Search Results (47,229)

The upscaling of turbines in the offshore wind industry has been unprecedented, as compared to 5–6 MW rated turbines 10 years ago. A typical 20–26 MW rated turbine in modern commercial applications (MingYang MySE 18.X-20 MW installed in 2025 and 26 MW prototype by Dongfang Electric tested in 2025) has been demonstrated. This scaling has been made possible by increasing rotor diameters (>250 m) and hub heights (>150–180 m) to achieve capacity factors of up to 55–65%, annual energy generation of more than 80 GWh/turbine, and significant decreases in levelised cost of energy (LCOE) to current values of up to 63–65 USD 2023/MWh globally averaged in 2023 (with minor variability in 2024 due to market changes and new regional areas). The paper analyses turbine upscaling over three levels of hierarchy, including turbine scale—rated capacity and physical aspect, project scale—multi-gigawatts of farms, and market scale—the global pipeline > 1500 GW level, and combines techno-economic evaluation, structural evaluation of loads, and infrastructure needs assessment. The upscaling has the advantage of reducing the number of turbines dramatically (e.g., 500 to 67 turbines in a 1 GW farm, as turbine size is increased to 15 MW) and balancing-of-plant (BoP) CAPEX (turbine-to-turbine foundations and cables) by some 20 to 30 percent per unit of capacity, and serial production learning rates of between 15 and 18% per doubling of capacity. But the problems that come with the increase in ultra-large designs are nonlinear increments in mass and load (i.e., blade-root and tower-bending moments), logistical constraints (blades > 120 m, nacelle up to 800–1000 tonnes demanding special vessels and ports), supply-chain issues (rare-earth materials, vessel shortages increase day rates by 30–50%), and technology limitations (aeroelastic compounded by numerical differences between reference 5 MW, 10 MW, and 15 MW models), it becomes evident that there is a significant increase in deflections of the tower and blades and platform surge/pitch responses with continued increases in power levels, but without a correspondingly mature infrastructure. The regional differences (mature ports of Europe vs. U.S. Jones Act restrictions vs. scale-up of vessels/manufacturing in China) lead to the necessity of optimisation depending on the context. The analysis concludes that, to the extent of mature markets with adapted logistics, continuous upscaling is an effective business strategy and can result in 5 to 12 percent further reductions in LCOE, but beyond that point, gains become marginal or even negative, as risks and costs increase. The competitiveness of the future depends on multi-scale/multi-market-based approaches—modular-based families of turbines, programmatic standardisation, vibration control innovations, and industry coordination towards supply-chain alignment and standards. Its major strength is that it transcends mere size–cost relationships and shows how nonlinear structural processes, aero-hydro-servo-elastic interactions, and bottlenecks in logistical systems are becoming more determinant of the efficiency of ultra-large turbines. The study demonstrates that upscaling turbines has LCOE benefits through the support of associated improvements in installation facility, supply-chain preparedness, and structural vibration control potential, based on the comparisons of quantitative loads, techno-economic scaling trends, and regional market differentiation. Full article

It is difficult to capture the realities of the illicit antiquities market because of the lack of accessible, unsiloed data from underground trade networks. Despite existing literature on social network analyses and machine-learning experiments with antiquities data, there is a gap in simple open-source methodologies accessible to the non-academic public. By using a provenance-based analysis, we present a case study of the Italian antiquities trafficking networks that more fully captures their complexity. This study culls provenance data from repatriated antiquities gathered in the Museum of Looted Antiquities’ dataset to create a network visualization for analysis. Using open-source provenance and repatriation data from 1950 to July 2025, we built a dataset of 233 repatriation events with 15.858 objects to produce a network that reveals central actors, roles, and locations while staying within ethical privacy limits. This study captures large portions of the trafficking network by using accessible data and produces a reproducible, ethically framed workflow. Full article

