Effective domestic waste separation at the source is a cornerstone of sustainable waste management, yet its implementation faces significant challenges globally, particularly at the household level. This article investigates the determinants of household intention to separate domestic waste, applying Ajzen's Theory of Planned Behavior (TPB) as the guiding theoretical framework. The TPB posits that an individual's intention to perform a behavior is influenced by their attitude towards the behavior, subjective norms, and perceived behavioral control. Through a comprehensive review of existing literature, this study synthesizes findings on how these constructs, alongside other contextual factors, influence household waste separation intentions. Understanding these drivers is crucial for designing targeted interventions and policies that promote greater participation in waste segregation programs, ultimately contributing to improved resource recovery and reduced environmental impact.

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.

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.

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.

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.

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.