This paper explores the integration of AI-based assessment techniques in engineering education, highlighting their potential to enhance personalized feedback, improve learning outcomes, and streamline assessment processes. It examines various AI tools and their applications in automated grading, adaptive testing, and intelligent tutoring systems. Additionally, it addresses the challenges of implementing AI in educational settings, including fairness, data privacy, and integration with existing systems. The paper concludes with a discussion on strategic planning and continuous improvement to optimize AI's role in fostering an effective assessment framework for engineering education.

The self-balancing robot represents a significant advancement in the realm of mobile robotic platforms. This paper introduces the design of an intelligent embedded system aimed at managing the direction and speed of the stepper motor that drives the self-balancing robot. Controlling the speed and direction of the stepper motor is crucial for maintaining the stability of the two-wheeled robot. Stability is achieved by keeping the robot in an upright walking position. The proposed smart embedded system is engineered to handle sensing, control, and actuation functions.

This paper explores the transformative potential of gamification and immersive learning experiences, enhanced by artificial intelligence (AI), in modern education. Gamification leverages game design elements to boost engagement, motivation, and learning outcomes, while immersive technologies such as virtual reality (VR) and augmented reality (AR) create interactive, experiential learning environments. AI plays a pivotal role by personalizing learning experiences, adapting content to individual needs, and providing real-time feedback. This study reviews existing literature presents case studies of successful implementations, and discusses the benefits and challenges associated with these technologies. By integrating AI with gamification and immersive learning, educators can create dynamic, engaging, and effective educational experiences. The paper also addresses ethical considerations, accessibility issues, and future research directions, ultimately highlighting the significant impact of AI-driven gamification and immersive learning on the future of education.

MOTIVATION: There are high demands for joint genotyping of structural variations with short-read sequencing, but efficient and accurate genotyping in population scale is a challenging task. RESULTS: We developed muCNV that aggregates per-sample summary pileups for joint genotyping of > 100,000 samples. Pilot results show very low Mendelian inconsistencies. Applications to large-scale projects in cloud show the computational efficiencies of muCNV genotyping pipeline. AVAILABILITY: muCNV is publicly available for download at: https://github.com/gjun/muCNV. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

State of the art research for date-time 1 entity extraction from text is task agnostic.Consequently, while the methods proposed in literature perform well for generic date-time extraction from texts, they don't fare as well on task specific date-time entity extraction where only a subset of the date-time entities present in the text are pertinent to solving the task.Furthermore, some tasks require identifying negation constraints associated with the date-time entities to correctly reason over time.We showcase a novel model for extracting task-specific date-time entities along with their negation constraints.We show the efficacy of our method on the task of date-time understanding in the context of scheduling meetings for an email-based digital AI scheduling assistant.Our method achieves an absolute gain of 19% f-score points compared to baseline methods in detecting the date-time entities relevant to scheduling meetings and a 4% improvement over baseline methods for detecting negation constraints over date-time entities.

The Long interspersed nuclear element 1 (LINE-1) is a primary source of genetic variation in humans and other mammals. Despite its importance, LINE-1 activity remains difficult to study because of its highly repetitive nature. Here, we developed and validated a method called TeXP to gauge LINE-1 activity accurately. TeXP builds mappability signatures from LINE-1 subfamilies to deconvolve the effect of pervasive transcription from autonomous LINE-1 activity. In particular, it apportions the multiple reads aligned to the many LINE-1 instances in the genome into these two categories. Using our method, we evaluated well-established cell lines, cell-line compartments and healthy tissues and found that the vast majority (91.7%) of transcriptome reads overlapping LINE-1 derive from pervasive transcription. We validated TeXP by independently estimating the levels of LINE-1 autonomous transcription using ddPCR, finding high concordance. Next, we applied our method to comprehensively measure LINE-1 activity across healthy somatic cells, while backing out the effect of pervasive transcription. Unexpectedly, we found that LINE-1 activity is present in many normal somatic cells. This finding contrasts with earlier studies showing that LINE-1 has limited activity in healthy somatic tissues, except for neuroprogenitor cells. Interestingly, we found that the amount of LINE-1 activity was associated with the with the amount of cell turnover, with tissues with low cell turnover rates (e.g. the adult central nervous system) showing lower LINE-1 activity. Altogether, our results show how accounting for pervasive transcription is critical to accurately quantify the activity of highly repetitive regions of the human genome.

BACKGROUND: In recent years there has been a growing interest in the role of copy number variations (CNV) in genetic diseases. Though there has been rapid development of technologies and statistical methods devoted to detection in CNVs from array data, the inherent challenges in data quality associated with most hybridization techniques remains a challenging problem in CNV association studies. RESULTS: To help address these data quality issues in the context of family-based association studies, we introduce a statistical framework for the intensity-based array data that takes into account the family information for copy-number assignment. The method is an adaptation of traditional methods for modeling SNP genotype data that assume Gaussian mixture model, whereby CNV calling is performed for all family members simultaneously and leveraging within family-data to reduce CNV calls that are incompatible with Mendelian inheritance while still allowing de-novo CNVs. Applying this method to simulation studies and a genome-wide association study in asthma, we find that our approach significantly improves CNV calls accuracy, and reduces the Mendelian inconsistency rates and false positive genotype calls. The results were validated using qPCR experiments. CONCLUSIONS: In conclusion, we have demonstrated that the use of family information can improve the quality of CNV calling and hopefully give more powerful association test of CNVs.

The traditional relationship between the car, driver, and city can be described as waypoint navigation with additional traffic and maintenance information. The car can receive and store waypoint information, find the shortest route to these waypoints, integrate traffic information, find points-of-interest, and alert the driver of a pre-programmed set of maintenance issues related to the car. Here, we propose a new route system that is multi-goal-centric rather than waypoint-centric. Instead of focusing on determining the route to a specified waypoint, as done in commercially available navigation systems, the system will analyze the driver's behavior in order to extract the potential set(s) of goals that the driver would like to achieve. The system must also understand the city on a number of levels: physical, social, and commercial. This provides the foundation for a social and intelligent driving assistant, that helps the driver achieve his goals and helps the city perform better through interaction between both entities.