Vol. 7 No. 07 (2025)

This study examines targeted degradation of the transcription factor MYB as a novel therapeutic avenue in acute myeloid leukemias. Its relevance stems from the limited efficacy of existing treatment regimens and the emergence of drug‐resistant disease forms. The work’s novelty lies in the integrated comparison of genetic models for MYB destabilization, chemical dTAG approaches, and the repurposing of low-molecular-weight compounds mebendazole and vitaferin A. Data are synthesized on MYB’s transformational properties critical for leukemic-clone maintenance, and experimental findings are reviewed on the suppression of transcriptional programs and induction of blast-cell death following complete factor elimination. Special attention is devoted to prospects for developing PROTAC degraders and molecular glues capable of catalyzing ubiquitin-dependent proteolysis of MYB at low compound doses. The research aims to construct a comprehensive assessment of the efficacy and safety of MYB-degradation strategies and to identify directions for further preclinical investigation. Methods include comparative analysis of peer-reviewed literature, critical appraisal of in vitro and in vivo data, and synthesis of pharmacodynamic profiles of the repurposed agents. The overall evaluation highlights the approach’s potential to overcome therapeutic resistance and improve patient survival. The findings will interest pharmacologists, oncologists, clinical researchers, and specialists in chemical biology.

The automotive industry is undergoing a tremendous increase in data generation, mostly driven by advancements in vehicle technology, connectivity, and autonomous driving features. The Apache Hadoop data lake was adopted by companies to store and analyze the huge volume, velocity, and variety of automotive data. However, with technological advancement and the need for real-time analytics, operational complexity, scalability, and cost efficiency, Apache Hadoop-based data lakes started presenting challenges. Google BigQuery, on the other hand, is a fully managed, serverless data warehouse and analytics platform that offers a good alternative with its scalable architecture, high performance, ease of use, and integration with advanced analytics and machine learning services. Migrating this massive amount of automotive data from Hadoop to BigQuery needs careful planning and execution, especially while making sure there are fewer disruptions with the ongoing business and avoiding downtime. This paper explores the typical architecture and use case of Hadoop-based data lakes in the automotive sector, explores BigQuery as an alternative option while also considering its benefits, and analyzes various strategies and methods for a seamless migration. Further, it delves into techniques and best practices for achieving zero downtime during the migration of large automotive datasets, addresses the specific challenges and considerations involved in handling automotive data’s unique characteristics, examines relevant case studies of successful migrations, investigates methods for ensuring data consistency and integrity, and researches approaches to optimize data processing and analytics workflows on BigQuery post-migration.

The use of Snowflake as a cloud-native data warehouse has dramatically changed the management of analytics workload for Property and Casualty (P&C) insurers, while simultaneously presenting serious cost governance challenges. The heavy volume of searches, big data retention, and decentralized business intelligence operations are industry-standard procedures that tend to lead to uncontrolled credit usage and overspending on storage. This research introduces a modular five-layer optimization framework focused on property and casualty insurance data, combining workload segmentation, and compute sizing with Snowflake's account usage metadata. The framework is tested and validated using Kaggle’s Insurance Agency Data, representing real-world P&C operations across 17 states. Benchmark queries simulating core insurance workloads were designed using modified TPC-H logic, a standard decision support benchmark that enables realistic performance evaluation under analytical query conditions, achieving up to 82% cost reduction and a 64% reduction in execution time without compromising the results. These results highlight the efficiency of the framework to facilitate proactive and elastic cost control. Future studies can investigate AI-driven query forecasting, scalable warehouse dynamics, and real-time anomaly detection to further advance cloud-native data ecosystem governance.

A large proportion of the worldwide emissions caused by greenhouse gases is attributed to the construction sector and the manufacturing industry, with much of it related to embodied carbon or emissions associated with the extraction of materials, their production, transportation, and assembly. This paper involves the conceptualization and validation of a real-time carbon dashboard meant to monitor embodied emissions in supply chains and project stages. The dashboard is designed to provide dynamic monitoring, predictive analysis, and forecasting of emissions, integrating technologies from the Internet of Things (IoT) and Life Cycle Assessment (LCA), and presenting the results in visual forms. An on-site pilot test at a commercial construction project demonstrated that the system conducted time-stamped emission logging and alerted to high-impact building materials, and can transform procurement and operational practices. The article describes the architecture of the dashboard, the methods of data acquisition, the validation process, and the practical implications, as well as its opportunities to facilitate sustainable decision-making and stakeholder engagement. The barriers to cost implementation, data quality, and system integration will be discussed, as well as future challenges such as integrating machine learning and blockchain. Carbon tracking, specifically real-time embodied carbon tracking, has been identified as a crucial tool for achieving net-zero targets, ensuring compliance, and facilitating ESG reporting. Not only does the dashboard enhance the visibility of emissions, but it also serves as a strategic lever to advocate for building towards carbon-mindful action, which is applicable across the built environment.

