Data Analytics Research Articles

Financial reporting quality, industrial revolution 4.0 and social well-being among Malaysian public companies

PurposeThis study examines the relationship between financial reporting quality, Industrial Revolution 4.0 and social well-being of stakeholders among public companies in Malaysia.Design/methodology/approachThe sample of the study includes 232 firm-year observations of Malaysian publicly listed companies from 2013 to 2017. Social well-being is measured using social pillar scores from the Environmental, Social and Governance (ESG) data provided by Refinitiv. The study identified companies as an adopter of IR 4.0 based on their disclosure on the use of autonomous robots, simulation, cloud, horizontal and vertical system integration, cybersecurity, additive manufacturing, augmented reality and big data analytics in their financial reports. Financial reporting quality is measured using discretionary accruals.FindingsThis study found that financial reporting quality and IR 4.0 are related to social well-being, particularly the workforce. These results imply that companies with higher adoption of IR 4.0 are more likely to provide more information concerning job satisfaction, a healthy and safe workplace, maintaining diversity, equal and development opportunities for its workforce. Furthermore, the results show that firms with lower discretionary accruals (i.e. higher quality of financial reporting) are more likely to provide more information about social well-being. The results are robust even after addressing endogeneity issues.Research limitations/implicationsThis research contributes new insights into the role of financial reporting quality and IR 4.0 in enhancing social well-being in Malaysia. These findings offer valuable input for regulators striving to advance the United Nations' 2030 Agenda for Sustainable Development.Practical implicationsThis study carries substantial practical implications for policymakers and businesses alike. It underscores the importance of embracing IR 4.0 technologies and integrating them into strategic planning to foster social well-being. These insights can guide policymakers in shaping economic strategies and assist businesses in prioritizing financial reporting quality while engaging stakeholders to promote social well-being.Originality/valueThis is the first study to investigate the combined relationship of financial reporting quality and IR4.0 on social well-being, which provides valuable evidence in this novel domain. While previous studies have primarily explored the relationship of IR4.0 on sustainability from an environmental and human resource perspective, this study sheds light on the specific dimension of social well-being, hence promoting sustainable development goals by the United Nations in 2030.

Navigating business intelligence and data analytics: Trends, foundations, strategies, and future directions

In the current digital age, business intelligence (BI) and data analysis are rapidly evolving, but the driving force is in the progress of technology and exponential increase in data. In this paper, we consider a new trend, theoretical basis, implementation strategy, and the future direction of BI and data analysis. In this paper, we consider what innovative roles in artificial intelligence (AI), the Internet (IOT), and block chain technology reconstruct data analysis practices. In addition, this paper investigates ethical considerations and privacy issues related to data driven decisions, emphasizing the importance of responsible data management practices. In this paper, we introduce the strategy of implementing data analysis in business processes, overcoming challenges, and successful case studies. Finally, we emphasize the new trend and emphasize the essence of BI that will continue to evolve through advanced analysis.

AI-POWERED PREDICTIVE ANALYTICS FOR INTELLECTUAL PROPERTY RISK MANAGEMENT IN SUPPLY CHAIN OPERATIONS: A BIG DATA APPROACH

The rapid advancement of technology and the increasing complexity of global supply chains have heightened the need for robust intellectual property (IP) risk management strategies. This study explores the application of artificial intelligence (AI) and big data analytics in enhancing IP risk management within supply chains. A comprehensive literature review was conducted using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, identifying 578 records through database searches and an additional 90 records through other sources. After removing duplicates, 568 records were screened, with 196 full-text articles assessed for eligibility. Ultimately, 135 articles were included in the final synthesis. The findings reveal that AI-driven predictive analytics significantly enhance the detection and mitigation of IP risks by analyzing large volumes of data from various sources, such as patent filings, market trends, and social media. Big data analytics tools like Hadoop and Spark facilitate real-time monitoring and early identification of potential IP threats, providing a comprehensive view of the supply chain landscape. Several successful case studies across different industries, including pharmaceuticals, electronics, and fashion, demonstrate the practical applications of these technologies in addressing IP risks. However, the review also highlights several challenges, including data quality, scalability, model interpretability, data privacy, and integration with legacy systems. Despite these challenges, the benefits of AI and big data analytics in IP risk management are substantial, enabling organizations to protect their intellectual assets more effectively. The study underscores the need for future research to address these challenges and explore innovative solutions to maximize the potential of AI and big data analytics in IP risk management. By investing in the necessary infrastructure and expertise, organizations can enhance their resilience and maintain a competitive edge in the global market.

