Search Results (5,488)

The unit costs of power generation of onshore wind and photovoltaics in China have dropped rapidly and significantly since 2010. Recent studies have indicated that the learning effect on cost reduction could have been overestimated due to the exclusion of the equipment-level installed capacity and the price of capital. To address this estimation bias, we constructed a research framework comprising a one-factor analysis model (OFAM), a two-factor analysis model (OFAM), and a multi-factor analysis model (MFAM) based on the Cobb–Douglas function and the cost minimization problem. This framework examines the determinants of unit costs in renewable energy generation in consideration of learning effects, scale effects, and price effects. This paper uses data from institutions such as IRENA and the World Bank to empirically analyze the contributions of these factors to reductions in the cost of onshore wind and photovoltaic power generation in China from 2010 to 2022. The results indicate that the learning-by-doing (LBD) effect has been overestimated, with scale effects accounting for a significant portion of the cost reduction. Moreover, the price of capital exerts a more pronounced influence on the levelized cost of electricity (LCOE) for photovoltaics. After factoring in equipment scale and capital costs, LBD continues to significantly reduce the LCOE of photovoltaics, with the LBD learning rate declining from 23.85% to 6.30%. Meanwhile, the impact of LBD on the LCOE of onshore wind technology ceases to be significant. Both technologies exhibit economies of scale, with scale effects accounting for 41.60% and 34.12% of the LCOE reductions for onshore wind and photovoltaics, respectively. Capital costs accounted for 32.50% of the LCOE reduction for photovoltaics. Therefore, future large-scale deployments of other costly renewable energy technologies may also benefit from the equipment-level scale and favorable bank interest rates in addition to learning-by-doing. Full article

Background: Autologous fat grafting is widely used in reconstructive, aesthetic and regenerative surgery and often requires repeated applications. Cryopreservation of lipoaspirate enables autologous fat banking and off-the-shelf availability; however, its clinical implementation is limited by freezing-induced tissue injury, regulatory requirements and uncertainties regarding the optimal preservation protocol. Glycerol is a biocompatible cryoprotective agent with promising preliminary data. Nevertheless, the optimal concentration for lipoaspirate cryopreservation remains unknown. The aim of this study was to determine the optimal glycerol concentration for preservation of adipose tissue processed according to the Cell-Enriched Lipotransfer (CELT) protocol in clinically relevant volumes under GMP-compatible conditions. Methods: Lipoaspirates from 10 patients were processed by centrifugation according to the CELT protocol and allocated into experimental groups: fresh unfrozen control, frozen samples without cryoprotectant, frozen samples with PBS, and frozen samples supplemented with glycerol in concentrations ranging from 10% to 60%. Samples were cryopreserved using a controlled freezing rate at a temperature of −80 °C for 24 h. Large-volume cryopreservation was additionally performed with the best concentration of glycerol. Post-thaw tissue quality was assessed by resazurin assay of whole tissue, stromal vascular fraction (SVF) cell live/dead counting, and resazurin assay after short-term cell culture. Results: Glycerol supplementation improved post-thaw tissue viability compared with cryopreservation without cryoprotectant or with PBS alone. An optimal concentration range between 10% and 30% glycerol was identified, with highest preservation of metabolic activity and surviving cell yield observed at 20%. Higher glycerol concentrations resulted in a marked decline in tissue quality. Cryopreservation in large volume was feasible and did not impair post-thaw viability compared with small-volume samples. Conclusions: Glycerol-based cryopreservation allows effective and GMP-compatible preservation of human lipoaspirate. An optimal glycerol concentration range was identified, enabling large-volume fat banking without compromising tissue quality. This protocol provides a clinically applicable strategy for autologous fat storage and may facilitate repeated reconstructive and regenerative treatments. Full article

N. chui is an economically important marine fish species distributed along the coastal waters of China, renowned for its delicate flesh texture and high-quality dried swim bladder. However, its scientific name and taxonomic relationship with N. coibor have long remained controversial, hindering accurate resource assessment and germplasm management. To address this issue, we sequenced and annotated the first complete mitochondrial genome of N. chui (GenBank accession: PZ024444). The circular mitogenome is 16,504 bp in length and contains 37 typical genes, with gene arrangement, nucleotide composition (A + T content: 52.07%), and codon usage patterns consistent with general teleost characteristics. Phylogenetic analyses based on 13 concatenated protein-coding genes revealed that N. chui and N. coibor form a maximally supported monophyletic clade (bootstrap support = 100%), with a pairwise genetic distance of 0. These mitochondrial results strongly suggest that the two nominal taxa are very closely related and may represent the same species. However, formal taxonomic synonymy cannot be established on mitochondrial evidence alone and requires further evaluation through examination of type material and comparative morphological study. Gene-specific selection pressure analyses showed that most mitochondrial protein-coding genes were subject to purifying selection, with ATP8 exhibiting the highest mean ω among genes with ω < 1, whereas ND5 and ND6 showed elevated ω values that warrant cautious interpretation. This study provides essential mitochondrial genomic resources for future research on species delimitation, phylogeny, and conservation of this important sciaenid fish. Full article

