Revisiting steel slag as a modified catalyst in advanced oxidation processes for the degradation of emerging pollutants in water: A review.

A physical, low-environmental-impact, and easily applicable technology for enhancing the performance of recycled concrete: Multiscale insights

Effects of dietary nutrient levels on the emission of pollutants from pig manure

Mink coronavirus (MCoV) infection poses a serious threat to animal health, resulting in substantial economic losses in mink farming and impeding the sustainable development of the fur industry. Thus, developing both sensitive and specific molecular diagnostics is imperative for effective MCoV surveillance and disease control. In the present study, specific oligonucleotide primers were designed to target the 1b and N genes of MCoV, and two SYBR Green-based RT-qPCR assays - designated as MCoV-1b and MCoV-N, respectively - were established. The assays exhibited extremely high sensitivity, with lower limits of detection of 1.79 × 10 ¹ copies/μL for MCoV-1b and 1.29 × 10 ¹ copies/μL for MCoV-N. No cross-reactivity was observed with other common viruses and melting curve analyses revealed a single sharp peak for each assay, confirming specific amplification of MCoV targets. Reproducibility across three independent experimental runs exhibited low standard deviations and coefficients of variation (CV) of cycle threshold (Ct) values across broad dynamic ranges, indicating stable and consistent performance (CV < 5%). When applied to faecal clinical samples (n = 236), the assays yielded overall positivity rates of 77.97% for the 1b target and 77.54% for the N targets, which are consistent with that from high-throughput sequencing, validating their utility. Collectively, the two RT-qPCR assays developed in this study offer a reliable technical platform for rapid screening, accurate quantification, and epidemiological monitoring of MCoV. The reported molecular diagnostics hold significant practical value in mitigating the risks of MCoV transmission, supporting the sustainable growth of the global mink farming industry and diminishing potential global public health threats.

• VSM is a precursor of TPM and JIT in the manufacturing industry. • JIT and TPM encourage the implementation of social, environmental, and economic sustainability. Economic sustainability is accounted for by TPM and JIT at 41.9 %. • VSM, JIT, and TPM are obtained within five years of implementation. • 5S is reached at 100 % at 14 years of age and social sustainability at 15 years. The Mexican maquiladora industry faces high pressure to achieve sustainability while maintaining operational efficiency. This study investigates how key Lean Manufacturing (LM) tools—5S, Value Stream Mapping (VSM), Total Productive Maintenance (TPM), and Just-in-Time (JIT)—contribute to economic, environmental, and social sustainability in this context. To analyze these relationships, a structural equation model (SEM) was developed based on 411 validated survey responses collected from mid- and senior-level personnel working in operations, production, quality, and maintenance areas. Additionally, system dynamics (SD) modeling was applied to simulate the long-term effects of LM implementation. The results show that VSM and JIT strongly influence economic sustainability, whereas TPM plays a key role in promoting environmental and social sustainability. The findings also indicate that social sustainability takes longer to consolidate, up to 15 years, while LM tools are implemented more rapidly. By integrating SEM and SD, this study offers a dynamic perspective on sustainability outcomes and provides practical guidance for managers and policymakers to align LM strategies with their long-term sustainability goals.

SME development for poverty alleviation using GIS and AHP-based multi-criteria decision analysis

The mutual impacts of climate change and wastewater treatment: Critical review of challenges and solutions

Green manufacturing synthesis of nicotinamide mononucleotide: Pathways, catalysts, and prospects.

Simulation of 3D oil spill trajectories and weathering processes based on the SCHISM.

Efficient and selective capture of uranium(VI) by MIL-125/MXene nanocomposites: Synergistic enhancement via interfacial engineering.

Astral-based DIA proteomics explored the cold response mechanism in the liver of juvenile greenfin horse-faced filefish (Thamnaconus septentrionalis).

