Data Analysis Methodology Research Articles

The role of work ethic mediation on the impact between job motivation on performance and knowledge sharing of SMEs in East Java

This study examines the influence of SME performance, knowledge sharing, work motivation, and the work ethic of SME proprietors. This research was conducted on Small and Medium Enterprises in East Java. The study population comprised 293 proprietors of small and medium-sized enterprises (SMEs) employing between 3 and 10 individuals. The participants in the study were owners of small and medium enterprises situated across East Java. Data collection for each variable was performed using questionnaires. Data analysis methodologies utilize descriptive analysis techniques and quantitative data analysis techniques, such as Structural Equation Modeling (SEM) with Partial Least Squares (PLS). The results demonstrated that the mediating effect of work ethics on the relationship between work motivation and company performance was insignificant. The mediating effect of work ethics on the relationship between work motivation and knowledge sharing was substantial. The correlation between work motivation, work ethics, and knowledge sharing was positive and significant; however, the influence of motivation on company performance was not significant. The motivation of employees negatively affects the performance of small and medium-sized enterprises (SMEs) in East Java. This study offers guidance to managers of small and medium enterprises on implementing work ethics to enhance employee motivation, performance, and knowledge sharing.

The Role of Social Media in Enhancing Communication among Individuals: Prospects and Problems

This study is a discussion about the role that social media plays in enhancing communication among individuals, highlighting its prospects and related challenges. This research examines the impact of social media on improving communication among individuals and its prospects and challenges. This study had taken an integrated approach of secondary data analysis methodology through an extensive systematic review of the existing literature, along with the Canadian Research Data Collection (CREDC) national web-survey, to collect the attitudinal and behavioral insight of social media users on different platforms like Facebook, Twitter, and LinkedIn considering theoretical frameworks like Social Presence Theory and Media Richness Theory. Further, this conceptual review used PRISMA guidelines to examine how social media improves communication. Eligibility and exclusion criteria ensured peer-reviewed article selection and credibility. Study reveals that social media platforms significantly enhance interpersonal communication by fostering increased interconnectedness, more accessible information, and community formation. Notably, while these digital environments have considerable benefits, such as the facilitation of learning and support networks, they are also fraught with challenges, including misinformation, privacy concerns, and potential adverse effects on mental health. These findings therefore have dual approaches to reaping the benefits of social media in communication: the promotion of user education to reduce misinformation and building up digital literacy, while there is also a need to consider regulations that will protect against violation of privacy and cyberbullying. It is at this point that stakeholders will, arguably, have fewer difficulties in making use of social media to continue developing active citizenship and rich interpersonal relationships. Future research is encouraged to further explore the implications of emerging technologies on social media communication practices.

Teacher Professionalism: Opportunities and Challenges in the Education Delivery System

The study set out to characterize the professionalism of teacher preparation programs offered by islamic schools and institutions. This kind of research combines a review of the literature with qualitative methods. They gather data by locating and compiling information from various sources, including books, journals, and previous studies. Meanwhile, data analysis was done using data triangulation techniques, member checking, and auditing, as well as Miles & Huberman's data analysis methodologies, which include data reduction, data display, and conclusions. The professionalism of teachers is a good indicator of the quality of education, according to research findings. Several activities are conducted to enhance teacher professionalism, such as workshops, seminars, M.G.M.P., and training. They are improving the calibre of teachers by organizing, carrying out, and monitoring changes. Professional teachers in schools are beneficial in improving the quality of education, one of which is by conducting training related to experienced teachers in schools. Skilled teachers are applied by meeting the tools of teaching needs, attendance, making academic calendars, and having work meetings with other teachers. They are also proven by attending professional teacher improvement training in schools. Teachers must be competent to carry out the learning process utilizing the current I.T. systems since teacher professional education presents both opportunities and obstacles. Teacher quality is achieved by mastering learning supported by understanding their duties and responsibilities.

Supreme Court's Legal Advice: Limits, Procedures, and Need for Change

The Supreme Court offers legal advice to state organs and government agencies. Nonetheless, there are no regulations governing the execution of Supreme Court Legal Advice. This paper analyses the limitations and procedures of Supreme Court legal advice. This is a normative legal analysis grounded in conceptual and statutory law. This study utilized legal texts, doctrinal sources, and scientific material. This study employs Miles and Huberman's data analysis methodology. This study demonstrates that Article 37 of the 1985 Supreme Court Law and Article 22 paragraph (1) of the 2009 Judicial Power Law are contradictory. The dispute pertains to the petitioner seeking Supreme Court Legal Advice. The Supreme Court's Legal Advice is only extended to state organs and government agencies. The Supreme Court lacks the authority to offer legal advice to local governments, community organizations, and non-governmental organizations. The Chief Justice of the Supreme Court offers legal advice to state organs, while the Chairman of the Supreme Court Chamber provides legal advice to government agencies. Both legal advice continues to be designated as Supreme Court Legal Advice.

