bluuebunny/crossref_metadata_2025 huggingface dataset:
Perputaran Piutang Pada Pasien BPJS di Rumah Sakit AB Gresik Tahun 2022
Authors: Yunita Dewi Nurjanah  , Wasti Reviandani
Published: January 2024

Perputaran piutang pasien BPJS di Rumah Sakit AB pada tahun 2022 adalah subjek penelitian kualitatif deskriptif. melalui wawancara dan observasi Rumah Sakit AB sangat baik dalam mengelola piutang. Meskipun demikian, hasil perhitungan yang didasarkan pada kriteria Rumah Sakit AB menunjukkan bahwa Receivable Turnover tahun 2020 ini dapat dikategorikan rendah. Sesuai dengan standar keuangan Rumah Sakit AB,  Avarage Collection Period (ACP) Piutang lancar dianggap cukup baik karena memenuhi persyaratan rumah sakit, dan ACP Piutang Tak Lancar dianggap rendah.
SISTEM PENGENDALIAN INTERNAL PIUTANG DALAM MEMINIMALKAN PIUTANG TAK TERTAGIH PADA PT PCS
Authors: Dewi Dewi Safitri , Reviandani Wasti
Published: January 2024

Abstrak

Sistem pembayaran yang digunakan PT PCS umumnya dapat dilakukan secara tunai atau kredit, meningkatnya penjualan secara kredit menjadi faktor penghambat dalam peningkatan laba perusahaan, karena banyaknya customer yang telat membayar piutang hingga melebihi jatuh tempo pembayaran. Masalah tersebut terjadi karena belum adanya sistem pengendalian piutang yang efektif pada PT PCS. Penelitian ini bertujuan untuk menganalisis sistem pengendalian internal piutang dalam meminimalkan piutang tak tertagih pada PT PCS. Metode yang digunakan yaitu, metode kualitatif deskriptif untuk menjabarkan representasi mengenai pengendalian internal dan piutang tak tertagih pada PT PCS melalui kegiatan wawancara dan observasi. Hasil yang diperoleh peneliti ialah, PT PCS belum menerapkan sistem pengendalian internal piutang yang baik, dibuktikan dengan banyaknya karyawan PT PCS yang melakukan penagihan piutang tanpa memperhatikan syarat dan ketentuan yang sesuai dengan Standard Operating Procedure penjualan kredit yang berlaku di PT PCS, dan berdasarkan perhitungan rasio penagihan piutang, membuktikan bahwa piutang tak tertagih pada PT PCS dari tahun 2018 sampai dengan tahun 2022 meningkat secara signifikan.

Kata Kunci : Pengendalian Internal, Pengendalian Internal Piutang, Piutang Tak Tertagih
The Competence in the Digital Era in Improving Public Service Performance
Authors: Nur Hafni Rizanuddin
Published: January 2024

This study will explain the competence of human resources in the digital era to influence better performance. The human resources needed by the industry today are those who have competence in using digital technology. This competency is to realize intelligent factories, such as the Internet of Things (IoT). The era of the industrial revolution 4.0 opens opportunities for human resources (HR) to have skills following the latest technological developments. For this reason, civil servants who are state apparatus must be able to answer the challenges and master competencies in the digitalization era, including complex problem solving, critical thinking, creativity, human resource management, coordination with others, emotional intelligence, judgment and decision making, orientation service, Negotiation, and cognitive flexibility. This study uses quantitative research methods through the distribution of questionnaires or questionnaires to respondents who have been determined using sampling. The analytical technique used is structural equation modeling analysis. The results show that the appropriate competencies in the digitalization have a significant influence on the performance of public services.
Pengembangan LKPD Berbasis Tri-N (Niteni, Nirokke, Nambahi) Pada Muatan Materi IPA Kelas IV Sekolah Dasar Palembang
Authors: Yusindra  , Sylvia Lara Syaflin  , A. Heryanto
Published: January 2024

This research uses the Research and Development (R&D) research methodology. The aim of this development research is to produce Tri N-based LKPD in learning force and movement in class IV elementary school that is valid and practical. This research was conducted on 6 fourth grade students at SD Negeri 93 Palembang, using a limited trial. This limited trial basis was carried out to determine the feasibility of the Tri N-based LKPD product. Based on the results of the needs analysis, it is known that the learning process for class IV students at SD Negeri 93 Palembang is currently not optimal, considering the lack of student enthusiasm in participating in learning. LKPD developed using a TRI N-based method. Material expert validation results show a percentage of 55% (quite valid), media expert validation results show a percentage of 91% (very valid), and language expert validation results show a percentage of 80% (valid), meaning that the media has been declared valid in terms of material, media and language. The level of practicality of the TRI N (Niteni, Nirokke, Nambahi) based LKPD in grade IV science material, through student responses showed a score of 90.5%, which proves that the LKPD developed is very practical in its use. This is supported by teacher responses which show a practicality level of 96%. It was proven that during the limited trial, there was an increase in the test results of students who used the TRI N (Niteni, Nirokke, Nambahi) based LKPD on class IV science content, which initially obtained an average score of 64.2, increasing to 96.7.
Landasan Fılosofıs Dan Analısıs Teorı Belajar Dalam Implementası Kurıkulum Merdeka Dı Sekolah Dasar
Authors: Noer Roby Zulkarnain , Deni Mustopa  , Rizal Arizaldy Ramly  , Mochamad Nursalim  , Fajar Arianto
Published: January 2024

