Search Results (122,491)

The flow and heat transfer inside the mold play an important role in the quality of the casting billet during continuous casting. In this work, a three-dimensional coupled model of flow and heat transfer was established, and the flow field and temperature distribution characteristics of molten steel were explored in depth. The results indicated that the narrow impact position is 315 mm away from the meniscus. The maximum turbulence kinetic energy of the centerline reached 0.00284 m²∙s⁻², 108 mm from the narrow surface. The temperature of the steel liquid on the path of the two splitting strands located in the upper and lower circulation zones was above 1781 K. The temperature range from the center of the billet to the narrow 1/4 section, which was enclosed by the upper annular flow zone and 400 mm below the liquid level, was relatively low and lower than the liquidus temperature. The model can provide guidance for improving and optimizing the quality of continuous casting billets. Full article

Background/Objectives: Bacterial blight (BB) represents one of the most devastating diseases threatening global rice production. Exploring and characterizing disease resistance (R) genes provides an effective strategy for controlling BB and enhancing rice resilience. Common wild rice (Oryza rufipogon) serves as a valuable reservoir of genetic diversity and disease resistance resources. In this study, we identified and functionally characterized a novel NLR gene, YPR1, from common wild rice (Oryza rufipogon), which exhibited significant spatial, temporal, and tissue-specific expression patterns. Methods: Using a combination of conventional PCR, RT-PCR, bioinformatics, transgenic analysis, and CRISPR/Cas9 gene-editing approaches, the full-length YPR1 sequence was successfully cloned. Results: The gene spans 4689 bp with a coding sequence (CDS) of 2979 bp, encoding a 992-amino acid protein. Protein domain prediction revealed that YPR1 is a typical CNL-type NLR protein, comprising RX-CC_like, NB-ARC, and LRR domains. The predicted molecular weight of the protein is 112.43 kDa, and the theoretical isoelectric point (pI) is 8.36. The absence of both signal peptide and transmembrane domains suggests that YPR1 functions intracellularly. Furthermore, the presence of multiple phosphorylation sites across diverse residues implies a potential role for post-translational regulation in its signal transduction function. Sequence alignment showed that YPR1 shared 94.02% similarity with Os09g34160 and up to 96.47% identity with its closest homolog in the NCBI database, confirming that YPR1 is a previously unreported gene. To verify its role in disease resistance, an overexpression vector (Ubi–YPR1) was constructed and introduced into the BB-susceptible rice cultivar JG30 via Agrobacterium tumefaciens-mediated transformation. T₁ transgenic lines were subsequently inoculated with 15 highly virulent Xanthomonas oryzae pv. oryzae (Xoo) strains. The transgenic plants exhibited strong resistance to eight strains (YM1, YM187, C1, C5, C6, T7147, PB, and HZhj19), demonstrating a broad-spectrum resistance pattern. Conversely, CRISPR/Cas9-mediated knockout of YPR1 in common wild rice resulted in increased susceptibility to most Xoo strains. Although the resistance of knockout lines to strains C7 and YM187 was comparable to that of the wild type (YP^WT), the majority of knockout plants exhibited more severe symptoms and significantly lower YPR1 expression levels compared with YP^WT. Conclusions: Collectively, these findings demonstrate that YPR1 plays a crucial role in bacterial blight resistance in common wild rice. As a novel CNL-type NLR gene conferring specific resistance to multiple Xoo strains, YPR1 provides a promising genetic resource for the molecular breeding of BB-resistant rice varieties. Full article