Fourier Transform Infrared (FTIR) Spectroscopy as a Tool to Characterize Exercise and Physical Activity: A Systematic Review.

Over the past few decades, the scientific community has recognized the impact of physical activity on health and performance. In parallel, researchers have been actively exploring novel methodologies to analyze the physiological and metabolic responses to exercise. Fourier transform infrared spectroscopy has emerged as a powerful tool in this effort, offering the potential to provide unique insights into exercise-related changes at the molecular level. The primary goal of this systematic review is to confirm the viability of utilizing Fourier transform infrared spectroscopy for the analysis of the biochemical changes associated with physical exercise and its potential applications. This systematic review adhered to PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines and examined studies employing Fourier transform infrared spectroscopy to analyze exercise and physical activity, focusing on a biological sample collection and spectral analysis. Four databases (PubMed, SPORTDiscus, Web of Science, and Scopus) were searched, and inclusion criteria encompassed original English-language studies involving human participants aged 18-50years, a biological sample collection (urine, saliva, and blood), and the use of Fourier transform infrared spectroscopy. The studies were analyzed considering the type of exercise or sport that was investigated, and also the type of spectral analysis conducted. The review encompassed 15 studies that demonstrated the versatility of Fourier transform infrared spectroscopy in assessing various aspects of exercise, including metabolism, cardiovascular responses, and muscular fatigue. The largest study evaluated 57 athletes from several different sports. On average, almost all the studies were performed with around 20 athletes. Notably, the technique's holistic approach allows for a comprehensive analysis of the complex network of metabolites and proteins within the human body. Data analysis methodologies, particularly when coupled with machine learning, show great potential for advancing the field of sports science. Fourier transform infrared spectroscopy emerges as a promising tool for monitoring and enhancing the performance of high-level athletes, preventing overtraining or even over-reaching, and assessing metabolism. Its accuracy, efficiency, and affordability also make it a candidate for broader applications in assessing the health and fitness of the general population. Future research should explore its applicability across diverse exercise modalities and demographic groups, aiming to prescribe exercise plans that consider a multitude of parameters for larger, more intricate exercise cohorts. The study protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO) under the ID number CRD42023441965.

Formation and dynamics of prefabricated building innovation network

PurposeWith the uptake of prefabricated construction (PC) facing serious obstacles in China, networked innovation can break the technical constraints while also containing the risks in individual innovation. However, the construction community has paid little attention to PC innovation, especially networked innovation. This study aims to gain deep insights into what impacts the formation and dynamics of a prefabricated construction innovation network (PCIN). With the uptake of PC facing serious obstacles in China, networked innovation can break the technical constraints while also containing the risks in individual innovation.Design/methodology/approachThe research design follows a sequential mixed methodology of qualitative and quantitative data collection and analysis. It first conceptualizes the PCIN based on the quadruple helix model and formulates a corresponding system dynamics model based on causality analysis. After validating the PCIN model using empirical data, simulations are carried out using Vensim PLE software. Finally, this study identifies key factors that promote the formation of PCIN in China through sensitivity analysis.FindingsThe results show that PC predicts a continuous increase in practice as of 2030. The tested drivers all positively influence the formation of the PCIN, with market demand and risk sharing having the greatest influence, followed by competitive pressure, profit government support, scientific and technological advancement and collaborative innovation strategy.Originality/valueThe study makes three major contributions. First, it provides a novel angle for a deeper understanding of the PC innovation. Second, it proposes a new approach for probing the formation and dynamics of the PCIN. Finally, it offers a theoretical reference for promoting the formation of innovation networks and the development of PC.

