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Found 6273 publications
Deep-learning based adaptive fusion of CC and MLO views for improved mammographic cancer diagnosis
Publication Name: Methodsx
Publication Date: 2026-06-01
Volume: 16
Issue: Unknown
Page Range: Unknown
Description:
Breast cancer remains the most prevalent malignancy among women worldwide. The timely detection of this cancer type is critical for improving survival outcomes. Despite advancements, mammogram classification using deep learning strategies still faces challenges. These include inter-view feature inconsistency, loss of diagnostic details, and limited interpretability. In order to address these issues, MammoFusion-Net, a dual-branch deep learning framework, is proposed for mammogram-based breast cancer classification. Using residual convolutional streams, the framework processes craniocaudal (CC) and mediolateral oblique (MLO) views independently. This supports preservation of view-specific anatomical information. In the proposed framework, a Gates Cross-View Fusion mechanism adaptively integrates features across views. As a result of experimental analysis, the proposed framework achieved 92.116 % (VinDr-Mammo dataset) and 95.556 % (INBreast dataset) of improved classification performance.•Employs a dual-branch architecture to independently process CC and MLO views using residual convolutional streams.•Integrates Gated Cross-View Fusion and attention mechanisms adaptively and refines multi-view features for stronger discrimination.•Demonstrates the explainability of the model through Grad-CAM visualizations that highlight lesion-relevant regions.
Open Access: Yes
How can green credit reduce environmental costs? Analysis based on an extended economic growth model with environmental constraints
Publication Name: Structural Change and Economic Dynamics
Publication Date: 2026-06-01
Volume: 78
Issue: Unknown
Page Range: 326-342
Description:
Green credit is a financial tool stimulating environmental protection by allocating credit funds for cleaner and energy-efficient activities. However, the mechanisms of credit allocation can affect the effectiveness of green credit. This study discusses the concepts of lending discrimination and endogenous clean technology based on the economic growth model with environmental constraints. This allows explaining the mechanism through which lending discrimination and green innovation influence the effect of green credit on (reduction of) environmental impact. Furthermore, empirical data on Chinese provinces from 2012–-2022 confirm that lending discrimination weakens the positive effect of green credit in reducing environmental impact. Additionally, green credit can reduce environmental impact by enhancing corporate green innovation. This study provides theoretical and empirical evidence for understanding the relationship between green credit and environmental impact and offers insights for optimizing green credit.
Open Access: Yes
Reliability-first, emissions reduction in grid-connected PV-coal systems: Optimal PV integration and coal dispatch under emission caps
Publication Name: Results in Engineering
Publication Date: 2026-06-01
Volume: 30
Issue: Unknown
Page Range: Unknown
Description:
Coal-dependent power systems must reduce cost volatility and emissions while maintaining reliable supply under rising demand. This study assesses whether a practical transition architecture, high-penetration photovoltaic (PV) generation combined with a dispatchable coal unit and grid support, can improve techno-economic and environmental performance without sacrificing feasibility. A grid-connected PV-coal-grid hybrid system was modelled and optimized in HOMER Pro, and a sensitivity campaign was conducted by varying coal fuel price, global horizontal irradiance (GHI), and load demand to test robustness and dispatch shifts. The least-cost feasible solution within the explored design space comprises 145 MW PV and a 75 MW coal power plant with grid interaction. Under baseline conditions, the optimized system achieves a net present cost (NPC) of $632 million and a levelized cost of electricity (COE) of $0.049/kWh. Sensitivity results show that increased GHI consistently reduces NPC and COE, while coal price increases drive greater PV utilization in dispatch without undermining feasibility. Load growth increases total system cost due to higher capital and operating requirements, yet COE changes remain modest, indicating improved utilization of installed assets at higher demand levels. The optimized configuration’s emissions inventory quantifies the residual environmental footprint of the least-cost reliable solution, including 429.2 million kg/yr CO₂, 3.30 million kg/yr SO₂, 0.44 million kg/yr NOₓ, 2.30 million kg/yr CO, 19.7 thousand kg/yr particulate matter, and 122 thousand kg/yr unburned hydrocarbons, reflecting reduced coal combustion through PV displacement during high-resource periods. These findings demonstrate that an optimized PV-coal-grid hybrid can deliver cost-competitive electricity, operational robustness to fuel/resource/demand uncertainty, and measurable multi-pollutant emissions mitigation, offering a realistic transition pathway for coal-reliant systems.
