bluuebunny/crossref_metadata_2025 huggingface dataset:
The Impact of Knowledge Management on Competitive Advantage in Private Sector Companies: A Case Study of Al-Hayat Investment Company
Authors: Al-Saif Rawan , Mohamed Al-Faisal , Jarad Fayez
Published: August 2024

This study aimed to demonstrate the impact of knowledge management in achieving competitive advantage in Al-Hayat Investment Company. For the purpose of achieving this, the researcher followed the descriptive analytical approach in its social survey style, where an electronic questionnaire was distributed to a sample of (86) individuals from the company’s employees, and (65) individuals responded. Of them, this percentage constitutes 74%. The “SPSS” Statistical Package for the Social Sciences application was relied upon to perform descriptive and analytical analyzes of the data, after the stability and validity of the study tool was proven through the Cronbach’s alpha reliability coefficient and the Pearson correlation coefficient. The study found a high level of knowledge management and competitive advantage at Al-Hayat Investment Company. The study also found that there is a positive moral effect of knowledge management on the competitive advantage at Al-Hayat Investment Company in general. In detail, the dimension of knowledge generation does not affect competitive advantage similar to the rest of the dimensions of knowledge management that have proven effective in Enhancing competitive advantage. The study also found that there are no statistical differences attributable to the variables of gender and practical qualification, with limited differences attributable to the years of experience variable in favor of the 5 – 10 year group, but these differences are limited to the application of knowledge, and the competitive advantage is lost and there are no statistical differences. Towards the variable of years of experience towards any other dimension. The study concluded with some recommendations, the most important of which are: the need for the company to adopt new methods of shopping, target new markets, and innovate modern promotional methods.
Analysis of the of the Career System at Varo Tech Indonesia Limited Liability Company
Authors: Pratiwi Ashfi , Darmastuti Dhian , Haq Ahmas Muhashisul
Published: August 2024

System career is something very important in development system employment at a company. Meanwhile, from the employee system, a career can become an encouraging thing and increase rank or position. The mobility system of a career at the company covers promotion, transfer, or demotion positions. The purpose of doing this study is to know if the system of career at Varo Tech Indonesia Limited Liability Company already walks in accordance with planning and if the company already supports the development of career employees. Research This nature is qualitative-descriptive, with an approach that is qualitative, through an interview and study process. References. Research result This discloses that the Career System at Varo Tech Indonesia Limited Liability Company has been implemented in the company and delivered to employees. Where is the system of careers at the company? This is not rigid, so the employees can make it easier. If there is a constraint or desire to move positions within the company, Besides opportunity employee for promoted open for all over employee. Mobility careers in this company are flexible so that every employee can keep going and develop their career. Result of the study It can be concluded that the planning system career has been implemented by Varo Tech Indonesia Limited Liability Company in the city of Tangerang, and as far as this has already been accomplished.
Implications of Therapeutic Communication for Avoiding Lawsuits in Health
Services
Authors: Sriwijayanti Luh Putu Eka , Artawan Eka Putra I. Wayan Gede , Kuswardhani R.A. Tuty , Bagiastra I. Nyoman
Published: August 2024

Health services in Indonesia have a very important role in improving the welfare of the Republic
of Indonesia. To prevent misunderstandings between patients and paramedics in health services,
good communication is needed. Communication in health services can be called therapeutic
communication. Previous research mostly only highlights the importance of using informed
consent as valid proof of a therapeutic agreement, therefore in this research the important point is
that in implementing informed consent, adequate, detailed, and easy to understand information
must be given to the patient, this can be done by providing information using therapeutic
communication. This article uses a literature study of legal research methods with a normative
approach and a conceptual approach. Sources of research material used in this literature review
include journal articles, legal regulations, and books. Therapeutic communication should be
carried out in the entire series of patient care, especially in conveying informed consent, where the
patient must receive a detailed explanation regarding the diagnosis, therapy plan, risks if treatment
is carried out or not carried out, and the treatment prognosis, so that the patient can determine
properly what treatment is needed. will be chosen consciously. When the treatment carried out is
determined by the patient and carried out based on mutual agreement, this will reduce the
possibility of disputes between the doctor and the patient. Therapeutic communication cannot
completely prevent lawsuits and medical disputes, but by carrying out effective therapeutic
communication, patients and paramedics can have equal views regarding the patient's condition,
treatment plans, and the risks that may occur if medical action is or is not carried out.
Komparasi studi kasus pemerintah Jepang dan Indonesia dalam pendidikan pengurangan risiko bencana dalam konteks masa pandemi covid-19
Authors: Kuncoro Wahyu
Published: August 2024

