Search Results (69)

A 9.4T brain MRI is the highest resolution MRI scanner in the public market. It offers submillimeter brain imaging with exceptional anatomical detail, making it one of the most powerful tools for detecting subtle structural changes associated with neurological conditions. Current segmentation models are optimized for lower-field MRI (1.5T–3T), and they struggle to perform well on 9.4T data. In this study, we present the GA-MS-UNet++, the world’s first deep learning-based model specifically designed for 9.4T brain MRI segmentation. Our model integrates multi-scale residual blocks, gated skip connections, and spatial channel attention mechanisms to improve both local and global feature extraction. The model was trained and evaluated on 12 patients in the UltraCortex 9.4T dataset and benchmarked against four leading segmentation models (Attention U-Net, Nested U-Net, VDSR, and R2UNet). The GA-MS-UNet++ achieved a state-of-the-art performance across both evaluation sets. When tested against manual, radiologist-reviewed ground truth masks, the model achieved a Dice score of 0.93. On a separate test set using SynthSeg-generated masks as the ground truth, the Dice score was 0.89. Across both evaluations, the model achieved an overall accuracy of 97.29%, precision of 90.02%, and recall of 94.00%. Statistical validation using the Wilcoxon signed-rank test (p < 1 × 10⁻⁵) and Kruskal–Wallis test (H = 26,281.98, p < 1 × 10⁻⁵) confirmed the significance of these results. Qualitative comparisons also showed a near-exact alignment with ground truth masks, particularly in areas such as the ventricles and gray–white matter interfaces. Volumetric validation further demonstrated a high correlation (R² = 0.90) between the predicted and ground truth brain volumes. Despite the limited annotated data, the GA-MS-UNet++ maintained a strong performance and has the potential for clinical use. This algorithm represents the first publicly available segmentation model for 9.4T imaging, providing a powerful tool for high-resolution brain segmentation and driving progress in automated neuroimaging analysis. Full article

To systematically reveal the influence mechanisms and spatial effects of industrial intellectual property (IP) reform strategies and manufacturing craftsmanship spirit on regional energy intensity, this study aims to provide theoretical support and practical pathways for emerging market economies pursuing dual goals of energy efficiency governance and manufacturing transformation. Based on a “technology–culture synergistic innovation ecology” theoretical framework, the study deepens the understanding of energy intensity governance and introduces two spatial weight matrices—the economic distance matrix and the nested economic–geographic matrix—to uncover the spatial heterogeneity of policy and cultural effects. Using panel data from 30 Chinese provinces from 2010 to 2022 (excluding Tibet, Hong Kong, Macao, and Taiwan), we construct an index of manufacturing craftsmanship spirit (CSM) and its four dimensions—excellence in detail, persistent dedication, breakthrough orientation, and innovation inheritance—via the entropy method. Empirical analysis is conducted through Spatial Difference-in-Differences (SDID) and Double Machine Learning (DML) models. The results show that: (1) Industrial IP reform strategies significantly reduce local energy intensity through improved property rights definition and technology transaction mechanisms, but may increase energy intensity in economically proximate regions due to intensified technological competition. (2) All four dimensions of craftsmanship spirit indirectly mitigate regional energy intensity via distinct pathways, with particularly strong mediating effects from persistent dedication and innovation inheritance. In contrast, breakthrough orientation shows no significant impact, possibly due to limitations from the current stage of the technology lifecycle. (3) Spatial spillover effects are heterogeneous: under the nested economic–geographic matrix, IP reform strategies reduce neighboring regions’ energy intensity through synergistic effects, while under the economic distance matrix, competitive spillovers lead to an increase in adjacent energy intensity. Based on these findings, we propose the following: deepening IP reform strategies to build a technology–culture synergistic ecosystem; enhancing regional policy coordination to avoid technology lock-in; systematically cultivating the core of craftsmanship spirit; and establishing a dynamic incentive mechanism for breakthrough orientation. These measures can jointly drive systemic improvements in regional energy efficiency. Full article

