Mako robotic-assisted glenoid preparation in reverse shoulder arthroplasty: a tail-risk reduction perspective compared with manual and patient-specific guide techniques.

From energy crisis of the pandemic to opportunity: Valuable lesson learned on technology innovations, energy sources, environmental sustainability, and economic implications

Recent developments and innovations in solar chimney power technologies: A focus on the last two decades

Condensed tannin extracts in oenology: chemistry, botanical sources and applications in winemaking and aging processes.

Spatio-temporal dataset of energy infrastructure attacks in Nigeria (2009-2025).

Economic benefit of expanding mammography screening for breast cancer in Colombia: A cost modelling analysis.

Reassessing the economic implications of environmental protection measures: the case of the EU’s green payments

In regions with scarce data, such as Norway, predicting cost performance in large-scale road (LSR) projects presents a unique challenge due to the high risk of cost overruns and their significant economic implications. This study aims to develop a data-driven framework for predicting cost performance in LSR projects by combining synthetic data generation and machine learning models. The approach employs synthetic data generation via Conditional Generative Adversarial Networks (CTGAN) to enhance the data pool and improve predictive accuracy. By integrating 173 synthetically generated samples with 52 actual project samples, a robust dataset of 225 road projects was created. Three machine learning classifiers (i.e., XGBoost, MLP, and SVM) were applied to this enriched dataset. The models achieved an average accuracy of 0.76 and an F1 score of 0.74 when tested against real-world data, demonstrating substantial alignment with actual project outcomes. Further validation with 5-fold cross-validation on the combined datasets confirmed the consistency of these results, with similar accuracy and F1 scores. This research highlights the effectiveness of synthetic data in overcoming the limitations of small datasets and underscores its potential to substantially improve decision-making in highway engineering by providing more accurate, data-driven insights for project planning, design, and management.

Bacteriocin-producing Saccharomyces cerevisiae inhibits bacterial contamination in raw starch-to-ethanol fermentations.

Although economic growth remains a central objective in policymaking and political discourse, its compatibility with environmental sustainability is increasingly contested. Yet it remains unclear whether this debate is reflected in public opinion. We therefore undertake a cross-national survey of 16,781 people across 13 EU countries to assess citizens' attitudes in this regard. We find that nearly 60% of citizens express pro-growth views, seeing economic growth as essential for a sustainable society. Of these, more than half hold a moderate and less than half a strong pro-growth view. Additionally, a third of respondents show indifference about growth, while less than 10% hold views that are sceptical about growth. Wealthier countries with less income inequality show lower support for economic growth. Pro-growth attitudes correlate positively with both self-enhancement and self-transcendence values, suggesting that citizens may view growth not only as a means for personal advancement but also as a pathway to collective wellbeing. Growth attitudes have no significant association with climate concern or climate policy support, suggesting a more nuanced picture than a traditional trade-off narrative.

Conventional age-based breast cancer screening ignores substantial inter-individual risk variation, contributing to overdiagnosis, false positives, and missed opportunities for earlier detection in high-risk women. Mammography-based artificial intelligence (AI) may enable risk-stratified screening and more efficient workflows. To systematically review evidence on mammography-based AI for personalized breast cancer screening, covering risk prediction, detection/triage, decision support, and associated ethical, economic, and equity implications. We searched MEDLINE/PubMed, Embase, Scopus, Web of Science, and the Cochrane Library (January 2015-November 2025) for studies evaluating AI-enabled personalization in breast cancer screening. Two reviewers independently screened 612 records, assessed 77 full texts, and included 30 studies; data were synthesized narratively. Image-based deep-learning risk models consistently outperformed traditional clinical risk tools and enriched future cancers within small high-risk strata, including cancers presenting as interval cancers in recent validations. Prospective trials and real-world implementations indicate that AI-supported reading can maintain or modestly improve cancer detection while reducing radiologist workload by roughly 40-50%. Decision-analytic models suggest that AI-enabled risk-stratified policies may be cost-effective but rely on assumptions and lack prospective confirmation of long-term endpoints. Key evidence gaps include prospective demonstration that acting on AI-derived risk reduces interval/advanced cancers, subgroup equity performance, explainability, and governance. AI is a credible enabler of personalized mammography screening, with the most mature near-term use cases being identification of women at highest short-term risk for intensified surveillance and workflow optimization via AI-supported reading. Interval extension for low-risk groups should be implemented only within carefully monitored pilots and prospective outcome studies, with predefined safety, equity audits, and governance safeguards.

Economic choices depend on our predictions of the future. Yet, at times, predictions are not based on all possible outcomes, but instead on the single most likely one, which is treated as though certainly the case-that is, digitally. Four sets of studies test whether this digitization bias occurs in higher stakes economic contexts. When making predictions about the future asset prices, participants ignored conditional probability information given relatively unlikely events and relied entirely on conditional probabilities given the more-likely events. This effect was found for both financial aggregates and individual stocks, for binary predictions about the direction and continuous predictions about expected values, and even when the "unlikely" event explicitly had a probability as high as 30%; further, it occurred in incentive-compatible conditions and among financial professionals. Implications for probabilistic cognition and behavioral economics are discussed. (PsycInfo Database Record (c) 2026 APA, all rights reserved).