Background/Objectives: Puerto Rican Hispanic/Latino (PR H/L) men experience a heightened incidence and mortality rate of aggressive forms of prostate cancer. The underlying causes of this increased disease burden likely include a complex interplay of socio-economic and biological factors. This pilot study leveraged the first cancer tissue biobank at a Hispanic-Serving Institution (Puerto Rico BioBank) and aimed to provide an initial description of the genomic features of prostate cancer in 35 PR H/L men. Methods: Whole-exome and RNA sequencing were performed on prostate adenocarcinoma tumor samples to investigate the genomic features associated with prostate cancer. Results: Our analysis suggests that mutation profiles and gene expression pattern differences are observed in this population and may be associated with disease aggressiveness and progression. Notably, mutations in TP53 and TMPRSS2-ERG gene fusions, which are common in broader populations, were less prevalent in the PR H/L cohort. Conclusions: While this study contributes to the understanding of ethnicity-specific genetic factors in prostate cancer, underscoring the need for inclusive genomic studies, continued expansion to larger cohorts of patients under-represented in large genomic studies will be needed to more robustly characterize the full range of genomic features of prostate cancer. A broader understanding of the genomic features of prostate cancer in PR H/L men may lead to future opportunities for delivering more personalized prognoses and treatment options, helping to ensure that treatment advances and better outcomes are available to all patients. Full article

Transboundary river basins face water stress exacerbated by data scarcity and political instability, and most allocation models require ideal conditions that ordinarily do not exist. This study operationalizes Water Diplomacy Theory (WDT) for data-scarce, conflict-prone basins through quantifiable allocation rules—a critical gap as 310 transboundary basins worldwide face similar challenges. We address: (1) How can a four-stage allocation framework reduce basin-wide water stress under varying Institutional Capacity (IC), Data Transparency (DT), and Stakeholder Inclusion (SI)? (2) What treaty provisions achieve bindingness under upstream-downstream power asymmetries? (3) How does this framework advance beyond existing models in equity, efficiency, and adaptive capacity? We synthesize Water Diplomacy Theory with Hydro-political Security Complex Theory to construct a novel four-stage framework: initial allocation with ecological floors, conditional reallocation triggers, interannual water banking, and satellite-verified compliance. Drawing on 14 treaty precedents and 30-year hydrological data for the Salween River, we embed these rules in an open-source water banking model. Results demonstrate that increasing IC from low to high reduces basin-wide water stress by 34% (±7%, 95% IC) under drought conditions. Stakeholder Inclusion decreases allocation conflicts by 52%. Water banking outperforms priority rules by 23% across climate scenarios. Cooperation becomes self-enforcing when IC exceeds 0.55. The novelty and contribution to existing literature our study provides are: (1) first operationalization of hybrid commons-to-treaty transition with 85.7% empirically grounded clauses; (2) evidence that binding cooperative treaty design is achievable in weak-state contexts through institutional design; and (3) a portable template for data-scarce conflict-affected basins. Full article

This study examines the psychological mechanisms through which anthropomorphic artificial intelligence (AI) relates to consumer adoption intentions in fragile, low-trust economies. Integrating the Stimulus–Organism–Response framework with the Computers Are Social Actors paradigm, Institutional Trust Theory, and Privacy Calculus Theory, we investigate how human-like AI design shapes cognitive and affective responses within Sierra Leone’s banking sector. Using survey data from 277 banking customers and partial least squares structural equation modeling, we find that AI anthropomorphism exhibits no direct association with adoption intention (β = −0.013, p = 0.760). Instead, its influence is entirely indirect—transmitted in parallel through perceived social presence (β = 0.144, 95% CI [0.062, 0.226]) and trust in the AI system (β = 0.139, 95% CI [0.068, 0.210]). Critically, customer skepticism—shaped by institutional fragility—functions as a boundary condition that substantially attenuates both pathways: among highly skeptical users (+1 SD), anthropomorphism’s conditional effect on social presence becomes non-significant (β = 0.098, p = 0.124) compared to low-skepticism users (β = 0.412, p < 0.001), while its effect on trust is reduced by more than half (β = 0.118 vs. 0.284). These findings identify a critical boundary condition on human-like AI design: in low-trust environments, anthropomorphism operates not as a standalone adoption driver but as a relational amplifier whose efficacy depends on foundational trust and is substantially weakened when skepticism is high. The study challenges universalist assumptions in human–AI interaction research and underscores the need for institutionally sensitive design approaches in fragile economies. Full article