Latar Belakang: Sejak dinyatakan adanya pandemi COVID-19 oleh WHO, banyak negara menutup sekolah-sekolah untuk melindungi anak-anak dari keterpaparan virus yang dapat saja terjadi pada saat berada di lingkungan sekolah. Kekhawatiran semakin lama anak-anak tidak kembali ke sekolah dapat mengancam ketercapaian Tujuan Pembangunan Berkelanjutan ke empat yaitu pemenuhan hak mendapatkan Pendidikan Berkualitas di tahun 2030. Studi ini bertujuan untuk menganalisa kebijakan yang dilakukan oleh pemerintah Jepang dan Indonesia dalam merespon dampak pandemi COVID-19 di sektor pendidikan. Metode: Studi ini menggunakan metode peninjauan literatrasi yang berkaitan dengan kebijakan sektor pendidikan di kedua negara. Temuan: Hasil dari tinjauan literatur didapatkan bahwa baik pemerintah Jepang maupun pemerintah Indonesia merespon dampak pandemic pada sektor pendidikan dengan cara yang sama yaitu melakukan penutupan sekolah-sekolah untuk melindungi keselamatan dan Kesehatan dari warga sekolah. Kedua pemerintah juga mengeluarkan kebijakan terkait dengan pemberian bantuan dalam mendukung metode belajar dari rumah. Perbedaan yang mendasar adalah anjuran penutupan sekolah tidak menggunakan legalitas dokumen, sementara pemerintah Indonesia menggunakan surat edaran Menteri. Kesimpulan: Kebijakan yang dilakukan oleh pemerintah Jepang dan Indonesia memperlihatkan bahwa dalam situasi darurat respon yang dilakukan perlu didukung oleh kebijakan.
Attitudes and Willingness to Pay Dengue Vaccine: A Survey Data Set Towards Public’s Acceptance in Peninsular Malaysia
Authors: Hisham Muhammad Fauzannaim Badrul , Arham Ahmad Firdhaus , Hasim Nur Asmadayana , Rusly Noor Sharizad , Yusoff Yusnaini Md , Aziz Muhammad Firdaus , Arham Ahmad Fadhly , Norizan Nor Sabrena , Ibrahim Ahmad Nazrul Hakimi
Published: August 2024

The paper analyses data concerning trust in key players, attitudes towards technology, religiosity, perceived benefits and barriers, perceived risks and severity, self-efficacy, cues to action towards attitudes, and the willingness to pay for the dengue vaccine among stakeholders in Peninsular Malaysia. The validated questionnaire was conducted using an online survey platform on the public in Malaysia starting from March to June 2024. The survey data were collected from 580 adults over the age of 18. The participants were 339 females and 241 males. Among the respondents, 281 were willing to pay between 0 and 49 Malaysian Ringgit for the immunization, while the rest were willing to pay more than MYR 50. Generally, the public in Peninsular Malaysia shows a positive attitude towards dengue vaccine and is willing to pay for it. They consider this dengue vaccine as beneficial and a high level of cues to action, translating into high acceptance. However, they also acknowledged concerns about the medium-level dengue vaccine's risks, perceived barriers, and severity. The extensive findings give several possibilities for evaluating the public's reception of the dengue vaccine, supporting the formulation of policies and action plans, and promoting more studies by other researchers interested in the methodology and data presented.
Factors Influencing Electric Vehicle Purchases in India: A Quantitative Study
Authors: Bhasin Jay , Srivastava Nandini
Published: August 2024

Environmental concerns and dwindling fossil fuels drive the transition from internal combustion engines to electric vehicles (EVs). This study investigated the factors influencing Indian consumers' EV purchase intentions. Data from 439 respondents, including EV and non-EV owners, were collected using a 12-item survey. A binomial logistic regression model assessed the impact of environmental benefits (EB), cost and economic benefits (CEB), performance and technology benefits (PTB), and social image (SI) on EV purchase decisions among Indian consumers. Results indicate that environmental benefits, cost and economic benefits, and performance and technology benefits significantly influence consumer decisions, while social image does not.  These findings underscore the crucial role of financial factors and vehicle performance in driving EV adoption in India. Implications for government policies and consumer decision-making are discussed.
The Effect of Using Local Wisdom-Based Science Modules, Learning Readiness, And Intrinsic Motivation on Understanding Science Materials in Elementary Schools
Authors: Alwi Mijahamuddin , Arnyana Ida Bagus Putu , Lasmawan I Wayan , Margunayasa I. Gede
Published: August 2024

This study aims to examine the direct and indirect effects of using science modules based on local wisdom, Intrinsic Motivation, and Learning Readiness on Understanding Science Materials in Elementary Schools. The local wisdom approach in the science module is expected to increase the relevance and connection of the material to students' daily lives while learning readiness and intrinsic motivation are considered important factors in the learning process. This study used a quantitative method. This study was conducted at State Elementary School 3 Pancor in class IV consisting of 3 classes as the population. the sample in this study was class IV B which was taken randomly using the lottery method. Data were collected through questionnaires that measured students' perceptions of science modules, learning readiness levels, and intrinsic motivation while understanding science materials using tests. The results of the analysis showed that the use of local wisdom-based science modules had a significant positive effect on the understanding of science materials. Learning readiness and intrinsic motivation also showed a significant positive effect on science comprehension. In addition, intrinsic motivation was found to mediate the effect of using local wisdom-based science modules and learning readiness on understanding science materials.
Evaluation of a novel osmotically volumetric urine index as a rapid and inexpensive marker for certain renal conditions
Authors: Hnativ Volodymyr , Hnativ Yurii , Korda Mykhaylo
Published: August 2024