Open Access: Yes
Experiential learning and governance in the socio-technical era: Modeling responsible AI performance via explainability and adaptability
Publication Name: Technological Forecasting and Social Change
Publication Date: 2026-06-01
Volume: 227
Issue: Unknown
Page Range: Unknown
Description:
The concept of artificial intelligence (AI) is altering the way organizations operate. AI systems will deliver more intelligent results in a shorter period of time, starting with decision-making up to innovation. However, the more it is adopted, the more issues to do with fairness, transparency, and accountability are raised. Most organizations are finding it difficult to reconcile innovation and ethical responsibility. This study discusses the role of internal capabilities in making firms govern AI responsibly. The study proposes a model linking four key organizational capabilities, i.e., explainable AI capability, contextual learning adaptability, experiential learning orientation, and organizational ethical alignment to responsible AI performance. The impact of these capabilities on user interpretability and trust, responsible AI governance maturity, and decision transparency is also examined in this study. The results show that explainable AI capability and learning adaptability enhance user trust, while experiential learning orientation and organizational ethical alignment significantly improve governance maturity. Governance maturity and decision transparency lead to stronger responsible AI performance. Interestingly, not all expected paths held as user interpretability trust and governance maturity did not directly predict decision transparency. The findings show that building technical and cultural capabilities inside firms is essential not just to deploy AI effectively, but to do it responsibly. For leaders, this means moving beyond checklists and toward meaningful governance rooted in learning, transparency, and ethical alignment.
Open Access: Yes
Transferring and scaling innovation in urban green-blue Infrastructure: beyond one-size-fits-all solutions and preconceptions
Publication Name: Sustainable Futures
Publication Date: 2026-06-01
Volume: 11
Issue: Unknown
Page Range: Unknown
Description:
Green and Blue Infrastructure (GBI) innovations are increasingly promoted in Europe, yet their transfer to new socio-political contexts remains poorly understood. This study applies the Strategic Niche Management (SNM) framework to analyse the conditions under which GBI innovations can be replicated and scaled beyond their original settings. We examine six GBI projects from five EU Member States and assess their perceived transferability through three expert workshops in Belarus, Russia and Ukraine. Using a comparative qualitative design, workshop transcripts were analysed to identify how actors interpret innovation, allocate responsibilities, and negotiate risks within their socio-technical regimes. Across cases, successful transfer depended on leadership by municipal actors, a supportive knowledge base, flexible regulatory arrangements, and targeted communication that strengthens public acceptance. Major constraints included entrenched “business-as-usual’’ routines in administrative and epistemic communities, misconceptions about the costs and maintenance of GBI, weak participatory traditions, and corruption risks. The findings demonstrate that GBI diffusion is highly context-dependent: local actors may be unexpectedly supportive of nature-based solutions, while bottom-up initiatives can serve as viable entry points even within hierarchical governance systems. The study contributes empirical insights from an under-researched region and illustrates how SNM can be operationalised to guide GBI innovation transfer and regime change.
Open Access: Yes
Mapping the scholarly literature on the infodemic using topic modelling
Publication Name: Social Sciences and Humanities Open
Publication Date: 2026-06-01
Volume: 13
Issue: Unknown
Page Range: Unknown
Description:
The present study aims to map the scholarly evolution of the infodemic as a research subject through a scientometric analysis of 852 peer-reviewed articles indexed in Web of Science between 2020 and 2024 building on scientometric methods and Structural Topic Modeling (STM). Findings reveal a sharp rise in publications during the pandemic years, peaking in 2022, followed by a remarkable decline in both output and citation impact. The STM uncovered 20 distinct topics, with dominant themes centred on health communication, misinformation, social media, and institutional trust. While several themes peaked early in the pandemic, others, such as institutional or public trust, gained prominence later. Topic correlations showed dense interlinkages but low modularity suggested conceptual fragmentation and weak field consolidation. The results highlight that infodemic scholarship remains an emergent, interdisciplinary domain, however, there is a need for stable theoretical foundations.