The lack of a reliable national sampling frame poses a major challenge for conducting representative population and household surveys, particularly in developing countries affected by displacement and rapid territorial change. This study addresses this gap by developing Armenia’s first digitized national sampling frame, where accessible survey frames are severely limited. We introduce an innovative pre-EA tool to semi-automatically construct the digital sampling frame using publicly available datasets. Compared with traditional approaches, this method outperforms in several ways: it enables rapid, semi-automated frame construction, minimizes resource requirements, eliminates geometric errors associated with manual digitization, and produces pre-census EAs (pre-EAs) that both nest within administrative boundaries and align with visible ground features. The approach also integrates gridded population data to reflect recent urbanization and migration, generating pre-census EAs and urban–rural classifications suitable for national surveys. The sampling frame was successfully applied in the World Bank’s “Listening to Armenia” survey. Overall, the study demonstrates that automated, data-driven approaches can efficiently produce accurate, scalable, and adaptable national sampling frames, offering potential utility in other countries facing similar constraints. Full article

To address the challenges associated with identifying causal relationships among event mentions in the event causality identification (ECI) task, ECI has emerged as a pivotal area of research for comprehending event structures. Recent studies have leveraged Transformer-based models, augmented by auxiliary components, to develop effective contextual representations for causality prediction. A critical step in ECI models involves transforming intricate event context representations into causal label representations, thereby facilitating the logical score calculations necessary for both training and inference. However, existing models frequently depend on simplistic feedforward networks for this transformation process, which often struggle to bridge the semantic gap between complex event contexts and target causal labels, particularly in linguistically nuanced scenarios. To address these limitations, we propose Contrastive Learning for Event Causality Identification (CLECI), an innovative ECI framework that enhances representation learning through the integration of contrastive learning techniques, a generator-discriminator mechanism with causal label embeddings. In contrast to traditional direct transformation methods, CLECI generates latent causal label embeddings that filter out irrelevant information while aligning with potential label representations. By incorporating contrastive learning principles, CLECI further augments the discriminative capability of event representations by constructing positive and negative pairs of events. Experimental evaluations conducted on the EventStoryLine (ESL), Causal-TimeBank (CTB), and MECI datasets demonstrate that CLECI achieves competitive performance, with F1-score improvements of 4.3%, 7.9%, and 2.5%, respectively, compared with the strongest baseline methods, while maintaining strong robustness in complex and noisy multilingual event contexts. Full article

The role of Corporate Social Responsibility (CSR) in advancing economic, social, and environmental well-being has been increasingly acknowledged in the broader context of the United Nations Sustainable Development Goals. For instance, CSR in Saudi Arabia is increasingly framed as a mechanism to support Vision 2030—a national strategy aimed at transforming Saudi Arabia to a sustainable economy. However, evidence on how financial institutions disclose and prioritize CSR at the country level remains fragmented. This study examines the extent and patterns of CSR disclosure across the Saudi banking sector by analyzing publicly available documents, e.g., annual reports and ESG/CSR reports (n = 36) from 10 banks (4 Islamic and 6 commercial). Findings indicate that CSR disclosures were primarily clustered into four macro themes—society, economic contribution, internal stakeholders, and environment—with a strong thematic emphasis on philanthropic activities, financial donations, disability support, and financing for Small and Medium Enterprises (SMEs). Environmental initiatives were disclosed less frequently and were generally narrower in scope, focusing on resource efficiency, recycling, and selective green financing. In addition, a comparative analysis between Commercial and Islamic banks revealed that the latter focused on values-based CSR, while commercial ones emphasized governance-oriented CSR. Full article

