Perform Data Analysis Research Articles

The triple-helix model as foundation of innovative entrepreneurial ecosystems

Purpose Recommend ways to increase the entrepreneurial performance score for city-centered entrepreneurial ecosystems (EES) and the possible role within the triplex helix concept. The former applies to lower-ranked EESs. For the latter, the authors hypothesize that the standard triple helix model of counting on some existing, well known multi-party interest in bringing together businesses and other stakeholders (especially universities and government) is insufficient. Including more players and alternative cooperation models may truly enable a long lasting productive interplay of cooperative and competitive forces leading to genuinely innovative (non-marginal, non-opportunistic) businesses and also to more regional well-being. This study aims at leveraging the city-based view on entrepreneurial ecosystems, to enable new ways of boosting the functionality of triple-helix minded programs, especially so for emerging economies and their cross-border regions. Design/methodology/approach Methodology is dual: (1) paying tribute to the history of entrepreneurial behavior and reviewing the diverse sources of support (institutional and otherwise), eventually leading to today’s concentrations of successful innovative business, and (2) analyzing modern data on performance of city-centered EESs. Putting emphasis on path-dependence (meaning that history matters) the authors analyze data distributions of measures concerning city-centered EESs and also nation-wide entrepreneurial aggregates, searching signs of long-tail statistic, an indicator of path dependence. Furthermore the authors statistically analyze if and to what extend, key university performance indices translate into successful EESs. From these results, recommendation follow. Findings Historical developments and cultural backgrounds of different countries lead to different ways of support for new ideas and approaches. Entrepreneurship success is path-dependent and there are entrepreneurial approaches more easy – but others way more difficult – to imitate (e.g. “Hidden Champions”). Statistical analysis of entrepreneurial performance data confirms power law decay, which confirms the action of path-depend processes. Statistical models show that university performance is just weakly influencing entrepreneurial success. This leads to proposing a new, polycentric approach for organizing robust cooperation between the stakeholders, potentially capable of lifting low-score EESs into a more productive regime. Research limitations/implications Research limitation primarily concern data availability, as potentially useful information is not publicly available. The findings lead to further questions, and to questioning too optimistic expectations about cooperation readiness of the implied triple helix stakeholders. As pointed out repeatedly, other important players are non-university research organizations with more focused goals (national labs, etc.), which should be explicitly accounted for. Another directions is distinguishing between support for (a) short-term high-tech, and for (b) longer term, deep-tech entrepreneurs. The latter may require new evaluation techniques and specially tailored private-public partnerships. Finally, polycentric and polycentric cross-border cooperation requires more research into networked organizations. Practical implications Triple-helix based, cooperative agreements should rethink the effective functionality of EESs. City-centered, EESs tend to be most expressive in their projects but also subject to a stronger long tail-effect. This implies “success-breads-success” for the front-runners, enjoying very low ranking mobility. Lower-positioned EESs may profit from the mobility within this score region. However, the still lower positioned ones face strong downward rank mobility, implying that special effort (support) is needed. The authors’ proposal of polycentric cooperation, especially the cross-border variant, would credibly enable such support by bringing in both, more players and more levels of play, helping robust networking. Social implications This research has potentially profound social implications. Acknowledging the path-dependence of intellectual capital formation and emergence of entrepreneurial spirit in the modern sense helps to understand which techno-entrepreneurial endeavors would have increased chance of success of being brought up in the respective EES. It is also useful to identify the oversold (over-hyped) entrepreneurial projects. Concerning the cross-border polycentric organizations harboring EESs one may have to concede that national borders are sub-optimal, at least with regard to lifting up the entrepreneurial reputation of border-adjacent regions. Implicitly, opportunistic and marginal entrepreneurship (e.g. in Eastern Europe) would reduce. Originality/value This research claims originality for combining a qualitative-historical approach with a statistical modeling approach, which can explain the dynamic nature of the involved entrepreneurial processes (long-tails). While the historical approach is deemed to be necessary for understanding the reasons for the large differences in entrepreneurial success across nations and regions, the statistical modeling is helping to identify lower-score intervals within the entrepreneurial score hierarchy which are more easy, or, alternatively, very hard to escape. This in turn leads to adapted recommendations and mobilizing special efforts for creating new, potentially robust, cooperative arrangements beyond the triple-helix concept.

