Houston Independent School District

Abstract In Texas, all new teachers with 2 years or less experience must have a mentor teacher assigned to them by the campus or district. This mentor teacher must have at least “three years of experience and a superior history of improving student performance and have completed a research-based mentor and induction training program approved by the commissioner” (Texas Education Code, §21.458). Often there is a mismatch between the mentors and the mentees, which can mean the difference between a novice teacher staying in the profession or leaving. This narrative inquiry takes up an autobiographical exploration into the influence of mentors on my beginning teacher classroom experiences and on my decision to stay or leave teaching as a profession. Drawing on my experiences over the span of a career, my stories of experience pertaining to being both a mentee and a mentor are examined.

With the exponential growth of remote sensing data, efficiently analyzing large-scale satellite imagery remains challenge due to computational complexity and resource constraints. This paper in turn suggests a superior serverless cloud-computing architecture to overcome such constraints with Hunger Game Optimization (HGO) to introduce live resource scheduling and Deep Reinforcement Learning (DRL) to introduce intelligent workflow. The suggested architecture processes cloudless Landsat images between the years 1991 and 2022 in both dry and rainy seasons. Data will be obtained on the Landsat series, namely <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$5,7,8$</tex> and 9, with a 30meter spatial resolution, therefore providing a vast temporal and spatial coverage to be used in the analysis to follow. Experimentally, the framework has been shown to provide an average <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$7.8 x$</tex> speedup compared to traditional server-based models, and at the same time have a 28% higher resource utilization. Notably, the classification accuracy is maintained at a high rate, with the overall F1-score of 92.3%. The findings demonstrate the effectiveness of combining HGO and DRL in a serverless cloud, which is a scalable, cost-efficient, and accurate solution to long-term remote sensing data analysis, and environmental monitoring.

Abstract The global quest for greater value from wells achieved success through well construction improvements contributing to economic production. The stage is set to unlock further value through a well lifecycle approach. Well Lifecycle applications need to solve two interconnected challenges: ability to uniformly quantify all the constituent factors contributing to the long-term outcome of a well, and ability to connect multiple static, transient, and in motion data in a meaningful manner across multiple disciplines. Increased value from wells requires a shift from siloed behaviors to full lifecycle unhindered workflow. A system of systems framework can create a context-driven federation for linkage of sub systems and multiple data sources for their intended use. Fully leveraged value can only be achieved when the well's data is comprehensively managed, contains context and is accessible in time to all involved for comprehensive lifecycle asset value while safeguarding against risk. This paper proposes an approach to well lifecycle workflow and data management that maximizes the well value contribution to enhance field development value. Data is an asset that must be competently managed and utilized to maximize the impact it has on business value. Currently, the two-way flow of information across the well lifecycle is broken or stifled. For example, the final user has limited input and access to the initial design. This failure to flow accurate data drives up risk across multiple aspects of the well lifecycle. The System of Systems approach has been successfully applied to both the defense and space exploration sectors as they evolved over the last decade. This approach offers an opportunity to deliver a framework onto which robust modern data management and utilization methods can be constructed. In time, access to all well data regardless of the stage in the lifecycle in a manner that is efficient and communicates the data context can be achieved with modern data management systems. This will enable significantly greater value realization from the operation of the wells and the associated subsurface modelling. Currently this is not practiced in wells lifecycle, for example, through the poor management of wellbore survey data routinely leading to greater uncertainty in subsurface models negatively impacting field development decisions. This paper furthers the application of a System of Systems methodology and workflow to manage the inherent complexity of interconnected interactions throughout a wells lifecycle. It also introduces industry best practices for managing data in this environment. These best practices will include data attributes (frequency, latency, …), context (data set description and changes made over time), uncertainty (deviations from the intended source value or derived parameters), various industry data standards, etc.

Demand forecasting is a critical function in Enterprise Resource Planning (ERP) systems, directly influencing inventory management, production planning, and overall operational efficiency. Traditional statistical models often fall short in handling the complexity and variability of modern supply chains. This study investigates the application of Artificial Intelligence (AI), specifically Machine Learning (ML) algorithms, to enhance demand forecasting accuracy within ERP environments. I conduct a comparative analysis of four widely used ML models: Linear Regression, Random Forest, Support Vector Regression (SVR), and Long Short-Term Memory (LSTM) networks. Using real-world ERP datasets, each model is evaluated based on Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and computational performance. The results reveal that while Random Forest and LSTM models outperform others in terms of accuracy, their complexity and training time vary significantly. My findings highlight the trade-offs between model accuracy and computational efficiency, offering practical insights for ERP stakeholders. This study contributes to the growing field of AI-driven enterprise analytics and provides guidance on selecting appropriate ML techniques tailored to specific forecasting needs within ERP systems.

Highlights Abstract Introduction: The arteriovenous fistula (AVF) is the preferred vascular access for hemodialysis; however, it is associated with complications such as failure to mature and aneurysms. Alternatives like arteriovenous grafts (AVGs) and tunneled dialysis catheters (TDCs) have their own issues, including higher rates of thrombosis and infection. A novel vascular access device was developed to facilitate direct arteriovenous hemodialysis access, potentially removing the need for AVFs or AVGs. In this proof-of-concept study, we aim to evaluate the feasibility of our novel vascular access device in a rabbit model and to investigate the effects of repeated arterial cannulation. Methods: The device was implanted in a rabbit model around the femoral artery, with twice-weekly blood draws performed over 4 weeks to assess feasibility and effects of repeated cannulation. Postsurgical monitoring focused on identifying complications after device implantation. Results: The device was implanted successfully with minimal surgical time and without complications. Over the 4 weeks, it facilitated easy and reproducible cannulation without infection, ischemia, or hematoma. The rabbit showed stable behavior, normal food intake, and no distress. The distal limb exhibited normal temperature and capillary refill, indicating no blood flow impairment. Complete blood count values remained stable throughout. These results support the device’s feasibility for future studies. Discussion: The results in this study confirm the feasibility of using a rabbit model for further testing and suggest that repeated arterial cannulation with the novel device may be safe and effective for hemodialysis. Despite a small sample size, the device appears promising for advancing vascular access solutions in hemodialysis.

The rapid growth of cyber threats and the increasing complexity of attack techniques demand advanced solutions for protecting systems, networks, and sensitive data. Artificial Intelligence (AI) and Machine Learning (ML) have proven highly effective in a wide range of cybersecurity applications, from detecting malicious activities to automating defense mechanisms. These technologies are transforming the way security systems are designed and operated. This has become essential in addressing the evolving landscape of digital threats. With the exponential rise in cyberattacks and the sophistication of adversaries, traditional methods of cybersecurity are proving inadequate. AI and ML offer innovative solutions by enhancing threat detection, improving response times, and automating tasks that previously required significant human intervention. These technologies are widely employed in applications such as anomaly detection, intrusion detection systems (IDS), malware analysis, and fraud detection. The primary benefits of AI and ML in cybersecurity include real-time threat analysis, predictive capabilities, and the ability to process large volumes of data. However, they also present challenges, such as high implementation costs, the need for skilled professionals, and the potential for adversarial attacks that can exploit machine learning models. This review aims to provide a comprehensive understanding of the integration of AI and ML into cybersecurity, highlighting their current applications, benefits, limitations, and future prospects. Key findings indicate that while these technologies significantly enhance cybersecurity capabilities, their success depends on overcoming challenges related to data quality, model robustness, and the dynamic nature of cyber threats.