Education is an important need for human life, every individual has the same right to receive educational services. Implementing an independent curriculum requires independent curriculum planning, philosophical values such as humanism, progressivism and constructivism can be implemented to create learning that is more relevant, interactive and respects the uniqueness and potential of students. The method used in writing and preparing this article is literature study. The results of this literature review are related to the philosophical basis and analysis of learning theory in implementing the independent curriculum. In implementing the Merdeka Curriculum, teachers also have an important role in recognizing students' potential, creating relevant learning, and implementing fun learning through a project-based approach, integrating relevant educational philosophies and the principles of the Merdeka Curriculum, it is hoped that learning can become more meaningful , according to the needs of students, and prepare them with relevant skills to face future challenges.
Literature Review : Penggunaan Modul IPA Berbasis Etnosains untuk Meningkatkan Literasi Sains Pada Siswa SD
Authors: Abdul Muizz Abdul Muizz , Suryanti  , Binar Kurnia Prahani
Published: January 2024

The development of the 2013 curriculum into an independent curriculum is the right way to respond to globalization. Science is one of the main subjects at the elementary level, but in its implementation it can be done separately, namely science and social studies. In reality, science subjects are less desirable, students are not interested and consider science subjects difficult to learn. Thus, at least teachers offer alternative ways to improve students' abilities by emphasizing lessons related to everyday life or culture, namely with ethnoscience, which aims to attract students' interest in learning. This study aims to analyze the use of ethnoscience-based science modules to improve science literacy in elementary school students.  The method used is in the form of literature review. By looking for references to at least six articles in journals that are analyzed in inclusion and exclusion so that they are referenced in accordance with the goals to be achieved, especially with the help of Harzing's Publish or Perrish application and VOSViewer based on Google schoolar. From the results of the data collection, 200 articles were filtered and re-selected to produce 4 articles which were the results of selection according to more in-depth inclusion and exclusion criteria including titles, abstracts and discussion content. Based on the results of the analysis, no research results were found related to the use of ethnoscience-based science modules that can improve science literacy in elementary school students, and the lack of discussion words on VOSViewer that appeared and was less related to the research discussion. Thus, it can be concluded that the use of the IPA module is less of a trend in 2018-2023. Meanwhile, in terms of the feasibility of ethnoscience-based science modules, there is nothing to prove that the mod is feasible for elementary school students, because there is no suitability of scientific articles found, so further research is still needed regarding validity, practicality, and effectiveness. Thus, based on the results of this study, it can be a good opportunity for new researchers to conduct research on the topic to be a reference for researchers and teachers.
PENGARUH EFIKASI DIRI DAN PENGETAHUAN MANAJEMEN KEUANGAN TERHADAP INTENSI BERWIRAUSAHA DI DESA SALUTAMBUN KABUPATEN MAMASA
Authors: Januarti Apfia , Yahya Muh , Burhan Rizka Rayhana , Mattunruang Andi Aris
Published: January 2024

Penelitian ini bertujuan untuk mengetahui pengaruh efikasi diri dan pengetahuan pengelolaan keuangan terhadap niat berwirausaha di Desa Salutambun Kabupaten Mamasa. Penelitian ini dilakukan dengan menggunakan pendekatan kuantitatif dengan metode pengumpulan data melalui penyebaran kuesioner kepada 55 responden. Teknik pengolahan data dilakukan dengan bantuan aplikasi SPSS Versi 20. Data dianalisis dengan menggunakan teknik pengujian yaitu uji validitas, uji reliabilitas, uji asumsi klasik, uji multikolinearitas, uji heteroskedastisitas, uji regresi linier berganda dan uji hipotesis. Hasil penelitian menunjukkan bahwa dari hasil uji t pada variabel self-efisiensi (X1) diperoleh nilai thitung (2,845) > ttabel (2,00574) dengan nilai signifikansi 5%, maka variabel tersebut X1 berpengaruh terhadap variabel niat berwirausaha (Y). pada variabel pengetahuan pengelolaan keuangan (X2) diperoleh nilai thitung (3,074) > ttabel (0,00574) dengan nilai signifikansi 5% maka variabel 0,001 lebih kecil dari 0,05 maka variabel X1 niat berwirausaha.
PENGARUH CUSTOMER VALUE DAN CUSTOMER EXPERIENCE TERHADAP PURCHASE INTENTION DAN TERHADAP CUSTOMER SATISFACTION
Authors: Ningsih Kurnia Setyo Widiyah , Sutedjo Bambang
Published: January 2024

