Aim/Purpose: Although cassava is one of the crops that can be grown during the dry season in Northeastern Thailand, most farmers in the region do not know whether the crop can grow in their specific areas because the available agriculture planning guideline provides only a generic list of dry-season crops that can be grown in the whole region. The purpose of this research is to develop a predictive model that can be used to predict suitable areas for growing cassava in Northeastern Thailand during the dry season. Background: This paper develops a decision support system that can be used by farmers to assist them determine if cassava can be successfully grown in their specific areas. Methodology: This study uses satellite imagery and data on land characteristics to develop a machine learning model for predicting suitable areas for growing cassava in Thailand’s Nakhon-Phanom province. Contribution: This research contributes to the body of knowledge by developing a novel model for predicting suitable areas for growing cassava. Findings: This study identified elevation and Ferric Acrisols (Af) soil as the two most important features for predicting the best-suited areas for growing cassava in Nakhon-Phanom province, Thailand. The two-class boosted decision tree algorithm performs best when compared with other algorithms. The model achieved an accuracy of .886, and .746 F1-score. Recommendations for Practitioners: Farmers and agricultural extension agents will use the decision support system developed in this study to identify specific areas that are suitable for growing cassava in Nakhon-Phanom province, Thailand Recommendation for Researchers: To improve the predictive accuracy of the model developed in this study, more land and crop characteristics data should be incorporated during model development. The ground truth data for areas growing cassava should also be collected for a longer period to provide a more accurate sample of the areas that are suitable for cassava growing. Impact on Society: The use of machine learning for the development of new farming systems will enable farmers to produce more food throughout the year to feed the world’s growing population. Future Research: Further studies should be carried out to map other suitable areas for growing dry-season crops and to develop decision support systems for those crops.

Aim/Purpose: In the United States, the Centers for Disease Control and Prevention (CDC) tracks the disease activity using data collected from medical practice's on a weekly basis. Collection of data by CDC from medical practices on a weekly basis leads to a lag time of approximately 2 weeks before any viable action can be planned. The 2-week delay problem was addressed in the study by creating machine learning models to predict flu outbreak. Background: The 2-week delay problem was addressed in the study by correlation of the flu trends identified from Twitter data and official flu data from the Centers for Disease Control and Prevention (CDC) in combination with creating a machine learning model using both data sources to predict flu outbreak. Methodology: A quantitative correlational study was performed using a quasi-experimental design. Flu trends from the CDC portal and tweets with mention of flu and influenza from the state of Georgia were used over a period of 22 weeks from December 29, 2019 to May 30, 2020 for this study. Contribution: This research contributed to the body of knowledge by using a simple bag-of-word method for sentiment analysis followed by the combination of CDC and Twitter data to generate a flu prediction model with higher accuracy than using CDC data only. Findings: The study found that (a) there is no correlation between official flu data from CDC and tweets with mention of flu and (b) there is an improvement in the performance of a flu forecasting model based on a machine learning algorithm using both official flu data from CDC and tweets with mention of flu. Recommendations for Practitioners: In this study, it was found that there was no correlation between the official flu data from the CDC and the count of tweets with mention of flu, which is why tweets alone should be used with caution to predict a flu out-break. Based on the findings of this study, social media data can be used as an additional variable to improve the accuracy of flu prediction models. It is also found that fourth order polynomial and support vector regression models offered the best accuracy of flu prediction models. Recommendations for Researchers: Open-source data, such as Twitter feed, can be mined for useful intelligence benefiting society. Machine learning-based prediction models can be improved by adding open-source data to the primary data set. Impact on Society: Key implication of this study for practitioners in the field were to use social media postings to identify neighborhoods and geographic locations affected by seasonal outbreak, such as influenza, which would help reduce the spread of the disease and ultimately lead to containment. Based on the findings of this study, social media data will help health authorities in detecting seasonal outbreaks earlier than just using official CDC channels of disease and illness reporting from physicians and labs thus, empowering health officials to plan their responses swiftly and allocate their resources optimally for the most affected areas. Future Research: A future researcher could use more complex deep learning algorithms, such as Artificial Neural Networks and Recurrent Neural Networks, to evaluate the accuracy of flu outbreak prediction models as compared to the regression models used in this study. A future researcher could apply other sentiment analysis techniques, such as natural language processing and deep learning techniques, to identify context-sensitive emotion, concept extraction, and sarcasm detection for the identification of self-reporting flu tweets. A future researcher could expand the scope by continuously collecting tweets on a public cloud and applying big data applications, such as Hadoop and MapReduce, to perform predictions using several months of historical data or even years for a larger geographical area.