Background: Ventilator-associated pneumonia (VAP) is a major cause of morbidity and mortality in intensive care units (ICUs), particularly in low- and middle-income countries (LMICs). Evidence-based practice (EBP) bundles have shown effectiveness in reducing VAP; however, the implementation in Bangladesh remains limited. This study aimed to evaluate the effectiveness of EBP to reduce the incidence rate of VAP among adult ICU patients in Bangladesh. Methods: A quasi-experimental study with a historical control group was conducted among 347 eligible ICU patients from October 2024 to April 2025. The intervention included nurse training on VAP bundle practices with advanced equipment support. Data on VAP incidence as a primary endpoint and VAP-related patients’ outcomes were analyzed. Results: The clinically suspected VAP incidence was 30.1 and 51.1 per 1000 ventilator-days, and the prevalence decreased significantly in the intervention group compared to the control group (26.9% vs. 46.1%; p < 0.001), respectively. Logistic regression indicated VAP bundle implementation was associated with reduced VAP (Exp(B) = 0.417, 95% CI: 0.262–0.666), while ventilation ≥96 h was a significant risk factor (Exp(B) = 2.6, 95% CI: 1.385–4.881). Early-onset VAP was reduced (25.0% vs. 10.2%), though late-onset predominated in the intervention group (75.0% vs. 89.8%). Conclusion: Implementation of an EBP-based VAP bundle by trained nurses significantly reduced VAP incidence. However, increased overall ICU mortality highlights the need for broader critical care improvements, including advanced comorbidity management and comprehensive ICU services. This study underscores the feasibility and effectiveness of VAP bundle implementation in the ICU of an LMIC. Full article

The CRISPR-Cas9 system has transformed biomedical research by enabling precise genetic modifications. However, efficient delivery of CRISPR components remains a major hurdle for therapeutic applications. To address this, we employed a new modified cationic hyper-branched cyclodextrin-based polymer (Ppoly) system to deliver an integrating GFP gene using the TILD-CRISPR method, which couples donor DNA linearization with RNP complexes. The physicochemical properties, loading efficiency, and cellular uptake of RNP with Ppoly were studied. After transfection, antibiotic selection and single-cell cloning were performed. Junction PCR was then performed on the isolated clones, and we compared the knock-in efficiency of Ppoly with that of the commercial CRISPRMAX™ reagent (Thermo Fisher, Invitrogen™, Waltham, MA, USA). The results demonstrate the encapsulation efficiency of over 90% for RNP and Ppoly, and cell viability remaining above 80%, reflecting the minimal toxicity of this approach. These attributes facilitated successful GFP gene integration using the TILD-CRISPR with RNP delivered via cyclodextrin-based nanosponges. The present method achieved a remarkable 50% integration efficiency in CHO-K1 cells, significantly outperforming the 14% observed with CRISPRMAX™ while maintaining lower cytotoxicity. This study highlights a promising platform for precise and efficient genome editing, with strong potential for therapeutic and regenerative medicine applications. Full article

The identification of anomalous data objects within massive datasets is a critical technique in financial auditing. Most existing methods, however, focus on global outlier anomalies detection with less effective in contexts such as Chinese financial budget auditing, where local outliers are often more prevalent and meaningful. To overcome this limitation, a unified outlier detection framework is proposed that integrates both global and local detection mechanisms using k-nearest neighbors (KNN) and kernel density estimation (KDE) methodologies. The global outlier score is redefined as the sum of the distances to the k-nearest neighbors, while the local outlier score is computed as the ratio of the average cluster density to the kernel density—replacing the cutoff distance employed in Density Peak Clustering (DPC). Furthermore, an adaptive adjustment coefficient is further incorporated to balance the contributions of global and local scores, and outliers are identified as the top-ranked objects based on the combined outlier scores. Extensive experiments on synthetic datasets and benchmarks from Python Outlier Detection (PyOD) demonstrate that the proposed method achieves superior detection accuracy for both global and local outliers compared to existing techniques. When applied to real-world Chinese financial budget data, the approach yields a substantial improvement in detection precision-with 38.6% enhancement over conventional methods in practical auditing scenarios. Full article

This study compares unsupported NiO nanoflowers (NiEG) and ZrO₂-supported NiO (25NiZ) for methane activation and hydrogenation, focusing on the impact of catalyst morphology. The NiEG catalyst demonstrated superior performance, achieving a high methane activation rate of 1.79 mol/(s·g_NiO) and unique product selectivity. It produced ethylene and ethane at 503 K and higher hydrocarbons (C₄–C₆) at 593 K. Furthermore, the NiEG catalyst exhibited enhanced coke resistance, forming less-deactivating carbon nanotubes compared to the filamentous coke prevalent on the 25NiZ catalyst. We attribute this performance to the nanoflower morphology, which provides highly exposed and stable Ni sites that facilitate C-H cleavage and stabilize reaction intermediates. Full article