Seiring dengan perkembangan dunia yang semakin pesat memiliki banyak pengaruh dari berbagai aspek bagi penggunanya. Perkembangan dirasakan salah satuya dalam bidang bisnis dimana para pelaku bisnis memanfaatkan digitalisasi dengan penjualan secara online untuk sarana dan prasarana berbisnis. Penggunaan digitalisasi saat ini salah satunya dengan menggunakan teknologi yaitu e-commerce. Banyak e-commerce yang bisa ditemui di Indonesia salah satunya adalah shopee. Penelitian yang dilakukan bertujuan untuk menganalisis pengaruh customer value dan customer experience terhadap purchase intention dan terhadap customer satisfaction pada pelanggan produk fashion di shopee. Dengan harapnnya dari penelitian yang dilakukan ini dapat memberikan masukan untuk e- commerce shopee penelitian yang dilakukan menggunakan populasi pelanggan yang membeli produk fashion di e-commerce shopee. Dengan metodologi analisis yang digunakan yaitu metode kuantitatif dengan menggunakan 100 responden. Pengumpulan data dengan menggunakan kuisioner yang disebar secara offline maupun dengan google form. Data yang dianalisis menggunakan program SPSS versi 25. Dengan hasil analisis mendapatkan bahwa customer value dan customer experience berpengaruh terhadap purchase intention dan purchase intention berpengaruh terhadap customer satisfaction.
Effect of Maternal Adverse Childhood Experiences (ACEs) and Maternal Resiliency on Early Childhood Outcomes on Vaccination Adherence and Emergency Department Visits
Authors: Abraham Leslie , Banks Hsiao-Wei , Gaughan John , Kushnir Alla
Published: January 2024

Introduction: Adverse Childhood Experiences (ACEs) are traumatic events that occurred to an individual during ages 0-17 years. The 1998 Adverse Childhood Experiences (ACE) suggested higher ACE scores contributed to negative future health outcomes. The tool is now used to screen at-risk populations for the development of chronic health conditions. Protective factors, such as resilience, were later studied and found to offset the negative health impacts of traumatic childhood events. The 7Cs questionnaire was a tool developed to measure resilience in adolescents and demonstrated better health outcomes despite the presence of higher ACE scores. This pilot study evaluated whether higher maternal ACEs would result in more frequent pediatric ED visits and less vaccination adherence and whether higher 7Cs would be protective.

Methods: This IRB approved study recruited pregnant women and new mothers in a tertiary NJ urban hospital, between their second trimester and their child’s one month well visit at the pediatric clinic. All subjects completed the Adverse Childhood Events (ACEs) survey, 7Cs resilience tool, and a Maternal Health questionnaire at the time of recruitment. A review of children’s electronic medical records was conducted at 2-, 4-, 6-, and 12-months to evaluate pediatric outcomes, adherence to AAP recommended vaccine schedule and emergency department visits.

Results: Total of 34 women were enrolled, their and their children’s medical and demographic data collected. There was no difference in higher ACE scores and number of ED visits for the babies (OR 0.9645 [CI 0.7643 - 1.2172]), with no effect seen with higher resilience scores and number of ED visits (OR 0.8477 [0.6192 – 1.1604]). Increase in ACE scores resulted in reduction in vaccination adherence (OR 0.3555 [CI 0.1417 – 0.8923]). Increase in resilience scores decreased vaccination adherence by 44% (OR 0.5578 [CI 0.4164 - 0.7471]).

Conclusion: There was no significant relationship between ACE or resilience scores and pediatric ED visits. A higher ACE score and lower levels of resilience were associated with decreased child vaccine adherence rate. ACE scores relate to higher traumatic childhoods, suggest poorer outcomes in offspring. Women with higher ACE scores could benefit from closer interventions and resources from obstetricians, and closer follow up of their children by the pediatricians.
Transductive classification via patch alignment
Authors: Song Zhijun , Wu Zhaoli , Chen Shu-Wen , Zhu Hui-Sheng
Published: January 2024

In this paper, a novel approach for transductive classification is proposed. Unlike existing methods that heavily rely on constructing the Laplacian matrix to capture data distribution, the proposed approach takes a unique path. It employs a linear transformation model to create local patches for each data point and then unifies them in an objective function to build the Laplacian matrix. Incorporating this Laplacian matrix into the transductive classification framework allows us to assign optimal class labels globally. The experimental results from toy data and real world databases demonstrate that the proposed approach achieves more efficient and stable performance, while this approach is insensitive to the parameters. Notably, our method exhibits robustness to parameter variations, making it highly adaptable to practical applications.
Outlet Recognition Based on Deep Learning and Sensors for Large-Scale Cable Laying
Authors: Chen Mengbo , Wang Weiwei , Geng Junhao
Published: January 2024

The outlet recognition for large-scale cable laying has problems such as difficulty obtaining prior information, similarity in local recognition information, and low recognition accuracy, making it difficult for most outlet recognition based on real prior information for large-scale cable laying to proceed smoothly. In response to the above issues, this paper proposes an outlet recognition method based on deep learning and sensors for large-scale cable laying. A system of outlet recognition based on deep learning and sensors for large-scale cable laying is formed based on the self-labeling of outlet images, data augmentation of outlet features, and relative pose constraints. The feasibility and effectiveness of this technology have been verified by recognizing outlets at different locations in a large-scale cable laying scene. This technology can recognize and locate outlets, effectively solving the problem of misrecognition at different positions.
Ordered Tree Billing Algorithm and Its Application in Power Marketing System
Authors: Zhu Ke , Kong Juan , Xiong Gen-Xin , Shi Wen-Juan , Li Yun , Ouyang Hong , Liu Yu-Xi
Published: January 2024