Aim/Purpose: The aim of this study was to analyze various performance metrics and approaches to their classification. The main goal of the study was to develop a new typology that will help to advance knowledge of metrics and facilitate their use in machine learning regression algorithms Background: Performance metrics (error measures) are vital components of the evaluation frameworks in various fields. A performance metric can be defined as a logical and mathematical construct designed to measure how close are the actual results from what has been expected or predicted. A vast variety of performance metrics have been described in academic literature. The most commonly mentioned metrics in research studies are Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), etc. Knowledge about metrics properties needs to be systematized to simplify the design and use of the metrics. Methodology: A qualitative study was conducted to achieve the objectives of identifying related peer-reviewed research studies, literature reviews, critical thinking and inductive reasoning. Contribution: The main contribution of this paper is in ordering knowledge of performance metrics and enhancing understanding of their structure and properties by proposing a new typology, generic primary metrics mathematical formula and a visualization chart Findings: Based on the analysis of the structure of numerous performance metrics, we proposed a framework of metrics which includes four (4) categories: primary metrics, extended metrics, composite metrics, and hybrid sets of metrics. The paper identified three (3) key components (dimensions) that determine the structure and properties of primary metrics: method of determining point distance, method of normalization, method of aggregation of point distances over a data set. For each component, implementation options have been identified. The suggested new typology has been shown to cover a total of over 40 commonly used primary metrics Recommendations for Practitioners: Presented findings can be used to facilitate teaching performance metrics to university students and expedite metrics selection and implementation processes for practitioners Recommendation for Researchers: By using the proposed typology, researchers can streamline development of new metrics with predetermined properties Impact on Society: The outcomes of this study could be used for improving evaluation results in machine learning regression, forecasting and prognostics with direct or indirect positive impacts on innovation and productivity in a societal sense Future Research: Future research is needed to examine the properties of the extended metrics, composite metrics, and hybrid sets of metrics. Empirical study of the metrics is needed using R Studio or Azure Machine Learning Studio, to find associations between the properties of primary metrics and their “numerical” behavior in a wide spectrum of data characteristics and business or research requirements

Aim/Purpose: To predict the change-proneness of software from the continuous evolution using machine learning methods. To identify when software changes become statistically significant and how metrics change. Background: Software evolution is the most time-consuming activity after a software release. Understanding evolution patterns aids in understanding post-release software activities. Many methodologies have been proposed to comprehend software evolution and growth. As a result, change prediction is critical for future software maintenance. Methodology: I propose using machine learning methods to predict change-prone classes. Classes that are expected to change in future releases were defined as change-prone. The previous release was only considered by the researchers to define change-proneness. In this study, I use the evolution of software to redefine change-proneness. Many snapshots of software were studied to determine when changes became statistically significant, and snapshots were taken biweekly. The research was validated by looking at the evolution of five large open-source systems. Contribution: In this study, I use the evolution of software to redefine change-proneness. The research was validated by looking at the evolution of five large open-source systems. Findings: Software metrics can measure the significance of evolution in software. In addition, metric values change within different periods and the significance of change should be considered for each metric separately. For five classifiers, change-proneness prediction models were trained on one snapshot and tested on the next. In most snapshots, the prediction performance was excellent. For example, for Eclipse, the F-measure values were between 80 and 94. For other systems, the F-measure values were higher than 75 for most snapshots. Recommendations for Practitioners: Software change happens frequently in the evolution of software; however, the significance of change happens over a considerable length of time and this time should be considered when evaluating the quality of software. Recommendation for Researchers: Researchers should consider the significance of change when studying software evolution. Software changes should be taken from different perspectives besides the size or length of the code. Impact on Society: Software quality management is affected by the continuous evolution of projects. Knowing the appropriate time for software maintenance reduces the costs and impacts of software changes. Future Research: Studying the significance of software evolution for software refactoring helps improve the internal quality of software code.