Geographical health inequalities in India: the impact of the COVID-19 pandemic on healthcare access and healthcare inequality

PurposeWith an emphasis on spatial health disparities, this study examines how COVID-19 has affected healthcare access and inequality in India. The study developed the Healthcare Access Index (HAI) and Healthcare Inequality Index (HII) to assess the pandemic’s effects on healthcare. The study addresses spatial health disparities in healthcare access and inequality, filling gaps in the literature. The final aim of the study is to offer policy suggestions to lessen healthcare inequities in India, particularly in the context of COVID-19.Design/methodology/approachThe study incorporates secondary data from publicly accessible databases such as the National Family Health Survey, Niti-Ayog and Indian Census databases and employs a quantitative research design. The impact of the COVID-19 pandemic on healthcare access and healthcare inequality in India is examined using the HAI and the HII. The five dimensions of healthcare access – availability, accessibility, accommodation, cost and acceptability – were used in developing the HAI. The study uses a panel data analysis methodology to examine the HAI and HII scores for 19 states over the pre-COVID-19 (2015) and post-COVID-19 (2020) periods. In order to investigate the connection between healthcare access, healthcare inequality and the COVID-19 pandemic, the analysis employs statistical tests such as descriptive statistics, correlation analysis, factor analysis and visualization analysis.FindingsAccording to the study, COVID-19 impacted healthcare access and inequality in India, with notable regional inequalities between states. The pandemic has increased healthcare disparities by widening the gap between states with high and low HII ratings. Healthcare access is closely tied to healthcare inequality, with lower levels of access being associated with more significant levels of inequality. The report advises governmental initiatives to lessen healthcare disparities in India, such as raising healthcare spending, strengthening healthcare services in underperforming states and enhancing healthcare infrastructure.Practical implicationsFor Indian healthcare authorities and practitioners, the study has significant ramifications. In light of the COVID-19 pandemic, there has been a main focus on addressing geographic gaps in healthcare access and inequality. The report suggests upgrading transportation infrastructure, lowering out-of-pocket costs, increasing health insurance coverage and enhancing healthcare infrastructure and services in underperforming states. The HAI and the HII are tools that policymakers can use to identify states needing immediate attention and appropriately spend resources. These doable recommendations provide a framework for lowering healthcare disparities in India and enhancing healthcare outcomes for all communities.Originality/valueThe study’s originality resides in establishing the HAI and HII indices, using panel data analysis and assessing healthcare inequality regarding geographic disparities. Policy choices targeted at lowering healthcare disparities and enhancing healthcare outcomes for all people in India can be informed by the study’s practical consequences.

Solutions for Lithium Battery Materials Data Issues in Machine Learning: Overview and Future Outlook.

The application of machine learning (ML) techniques in the lithium battery field is relatively new and holds great potential for discovering new materials, optimizing electrochemical processes, and predicting battery life. However, the accuracy of ML predictions is strongly dependent on the underlying data, while the data of lithium battery materials faces many challenges, such as the multi-sources, heterogeneity, high-dimensionality, and small-sample size. Through the systematic review of the existing literatures, several effective strategies are proposed for data processing as follows: classification and extraction, screening and exploration, dimensionality reduction and generation, modeling and evaluation, and incorporation of domain knowledge, with the aim to enhance the data quality, model reliability, and interpretability. Furthermore, other possible strategies for addressing data quality such as database management techniques and data analysis methodologies are also emphasized. At last, an outlook of ML development for data processing methods is presented. These methodologies are not only applicable to the data of lithium battery materials, but also endow important reference significance to electrocatalysis, electrochemical corrosion, high-entropy alloys, and other fields with similar data challenges.

Application of UAVs and Remote Sensing Technologies for Atmospheric CO2 Capturing: A Study Application of UAVs and Remote Sensing in CO2 Reductions

Human activities are a significant contributor to climate change, with rising levels of CO₂ in the atmosphere. Several carbon capture and sequestration (CCS) methods have been developed to address this issue. Uncrewed Aerial Vehicles (UAVs) and remote sensing technologies are emerging as significant improvements to the efficiency and effectiveness of atmospheric carbon capture initiatives. This research examines using UAVs and remote sensing technologies to monitor, quantify, and manage atmospheric CO₂ levels. Furthermore, the study explores the implications of integrating robotic-drone technology, emphasizing their ability to contribute to a sustainable future. These technologies, incorporating modern data collection and analysis methodologies, provide promising answers for climate change mitigation and long-term environmental sustainability.

Enhanced Brain Tumor Detection and Privacy Preserving Using Federated Learning

Brain cancers pose significant difficulties for both diagnosis and treatment, underscoring the necessity for precise and private-protecting detection techniques. Federated learning is used to solve this, allowing several healthcare facilities to work together to train detection models without jeopardizing patient privacy. This paper presents an approach called federated learning that may be used to improve brain tumor identification while protecting patient privacy. Brain tumors are dangerous medical disorders that need to be accurately diagnosed in order to be effectively treated. However, sharing private patient information is a common practice in traditional medical data analysis methodologies, which raises privacy issues. Federated learning helps with this by enabling cooperative training of a common model amongst several hospitals or institutions without requiring the exchange of raw data. This method protects patient privacy by having each institution train the model using its own local data and only sharing model updates. We show through trials that our method is efficient in reliably identifying brain tumors while upholding privacy norms, presenting a viable option for improving medical diagnosis without jeopardizing patient privacy.

