Standards

This article examines the global efforts to govern and regulate Artificial Intelligence (AI) in response to its rapid development and growing influence across many parts of society. It explores how governance takes place at multiple levels, including international bodies, national governments, industries, companies, and communities. The study draws on a wide range of official documents, policy reports, and international agreements to build a timeline of key regulatory and standardization milestones. It also analyzes the challenges of coordinating across different legal systems, economic priorities, and cultural views. The findings show that while some progress has been made through soft-law frameworks and regional partnerships, deep divisions remain. These include unclear responsibilities, uneven enforcement, and risks of regulatory gaps. The article argues that effective AI governance requires stronger international cooperation, fair and inclusive participation, and awareness of power imbalances that shape policy decisions. Competing global and commercial interests can create obstacles to building systems that prioritize the public good. The conclusion highlights that future governance models must be flexible enough to adapt to fast-changing technologies, yet consistent enough to protect rights and promote trust. Addressing these tensions is critical for building a more just and accountable future of AI.

Driver fatigue and drowsiness are the causes of many road accidents. In this work, a low-cost hybrid system is proposed for determining driver fatigue level based on four metrics: last sleep quality, blinking rate, heart rate variation, and yawning. The values of these metrics were experimentally determined for two age groups (20–25 and 55–60 years) and three time intervals of a day (9:00–10:00, 12:00–14:00, and 21:00–24:00). It should be mentioned that it is not always necessary to calculate the values of the four metrics, depending on the state of the driver and the state of the vehicle. In comparison to other proposed systems, our proposed system uses the four metrics in a single system, with their measurement being interdependent. Experimental results as well as future research directions are presented in this paper.

Phytoremediation has emerged as a sustainable and cost-effective strategy for mitigating contamination in soil and water systems, utilizing plants and their associated microbial consortia to uptake, degrade, or immobilize pollutants. This review synthesizes findings from over 100 peer-reviewed publications and case studies to identify key parameters influencing phytoremediation efficiency, including contaminant bioavailability, chemical speciation, concentration levels (ranging from trace to >100 mg/L), plant species suitability, hydraulic retention time, and temperature ranges (10–35 °C). Despite its proven potential, the absence of standardized design frameworks limits consistent implementation and cross-site performance comparability. To address this, the study proposes a conceptual system design framework supported by measurable performance metrics—such as pollutant removal efficiency (often >70% for heavy metals) and biomass uptake capacity. The review further examines regulatory and policy gaps that hinder the technology’s integration into national remediation strategies, particularly in low- and middle-income countries. It underscores the need for technical guidelines, regulatory benchmarks, and protocols for post-treatment biomass management to enable safe, effective, and scalable deployment. By advocating a multi-stakeholder, evidence-based approach, the study aims to bridge the gap between scientific innovation and environmental governance, positioning phytoremediation as a viable tool for pollution control, ecosystem restoration, and alignment with global sustainability targets.