Aim/Purpose: Previous research has generally concentrated on identifying the variables that most significantly influence customer churn or has used customer segmentation to identify a subset of potential consumers, excluding its effects on forecast accuracy. Consequently, there are two primary research goals in this work. The initial goal was to examine the impact of customer segmentation on the accuracy of customer churn prediction in the banking sector using machine learning models. The second objective is to experiment, contrast, and assess which machine learning approaches are most effective in predicting customer churn. Background: This paper reviews the theoretical basis of customer churn, and customer segmentation, and suggests using supervised machine-learning techniques for customer attrition prediction. Methodology: In this study, we use different machine learning models such as k-means clustering to segment customers, k-nearest neighbors, logistic regression, decision tree, random forest, and support vector machine to apply to the dataset to predict customer churn. Contribution: The results demonstrate that the dataset performs well with the random forest model, with an accuracy of about 97%, and that, following customer segmentation, the mean accuracy of each model performed well, with logistic regression having the lowest accuracy (87.27%) and random forest having the best (97.25%). Findings: Customer segmentation does not have much impact on the precision of predictions. It is dependent on the dataset and the models we choose. Recommendations for Practitioners: The practitioners can apply the proposed solutions to build a predictive system or apply them in other fields such as education, tourism, marketing, and human resources. Recommendation for Researchers: The research paradigm is also applicable in other areas such as artificial intelligence, machine learning, and churn prediction. Impact on Society: Customer churn will cause the value flowing from customers to enterprises to decrease. If customer churn continues to occur, the enterprise will gradually lose its competitive advantage. Future Research: Build a real-time or near real-time application to provide close information to make good decisions. Furthermore, handle the imbalanced data using new techniques.

Aim/Purpose: While existing literature has extensively explored factors influencing the success of big data projects and proposed big data maturity models, no study has harnessed machine learning to predict project success and identify the critical features contributing significantly to that success. The purpose of this paper is to offer fresh insights into the realm of big data projects by leveraging machine-learning algorithms. Background: Previously, we introduced the Global Big Data Maturity Model (GBDMM), which encompassed various domains inspired by the success factors of big data projects. In this paper, we transformed these maturity domains into a survey and collected feedback from 90 big data experts across the Middle East, Gulf, Africa, and Turkey regions regarding their own projects. This approach aims to gather firsthand insights from practitioners and experts in the field. Methodology: To analyze the feedback obtained from the survey, we applied several algorithms suitable for small datasets and categorical features. Our approach included cross-validation and feature selection techniques to mitigate overfitting and enhance model performance. Notably, the best-performing algorithms in our study were the Decision Tree (achieving an F1 score of 67%) and the Cat Boost classifier (also achieving an F1 score of 67%). Contribution: This research makes a significant contribution to the field of big data projects. By utilizing machine-learning techniques, we predict the success or failure of such projects and identify the key features that significantly contribute to their success. This provides companies with a valuable model for predicting their own big data project outcomes. Findings: Our analysis revealed that the domains of strategy and data have the most influential impact on the success of big data projects. Therefore, companies should prioritize these domains when undertaking such projects. Furthermore, we now have an initial model capable of predicting project success or failure, which can be invaluable for companies. Recommendations for Practitioners: Based on our findings, we recommend that practitioners concentrate on developing robust strategies and prioritize data management to enhance the outcomes of their big data projects. Additionally, practitioners can leverage machine-learning techniques to predict the success rate of these projects. Recommendation for Researchers: For further research in this field, we suggest exploring additional algorithms and techniques and refining existing models to enhance the accuracy and reliability of predicting the success of big data projects. Researchers may also investigate further into the interplay between strategy, data, and the success of such projects. Impact on Society: By improving the success rate of big data projects, our findings enable organizations to create more efficient and impactful data-driven solutions across various sectors. This, in turn, facilitates informed decision-making, effective resource allocation, improved operational efficiency, and overall performance enhancement. Future Research: In the future, gathering additional feedback from a broader range of big data experts will be valuable and help refine the prediction algorithm. Conducting longitudinal studies to analyze the long-term success and outcomes of Big Data projects would be beneficial. Furthermore, exploring the applicability of our model across different regions and industries will provide further insights into the field.

