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.

Computer Vision (CV) is undoubtedly one of the most popular forms of Artificial Intelligence (AI) and its implementation has gained considerable ground in all aspects of our lives, from security and automotive, to the night sky observation and astronomy. In general, CV uses pattern recognition techniques for identifying objects in visual media (both static and moving images). The current archetype in CV is largely based on supervised AI, which uses large data sets of human-labelled images for training. Machine Learning (ML) and Deep Learning (DL) models in computer vision have undergone a period of extremely rapid development in recent past years; in particular for object recognition and localisation tasks. An area of study with great interest in practical applications that concerns this essay, is astronomical images analysis. However, one of the main challenges facing researchers these days is the existence of large quantities of annotated data sets, in the appropriate resolution and scale. This challenge consequently asks for huge amounts of storage and high computational power. In this paper, we systematically review and analyze different challenges faced by astronomers and continue with state-of-the-art methodologies that were conducted over the last decade.

Background The recent economic recession has hit Greece with economic, political, and social repercussions. Structural reforms in the real economy and public sector are considered to be of paramount importance for introducing a new consumption and production paradigm to achieve sustainable economic growth. Methods The present paper aims to highlight Big Data and the Internet of Things as part of the 4th Industrial Revolution as a potential enabler for the necessary leap in the 21st century for the Greek public sector on the theoretical basis of the Unified Growth Theory and the Washington Doctrine. In the present paper, an evaluation of the Greek public sector is attempted by using two different indices, the Digital Maturity Index of the Hellenic Federation of Enterprises (SEV), and the Digital Economy and Society Index (DESI) of the EU. Results Findings strongly indicate that the introduction of digital skills in the educational system along with vocational training of older groups regarding the use of digital public services is an important factor for digital services implementation in the case of Greece. Moreover, the findings also underlie that demand for digital public services is not merely a matter of economic power. Other causes that lie in cultural, geographical, and behavioural habits should also be considered. Conclusions Greece is not in the pole position regarding Big Data implementation. High-speed broadband, both fast and ultrafast, lacks widespread availability, while prices remain relatively high compared to other European countries. Internet user skills and advanced IT skills remain mostly misused in the private and the public sector, while business digitization, e-commerce, e-Government, and e-health remain relatively low.

Recent advancements in space exploration have significantly increased the volume of astronomical data, heightening the demand for efficient analytical methods. Concurrently, the considerable energy consumption of machine learning (ML) has fostered the emergence of Green AI, emphasizing sustainable, energy-efficient computational practices. We introduce the first large-scale Green AI benchmark for galaxy morphology classification, evaluating over 30 machine learning architectures (classical, ensemble, deep, and hybrid) on CPU and GPU platforms using a balanced subset of the Galaxy Zoo dataset. Beyond traditional metrics (precision, recall, and F1-score), we quantify inference latency, energy consumption, and carbon-equivalent emissions to derive an integrated EcoScore that captures the trade-off between predictive performance and environmental impact. Our results reveal that a GPU-optimized multilayer perceptron achieves state-of-the-art accuracy of 98% while emitting 20× less CO₂ than ensemble forests, which—despite comparable accuracy—incur substantially higher energy costs. We demonstrate that hardware–algorithm co-design, model sparsification, and careful hyperparameter tuning can reduce carbon footprints by over 90% with negligible loss in classification quality. These findings provide actionable guidelines for deploying energy-efficient, high-fidelity models in both ground-based data centers and onboard space observatories, paving the way for truly sustainable, large-scale astronomical data analysis.

Product management (PM) is nearing its centennial. The father of modern PM, Neil H. McElroy, laid the foundation for this ever-expanding role. Since 1931, this role has undergone many changes and additions; a lot of literature covers these aspects. However, two of the most interesting aspects of this role are the introduction of PM in technology and the new product development theory (NPD). The goal of this paper is to examine and compare the PM practices of two giant corporations, Amazon and IBM, that seemingly use different techniques and approaches when it comes to their PM implementation and strategy. The research methods applied are two case studies and a comparative analysis. Through examining relevant literature and publicly accessible information, the comparative analysis is performed, and the specific practices of strategy, product development, validating customer needs, service innovation, portfolio management, new product development, and diffusion or adoption are selected to provide a spherical approach and information for the two selected companies. A plethora of literature papers are reviewed, and more emphasis is, of course, given to the aforementioned practices that information is more available, and for the other topics, a brief introduction is accomplished, and space for further research is set.

Artificial Intelligence plays an exponentially growing role in producing data-driven policy insights. In this policy-oriented case study, AI technology is examined as a necessary coordination node through evidence-based and data-enhanced policies, which can efficiently balance the processes of different and possibly competing sectors, such as agriculture and tourism. The focus is on the NUTS 1 region of the Aegean Islands and Crete (EL4) in Greece. The analysis aims to create a viable and resilient ecosystem of environmental, economic and social sustainability through innovation. Applying a “Growth Pole Theory” approach, key public administration frameworks like the European Interoperability Framework (EIF) and TAPIC (Transparency, Accountability, Participation, Integrity, Capacity) governance framework are discussed and analysed to structure the AI deployment and policy considerations for sustainable development. The paper argues in favour of AI’s transformative potential across both the agriculture and tourism sectors.