THE CULTURE OF ARABIC LANGUAGE LEARNING IN THE PERSPECTIVE OF BERNARD SPOLSKY'S LANGUAGE POLICY

<p><em>This article examines the culture of Arabic language learning through the perspective of Bernard Spolsky's language policy. Spolsky defines language policy as language practice, language belief, and language management. In the context of Arabic language learning, language policy includes the implementation of appropriate curricula, effective teaching methods, and sustainable management of education. </em><em>This research uses qualitative studies. Data collection techniques are carried out through observation, interviews and documentation. To perform data analysis, researchers use analytical theory according to Miles and Hiberman by condensing data, presenting data and drawing conclusions. At the data validity checking stage, researchers used triangulation methods and peer discussions.The study's findings are as follows: First, the ministry of religion's policy teaching materials are implemented using the minister of religious affairs' decree number 183 of 2019, and the administration of both madrasas construct learning plans, selecting appropriate learning media and methods. In madrasah Aliyah Negeri 2 Malang, the concept is similar. In developing learning activities, there involves planning for learning. Second, in both madrasas, the actuality of studying Arabic is based on the 2013 curriculum. And the ministry of religion's policy in the field to be more innovative and intensive on learning Arabic, as well as the idea of dimadrasah Aliyah negeri 2 Malang for arabic language learning to be done in a network or from home.</em><em></em></p>

Multiple-wave admixture and adaptive evolution of the Pamirian Wakhi people.

While whole-genome sequencing has been applied extensively to investigate the genetic diversity of global populations, ethnic minority groups in Pakistan are generally underrepresented. In particular, little is known about the genetic origin and highland adaptation of the Pamirian Wakhi people. According to Chinese historical records, the geographical location and language usage of Wakhi may be closely related to Xinjiang Tajiks (XJT). In this study, based on high-coverage (∼30×) whole-genome sequencing of eight Wakhi and 25 XJT individuals, we performed data analyses together with worldwide populations to gain insights into their genetic composition, demography, and adaptive evolution to the highland environment. The Wakhi derived more than 85% of their ancestry from West Eurasian populations (European ∼44.5%, South Asian ∼42.2%) and 10% from East Eurasian populations (Siberian ∼6.0%, East Asian ∼4.3%). Modeling the admixture history of the Wakhi indicated that the early West-East admixture occurred approximately 3,875-2,250 years ago and that the recent admixture occurred 750-375 years ago. We identified selection signatures across EGLN3, in particular, a distinctive evolutionary signature was observed, and a certain underlying selected haplotype showed higher frequency (87.5%) in the Wakhi than in nearby XJT and other highlanders. Interestingly, we found high-frequency archaic sequences in the Wakhi genome, which overlapped with several genes related to cellular signaling transduction, including MAGI2, previously associated with high-altitude adaptation. Our analysis indicates that the Wakhi are distinct from the XJTs and Tajikistan Tajiks, and shed light on the Wakhi's ancestral origin and genetic basis of high-altitude adaptation.

Operation Data Analysis and Performance Optimization of the Air-Cooled System in a Coal-Fired Power Plant Based on Machine Learning Algorithms

Operation Data Analysis and Performance Optimization of the Air-Cooled System in a Coal-Fired Power Plant Based on Machine Learning Algorithms

Data Mining Methods in Educational Process Management

The paper addresses the challenge of effectively managing the educational process by leveraging intelligent data analysis of student performance during learning activities. It introduces an approach centered around data clustering, specifically applied to the study of programming disciplines and languages. By utilizing clustering techniques, the paper aims to identify the most challenging topics within a given academic subject, track students’ learning paths, evaluate and enhance teaching methodologies, and create personalized learning plans tailored to individual students’ needs. This approach enables educators to better understand and address the diverse learning requirements of students, ultimately enhancing the overall educational experience.

Examining refusal and acceptance sequences employing a data-driven binary rating approach

This paper presents the development and preliminary validation of a data-driven analytic binary rating rubric for assessing roleplay-elicited pragmatic performance of learners. The roleplay task consists of two items—one lunch invitation to elicit a refusal and one gift offer to elicit an acceptance in Chinese, both of which involve initiating proposals to return an earlier favor. Responses to return-to-favor invitations or offers in Chinese are challenging for L2 learners as they involve complicated discursive features. To create a practical, reliable tool for evaluating L2 performance, the researchers conducted a discourse analysis of L1 Chinese performance data (N = 22) to identify key discursive features in two domains— Intention (i.e., refusing the return-for-favor intention) and Decision (i.e., refusing/accepting the offer/invitation). A roleplay can receive up to 4 points in both domains, including one point each for orientation/directness, position, modification, and justification, for 8 total possible points. Scores assigned to both L1 and L2 performance according to the rubric reflected high inter-rater reliability. The effect of proficiency was significant on learner scores and interacted with rating domains and scenarios. Lower-level learners scored lower with addressing the interlocutor's return-for-favor intention than with the invitation or offer itself; they also scored lower with refusing than accepting an offer. By focusing on key discursive features rather than exhaustive interactional details, the rubric streamlines the evaluation process while maintaining accuracy and reliability. Future studies may extend the use of the rubric to other speech act sequences and languages and apply it to automated grading systems.