The study assessed the health and economic implications as well as the cost-utility of implementing universal alcohol use disorder (AUD) screening in 15-74 years population at the primary healthcare level compared with the current practice of diagnosis and management of symptomatic AUD patients seeking formal healthcare. Model-based cost-utility analysis using a hybrid model comprising a decision tree and lifetime age- and gender-specific Markov models for alcohol attributable conditions, including road traffic accident injuries, alcohol-related liver disease and head and neck cancers. The analysis was undertaken from both an abridged societal (consideration of direct cost of care) and a societal (consideration of direct and indirect costs) perspective. India (national and sub-national level analysis). 15-74 years population segregated by gender. The intervention was 10-year annual population-based screening for alcohol use disorders using alcohol use disorder identification test by community health workers at primary care facilities. The comparator was 'usual care' scenario of diagnosis and management of symptomatic AUD patients, considering care seeking patterns in India. Differences in life years, quality-adjusted life years (QALYs), alcohol attributable deaths and morbidities, direct costs and indirect costs in the comparative scenarios, along with incremental cost-utility ratio (ICUR), benefit-cost ratio and net monetary benefit. ICUR was evaluated using the per-capita gross domestic product (GDP) threshold of ₹171 498 (US$2182), as per Indian economic evaluation guidelines. Probabilistic and deterministic sensitivity analysis was conducted to identify the parameters that are likely to have an impact on efficiency of the screening programme. The AUD universal screening programme was associated with a gain of 71.16 million QALYs at population level, with approximately one-fourth reduction in the incidence of alcohol-attributable conditions. The ICUR value indicated that the programme is likely to be cost-effective from an abridged societal perspective. The intervention is projected to generate a gain of ₹8.21 (US$1.03) trillion, equivalent to per year gain of 0.59% of GDP, based on the abridged societal perspective. The deterministic sensitivity analysis indicated that reductions in diagnostic accuracy of the screening method, prevalence of AUD and treatment coverage had an inverse impact on the ICURs and could impact efficiency of the programme. There is good health and economic evidence to support the integration of alcohol use disorder screening and management within routine primary care. It would be essential to deploy measures for effectiveness of the screening tool and continuity of care to enhance efficiency of the programme.

Sustainability impacts of green hydrogen production technology: An agent-based model simulation.

Financial institutions now face three pressures that do not pull in the same direction. Risk detection has to work at microsecond timescales. Compliance rules demand verifiable provenance for every decision that gets made. And the optimisation problems underlying modern portfolio construction and derivative pricing are growing faster than classical computing can keep up with. Most of the existing scholarship treats artificial intelligence, blockchain, and quantum computing as three separate technological trajectories, each studied for the incremental improvement it promises in the problem it addresses. This study argues that the more consequential transformation lies in their convergence, not in their individual adoption. The literature has not yet articulated an integrated architecture that assigns complementary roles to these three technologies within a single risk-and-compliance stack, and that is the gap the paper addresses. Drawing on recent work in financial economics, cryptography, and quantum information science, the paper proposes the ABQ Financial Risk Architecture, which organises three layers: an AI-driven Detection Layer, a blockchain-anchored Trust and Audit Layer, and a quantum-enabled Optimisation Layer. A testable proposition follows. Institutions that integrate all three layers should achieve materially better risk-adjusted compliance outcomes than institutions deploying any subset on its own, with the gap widening as the underlying problems grow more complex. The paper then examines economic implications for developed markets, regulators, and emerging economies, with particular attention to India's National Quantum Mission and the Reserve Bank of India's evolving regulatory posture. Contributions include the architectural conceptualisation itself, three analytical figures depicting layer structure and information flows, a comparative mapping of technology roles, and a research agenda for validating convergence effects empirically. The analysis suggests that real leapfrog opportunities exist for jurisdictions without deeply entrenched legacy systems, though three constraints will bind near-term deployment: quantum hardware immaturity, regulatory asymmetry across jurisdictions, and unsolved interoperability across the three technology domains.

Rabies is a vaccine-preventable but fatal zoonotic disease. In India, where animal bite incidents are common, many reexposed patients do not have documentation of prior postexposure prophylaxis. This results in unnecessary administration of anti-rabies vaccines (ARVs) and rabies immunoglobulin (RIG), which leads to waste of resources and increased healthcare costs. This study aimed to quantify the burden and underlying causes of missing documentation in rabies reexposure cases. A prospective cross-sectional observational study was conducted at a tertiary Anti-Rabies Clinic in North India from August 2024 to November 2024, enrolling 200 patients with category 2 or 3 animal bites. Data were collected through validated questionnaires and analyzed using descriptive and inferential statistics. Of 61 patients with a prior bite exposure history, 78.7% lacked documentation, with the primary reasons being loss of documents (56.2%) and lack of awareness (31.2%). Undocumented patients incurred 2.18 times higher treatment costs, spending ₹13,200 on ARVs and ₹12,960 on RIG. Significant associations were found between documentation status and occupation (P = 0.02) and time since the last bite (P = 0.002). The high rates of undocumented prior rabies exposure contribute to avoidable biological and financial burdens on healthcare systems. This study suggests integrating digital health tools such as short messaging service reminders, mobile applications, and centralized electronic health records to lower costs and improve case management, thereby enhancing the efficiency of rabies prevention programs in resource-limited settings.