Online monitoring technology for transformers is a crucial safeguard for power supply, and diagnosing dissolved gases in 10 kV distribution transformer oil is considered an effective criterion for transformer fault detection. Using density functional theory, this paper simulated the adsorption process of five dissolved gases in a 10 kV distribution transformer on Os-modified C₃N nanosheets, and by calculating the band structure, differential charge density, density of states, and work function, the related sensing and adsorption mechanisms were revealed. The results indicate that Os modification significantly enhances the gas-sensing response of C₃N nanosheets, particularly for capturing C₂H₂ and CO, which is primarily attributed to the d-orbital electrons of the doped metal. The adsorption capability of Os-modified C₃N nanosheets of dissolved gases follows the order C₂H₂ > CO > H₂ > CO₂ > CH₄, with the adsorption type being physico-chemical adsorption, and these findings provide a theoretical foundation for developing high-sensitivity gas sensors for detecting dissolved gases in a 10 kV distribution transformer. Full article

In autonomous driving, detecting small and occluded objects remains a substantial challenge due to the complexity of real-world environments. To address this, we propose RSO-YOLO, an enhanced model based on YOLOv12. First, the bidirectional feature pyramid network (BiFPN) and space-to-depth convolution (SPD-Conv) replace the original neck network. This design efficiently integrates multi-scale features while preserving fine-grained information during downsampling, thereby improving both computational efficiency and detection performance. Additionally, a detection head for the shallower feature layer P2 is incorporated, further boosting the model’s capability to detect small objects. Second, we propose the feature enhancement and compensation module (FECM), which strengthens features in visible regions and compensates for missing semantic information in occluded areas. This module improves detection accuracy and robustness under occlusion. Finally, we propose a lightweight global cross-dimensional coordinate detection head (GCCHead), built upon the global cross-dimensional coordinate module (GCCM). By grouping and synergistically enhancing features, this module addresses the challenge of balancing computational efficiency with detection performance. Experimental results demonstrate that on the SODA10M, BDD100K, and FLIR ADAS datasets, RSO-YOLO achieves mAP@0.5 improvements of 8.0%, 10.7%, and 7.2%, respectively, compared to YOLOv12. Meanwhile, the number of parameters is reduced by 15.4%, and model complexity decreases by 20%. In summary, RSO-YOLO attains higher detection accuracy while reducing parameters and computational complexity, highlighting its strong potential for practical autonomous driving applications. Full article

The increasing complexity of urban energy systems requires decentralized, sustainable, and scalable solutions. The paper presents a new multi-layered framework for smart energy management in microgrids by bringing together advanced forecasting, decentralized decision-making, evolutionary optimization and blockchain-based coordination. Unlike previous research addressing these components separately, the proposed architecture combines five interdependent layers that include forecasting, decision-making, optimization, sustainability modeling, and blockchain implementation. A key innovation is the use of Temporal Fusion Transformer (TFT) for interpretable multi-horizon forecasting of energy demand, renewable generation, and electric vehicle (EV) availability which outperforms conventional LSTM, GRU and RNN models. Another novelty is the hybridization of Genetic Algorithms (GA) and Particle Swarm Optimization (PSO), to simultaneously support discrete and continuous decision variables, allowing for dynamic pricing, efficient energy dispatching and adaptive EV scheduling. Multi-Agent Reinforcement Learning (MARL) which is improved by sustainability shaping by including carbon intensity, renewable utilization ratio, peak to average load ratio and net present value in agent rewards. Finally, Ethereum-based smart contracts add another unique contribution by providing the implementation of transparent and tamper-proof peer-to-peer energy trading and automated sustainability incentives. The proposed framework strengthens resilient infrastructure through decentralized coordination and intelligent optimization while contributing to climate mitigation by reducing carbon intensity and enhancing renewable integration. Experimental results demonstrate that the proposed framework achieves a 14.6% reduction in carbon intensity, a 12.3% increase in renewable utilization ratio, and a 9.7% improvement in peak-to-average load ratio compared with baseline models. The TFT-based forecasting model achieves RMSE = 0.041 kWh and MAE = 0.032 kWh, outperforming LSTM and GRU by 11% and 8%, respectively. Full article

