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In the field of financial risk management, the accuracy of portfolio Value-at-Risk (VaR) forecasts is of critical importance to both practitioners and academics. This study pioneers a comprehensive evaluation of a univariate model that leverages high-frequency intraday data to improve portfolio VaR forecasts, providing a novel contrast to both univariate and multivariate models based on daily data. Existing research has used such high-frequency-based univariate models for index portfolios, it has not adequately studied their robustness for portfolios with diverse risk profiles, particularly under changing market conditions, such as during crises. Our research fills this gap by proposing a refined univariate long-memory realized volatility model that incorporates realized variance and covariance metrics, eliminating the necessity for a parametric covariance matrix. This model captures the long-run dependencies inherent in the volatility process and provides a flexible alternative that can be paired with appropriate return innovation distributions for VaR estimation. Empirical analyses show that our methodology significantly outperforms traditional univariate and multivariate Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) models in terms of forecasting accuracy while maintaining computational simplicity and ease of implementation. In particular, the inclusion of high-frequency data in univariate volatility models not only improves forecasting accuracy but also streamlines the complexity of portfolio risk assessment. This research extends the discourse between academic research and financial practice, highlighting the transformative impact of high-frequency data on risk management strategies within the financial sector.

The environmental concerns associated with energy-intensive cryptocurrencies have led to the rise of clean cryptocurrencies, which aim to balance financial innovation and sustainability. This study investigates whether clean cryptocurrencies improve portfolio resilience while promoting environmental goals in the cryptocurrency market. Using dynamic correlation-based hedge and safe-haven regression models, relative risk ratio analysis with higher-order moments risks, and multiple portfolio optimization strategies, we assess the impact of integrating clean cryptocurrencies into portfolios composed mainly of traditional cryptocurrencies. The results show that clean cryptocurrencies consistently reduce tail risk during periods of market stress; however, this risk reduction does not always result in higher returns or better risk-adjusted performance. These findings have important implications for both investors and policymakers. Clean cryptocurrencies can help investors manage tail risk and align with ESG goals, but their implementation requires a careful assessment of return expectations and investment constraints. Policymakers are encouraged to create a regulatory framework that fosters sustainable digital asset development while protecting investors and ensuring market stability. This study contributes to a deeper understanding of clean cryptocurrencies’ role in sustainable investment strategies within the evolving digital asset landscape.

The increasing focus on sustainable finance has highlighted the critical need for accurate risk assessment in clean energy investments. However, existing research often overlooks the sector’s distinctive volatility characteristics, resulting in ineffective risk management approaches that fail to distinguish between the varied risk profiles associated with long and short positions in clean energy equities. This study addresses this gap by improving the forecasting accuracy of tail risk assessments through novel adaptations of existing volatility modeling frameworks. We demonstrate that different modeling paradigms, which assume different statistical properties for price volatility and return distributions, are required for accurate forecasting of long and short positions. Specifically, models incorporating asymmetric volatility responses and heavy-tailed distributions excel for long holdings, while models allowing for highly persistent volatility effects combined with skewed distributions perform best for short positions. This differentiated approach reflects the intrinsic asymmetries in clean energy markets. Our rigorous empirical investigation, spanning more than a decade and including severe market upheavals, reveals that these tailored models significantly outperform standard methods. The findings provide practical insights for investors and regulators by demonstrating how targeted modeling methodologies can effectively capture the complex dynamics of clean energy investments, thus supporting the broader goals of sustainable finance.

