves). However, most people have little insight into how much urban lifestyles depend on geosphere functions. That ignorance is a systemic risk for modern societies, which Earth/Geosciences professionals should mitigate, and meteorology gives an example of ‘how’. Modern meteorologists go public daily, combing weather forecasts with information on meteorological phenomena and impacts on economic and social activities. They show how weather forecasts determine people's work and life, demonstrating the wealth of geoscientific information and professional practices. The yet-to-answer question: How to do alike?
PENGARUH STRUKTUR MODAL DAN LIKUIDITAS TERHADAP PROFITABILITAS PERUSAHAAN OTOMOTIF DAN KOMPONEN TERDAFTAR BEI PERIODE 2019-2022
Authors: Noviadry Nur Tamtama  , Riantisari Rahmawati
Published: July 2023

Growth in the quantity of motorized transport continues to increase every year. These conditions will certainly have an impact on the profitability of automotive and component companies. There are many studies studying the factors that impact profitability. This study focuses on capital structure and liquidity factors. This variable was chosen because it found various research results. The population of this study are automotive and component companies listed on the IDX. The research sample is a company that includes annual financial reports for the 2019-2022 period, namely 9 companies. The data collection method is through observation of financial data analysis which is then processed to see the results of DER, CR and ROA calculations. Analysis to test the correctness of the data using the normality test, multicollinearity test, heteroscedasticity test and autocorrelation test. Testing the truth of the hypothesis using multiple regression test. The results of the study prove that capital structure has an effect on profitability. Liquidity has no impact on company profitability. Through the results of the research and discussion it is concluded that the capital structure has an impact on the profitability of the institution or institutions. Liquidity has no effect on institutional profitability. Through the results of a review of the suggestions provided, the institution should be more careful about the composition of the components of the capital structure.

Key Words: Capital Structure, Liquidity, Profitability, Automotive and Components, IDX
PERAN PROFITABILITAS SEBAGAI VARIABEL PEMODERASI PENGARUH KEBIJAKAN DIVIDEN DAN KEBIJAKAN HUTANG TERHADAP NILAI PERUSAHAAN
Authors: Firdarini Khoirunnisa Cahya , Zulkifli  , Tjahjono Achmad
Published: July 2023

This study aims to examine the role of profitability as a moderating variable of dividend policy and debt policy on company value in Basic Industrial and Chemical sector companies listed on the Indonesia Stock Exchange during the period 2016 to 2020. The research method used in this study is a quantitative method based on financial statement data of companies that are the study population.
The total sample in this study was 100 firm years. The sampling method using purposive sampling techniques and presenting the hypothesis used in this study is multiple linear regression analysis. The hypothesis proposed in this study is the effect of dividend policy on company value, the effect of debt policy on company value and the effect of profitability as a moderating variable for the effect of dividend policy and debt policy on company value. Dividend policy and debt policy are proven to affect company value; profitability is also proven to be able to moderate dividend policy to company value but not proven to moderate debt policy to company value.
Sorption technology for obtaining uranium from technogenic and hydromineral raw materials using natural modified sorbents
Authors:   L. N. Gumilyov Eurasian National University, Abdualiyev B. , Satanova B. ,   L. N. Gumilyov Eurasian National University, Abuova F. ,   L. N. Gumilyov Eurasian National University, Aralbayeva G. ,   L. N. Gumilyov Eurasian National University
Published: July 2023

This work is devoted to the study of sorption technology for obtaining uranium from technogenic and hydromineral raw materials using natural modified sorbents. The main purpose of the study is to determine the effectiveness and prospects of this technology in the field of uranium mining and processing.The paper analyzes various types of naturally modified sorbents, their sorption capacity and the specificity of uranium. Special attention is paid to the environmental aspects of this technology, since the use of natural modified sorbents minimizes the negative impact on the environment.The paper also discusses methods of recovery and reuse of sorbents, which contributes to the resource efficiency of this technology. The economic benefits of the use of sorption technology are also considered in the work, taking into account the reduction of costs for the extraction and processing of uranium.The results of the study show that the sorption technology for producing uranium using naturally modified sorbents has high efficiency and prospects for industrial applications. The need for further research and innovation in this area is noted in order to improve processes and develop new types of sorbents.
Voice recognition methods for the development of an intelligent virtual consultant
Authors:   Al-Farabi Kazakh National University, Abdildayeva A. , Zhyilyssova D. ,   Al-Farabi Kazakh National University, Nazar G. ,   Al-Farabi Kazakh National University
Published: July 2023