The electricity is essentially a function of electricity customers and their electricity consumption. The electricity contract generally stipulates the calculation method of electricity price rather than one or a set of clear and fixed prices. The electricity price is subject to provincial policies, user classification, electricity price type, The impact of new energy, etc., will generate electricity charges, refunds and other services. In addition, in the absence of unified national supervision, it is difficult to update the electricity billing system in each province, and at the same time, the calculation of electricity bills is extremely complicated. This paper proposes a general billing algorithm based on ordered tree, which can construct a variety of complex rules through flexible assembly of basic rules, which can meet the billing needs of utility companies. The power charge engine implemented based on this algorithm can adapt to the different electricity bill calculation rules in different provinces, changing the unfavorable situation that the original marketing system lacks a unified billing module, and it is difficult to quickly respond to government and market requirements, as well as the headquarters’ management and supervision requirements for provincial and municipal companies, and the operating efficiency is higher than the original hard-coded billing module. Finally, the power charge engine also effectively improves the versatility, reliability and operational efficiency of the existing State Grid marketing system.
Development of a High Precision Instrument for Strain Sensors
Authors: Xu Jian , Zhang Yizhi , Qin Haifeng , Chen Kexing
Published: January 2024

This paper introduces the design of a high-precision instrument for strain sensors, which accuracy class is 0.0005. In order to reach that high accuracy AC excitation voltage with 225Hz frequency, pulse width modulation multi-slope integration and high-precision voltage reference are developed. To verify the consistency of the measurement accuracy and uncertainty of the instrument, the comparison test of the DMP9000H high-precision instrument and the DMP41 high-precision instrument (made by HBM Germany) is carried out in two research institutions, the comparison results show that the relative deviation between DMP9000H and DMP41 measured at the comparison point is within ± 0.0005%, and the relative expanded uncertainty of DMP9000H measured at the comparison point is 1.0 × 10-5 (k = 2); |En| of the measurement results of each comparison point are less than 0.5, which indicates that the difference between DMP9000H and DMP41 is within reasonable expectation, and the comparison result is satisfied.
Two-Layer Optimal Dispatching Strategy of Distribution Network Considering Demand Side Load
Authors: Wang Qing , Zhu Xiaohu , Zhou Xiaozhuang
Published: January 2024

The traditional optimal dispatching of distribution network usually takes the demand load measurement as the rigid load. However, as the demand for electricity continues to rise, a large amount of distributed renewable energy generation is incorporated into the power grid. It is difficult to ensure the safe and reliable operation of the electrical system if only the optimal dispatching of the generating side is carried out. This paper proposes a two-layer optimal distribution network scheduling strategy that considers the demand side load. The upper layer model chooses controllable load output as the objective and aims to achieve the lowest overall operation cost of the distribution network, as well as the lowest comprehensive load fluctuation to reflect the power supply reliability of the distribution network. The adaptive particle swarm optimization algorithm based on genetic algorithms is implemented to solve the model based on its characteristics. Simulation verification is performed on the IEEE33-node distribution system, and the results depict that the proposed two-layer optimization method reduces the operating cost of the distribution network, effectively stabilizes the peak valley difference of load, and improves the overall stability of the distribution network.
Robot Sliding Mode Control Based on Adaptive Fuzzy Compensation
Authors: Li Zhengqiang , Tan Jia , Lv Weihong , Lai Jianfang
Published: January 2024

With the continuous development of digital technology, the mechanical arm has been widely used because of its flexibility and convenience. The dynamic equation of flexible manipulator is complex, and there are external disturbances and model uncertainties. A sliding mode control (SMC) method of robot based on adaptive fuzzy compensation is proposed. The controller design of nonlinear system adopts the method of combining sliding mode control with fuzzy approximation, and the fuzzy system is used to realize the adaptive approximation of the unknown part of the robot model. Matlab/Simulink simulation results show that the control system based on adaptive fuzzy compensation can be stable, effectively reduce the fuzzy gain and meet the expected dynamic performance index.
A 3D Information Hiding Algorithm Based on Point Cloud Increment
Authors: Ren Shuai , Jin Yulin , Wang Binbin
Published: January 2024

In order to solve the problem of low capacity in current 3D model information hiding algorithms based on single carrier, we propose a 3D information hiding algorithm based on point cloud increment building upon the research findings of existing single carrier information hiding technology for 3D models. Firstly, the algorithm performs hierarchical clustering on the 3D model using a plane fitting technique, completing the preprocessing of secret information. Secondly, employing a point simplification and compression method, we classify the layered point cloud into four distinct point sets. Finally, utilizing the spatial characteristics between the four point sets, double secret information was embedded through vertex segmentation in two sets of point sets. The application of double embedding ideas in the algorithm greatly improves robustness. The segmentation of points increases the number of point clouds and makes the model smoother, while increasing the algorithm’s capacity. The experimental results show that the information hiding algorithm proposed in this article outperforms the comparison algorithm in all aspects, breaking the balance between robustness, invisibility, and capacity to a certain extent, and has a positive impact on the development of national network security.
Digital Twin-Based Identification of Effective Partial Discharge Signals for GIS
Authors: Fang Yizhi , Lin Yuling , Liang Yuxiong , Wu Weiwen , Jiang Zhibin , Shen Daoyi , Zeng Dan
Published: January 2024