An open question remains the assessment of the patient's condition in various kidney diseases using inexpensive laboratory methods. The aim of this study was to evaluate the diagnostic tool, the osmolal-volume index of urine, calculated based on urine density and hourly diuresis. A retrospective study of medical records of 86 intensive care unit patients was carried out (34 – with diabetes insipidus, 30 – with acute renal failure, 22 – with chronic renal failure), as well as a prospective study involving 22 healthy individuals without renal pathology. Urine samples were collected three times over a three-hour period; the index and volume of each fraction were measured and averaged. One-way Analysis of Variance was used to evaluate the influence of study groups on osmotically volumetric urine index; means were separated using Fisher’s Least Significant Difference procedure (p < 0.01). There was a significant difference between study groups regarding the proposed index (p < 0.01), and in healthy individuals it ranged from 8.0 to 12.0. In diabetes insipidus, the proposed index sharply decreased, acquiring values below 1.0. At the initial stage of acute renal failure, its value increased (22.0 ± 5.5), while at the stage of polyuria decreased to 2.0. Chronic renal failure was manifested by the index decrease (4.2±2.1). The osmotically volumetric urine index is a dynamic indicator of the efficiency of excretory and concentration renal function applicable for the field hospitals where necessary lab equipment and reagents are unavailable and history of patient’s water consumption, retention, and loss, is known. In case of impaired renal function, this index can vary significantly from 0.02 to 30. The simplicity of the method, its non-invasiveness, plus as the communicativeness, deserve the introduction of this marker into clinical practice
Anthropometric indices, a predictive marker for stroke and other metabolic disorders
Authors: Orupabo Clinton David , Owualah Solomon David , David Iberedem Clinton
Published: August 2024

Although a lot of studies have been conducted on stroke and its prevention, stroke is yet a common occurrence in Nigeria. Hence, it becomes imperative to identify reliable and easily accessible predictive markers for stroke and other metabolic disorders. This research therefore aimed to investigate the prediction of stroke and other metabolic disorders using anthropometric indices. The study population included 211 subjects with 86 males and 125 females at an age range of 26 to 95 years, and the selection process employed a simple random sampling method. Anthropometric indices like body mass index, waist-hip ratio, waist circumference, hip circumference and abdominal circumference were measured. The body mass index and waist-hip ratio in females compared to their male counterparts were 32.32 and 28.90 respectively for body mass index and 0.95 and 0.94 respectively for the waist-hip ratio. the male subjects had an abnormal body mass index, with peak values at ages greater than 36. The most number of stroke and metabolic disease patients were documented at the ages of 56 to 65 years for both male and female subjects. All females diagnosed with stroke and other metabolic diseases had an abnormal waist-hip ratio (>0.80) while most of the males had a high normal waist-hip ratio. There was also a statistically significant sex variation (p ≤ 0.05) in the body mass index and hip circumference for patients diagnosed with stroke and other metabolic diseases. No correlation exists between age and the occurrence of stroke and other metabolic diseases. The findings show that the anthropometric indices: body mass index, waist hip ratio and hip circumference were better predictive markers than the age of the subjects. Again, the study showed that high normal waist hip ratio was a good predictive marker for stroke and other metabolic disorders in males. The findings of this study will be relevant for medical experts, nutritionists, and stroke prevention associations
Comparison of the effectiveness of training methods for medical practitioners in Ukraine regarding anaphylaxis
Authors: Pokryshko Alina , Dutchak Olha
Published: August 2024

The incidence of аnaphylaxis is increasing worldwide. Therefore, it is crucial for medical practitioners to be aware of anaphylaxis symptoms and respond effectively. The purpose of this study was to assess the level of Ukrainian doctors’ knowledge regarding anaphylaxis, ability to recognise variable presentations of anaphylaxis, and knowledge of adrenaline administration depending on their form of training. A cross-sectional study was conducted between May 2023 and June 2023, involving physicians from different specialities, medical paramedics, medical students, and interns in Ukraine. A standardised anonymous questionnaire based on the European Academy of Allergy and Clinical Immunology guidelines for anaphylaxis (2021 update) was used to evaluate knowledge of clinical criteria for diagnosing and managing anaphylaxis. The study compared two groups: one with completed European Resuscitation Council courses in simulation centres (Group 1) and the other without such practical reinforcement (Group 2). Respondents primarily acquired knowledge about anaphylaxis during university studies and from Ukrainian guidelines, with only a minority referring to international guidelines. Approximately half of the participants completed European Resuscitation Council courses in simulation centres, suggesting the potential positive impact of practical reinforcement of theoretical knowledge. Group 1 demonstrated a higher percentage of recognising anaphylaxis in scenarios that combined symptoms of the respiratory and gastrointestinal systems without involving mucosal tissue and skin manifestations after exposure to a potential allergen, and they also provided more accurate responses regarding the route of adrenaline administration. Nonetheless, there was also a tendency for overdiagnosis by respondents in Group 1. The study revealed differences in the diagnosis and management of anaphylaxis among medical practitioners, with an advantage for those who completed European Resuscitation Council courses. Continuous education and simulation-based training are crucial for reducing anaphylaxis mortality and improving treatment outcomes
F. POULENC'S VOCAL CYCLE “CALLIGRAMMES” ON G. APOLLINAIRE'S POEMS: SYNTHESIS OF MUSIC, POETRY AND GRAPHICS
Authors: Хилько Н.П. , Трусова В.Г.
Published: August 2024