In this article, methods of voice recognition were studied for the development of an intelligent virtual consultant, which plays an important role as an auxiliary tool when working with business process information systems, or rather with an ERP system. This study is intended to describe an intelligent virtual consultant in assessing the usefulness and effectiveness of using real functions in a business process modeling software system for enterprise resource planning (ERP). An intelligent virtual consultant is integrated with a Web-ERP prototype. In this study, natural language processing (NLP) was based using Levenberg Marquardt (LM) and Broyden-Fletcher Goldwarb-Shanno (BFGS) methods with the Mel kepstral frequency coefficients algorithm. A comparative analysis of these methods was also carried out to determine the effectiveness of the methods used. The big advantage of this virtual consultant is the voice input of long text data fields when working in Web-ERP. This system is relevant, as it significantly expands the search for business workflows. The system allows you to convert speech into text by receiving information system (IS) instructions, which, in turn, are transmitted to an ontological database, where a query based on terms is executed to obtain a set of commands available for execution. The introduction of an intelligent virtual consultant not only expands the application’s capabilities for users through the system, but also allows users to interpret data using speech synthesis. This prototype will be used for further research on the integration of intelligent virtual consultant and data analysis.
Investigation of boring pile integrity of road systems in engineering-geological condition of Kazakhstan
Authors:   L.N. Gumilyov Eurasian National University, Shakhmov Zh. , Tleulenova G. ,   L.N. Gumilyov Eurasian National University
Published: July 2023

The paper considers the testing of pile foundations for continuity at Atbasar pipeline facility. For testing monolithic concrete and reinforced concrete structures, a variety of the pit (pile integrity test) method is used – low-deformation dynamic tests. This is the most modern technique in the world practice of recent years. It allows you to check the continuity of all types of monolithic concrete and reinforced concrete structures, including concrete (reinforced concrete) piles, to identify defects in them. Based on the obtained pile data, as a result of testing for the continuity of foundations, the number of non-defective piles and defective piles are reflected, and solutions are proposed to ensure the reliability and safety of the pipeline operation. A total of 10 bored piles, which were located in 4 separately standing foundations, were investigated in the work. The results showed that 3 piles could have defects in the form of cracks or narrowing of the cross-section of the bored piles.
SISTEM PENDUKUNG KEPUTUSAN PENILAIAN KINERJA GURU SDN 5 YEHEMBANG KAUH DENGAN MENGGUNAKAN METODE ANALYTICAL HIERARCHY PROCESS
Authors: Widiantari Ni Made Putri , Made Dwi Utami Putra Desak , Pande Ni Kadek Nita Noviani
Published: July 2023

Penilaian Kinerja Guru merupakan penilaian yang dilaksanakan pada tiap butir tugas pokok guru yang bertujuan untuk pembinaan karier, jabatan dan kepangkatannya. SD Negeri 5 Yehembang Kauh setiap semester memberikan penilaian kepada guru untuk menentukan guru yang akan mendapatkan penghargaan atau reward sebagai bentuk apresiasi atas kerja kerasnya dalam memberikan yang terbaik untuk instansi. Dalam proses seleksi SD Negeri 5 Yehembang Kauh masih melakukan proses seleksi secara manual dengan memberikan penilaian terhadap masing-masing guru yang selanjutnya nilai tersebut akan direkap dan kemudian input kedalam file excel. Berdasarkan sistem penilaian yang berjalan saat ini tentunya kurang efisien dalam memberikan penilaian terhadap masingmasing guru. Untuk membantu proses penilaian kinerja guru pada SD Negeri 5 Yehembang Kauh digunakan sebuah Sistem Pendukung Keputusan (SPK). Sistem Pendukung Keputusan Penilaian Kinerja Guru dibangun berbasis website dan proses perhitungan hasil seleksi menggunakan metode Analytical Hierarchy Process (AHP) berdasarkan kriteria yang diambil dari unsur penilaian perilaku kerja. Kriteria yang digunakan yaitu Orientasi Pelayanan, Integritas, Komitmen, Disiplin, Kerjasama, dan Kepemimpinan. Sistem Pendukung Keputusan yang dihasilkan dalam penelitian ini dapat menampilkan peringkat guru yang sesuai dengan penilaian yang dilakukan dengan menggunakan metode Analytical Hierarchy Process (AHP).