Horse breeding and management have a globally extensive history, with horses serving vital roles in transportation, warfare, agriculture, and cultural activities. In China, particularly in Inner Mongolia, the horse industry faces challenges such as harsh winters and limited nutritional research for local breeds, especially during late gestation. This critical period is essential for fetal development and maternal health, and inadequate nutrition can lead to complications like nutritional abortions and poor foal growth. Similar issues exist worldwide in cold climates or regions with seasonal pasture variations, emphasizing the need for comprehensive equine nutrition research. This study investigated the effects of supplementary feeding with concentrates of different energy levels-high (HEN), medium (MEN), and low (LEN)-on Mongolian broodmares during late gestation. Supplementation with MEN concentrate significantly improved the final body weight of mares, tended to increase average daily gain (ADG), and enhanced nutrient digestibility. Blood biochemical indicators showed that lactate dehydrogenase (LDH) activity was significantly higher in the MEN group compared to HEN and LEN groups, indicating enhanced anaerobic glycolysis. Conversely, aspartate aminotransferase (AST) activity was significantly lower in the MEN group, suggesting reduced hepatic or muscular stress. Antioxidant indices demonstrated reduced oxidative stress, with catalase (CAT) levels significantly higher and malondialdehyde (MDA) levels lower in the MEN group. Reproductive hormone analysis revealed that MEN supplementation increased follicle-stimulating hormone (FSH) levels, while the LEN group had significantly higher progesterone (P4) and estradiol (E2) levels. Fecal microbiota analysis indicated altered microbial community composition favoring beneficial bacteria, and metabolomic analysis revealed significant differences in metabolites related to energy metabolism and other physiological pathways. The study provides compelling evidence that medium-energy (MEN) concentrate supplementation is an effective nutritional strategy for late-gestation Mongolian broodmares, promoting maternal health, metabolic efficiency, and physiological homeostasis. These findings have global implications, offering insights to optimize broodmare nutrition worldwide, particularly in cold climates or regions with seasonal pasture shortages. By adopting similar strategies, breeders can enhance reproductive outcomes and contribute to the sustainable development of the horse industry on a global scale.

Indole and skatole are major odorous compounds generated in pig farms and their emissions affect the sustainable development of pig industry seriously. At present, microbial interventions represent an efficient and environmentally sustainable strategy for reducing odor emissions. Research on functional microorganisms specifically targeting indole and skatole emission reduction in pigs remains limited. To compare the concentrations of indole and skatole in Hezuo and DLY pigs, and analysis differences in microbial community characteristics between the two pig breeds, aimed to screen the potential functional gut microbiota in reducing indole and skatole production. Indole and skatole concentrations in the cecum, colon and rectum of Hezuo pigs were reduced compared with DLY pigs (p < 0.05 or p < 0.01). The relative abundances of Treponema_D, Lactobacillus, Coprococcus_A, Anaeroplasma, and Faecalibacillus were higher in Hezuo pigs (p < 0.05). The relative abundances of Escherichia, Streptococcus, Corynebacterium, and Enterococcus_B were higher in DLY pigs (p < 0.05). Correlation analysis indicated that Lactobacillus ultunensis, Lactobacillus amylovorus, Anaeroplasma, Faecalimonas, and Faecalibacillus were negatively associated with indole and skatole concentrations (p < 0.05 or p < 0.01), whereas Streptococcus hyointestinalis, Catenibacterium, Corynebacterium, and Enterococcus_B showed positive associations (p < 0.05 or p < 0.01). Specific gut microbiota, including Lactobacillus ultunensis, Lactobacillus amylovorus, Anaeroplasma, Faecalimonas and Faecalibacillus are associated with reduced indole and skatole level, indicating their potential as probiotics for mitigating odor emissions in pig production.