В центре внимания авторов статьи – вокальный цикл Ф. Пуленка «Каллиграммы» (1948), созданный на поэтические тексты из одноименной книги стихов Г. Аполлинера. Сочинение проанализировано в аспекте музыкального воплощения фигуративной поэзии и принципов организации цикла. В своей интерпретации вербально-визуального первоисточника композитор опирается на гармонические средства с закреплённой семантикой, звукоизобразительные приёмы, жанровую конкретизацию. Для построения лирического сюжета и объединения миниатюр в цикл он формирует интонационные, тональные связи, создаёт систему особых гармоний. «Каллиграммы» Аполлинера – Пуленка можно трактовать и как дневник человека, прошедшего войну, и как музыкальный памятник всему «потерянному поколению» (Г. Стайн).

The authors of the article focuson F. Poulenc's vocal cycle “Calligrammes” (1948), based on poetic texts from the collectionof poems of the same name by G. Apollinaire. The composition is analyzed in the aspect of the musical embodiment of figurative poetry and the principles of the organization of the cycle. In his interpretation of the verbal-visual primary source, the composer relies on harmonic means with fixed semantics, sound-descriptive techniques, and genre concretization. To build a lyrical plot and integrate miniatures into a cycle, he forms intonation and tonal connections, and creates a system of special harmonies. Apollinaire–Poulenc's “Calligrammes” can be interpreted both as a diary of a man has gone through the war, and as a musical monument to the entire “lost generation” (G. Stein).
Poesididaktik och kulturell hållbarhet – bortom mätbarhetens horisont
Authors: Pettersson Karolina
Published: August 2024

Artikelns övergripande syfte är att diskutera poesididaktik i relation till kulturell hållbarhet. Artikeln utgår ifrån en tidigare genomförd studie om hur poesin hanteras inom ämneslärarprogrammets svenskämne. Det empiriska materialet består av åtta intervjuer med universitetslärare från åtta olika universitet om deras yrkesmässiga förhållande till poesi. Resultaten visar att förekomsten av poesi skiljer sig markant åt på de olika lärosätena och samtliga lärarutbildare anser att poesin har eller borde ha en viktig roll inom ämneslärarutbildningen och anger pragmatiska, didaktiska, kulturbärande samt emancipatoriska argument för detta. Lärarutbildarna anser att poesin får stå tillbaka till förmån för mer prosaorienterade texter och de menar att både studenter och många lärare tycker att poesin är svår och abstrakt att arbeta med, vilket enligt lärarutbildarna delvis har att göra med kravet på mätbarhet inom gymnasieskolan och inom ämneslärarutbildningen. I artikelns avslutande del diskuteras didaktiska implikationer från ovan nämnda studie utifrån ett kulturellt hållbarhetsperspektiv. Artikeln är relevant för både svensklärarutbildningen och gymnasieskolan och den kan bidra till att synliggöra poesididaktikens möjligheter ur ett kulturellt hållbarhetsperspektiv i gymnasieskolans svenskämne och inom svensklärarutbildningen.
Can we understand sustainability through poetry didactics, and can poetry didactics be developed through theories about sustainability?
The overall purpose of this article is to discuss poetry didactics in relation to cultural sustainability. The article is based on a previously conducted study of how poetry is handled within the subject Swedish in teacher training education. The empirical material consists of interviews with eight university teachers from eight different universities about their professional relationship to poetry. The results shows that the prevalence of poetry differs significantly at the different universities. The teacher educators believe that poetry has or should have an important role in teacher education, and they state pragmatic, didactic, cultural and emancipatory arguments for this. The teacher educators argues that poetry is left behind in favor of more prose-oriented texts and they also express that many teachers find that poetry is difficult and abstract to work with, which partly has to do with the requirement for measurability. In the concluding part of the article, didactic implications from the above-mentioned study are discussed from a cultural sustainability perspective. The article is relevant for both Swedish teacher education and upper secondary school and it can contribute to make the possibilities of poetry didactics visible from a cultural sustainability perspective in upper secondary school and within Swedish teacher education.
”Jag vet att man inte kan förändra världen, men man kan ha ambitioner” – sociala hållbarhetsperspektiv på ett musik- och poesiprojekt
Authors: Dahlbäck Katharina , Schmidt Catarina
Published: August 2024