Adaptive Location-based Routing Protocols for Dynamic Wireless Sensor Networks in Urban Cyber-physical Systems

This study investigates the development and enhancement of adaptive location-based routing protocols within dynamic wireless sensor networks (WSNs) in urban cyber-physical systems, recommending the implementation of the study’s innovative Urban Adaptive Location-based Routing Protocol (UALRP). This innovative protocol integrates real-time data analytics and adaptive machine learning models into its algorithmic framework to dynamically optimize routing decisions based on continuously changing urban conditions. Through the utilization of data-driven simulation models and machine learning techniques, the research sought to significantly improve the efficiency, reliability, and scalability of urban WSNs. Existing protocols such as Geographic Adaptive Fidelity (GAF), Greedy Perimeter Stateless Routing (GPSR), and Dynamic Source Routing (DSR) were critically assessed under urban settings using extensive datasets detailing New York City's traffic patterns and environmental variables. The analysis demonstrated that while GPSR showed superior performance in terms of latency, throughput, and energy efficiency among the traditional protocols, the introduction of UALRP, with its advanced predictive and adaptive capabilities, can further optimize these metrics. The study affirms the critical role of enhancing location accuracy and the ongoing advancement of machine learning models within urban routing protocols. These insights advocate for the broader implementation of adaptive strategies like UALRP to foster the development of more resilient and efficient urban cyber-physical systems.

One Meter Triboelectric Nanogenerator for Efficient Harvesting of Meter‐Scale Wave Energy

AbstractTriboelectric nanogenerators (TENGs) are effective in harvesting high‐entropy low‐frequency wave energy, yet traditional TENGs struggle to align with the meter‐scale wavelengths prevalent in marine environments, significantly impairing their wave energy conversion efficiency. Addressing this, the One Meter TENG (OM‐TENG) is introduced, a pioneering advancement in TENG design that extends the shell size to 1 m. This innovation features a novel lantern‐shaped stacked silicon‐manganese steel sheet configuration, optimizing internal space usage, and achieving a single‐cycle transfer charge of 60.82 µC and a friction layer area density of 1.76 cm−1. Furthermore, by integrating a segmented limiter device for rational control of different sliders, effective contact separation among numerous TENG units is realized. Comprehensive evaluations through theoretical analysis, multiphysics field numerical simulations, sensor data analytics, and comparative experimental measurements have unequivocally demonstrated OM‐TENG's superior capability in capturing meter‐scale wave energy under actual environmental conditions, alongside its remarkable anti‐capsize performance. Achieving peak outputs of 584 V and 444 µA, OM‐TENG can power 96 2 W LEDs and drive wireless communication modules, marking a significant step forward in the efficient utilization of wave energy and the design of large‐scale TENGs.

Management and prediction of employee turnover in enterprises based on big data analytics and machine learning

In the context of aging population and social transformation, employees also put forward higher demands on corporate culture and values, which also increases the risk of employee turnover. This paper explores the correlation between various indicators and leavers through statistical analysis and visualization of various data indicators and the distribution of leavers and non-leavers on various indicators by using whether they left the company as a classification criterion. In addition, this paper also carries out Pearson correlation analysis for each indicator and draws a correlation heat map to quantitatively explore the correlation between indicators. In order to predict whether employees will leave the company, this paper uses random forest model, support vector machine model, KNN model, plain Bayesian model and logistic regression model for training and testing. The results show that the best prediction in terms of employee turnover is the Random Forest model with a prediction accuracy of 98.8%. This was followed by the Support Vector Machine model with an accuracy of 95.1%. In addition, the KNN model also achieved an accuracy of 94.8%. Ordinary Bayesian model and logistic regression model have lower accuracy rates of 80.4% and 77.2% only. This is of great significance for enterprises to realize sustainable development, and is worthy of in-depth study and practice by enterprise managers.