Releasing structured microdata requires balancing utility and privacy under group-based disclosure risks. We propose AE-LRHMA, a hybrid anonymization framework that performs Mondrian-style hierarchical partitioning in an autoencoder-learned latent space and integrates local (k,e)-microaggregation. To explicitly control sensitive-value concentration and diversity within each equivalence class, we introduce a tunable constraint set consisting of k, a maximum sensitive proportion threshold, and an optional sensitive-entropy threshold (used as a hard gate when enabled and otherwise as a soft term in split scoring). The anonymized output is generated via standard interval/set generalization in the original space. Experiments on Adult and Bank Marketing demonstrate that AE-LRHMA yields lower information loss and more stable group structures than representative baselines under comparable settings. We further report linkage-attack-oriented risk metrics to empirically characterize relative disclosure trends without claiming formal guarantees, such as differential privacy. Full article

Tirzepatide, a dual glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide 1 (GLP-1) receptor agonist, introduces a mechanistically distinct approach within incretin-based therapies. While its efficacy is established, real-world data comparing post-marketing safety with established GLP-1 receptor agonists remain limited. This study assessed System Organ Class (SOC)-level reporting patterns for tirzepatide versus semaglutide and liraglutide using EudraVigilance data. Aggregated individual case safety reports (ICSRs) were analyzed using pairwise disproportionality analyses based on a case/non-case approach. Reporting odds ratios (RORs) with 95% confidence intervals were calculated. False discovery rate (FDR) correction using the Benjamini–Hochberg procedure and sensitivity analyses restricted to serious and healthcare professional–reported cases were performed to assess robustness. After FDR adjustment, 20 SOCs were significant in tirzepatide–semaglutide and 23 in tirzepatide–liraglutide comparisons; eight SOCs remained significant across all analytical conditions. Compared with semaglutide, tirzepatide showed higher reporting for immune (ROR 1.97, 95% CI 1.75–2.21) and hepatobiliary disorders (ROR 1.71, 95% CI 1.61–1.82). Versus liraglutide, higher odds occurred for musculoskeletal (ROR 2.02, 95% CI 1.85–2.21) and psychiatric disorders (ROR 2.14, 95% CI 1.99–2.30), and lower odds for neoplasms (ROR 0.28, 95% CI 0.26–0.31). Tirzepatide shows heterogeneous reporting patterns compared with GLP-1 receptor agonists, with consistent excess reporting for hepatobiliary, immune, and musculoskeletal disorders. These findings are hypothesis-generating and warrant confirmation in exposure-adjusted studies. Full article

About 10% of plastic products are recycled worldwide, highlighting the need for technology improvements based on deeper material understanding. In packaging, which holds the highest market share in plastics demand, odor and potential hazards remain critical barriers to high-quality recycling. Conventional characterization relies on chromatography with extensive sample preparation. A gas chromatography system equipped with thermal desorption and dual flame ionization and mass spectrometric detection (ATD-GC/FID-MS) was established to analyze recyclates directly, thereby accelerating technology adaptation and guiding follow-up analyses. For calibration and validation, liquid standards were introduced into TenaxTA-filled tubes via a packed column injector and compared to a loading rig. The injector exhibited losses for higher-molar-mass compounds and solvent-dependent signal shifts. A storage study on compounded recycled polypropylene stored under various conditions showed that samples not frozen in sealed containers should be analyzed within 30 days. Experiments with varying sample geometries demonstrated that higher surface-to-volume ratios increase volatile release and variability in results, highlighting the need for uniform shapes. Applying the method to recycled yogurt cups enables the identification and quantification of contaminants, facilitating optimization of the washing process. Overall, ATD-GC/FID-MS provides a rapid screening tool for recyclate quality control and supports the improvement of recycling technologies. Full article

Traditional markets play an important role in the exchange of plant resources and the preservation of traditional food knowledge. This study documents the diversity of food plants traded in Renmin Market, located in Youjiang District, Baise City, Guangxi, China, and evaluates their cultural importance using the Cultural Food Significance Index (CFSI). Field surveys were conducted through market observations and interviews with vendors and local informants. All edible plant species were recorded, including their scientific names, vernacular names, used parts, and modes of consumption. A total of 104 food plant taxa were documented, representing a wide range of plant families and growth forms. The recorded plants were used in four main utilization categories: vegetables, spices, fruits, and beverages. Frequently used plant parts included fruits, leaves, shoots, and underground organs such as roots, rhizomes, and tubers. The CFSI values showed considerable variation in cultural importance among species, ranging from 21.6 to 1764. The highest CFSI values were recorded for Cucurbita pepo, Allium cepa, Cucurbita maxima, and Houttuynia cordata, reflecting their frequent consumption and versatility in local cuisine. Comparative analysis with previous studies in Baise City indicated that 38 species were shared among three markets, while 30 species were recorded exclusively in Renmin Market. These findings highlight the diversity of food plants available in local markets and their importance in maintaining regional culinary traditions and plant-based dietary diversity. Full article