Att utforska olika problemställningar genom konst kan vara ett sätt att ge nya perspektiv på komplexa frågor i arbetet med att uppfylla målen i Agenda 2030. När samhället ska anpassas till ekologiska, ekonomiska och sociala förändringar kan estetiska uttryck spela en viktig roll för att hitta nya vägar till lösningar och visa olika möjligheter för hur vi kan leva våra liv. Denna artikel utgår från ett musik- och poesiprojekt som pågick under ett år. Syftet är att undersöka möjligheter och hinder för konstnärliga samverkansprojekt i skolan samt relationen mellan rumslig, social och didaktisk inkludering och betydelsen av dessa ur ett hållbarhetsperspektiv. Intervjuer har valts som metod, och Illeris teori, Art Education for Sustainable Development, används som teoretisk utgångspunkt. I resultatet synliggörs hur elever inkluderades såväl rumsligt och socialt som didaktiskt i en konstnärlig process. Projektet var en deltagarorienterad intervention och kan ses som ett exempel på hur olika former av inkludering är en förutsättning för ett socialt hållbart samhälle.
"I know that you can´t change the world, but you can have ambitions" - social sustainability perspectives on a music and poetry project
Exploring different issues through art can be a way to provide new perspectives on complex issues in the work of meeting the goals of Agenda 2030.When society has to adapt to ecological, economic and social changes, aesthetic expressions can play an important role in exploring new ways to find solutions and show different possibilities for how to live our lives. The article is based on a music and poetry project that lasted for one year. The aim is to investigate opportunities and obstacles for artistic collaboration projects in schools, as well as the relationship between spatial, social and didactic inclusion, and the significance of these from a sustainability perspective. Interviews have been chosen as a method, and Illeris’ art theory, Art Education for Sustainable Development, is used as a theoretical starting point for analysing the material. The results highlight how students were included spatially, socially and didactically in an artistic process. The project was a participant-oriented intervention and can be seen as an example of how different forms of inclusion are a prerequisite for a socially sustainable society.
OPTIMIZING THE GENERAL STORE AT HOTEL 88 EMBONG KENONGO DI SURABAYA
Authors: Fri Medistya Anke Priyono Merrinda Tri Amalia,
Published: August 2024

This study describes the optimization of the general store by changing the habits of employees at Hotel 88 Embong Kenongo Surabaya. With the VHP system in the hotel, employees should comply with hotel procedures by using the VHP system if there is a demand for goods in their respective departments in the general store. The purpose of this study was to determine the final results of the inventory carried out at the end of the month by the storekeeper the same as the VHP system at Hotel 88 Embong Kenongo in order to facilitate the work carried out by the storekeeper. The results of this study are descriptive explanations of Store Requirements obtained from the VHP system in the form of hardcopy.
Research on Keywords Extraction of Film Reviews Based on the KeyBERT Model
Authors: Huang Qikang
Published: August 2024

Current film and television platforms still struggle to effectively gather information for users and companies through public film reviews. There is a lack of applications or research on keyword extraction from film reviews. This study aims to evaluate the effectiveness and feasibility of the KeyBERT model for extracting keywords from film reviews. The precision and recall rate are utilized to assess the impact of model extraction. The test results indicate that the average precision and recall rate of film review extraction are 0.600 and 0.387, respectively, which are slightly lower than those of other types of text. Specifically, the precision rate and recall rate of plot description film reviews are 0.80 and 0.50, respectively, which are higher than the rates for multidimensional subjective analysis film reviews (0.40 and 0.33). Furthermore, they surpass the precision rate of 0.20 and the recall rate of 0.20 for subjective emotional expression type reviews. It is worth noting that the precision rates decrease from 0.80 to 0.20 as the number of words reviewed increases from 100 to 500 in subjective emotional expression type reviews. While the KeyBERT model is suitable for extracting keywords from movie reviews, it is essential to consider the classification of such reviews, the structural breakdown of lengthy text, and minimizing personal bias as much as possible.
NeRF-based Real-Time Rendering Photo-Realistic Graphics Methods Review
Authors: Yang Shunyu
Published: August 2024

Recently, the publication of the Neural Radiance Field (NeRF) has sparked a surge in further research, unveiling a wealth of innovative solutions in related domains. With its potent predictive capabilities and operational efficiency, neural networks have addressed the traditional trade-off dilemma, enabling real-time rendering at state-of-the-art (SOTA) quality. This breakthrough has introduced a novel rendering approach in computer graphics (CG) and unlocked fresh opportunities for real-time applications such as video games, augmented reality (AR), and virtual reality (VR). This paper aims to offer a comprehensive overview of recent NeRF-like methodologies and explore potential pathways for enhancing NeRF to achieve both real-time performance and photorealistic standards. Our study includes a comparative analysis of these methodologies in terms of their efficacy and hardware requirements. Ultimately, this paper outlines potential future advancements in this field. The objective of this paper is to familiarize both newcomers and researchers with NeRF, catalyze in-depth investigations, propose enhanced methodologies.
Comparative Analysis of Convolutional Neural Network and Multilayer Perceptron on Power Quality Classification
Authors: Wang Jiemin
Published: August 2024