In the actual substation complex environment, in addition to the influence from the environmental noise, the Gas Insulated Switchgear (GIS) online monitoring system will also receive the interference from the invalid partial discharge signal from the neighboring high-voltage equipment. Aiming at the current GIS partial discharge online monitoring system can not identify the defects of effective PD signals, combined with the advantages of digital twin technology, this paper carries out a study on the identification of effective PD signals of GIS based on digital twins, proposes a digital twin-based judgment mechanism for effective PD signals of GIS, establishes a typical digital twin model of GIS, and analyzes the decay rate of the UHF signals in the various components, based on the theoretical signal decay and actual signal decay. According to the comparison between the theoretical signal attenuation and the actual signal attenuation to determine whether the partial discharge signal is from the target high-voltage equipment, the effectiveness of this paper’s algorithm is verified through simulation experiments.
Coping Strategies for Negative Psychological Emotions Among Higher Vocational Colleges EFL Students in the Internet Age: A Cognitive Psychology Perspective
Authors: Jiang Liping , Chen Xia , Cheng Mengmeng
Published: January 2024

The mental well-being of students from higher vocational colleges (HVCs) is a critical concern for personal development and a nation’s future. While scholars have explored strategies to address negative psychological emotions in English as Foreign Language (EFL) students in internet era, few have delved into the underlying cognitive psychology aspects. Cognitive psychology, with its focus on information processing, offers a unique perspective. It reveals cognitive biases in individuals with emotional issues, shedding light on causal relationships. By studying EFL students’ mental states through a cognitive psychology lens, new avenues for mental health improvement are opened. This approach, grounded in cognitive psychology theory, provides innovative strategies for enhancing mental well-being and offers valuable insights for coping with negative psychological emotions, bridging the gap between psychological processes and behavior.
Imaging of conducting materials via the Kernel Method
Authors: Tamburrino Antonello , Mottola Vincenzo
Published: January 2024

In this work, we present a new non-iterative imaging method for Electrical Resistance Tomography (ERT). The problem in ERT is retrieving the spatial behaviour of the electrical conductivity by means of boundary measurements in steady-state conditions. Specifically, the interest is focused on the inverse obstacle problem, that consists in reconstructing the shape, position and dimension of one or more anomalies embedded in a known background. The proposed method, called Kernel Method, is based on the idea that if there exists a current density Jn that applied at the boundary ∂𝛺 of the domain under investigation 𝛺 produces the same scalar potential (on ∂𝛺), with and without anomalies, then the power density corresponding to Jn, evaluated on a configuration without anomalies, is vanishing in the region occupied by the latter. The proposed method has a very low computational cost. Indeed, the evaluation of the desired current density Jn on ∂𝛺 requires a negligible computational effort, and the reconstructions require only one forward problem.
An integrated description of election information using domination and dual domination in picture fuzzy graph
Authors: Khan Sami Ullah , Al Ghour Samer , Hussain Shoukat , Saeed Maha Mohammed , Hussain Fiaz , Mehmood Arif , Park Choonkil
Published: January 2024

The concept of the fuzzy graph (FG) and the simplification of its type have been designed with many real-world concerns, decision-making, network problems, and other types of impression in mind. This article is essentially a generalization of fuzzy graph theory with their association in settings where four organizations of membership grade are characterized by imprecision. The contemporary idea of domination in picture fuzzy graph (PFG) is given the generalization of fuzzy graph domination and intuitionistic fuzzy graph (IFG) domination is unit on the innovative idea of picture fuzzy graph. This article is the main focus on the graph of the picture fuzzy set. Additionally, this research paper concludes with the concept of domination theory (DT) and double domination theory (DDT) in the concept of environment. Obviously, the framework of picture fuzzy graph is purposed and their connected theory is obtained together with presented examples. Supplementary the domination theory (DT) in the picture fuzzy graph. A few order, strength, cardinality, and domination of completeness, neighbors in a picture fuzzy graph, and the DDS and given some examples are likewise studied in the concept of picture fuzzy graph in the environment. The application of the politician campaign is novel exploiting the projected study and curious how to double dominate would relationship with the politician campaign. Finally, a comparison between the current effort and the prior effort is made, and the advantages of the work done in the context of a picture fuzzy graph are discussed.
Research on the urban green transportation development level evaluation based on the triangular pythagorean fuzzy multiple attribute decision making
Authors: Yu Huan
Published: January 2024

