Search Results (3,643)

Τhe Greek Seas are one of the most favorable locations for offshore wind energy development in the Mediterranean basin. In 2023, the Hellenic Hydrocarbons & Energy Resources Management Company SA published the draft National Offshore Wind Farm Development Programme (NDP-OWF), including the main pillars for the design, development, siting, installation, and exploitation of offshore wind farms, along with the Strategic Environmental Impact Assessment. The NDP-OWF is under assessment by the relevant authorities and is expected to be finally approved through a Joint Ministerial Decision. In this work, the preliminary offshore wind energy assessment of the Greek Seas is performed using the CERRA wind reanalysis data and in situ measurements from six offshore locations of the Greek Seas. The in situ measurements are used in order to assess the performance of the reanalysis datasets. The results reveal that CERRA is a reliable source for preliminary offshore wind energy assessment studies. Taking into consideration the potential offshore wind farm organized development areas (OWFODA) according to the NDP-OWF, the study of the local wind characteristics is performed. The local wind speed and wind power density are assessed, and the wind energy produced from each OWFODA is estimated based on three different capacity density settings. According to the balanced setting (capacity density of 5.0 MW/km²), the annual energy production will be 17.5 TWh, which is equivalent to 1509.1 ktoe. An analysis of the wind energy correlation, synergy, and complementarity between the OWFODA is also performed, and a high degree of wind energy synergy is identified, with a very low degree of complementarity. Full article

As Chinese open-pit mines progressively transition to deeper operations, challenges such as rising stripping ratios, declining slope stability, and environmental degradation have become increasingly pronounced. The sustainability of traditional open-pit mining models faces substantial challenges. Underground mining, offering higher resource recovery rates and minimal environmental disruption, is emerging as a pivotal technological pathway for the green transformation of mining. Consequently, the transition from open-pit to underground mining has emerged as a central research focus within mining engineering. This paper provides a comprehensive review of key technological advancements in this transition, emphasizing core issues such as mine development system selection, mining method choices, slope stability control, and crown pillar design. A typical case study of the Anhui Xinqiao Iron Mine is presented to analyze its engineering approaches and practical experiences in joint development, backfilling mining, and ecological restoration. The findings indicate that the mine has achieved multi-objective optimization of resource utilization, environmental coordination, and operational capacity while ensuring safety and recovery efficiency. This offers a replicable and scalable technological demonstration for the green transformation of similar mines around the world. Full article

Plastic waste accumulation is one of the most pressing environmental challenges of the 21st century, owing to the widespread use of synthetic polymers and the limitations of conventional recycling methods. Among available strategies, chemical upcycling via depolymerization has emerged as a promising circular approach that converts plastic waste back into valuable monomers and chemical feedstocks. This article provides an in-depth narrative review of recent progress in the upcycling of major plastic types such as PET, PU, PS, and engineering plastics through thermal, chemical, catalytic, biological, and mechanochemical depolymerization methods. Each method is critically assessed in terms of efficiency, scalability, energy input, and environmental impact. Special attention is given to innovative catalyst systems, such as microsized MgO/SiO₂ and Co/CaO composites, and emerging enzymatic systems like engineered PETases and whole-cell biocatalysts that enable low-temperature, selective depolymerization. Furthermore, the conversion pathways of depolymerized products into high-purity monomers such as BHET, TPA, vanillin, and bisphenols are discussed with supporting case studies. The review also examines life cycle assessment (LCA) data, techno-economic analyses, and policy frameworks supporting the adoption of depolymerization-based recycling systems. Collectively, this work outlines the technical viability and sustainability benefits of depolymerization as a core pillar of plastic circularity and monomer recovery, offering a path forward for high-value material recirculation and waste minimization. Full article

This paper examines the farming systems and operational structures in the Region of Western Macedonia (RWM), Greece and constructs a typology of farms based on structural, operational, and socio-economic characteristics. Agriculture remains a vital pillar of the regional economy, particularly in the context of RWM’s ongoing transition to a post-lignite development model. Using farm-level data from the 2018 Farm Accountancy Data Network (FADN), Principal Component Analysis (PCA) identified four latent dimensions of farm heterogeneity—income and productivity, asset base, land size, and labour structure. Hierarchical and K-means cluster analysis revealed three distinct farm types: (1) medium-sized, high-efficiency farms with moderate reliance on subsidies (30% of the sample); (2) small-scale, family farms with modest productivity and limited capitalisation (48%); and (3) large, asset-rich farms exhibiting structural inefficiencies and lower output per hectare (22%). These findings highlight structural vulnerabilities, particularly the predominance of undercapitalised smallholdings, and provide a data-driven foundation for Thdesigning differentiated policies that support farm resilience, generational renewal, and sustainable rural development. Full article