Enhancing Supply Chain Safety and Security: A Novel AI-Assisted Supplier Selection Method

The "Make or Buy” decision and the supplier selection are critical steps for the efficient operation of companies' supply chains. Safety and security are paramount considerations, especially in industries like logistics, where supply chains are vulnerable to external threats and disruptions. In this scientific article, we present a novel Artificial Intelligence (AI)-assisted supplier selection method that significantly enhances the safety and security of suppliers. During our research project, we have created an expert system and a corresponding knowledge base with the relevant rules to support supply chain decision-makers in selecting logistics service providers for warehousing services. The foundation of the AI-assisted supplier selection method is advanced data analytics and the application of expert systems, enabling companies to evaluate potential suppliers in detail from a safety and security perspective. The applied expert systems can identify potential risks and make predictions about supplier performance in the future. In the turbulent environment of the global supply chain, selecting long-term partners like warehousing services providers is critical for the success of the organization. A wrong supplier selection can hardly be reversed in warehousing services, as the cost of change is usually high. The article demonstrates the practical application of the expert system-assisted supplier selection method in a real-world supply chain environment and thoroughly analyzes the achieved results and advantages. The results show that the expert system-assisted method not only increases supplier safety and security but also contributes to improving the efficiency and sustainability of the supply chain. This article provides valuable guidance and solutions for companies looking to enhance their supplier selection using expert system technologies, thereby increasing the safety and security of their supply chains.

Editorial Note

Abstract This Special Issue (SI) on Advanced Data Analytics and Technologies for Decoding Human Health & Well-being in Built Environments that comes to partial closure in this issue is a necessary first step in a larger and longer conversation centered on the well-being of the people within built environments. The discussion started in 2022 with an International Workshop in Nottingham, UK, funded by the US National Science Foundation. The workshop, titled “Biosensing-enabled, Wellbeing-Centric Sustainable Built Environment Ecosystems,” covers the topics of human health and well-being, and the use of data and building energy technologies were discussed amply by a group of experts from the US and the UK. Fundamental questions in this conversation include how it may be possible to measure and quantify human well-being in different contexts of indoor and outdoor environments, workplaces, or health centers and how technology at different scales and components, from the individual scale to the outdoor, community environments, can enable a reasonable state of well-being.

Developing a smart forest protection mini program with Spring Boot: Integrating big data and image recognition technologies

Forests are essential for ecological stability yet pose significant management challenges due to their expansive and remote nature. Despite advancements in data acquisition, there is a lack of tools that effectively combine image recognition with big data analytics for real-time monitoring. This study introduces a "Smart Forest Protection" mini program, developed using Spring Boot, which integrates these technologies to facilitate immediate and efficient forest management. The program features intelligent plant recognition, user-driven incident reporting, waste recycling point displays, and a platform for disseminating forest conservation knowledge. Additionally, a big data visualization platform illustrates the spatiotemporal distribution of forests in Zhuhai, aiding governmental and environmental agencies in making informed decisions that enhance forest conservation and promote sustainable practices.

Impact of Music Teaching on Student Mental Health Using IoT, Recurrent Neural Networks, and Big Data Analytics

Impact of Music Teaching on Student Mental Health Using IoT, Recurrent Neural Networks, and Big Data Analytics

The influence of business analytics on supply chain ambidexterity: the mediating role of market learning

Purpose The main purpose of this study is to assess the influence of business analytics (BA) on supply chain ambidexterity (SCA) and market learning (ML) in the context of Iran as a developing country. Design/methodology/approach The study population encompasses a range of key positions such as senior managers, supply chain managers, senior IT managers and senior marketing and marketing research managers in Iran. Through a survey, a questionnaire was designed to gather data from these individuals. The data collected from a total of 214 participants underwent rigorous analysis using structural equation modeling. Findings Findings revealed BA has a positive influence on SCA and ML. Furthermore, the study found that distinct facets of ML, namely, exploratory and exploitative learning, exerted a positive influence on SCA. Additionally, the investigation uncovered that the mechanisms of exploratory ML and exploitative ML play a partially mediating role in the relationship between BA and SCA. Research limitations/implications It is prudent to acknowledge that the study’s sampled entities were exclusively Iranian companies, potentially curtailing the extent of generalizability of our findings. Originality/value This research contributes valuable theoretical insights and practical implications to policymakers and top managers of organizations, particularly the surveyed organizations to formulate and implement an appropriate strategy to avail of BA techniques toward enhancing SCA. Also, this study provides significant insights into the determinants of SCA and demonstrates how organizations can leverage data analytics and ML to attain sustained growth and ambidexterity within the supply chain context.

