Search Results (957)

The frequent blasting in underground mines results in stress waves of different intensities, which is one of the main factors leading to backfill collapse. Improving the strength of backfill is an effective way to reduce the backfill damage. In this study, rice straw fiber and graded tailings were used as raw materials to prepare rice straw fiber-reinforced cemented tailings backfill (RSCTB). An orthogonal experimental design was employed to perform unconfined compressive strength (UCS) tests, diffusivity measurements, and Split Hopkinson Pressure Bar (SHPB) tests. The results showed that straw fibers slightly reduce slurry fluidity. The UCS of RSCTB at a specific mix ratio was more than 50% higher than that of cemented tailings backfill (CTB) without rice straw. The dynamic unconfined compressive strength (DUCS) of RSCTB increased linearly at different strain rates. The effect of rice straw fibers on the UCS and DUCS was much smaller than that of cement content and solid mass concentration. Excessively long and abundant straw fibers are not conducive to improving the long-term impact resistance of RSCTB. Full article

The acceptance of generalized distributions has significantly improved over the past two decades. In this paper, we introduce a new generalized distribution: Topp–Leone odd Weibull flexible-G family of distributions (FoD). The new FoD is a combination of two FOD; the Topp–Leone-G and odd Weibull-flexible-G families. The proposed FoD possesses more flexibility compared to the two individual FoD when considered separately. Some selected statistical properties of this new model are derived. Three special cases from the proposed family are considered. The new model exhibits symmetry and long or short tails, and it also addresses various levels of kurtosis. Monte Carlo simulation studies were conducted to verify the consistency of the maximum likelihood estimators. Two real data examples were used as illustrations on the flexibility of the new model in comparison to other competing models. The developed model proved to perform better than all the selected competing models. Full article

Mouse papillomavirus (MmuPV1) is the first papillomavirus known to infect laboratory mice, making it an irreplaceable tool for research on papillomaviruses. Despite wide use, standardized techniques for conducting MmuPV1 animal research are lacking. In this report, we describe an unexpected MmuPV1 outbreak causing recurrent papillomatosis in a specific pathogen-free animal research facility. The infected mice displayed characteristic papillomatosis lesions from the muzzles, tails, and feet with histological signs including anisocytosis, epithelial dysplasia, and typical koilocytosis. Etiology studies showed that the papilloma tissues exhibited MmuPV1 infection with expression of viral early and late genes detected by RNA-ISH using MmuPV1 antisense probe to viral E6E7 region and antisense probe to viral L1 region. The viral L1 protein was detected by an anti-MmuPV1 L1 antibody. PCR amplification and cloning of the entire viral genome showed that the origin of the outbreak virus, named MmuPV1 Bethesda strain (GenBank Acc. No. PX123224), could be traced to the MmuPV1 virus previously used in studies at the same facility. Our data indicate that MmuPV1 could exist in a contaminated environment for a long period of time, and a standardized international animal protocol discussing how to handle MmuPV1 studies is urgently needed. Full article

The neuropeptide’s multifaceted involvement in various components of neural homeostasis impacts pain and behavioral regulation. One of the highly potent neuropeptides is dermorphin, extracted from the skin of the Amazon frog (Phyllomedusa sauvagei). The unique feature of dermorphin is the D-Ala residue in its sequence, which has inspired researchers to search for dermorphin analogs for use as pharmaceuticals. The primary objective of this study is to synthesize several new linear and cyclic dermorphin analogs and evaluate them as potential non-invasive analgesics. By exploring our method for converting linear peptides into 2,5-diketopiperazine(2,5-DKP) derivatives, which stabilize peptide structures, we synthesize several new dermorphin linear peptides and chimeric cyclopeptidomimetics. These compounds were tested in vitro and in vivo to determine their biological activities and potential applicability as pharmaceuticals. For the evaluation of in vitro opioid activity, the “Guinea Pig Ileum” (GPI) test was used. D2 showed the highest activity, and cyclopeptides D3 and D4 showed high activity. We can assume that dermorphin analogues D2, D3, and D4 are potent agonists of µ-type opioid receptors and have high opioid activity. However, this needs to be verified using molecular modeling methods in further research. The analgesic effects of dermorphins have been evaluated in the “Hot-Plate” and “Tail-Flick” tests. In rats, D2 dermorphin analogues demonstrated dose-dependent analgesic effect in the “Water Tail-Flick” test after intranasal administration. A smaller dose of 0.5 µg/kg resulted in 40% analgesia and a short-term state of stupor. The maximum long-lasting analgesia was observed at a dose of 1.0 µg/kg, which induced complete stupor. The analgesic effect of peptide D2 after intraperitoneal administration at a 5.0 mg/kg dose was over 50%. The “Open-Field” test demonstrated a dose-dependent (15, 50, 150 μg/kg) peptide D2 suppression effect on behavioural reactions in rats following intranasal administration. A new modification of linear peptides, combined with a 2,5-DKP scaffold (D3 and D4), proved promising for oral use based on the results of analgesic effect evaluation in mice following intragastric administration. Full article