Type 1 diabetes (T1D) is an autoimmune disease with a strong genetic component (~70% heritability). Early identification of individuals at risk is crucial for early intervention or risk assessment. Although polygenic risk scores (PRS) have shown promise in risk assessment, most current approaches remain constrained by linear assumptions and limited generalizability. We aimed to develop a neural network-driven classifier using T1D-associated single nucleotide polymorphisms (SNPs). In addition, we explored the inclusion of an entropy-derived feature as a complementary variable, representing the degree of genetic variability within an individual’s genotype profile across the 67 T1D-associated SNPs, to evaluate its potential additive contribution to the model performance. We analyzed genotype data from 11,909 individuals in the UK BioBank (546 T1D cases and 11,363 controls). Sixty-seven well-known SNPs associated with T1D were utilized as inputs to the model, using two distinct allele-encoding strategies. A feed-forward neural network was evaluated under varying case–control ratios through five-fold cross-validation. Performance was assessed using the area under the receiver operating characteristic curve (AUC) on a held-out test set and on an external European cohort as a validation cohort. Across five-fold cross-validation, the best configuration achieved a median AUC of 0.903. On the held-out UK Biobank test set, the model generalized well, with an AUC of 0.8889 (95% CI: 0.8516–0.9262). A probability-based risk framework, constructed using five risk groups (“very low”, “low”, “intermediate”, “high”, and “very high” risk), yielded a negative predictive value (NPV) of 98.9% for the “very low” risk group and a Positive Predicted Value (PPV) of 61.9% with a specificity of 97.3% for the “very high” risk group, assuming a 10% T1D prevalence. External validation in the German Diabetes Study reproduced clear case–control separation; for individuals with recent onset diabetes and glutamic acid decarboxylase antibodies (GADA+) vs. controls, specificity reached 91.9% in the “high” risk group (PPV of 94.3%) and 97.6% in the “very high” risk group (PPV of 95.7%). The proposed neural network reliably predicts T1D genetic risk using a compact SNP panel of 67 SNPs and maintains accuracy in both internal and external European cohorts. Its probabilistic output enables clinically interpretable risk thresholds, while entropy features contributed modestly to performance. These results demonstrate that a neural network-based approach achieves discriminative performance that is comparable to established T1D genetic risk models, while offering flexible probability-based risk stratification and architectural extensibility for future integration of additional features. Full article

Existing research on green transformation predominantly emphasizes “hard constraints” such as carbon taxes and environmental regulations, while neglecting “soft constraints” shaped by environmental sentiment expressions from key actors such as the public, financial institutions, media, and government. In particular, the collective influence of these multi-actor environmental sentiments remains insufficiently explored. This study fills that gap by constructing a collaborative governance framework using multi-source heterogeneous data from China spanning 2013–2023, including 330 provincial government work reports, 1862 bank annual reports, 2472 newspaper articles, and 68,519 Weibo posts, matched to 4708 firm-year observations of Chinese A-share energy companies. We quantify environmental sentiment tendencies through natural language processing, calculating the index as (negative word frequency − positive word frequency)/total word frequency at the province-year level, thus higher index value indicates more negative sentiment tendency, while green transformation is proxied by ln(green patent applications + 1). The results reveal the following: (1) More negative environmental sentiment tendencies from financial institutions, media, public, and government significantly promote green transformation in energy enterprises, with stronger effects observed from financial institutions and government. (2) Economic and climate policy uncertainty selectively weaken the impact of financial institutions’ sentiment, while the moderating effects for other actors are statistically insignificant. (3) The effect of multi-actor environmental sentiment is more pronounced for firms located in eastern China, operating under high competition or stricter environmental regulations. This study provides a novel, quantified approach to assessing multi-actor environmental sentiment tendencies, affirms the effectiveness of informal governance, and highlights the importance of stable policy in guiding corporate green transformation in emerging economies. Full article

Advancing hydropower development is crucial for supporting China’s “Dual Carbon” strategy and ensuring energy security. A key safety challenge in this endeavor is the stability of arch dam abutments under the combined action of high in situ stress and reservoir water loads. This study addresses this issue by proposing an integrated methodology that links detailed geological characterization, in situ stress quantification, and mechanical stability analysis. Using the Guxue high-arch dam as a case study, we first established a three-dimensional geological model to identify controlling discontinuities and delineate potential sliding blocks. A finite difference model was then developed to simulate the in situ geo-stress field and operational water pressures. Through stress tensor transformation, the stress state on potential slip surfaces was accurately determined, and safety factors were calculated based on the Mohr–Coulomb strength criterion. The results show that the critical left and right abutment rock blocks exhibit safety factors of 1.30 and 1.24, respectively, meeting design specifications while indicating a relatively lower safety margin on the right bank. The proposed approach, grounded in precise stress analysis, provides a reliable framework for assessing abutment stability under complex loading conditions, offering practical support for the safety evaluation and targeted reinforcement of high-arch dam projects in similar geological settings. Full article