Open Access: Yes
Making the invisible visible: Non-intrusive scalable digitalisation using existing control signals in legacy medical device manufacturing equipment
Publication Name: Journal of Manufacturing Systems
Publication Date: 2026-06-01
Volume: 86
Issue: Unknown
Page Range: 1048-1065
Description:
With the advent of Industry 4.0 and Industrial Internet of Things, it is appreciated that data availability is essential to provide information to facilitate decision-making in relation to effective production operation and maintenance strategies. This study presents a case application of digitalisation to improve operational efficiency in a regulated medical device manufacturing environment. The case study focuses on a legacy ureteral stent production process in which ageing sideporting machines—classified as critical equipment — pose reliability challenges that impact operational efficiency. Regulatory constraints and cleanroom requirements limit the ability to retrofit sensors or replace legacy controllers, creating a significant gap in data availability. This paper describes a brownfield integration study enabling the transition to a more data-driven production environment. To address these challenges, a non-intrusive data aggregation solution was implemented using an Omron NX102 controller, enabling near-real-time monitoring of machine cycle counts, run times, and punch changes without altering validated equipment. A custom Human Machine Interface (HMI) and local CSV logging ensured traceability and compliance. The collected data was analysed using statistical methods and machine learning algorithms to predict maintenance needs. This approach facilitated the calculation of previously unavailable metrics such as Overall Equipment Effectiveness (OEE) and supported targeted maintenance planning and operator training. The results demonstrate that applying Industry 4.0 principles to brownfield legacy systems in regulated environments can extend equipment life, reduce downtime, and enable data-driven decision-making. This work provides a practical roadmap for integrating legacy equipment with enterprise manufacturing systems in highly regulated manufacturing settings, bridging the gap between traditional processes and smart IIoT-enabled strategies.
Open Access: Yes
Enabling industry symbiosis between energy-intensive industries via optimal integration of thermal energy storage
Publication Name: Thermal Science and Engineering Progress
Publication Date: 2026-06-01
Volume: 74
Issue: Unknown
Page Range: Unknown
Description:
Energy-based industrial symbiosis is a potential decarbonisation strategy for energy-intensive industries, which contribute significantly to carbon emissions. Thermal energy storage (TES) can be integrated to enhance energy efficiency and operational flexibility, while addressing issues related to supply–demand fluctuations. Nonetheless, the economic feasibility of TES-supported interplant heat recovery depends on the costs and properties of the storage media incorporated. Therefore, this work presents a systematic framework for optimising TES selection across a spectrum of storage options for interplant indirect heat integration. The objective is to minimise the total annualised cost (TAC), comprising energy and storage capital costs. The optimal TES option can then be identified based on its respective TAC ranking. A case study that compares the effectiveness of the indirect method against the intraplant and direct methods is conducted. The results show that among the 33 TES options evaluated, silica fire brick offers the lowest TAC and energy-related carbon emissions, leading to a reduction of 21.60% and 13.16%, respectively, as compared to the intraplant method. Subsequently, a sensitivity analysis is performed to explore the impacts of varying stream flowrates and storage capacity redundancy allocation on the TES selection. This provides insights into the performance of various TES options under intraplant, direct, and indirect heat integration methods. Finally, the threshold (i.e., stream flowrate required to provide economic gain under a given redundant allocation scenario) aligned with the strategic planning can be determined. This work demonstrates that TES integration can improve the economic feasibility and sustainability of industrial symbiosis in energy-intensive industries.
Open Access: Yes
Pricing sustainability risk: Climate policy uncertainty and energy market dynamics
Publication Name: Development and Sustainability in Economics and Finance
Publication Date: 2026-06-01
Volume: 10
Issue: Unknown
Page Range: Unknown
Description:
This study investigates the dynamic temporal relationship between climate policy uncertainty (CPU) and international energy prices across nine commodities from January 1992 to June 2024. The research examines whether CPU systematically drives energy price movements and how these relationships evolve over time. We employ dynamic time warping (DTW), a nonparametric pattern recognition technique that accommodates non-linear temporal alignments between time series. Unlike conventional econometric methods that impose fixed lag structures, DTW flexibly maps how CPU influences on energy prices evolve across different periods. We analyse crude oil (WTI and Brent), gasoline, heating oil, coal, liquefied natural gas, natural gas, palm oil, and sunflower oil using multiple DTW step patterns (Rabiner–Juang VI-c, Symmetric1) with Sakoe–Chiba window constraints. Lead–lag analysis quantifies quarterly shifts in temporal precedence between CPU and energy prices. Energy prices demonstrate increasing sensitivity to CPU, particularly after the early 2000s. Traditional fossil fuels show pronounced alignment with CPU during major policy shifts, including the post-2008 financial crisis and 2015 Paris Agreement. CPU often leads energy price movements during regulatory transitions, suggesting markets price anticipated policy risks. Recent years reveal temporal reversals: natural gas and coal prices increasingly lead CPU, indicating market dynamics now drive subsequent policy adjustments as energy transitions accelerate. This research introduces sophisticated temporal analysis using DTW and lead–lag methods to explore evolving CPU-energy price relationships. The study provides fresh insights into how climate-related regulations drive energy market volatility during transitions toward sustainable energy systems, with implications for portfolio management and regulatory design.
Open Access: Yes