Linguistic summarization of visual attention and developmental functioning of young children with autism spectrum disorder

Diagnosing autism spectrum disorder (ASD) in children poses significant challenges due to its complex nature and impact on social communication development. While numerous data analytics techniques have been proposed for ASD evaluation, the process remains time-consuming and lacks clarity. Eye tracking (ET) data has emerged as a valuable resource for ASD risk assessment, yet existing literature predominantly focuses on predictive methods rather than descriptive techniques that offer human-friendly insights. Interpretation of ET data and Bayley scales, a widely used assessment tool, is challenging for ASD assessment of children. It should be understood clearly to perform better analytic tasks on ASD screening. Therefore, this study addresses this gap by employing linguistic summarization techniques to generate easily understandable summaries from raw ET data and Bayley scales. By integrating ET data and Bayley scores, the study aims to improve the identification of children with ASD from typically developing children (TD). Notably, this research represents one of the pioneering efforts to linguistically summarize ET data alongside Bayley scales, presenting comparative results between children with ASD and TD. Through linguistic summarization, this study facilitates the creation of simple, natural language statements, offering a first and unique approach to enhance ASD screening and contribute to our understanding of neurodevelopmental disorders.

Concepts of management accounting in the context of its definition and relation to the accounting essence

Subject. This article examines the present-day essential content of the category promoted by foreign professional associations, and assesses its compliance with the functionality used in the Russian Federation. Objectives. The article aims to find and describe the essential content of what is called Management Accounting in Russia and abroad due to the lack of a clear institutional scientific justification. Methods. For the study, we used general scientific methods of cognition, namely induction and deduction, analysis and synthesis, modeling, comparison, extrapolation, as well as the comprehensive, and functional-and-targeted approaches. Results. The article identifies and describes significant problems that underlie the versatility of the semantic content of the term Management Accounting. Conclusions and Relevance. The Global Principles of Management Accounting is a framework document that defines the principles of management based on accounting and analytical data, but not management accounting in the sense of collecting information and recording and summarizing it. It would be logical to break down the categories into accounting for management, that is management accounting, and management based on accounting data, that is analytical (accounting) management. Language (term translation) inconsistencies can also be considered a negative impact on the understanding of foreign documents. The results of the study of the subject and functional area of management accounting, presented in the article, are useful for continuing the discussion and understanding the semantic content of individual accounting categories.

On a Mott formalism for modeling oxide thin-film transistors

We report on a device model for thin-film transistors (TFTs) in the framework of Mott's trap-and-release transport theory for disordered semiconductors. The model features a so-called Mott function that is demonstrated to be powerful for analytically deriving the terminal characteristics and other critical parameters of TFTs, including threshold voltage, subthreshold swing, and field-effect mobility. The model is validated by way of application to an amorphous InGaZnO (IGZO)-based TFT, for which good agreement between the analytical and experimental data are obtained. This study offers a simple yet powerful formalism for effective parameter characterization and extraction for oxide and other related TFTs, including those from the organic materials family.

Big Data Analytics and its Approaches: The Digital Transformation of the Future

This study is all about the importance- of methods in big data study, and what they might mean for future- digital growth projects. The rese-arch explores current patte-rns, issues, and smart plans. This gives valuable information for groups figuring out the- tricky mixture of data study and digital growth. The focus of the pape-r is on important methods like batch handling, stream handling, and machine- learning. It looks at their strengths, limits, and use-s. We see the-ir effects on growing and improving. We fore-cast future patterns, tackle issue-s and suggest inventive solutions for succe-ss. This research acts like a guide- for groups that want to line up their digital changes with the- best methods for continuous success.

Using near misses, artificial intelligence, and machine learning to predict maritime incidents: A U.S. Coast Guard case study.