Aim/Purpose: This paper aims to investigate and understand the intentions of management undergraduate students in Hangzhou, China, regarding the adoption of Artificial Intelligence (AI) technologies in their education. It addresses the need to explore the factors influencing AI adoption in the educational context and contribute to the ongoing discourse on technology integration in higher education. Background: The paper addresses the problem by conducting a comprehensive investigation into the perceptions of management undergraduate students in Hangzhou, China, regarding the adoption of AI in education. The study explores various factors, including Perceived Relative Advantage and Trialability, to shed light on the nuanced dynamics influencing AI technology adoption in the context of higher education. Methodology: The study employs a quantitative research approach, utilizing the Confirmatory Tetrad Analysis (CTA) and Partial Least Squares Structural Equation Modeling (PLS-SEM) methodologies. The research sample consists of management undergraduate students in Hangzhou, China, and the methods include data screening, principal component analysis, confirmatory tetrad analysis, and evaluation of the measurement and structural models. We used a random sampling method to distribute 420 online, self-administered questionnaires among management students aged 18 to 21 at universities in Hangzhou. Contribution: This paper explores how management students in Hangzhou, China, perceive the adoption of AI in education. It identifies factors that influence AI adoption intention. Furthermore, the study emphasizes the complex nature of technology adoption in the changing educational technology landscape. It offers a thorough comprehension of this process while challenging and expanding the existing literature by revealing the insignificant impacts of certain factors. This highlights the need for an approach to AI integration in education that is context-specific and culturally sensitive. Findings: The study highlights students’ positive attitudes toward integrating AI in educational settings. Perceived relative advantage and trialability were found to impact AI adoption intention significantly. AI adoption is influenced by social and cultural contexts rather than factors like compatibility, complexity, and observability. Peer influence, instructor guidance, and the university environment were identified as pivotal in shaping students’ attitudes toward AI technologies. Recommendations for Practitioners: To promote the use of AI among management students in Hangzhou, practitioners should highlight the benefits and the ease of testing these technologies. It is essential to create communication strategies tailored to the student’s needs, consider cultural differences, and utilize the influence of peers and instructors. Establishing a supportive environment within the university that encourages innovation through policies and regulations is vital. Additionally, it is recommended that students’ attitudes towards AI be monitored constantly, and strategies adjusted accordingly to keep up with the changing technological landscape. Recommendation for Researchers: Researchers should conduct cross-disciplinary and cross-cultural studies with qualitative and longitudinal research designs to understand factors affecting AI adoption in education. It is essential to investigate compatibility, complexity, observability, individual attitudes, prior experience, and the evolving role of peers and instructors. Impact on Society: The study’s insights into the positive attitudes of management students in Hangzhou, China, toward AI adoption in education have broader societal implications. It reflects a readiness for transformative educational experiences in a region known for technological advancements. However, the study also underscores the importance of cautious integration, considering associated risks like data privacy and biases to ensure equitable benefits and uphold educational values. Future Research: Future research should delve into AI adoption in various academic disciplines and regions, employing longitudinal designs and qualitative methods to understand cultural influences and the roles of peers and instructors. Investigating moderating factors influencing specific factors’ relationship with AI adoption intention is essential for a comprehensive understanding.

Aim/Purpose: This paper introduces a Social Machine for collaborative sensemaking that the developers have configured to the requirements and challenges of transdisciplinary literature reviews. Background: Social Machines represent a promising model for unifying machines and social processes for a wide range of purposes. A development team led by the author is creating a Social Machine for activities that require users to combine pieces of information from multiple online sources and file types for various purposes. Methodology: The development team has applied emergent design processes, usability testing, and formative evaluation in the execution of the product road map. Contribution: A major challenge of the digital information age is how to tap into large volumes of online information and the collective intelligence of diverse groups to generate new knowledge, solve difficult problems, and drive innovation. A Transdisciplinary Social Machine (TDSM) enables new forms of interactions between humans, machines, and online content that have the potential to (a) improve outcomes of sensemaking activities that involve large collections of online documents and diverse groups and (b) make machines more capable of assisting humans in their sensemaking efforts. Findings: Preliminary findings suggest that TDSM promotes learning and the generation of new knowledge. Recommendations for Practitioners: TDSM has the potential to improve outcomes of literature reviews and similar activities that require distilling information from diverse online sources. Recommendation for Researchers: TDSM is an instrument for investigating sensemaking, an environment for studying various forms of human and machine interactions, and a subject for further evaluation. Impact on Society: In complex areas such as sustainability and healthcare research, TDSM has the potential to make decision-making more transparent and evidence-based, facilitate the production of new knowledge, and promote innovation. In education, TDSM has the potential to prepare students for the 21st century information economy. Future Research: Research is required to measure the effects of TDSM on cross-disciplinary communication, human and machine learning, and the outcomes of transdisciplinary research projects. The developers are planning a multiple case study using design-based research methodology to investigate these topics.