Toxoplasma gondii is one of the most widely distributed intracellular parasites worldwide, which can infect humans and a wide range of warm-blooded animals including sheep, with felines serving as its definitive host. T. gondii infection in sheep can lead to premature births, abortions and stillbirths, causing significant economic losses to the sheep industry. Sheep farming has become a key pillar of the agricultural economy in Shanxi Province, North China, but little is known about T. gondii infection in sheep in this province. In the present study, a total of 755 sheep tissue samples (682 muscle tissue samples and 73 lymphatic tissue samples) were collected from different markets in 10 different cities of Shanxi Province. The genomic DNA of all samples was extracted and the B1 gene of T. gondii was amplified by PCR. The B1 gene-positive samples were genotyped at 12 genetic markers employing the multilocus nested PCR-restriction fragment length polymorphism (Mn-PCR-RFLP). The molecular prevalence of T. gondii infection in sheep tissues in Shanxi Province was 20.5% (155/755). The T. gondii genotype ToxoDB#9 was identified in one positive T. gondii sample, with complete genotyping at all 12 genetic markers based on Mn-PCR-RFLP. This is the first report of molecular prevalence and genotype of T. gondii infection in sheep in Shanxi Province. These results reveal the widespread distribution of T. gondii in sheep in Shanxi, which is of significant public health importance. Full article

Soybean (Glycine max) seeds are rich in amino acids, offering key nutritional and physiological benefits. In this study, 290 soybean accessions from the USDA Germplasm Collection based in Urbana, IL Information Network (GRIN) were analyzed. Four Genome-Wide Association Study (GWAS) models—Bayesian-information and Linkage-disequilibrium Iteratively Nested Keyway (BLINK), Mixed Linear Model (MLM), Fixed and Random Model Circulating Probability Unification (FarmCPU), and Multi-Locus Mixed Model (MLMM)—identified two significant Single Nucleotide Polymorphisms (SNPs) associated with arginine content: Gm06_19014194_ss715593808 (LOD = 9.91, 3.91% variation) at 19,014,194 bp on chromosome 6 and Gm11_2054710_ss715609614 (LOD = 9.05, 19% variation) at 2,054,710 bp on chromosome 11. Two candidate genes, Glyma.06g203200 and Glyma.11G028600, were found in the two SNP marker regions, respectively. Genomic Prediction (GP) was performed for arginine content using several models: Bayes A (BA), Bayes B (BB), Bayesian LASSO (BL), Bayesian Ridge Regression (BRR), Ridge Regression Best Linear Unbiased Prediction (rrBLUP), Random Forest (RF), and Support Vector Machine (SVM). A high GP accuracy was observed in both across- and cross-populations, supporting Genomic Selection (GS) for breeding high-arginine soybean cultivars. This study holds significant commercial potential by providing valuable genetic resources and molecular tools for improving the nutritional quality and market value of soybean cultivars. Through the identification of SNP markers associated with high arginine content and the demonstration of high prediction accuracy using genomic selection, this research supports the development of soybean accessions with enhanced protein profiles. These advancements can better meet the demands of health-conscious consumers and serve high-value food and feed markets. Full article

In the context of electricity and carbon markets, with the in-depth research of virtual power plants and to realize the mutual assistance of electric energy in different regions within the same distribution network, a scheduling strategy of virtual power plant alliance based on dynamic electricity and carbon pricing using the Master–Slave game is proposed. Firstly, an interactive framework of virtual power plant alliance is designed in which the alliance operator formulates the electricity and carbon prices, and each user entity formulates the operation plan according to the prices. Secondly, the information gap decision theory is adopted to handle the uncertainties on the source–load side. Based on the Master–Slave game and source–load interaction, an economic optimal dispatching model for the virtual power plant alliance is established. Finally, the particle swarm optimization algorithm nested with the CPLEX solver is used to solve the model, and the rationality and effectiveness of the proposed strategy are demonstrated through case analysis. The simulation results show that, after considering the electricity energy interaction and dynamic electricity–carbon pricing, the daily operation cost of the virtual power plant alliance was reduced by 47.7%, carbon emissions decreased by 24.6%, and comprehensive benefits increased by 77.2%. Full article