The escalating climate crisis and global disruptions have prompted a critical re-evaluation of operations management within manufacturing and supply systems. This conceptual article addresses the theoretical and strategic gap in aligning resilience and sustainability by proposing an Integrated Sustainable Operational Strategy (ISOS) framework. Drawing on systems theory, circular economy principles, and sustainability science, the framework synthesizes multiple operational domains—circularity, localization, digital adaptation, and workforce flexibility—across macro (policy), meso (organizational), and micro (process) levels. This study constructs a conceptual model that explains the interdependencies and trade-offs among strategic operational responses in the Anthropocene era. Supported by multi-level logic and a synthesis of domain constructs, the model provides a foundation for empirical investigation and strategic planning. Key propositions for future research are developed, focusing on causal relationships and boundary conditions. The novelty of ISOS lies in its simultaneous integration of three strategic pillars—circularity, localization, and digital resilience—within a unified, multi-scalar architecture that bridges fragmented operational theories. The article advances theory by redefining operational excellence through regenerative logic and adaptive capacity, responding directly to SDG 9 (industry innovation), SDG 12 (responsible consumption and production), and SDG 13 (climate action). This integrative framework offers both theoretical insight and practical guidance for transforming operations into catalysts of sustainable transition. Full article

Background/Objectives: Guidelines recommend patients with heart failure with reduced ejection fraction (HFrEF) receive four-pillar heart failure (4P-HF) therapy, which significantly reduces cardiac morbidity and mortality. However, implementing these guidelines effectively into clinical practice remains challenging. Methods: Patients with HFrEF on submaximal 4P-HF therapy were identified from a large, multicentre Cardiology network database using a natural language processing tool, supported by manual file review. A nurse-led, remotely delivered, medication uptitration program aimed to optimise therapy in this real-world cohort. Results: The final cohort included 2004 patients with a mean age of 72.7 ± 11.6 years. Utilisation of 4P-HF increased from 11.1% at baseline to 49.8% post intervention, and each individual medication class increased significantly post intervention (all p < 0.001). The largest increase was observed with the use of sodium–glucose cotransporter 2 inhibitors, which rose from 17.3% to 73.9%, followed by mineralocorticoid receptor antagonists (51.6% to 65.7%), beta-blockers (88.4% to 97.0%), and angiotensin-converting enzyme inhibitors/angiotensin receptor blockers/angiotensin receptor blocker–neprilysin inhibitors (89.8% to 96.4%). In patients on submaximal therapy, barriers were documented in all cases. Following medication optimisation, left ventricular ejection function (LVEF) improved significantly (38.5% ± 10.8% vs. 42.5% ± 11.7, p < 0.001). Conclusions: This nurse-led, remotely delivered, medication optimisation program significantly improved the adoption of 4P-HF therapy and LVEF in patients with HFrEF. The program demonstrates a practical, scalable solution for the optimisation of HFrEF therapy across a large healthcare network. Full article

Ice accumulation on exposed surfaces presents substantial economic and safety challenges across various industries. To overcome limitations associated with traditional anti-icing methods, such as the use of nanoparticles, this study introduces a novel and facile approach for fabricating superhydrophobic and anti-icing microstructures using cost-effective LCD 3D printing technology. The influence of diverse pillar geometries, including square, cylindrical, hexagonal, and truncated conical forms, was analyzed to assess their effects on the hydrophobic and anti-icing/icephobic performance in terms of wettability, ice adhesion strength, and icing delay time. The role of microstructure topography was further investigated through cylindrical patterns with varying geometric parameters to identify optimal designs for enhancing hydrophobic and icephobic characteristics. Furthermore, the effectiveness of surface functionalization using a low surface energy material was evaluated. Our findings demonstrate that the synergistic combination of tailored microscale geometries and surface functionalization significantly enhances anti-icing performance with reliable repeatability, achieving ice adhesion of 13.9 and 17.9 kPa for square and cylindrical pillars, respectively. Critically, this nanoparticle-free 3D printing and low surface energy treatment method offers a scalable and efficient route for producing high-performance hydrophobic/icephobic surfaces, opening promising avenues for applications in sectors where robust anti-icing capabilities are crucial, such as renewable energy and transportation. Full article