Panoptic scene graph generation (PSG) aims to simultaneously segment both foreground objects and background regions while predicting object relations for fine-grained scene modeling. Despite significant progress in panoptic scene understanding, current PSG methods face challenging problems: relation prediction often only relies on visual representations and is hindered by imbalanced relation category distributions. Accordingly, we propose IntegraPSG, a single-stage framework that integrates large language model (LLM) guidance with multimodal feature fusion. IntegraPSG introduces a multimodal sparse relation prediction network that efficiently integrates visual, linguistic, and depth cues to identify subject–object pairs most likely to form relations, enhancing the screening of subject–object pairs and filtering dense candidates into sparse, effective pairs. To alleviate the long-tail distribution problem of relations, we design a language-guided multimodal relation decoder where LLM is utilized to generate language descriptions for relation triplets, which are cross-modally attended with vision pair features. This design enables more accurate relation predictions for sparse subject–object pairs and effectively improves discriminative capability for rare relations. Experimental results show that IntegraPSG achieves steady and strong performance on the PSG dataset, especially with the R@100, mR@100, and mean reaching 38.7%, 28.6%, and 30.0%, respectively, indicating strong overall results and supporting the validity of the proposed method. Full article

This study develops an integrated risk modeling framework to assess capital adequacy and optimize portfolio performance for Thai life and non-life insurers. Leveraging ARMA–GJR–GARCH models with skewed Student-t innovations, extreme value theory, and dynamic R-vine copulas, the framework effectively captures volatility, tail risks, and evolving asset interdependencies. Utilizing daily data from 2014 to 2024, the models generate value-at-risk forecasts consistent with international standards such as Basel III’s 10-day 99% VaR and rolling Sharpe ratios for portfolios integrating green bonds compared to traditional asset allocations. The results demonstrate that green bonds, fixedincome instruments funding renewable energy and other environmental projects, significantly improve risk-adjusted returns and have the potential to reduce capital requirements, particularly for life insurers with long-term sustainability mandates. These findings underscore the importance of portfolio-level capital assessment and support the proactive integration of ESG considerations into supervisory investment guidelines to enhance financial resilience and align the insurance sector with Thailand’s sustainable finance agenda. Full article

Artificial intelligence has shown great promise in advancing intelligent surgical systems. Among its applications, surgical video action recognition plays a critical role in enabling accurate intraoperative understanding and decision support. However, the task remains challenging due to the temporal continuity of surgical scenes and the long-tailed, semantically entangled distribution of action triplets composed of instruments, verbs, and targets. To address these issues, we propose TriQuery, a query-based model for surgical triplet recognition and classification. Built on a multi-task Transformer framework, TriQuery decomposes the complex triplet task into three semantically aligned subtasks using task-specific query tokens, which are processed through specialized attention mechanisms. We introduce a Multi-Query Decoding Head (MQ-DH) to jointly model structured subtasks and a Top-K Guided Query Update (TKQ) module to incorporate inter-frame temporal cues. Experiments on the CholecT45 dataset demonstrate that TriQuery achieves improved overall performance over existing baselines across multiple classification tasks. Attention visualizations further show that task queries consistently attend to semantically relevant spatial regions, enhancing model interpretability. These results highlight the effectiveness of TriQuery for advancing surgical video understanding in clinical environments. Full article

This paper investigates the time-varying efficiency of Western European stock markets and examines how macroeconomic events defined as endogenous and exogenous shocks influence the degree of efficiency by either long-range dependence or mean reverting. We apply a rolling-window detrended fluctuation analysis (DFA) with two window sizes, complemented by the Efficiency Index to synthetize multiple measures of market efficiency. The results confirm that efficiency evolves dynamically in response to macroeconomic disruptions. Specifically, endogenous shocks tend to generate anti-persistent behavior, while exogenous shocks are associated with long-memory effect. These shifts in efficiency are also reflected in rolling Kurtosis estimates, suggesting that only the most severe shocks produce spikes in Kurtosis, fat-tailed returns distributions, and structural inefficiencies. This dual approach allows us to classify shocks as major or minor based on their joint impact on both market efficiency and tail behavior. Overall, our findings support the adaptive market hypothesis and extend its implications through the fractal market hypothesis by underlining the role of heterogenous investment horizons during periods of turmoil. The combined use of dynamic DFA and Kurtosis offer a framework to assess how financial markets adapt to different types of macroeconomic shocks. Full article