Background We estimated the incidence and mortality of nasopharyngeal carcinoma (NPC) in China in 2010 according to the data of 145 domestic population‐based cancer registries in 2014, and no such reports since then. Hence, to further and better understand its epidemiology in China and to provide more precise scientific information for its control and prevention in China, we analyzed the NPC incidence and mortality of 255 domestic population‐based cancer registries, and estimated the national rates in 2013 again. Methods NPC incidence and mortality data of 255 domestic cancer registries in 2013, accepted by the 2016 National Cancer Registry Annual Report, were collected and collated, and the indices of NPC such as the numbers of new cases and deaths, crude rates, age‐standardized rates, and truncated rates of incidence and mortality were calculated and analyzed. The incidence and mortality in China and its constituent areas were estimated according to the national population in 2013. Results An estimated 42,100 new cases and 21,320 deaths were attributed to NPC in China in 2013, accounting for 1.14% of all new cancer cases and 0.96% of all cancer‐related deaths that year in China. Crude incidence and mortality of NPC were 3.09/100,000 and 1.57/100,000, respectively. World age‐standardized incidence and mortality were 2.17/100,000 and 1.08/100,000, respectively. The incidence and mortality of males were obviously higher than those of females and slightly higher in urban areas than in rural areas. Among seven Chinese administrative regions, NPC incidence and mortality were obviously higher in South China than in other regions and lowest in North China. Top 3 incidence and mortality provinces and registering areas all located in South China. The age‐specific incidence and mortality rose quickly from age 25–29 and 35 to 39 years, respectively, peaked at different ages and varied by location. Conclusions These results demonstrated that NPC incidence and mortality in China in 2013 were also at high levels worldwide, which suggested that its control and prevention should be enhanced.

Anthropogenic activities accompanied by heavy metal waste threaten the environment. Heavy metal pollution alters the soil microbial community composition, and the microorganisms that adapt to this stress increase in abundance. The remediation process of contaminated soil not only reduces the concentration of heavy metals but also alters the bacterial communities. High-throughput 16S rDNA sequencing techniques were applied to understand the changes in soil microbial communities. Using the remediation approach of the soil mixing, the concentrations of heavy metals in the contaminated areas were diluted and the soil environment was changed. The change of soil environment as a disturbance contributed to the alteration of microbial diversity of the remediated areas. The pH and heavy metals (Cr, Cu, Ni, and Zn) were the most influential factors driving the changes in community structure. The bacterial community structure was significantly different among sample areas. The decrease of heavy metals in soil may be the important factors that changed the microbial composition. This study provides the better understanding of the changes in composition of microbial communities affected by the remediation process in heavy metal-contaminated soil.

Microorganisms capable of decomposing cellulose, xylan, starch and protein were individually isolated from swine manure compost and soil in this study. The correlations with pH, carbon source concentration, C/N ratio and enzyme activity among these isolated microorganisms were also investigated. Furthermore, the effect of additional inoculation in the compost was studied by measuring variations in the C/N ratio, enzyme activity and compost maturation rate. The inoculated microorganisms used in this study included four bacterial isolates and one commercial microorganism Phanerochaete chrysosporium . The results indicated that the isolated Kitasatospora phosalacinea strain C1, which is a cellulose-degraded microorganism, presented the highest enzyme activity at 31 ℃ and pH 5.5, while the C/N ratio was 0.8%. The isolated xylan-degraded microorganism Paenibacillus glycanilyticus X1 had the highest enzyme activity at 45 ℃ and pH 7.5, while the C/N ratio was 0.5%. The starch-degraded microorganism was identified as Bacillus licheniformis S3, and its highest enzyme activities were estimated to be 31 ℃ and pH 7.5 while the C/N ratio was 0.8%. The highest enzyme activity of the protein-degraded microorganism Brevinacillus agri E4 was obtained at 45 ℃ and pH 8.5, while the C/N ratio was 1.0%. The rate of temperature increase in the compost inoculated with P. chrysosporium was only higher than that of the compost without inoculation, and its compost maturation level was also lower than that of other composts with additional inoculation. The optimal initial C/N ratio of the compost was 27.5 and the final C/N ratio was 18.9. The composting results also indicated that the secondary inoculation would benefit compost maturation, and the lowest final C/N ratio of 17.0 was obtained.

Glioma is the most common malignant brain tumor and long non-coding RNAs (lncRNAs) have been reported to play an important role in the growth and angiogenesis of glioma. However, the potential mechanisms of lncRNA H19 in glioma remain unclear. In the present study, the effects of lncRNA H19 on glioma cell proliferation, migration, and angiogenesis were evaluated. The expression levels of H19, miR-342, and Wnt5a in glioma tissues and cells were detected by RT-qPCR or Western blotting. Dual luciferase reporter assay confirmed the interaction between H19, miR-342, and Wnt5a. Cell proliferation, migration, and angiogenesis were analyzed by colony formation, transwell, and tube formation assays, respectively. IHC was performed to test the angiogenesis-related factor CD31. H19 and Wnt5a expression were remarkably upregulated in glioma tissues and cells, whereas miR-342 expression was downregulated. Moreover, functional analysis confirmed that knockdown of H19 or overexpression of miR-342 suppressed glioma cell proliferation, migration, and angiogenesis in vitro. Besides, H19 was found to directly target miR-342 to promote Wnt5a expression and activate β-catenin pathway in glioma cells. Moreover, suppression of miR-342 or overexpression of Wnt5a reversed the inhibitory effect of sh-H19 on glioma growth and metastasis. Additionally, we verified that H19 promoted glioma cell proliferation, migration, and angiogenesis via miR-342/Wnt5a/β-catenin axis in vivo. H19 regulates glioma cell growth and metastasis through miR-342 to mediate Wnt5a/β-catenin signaling pathway, which provides new therapeutic targets for glioma treatment.