The imbalance of the renin–angiotensin system (RAS), characterized by the overactivation of the pro-tumor ACE/AngII/AT1R axis, is closely linked to tumor growth, angiogenesis, metastasis, and poor prognosis. Natural polyphenols, such as EGCG and resveratrol, exert anti-cancer effects by dual-regulating RAS: they inhibit the pro-tumor axis by blocking renin, ACE activity, and AT1R expression, while simultaneously activating the protective ACE2/Ang(1-7)/MasR axis. Furthermore, polyphenols and their autoxidation products (e.g., EAOP) modify thiol-containing transmembrane proteins (such as ADAM17 and integrins) and interact with RAS components, further disrupting oncogenic pathways (including MAPK and PI3K/Akt/mTOR) to induce apoptosis, suppress invasion, and reduce oxidative stress. Notably, EAOP exhibits stronger RAS-modulating efficacy than its parent polyphenols. However, challenges such as low bioavailability, insufficient targeting, and limited clinical evidence impede their application. This review provides a comprehensive overview of the anti-cancer mechanisms of polyphenols through RAS regulation, discusses the associated challenges, and proposes potential solutions (including nanodelivery and structural modification) and strategies to advance natural product-based adjuvant treatments. Full article

Background/Objectives: Breast cancer is a heterogeneous disease, and the role of the transcription factor SPDEF remains controversial. We aimed to clarify the prognostic value of SPDEF, explore demographic and molecular correlates of its expression, and investigate potential regulatory mechanisms underlying its dysregulation. Methods: Genomic and clinical data for 1218 breast cancer tumors were obtained from The Cancer Genome Atlas (TCGA). SPDEF mRNA expression was compared across intrinsic subtypes, age, and race, and prognostic significance was evaluated by Kaplan–Meier analysis. Promoter methylation patterns and DNA methyltransferase (DNMT) expression were examined as potential regulatory drivers. Co-expression analysis was performed using gene panels representing luminal differentiation, basal identity, EMT, proliferation, DNA repair, and immune signaling. Results: Low SPDEF expression was significantly associated with worse overall, relapse-free, and metastasis-free survival across all breast cancers. Expression was lowest in Basal tumors, as well as among younger and Black or African American patients. Promoter methylation at six CpG islands correlated with both reduced SPDEF expression and inferior survival, and DNMT1, DNMT3A, and DNMT3B overexpression also aligned with poor prognosis and Basal enrichment. Co-expression analysis revealed that SPDEF downregulation coincided with loss of luminal markers and increased EMT, proliferation, DNA repair, and immune pathways. Conclusions: SPDEF functions as a tumor suppressor in breast cancer, with reduced expression linked to poor outcomes, aggressive molecular features, and epigenetic regulation. These findings highlight SPDEF and DNMT-driven methylation as potential prognostic biomarkers for enhanced risk stratification and targets for novel therapies, particularly in Basal breast cancers. Full article

Background/Objectives: According to the World Drug Report 2025, cannabis is the most abused drug in the world, being sold in illicit markets in various physical forms ranging from herbal cannabis to cannabis resin and liquid cannabis. Currently, the methods used for cannabis identification are largely based on the morphological features and chemical content of the product. In this respect, identification could be severely impacted if the product is highly fragmented or pulverised. As such, DNA-based molecular techniques offer a viable alternative detection approach. In this study, we have developed a robust DNA testing method for cannabis identification, with high sensitivity and specificity. Methods/Results: Two plant DNA barcode regions, rbcL and matK, were successfully amplified in a cohort of 54 cannabis plant samples. DNA sequences obtained from these samples were blast-searched against GenBank and resulted in returned matched identity of at least 99% compared to their corresponding Cannabis sativa reference sequences. In addition, the amplification of two cannabis-unique markers, the tetrahydrocannabinolic acid synthase (THCAS) and cannabidiolic acid synthase (CBDAS) genes, produced amplicons with expected sizes only in cannabis samples; these amplicons were not detected in those plants closely related to cannabis. Sequence comparison of the majority of samples yielded at least 97% matched identity against C. sativa reference sequences in GenBank. The THCAS and CBDAS markers detected only the cannabis DNA in varying levels of cannabis–hops and cannabis–tobacco DNA mixtures. Lastly, the use of the four markers could effectively differentiate between cannabis and non-cannabis in 27 blinded samples, including 18 actual casework samples. Conclusions: In conclusion, these four genetic markers can be used to discriminate cannabis from other plant species at the genus level, especially in challenging forensic samples lacking morphological features which therefore cannot be determined by traditional detection methods. As such, this method can complement existing techniques to identify a myriad of cannabis samples. Full article