Digital Youth and Family Engagement Program for Adolescents Who Receive Outpatient Mental Health Services: Qualitative Evaluation.

Incidents of depression, anxiety, and suicidal ideation among adolescents have increased in recent years. Mental health interventions tailored to adolescents and families need to consider mechanisms for increasing enrollment and sustaining program engagement. A telephone-based, health coach intervention for adolescents and families was implemented at a Southeastern US health system with the goals of improving psychiatric appointment attendance, medication adherence, reduction in emergency department visits, and assisting with crisis management ("Youth and Family Engagement" [YFE] program). This study aims to explore patients' and parents' perceptions of a mental health program and the factors that impact enrollment and sustained engagement. Semistructured interviews were conducted with adolescent patients (n=9, 56%), parents (n=11, 92%), and clinicians who placed patient referrals (n=6, 100%). Interviews were in English (participants: 19/26, 73%) or Spanish (parents: 7/11, 64%), depending upon participants' preference. Interviews explored perceptions of the YFE program, experiences working with health coaches, suggestions for program changes, and program goals. The data were analyzed using inductive coding methodologies, with thematic analysis used to organize emergent themes. Two qualitatively trained researchers, one bilingual in English and Spanish, facilitated all data collection and collaboratively performed data analysis. The YFE program's structure was often mentioned as promoting engagement, with telephone appointments and health coaches' ability to accommodate inflexible work or school schedules alleviating participation barriers. Skills learned from health coaches were frequently referenced, with adolescents generally citing internal processes, such as positive thinking and mindfulness. Parents discussed behaviors relative to their children, such as improvements with discipline, setting boundaries, and improved parent-child communication. Many participants discussed the importance of health coaches assisting families in navigating social systems, such as accessing resources (eg, housing) and navigating school processes (eg, individualized educational plans), with clinicians suggesting an increased emphasis on adolescents' nutrition and engagement in primary care. Spanish-speaking parents highlighted numerous advantages of working with bilingual health coaches, emphasizing both enhanced communication and cultural understanding. They specifically noted the coaches' ability to grasp their lived experiences and challenges as immigrants in the United States, which significantly enriched their participation in the program. Prioritizing convenient engagement for adolescents and families may be important for sustained program participation, as inflexible schedules and competing priorities pose barriers to traditional appointments. Future programs should carefully consider health coach-participant relationships, specifically cultural competency, providing services in native languages, and assisting families with wraparound care, as these may be crucial to sustained engagement.

Integrating Real-World Data into Actuarial Science Teaching: Examples and Challenges

With the increasing availability of data and advancements in statistical tools, the insurance industry is eagerly seeking students capable of performing data analysis and developing business solutions. To meet this growing demand, many universities and institutions are innovating their actuarial science programs. This trend has led us to develop two types of experiential learning courses that integrate real-world data: a case study with actuarial professionals and an actuarial science senior capstone. In this paper, we provide two specific examples and methodologies. Then we discuss the challenges faced along with recommendations derived from these project experiences.

SolarWinds Attack: Stages, Implications, and Mitigation Strategies in the Cyber Age

SolarWinds is a software company based in the United States that provides IT monitoring and management tools. Founded in 1999, SolarWinds offers a variety of products that help organizations manage networks, systems, IT infrastructure, applications and cloud-based services. SolarWinds products are used for performance monitoring, log management, IT security, and data analysis. The company became widely known after a major cybersecurity incident came to light in late 2020, in which their network management software, Orion, was used as a vector for attacks by a state-backed hacking group. These attacks affected many organizations, including government agencies and private companies, and led to an increased focus on software supply chain security. This paper has reviewed stages, Implications, and mitigation strategies of SolarWinds.