The AI-Powered Evolution of Big Data

The rapid advancement of artificial intelligence (AI), coupled with the global rollout of 4G and 5G networks, has fundamentally transformed the Big Data landscape, redefining data management and analysis methodologies. The ability to manage and analyze such vast and varied datasets has exceeded the capacity of any individual or organization. This study introduces an enhanced framework that expands upon the traditional four Vs of Big Data—volume, velocity, volatility, and veracity—by incorporating six additional dimensions: value, validity, visualization, variability, volatility, and vulnerability. This comprehensive framework offers a novel and straightforward approach to understanding and addressing the complexities of Big Data in the AI era. This article further explores the use of ‘Big D’, an AI-driven, RAG-based Big Data analytical bot powered by the ChatGPT-4o model (ChatGPT version 4.0). This article’s innovation represents a significant advance in the field, accelerating and deepening the extraction and analysis of insights from large-scale datasets. This will enable us to develop a more nuanced and comprehensive understanding of intricate data landscapes. In addition, we proposed a framework and analytical tools that contribute to the evolution of Big Data analytics, particularly in the context of AI-driven processes.

Harnessing Wearables and Digital Technologies to Decode the Cardiovascular Exposome.

The cardiovascular exposome encompasses the array of external and internal factors affecting cardiovascular health throughout life, inviting comprehensive monitoring and analysis to enhance prevention, diagnosis, and treatment strategies. Wearable and digital technologies have emerged as promising tools in this domain, offering longitudinal, real-time data on physiological parameters such as heart rate, heart rhythm, physical activity, and sleep patterns. This review explores the advancements in wearable sensor technology, the methodologies for data collection and analysis, and the integration of these technologies into clinical practice and research. Primary findings indicate significant improvements in device accuracy and functionality, facilitated by enhanced sensor technology, artificial intelligence, and data connectivity. These advancements enable precise monitoring, early detection of cardiovascular anomalies, and personalized healthcare interventions. Ultimately, wearables and digital health technologies have the potential to facilitate a deeper understanding of cardiovascular disease and behavior and bridge gaps in traditional healthcare models to help usher in more efficient, personalized, patient-centered care.

Pengendalian Persediaan Sparepart dengan Menggunakan Metode Fifo di Warehouse di PT.Heinz ABC Indonesia Pasuruan

Spare parts inventory is a common problem faced by many companies that use lots of machines or robots in their production processes. Where the spare parts warehouse in the company is required to always be prepared and there is no delay in the availability of spare parts to continue to support production machine activities. If the availability of spare parts is late or empty, it will hamper the production process activities within the company. Seeing this problem, researchers are trying to maintain the availability of spare parts using the FIFO (first in first out) method so as to prevent delays or stock shortages. The FIFO method is a method of inventory management that uses product stock in the warehouse based on the time of entry. By using this method, it is hoped that there will be no delays or stock vacancies in the warehouse due to irregularities in picking up spare parts in the warehouse. The aim of this research is to ascertain how the use of the FIFO method affects the warehouse's ability to manage spare parts inventory efficiently. Field research is this type of research. This research uses a qualitative data analysis methodology and inductive reasoning, which starts with precise and real facts before drawing conclusions from them. The research results provide significant benefits in increasing operational efficiency and reducing the possibility of excess or shortage of stock, which can disrupt production and delivery processes. Efficient receiving also helps in maintaining spare parts quality, ensuring that only items that meet quality standards are received and stored. Application of the FIFO method in the spare parts warehouse at PT. Heinz ABC Indonesia also has a positive impact on inventory recording and reporting. With a more regular flow of goods in and out, the recording system becomes more accurate and easier to track. This makes it easier for the accounting and management team to carry out audits and identify potential problems.