The electrical ventricular storm (VES) is defined as multiple sustained ventricular arrhythmias arising in a short time, often refractory to standard antiarrhythmic treatment. The three pillars of the physiopathogenesis of the VES are autonomic dysfunction, triggers, and an altered ventricular substrate. Incessant or highly recurrent ventricular arrhythmia impacts the hemodynamic status by worsening heart failure and increasing mortality. A stepwise, team-based, and tailored therapeutic approach is required to stop ventricular arrhythmia and regain the hemodynamic and electric stability of the patient. The authors focused on describing all currently available therapeutic approaches for VES, intending to establish the best VES therapeutic approaches. This process involves considering the patient’s specific condition, responses to previous treatments, and the potential risks and benefits of each approach. The options range from adjusting antiarrhythmic therapy to reprogramming of the ICD, sedation, epidural anaesthesia, stellate ganglia anaesthetic block, and the use of ECMO or left ventricular assist devices and radiofrequency catheter ablation. Particular attention is paid to the detailed management of genetic primary arrhythmia syndromes like long-QT syndrome, catecholaminergic polymorphic ventricular tachycardia, Brugada syndrome and Wolff–Parkinson–White syndrome, early repolarisation syndrome, right ventricular arrhythmogenic dysplasia, and idiopathic ventricular fibrillation. After overcoming the acute events of VES and obtaining hemodynamic stability, the treatment should shift toward an optimal balance of heart failure therapy, controlling the substrate by revascularisation procedures and resolving other pathology-generating ventricular arrhythmias. This article provides a comprehensive overview of ESV’s current management options using the most efficient strategies known to date. Full article

This study aimed to analyze the influence of local politics, village facilitators, recruitment of administrators, training and education, and organizational culture on work motivation and management performance. The study was conducted in Wajo Regency, South Sulawesi Province, Indonesia, utilizing primary data collected from 250 participants, including administrators of village-owned enterprises (BUMDES), community leaders, and representatives from the private sector. The data were analyzed using structural equation modeling (SEM) with the LISREL program. The results indicated that the latent variables of local politics, village facilitator, recruitment of administrators, training and education, and organizational culture had a positive and significant impact on work motivation and management performance. These findings are valuable key indicators and provide essential insights for promoting and driving the BUMDES as a pillar of rural development strategies. Based on these findings, it is recommended that the local government revitalize the local political system, reorient the organizational culture of the BUMDES toward a modern business-oriented culture suited to rural conditions, and enhance the training and education of village facilitators to improve their motivation and performance. This recommendation will empower the BUMDES to promote rural economic improvement and sustainable rural development by enhancing work motivation and management performance. Full article

Camellia oleifera is one of the four largest woody oil species in the world, with more than 5 million hectares planted in China alone. Reducing bud damage and improving harvesting net rate and efficiency have become the key challenges to mechanized harvesting of Camellia oleifera fruits. This paper presents a novel comb-spiral impact harvesting device primarily composed of four parts, which are lifting mechanism, picking mechanism, rotating mechanism, and tracked chassis. The workspace of the four-degree-of-freedom lifting mechanism was simulated, and the harvesting reachable area was maximized using MATLAB R2021a software. The picking mechanism, which includes dozens of spirally arranged impact pillars, achieves high harvesting efficiency through impacting, brushing, and dragging, while maintaining a low bud shedding rate. The rotary mechanism provides effective harvesting actions, and the tracked chassis guarantees free movement of the equipment. Simulation experiments and field validation experiments indicate that optimal performance can be achieved when the brushing speed is set to 21.45 r/min, the picking finger speed is set to 341.27 r/min, and the picking device tilt angle is set to 1.0°. With these parameters, the harvesting quantity of Camellia oleifera fruits is 119.75 kg/h, fruit shedding rate 92.30%, and bud shedding rate as low as 9.16%. This new model for fruit shedding and the comb-spiral impact harvesting principle shows promise as a mechanized harvesting solution for nut-like fruits. Full article

The olive oil sector constitutes a fundamental pillar in the Mediterranean region from socio-economic and cultural perspectives. Nonetheless, it produces significant amounts of waste, leading to numerous environmental issues. These waste streams contain valuable compounds that can be recovered and utilized as inputs for various applications. This study introduces a novel value chain for olive wastes, focused on extracting lignin from olive pomace by ionic liquids and polyphenols from olive mill wastewater, which are then incorporated as hybrid nanoparticles in the formulation of an innovative starch-based biofertilizer. This biofertilizer, obtained by using residual wastewater as a source of soluble nitrogen, acting at the same time as a plasticizer for the biopolymer, was demonstrated to surpass traditional NPK biofertilizers’ efficiency, allowing for root growth and foliage in drought conditions. In order to recognize the environmental impact due to its production and align it with the technical output, the circularity and environmental performance of the proposed system were innovatively evaluated through a combination of Life Cycle Assessment (LCA) and the Material Circularity Indicator (MCI). LCA results indicated that the initial upcycling process was potentially characterized by significant hot spots, primarily related to energy consumption (>0.70 kWh/kg of water) during the early processing stages. As a result, the LCA score of this preliminary version of the biofertilizer may be higher than that of conventional commercial products, due to reliance on thermal processes for water removal and the substantial contribution (56%) of lignin/polyphenol precursors to the total LCA score. Replacing energy-intensive thermal treatments with more efficient alternatives represents a critical area for improvement. The MCI value of 0.84 indicates limited potential for further enhancement. Full article