This study examined audience responses toward the Pelangi di Mars movie trailer on YouTube using sentiment analysis and topic modelling approaches. The data were collected from the YouTube comment section on March 10, 2026, resulting in 3,006 comments used as the research dataset. The comments were processed through several preprocessing stages, including text cleaning, tokenization, stopword removal, stemming, and normalization. Sentiment analysis was conducted using the IndoBERT pre-trained model to classify comments into positive, neutral, and negative categories, while Latent Dirichlet Allocation (LDA) was applied to identify dominant discussion topics within audience comments. The findings show that positive sentiment dominates audience responses, accounting for 73.7% of the total comments, followed by 13.9% neutral comments and 12.4% negative comments. Topic modelling reveals five major discussion themes related to animation quality, cinema enthusiasm, Hollywood comparison, story appreciation, and the development of the Indonesian animation industry. In addition, the IndoBERT model achieves an accuracy score of 94%, indicating strong performance in classifying Indonesian-language YouTube comments. The study concludes that YouTube comments function as electronic word of mouth (e-WOM) that influences public perception and supports digital film promotion and audience engagement in the entertainment industry.

The relationship between socio-economic development and ecological protection is a dynamic and interactive process. How to balance and coordinate the achievement of both objectives is an eternal topic concerning the sustainable development of nations and regions. Industry is a key factor driving economic growth. The development of industry brings technological progress and the acceleration of urbanization, but also leading to excessive consumption of resources, environmental pollution and the destruction of ecosystem. The health of ecosystem is closely related to the stability of the ecosystem services (ES) required by industrial production activities. Therefore, it is a prerequisite for realizing sustainable social and economic development to scientifically understand the impact of industrial development on ES. In summary, this study systematically analyzes the bidirectional influence mechanism between industry and ecosystems, proposing a research framework to explore the complex interactions between the two. Based on this research framework, this paper constructs a quantitative analysis model of the interaction between industry and ES using spatial simultaneous equation modeling techniques, revealing the complex feedback loop relationship between the two. This research can provide scientific references for ecological protection and high-quality development strategies.

Travel recommendation (TR) demonstrates immense commercial potential in global economic development. However, previous research has relied solely on single-form interaction attributes between users and travel products, overlooking the impact of different interaction attributes on the final travel product and the distinctions among these attributes. Additionally, travel data inherently suffers from data sparsity. To bridge this gap, this paper proposes a novel diffusion-guided multiplex graph contrastive learning (CL) for TR framework, namely DMGCLTR. It leverages an enhanced Diffusion Model (DM) to capture global interaction patterns across diverse attributes between users and travel products. Through a designed attribute-chain representation learner and perceptual encoder, it enables granular discovery of how different interaction attributes influence and depend upon the final selected travel product. Furthermore, we adopt a contrastive learning approach to jointly optimize these tasks and enhance its symmetry. Extensive experiments across three real-world datasets demonstrate DMGCLTR's superior performance. On average, it outperforms the best baseline by 5.20% in HR@5 and 7.01% in MRR across all datasets. This enhanced TR method offers a novel perspective for innovation and integrated development in the smart tourism industry.

Mink self-biting behaviour causes skin damage, reducing fur quality and restricting the development of the mink farming industry. The aim of this study was to investigate the genetic mechanisms underlying self-biting behaviour in minks from a genomic perspective. Using weighted gene coexpression network analysis (WGCNA), we identified functional gene modules and then explored their associations with phenotype, thereby identifying key genes influencing this behaviour. The daily behaviours of minks were analysed through behavioural observation. Gene expression profiles of hippocampi from self-biting and healthy minks (8 males and 10 females in each group) were constructed using transcriptome sequencing. The results revealed significant differences in self-biting, feeding, playing, and lying behaviour between self-biting and healthy minks (P < 0.01). Notably, grooming was significantly correlated with self-biting behaviour (P < 0.05), and slow pace, lying, playing, shaking, and feeding behaviours were strongly correlated with self-biting behaviour (P < 0.01). Differentially expressed genes between self-biting and healthy minks were identified and analysed via WGCNA; then, the correlation coefficient method was used to identify 50 and 20 single genes that may affect mink self-biting in females and males, respectively. Database annotation identified a single gene in the female group, corresponding to Hspa2. This gene is associated with neurological disorders. The Hspa2 gene showed functionally enriched expression under cold and heat stress. Therefore, we speculate that such stress induces abnormal changes in the Hspa2 gene in mink hippocampal tissue, leading to nerve stimulation and neurological disorders, including self-biting behaviour, in mink.