Two recent trends made this project possible: (1) The recognition that near misses can be predictors of future negative events and (2) enhanced artificial intelligence (AI) and machine learning (ML) tools that make data analytics accessible for many organizations. Increasingly, organizations are learning from prior incidents to improve safety and reduce accidents. The U.S. Coast Guard (USCG) uses a reporting system called the Marine Information for Safety and Law Enforcement (MISLE) database. Because many of the incidents that appear in this database are minor ones, this project initially focused on determining if near misses in MISLE could be predictors of future accidents. The analysis showed that recent near-miss counts are useful for predicting future serious casualties at the waterway level. Using this finding, a predictive AI/ML model was built for each waterway type by vessel combination. Random forest decision tree AI/ML models were used to identify waterways at significant accident risk. An R-based predictive model was designed to be run monthly, using data from prior months to make future predictions. The prediction models were trained on data from 2007 to 2022 and tested on 10 months of data from 2022, where prior months were added to test the next month. The overall accuracy of the predictions was 92%-99.9%, depending on model characteristics. The predictions of the models were considered accurate enough to be potentially useful in future prevention efforts for the USCG and may be generalizable to other industries that have near-miss data and a desire to identify and manage risks.

Leveraging Artificial Intelligence for Enhanced Performance in Matrix Organizations: A Research Perspective

In today's complex business landscape, matrix organizations represent a prevalent structural framework characterized by overlapping reporting structures and cross-functional project teams. This organizational model, while offering flexibility and specialization, also presents unique challenges in resource allocation, decision-making, collaboration, and project management. This research paper investigates the potential of artificial intelligence (AI) to address these challenges and enhance the performance of matrix organizations. Drawing upon an extensive review of existing literature and relevant case studies, the paper provides a comprehensive analysis of AI's role in optimizing various aspects of matrix organization operations. Specifically, it examines how AI technologies can streamline resource allocation processes by analyzing project requirements, team capabilities, and individual skill sets to ensure optimal staffing levels and skill matching across projects. Additionally, the paper explores AI's capabilities in providing decision support to managers and team leaders by leveraging data analytics to offer insights into project progress, risk assessment, and performance evaluation.

Conceptual foundations of Tech-Driven logistics and supply chain management for economic competitiveness in the United Kingdom

The concept paper explores the transformative potential of technology in enhancing the logistics and supply chain sector to bolster the UK's economic competitiveness. This executive summary highlights the paper's primary objectives, theoretical frameworks, and anticipated outcomes, focusing on the integration of advanced technologies to streamline supply chain operations and drive economic growth. This paper aims to provide a comprehensive framework for understanding how technology can be leveraged to optimize logistics and supply chain management in the UK. It underscores the importance of a robust and efficient supply chain as a cornerstone of economic competitiveness, enabling businesses to reduce costs, improve delivery times, and enhance overall operational efficiency. Central to this paper is the exploration of various technological innovations that can revolutionize supply chain management. It discusses the impact of emerging technologies such as the Internet of Things (IoT), artificial intelligence (AI), blockchain, and big data analytics on the logistics sector. These technologies enable real-time tracking, predictive analytics, enhanced transparency, and improved decision-making processes, which are crucial for maintaining a competitive edge in the global market. The paper delves into theoretical models and frameworks that support the integration of technology into supply chain management. It examines concepts such as the Digital Supply Chain (DSC) model, which emphasizes the interconnectedness of digital technologies in creating a seamless supply chain network. Additionally, the paper explores the Supply Chain Operations Reference (SCOR) model, which provides a structured approach to measuring and improving supply chain performance through technology-driven metrics and best practices. Addressing the practical challenges of implementing tech-driven logistics, the paper highlights issues such as cybersecurity risks, high initial investment costs, and the need for skilled labor. It proposes solutions like fostering public-private partnerships, investing in cybersecurity measures, and developing training programs to build a tech-savvy workforce capable of managing advanced supply chain systems. The expected outcomes of tech-driven logistics and supply chain management include increased operational efficiency, reduced operational costs, improved customer satisfaction, and enhanced agility in responding to market changes. These improvements can significantly contribute to the UK's economic competitiveness, positioning it as a leader in the global supply chain landscape. The paper provides a strategic roadmap for integrating advanced technologies into the logistics sector. By leveraging these technologies, the UK can enhance its supply chain efficiency, drive economic growth, and maintain a competitive advantage in the global economy. The paper calls for concerted efforts from policymakers, industry leaders, and academia to foster an environment conducive to technological innovation and supply chain excellence. Keywords: Logistic, Supply Chain, Technology, Cyber Security, UK.