With the acceleration of urbanization and the significant improvement of people’s living standards, the motorization of urban transportation in China has developed rapidly, and the number of urban motor vehicles has sharply increased. This has also caused a series of problems such as increasingly severe urban road traffic congestion, increased traffic energy consumption, and atmospheric environmental pollution. Unprecedented social and environmental pressures have put forward higher requirements for the development model of urban transportation. Against the backdrop of increasingly severe conflicts between urban transportation and resource environment in China, green transportation with the goal of “meeting maximum demand with minimum consumption” has gradually received widespread attention from the academic community. The urban green transportation development level evaluation is a classical multiple attribute decision making (MADM). In this paper, we define the triangular Pythagorean fuzzy sets (TPFSs) and investigate the MADM problems under TPFSs. Based on the traditional geometric BM (GBM) operator and generalized weighted GBM (GWGBM) operator, some triangular Pythagorean fuzzy operators are proposed: triangular Pythagorean fuzzy generalized GBM (TPFGGBM) operator and triangular Pythagorean fuzzy generalized WGBM (TPFGWGBM) operator. Accordingly, we have took advantage of these operators to develop some approaches to work out the triangular Pythagorean fuzzy MADM. Ultimately, a practical example for urban green transportation development level evaluation is took advantage of to validate the developed approach, and an influence analysis of the parameter on the final results is been presented to attest its availability and validity.
Modified method for college students’ career planning evaluation based on the probabilistic hesitant fuzzy multiple attributes group decision making
Authors: Yang Guangfu , Xiao Chunyun
Published: January 2024

The employment of college graduates is related to the overall situation of China’s social development, and the difficulty of employment has become a social problem that cannot be ignored. Through the analysis of the current situation of employment, it is found that the lack of employment guidance in colleges and universities and the lack of employment concept of college students are important factors for the difficulty of college students’ employment, and college counselors play an irreplaceable role in college students’ career planning. Based on the characteristics of college counselors’ work, the paper constructs a career planning evaluation system, hoping to provide new ideas for counselors’ employment guidance. The college students’ career planning evaluation is a multiple attributes group decision making (MAGDM). Recently, the TODIM and GRA technique has been employed to manage MAGDM. The probabilistic hesitant fuzzy sets (PHFSs) are employed as a useful tool for depicting uncertain information during the college students’ career planning evaluation. In this paper, the probabilistic hesitant fuzzy TODIM-GRA (PHF-TODIM-GRA) technique is built to manage the MAGDM under PHFSs. At last, the numerical example for college students’ career planning evaluation is employed to show the PHF-TODIM-GRA technique. The main contribution of this paper is outlined: (1) the TODIM technique based on GRA technique has been extended to PHFSs based on CRITIC technique; (2) the CRITIC technique is employed to derive weight values under PHFSs. (3) the PHF-TODIM-GRA technique is founded to manage the MAGDM under PHFSs; (4) a numerical case study for college students’ career planning evaluation and some comparative analysis is supplied to validate the proposed PHF-TODIM-GRA technique.
Comprehensive empirical investigation for prioritizing the pipeline of using feature selection and data resampling techniques
Authors: Tyagi Pooja , Singh Jaspreeti , Gosain Anjana
Published: January 2024

The contemporary real-world datasets often suffer from the problem of class imbalance as well as high dimensionality. For combating class imbalance, data resampling is a commonly used approach whereas for tackling high dimensionality feature selection is used. The aforesaid problems have been studied extensively as independent problems in the literature but the possible synergy between them is still not clear. This paper studies the effects of addressing both the issues in conjunction by using a combination of resampling and feature selection techniques on binary-class imbalance classification. In particular, the primary goal of this study is to prioritize the sequence or pipeline of using these techniques and to analyze the performance of the two opposite pipelines that apply feature selection before or after resampling techniques i.e., F + S or S + F. For this, a comprehensive empirical study is carried out by conducting a total of 34,560 tests on 30 publicly available datasets using a combination of 12 resampling techniques for class imbalance and 12 feature selection methods, evaluating the performance on 4 different classifiers. Through the experiments we conclude that there is no specific pipeline that proves better than the other and both the pipelines should be considered for obtaining the best classification results on high dimensional imbalanced data. Additionally, while using Decision Tree (DT) or Random Forest (RF) as base learner the predominance of S + F over F + S is observed whereas in case of Support Vector Machine (SVM) and Logistic Regression (LR), F + S outperforms S + F in most cases. According to the mean ranking obtained from Friedman test the best combination of resampling and feature selection techniques for DT, SVM, LR and RF are SMOTE + RFE (Synthetic Minority Oversampling Technique and Recursive Feature Elimination), Least Absolute Shrinkage and Selection Operator (LASSO) + SMOTE, SMOTE + Embedded feature selection using RF and SMOTE + RFE respectively.
Enterprise technological innovation capability evaluation using a spherical fuzzy number based CSM-EDAS model
Authors: Wang Kai , Bai Yameng
Published: January 2024