Kazakhstan is one of the world’s major wheat producers and exporters, playing an important role in regional and global food security. However, increasing quality requirements in domestic and export markets have exposed limitations in the country’s capacity to consistently supply high-quality spring bread wheat (Triticum aestivum L.). This review aims to assess the current status of spring wheat grain quality in Northern Kazakhstan, identify the main factors driving its variation, and outline pathways for quality improvement. The analysis is based on published literature, official statistics, national quality standards, and recent data on wheat production, grading, breeding systems, agronomic practices, and trade patterns. The review reveals that wheat production is dominated by medium-quality grain (primarily class 3), while high-quality classes suitable for premium and improver markets represent a small share. Compared with major exporters such as Canada, the United States, and Australia, Kazakh wheat is generally inferior across key quality parameters. Structural constraints include the limited integration of quality assessments within breeding programs, insufficient laboratory infrastructure, weak agroecological zoning by quality classes, and suboptimal agronomic management, particularly regarding nitrogen use. Environmental heterogeneity and climate change further influence the yield–quality balance. Overall, the findings suggest that improving wheat grain quality in Kazakhstan will require coordinated advances in breeding, agronomy, institutional capacity, and market alignment, enabling a gradual shift toward a more competitive, quality-oriented wheat production system. Full article

The agriculture sector plays a pivotal role in global economies, and optimizing its perishable food supply chain (PFSC) is vital to ensuring food security and transparency. The purpose of the study is to develop a blockchain-based smart contract to secure and provide transparency about perishable goods in the PFSC while delivering the goods between the stakeholders, such as farmers, mandis, and wholesalers. The study enhances collaboration between stakeholders by implementing smart contracts. The delivery status and the transactions have been safely recorded and verified by the stakeholder in the PFSC to ensure data integrity all the way through. The blockchain application has reduced fraud and streamlined the flow of goods and information. Moreover, this study emphasizes providing farmers with a straightforward route to the market to empower them. The benefits for the stakeholders are optimizing inventory control and developing appropriate decision-making skills. A three-echelon PFSC can become more resilient and is able to meet changing market demands by implementing blockchain-based smart contracts. Finally, the study employs blockchain technology to establish a decentralized and efficient PFSC, confirming a tamper-resistant system and enhancing stakeholder trust and collaboration. Full article

This study uses multidimensional indicators of macroeconomics, supply and demand, cost, and market microstructure to construct an intelligent framework integrated with optimized Exponentially Weighted Moving Average (EWMA) denoising for price forecasting and black early warning for egg futures in China from 2014 to 2023. Black early warning serves as a non-parametric early warning method that identifies abnormal price increases and falls based on historical fluctuation thresholds. As the first livestock future contract listed in China, accurate egg price forecasting is crucial for risk prevention and market control and regulation. First, LASSO regression was used to screen the core driving factors of egg futures prices. Nine key indicators were identified and input into the hybrid Temporal Convolutional Network–Gated Recurrent Unit (TCN-GRU) prediction model. To address the high-frequency noise in the original price series, two-dimensional optimization was performed on traditional EWMA denoising to achieve more adaptive noise filtering. By applying the black early warning method, the obtained future egg price fluctuations were more consistent with the actual situation. In addition, empirical analysis of multi-horizon forecasting and early warning for t + 1, t + 5, and t + 10 was carried out to further verify the model’s prediction accuracy. The results show that compared with the single TCN model, the single GRU model, and the TCN-GRU model without denoising, the TCN-GRU model integrated with optimized EWMA denoising achieves better prediction performance on the test set. In terms of the early warning matching rate, it reaches 83.33% for the t + 1 horizon, and the prediction accuracy for the t + 5 and t + 10 horizons decreases regularly but remains stable above 60%. In contrast, the highest early warning matching rate of the model without denoising is only 22.22% across all horizons, which has no practical early warning value. The early warning signals generated by the optimized EWMA denoising-based TCN-GRU model can effectively identify abnormal sharp rises and falls in egg futures prices, providing effective support for hedging and risk management for market participants. The study’s limitations are discussed, as well as future research directions. The findings provide a basis for decision making for agricultural producers and future investors and support the development of China’s agricultural product market. Full article