Aim/Purpose: This paper examines the impact of the transformational, servant, and paternalistic leadership styles on employee performance at the middle and senior levels. Background: Transdisciplinary research promotes the integration and development of various sciences. It provides more choices for leaders to adopt ways and practical activities to promote enterprise development. Complexity leadership theory emphasizes that effectively functioning organizations need distinct forms of leadership to work together. Leaders rely on different leadership practices in an emergent collaborative context, and finding an optimal balance is challenging. Many scholars have attempted to explore which leadership styles have a more significant impact on employees by distinguishing and defining types of leadership styles and explaining the process by which they influence employee behavior and performance. Various scholars have further explored and empirically demonstrated the impact of these three types of leadership styles (transformational, servant, paternalistic)on employee performance. While transformational and servant leadership have their roots in the West, paternalistic leadership has roots in China. Few scholars have conducted comparative studies on their positive impact on employee performance. How do these three leadership styles affect employee performance at the middle and senior levels in the Chinese context? Which combination of middle and senior leadership styles performs best? These are the second area that this paper will attempt to explore. Methodology: This study constructs a three-tier model at the senior, middle, and grassroots levels. A questionnaire survey was used to collect data. SPSS 22.0 and Amos were used for data analysis. Contribution: Through its construction of a three-tier model (senior, middle, and grassroots levels), the paper explores the combined effect of three leadership styles (transformational, servant, and paternalistic) on grassroots employees. It explores the impact of senior leaders across levels on grassroots employee performance, which is expected to provide a valuable addition to theories on leadership styles. It is also instructive to examine which leadership style performs better and what middle and senior leadership configurations are more conducive to driving beneficial employee behavior and, ultimately, corporate growth. Findings: The transformational, servant, and paternalistic leadership styles, both at the top and middle levels, have a significant positive relationship with employee performance; the middle leadership style plays a positive mediating role between the top leadership style and employee performance. In terms of impact on employee performance, transformational leadership shows the best results at both the top and middle levels, with paternalistic leadership second and servant leadership at the same level. Regarding which middle and senior leadership style pairing is the best, the sample is relatively small, and the gap between various pairing combinations is not evident from the data. If the sample size is enlarged, the coefficient will likely expand year-on-year. Therefore, we can assume that the pairing effect of top servant leadership and middle transformational leadership is the best, top paternalistic leadership and middle transformational leadership is the second-best, and the combination of top paternalistic leadership and middle-level servant leadership leaders is the weakest. Recommendation for Researchers: This paper extends the study of top and middle leadership’s combined effect on employee performance as a positive response to the call for multi-layer or cross-layer analysis in leadership research. The findings further enrich the literature on leadership style-related theories. The middle leadership style plays a positive mediating role between the top leadership style and employee performance. The trickle-down effect is further verified, i.e., the top leadership will have a permeating influence on employees through the middle leadership, and the top’s influence on the middle is generally more significant than the influence on grassroots employees. However, the difference between the influence of the middle leadership on the grassroots and that of the top on the grassroots is not apparent, which is inconsistent with the trickle-down effect that the middle leadership communicates more with the grassroots and has more influence on the grassroots, and further verification is needed. All three types of leaders positively affected employee performance, with the best being transformational leadership, paternalistic leadership, and servant leadership. This finding is consistent with some scholars and inconsistent with some scholars. The interested scholars can do further research. The better performance of diverse pairings in middle and senior leadership combinations is consistent with previous research suggesting that leadership styles have their own strengths and can be complementary. This paper further provides a comparative study of multiple leadership styles to validate the recognition and adaptability of leadership styles and further explain the complex relationship between leadership styles and employee job performance. Scholars can conduct comparative research on other leadership styles, and there may be different results. Future Research: Because of the cross-sectional data taken, the findings’ generalizability still needs further validation. There are many types of leadership styles, and there are other types of leadership styles that can be explored comparatively, perhaps leading to different findings. From another point of view, various leaders have their strengths, and they are not mutually hindering. More research is needed on team formation in a variety of contexts. Organic organizational structure enables knowledge creation and integration through the process of organizational learning through deep and continuous social interaction or dialogue. So we can further examine the influence process of leaders on employees from how to give full play to their advantages, such as improving shared leadership and shared communication.