Uracil−DNA glycosylases (UNGs) are DNA repair enzymes responsible for the removal of uracil, a canonical RNA nucleobase, from DNA, where it appears through cytosine deamination or incorporation from the cellular dUTP pool. While human and Escherichia coli UNGs have been extensively investigated, much less is known about their plant counterparts, of which UNGs from Arabidopsis thaliana are the only studied examples. Here, we show that in sugar beet (Beta vulgaris L.), an important crop species, cold and salt stress induce the expression of the UNG gene (BvUNG) and modulate the level of the uracil-excising activity in the roots. Purified recombinant BvUNG efficiently removes uracil from DNA both in vitro and in an E. coli reporter strain but does not excise 5-hydroxyuracil, 5,6-dihydrouracil, or 5-hydroxymethyluracil. The activity is abolished by Ugi, a protein UNG inhibitor from PBS1 bacteriophage, and by a mutation of a conserved active site His residue. Structural modeling shows the presence of a disordered N-tail prone to undergo phase separation, followed by a long α helix oriented differently from its counterpart in human UNG. Overall, BvUNG is a functional uracil–DNA glycosylase that might participate in the response to abiotic stress. Full article

This study evaluated the feasibility of reusing abandoned copper mine tailings (Cu tailings) as a precursor in the production of fly-ash-based alkali-activated materials (FA-AAMs). Two formulations were developed by combining FA and Cu tailings with a mixture of sodium silicate and sodium hydroxide as alkaline activators at room temperature (20 °C). Formulation G1 consisted of 70% Cu tailings and 30% fly ash (FA), whereas G2 included the same composition with an additional 15% ordinary Portland cement (OPC). The materials were characterized using X-ray fluorescence (XRF), -X-ray diffraction (XRD), field emission scanning electron microscopy with energy-dispersive spectroscopy (FESEM-EDS), and particle size analysis. While FA exhibited a high amorphous content (64.4%), Cu tailings were largely crystalline and acted as inert fillers. After 120 days of curing, average compressive strength reached 24 MPa for G1 and 41 MPa for G2, with the latter showing improved performance due to synergistic effects of geopolymerization and OPC hydration. Porosity measurements revealed a denser microstructure in G2 (35%) compared to G1 (52%). Leaching tests confirmed the immobilization of hazardous elements, with arsenic concentrations decreasing over time and remaining below regulatory limits. Despite extended setting times (24 h for G1 and 18 h for G2) and the appearance of surface efflorescence, both systems demonstrated good chemical stability and long-term performance. The results support the use of Cu tailings in FA-AAMs as a sustainable strategy for waste valorization, enabling their application in non-structural and moderate-load-bearing construction components or waste encapsulation units. This approach contributes to circular economy goals while reducing the environmental footprint associated with traditional cementitious systems. Full article

Estimating skewness and kurtosis from real-world data remains a long-standing challenge in actuarial science and financial risk management, where these higher-order moments are critical for capturing asymmetry and tail risk. Traditional moment-based estimators are known to be highly sensitive to outliers and often fail when the assumption of normality is violated. Despite numerous extensions—from robust moment-based methods to quantile-based measures—being proposed over the decades, no universally satisfactory solution has been reported, and many existing methods exhibit limited effectiveness, particularly under challenging distributional shapes. In this paper we propose a novel method that jointly estimates skewness and kurtosis based on a regression adaptation of the Cornish–Fisher expansion. By modeling the empirical quantiles as a cubic polynomial of the standard normal variable, the proposed approach produces a reliable and efficient estimator that better captures distributional shape without strong parametric assumptions. Our comprehensive simulation studies show that the proposed method performs much better than existing estimators across a wide range of distributions, especially when the data are skewed or heavy-tailed, as is typical in actuarial and financial applications. Full article

In water-stressed regions of South Africa, the expansion of extractive industries is compounding the effects of climate change and poor governance, threatening local water security and socio-ecological resilience for hydrosocial justice. This chapter examines the proposed Jindal iron-ore mine in Melmoth, KwaZulu-Natal and its anticipated impact on water availability, quality, and governance. Drawing on in-depth interviews with farmers, residents, and environmental stakeholders, the findings reveal a region already suffering from recurrent droughts, El Niño-related climate variability, and over-allocated water resources. Findings reveal concern that the mine would further strain surface and groundwater systems, especially given the industrial demands already placed on the Goedertrouw dam. Other concerns about potential water contamination from tailings, dust, and runoff echo experiences from neighbouring mining areas, where degraded water quality has affected both domestic use and cultural practices. The study also uncovers governance gaps, including weak regulatory oversight, non-compliance with environmental safeguards, and flawed consultation processes that overlook downstream impacts. By situating Melmoth within wider debates on extractivism, climate stress, and environmental justice, the paper calls for an urgent reconsideration of extractive approvals in ecologically vulnerable regions that threaten water security, livelihoods, cultural practices, and sense of place. Ignoring interconnected dimensions risks reinforcing existing vulnerabilities, undermining resilience, and entrenching long-term injustices. Full article