Pengaruh Artificial Intelligence dalam Pembuatan Laporan Keuangan

The presence of Artificial Intelligence (AI) has a significant impact on the preparation of financial reports, especially in terms of efficiency and accuracy. This article aims to examine the influence of AI on the process of preparing financial reports that were previously carried out conventionally and the challenges faced by companies in adopting it. This study uses a literature study method to identify trends and findings related to the use of AI in accounting. The results show that AI is able to improve operational efficiency, data analysis, and security, although there are challenges in its implementation, such as human resource adaptation and data security.

New Approaches in the Assessment of Pre-Service Teachers' Scientific Research Skills: Multidimensional-Many Facet Rasch Model Application

This survey study aimed to determine pre-service teachers’ skills in conducting and presenting scientific research process and to examine their peer-scoring behaviors. The participants consisted of 36 pre-service teachers and seven peer raters. The analytical rubric developed by the researchers and the performance task were used to collect data. The multidimensional many-facet Rasch measurement model was employed in data analysis. Rasch analysis was carried out with a fully crossed design. The analyses revealed that the least difficult criterion in the dimension of conducting the scientific research process was to perform data analysis while the most challenging criterion was to determine the research model and sample. In addition, the least difficult criterion in the dimension of making an oral presentation was to ensure the interest and participation of the audience, while the most challenging criterion was to make an effective start. The analysis of the rater facet demonstrated that the most severity rater was R3, while the most leniency rater was R4. The study argues that the multidimensional many-facet Rasch model can be used to present reliability and validity evidence in multidimensional performance evaluations.

The Proliferation of Artificial Intelligence in the Forklift Industry—An Analysis for the Case of Romania

This paper investigates the impact of artificial intelligence (AI) on the forklift industry, focusing on logistics and procurement within small and medium-sized enterprises (SMEs) in Romania. Using a mixed-methods approach, including interviews with seven managers from a benchmarked company in the forklift industry (BCFI) and quantitative analysis of operational data, we examine the transformative effects of AI integration. Key findings include a 30% reduction in inventory holding costs due to AI-powered predictive analytics; a 15% decrease in procurement costs through AI-driven supplier evaluation systems; a 25% increase in operational efficiency from AI-optimized route planning; a 40% boost in overall productivity attributed to AI-enabled automation; and a projected 20% reduction in low-skilled labor requirements over the next five years. The study employs environmental, social, and corporate governance (ESG), balanced scorecard (BSC), benchmarking, and activity-based management (ABM) models to analyze risks and implications of AI integration. A case study of a leading Romanian SME in the forklift industry is presented, examining financial strategies using McKinsey’s 7S framework. The paper concludes that while AI offers significant operational benefits, it also presents challenges in workforce transition and ethical considerations that require careful management.

AI-empowered Search Drone: Advanced AI-based Tiny Object Search Drone for Broad Areas

Abstract. Detecting the garbage distribution and position in broad terrains(beaches, grasslands, squares, etc) is extremely important for environmental protection and urban image. Manually screen these terrains is time-consuming and ineicient. Autonomous robot may find the wastes automatically, but they may hinder and even endanger the pedestrians. Unmanned aerial systems(UAS) are ideal tools for monitoring broad areas and detecting garbage. In this project we create an AI-empowered Search Drone for extensive garbage search in broad regions. The camera on our drone and the external image transmission module on the computer receive and process top-view image from the sky. The images from high altitude lead to the reduction in target size and object distortion. We innovately modified the network structure of the oicial YOLOv5 model by adding layers specifically for small object detection, enhancing its adaptability to small targets captured by drones.To address camera image distortion, we leveraged the OpenCV (cv2) library to obtain and apply a distortion correction matrix. Furthermore, we devised an algorithm that calculates the real-world location of targets based on their pixel positions, the drones height, and the ratio between pixel and real-world dimensions.To strengthen the models garbage search capabilities, we simulated various garbage search scenarios in open environments and collected image datasets using drones for specialized training. Finally, we utilized the PyQt library to develop a simplistic front-end interface for data analysis and comparison of the detection performance between the original and optimized models. Experimental results demonstrate that the optimized model outperforms the original model, effectively enhancing garbage search eiciency in extensive areas. This project represents a significant step forward in leveraging technology to combat environmental pollution.