Indonesian Teachers’ Perspectives on Gender Representation in EFL Textbook Visual Images for Elementary Schools

Background: The use of gendered visual imagery in English as a Foreign Language (EFL) textbooks has raised concerns about perpetuating gender stereotypes and the potential effect on students' conceptions of gender roles. Despite the widespread use of mandated textbooks in primary classrooms in Indonesia, less is known about instructors' perceptions of gendered visual imagery. Methodology: This qualitative research employed a mixed-methods approach, incorporating quantitative and qualitative data collection and analytic methodologies. An investigation was conducted in Samarinda East Kalimantan to collect the perspectives of 41 teachers who teach English in elementary school regarding the gendered visual representations present in the mandatory EFL textbooks. Furthermore, 12 teachers were chosen to partake in exhaustive interviews to present more informed and profound perspectives. Findings: They merely focused on how to teach the materials rather than scrutinizing the gender bias represented through texts and visual images. The study revealed that a significant proportion of teachers (80%) believed that gendered visual imagery in textbooks was not impartial and could influence the gender roles of pupils. Furthermore, a significant majority (75%) said they often adjusted or avoided using these pictures in their teaching methods. The interviews highlighted the teachers' concern about the possible negative impact of these pictures on students' social relationships and self-image. However, they merely focused on how to teach the materials rather than scrutinizing the gender bias represented through texts and visual images Conclusion: The findings suggest that elementary school teachers in Indonesia know the potential negative repercussions of gendered visual images in EFL textbooks and have implemented measures to mitigate these effects. The study underscores the importance of incorporating diverse and inclusive visual materials into educational resources to promote gender equality and positive gender representations. This concept is new to them. They agree that gender equality needs to be addressed and discussed as part of the content in classroom practices to raise students’ awareness of gender bias. Originality: Previous studies have investigated the frequency and occurrence of gender bias in textbooks, found in images and texts. However, there is a lack of investigation into teachers' awareness of gender stereotypes in textbooks. This study enhances the existing corpus of literature by examining the perspectives of teachers on the use of gendered visual images in EFL textbooks in elementary schools in Indonesia. It emphasizes the importance of teachers and policymakers advocating for more inclusive educational resources and considering the impact of visual materials on students' gender perceptions

Green cryptocurrency and business strategies: Framework and insights from a stewardship literature review

AbstractThis study critically evaluates the extant research on green cryptocurrency (GC). It incorporates the systematic literature review (SLR) approach of research executed through the analysis and compilation of 54 relevant studies. The focus is on identifying and interpreting the thematic foci and existing gaps about GC to inform potential areas of future research. The SLR findings aggregated the research around key themes: interconnectedness, portfolio diversification, environmental impact, green blockchain, behavioral impact, and regulatory policy. Additionally, the study develops the research profile of the selected studies in terms of data analysis methodology, research findings, variables investigated, and critical knowledge gaps in the literature corpus. The study proffers an integrated framework and actionable inferences for supporting the merits of novel research and regulatory project applications in GC. It is crucial for understanding and advancing research in the GC domain, which currently has scant academic literature and efforts toward sustainable investment.

OmicsFootPrint: a framework to integrate and interpret multi-omics data using circular images and deep neural networks.

The OmicsFootPrint framework addresses the need for advanced multi-omics data analysis methodologies by transforming data into intuitive two-dimensional circular images and facilitating the interpretation of complex diseases. Utilizing deep neural networks and incorporating the SHapley Additive exPlanations algorithm, the framework enhances model interpretability. Tested with The Cancer Genome Atlas data, OmicsFootPrint effectively classified lung and breast cancer subtypes, achieving high area under the curve (AUC) scores-0.98±0.02 for lung cancer subtype differentiation and 0.83±0.07 for breast cancer PAM50 subtypes, and successfully distinguished between invasive lobular and ductal carcinomas in breast cancer, showcasing its robustness. It also demonstrated notable performance in predicting drug responses in cancer cell lines, with a median AUC of 0.74, surpassing nine existing methods. Furthermore, its effectiveness persists even with reduced training sample sizes. OmicsFootPrint marks an enhancement in multi-omics research, offering a novel, efficient and interpretable approach that contributes to a deeper understanding of disease mechanisms.