In modern manufacturing, where customer demands change quickly and market forces are always changing, two key processes are essential for operational success: production planning and scheduling. To make sure that manufacturing processes are in accordance with business goals, that resources are spent intelligently, and that things are delivered on time, these actions must be taken. But conventional means of planning and scheduling production are having trouble at a time where individuals are continually seeking for ways to get better and come up with new ideas. These methods that used to function effectively don't work as well in today's intricate industrial environment, therefore it's time to come up with new ways to stay ahead in a competitive field. Old ways of planning production that can't keep up with a world that is changing swiftly cause a lot of issues in the manufacturing company. AI, or artificial intelligence, is a new technology that is revolutionizing the way things have always been done. Imagine a future where manufacturing goes smoothly because production lines can alter to meet market needs, resources are used more efficiently, and demand is predicted accurately. Because AI can transform things, this future is not simply a dream; it is occurring right now. AI is altering how things are manufactured by replacing rigid manufacturing processes and set schedules with smart systems that can learn, adapt, forecast, and become better at speeds never seen before. AI technologies are transforming how production planners and manufacturers do their jobs. Now they can make better decisions, manage their resources more wisely, and design strategies that function in the real world. This stu: AIoks at how complicated AI is when it comes to planning and scheduling production, with a focus on how important it is in ERP systems.

Consumer Packaged Goods (CPG) firms encounter distinct obstacles in handling the extensive data produced from sales, supply chains, customer interactions, and market patterns. To tackle these obstacles, organizations are progressively implementing a contemporary data stack. This document examines how using Microsoft Azure, Snowflake, and DBT technologies can create a comprehensive analytics stack that improves scalability, data transformation, and decision-making processes. The paper outlines the technical framework, best practices, and real-world examples that showcase the implementation of this modern CPG data stack. By utilizing cloud-based solutions, businesses can enhance operational efficiency, automate data processes, and acquire more profound insights. We will investigate performance metrics, security risks, cost-efficiency methods, and forthcoming trends that influence the advancement of CPG analytics.

This review paper analyses the implementation of Robotic Process Automation (RPA) in financial account reconciliations, integrating existing literature with a practical case study of a multinational life sciences firm that realized an 85% decrease in reconciliation duration. Although RPA has gained traction in enhancing repetitious financial processes, current research frequently lacks empirical specificity, sector-related constraints, and evaluations of post-implementation effects. This report identifies deficiencies in exception handling, scalability, and ERP system integration by comparing five academic and industrial sources with practical insights from the case study. A thorough RPA Reconciliation Framework is proposed, including process discovery, bot logic, error feedback, AI integration, compliance, and change management. The results underscore RPA's capacity to enhance speed, precision, and auditability, while indicating that future research should concentrate on hybrid RPA-AI systems and uniform maturity models.

This systematic review surveys advances in conversational AI from 2015 to 2025, focusing on the emergence of modular multi-agent architectures, hierarchical reinforcement learning, and self- evolving agents. A quantitative synthesis of 63 studies indicates that memory-augmented, long- horizon planners improve task success rates by approximately 30% over flat policies, while meta- learning and lifelong learning approaches halve sample complexity in data-scarce domains. Despite these gains, current systems remain brittle under distribution shifts, lack principled safety guarantees, and provide few benchmarks for diagnosing co-adaptive failure modes in mission-critical applications.

Achieving ultra-low latency (ULL) with end-to-end delays of 1–5 milliseconds is vital for real-time applications such as high-frequency trading, autonomous vehicles, and personalized e-commerce. This study defines latency as the time from initiating a data processing task to receiving its result, proposing a holistic approach to ULL through optimized hardware, software, and network components. Latency is broken down into network, I/O, processing, queuing, application, and security factors, reducing the standard ~19 ms latency to below 5 ms with targeted enhancements. Key strategies leverage high-performance hardware (NVMe SSDs, FPGAs, GPUs), low-latency interconnects (InfiniBand with RDMA), and efficient software. A real-time fraud detection scenario handling 10,000 concurrent queries per second is analyzed, detailing tiered technology stacks. The study contrasts networking protocols, emphasizing InfiniBand’s sub-microsecond latency advantage for ULL, and demonstrates feasibility with edge infrastructure, dedicated instances, and RDMA-enabled NVMe-oF. This framework offers practical guidance and cost estimates for 2025 ULL implementations, acknowledging that actual latency, performance, and costs may vary by use case.

The rapid growth of unstructured financial data—ranging from earnings calls and SEC filings to real-time social me- dia and global news—has outpaced the ability of traditional analysis tools to provide timely, contextual insights. Most natural language models are trained on static data and lack the capacity to integrate dynamic, real-world updates. Retrieval- Augmented Generation (RAG) bridges this gap by combining document retrieval with generative capabilities, creating a more grounded and up-to-date understanding of user queries. This paper presents a domain-adapted RAG-based framework for real-time financial analysis, using vector databases and domain-specific language models. The framework demon- strates improved contextual accuracy, reduced hallucination, and greater interpretability compared to traditional NLP mod- els. Our findings indicate that RAG has the potential to become a core component in next-generation financial intelli- gence systems.