Coopetition, the strategic blend of competition and collaboration, has emerged as a critical strategy for firms navigating today’s interconnected and resource-constrained global economy. While coopetition networks offer substantial benefits, such as fostering innovation, market expansion, and scalability, they are fraught with challenges like resource-sharing risks, trust deficits, and the inherent tension between collaboration and competition. Despite these hurdles, the transformative potential of technology in enabling and enhancing coopetition networks remains underexplored. This study addresses this gap by integrating Service-Dominant Logic (S-D Logic) and institutional work to propose a comprehensive framework for technology-driven coopetition networks. It identifies seven systemic building blocks—coopetition actors, resource integration, service exchange, institutions, nested ecosystems, operand technologies, and operant technologies—that facilitate sustainable value co-creation. These components enable firms to navigate dynamic market conditions by fostering trust, collaboration, and innovation. This research emphasizes technology’s pivotal role as a transformative enabler and strategic driver, enabling real-time interaction, seamless resource integration, and institutional alignment. Institutional work is highlighted as essential for managing regulatory, normative, and cognitive dimensions to ensure the Adaptability and longevity of coopetition ecosystems. By providing actionable insights into the design and management of resilient, technology-driven coopetition networks, this study offers a roadmap for sustainable and equitable value distribution. It contributes to the evolving discourse on strategic business networks, empowering organizations to harness the power of coopetition in an increasingly complex global marketplace. Full article

In the current era, the environmental component of ESG is recognized as a major driver due to the pressing challenges posed by climate change, population growth, global warming, and shifting weather patterns. The environment must be considered a critical factor, and as evidenced by existing research, it is regarded as the dominant component within ESG. In this study, the ESG score is derived primarily from the environmental score. The increasing importance of the environmental, social, and governance (ESG) factors in financial markets, along with the growing need for sentiment analysis in sustainability, has necessitated the development of advanced sentiment analysis techniques. A predictive model has been introduced utilizing a nested sentiment analysis framework, which classifies sentiments towards eco-friendly and non-eco-friendly products, as well as positive and negative sentiments, using FinBERT. The model has been optimized with the AdamW optimizer, L2 regularization, and dropout to assess how sentiments related to these product types influence ESG metrics. The “black-box” nature of the model has been addressed through the application of explainable AI (XAI) to enhance its interpretability. The model demonstrated an accuracy of 91.76% in predicting ESG scores and 99% in sentiment classification. The integration of XAI improves the transparency of the model’s predictions, making it a valuable tool for decision-making in making sustainable investments. This research is aligned with the United Nations’ Sustainable Development Goals (SDG 12 and SDG 13), contributing to the promotion of sustainable practices and fostering improved market dynamics. Full article

The present research explores the optimization of maintenance strategies for floating offshore wind (FOW) farms using nested genetic algorithms. The primary goal is to provide insights on the decision-making processes required for both immediate and strategic maintenance planning, crucial for the viability and efficiency of FOW operations. A nested genetic algorithm was coupled with discrete-event simulations in order to simulate and optimize maintenance scenarios influenced by various operational and environmental parameters. The study revealed that short-term maintenance timing is significantly influenced by wind conditions, with higher electricity prices justifying on-site spare parts storage to mitigate operational disruptions, suggesting economic incentives for maintaining on-site inventory of spare parts. Long-term strategic findings emphasized the impact of planned intervals between inspections on financial outcomes, identifying optimal strategies that balance operational costs with energy production efficiency. Ultimately, this study highlights the importance of integrating sophisticated predictive models for failure detection with real-time operational data to enhance maintenance decision-making in the evolving landscape of offshore wind energy, where future farms are likely to operate farther from onshore facilities and under potentially highly varying market conditions in terms of electricity prices. Full article