Power quality classification is essential for identifying and categorizing various power quality issues within electrical systems. With the increasing integration of electronic devices into power grids, including converters, rectifiers, and inverters, the significance of real-time monitoring and fault prediction has grown. This paper explores the application of image classification techniques, specifically Convolutional Neural Networks (CNN) and Multilayer Perceptron (MLP), in addressing power quality issues. Using a dataset consisting of signals representing different power quality conditions, models were trained and evaluated based on accuracy and loss metrics. The results indicate that while CNN achieved the highest accuracy and lowest loss, MLP models demonstrated efficiency in terms of computational resources. Additionally, the study discusses the potential of image classification technology in enhancing power quality monitoring and management, with implications for improving energy utilization efficiency and supply quality. The findings highlight the importance of leveraging deep learning techniques for addressing complex power quality challenges and paving the way for the intelligent and automated development of power systems.
High-order total differential solution of multivariate function based on Yang Hui triangle
Authors: Zhang Tianyuan
Published: August 2024

Under the current study, there are fewer ways to solve the higher-order full differential of multivariate functions. This paper introduces Yanghui Triangle and the higher-dimensional form of Yanghui Triangle to study the field of higher-order full differential solution of multivariate functions, to summarize the general formulas and to improve the efficiency of operation. Based on the assumptions of infinite partial derivatives and continuity of partial derivatives, this paper first proves the correctness of the higher-order total differentiation formula for binary functions by mathematical induction and tests it with examples, and the result proves that the formula is valid and has good applicability. Then, the higher-order total differential formula for ternary functions and more multivariate functions are mathematically inducted and proved to obtain a generalized formula, which improves the computational efficiency. The generalized formulas studied in this paper assume that multivariate functions can have partial derivatives and the partial derivatives are continuous, which is consistent with the assumptions of full differential computation of multivariate functions in practical applications of science and engineering technology. However, for the case where the partial derivatives of multivariate functions are not continuous, it needs to be proved in future studies.
Revolutionizing Surgery: The Impact of Machine Learning and Artificial Intelligence on Surgical Robotics
Authors: Yuan Weihang
Published: August 2024

This article examines the transformative impact of machine learning (ML) and artificial intelligence (AI) on surgical robotics, highlighting the advancements that have significantly enhanced precision, efficiency, and safety in surgeries. The integration of these technologies has enabled surgical robots to perform complex tasks autonomously, with accuracy rates approaching those of human surgeons. Key developments include improved surgical tool tracking, real-time data analysis, and enhanced decision-making capabilities during operations, which collectively contribute to reducing operation times and complication rates. The discussion extends to the potential future directions of these technologies, emphasizing continuous improvement in human-robot interaction, regulatory adaptations, and broader application across various medical fields. The anticipated advancements are expected to make high-quality surgical interventions more accessible, particularly in remote and underserved areas, ultimately revolutionizing patient care by making surgeries safer, faster, and more patient-centered. The article underscores the role of ongoing research and development in pushing the boundaries of what surgical robots can achieve, setting the stage for a new era in medical technology.
Research of Bio-Inspired Motion Control in Robotics
Authors: Du Fengze
Published: August 2024

Bio-inspired motion control in robotics draws inspiration from biological systems to enhance the movement capabilities of robots. This article explores the integration of bionics techniques in robots’ path planning, motion control, and design of moving parts, offering advantages over traditional robots control systems. In path planning, bio-inspired approaches, such as swarm intelligence algorithms and artificial neural networks, optimize trajectories and enable obstacle avoid ability in complex environments. Furthermore, bio-inspired design principles facilitate the creation of motion components tailored for specific locomotion modes, such as legged locomotion and aquatic propulsion, improving robots’ agility and adaptability. Reflex-based and vision-based control methods emulate biological responses and utilize visual sensors to enhance robots’ perception and responsiveness. Additionally, recent advancements include the exploration of Long Short-Term Memory Networks (LSTM) for predicting control inputs based on animal trajectories. Through a synthesis of biomechanical principles, materials science, and artificial intelligence integration, bio-inspired motion control revolutionizes robotic capabilities, with implications for autonomous navigation and task execution in dynamic environments. Future research directions include further investigation into biomechanical principles, advancements in materials science, and the integration of artificial intelligence algorithms for enhanced autonomy and adaptability in robotics.
Systematic analysis of Structural optimization design of post-disaster emergency rescue robot
Authors: Chu Kaixiang
Published: August 2024