With the rapid development of science and technology, the flow of information has become more convenient, and society has entered the era of knowledge economy. In this era, technological innovation capability is becoming increasingly important and has become an important weapon for enterprises to survive in fierce competition, especially for technology-based small and medium-sized enterprises. Nowadays, technology-based small and medium-sized enterprises have developed many technological innovation achievements through continuous technological innovation, and have created a large number of high-tech products and services. Technological innovation has been proven to effectively improve the core competitiveness and economic benefits of technology-based small and medium-sized enterprises. Therefore, evaluating the technological innovation capabilities of technology-based small and medium-sized enterprises has both theoretical and practical significance. The enterprise technological innovation capability evaluation from a low carbon perspective could be deemed as the multiple attribute group decision making (MAGDM) problem. Recently, the evaluation based on distance from average solution (EDAS) technique and cosine similarity measure (CSM) technique has been employed to manage MAGDM issues. The spherical fuzzy sets (SFSs) are used as an efficient tool for portraying uncertain information during the enterprise technological innovation capability evaluation from a low carbon perspective. In this paper, the spherical fuzzy number EDAS based on the CSM (SFN-CSM-EDAS) technique is cultivated to manage the MAGDM under SFSs. Finally, a numerical study for enterprise technological innovation capability evaluation from a low carbon perspective is supplied to validate the proposed technique. The main contributions of this paper are outlined: (1) the EDAS and CSM technique was extended to SFSs; (2) the CRITIC technique is used to derive weight based on CSM technique under SFSs. (3) the SFN-CSM-EDAS technique is founded to manage the MAGDM under SFSs; (4) a numerical case study for enterprise technological innovation capability evaluation from a low carbon perspective and some comparative analysis is supplied to validate the SFN-CSM-EDAS technique.
Uncertain least square support vector regression with imprecise observations
Authors: Zhang Hao , Sheng Yuhong
Published: January 2024

In this study, an innovative approach that combines least square support vector regression (LSSVR) with uncertainty theory to enhance its performance in dealing with low-quality or imprecise data from real-world be proposed. The resulting model, called uncertain least square support vector regression (ULSSVR), incorporates chance constraints and simplified parameter selection, which are critical to handle imprecise observations. A numerical algorithm called the conjugate residual method (CR) is introduced to reduce the computational complexity of the model solution. The experimental results using both small and medium-sized datasets demonstrate the superior performance of ULSSVR in terms of prediction accuracy and generalization ability compared to other models such as uncertain support vector regression (USVR), uncertain linear lodel, uncertain polynomial model, and uncertain growth models. ULSSVR not only improves prediction accuracy by at least 28.49% but also demonstrates faster computational speed. Overall, ULSSVR presents a promising solution for data science and internet applications where dealing with imprecise and low-quality data is a common challenge.
TODIM-GRA based multiple-attribute group decision-making technique for risk assessment of intangible cultural heritage tourism development under interval-valued pythagorean fuzzy circumstances
Authors: Kang Zhonghui
Published: January 2024

Intangible cultural heritage can be said to be an important component of tourism resources. With the rapid development of society in today’s era, tourism development and intangible cultural heritage protection have gradually attracted attention from Chinese society, and in recent years, it has attracted high attention from relevant departments of the Chinese government. Tourism development has a “dual” impact on the protection of intangible cultural heritage, with both positive and negative impacts. The risk assessment of intangible cultural heritage tourism development is a MAGDM problems. Recently, the TODIM and GRA technique has been employed to manage MAGDM issues. The interval-valued Pythagorean fuzzy sets (IVPFSs) are employed as a tool for characterizing uncertain information during the risk assessment of intangible cultural heritage tourism development. In this paper, the interval-valued Pythagorean fuzzy TODIM-GRA (IVPF-TODIM-GRA) technique is construct to manage the MAGDM under IVPFSs. Finally, a numerical case study for risk assessment of intangible cultural heritage tourism development is employed to validate the proposed technique.
Quantitative analysis of professional basketball: A qualitative discussion
Authors: Zhou Yukun , Li Tianyi
Published: January 2024

Quantitative analysis of professional basketball become an attractive field for experienced data analysts, and the recent availability of high-resolution datasets pushes data-driven basketball analytics to a higher degree. We present a qualitative discussion on quantitative professional basketball. We propose and discuss the dimensions, the levels of granularity, and the types of tasks in quantitative basketball. We review key literature in the past two decades and map them into the proposed qualitative framework, with an evolutionary perspective and an emphasis on recent advances. A list of questions around professional basketball that could be approached with quantitative tools is displayed, pointing to directions for future research. We touch on the new landscapes of virtual basketball at enriching the space for quantitative analysis. This report serves as a qualitative primer for quantitative analysis of professional basketball, exhibiting the growing prospect of the promising research area.
Winner prediction in an ongoing one day international cricket match
Authors: Agrawal Yash , Kandhway Kundan
Published: January 2024