Donor human milk (DHM) provided by human milk banks is considered the optimal feeding alternative to mother’s own milk for premature or medically compromised infants. Before distribution, DHM is subjected to Holder pasteurization (HoP) by milk banks to eliminate potential pathogens. In this study, FT-IR, GC and GC-MS were applied to characterize changes in the volatile organic compounds (VOCs) and lipid components of human milk (HM) samples that were treated by HoP. FT-IR analysis revealed changes in specific band regions, indicating modifications to triglycerides and fatty acid (FA) organization and possible disruption of the milk fat globule membrane. There was also an increase in ester groups, suggesting that HoP increases lipid oxidation. GC analysis showed a reduction in long-chain FAs, including certain omega-3 and omega-6 polyunsaturated FAs (PUFAs). GC-MS analysis showed that HoP-treated samples contained higher levels of alkanes, aldehydes, aromatics and ketones than raw HM. Conversely, other compounds, including furans, and alkynes, were found exclusively in pasteurized HM. These results show that HoP affects the lipid and VOC components of HM, highlighting the need for research into alternative pathogen elimination strategies in human milk bank practices. Full article

Banks around the globe are rapidly progressing towards the adoption of digital currency. However, its adoption rate has been consistently low among both emerging and advanced economies. This study examines the user adoption of the Indian digital currency, the e-Rupee, based on a primary survey conducted between July 2025 and September 2025 of 751 respondents. The study adopted a blend of TAM and nudge theory for the first time in the digital currency domain, using the stated preference method in finance literature to understand the willingness to shift to the e-Rupee in India. Using binary logit regression, we test two hypotheses. The results show that apart from socioeconomic predictors, adoption of the e-Rupee is significantly influenced by digital financial literacy. With respect to the willingness to shift to the e-Rupee, the study found TAM constructs like perceived convenience and perceived belief in the study as the key predictors. Unlike the current literature, our study finds that trust is not a significant predictor of e-Rupee adoption. This highlights the credibility of the central bank of the country and the future growth of its digital currency. The findings highlight the importance of digital financial literacy and behavioral intentions, rather than technical viability, as the key factors in digital currency adoption in India. Full article

Infertility is a prevalent clinical issue which disrupts normal human life and exerts an impact on fertility rates within the population. The increase in environmental pollutants, including acetyl tributyl citrate (ATBC), has given rise to concerns regarding their potential toxicity in infertility-related disorders. Icariin exhibits therapeutic effects on infertility, yet its mechanism of action against plasticiser-induced reproductive disorders remains unclear. This study aims to elucidate the potential toxicological targets and molecular mechanisms of ATBC-induced infertility, as well as the therapeutic targets and mechanisms of icariin in treating ATBC-induced reproductive disorders, through network toxicology, molecular-docking techniques and molecular dynamics simulation. Utilising the component-target database SwissTargetPrediction, the Similarity Ensemble Approach, PharmMapper, the ChEMBL database, and disease databases including the Therapeutic Target Database, OMIM, GeneCards, and DrugBank, 63 targets for ATBC-induced infertility and 33 targets for icariin treatment were identified. Screening via the STRING platform and Cytoscape 3.10.1 software yielded four core targets for ATBC-induced infertility—HSP90AA1, PIK3CA, CASP3, HRAS—and four core targets for icariin treatment—IL6, TNF, STAT3, and INS. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses revealed that ATBC-induced infertility correlates with pathways including pathways in cancer, prostate cancer, and PI3K-Akt signalling pathways. Conversely, the core targets of icariin therapy for related reproductive disorders are closely associated with tumour-associated signalling pathways and the AGE-RAGE signalling pathway. Molecular-docking and molecular dynamics simulation further confirmed the strong binding interactions between ATBC and infertility-related targets, as well as between icariin and core targets for treating reproductive disorders. This provides a theoretical foundation for understanding ATBC’s toxicological targets and the complex molecular mechanisms underpinning icariin’s treatment of infertility. It informs the development of strategies for icariin to prevent and treat infertility caused by exposure to ATBC-containing plastics or excessive ATBC contact. Full article