The recycling of Floating Production Storage and Offloading (FPSO) units has become an important economic and environmental challenge as a growing number of offshore assets reach end-of-life. This study evaluates the comparative economic, environmental, and social performance of alternative FPSO recycling scenarios evaluated using a stochastic Monte Carlo simulation, focusing on five FPSOs that operated in Brazil and were scheduled for recycling either domestically or in Denmark. Twelve performance indicators were aggregated into sustainability indices using a Monte Carlo simulation with 100,000 iterations, enabling analysis of robustness and variability across ten recycling scenarios. The results indicate that Brazilian recycling scenarios (P-32 and P-33) outperform the Danish scenarios in terms of global performance, with Global Sustainability Index values predominantly ranging from 0.59 to 0.75, compared to 0.37 to 0.61 for the Danish cases. Differences in performance are mainly associated with towing distance, cost structure, and emissions. Social indicators show limited variability and act as a stabilizing component across scenarios. Plasma cutting presents slightly better environmental and economic results than LPG cutting, although it does not alter the overall ranking of scenarios. These findings support decision-making on FPSO recycling scenarios by highlighting the role of uncertainty and contextual factors, particularly in emerging recycling markets. Full article

We examine whether speculative bubbles in shadow banking institutions contribute to the buildup and materialization of systemic risk. Using the Phillips–Shi–Yu (BSADF) bubble detection methodology and market-based systemic risk measures (ΔCoVaR and Marginal Expected Shortfall), we analyze daily data for 17 publicly listed U.S. shadow banking firms over the period 2010–2026. We document a pronounced pro-cyclical measurement puzzle. During bubble periods, firms exhibit higher market exposure and greater tail risk—Beta increases by 4.9% and Expected Shortfall by 7.9%—yet widely used systemic risk measures decline, with ΔCoVaR falling by 6.6%. This pattern suggests that conventional systemic risk metrics may underestimate vulnerabilities during speculative expansions. However, when bubbles burst, systemic risk materializes rapidly. During burst windows, ΔCoVaR increases by 7.9% and MES by 8.6%, indicating that vulnerabilities accumulated during bubble phases translate into significant systemic spillovers once speculative dynamics collapse. Our findings highlight a pro-cyclical bias in commonly used systemic risk indicators: these measures capture realized financial stress but fail to detect the buildup of fragility during expansion phases. Monitoring bubble dynamics in shadow banking may therefore provide valuable complementary signals for macroprudential surveillance. Full article

This study examines whether China’s Intelligent Manufacturing Demonstration Projects (IMDPs, 2015–2018) can improve firms’ environmental, social, and governance (ESG) performance and thereby strengthen the quality of green transformation in manufacturing. Exploiting the staggered rollout of IMDPs as a quasi-natural experiment, we combine propensity score matching with a multi-period difference-in-differences design using panel data on Chinese listed manufacturing firms from 2009 to 2022. We find that IMDP participation increases ESG ratings by approximately 0.14 rating levels relative to comparable non-participating firms. Mechanism analyses suggest that the effect operates through higher innovation efficiency and improved cost management, consistent with a channel of capability upgrading and resource reallocation toward sustainability-related activities. The effect is stronger for firms under intense competitive pressure, at the growth stage, and in capital-scarce industries, indicating that industrial policy can be particularly valuable where market frictions constrain green investment. Importantly, we go beyond ESG scores by constructing measures of greenwashing and ESG rating uncertainty, and show that IMDPs reduce greenwashing and lower ESG uncertainty. These results imply that intelligent manufacturing policies can generate economically meaningful benefits by improving firms’ sustainability performance and the credibility of ESG information, which are central to capital allocation and the effectiveness of green governance. Full article