Although the Circular Economy (CE) has made remarkable technological progress by offering a wide range of alternative engineering solutions, an obstacle for its large-scale commercialization is nested in the adoption of those business and financial models that accurately depict the value generated from resource recovery. Recovering a resource from a waste matrix conserves natural reserves in situ by reducing demand for virgin resources, as well as conserving environmental carrying capacities by reducing waste discharges. The standard business model for resource recovery is Industrial Symbiosis (IS), where industries organize in clusters and allocate the process of waste matrices to achieve the recovery of a valuable resource at an optimal cost. Our work develops a coherent microeconomic architecture of Chemical Leasing (Ch.L.) contracts within the analytical framework of the Sherwood Plot (SP) for recovering a Value-Added Compound (VAC) from a wastewater matrix. The SP depicts the relationship between the VAC’s dilution in the wastewater matrix and its cost of recovery. ChL is engineered on the SP as a financial contract, motivating industrial synergies for delivering the VAC at the target dilution level at the market’s minimum cost and with mutual profits. In this context, we develop a ChL market typology where information completeness on which industry is most cost-efficient in recovering a VAC at every dilution level determines market dominance via a Kullback–Leibler Divergence (D_KL) metric. In turn, we model how payoffs are allocated between industries via three ChL contract pricing systems, their profitability limits, and their fitting potential by market type. Finally, we discuss the emerging applications of ChL financial engineering in relation to three vital pillars of resource recovery and natural capital conservation. Full article

The term ‘sustainable consumer’ (SC) is used across various knowledge domains, from sustainable consumption, green marketing, sustainability, and social change to social marketing for sustainability issues. However, the term SC lacks a precise definition, which leads to the inaccurate evaluation and measurement of the impact of green marketing or social marketing campaigns on consumption—sustainable or otherwise. This paper develops a framework to clarify the term ‘sustainable consumer’ to assist both scholars and practitioners. The application of systems thinking was applied to the extant literature to theorise the SC. This conceptual paper provides a new framework for theorising SCs: the integrated model of the sustainable consumer (ISMC). This framework emphasises the interconnected relationships of influences within the SC profile to assist scholars in examining SCs within these systems with precision. We contend that, to promote and maintain the desired sustainable consumption for long-term effects, researchers and practitioners should consider the impact not only of the socio-psycho-demographic characteristics but also the connection of the person to the environment and their community, in addition to their worldviews. The framework presented here challenges linear models by proposing a nested, dynamic structure that recognizes the interconnected influences within the sustainable consumer’s ecosystem. The framework also enables a targeted intervention design according to the layer and element and permits more precise evaluations of behaviour change campaigns’ effectiveness. Full article

We are currently witnessing a global transition (albeit slow) towards new, more sustainable models of development and consumption. This transition activates and highlights a series of discrepancies between the various actors in agri-food marketing systems, including the institutions that govern regulatory and trade aspects. These discrepancies highlight that the global agri-marketing system does not provide adequate responses to the principles of sustainability. This is due to a mixture of opportunism, information asymmetries, and ‘lock-in effects’, which create serious market failures. This, in turn, brings structural holes, in which new forms of exchange are born. We identify these as nested markets: hybrid market forms that often use new information technologies and create a new form of proximity in which reciprocity and reputation play a central role. In this article, we argue that the market is not only the place where prices and quantities are assessed. Markets are complex social spaces, where more-or-less stable relationships are formed, based on values of reciprocity and reputation that contain opportunism. This article discusses the many well-documented cases of new markets. This article argues that these new markets are characterized by a strong specificity of the resources used (that include territory, sustainability, and solidarity). Full article