Natural disasters do great to human life and post-disaster relief is significantly essential. Moreover, the post-disaster emergency rescue robots play an important role in searching, rescuing, transportation and detection. This paper wants to enhance the stiffness and force limit through the characteristics of the mortise and tenon structure by applying the mortise and tenon structure to the mechanical arm of the post-disaster emergency rescue robot. By analyzing the mechanism of robotic arm movement and mortise and tenon structure, it demonstrates the feasibility of the optimization of mortise and tenon structure applied on the mechanical arm. Through the application examples of mortise and tenon structure in related fields, the advantages and disadvantages of mortise and tenon structure in mechanical arm are demonstrated. The future prospect of mortise and tenon structure in mechanical arm is very optimistic, which can be used as an auxiliary connection mode in the design of mechanical arm, combined with traditional methods such as welding and bolt connection, to balance performance, cost and maintainability.
Underwriting Risk Prediction Based on Moran Process and Fourier Fitting
Authors: Shen Tao , Liu Zheng , Li Kejin
Published: August 2024

Extreme-weather events are becoming a crisis for property owners and insurers, which has drawn widespread attention. For insurance companies, predicting regional losses from Extreme-weather events and studying the participation decisions of insurers can inform insurance strategies. This paper primarily focuses on the impact of extreme-weather events on insurance behavior, based on ten years of extreme-weather data of Michigan, we use Fourier fitting to predict the frequency of extreme-weather events in the subsequent year. Next, to measure underwriting risk, this paper considers economic losses and pay-out ratios, etc. to apply Biotic community Strategy-updated Moran process to simulate the social leaning and reciprocal mechanism in catastrophe insurance purchase decision-making combined with the prediction results and consumers with different levels of risk-aversion and expected utility perception. Based on the above analysis, the URA model is developed, and the validity of the model is demonstrated using Michigan as an example. The evolutionary dynamic analysis based on Moran process expands the research perspective of the insufficient demand for Extreme-weather insurance, and can be a reference for insurance company's insurance strategy.
Deep Reinforcement Learning for Motion Control Algorithms in Robotics
Authors: Liu Qisong
Published: August 2024

This article introduces the concept and practical applications of deep reinforcement learning (DRL), describes the core principles and main algorithms of DRL, and summarizes the advantages of DRL and its differences from traditional reinforcement learning (RL). At the same time, it introduces the broad application prospects of DRL in various fields and lists some examples about the mechanical field. This includes applications for classifying colored objects, agricultural applications for robotic arms to cut fruits, branches, or twigs, a DQN based learning system, a mobile robot that can perform selection and placement operations, A path-planning approach concerning crop picking robotic arms, and a decision algorithm for robot tracking applications, Examples of algorithms for unmanned underwater vehicles and improvements for DRL to solve trajectory tracking control problems in autonomous underwater robots. Finally, the powerful role of DRL in future industrial development and other fields was summarized. Despite the significant progress made in DRL, there are still challenges to be addressed, such as the need for large amounts of training data, the difficulty in designing appropriate reward functions, and the potential for instability during training, which present opportunities for future research and development in this rapidly evolving field.
NBA Player Salary Projections Based on Gradient Boost in 2022-23 Season
Authors: Wang Yuxuan
Published: August 2024

The National Basketball Association (NBA) game has a high international profile, and the level of NBA players and salary projections can help clubs make the right decisions. This study introduces the research topic of predicting NBA salaries for the 2022-23 season. The proposed method utilizes a gradient boosting algorithm to analyze player performance metrics and historical salary data. The specific process includes data preprocessing, feature selection, model selection and evaluation. The proposed method is evaluated through a large number of experiments, and the gradient boosting model outperforms other methods in terms of Mean-Square Error (MSE) and R-squared values. This study demonstrates the potential of machine learning in predicting the salary of NBA players. The results showed that the gradient boosting model achieved an R-squared value of 0.74, indicating that the model is proficient in capturing the relationship between player performance metrics and salary. Clubs can use the model to rationalize their overall layout by capturing the relationship between player performance metrics and salary.
Insurance Company’s Undertaking Model Based on ARIMA Algorithm
Authors: Wang Kaiyue , Xu Changcong
Published: August 2024

Extreme weather can cause economic losses to residents which can be reduced by purchasing insurance. From the perspective of insurance company, to earn maximum profit, they need to carefully consider whether to provide insurance services in a region, especially for where natural disasters happen frequently. The paper puts forward Natural disaster prediction model and Insurance company undertaking model. In the first part, the paper uses ARIMA algorithm to forecast when will the natural disasters happen, including extreme high and low temperature, droughts and floods, and tornadoes. Data form four different regions follow as example. In the second part, the paper applies C-L risk model, and converts it into Compensation forecast model. As an example, this model is also used to value the insurance details in Tempa, America and Jakarta, Indonesia. When the total insured value of the two places is greater than 44 billion dollars and 143 billion dollars respectively, the insurance company can make a profit.
Research on state volatility of tennis players based on sliding window technique
Authors: Sun Yanze , Chen Zhaoqi , Wang Qiutong
Published: August 2024