Despite widespread corporate endorsement of the Sustainable Development Goals (SDGs), systematic evidence on how top management in emerging economies prioritizes and frames SDG-related issues over time remains limited. Existing studies are often based on manual or single-year analyses, restricting comparability, scalability, and longitudinal insight. This study examines how corporate managerial communication aligns with and emphasizes SDGs across sectors and over time in two major emerging economies, India and South Africa. Using an AI-driven natural language processing (NLP) pipeline, we analyse 2400 annual reports from 600 publicly listed companies covering the period 2020–2023. A fine-tuned SDG-BERT multi-label classification model is applied to extract and classify SDG-related content from top management communications, enabling sectoral, temporal, and cross-country comparison of SDG relevance. The results reveal a strong and persistent emphasis on SDG 12 (Responsible Consumption and Production) across both countries, alongside sector-specific variation and differing patterns of SDG diversity over time. South African firms exhibit greater variation in SDG emphasis across years, while Indian firms display more concentrated and stable SDG framing. Overall, the findings highlight systematic imbalances in SDG-related managerial communication and persistent underrepresentation of several social SDGs. The study contributes methodologically by demonstrating the value of validated AI-assisted longitudinal text analysis for large-scale SDG research and empirically by providing comparative insights into how corporate SDG narratives evolve in emerging market contexts. Full article

Purpose: Opioids are widely used for pain management but are associated with adverse events that may differ between women and men. However, post-marketing safety data are rarely examined at the individual level to characterize these sex differences. This study aimed to identify sex disparities in opioid-associated adverse events using the FDA Adverse Event Reporting System (FAERS) to inform safer opioid selection for women. Methods: Opioid drugs were identified using the FDA’s Opioid Analgesic Risk Evaluation and Mitigation Strategy (REMS) list and official drug labeling. Relevant FAERS reports were extracted, and adverse events were classified into 27 System Organ Classes (SOCs) based on the Medical Dictionary for Regulatory Activities (MedDRA). Sex-specific signals of disproportionate reporting were evaluated using proportional reporting ratios and reporting odds ratios for drug–SOC pairs. Results: Across most opioid drugs and SOCs, adverse events were reported more frequently in women than in men. The largest sex disparities were observed for codeine, fentanyl, tapentadol, hydrocodone, and sufentanil, with significantly higher disproportionate reporting rates among women. These findings indicate pronounced sex-specific differences in the post-marketing safety profiles of several commonly used opioids. Conclusions: Women demonstrate higher disproportionate reporting of adverse events for certain opioid medications, particularly codeine and fentanyl. These results suggest the need for increased awareness of sex-specific safety differences and support sex-informed prescribing and monitoring strategies to improve opioid safety in women. Since pharmacists are medication experts and play a key role in promoting rational and safe use, our findings may further support pharmacists counseling patients and monitoring for opioid-related adverse events. Full article

The article examines the persistent stability of the weighted average cost of capital (WACC) disclosed by German DAX40 companies despite substantial declines in risk-free interest rates between 2004 and 2021. While theory suggests that WACC should reflect lower risk-free interest rates and decline as well with falling government bond yields, empirical evidence reveals minimal adjustment in reported WACC figures. Disclosed WACC of DAX40 companies remains between 7% and 8% as the yield of the ten-year German government bond fell from 4.1% to −0.2%. This study employs quantitative analyses to investigate whether systematic increases in risk exposure can explain this phenomenon. Using capital market data spanning from 2000 to 2023, we analyze five risk dimensions: systematic risk (beta factors), overall market volatility, risk aversion (lambda factors), earnings risk, and financial structure risk. Bootstrap analyses reveal a 41.5% reduction in beta factor variance, while volatility analyses demonstrate declining market risk exposure. The market price of risk analysis does not reveal definite findings. Earnings risk measures indicate improved financial stability, and debt ratios show modest declines. These findings suggest that observable risk parameters cannot explain persistent WACC levels, indicating a disconnect between theoretical WACC calculations and practitioner applications in investment project decision-making following value-based management principles. Full article