Natural disasters can be fatal and necessitate critical thinking in decision-making processes; thus, simulations of multiple scenarios must be evaluated to prepare for such imminent events. An Open World Machine Learning (OWML) framework has been proposed as a focal tool to improve decision-making in natural disaster management. To better understand and formulate a comprehensive foundation, a systematic review of different cases was conducted. In this study, four types of natural disasters-earthquakes, floods, wildfires, and hurricanes-were analyzed using machine learning techniques within the OWML framework. The essence of this approach lies in its ability to accumulate knowledge from various sources and adapt to new, unknown event types. Moreover, by explicitly incorporating both qualitative and quantitative characteristics, the framework enhances predictive accuracy and adaptability. The results demonstrate that OWML models can effectively process large geospatial datasets and respond to looming threats with greater precision. Albeit some limitations, such as data quality and model complexity, the findings suggest that OWML can serve as a foundational tool for governments to reduce expenditure and improve evacuation strategies. The evolutionary nature of the OWML framework allows for continuous learning and adaptation, which is crucial as natural disasters escalate in frequency and intensity due to climate change. In essence, this study provides a significant contribution to natural disaster management by explicitly demonstrating the potential of OWML frameworks in enhancing decision-making processes.

Various studies have been conducted in the fields of sustainable operations management (SOM), optimization, and wastewater treatment, yielding unsubstantiated recovery. In the context of Europe’s climate neutrality vision, this paper reviews effective decarbonization strategies and proposes sustainable approaches to mitigate carbonization in various sectors such as buildings, energy, industry, and transportation and how these interlink with wastewater management. The study also explores the role of digitalization in decarbonization and reviews policies that can direct governments’ actions towards a climate-neutral society. This paper presents a review of optimization approaches applied in the fields of science and technology, incorporating modern optimization techniques based on various peer-reviewed published research papers. It emphasizes non-conventional energy and distributed power-generating systems along with the deregulated and regulated environment. Additionally, this paper critically reviews the performance and capability of the micellar-enhanced ultrafiltration (MEUF) process in the treatment of dye wastewater. The review presents evidence of the simultaneous removal of co-existing pollutants and explores the feasibility and efficiency of biosurfactants instead of chemical surfactants. Lastly, the paper proposes a novel Firm–Regulator–Consumer-Technology Enablers/Facilitators interaction framework to study operations, decisions and interactive cooperation considering the relationships between the four agents through a comprehensive literature review of SOM. The proposed framework provides support for exploring future research opportunities and holistic sustainability initiatives.

The growing global freshwater scarcity urgently requires innovative wastewater treatment technologies. This study hypothesized that biomimicry-inspired automated machine learning (AML) could effectively manage wastewater variability through adaptive processing techniques. Utilizing decentralized swarm intelligence, specifically the Respected Parametric Insecta Swarm (RPIS), the system demonstrated robust adaptability to fluctuating influent conditions, maintaining stable effluent quality without centralized control. Bio-inspired oscillatory control algorithms maintained stability under dynamic influent scenarios, while adaptive sensor feedback enhanced real-time responsiveness. Machine learning (ML) methods inspired by biological morphological evolution accurately classified influent characteristics (F1 score of 0.91), optimizing resource allocation dynamically. Significant reductions were observed, with chemical consumption decreasing by approximately 11% and additional energy usage declining by 14%. Furthermore, bio-inspired membranes with selective permeability substantially reduced fouling, maintaining minimal fouling for up to 30 days. Polynomial chaos expansions efficiently approximated complex nonlinear interactions, reducing computational overhead by approximately 35% through parallel processing. Decentralized swarm algorithms allowed the rapid recalibration of system parameters, achieving stable pathogen removal and maintaining effluent turbidity near 3.2 NTU (Nephelometric Turbidity Units), with total suspended solids consistently below 8 mg/L. Integrating biomimicry with AML thus significantly advances sustainable wastewater reclamation practices, offering quantifiable improvements critical for resource-efficient water management.

Carbon-aware AI demands clear links between algorithmic choices and verified emission outcomes. This study measures and steers the carbon footprint of swarm-based optimization in HPC by coupling a job-level Emission Impact Metric with sub-minute power and grid-intensity telemetry. Across 480 runs covering 41 algorithms, we report grams CO₂ per successful optimisation and an efficiency index $\eta$ (objective gain per kg CO₂). Results show faster swarms achieve lower integral energy: Particle Swarm emits 24.9 g CO₂ per optimum versus 61.3 g for GridSearch on identical hardware; Whale and Cuckoo approach the best $\eta$ frontier, while L-SHADE exhibits front-loaded power spikes. Conservative scale factor schedules and moderate populations reduce emissions without degrading fitness; idle-node suppression further cuts leakage. Agreement between CodeCarbon, MLCO2, and vendor telemetry is within 1.8%, supporting reproducibility. The framework offers auditable, runtime controls (throttle/hold/release) that embed carbon objectives without violating solution quality budgets.

Wastewater treatment is evolving rapidly with the advent of advanced deep-learning AI, graph-based, and physics-informed approaches. This study integrates graph neural networks, physics-informed neural networks, and multi-agent reinforcement learning within a hybrid digital-twin framework, evaluated on multi-scale real-world datasets. The developed models achieved over 90% dissolved organic carbon removal and reduced aeration energy by up to 22% while maintaining process stability. The results demonstrate that graph–physics synergies not only boost operational efficiency but also reveal critical trade-offs between energy savings and hydraulic performance. Our findings establish a new benchmark for resilient, low-carbon wastewater treatment, highlighting the transformative role of data-driven system design.

This study introduces Logarithmic Positional Partition Interval Encoding (LPPIE), a novel lossless compression methodology employing iterative logarithmic transformations to drastically reduce data size. While conventional dictionary-based algorithms rely on repeated sequences, LPPIE translates numeric data sequences into highly compact logarithmic representations. This achieves significant reduction in data size, especially on large integer datasets. Experimental comparisons with established compression methods—such as ZIP, Brotli, and Zstandard—demonstrate LPPIE’s exceptional effectiveness, attaining compression ratios nearly 13 times superior to established methods. However, these substantial storage savings come with elevated computational overhead due to LPPIE’s complex numerical operations. The method’s robustness across diverse datasets and minimal scalability limitations underscore its potential for specialized archival scenarios where data fidelity is paramount and processing latency is tolerable. Future enhancements, such as GPU-accelerated computations and hybrid entropy encoding integration, are proposed to further optimize performance and broaden LPPIE’s applicability. Overall, LPPIE offers a compelling alternative in lossless data compression, substantially redefining efficiency boundaries in high-volume numeric data storage.