Direct real-world comparisons between osimertinib and second-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) are lacking, particularly regarding the comparative effectiveness, safety, and economic implications of different treatment sequencing strategies. We emulated a target trial using the TriNetX database to study adults with newly diagnosed metastatic EGFR-mutant non-small cell lung cancer (NSCLC). After 1:1 propensity score matching, 777 patients in the first-line osimertinib arm were compared with 777 patients in the second-generation TKI arm. The primary outcome was overall survival (OS), with secondary outcomes including health care utilization and toxicity. First-line osimertinib demonstrated significantly longer median OS compared with second-generation TKIs (53.4 v 33.2 months; hazard ratio [HR], 0.618). Although sequential therapy (second-generation TKI followed by osimertinib) achieved similar OS, it was associated with significantly higher toxicity. Patients with brain metastases derived greater benefit from osimertinib (HR, 0.563). Osimertinib also significantly reduced rates of hospitalization, intensive care unit admission, and severe infections, generating substantial health care savings ($5.73 million per 1,000 patients) despite higher drug costs. First-line osimertinib provides prolonged OS and meaningful economic benefits over second-generation TKIs. Given the higher toxicity burden of sequential therapy despite similar survival outcomes, our findings support the implementation of first-line osimertinib to optimize patient experience and reduce health care utilization in metastatic EGFR-mutant NSCLC.

The relationship between health and economy is complex and bidirectional, since it shows an inseparable link in which each health-related decision carries significant economic implications, and, at te same time, every economic decision directly or indirectly influences the health of the population, particularly in environments where resources are limited and healthcare costs continue to escalate. In this context, health economic evaluation emerges as a crucial tool that enables a systematic analysis of the costs and benefits associated with various interventions, thereby facilitating informed decision-making. This narrative review focuses on the principles and techniques of economic evaluation, examining their relevance to the planning and management of health systems. The different types of economic evaluation studies and their impact on the efficiency of health interventions are discussed, and the need for a balanced approach that considers both market efficiency and social welfare is emphasized.

This paper explores the intersection of sustainable development and marine tourism through an in-depth analysis of the “Blue Flag” certification program as applied to marinas in the Attica region of Greece. The study investigates the environmental, social, and economic implications of Blue Flag-certified marinas, using a sample of five facilities that consistently meet international sustainability standards. Drawing on primary data collected through structured questionnaires and supported by secondary data from relevant institutional sources, the research assesses operational efficiency, compliance with environmental protocols, and the broader benefits of certification. Findings indicate that Blue Flag certification enhances marina prestige, visitor satisfaction, and environmental performance, though it has limited direct impact on revenue. The paper identifies key challenges such as energy consumption and berth shortages, while highlighting best practices in environmental management and safety. The study concludes with strategic recommendations to strengthen sustainable practices and enhance the competitive positioning of certified marinas within the Blue Economy framework.

Accurate estimation of dynamic environmental phenomena through intelligent sensing systems plays a critical role in enabling reliable monitoring and decision-making in complex real-world scenarios. With the rapid development of artificial intelligence-driven sensing technologies and Internet of Things systems, modern agricultural monitoring is evolving from isolated data acquisition toward intelligent, multimodal perception and decision-making. However, traditional approaches predominantly rely on single data sources, making it difficult to simultaneously capture plant phenotypic variations and environment-driven mechanisms, thereby limiting model applicability in complex field scenarios. To address this issue, a multimodal pest density estimation framework, namely the Pest Density Estimation Framework (PDEF), is proposed, which integrates UAV-based imagery, trap monitoring data, and environmental sensor measurements. In this framework, crop canopy damage features are extracted using convolutional neural networks, while temporal encoding is employed to model dynamic environmental variations. Cross-modal feature alignment and environment-aware enhancement mechanisms are further introduced to achieve deep integration of multi-source information, enabling the construction of a unified feature representation space and improving estimation accuracy. Extensive experiments conducted on a constructed multimodal agricultural dataset demonstrate that the proposed method achieves MAE, RMSE, and MAPE values of , , and , respectively, significantly outperforming the Transformer-based fusion model (MAE , RMSE ). Meanwhile, the coefficient of determination reaches , indicating superior fitting capability and stability. In multimodal combination experiments, the three-modality fusion reduces error metrics by more than on average compared with single-modality models, validating the effectiveness of multi-source collaborative modeling. From the perspective of integrating plant phenotypic analysis and environmental perception, this study provides a novel AI-driven intelligent sensing framework for pest monitoring and crop management, contributing to improved pest prediction capability and enhanced intelligence in agricultural production systems. This study further provides practical implications for agricultural economics and supply chain optimization by enabling data-driven decision-making through intelligent sensing systems.