High-voltage ceramic insulators are routinely exposed to short-duration overvoltages such as lightning impulses, switching surges, and partial discharges. These events occur on microsecond to millisecond timescales and can produce highly localized thermal spikes that are difficult to measure directly but may compromise long-term material integrity. This paper addresses the estimation of the internal temperature distribution immediately after a lightning impulse by solving a three-dimensional inverse heat conduction problem (IHCP). The forward problem is modeled by the transient heat diffusion equation with constant thermal diffusivity, discretized using the finite element method (FEM). Surface temperature measurements are assumed available from a 12 kV ceramic post insulator and are used to reconstruct the unknown initial condition. To address the ill-posedness of the IHCP, a spatio-temporal regularization framework is introduced and compared against spatial-only regularization. Numerical experiments investigate the effect of measurement time ($T=60$ s, 600 s, and 1800 s), mesh resolution (element sizes of 20 mm, 15 mm, and 10 mm), and measurement noise ($\sigma =1$ K and 5 K). The results show that spatio-temporal regularization significantly improves reconstruction accuracy and robustness to noise, particularly when early-time measurements are available. Moreover, it is observed that mesh refinement enhances accuracy but yields diminishing returns when measurements are delayed. These findings demonstrate the potential of spatio-temporal IHCP methods as a diagnostic tool for the condition monitoring of ceramic insulators subjected to transient electrical stresses. Full article

Introduction: Chronic wounds are a growing healthcare challenge, with infections being major complications that delay healing. Biofilms are structured microbial communities encased in extracellular polymeric substances. Biofilms confer antimicrobial resistance, promote inflammation, and protect pathogens from host defenses. These mechanisms make eradication difficult with standard therapies. Methods: A focused literature review was conducted using PubMed (2010–2025) to examine the role of biofilms in chronic wounds, diabetic foot ulcers (DFUs), and burn injuries, as well as conventional and emerging treatment strategies. Studies are included if they addressed microbial composition, host–microbe interactions, or therapeutic outcomes in clinical or translational models. Discussion: Biofilms are implicated in up to 60% of chronic wounds and more than half of burn wounds. In DFUs, both bacterial and fungal biofilms contribute to chronicity and impaired healing. Conventional treatments such as debridement and antiseptics reduce surface biofilm burden but rarely achieve full eradication. Emerging approaches include quorum sensing inhibitors, bacteriophage therapy, matrix-degrading enzymes, electroceutical dressings, antifungal strategies, and nanotechnology. They show promise when integrated with standard wound care. Conclusions: Biofilms are central to the pathogenesis of chronic wounds, DFUs, and burns. Integrating mechanism-based antibiofilm therapies with standard care represents a key research priority to improve healing outcomes. Full article

Background: Skeletal muscle is essential not only for structural integrity but also metabolic homeostasis. Muscle atrophy, the loss of muscle mass and function, is closely linked to chronic and metabolic disorders and is driven by chronic inflammation, oxidative stress, impaired myogenesis, and disrupted protein homeostasis. The present study aimed to evaluate the protective effects and underlying mechanisms of Rosa canina extract (RCE), a polyphenol-rich plant known for its antioxidant and anti-inflammatory properties, in vitro and in vivo models of muscle atrophy. Methods: We investigated the effects of RCE in TNF-α-treated L6 myotubes and a mouse model (eight-week-old male C57BL/6N) of immobilization-induced muscle atrophy. Markers of inflammation, oxidative stress, myogenesis, protein turnover, and anabolic signaling were analyzed via RT-PCR, Western blotting and ELISA. Muscle mass, performance, micro-CT imaging, and histological cross-sectional area were assessed in vivo. Results: RCE suppressed pro-inflammatory cytokines, restored antioxidant enzyme expression, and preserved myogenic markers. It inhibited muscle proteolysis by downregulating the genes involved in protein degradation and promoted protein synthesis by via activation of the PI3K/Akt/mTOR pathway. In mice, RCE mitigated muscle mass loss, preserved fiber cross-sectional area, improved strength and endurance, and restored muscle volume. Conclusions: RCE attenuated muscle atrophy by targeting inflammation, oxidative stress, proteolysis, and impaired anabolism. These findings highlight RCE as a promising natural therapeutic for preserving muscle health and metabolic homeostasis. Full article