Ports in the Baltic Sea region play an integral role in advancing sustainable maritime practices in the area, due to their geographic interconnectedness, economic importance, and sensitivity to environmental challenges. While numerous port sustainability assessment methods exist, most of which are grounded in the Triple Bottom Line (TBL) metric, many tend to emphasise whether specific targets have been met, rather than evaluating port sustainability on a scalar basis. This study explores the sustainability strategies of seven selected ports in five Baltic Sea countries using an innovative qualitative evaluation framework developed by the Swedish Maritime Competence Centre (SMCC). The SMCC model integrates the three core pillars of sustainability-environmental, social, and economic dimensions, while incorporating energy efficiency and digitalisation as critical enablers of modern port operations. The findings reveal significant variation in sustainability performance among the selected ports, shaped by regional contexts, operational profiles, and prior engagement with sustainability initiatives. Also, the results bring into light the most common sustainable practices used in the ports, e.g., LED lightning, onshore power supply, and port information systems. Full article

Wildfires increasingly threaten human life, ecosystems, and infrastructure, with events like the 2025 Palisades and Eaton fires in Los Angeles County underscoring the urgent need for more advanced prediction frameworks. Existing physics-based and deep-learning models struggle to capture dynamic wildfire spread across both 2D and 3D domains, especially when incorporating real-time, multimodal geospatial data. This paper explores how generative artificial intelligence (AI) models—such as GANs, VAEs, and transformers—can serve as transformative tools for wildfire prediction and simulation. These models offer superior capabilities in managing uncertainty, integrating multimodal inputs, and generating realistic, scalable wildfire scenarios. We adopt a new paradigm that leverages large language models (LLMs) for literature synthesis, classification, and knowledge extraction, conducting a systematic review of recent studies applying generative AI to fire prediction and monitoring. We highlight how generative approaches uniquely address challenges faced by traditional simulation and deep-learning methods. Finally, we outline five key future directions for generative AI in wildfire management, including unified multimodal modeling of 2D and 3D dynamics, agentic AI systems and chatbots for decision intelligence, and real-time scenario generation on mobile devices, along with a discussion of critical challenges. Our findings advocate for a paradigm shift toward multimodal generative frameworks to support proactive, data-informed wildfire response. Full article

In response to growing environmental pressures and material constraints, circular economy principles are gaining traction across manufacturing sectors. However, most existing frameworks emphasize design and supply chain considerations, with limited focus on how circularity can be operationalized within internal manufacturing systems. This paper proposes a conceptual model that embeds circular operations at the core of production strategy. Grounded in circular economy theory, operations management, and socio-technical systems thinking, the model identifies four key operational pillars: circular input management, looping process and waste valorization, product-life extension, and reverse logistics. These are supported by enabling factors—digital infrastructure, organizational culture, and leadership—and mediated by operational flexibility, which facilitates adaptive, closed-loop performance. The model aims to align internal processes with long-term sustainability outcomes, specifically resource efficiency and operational resilience. Practical implications are outlined for resource-intensive industries such as automotive, electronics, and FMCG, along with a readiness assessment framework for guiding implementation. This study offers a pathway for future empirical research and policy development by integrating circular logic into the structural and behavioral dimensions of operations. The model contributes to advancing the Sustainable Development Goals (SDGs), particularly SDG 9 and SDG 12, by positioning circularity as a regenerative operational strategy rather than a peripheral initiative. Full article

As an important pillar of the global economic system, the cotton industry faces critical challenges from non-fibrous impurities (e.g., leaves and debris) during processing, which severely degrade product quality, inflate costs, and reduce efficiency. Traditional detection methods suffer from insufficient accuracy and low efficiency, failing to meet practical production needs. While deep learning models excel in general object detection, their massive parameter counts render them ill-suited for real-time industrial applications. To address these issues, this study proposes Cotton-YOLO, an optimized yolov8 model. By leveraging principles of symmetry in model design and system setup, the study integrates the CBAM attention module—with its inherent dual-path (channel-spatial) symmetry—to enhance feature capture for tiny impurities and mitigate insufficient focus on key areas. The C2f_DSConv module, exploiting functional equivalence via quantization and shift operations, reduces model complexity by 12% (to 2.71 million parameters) without sacrificing accuracy. Considering angle and shape variations in complex scenarios, the loss function is upgraded to Wise-IoU for more accurate boundary box regression. Experimental results show that Cotton-YOLO achieves 86.5% precision, 80.7% recall, 89.6% mAP50, 50.1% mAP50–95, and 50.51 fps detection speed, representing a 3.5% speed increase over the original yolov8. This work demonstrates the effective application of symmetry concepts (in algorithmic structure and performance balance) to create a model that balances lightweight design and high efficiency, providing a practical solution for industrial impurity detection and key technical support for automated cotton sorting systems. Full article