Flavor deficiency in cigar tobacco leaves (CTLs) limits cigar industry development. This study isolated two aroma-producing bacteria (Alcaligenes phenolicus Z2 and Bacillus subtilis C5) from CTLs during air-curing. Single strain inoculation resulted in treatment-specific aroma compound changes: Z2 treatment was associated with enhanced diterpene compounds (notably neophytadiene and 1-heptacosanol), while C5 markedly increased fatty alcohol concentrations (2-hexyl-1-decanol, 9.67-fold) and nicotine levels. Mixed inoculation generally showed intermediate rather than synergistic effects. Variable importance in projection (VIP) analysis identified nicotine, neophytadiene, and nonadecatrienediol as the most discriminatory compounds among treatments. Microbial community analysis revealed that exogenous strain inoculation significantly altered indigenous microbiota composition and interaction patterns, with Z2 treatment associated with positive microbial co-occurrence patterns, while mixed inoculation displayed network characteristics suggestive of increased competitive interactions. Correlation analysis revealed strain-specific microbial-metabolite associations, though causal relationships require further validation. These findings suggest that single strain inoculation produces distinct metabolic profiles with potential for strain-specific tobacco aroma modulation.

Purpose This study aims to examine workplace learning and competence development in the Swedish mining industry amid the green transition and ongoing technical development, the dual transition. It explores how miners’ learning expectations are experienced, negotiated and enacted in everyday work. Design/methodology/approach Two qualitative studies were conducted at three mines operated by two companies, involving 14 interviews, six workshops and two focus group discussions with mining personnel. Findings Miners demonstrate a strong willingness to learn, driven by professional pride and a desire to contribute to both the collective and the green transition. Collective workplace learning enables adaptation, yet may also normalize uneven organizational conditions and mask structural limits to competence development. While collective workplace learning remains vital, emerging competence demands under the dual transition may exceed what informal, peer-based learning alone can sustain. Practical implications Clearer alignment between competence demands and organizational arrangements is essential to support miners’ long-term learning and competence development as mining work evolves under the dual transition. Originality/value This study empirically shows how macro-level transformations, meso-level organizational arrangements and micro-level collective practices interact to shape workplace learning under the dual transition. Furthermore, by engaging sociocultural perspectives, this study examines how collective participation and shared norms mediate the dual transition, illustrating how continuity and transformation coexist and are negotiated in everyday work practices.

Spring frost during the flowering period poses a significant threat to the sustainable development of the apple industry, creating an urgent need for efficient and nondestructive monitoring techniques for frost stress. The soil plant analysis development (SPAD) value, serving as a reliable indicator of chlorophyll content, can directly reflect physiological damage induced by frost. However, a hyperspectral monitoring method specifically for assessing leaf SPAD under frost stress during the apple flowering stage is currently lacking. In this study, apple cultivar ‘Changfu 2’ were subjected to controlled low-temperature treatments (0 °C, −2 °C, and −4 °C for durations of 2, 4, and 6 hours) to obtain leaf hyperspectral reflectance and corresponding SPAD values under varying frost stress conditions. Based on the calibration dataset, all possible two-band normalized difference spectral index, ratio spectral index, and difference spectral index were systematically calculated to develop an optimal estimation model. The results showed that NDSI (R 842 , R 799 ) demonstrated the best performance for estimating SPAD, with the validation model achieving a coefficient of determination of 0.73, a root mean square error of 3.13, and a residual prediction deviation of 1.05. Compared to a series of published vegetation indices, this newly proposed index NDSI (R 842 , R 799 ) exhibited superior estimation accuracy can effectively monitor the dynamics of leaf SPAD in apple trees under flowering-stage frost stress.