Evidence supporting the clinical use of neuroprotective drugs for cerebral ischemia remains limited. Spatial and temporal disorientation, along with cognitive dysfunction, are among the most prominent long-term consequences of hippocampal neurodegeneration following cerebral ischemia. Cannabigerol (CBG), a non-psychotomimetic constituent of Cannabis sativa, has demonstrated neuroprotective effects in experimental models of cerebral injury. This study investigated the neuroprotective mechanisms of CBG in mitigating memory impairments caused by transient global cerebral ischemia in C57BL/6 mice using the bilateral common carotid artery occlusion (BCCAO) model. Mice underwent sham or BCCAO surgeries and received intraperitoneal (i.p.) injections of either a vehicle or CBG (1, 5, or 10 mg/Kg), starting 1 h post-surgery and continuing daily for 7 days. Spatial memory performance and depression-like behaviors were assessed using the object location test (OLT) and tail suspension test (TST), respectively. Additional analyses examined neuronal degeneration, neuroinflammation, and neuronal plasticity markers in the hippocampus. CBG attenuated ischemia-induced memory deficits, reduced neuronal loss in the hippocampus, and enhanced neuronal plasticity. These findings suggest that CBG’s neuroprotective effects against BCCAO-induced memory impairments may be mediated by reductions in neuroinflammation and modifications in neuroplasticity within the hippocampus. Full article

The aim of this study was to address the issue of significant performance degradation in existing defogging algorithms under extreme fog conditions. Traditional Taylor series-based deformable convolutions are limited by local approximation errors, while the heavy-tailed characteristics of the Cauchy distribution can more successfully model outliers in fog images. The following improvements are made: (1) A displacement generator based on the inverse cumulative distribution function (ICDF) of the Cauchy distribution is designed to transform uniform noise into sampling points with a long-tailed distribution. A novel double-peak Cauchy ICDF is proposed to dynamically balance the heavy-tailed characteristics of the Cauchy ICDF, enhancing the modeling capability for sudden changes in fog concentration. (2) An innovative Cauchy–Gaussian fusion module is proposed to dynamically learn and generate hybrid coefficients, combining the complementary advantages of the two distributions to dynamically balance the representation of smooth regions and edge details. (3) Tree-based multi-path and cross-resolution feature aggregation is introduced, achieving local–global feature adaptive fusion through adjustable window sizes (3/5/7/11) for parallel paths. Experiments on the RESIDE dataset demonstrate that the proposed method achieves a 2.26 dB improvement in the peak signal-to-noise ratio compared to that obtained with the TaylorV2 expansion attention mechanism, with an improvement of 0.88 dB in heavily hazy regions (fog concentration > 0.8). Ablation studies validate the effectiveness of Cauchy distribution convolution in handling dense fog and conventional lighting conditions. This study provides a new theoretical perspective for modeling in computer vision tasks, introducing a novel attention mechanism and multi-path encoding approach. Full article

With changing demands in regulation, understanding methane emissions from offshore oil and gas production infrastructure has become increasingly important. Reported emissions from facilities in the Gulf of Mexico range from zero to thousands of tons of methane per hour, but these is currently no clear understanding of how this range compares to expected emissions from normally operating facilities. To generate realistic emission estimates, we create two bottom-up models that simulate emissions from facilities operating in the Gulf of Mexico. We estimate type 1 prototypical facilities (typically unmanned, older, lower-producing platforms in shallow water with little processing equipment, compressors, or storage tanks) to emit an average of 13 kg CH₄ h⁻¹, which corresponds to a loss of 2.7% of the average facility production. Type 2 prototypical facilities (continuously manned, higher production and operate in deeper water with processing equipment, oil storage tanks, compressors and power generation) emit an average of 88 kg CH₄ h⁻¹, which corresponds to a loss of 2.5% of production. The average measured emission from type 1 facilities was 18 kg CH₄ h⁻¹ with a median production loss estimated at 8%. The average measured emission from type 2 facilities was 36 kg CH₄ h⁻¹ with a median production loss estimated at 2.4%. Using emission factors that consider the long-tail emission distribution partly reconciles the difference between modelled and measured emission estimates, but we suggest the current the fugitive emission estimate may be an underestimate and more data on the number and size of fugitive emissions could explain differences between the modelled and measured emission estimate. We suggest the bottom-up approach described here that uses production data coupled with facility equipment could be used to identify facilities that have abnormally large measured emissions, caused by methodological failure or larger than expected fugitive emissions, which should be targeted for further evaluation resulting in remeasurement or identification of source type so that a more accurate estimates can be made on the absolute emission. Full article