Transformers are important equipment in the power system and their reliable and safe operation is an important guarantee for the high-efficiency operation of the power system. In order to achieve the prognostics and health management of the transformer, a novel intelligent fault diagnosis of the transformer based on multi-source data fusion and correlation analysis is proposed. Firstly, data fusion for multiple components of transformer dissolved gases is performed by an improved entropy weighting method. Then, the combination of bidirectional long short-term memory network, attention mechanism, and convolution neural network is employed to predict the load rate, upper oil temperature, winding temperature data, and the fusion indices of dissolved gas components in the transformer. Furthermore, Apriori correlation analysis is performed on the transformer load rate and upper oil layer, winding temperature, and fusion indices of gas components by support and confidence levels to achieve a predictive assessment of the transformer state. Finally, the validity of the algorithm is verified by applying actual data from a power system monitoring platform. The results show that in the vicinity of sample point 88, the dissolved gas, upper oil temperature, and winding temperature data are not within the normal range of intervals, and it is presumed that the arc discharge phenomenon. Furthermore, the average correct fault diagnosis rate of 100 diagnoses of the transformer fault diagnosis model proposed in this paper is 0.917, and the mean square error of the correct rate is 0.018. The proposed model can achieve the prediction of the accident early warning, to prevent further expansion of the accident.

The P301L mutation in tau protein is a prevalent pathogenic mutation associated with neurodegenerative frontotemporal dementia, FTD. The mechanism by which P301L triggers or facilitates neurodegeneration at the molecular level remains unclear. In this work, we examined the effect of the P301L mutation on the biochemical and biological characteristics of pathologically relevant hyperphosphorylated tau. Hyperphosphorylated P301L tau forms cytotoxic aggregates more efficiently than hyperphosphorylated wildtype tau or unphosphorylated P301L tau in vitro. Mechanistic studies establish that hyperphosphorylated P301L tau exacerbates endoplasmic reticulum (ER) stress-associated gene upregulation in a neuroblastoma cell line when compared to wildtype hyperphosphorylated tau treatment. Furthermore, the microtubule cytoskeleton is severely disrupted following hyperphosphorylated P301L tau treatment. A hyperphosphorylated tau aggregation inhibitor, apomorphine, also inhibits the harmful effects caused by P301L hyperphosphorylated tau. In short, the P301L single mutation within the core repeat domain of tau renders the underlying hyperphosphorylated tau more potent in eliciting ER stress and cytoskeleton damage. However, the P301L mutation alone, without hyperphosphorylation, is not sufficient to cause these phenotypes. Understanding the conditions and mechanisms whereby selective mutations aggravate the pathogenic activities of tau can provide pivotal clues on novel strategies for drug development for frontotemporal dementia and other related neurodegenerative tauopathies, including Alzheimer’s disease.

As just transition debates extend into agricultural land use, this study examines landscape transition in Huwei Township, Taiwan, through a procedural justice lens. To address severe land subsidence, the state has promoted a shift from paddy rice cultivation to dryland farming, but the transition remains politically contested. Based on a systematic review of 55 empirical studies (2020–2026) and 12 semi-structured interviews, the study identifies a key mismatch in problem attribution: official accounts emphasize irrigation, whereas farmers point to urban development pressures and infrastructure burdens. The findings also show that cultivation-decoupled subsidies enable landowners to capture compensation while shifting operational risks onto tenant farmers and other vulnerable groups. The study argues that a socially sustainable transition depends on incorporating local knowledge and redesigning subsidy eligibility and risk-sharing rules to strengthen procedural justice, representativeness, and accountability.