Enhanced Condition Monitoring of Gearboxes using Fuzzy Comprehensive Evaluation and Group Decision-making: A Case Study

In this study, we introduce a novel methodology for the condition monitoring of gearboxes by integrating fuzzy comprehensive evaluation and group decision-making. Traditional risk assessment methods often suffer from subjective biases and uncertainties inherent in expert evaluations. Our approach mitigates these limitations by aggregating expert opinions using fuzzy membership functions and assigning weights based on the similarity of individual evaluations to the group consensus. This converts qualitative judgments into quantitative measures, resulting in more precise and objective Risk Priority Numbers (RPNs). We validate the efficacy of our methodology through a case study involving a gearbox. The primary failure modes identified include gear tooth wear, misalignment, bearing failure, lubrication failure, and thermal overload. Our results indicate a significant improvement in condition monitoring accuracy, with calculated fuzzy RPN values closely aligning with historical data and expert feedback. Comparative analysis highlights the advantages of our methodology over conventional RPN calculations, particularly in reducing subjective biases and enhancing the reliability of risk assessments. Our findings demonstrate that this methodology can be effectively applied in various industrial settings, establishing a robust framework for mechanical system condition monitoring. Future research should explore integrating advanced data analytics and machine learning techniques to enhance the methodology's accuracy and efficiency.

Integration of Waste Management in Textile Industries of India

The foundation of India's economic growth, the textile sector, has experienced unheard-of growth in recent decades, along with a rise in environmental issues brought on by significant waste generation. This comprehensive study sets out to look into and analyze how waste management techniques are incorporated into the rich history of the Indian textile sector. The study offers a thorough review of current waste management techniques, environmental impact evaluations, and creative solutions influencing the path toward sustainability, bridging the gap between qualitative and quantitative analysis. The introduction sets the context by outlining the mutually beneficial relationship between the expansion of the textile industry and its subsequent environmental load. The clothing industry has gained prominence due to the increase in demand for its products, however, a paradigm shift is required due to the waste generated by this industry. The study aims to perform a thorough analysis of waste management processes, conduct a quantitative assessment of the environmental implications, and investigate comprehensive strategies for sustainable waste management. The chapter on qualitative analysis explores the complex aspects of waste management in the textile industries of India. This section explains the nuances of waste generation at different phases of textile production by carefully examining existing methods, case studies, and industry dynamics. A thorough grasp of the qualitative landscape is revealed, encompassing everything from yarn and fabric waste to the difficulties presented by chemical residues and the disposal of end-of-life textiles. The importance of awareness and corporate responsibility in influencing waste management methods is also highlighted in this section. Case studies of top textile producers present creative approaches and offer insights into the possibilities of technological developments, cooperative efforts, and models of the circular economy. Nonetheless, difficulties ranging from technology constraints to cultural obstacles highlight how difficult it is to smoothly integrate sustainable practices throughout the sector. The chapter on integration strategies looks at potential directions that could change the way waste is managed in the textile industry in India. Models of the circular economy take center stage because they provide a comprehensive approach to production and disposal. Case studies from real-world applications highlight the revolutionary potential of upcycling and sophisticated recycling technology, offering insights into how these strategies could lessen the industry's environmental impact. Co-processing in cement kilns is a two-pronged method that solves waste disposal issues and produces useful resources. The potential for improved trash sorting and identification is demonstrated by the combination of artificial intelligence and data analytics, opening the door to increased process efficiency in waste management. The study's conclusion emphasizes that although the study has been helpful, the search for India's sustainable textile waste management is still an ongoing project. The industry is at a turning point in its evolution, and the factors pushing it in that direction will be sustained research and innovation leading the way toward a time when environmental stewardship and economic prosperity coexist together. Every thread woven into the fabric of the Indian textile industry offers a story of sustainability, adaptability, and responsible International Journal of Scientific Research in Engineering and Management (IJSREM) Volume: 08 Issue: 07 | July - 2024 SJIF Rating: 8.448 ISSN: 2582-3930 © 2024, IJSREM | www.ijsrem.com DOI: 10.55041/IJSREM36458 | Page 2 growth; therefore, the success of this journey will depend on the collective duty of industry stakeholders, policymakers, and consumers. Essentially, the goal of this extensive study is to serve as a compass for the Indian textile sector, offering a path forward to a time when environmental conservation and economic growth coexist peacefully.