This study introduces a multivariate extension to the class of stochastic volatility models, employing integrated nested Laplace approximations (INLA) for estimation. Bayesian methods for estimating stochastic volatility models through Markov Chain Monte Carlo (MCMC) can become computationally burdensome or inefficient as the dataset size and problem complexity increase. Furthermore, issues related to chain convergence can also arise. In light of these challenges, this research aims to establish a computationally efficient approach for estimating multivariate stochastic volatility models. We propose a multifactor formulation estimated using the INLA methodology, enabling an approach that leverages sparse linear algebra and parallelization techniques. To evaluate the effectiveness of our proposed model, we conduct in-sample and out-of-sample empirical analyses of stock market index return series. Furthermore, we provide a comparative analysis with models estimated using MCMC, demonstrating the computational efficiency and goodness of fit improvements achieved with our approach. Full article

The current formal common denominator of the electricity supply system in Europe has been the Harmonized Electricity Market Role Model (HEMRM) set up by ENTSO-E, ebIX, and EFET at the turn of the millennium; it introduced the concept of de-coupling and the vertical structuring of the system into the previously vertically integrated system. Since then, within demonstration projects, the system has been undergoing further changes in a controlled environment, generating bottom-up energy, caused by new technologies, business models, and new players, and extending the concept of the system to the level of energy communities and prosumers. Therefore, this paper proposes a coherent approach to the extension of HEMRM to the lowest levels in both the grid and market segments—full harmonization. This entails further structuring of both segments downwards and applying the principles of vertically nested subsystems—a system of systems approach—to a unit functional level of the electricity system, which can be the prosumer itself. At the lowest levels, the de-coupled system becomes coupled; additionally, it cross-sects with other energy vectors. Complete harmonization reduces the number of system and market segments and represents system standardization, leading to both subsystem and system-wide optimization. Prerequisites for it include the automated trading of flexibilities by the prosumers and implicit trading of energy transfer capacities along the distribution grids. The energy reservoirs, implicit and explicit, short-term, and long-term, play a vital role in techno-economic balancing. Full article

To limit the spread of bovine ringworm, control measures such as movement restrictions are highly recommended. In this context, calves at auction markets in Styria, Austria, displaying skin lesions characteristic for bovine ringworm, are excluded from the auctions. To investigate whether these clinical assessments correspond to laboratory diagnosis, a total of 166 samples taken from skin lesions assigned to the three clinical categories ‘ringworm very likely (v), likely (l) or unlikely (u)’ were mycologically examined using microscopy, culture, and nested PCR followed by amplicon sequencing. Further, the relationships of isolated dermatophytes were determined through multi-locus sequence typing (MLST). Overall, a high agreement between clinical assessment and laboratory results were observed with microscopy and nested PCR, providing more consistent results and molecular detection possessing an analytical sensitivity superior to that of cultural isolation (culture 21.7% vs. nested PCR 48.2%). Phylogenetic analyses revealed that most of the isolated dermatophytes belong to a unique Trichophyton verrucosum MLST genotype. In conclusion, clinical assessments were largely confirmed through laboratory diagnosis with nested PCR and sequencing, providing rapid, sensitive, and species-specific detection of dermatophytes in calves at auction markets displaying skin lesions typical for ringworm; this seems to be predominantly caused by a single Trichophyton verrucosum strain. Full article

In the insurance industry, life insurers are required by regulators to meet capital requirements to avoid insolvency caused by, for example, sudden mortality changes due to the COVID-19 pandemic. To prevent any large movements in this required capital, insurance companies are motivated to establish hedging strategies to mitigate the inherent risk exposures they face. Nonetheless, devising and implementing risk mitigation solutions to risk managing capital requirement is frequently impeded by the computational complexities stemming from the extensive simulations required. In this paper, we delve into a simulation quandary concerning the management of solvency capital risk associated with mortality and longevity. More specifically, we introduce a thin-plate regression spline method as a surrogate alternative to the standard nested simulation approach. Using this efficient simulation method, we further investigate hedging strategies that utilize mortality-linked securities coupled with stochastic mortality dynamics. Our simulation results provide a numerical justification to the market-making of mortality-linked securities in the context of mortality and longevity capital risk management. Full article