Cricket is a team sport with an intricate set of rules, where players specialize in multiple skills such as batting, bowling, and fielding. Playing conditions and home advantage also impact the game. Thus, it is quite challenging to build an accurate quantitative model for the game. In this paper, we provide a data driven approach to predict the winner of a cricket match. We divide the ongoing match into various states and provide a prediction for each state using supervised machine learning models. We employ dynamic features that account for the current match situation, together with the static features like team strength, winner of the toss, and the home advantage. We also use SHAP scores—an explainable AI technique—to interpret the proposed prediction model. We use ball-by-ball data from 1359 men’s one day international cricket matches played between January 2004 to January 2022 to present our results. We achieved the best in-play prediction accuracy of about 85% . SHAP scores reveal that during initial phases of the match, the model treats static features like team strength more important than others, in making the predictions. But as the match progresses, dynamic features capturing the current match situation become exceedingly important. Our work may be useful in preparing tools for in-play winner prediction for live cricket matches that can be used in websites and mobile applications covering the sport, in providing analytics during live television commentary, and in legal betting platforms.
Pollen profiles, physico-chemical parameters, in vitro antioxidant and anti-inflammatory activities of honeys and anti-browning effect of honeys on apple
Authors: Guenaoui Nawel , Mouhoubi-Tafinine Zina , Amessis-Ouchemoukh Nadia , Saimi Meriem , Saidi Hakima , Ayad Rabha , Sadou Dyhia , Moumeni Lynda , Ouchemoukh Salim
Published: January 2024

BACKGROUND:

Honey, a product of the beehive with high nutritional and taste value, is a food much appreciated by consumers for its nutritional value and therapeutic properties such as antioxidant, anti-inflammatory and antibacterial properties.

OBJECTIVE:

The main objectives of this work are to evaluate the quality of Algerian honeys, their chemical composition, their antioxidant, anti-inflammatory and anti-browning enzyme activities, and to confirm the qualitative superiority of local honeys over imported honeys.

METHODS:

Six local and imported honeys are analyzed for their physicochemical parameters, floral origin, phenolic compound content and in vitro antioxidant and anti-inflammatory activities. The enzymatic browning inhibitory power of Algerian honeys is also tested.

RESULTS:

The pollen study shows that the four local honeys were of monofloral origin, while the two imported honeys are pollen-free. Local and imported honeys are compared on 17 physico-chemical and biological parameters. The results reveal that single-flower honeys generally meet international standards in terms of HMF and proline content, unlike imported honeys. These honeys have lower antioxidant levels than other honeys: polyphenols (161 to 248 mg GAE/100 g) and flavonoids (64 to 74 mg QE/100 g). There is a correlation between honey color, antioxidant contents and biological activities.

CONCLUSION:

Darker Algerian honeys have higher antioxidant content and better biological properties than others samples.
Covert consciousness
Authors: Young Michael J. , Edlow Brian L. , Bodien Yelena G.
Published: January 2024

Covert consciousness is a state of residual awareness following severe brain injury or neurological disorder that evades routine bedside behavioral detection. Patients with covert consciousness have preserved awareness but are incapable of self-expression through ordinary means of behavior or communication. Growing recognition of the limitations of bedside neurobehavioral examination in reliably detecting consciousness, along with advances in neurotechnologies capable of detecting brain states or subtle signs indicative of consciousness not discernible by routine examination, carry promise to transform approaches to classifying, diagnosing, prognosticating and treating disorders of consciousness. Here we describe and critically evaluate the evolving clinical category of covert consciousness, including approaches to its diagnosis through neuroimaging, electrophysiology, and novel behavioral tools, its prognostic relevance, and open questions pertaining to optimal clinical management of patients with covert consciousness recovering from severe brain injury.
Natural Language Processing for Ontology Development in IoT-Enabled Smart Healthcare
Authors: Turkmen Aytug , Can Ozgu
Published: January 2024

The healthcare industry is a knowledge-driven system and continuously expanding with enormous volumes of narrative data that are typically stored in unstructured and non-standardized formats. Therefore, it is challenging for systems to manage massive amounts of narrative data, comprehend the contents of these data, find relevant and useful healthcare information, and make decisions. In the healthcare domain, ontologies provide a formal specification of health data for knowledge representation and data integration. Therefore, ontologies enable the representation of health information in a machine-readable form and allow this information to be shared, reused, and used to make deductions. In recent years, modern healthcare services have been significantly affected by the growth of the interconnectedness of systems and the improvements in the Internet of Things (IoT). Moreover, wearables and implantable appliances enhance people’s quality of life and ease their life by performing continuous health monitoring. IoT technologies facilitate data flow across multiple entities in healthcare systems and deal with different data formats. Semantic Web and ontologies provide interoperability among IoT ecosystems by describing concepts and relationships between different entities. Nevertheless, extracting concepts and relationships from the healthcare domain is one of the most important processes in ontology development. Therefore, Semantic Web technologies benefit from Natural Language Processing (NLP) technologies to convert unstructured health data to meaningful representations. Thus, the structured and unstructured data can be combined by integrating NLP and Semantic Web technologies. Hence, NLP is a fundamental capability for cognitive computing systems and is frequently characterized as a behavioral technology that assists computers in understanding and comprehending human language. In the health domain, NLP techniques are used to gather unstructured healthcare data, examine its grammatical structure, and ascertain its meaning. Consequently, NLP techniques increase the quality of healthcare services while reducing costs. This chapter presents the NLP methods applied to the IoT-enabled healthcare domain in the scope of semantic intelligence. For this purpose, the main principles of NLP for the Semantic Web, and methodologies for creating ontologies by utilizing the NLP techniques to process heterogeneous data sources are presented. Also, the relationship between NLP and Semantic Web is explored within the context of ontology creation and population for IoT-based healthcare systems. Finally, the recent studies, applications, opportunities, and challenges in the related field are examined.