Maritime Supply Chain Optimisation: A Case Study of Blockchain Integration in Port Logistics Management

This research examines the potential for maritime supply chain optimisation through the integration of blockchain technology in port logistics management. In the era of global digitalisation, the maritime sector faces challenges to improve operational efficiency and transparency. This study uses a case study approach to evaluate the implementation of blockchain in one of Indonesia's major ports. The research methodology involved qualitative and quantitative analyses, including interviews with key stakeholders, direct observation of logistics processes, and analysis of historical port operational data. The blockchain system was implemented over a six-month period, and its performance was compared with conventional logistics management systems. Key challenges identified include the need for a large initial investment, resistance to change from some stakeholders, and the need for industry-wide standardisation of blockchain protocols. This research demonstrates the great potential of blockchain technology in optimising maritime supply chains. Recommendations for further implementation and future research are discussed, with an emphasis on the importance of industry collaboration and regulatory support to accelerate the adoption of this technology in the maritime sector.

Research on the Innovation in College English Teaching under the Background of New Liberal Arts

This article provides a brief overview of the application and development of artificial intelligence (AI) in the field of education under the background of New Liberal Arts. It also highlights the importance and current status of innovative approaches in college English teaching. Furthermore, it presents the research objectives and methods. The application of artificial intelligence in education has witnessed significant growth and advancement in recent years. AI technologies, such as machine learning and natural language processing, have been utilized to enhance various aspects of education, including personalized learning, intelligent tutoring systems, and automated grading. These applications have shown promising results in improving students' learning outcomes and engagement. In the context of college English teaching, innovation is crucial to meet the evolving needs and expectations of students. Traditional teaching methods often fail to cater to individual learning styles and preferences, leading to limited effectiveness and student disengagement. Therefore, there is a pressing need to explore innovative approaches that leverage AI technologies to create more interactive and personalized learning experiences. The research aims to investigate the potential of integrating AI into college English teaching and its impact on students' learning outcomes and engagement. The study will employ a mixed-methods approach, combining quantitative analysis of students' performance data with qualitative interviews and surveys to gather comprehensive insights. By examining the effectiveness and challenges of AI integration in English teaching, this research seeks to provide valuable recommendations for educators and policymakers to enhance the quality of college English education. Above all , this study aims to explore the application of artificial intelligence in college English teaching, considering its potential to improve learning outcomes and engagement. By adopting a mixed-methods research approach, this study intends to provide valuable insights and recommendations for educators and policymakers to promote innovative and effective English teaching practices.

Latent factors affecting safer injection practices that can reduce infections and how education can improve them.

The incidence of healthcare-associated infections, particularly injection-related infections, can increase patient comorbidities even in countries with adequate medical resources. Although there are clear guidelines for injection practices to prevent infections, their application in clinical settings is insufficient. Therefore, the objective of this study was to identify factors affecting injection practices associated with reduced infections by conducting surveys targeting practicing healthcare providers involved in administering injections at each healthcare organization and performing data analysis. We administered a survey to healthcare providers responsible for injection practices at each healthcare organization that included items related to infection-safe injection practice guidelines. All survey questions were reviewed by an expert panel of infectious disease and infection control nurses. Survey contents were subjected to exploratory factor analysis (EFA), confirmatory factor analysis, and multivariable robust regression tests to determine the impact of each factor and their correlations. A total of 842 questionnaires were analyzed. Each questionnaire was classified into four factors: reuse and contamination, compliance with aseptic technique, exchange of infusion set, and use of multidose vials. Nurses with higher careers showed more compliance. Education within one year and awareness of each item of the questionnaire had positive associations with proper injection practice. Education is thought to be the most important factor in good injection practices that could reduce infections. Relevant knowledge through timely training is expected to have a positive impact on performance and compliance related to safe injections.

Perspektif Rasio Struktur Modal, Likuiditas, Sales Growth dan Ukuran Perusahaan terhadap Profitabilitas Perusahaan Sub Sektor Transportasi di Bursa Efek Indonesia pada Masa Pandemi Covid-19

Profitability is a statistic that assesses a business' capacity to produce revenue or profit over a predetermined time frame. This ratio also gives an indication of how well a company's management is performing, as evidenced by the profit from sales or investment income. The purpose of this study was to determine the effect of capital structure, liquidity, sales growth, company size on profitability. This type of research is quantitative using secondary data. The population in this study are the transportation sub-sector companies on the Indonesian stock exchange for the period 2019-2022. The sampling technique used purposive sampling and resulted in 38 companies. The data studied were 152 companies' financial reports which were the research samples. The SPSS test tool version 26 was used to perform data analysis for this investigation using multiple linear regression. The results of this study prove that liquidity, sales growth and firm size have an effect on profitability, while capital structure has no effect on profitability.