Aim/Purpose: The amount of information published has increased dramatically due to the information explosion. The issue of managing information as it expands at this rate lies in the development of information extraction technology that can turn unstructured data into organized data that is understandable and controllable by computers Background: The primary goal of named entity recognition (NER) is to extract named entities from amorphous materials and place them in pre-defined semantic classes. Methodology: In our work, we analyze various machine learning algorithms and implement K-NN which has been widely used in machine learning and remains one of the most popular methods to classify data. Contribution: To the researchers’ best knowledge, no published study has presented Named entity recognition for the Kikuyu language using a machine learning algorithm. This research will fill this gap by recognizing entities in the Kikuyu language. Findings: An evaluation was done by testing precision, recall, and F-measure. The experiment results demonstrate that using K-NN is effective in classification performance. Recommendation for Researchers: With enough training data, researchers could perform an experiment and check the learning curve with accuracy that compares to state of art NER. Future Research: Future studies may be done using unsupervised and semi-supervised learning algorithms for other resource-scarce languages.

Product innovation has become a crucial part of the sustainability of the coconut agro-industry in Indonesia, covering upstream and downstream sides. To overcome this challenge, it is necessary to create several model stages using a hybrid method that combines machine learning based on multiple criteria decision making and mass balance analysis. The research case study was conducted in Tembilahan district, Riau province, Indonesia, one of the primary coconut producers in Indonesia. The analysis results showed that potential products for domestic customers included coconut milk, coconut cooking oil, coconut chips, coconut jelly, coconut sugar, and virgin coconut oil. Furthermore, considering the experts, the most potential product to be developed was coconut sugar with a weight of 0.26. Prediction of coconut sugar demand reached 13,996,607 tons/year, requiring coconut sap as a raw material up to 97,976,249.

This book centers on the author's discovery, and moral disapproval, of expedient arrangements adopted by American firms in China, through which 'middlemen', operating as independent agents, pay bribes as part and parcel of the troublesome process of negotiating and clinching business deals there.

Carlo Lipizzi’s Societal impacts of artificial intelligence and machine learning offers a critical and comprehensive analysis of artificial intelligence (AI) and machine learning’s effects on society. This book provides a balanced perspective, cutting through the

The wethepeople "Chaos Machine 22" BMX Frame - 22 Inch is the signature BMX frame from the Australian BMX Trails legend Tyson Jones-Peni and comes in a 22" inch version for 22" inch wheels. The wethepeople "Chaos Machine 22" BMX Frame - 22 Inch features a long and stable geometry and mounts for a disc brake (and regular U-brake).

• Wheel Size: 20"
• Material: 100% 4130 Japanese CrMo, top tube and down tube gussets, butted tubes, integrated headset, integrated seatclamp, offset thickness dent resistant chainstays and mid bb for grind resistance
• Geometry:
  Top Tube Length (TT): 22.25"
  Chainstay Length (CS): 14" (35.56cm) - 14.75" (37.47cm)
  Head Tube Angle (HA): 74.25°
  Seat Tube Angle (SA): 71°
  Bottom Bracket Height: 12.25"
  Standover Height (SO): 9.75" (24.77cm)
• Seat Clamp: integrated
• Seat Post Diameter: 25.4mm
• Bottom Bracket: Mid BB
• Dropouts: Investment cast, 6mm thick, 14mm slots, CNC machined, 4130 CrMo
• Chain Tensioners: integrated
• Brake Type: U-Brake & Disc Brake
• Brakemounts: without
• Brakemounts included with delivery: No
• Gyro compatible: No
• Features: Tyson Jones-Peni Signature, long trail geometry, disc mount for 120mm - 160mm rotors, 127mm high head tube, strut tube with larger radius, stiffer & stronger back end, larger weld on the head tube, wider dropout for wide 2.4" tires
• Model Year: 2024

Jiang Yuyan breaks the Paralympic world record in the women's 100m backstroke

Washington had sanctioned China-based companies in October 2023 for supplying missile-applicable items to Pakistan

LEGO has revealed three Chinese Traditional Festival (CTF) sets and two Monkie Kid models at the 2024 China International Import Expo, which is currently taking place in Shanghai.

The subject matter may not mean much to some of us here in the west, but the CTF sets once again look to be exceptional, although not as packed with minifigs as they usually are. They will probably be good value, too.

View photos after the break, and keep up to date with everything that's been officially revealed for next year in our 2025 set listing.

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