This paper aims to study the state volatility of players in tennis matches and proposes a conceptual model of "momentum" based on sliding window technology. Firstly, Pearson correlation analysis is used to screen out the indicators that are highly relevant to the victory of the match, and the index system including point advantage, service advantage, break point, unforced error, ace, game won and distance run is constructed. Then, a formula for calculating "momentum" is proposed, and the sliding window algorithm is used to calculate the "momentum" value of each athlete. Finally, the change of "momentum" during the game is demonstrated visually, and the correlation between "momentum" and the outcome of the game is verified. In addition, the "momentum" transition point is defined, the "momentum" difference series is calculated by moving average method, and the randomness of the "momentum" transition point is analyzed by Run test. The results show that the change of "momentum" has a significant impact on the outcome of the match, and the change of "momentum" is not random.
Analyzing the Research and Application of the RSA Cryptosystem in the Context of Digital Signatures
Authors: Ding Mashuo
Published: August 2024

The article commences by laying the foundational knowledge of public key cryptographic systems, with a focus on RSA encryption and digital signature technology. It then utilizes RSA encryption as a paradigm to elucidate the application of this encryption in digital signature schemes, offering an algorithmic perspective. A critical link is established between the security of digital signatures and the robustness of RSA public key cryptographic systems. Subsequently, the article presents a concise overview of the potential threats facing RSA encryption and proposes pertinent strategies to mitigate these security challenges. It culminates in a synthesis of three distinct approaches, encompassing both algorithmic and hardware aspects, to augment the efficiency of modular exponentiation – a key operation in RSA encryption. Since the inception of the RSA public key cryptosystem, it has been subject to continuous refinement, standardization, and practical deployment. Enhancing the computational efficiency and security of RSA encryption remains a pivotal area of research and discussion within the cryptographic community. Furthermore, with the progressive advancements in quantum theory, traditional public key systems, including RSA and elliptic curve cryptography, confront emerging security vulnerabilities. Thus, the pursuit of post-quantum cryptography, capable of withstanding quantum attacks, represents a frontier scientific endeavor in the field of cryptography.
Prediction and Feature Importance Analysis for Heart Failure using Machine Learning Techniques
Authors: Zeng Yu
Published: August 2024

Heart failure is a clinically complex syndrome that affects millions of people globally and posts a heavy strain on healthcare systems because of its high rates of morbidity and mortality. To manage and treat it effectively, early detection and accurate prediction are essential. Machine learning algorithms present a viable technique. This research attempts to forecast heart failure under specific circumstances, using a heart disease dataset from kaggle.com that is accessible to the public. Five machine learning models: Random Forest (RF), Decision Tree (DT), Logistic Regression (LR), Support Vector Machine (SVM), and K-Nearest Neighbor (KNN), were used as they show strong performance in applications pertaining to machine. The performance of five models was evaluated by accuracy, precision, recall and f1-score with 11 symptoms. From the calculation, Random Forest shows better performance than the remaining models with an accuracy of 88% and ST_Slope_Up is tested to have the greatest impact on the prediction.
Research and Case Study on Insurance Underwriting Decision Making Based on LSTM, SVM and Random Forest Modeling
Authors: Zhou Yunzhi , Huang Xiubi
Published: August 2024

In recent years, the world has suffered from a variety of extreme weather events, which are gradually becoming a crisis for insurance companies and homeowners. To address the challenges of insurance companies in their underwriting decisions for a specific region, this paper proposes a binary classification model for determining whether they should be underwritten, with an output of 1 (underwritten) or 0 (not underwritten). Firstly, extreme weather events are identified by screening the number of casualties, property and crop losses and quantifying the impact of these events. Then, a system of metrics was developed, including the use of LSTM models to predict risk indicators. Correlation analyses were conducted to verify the validity of the indicator system, and then the quantitative data were fed into SVM models and random forest models to solve the classification problem. Finally, recommendations for insurance companies and homeowners were made based on the results, and two regions on different continents were selected to demonstrate the effectiveness of the model.
Multi-Master Model: Better Way to Calculate the Final Weights
Authors: Ma Bowen
Published: August 2024

Under the current age in which data privacy and security become more and more important, as the short-coming of traditional distributed machine learning algorithms, the Federated Learning algorithm (FL), became more apparent. We need a better algorithm framework to handle more complex training scenarios. The focus of this paper is on multi-master Federated Learning, specifically on the last part of how to handle the weights from different master nodes. In this algorithm, instead of using only one central server to receive all weights calculated by the clients, we use multiple master nodes, each taking care of a portion of the data. There is no interaction between clients under different master nodes. Finally, the master nodes process their weights together to get a final weight. In this paper, the number of master nodes is 3, and there are 3 approaches we propose: 1) do a simple average of the weights. 2) do a weighted average. 3) apply the traditional Fedavg algorithm on these weights.  As a result, we found that for simple dataset, three approaches to get final weights in multi-master FL perform similarly and are no worse than traditional FedAvg, while for complex real-world data, approach 3 has a slightly better result.