Photodiode-based focus monitoring in ultrashort-pulsed laser structuring of graphite anodes for lithium-ion batteries

In recent years, there has been an increased demand for elaborate monitoring techniques in laser material processing. This has been driven by the need for fast and cost-efficient quality assurance processes. At the same time, ultrashort-pulsed (USP) laser radiation has emerged as a promising technology for creating intricate microstructures in lithium-ion battery graphite anodes due to its high precision and negligible thermal impact. However, the integration of process monitoring in USP laser applications for graphite anode structuring is still unexplored. There is a lack of clarity on suitable sensors, observable parameters, and extractable process-relevant insights. The presented study addressed this gap by demonstrating the capability of state-of-the-art photodiode-based monitoring systems in collecting process-relevant data and deriving valuable insights. A sensor equipped with three photodiodes was employed to address these challenges. Exploratory data analysis and machine learning methodologies were leveraged to develop a data pipeline for processing the acquired information. The data were used to train convolutional neural networks that could accurately predict the focal position. At the same time, the limitations of traditional regression approaches could be shown. The findings advanced the understanding of the possibilities of process monitoring in USP laser applications and emphasized the significance of data-driven approaches in optimizing manufacturing processes.

A Novel Data Analytics Methodology for Discovering Behavioral Risk Profiles: The Case of Diners During a Pandemic

Understanding tourist profiles and behaviors during health pandemics is key to better preparedness for unforeseen future outbreaks, particularly for tourism and hospitality businesses. This study develops and applies a novel data analytics methodology to gain insights into the health risk reduction behavior of restaurant diners/patrons during their dining out experiences in a pandemic. The methodology builds on data relating to four constructs (question categories) and measurements (questions and attributes), with the constructs being worry, health risk prevention behavior, health risk reduction behavior, and demographic characteristics. As a unique contribution, the methodology generates a behavioral typology by identifying risk profiles, which are expressed as one- and two-level decision rules. For example, the results highlighted the significance of restaurants’ adherence to cautionary measures and diners’ perception of seclusion. These and other factors enable a multifaceted analysis, typology, and understanding of diners’ risk profiles, offering valuable guidance for developing managerial strategies and skill development programs to promote safer dining experiences during pandemics. Besides yielding novel types of insights through rules, another practical contribution of the research is the development of a public web-based analytics dashboard for interactive insight discovery and decision support.

Overview of Publications on Payment for Environmental Services: Focus on Brazilian Contributions

Objective: This study aims to analyze publications on Payment for Environmental Services (PES), with a focus on Brazil's leadership and the evaluation of case studies, empirical and theoretical research in the Web of Science and SciELO databases up to April 2024. Theoretical Framework: PES is discussed as a tool for environmental conservation and sustainable development, emphasizing challenges such as social inclusion and participatory governance. Method: A systematic literature review was conducted, focusing on publications indexed in the Web of Science and SciELO databases up to April 2024. The exploratory research employed both quantitative and qualitative methodologies in data analysis. Results and Discussion: The analysis revealed that Brazil plays a prominent role in PES publications, both nationally and internationally. Within the Brazilian context, the states of São Paulo and Minas Gerais stand out, with the Conservador das Águas project in Extrema frequently cited in the literature. Research Implications: The results provide a foundation for the development of public policies aimed at expanding PES initiatives in Brazil. Mapping existing projects can encourage the replication of best practices and foster research in underexplored areas, promoting climate mitigation strategies. Originality/Value: This study contributes to the literature by identifying and mapping PES projects, providing valuable insights for the formulation of strategies aimed at improving climate conditions.

From Molecular Electronics to Molecular Intelligence.

Molecular electronics is a field that explores the ultimate limits of electronic device dimensions by using individual molecules as operable electronic devices. Over the past five decades since the proposal of a molecular rectifier by Aviram and Ratner in 1974 ( Chem. Phys. Lett.1974,29, 277-283), researchers have developed various fabrication and characterization techniques to explore the electrical properties of molecules. With the push of electrical characterizations and data analysis methodologies, the reproducibility issues of the single-molecule conductance measurement have been chiefly resolved, and the origins of conductance variation among different devices have been investigated. Numerous prototypical molecular electronic devices with external physical and chemical stimuli have been demonstrated based on the advances of instrumental and methodological developments. These devices enable functions such as switching, logic computing, and synaptic-like computing. However, as the goal of molecular electronics, how can molecular-based intelligence be achieved through single-molecule electronic devices? At the fiftieth anniversary of molecular electronics, we try to answer this question by summarizing recent progress and providing an outlook on single-molecule electronics. First, we review the fabrication methodologies for molecular junctions, which provide the foundation of molecular electronics. Second, the preliminary efforts of molecular logic devices toward integration circuits are discussed for future potential intelligent applications. Third, some molecular devices with sensing applications through physical and chemical stimuli are introduced, demonstrating phenomena at a single-molecule scale beyond conventional macroscopic devices. From this perspective, we summarize the current challenges and outlook prospects by describing the concepts of "AI for single-molecule electronics" and "single-molecule electronics for AI".