Big Data Analysis of the Speed Performance of a 176k DWT Bulk Carrier in Real Operating Conditions

Assessment of ship performance under in-service conditions is challenging due to the complex effects of many environmental disturbances. ISO 15016 and ISO 19030 standards are commonly used to evaluate ship operating performance. However, ISO 15016 requires numerous variables, a complex calculation formula, and considerable time and cost, and ISO 19030 only evaluates the reduction of ship speed caused by wind and neglects the effect of waves. To improve both standards and achieve a more accurate ship performance assessment, this study proposes a new performance prediction model, the multi-input single-output (MISO) system, which assumes that each ship has specific frequency characteristics according to type and size. Based on this new model, in-service navigation data collected from a 176k DWT bulk carrier, which amount to 5.7 million data points, are analyzed to assess the speed performance of the vessel subject to environmental disturbances. The proposed model was validated by comparing its results with ISO 19030 and specifically assessing the speed–power curves and speed reduction measured in operational data with the influence of environmental disturbances removed.

Analysis and Research on Intelligent Logistics Data under Internet of Things and Blockchain

ABSTRACT To explore the security performance of logistics data transmission in an Intelligent Logistics System (ILS) under the Internet of Things (IoT) and Blockchain (BC), IoT is introduced to optimize the logistics transportation process. First, BC is adopted to improve the ILS under IoT, and ILS under IoT combined with BC is established. Next, an analysis of the relationship between the information service provider and the transportation provider in the logistics transaction process is conducted to perform data analysis and to establish an incentive mechanism for logistics transportation under BC. Finally, the simulation analysis of the constructed model is implemented. The results reveal that the constructed model is highly efficient. It can effectively solve the traceability problem of logistics transaction information, and the calculation cost is stable within 1,000 ms. In the comparative analysis of system transmission performance, the model exhibits outstanding results in transmission latency, consistently maintaining an average delay of approximately 350 ms. This stability is primarily attributed to the seamless integration of BC technology, which optimizes data packet transmission paths and mitigates waiting time. Furthermore, the system efficiently manages high data volumes while upholding robust data processing capabilities, owing to the optimization of smart contracts and decentralized storage mechanisms. As for energy consumption, the model notably reduces overall energy usage within the logistics network by curtailing unnecessary data transmission and computation, particularly in mobile nodes and wireless communication channels. The proposed algorithm excels across various transmission metrics, including transmission delay, data successful acceptance rate, network throughput, and energy consumption are all shown to be the best transmission performance of the proposed algorithm, in which the data successful acceptance rate is close to 1. As a result, the developed ILS model not only guarantees low latency performance but also demonstrates high data transmission security, facilitating more efficient and precise real-time information transmission. In this study, within the ILS framework, the relationship between information service providers and transportation service providers is established through BC, and an incentive mechanism is constructed. However, some shortcomings in this incentive mechanism are recognized. For instance, during the initial stages of BC integration, there are issues of information asymmetry concerning factors such as credit, transportation capacity, and effort level. Moreover, the open access policy of couriers in the model raises concerns, as the access policy itself constitutes sensitive information, which may result in privacy breaches. Hence, as part of future work, the introduction of attribute encryption schemes is planned to obfuscate access policies, thereby safeguarding the privacy of logistics personnel’s access policies.

Comprehensive Analysis of Miscanthus Research: Sustainable Biomass and Bioenergy Through Knowledge Mapping

Miscanthus grass species have gained global research attention in the fields of biomass and bioenergy, witnessing remarkable advancements in the 21st century. This study systematically searched and selected relevant literature on primary research involving Miscanthus species, published between 2007 and 2023, from the China National Knowledge Infrastructure and Web of Science core databases. Utilizing CiteSpace as a bibliometric tool, the study generated quantitative statistics and performed data analysis on the selected articles. The examination covered publication trends, national and author publication outputs, author and journal co-citations, keyword research hotspots, and the temporal evolution of research topics in the Miscanthus research field, offering empirical support for future in-depth investigations and innovation in Miscanthus spp. The research findings reveal the following insights: (1) Publication trends can be divided into two phases (stable growth and fluctuating decline), with recent trends maintaining a relatively high level. (2) A collaborative publication network, led by core authors such as Zili Yi, John Clifton-Brown, and Iris Lewandowski, has been established, characterized by a diverse research focus and close cooperation. (3) Research hotspots encompass several aspects, including biomass production yield, efficiency, and energy products of Miscanthus spp., ecological restoration, genetics, plant physiological research, and chemical components. The primary species investigated are Miscanthus×giganteus, Miscanthus sinensis, Miscanthus floridulus, and Miscanthus lutarioriparius.