Search Results (1,606)

Slickwater fracturing has become an adopted technology for enhancing hydrocarbon recovery from unconventional, low-permeability reservoirs such as shale and tight formations, owing to its ability to generate complex fracture networks at a low cost. Polyacrylamide and polyacrylamide-based gels serve as key additives in these fluids, primarily functioning as drag reducers and thickeners. However, downhole environments of high-temperature (>120 °C) and high-salinity (>1 × 10⁴ mg/L) reservoirs pose challenges, leading to thermal degradation and chain collapse of conventional polyacrylamide, which results in performance loss. To address these limitations, synthesis methods including aqueous solution polymerization, inverse emulsion polymerization, and aqueous dispersion polymerization have been developed. This review provides an overview of molecular design methods aimed at enhancing performance stability of polyacrylamide-based polymers under extreme conditions. Approaches for improving thermal stability involve synthesis of ultra-high-molecular-weight polyacrylamide, copolymerization with resistant monomers, and incorporation of nanoparticles. Methods for enhancing salt tolerance focus on grafting anionic, cationic, or zwitterionic side chains onto the polymer backbone. The drag reduction mechanisms and gelation methods of these polymers in slickwater fracturing fluids are discussed. Finally, this review outlines research directions for developing next-generation polyacrylamide polymers tailored for extreme reservoir conditions, offering insights for academic research and field applications. Full article

Precise control of feeding rates is critically important in intensive shrimp farming for cost reduction, optimization of farming strategies, and protection of the aquatic environment. However, current assessment of residual feed in feeding trays relies predominantly on manual visual inspection, which is inefficient, highly subjective, and difficult to standardize. The residual feed particles typically exhibit characteristics such as small size, high density, irregular shapes, and mutual occlusion, posing significant challenges for automated visual detection. To address these issues, this study proposes a lightweight detection model named YOLO-Shrimp. To enhance the network’s capability in extracting features from small and dense targets, a novel attention mechanism termed EnSimAM is designed. Building upon the SimAM structure, EnSimAM incorporates local variance and edge response to achieve multi-scale feature perception. Furthermore, to improve localization accuracy for small objects, an enhanced weighted intersection over union loss function, EnWIoU, is introduced. Additionally, the lightweight RepGhost module is adopted as the backbone of the model, significantly reducing both the number of parameters and computational complexity while maintaining detection accuracy. Evaluated on a real-world aquaculture dataset containing 3461 images, YOLO-Shrimp achieves mAP@0.5 and mAP@0.5:0.95 scores of 70.01% and 28.01%, respectively, while reducing the parameter count by 19.7% and GFLOPs by 14.6% compared to the baseline model. Full article

Bovine mastitis remains a globally prevalent disease, with the limitations of antibiotic-based treatments—such as the rise in antimicrobial resistance and the presence of drug residues—highlighting the urgent need for alternative therapeutic approaches. Inflammation is intricately linked to various cytokines and immunomodulatory proteins, among which cyclophilin A (CypA) serves as a pivotal inflammatory mediator, significantly contributing to the initiation and amplification of inflammatory responses under such conditions. The acquisition of high-purity recombinant protein is a fundamental prerequisite for in vitro functional studies of bovine CypA. This study aimed to construct a eukaryotic expression vector for bovine CypA and verify its expression in CHO-K1 cells. Utilizing the bovine CypA gene sequence available in GenBank, the coding region was artificially synthesized and optimized for codon usage, subsequently being inserted into the pPB[Exp] backbone vector via BsrGI and BstEII double digestion. The resulting polycistronic expression vector contained a CAG promoter driving the CypA transcription, an EF1α promoter driving the EGFP reporter gene, a PGK promoter controlling the puromycin resistance gene, and a C-terminal His-tag. Restriction enzyme digestion and bidirectional Sanger sequencing confirmed that the inserted fragment sequence was completely consistent with the optimized design. Robust EGFP fluorescence was observed 24 h post-transfection and remained stable after puromycin selection. qPCR analysis showed that the Ct value of CypA in the experimental group was 16.20 ± 0.04, while no amplification signal was detected in the control group. Additionally, Western blot analysis identified a CypA-specific band at approximately 18 kDa, confirming the correct expression of the exogenous CypA protein in CHO-K1 cells. Collectively, these results demonstrate the successful construction and validation of a bovine CypA eukaryotic expression vector. The established CHO-K1 expression system exhibited stable and efficient expression, thereby providing a robust foundation for future research on the production and application of recombinant CypA protein. Full article

Early and accurate diagnosis is critical for disease surveillance, therapeutic guidance, and relapse monitoring. Sensor arrays have emerged as a multi-analyte detection tool via non-specific interactions to generate unique fingerprint patterns with high levels of selectivity and discrimination. Conjugated polymers (CPs), with their tunable π-conjugated backbones, exceptional light-harvesting capability, and efficient “molecular wire effect,” provide an ideal and versatile material platform for such arrays, enabling significant optical signal amplification and high sensitivity. This review systematically outlines the rational design and functionalization strategies of CPs for constructing high-performance sensor arrays. It delves into the structure–property relationships that govern their sensing performance, covering main-chain engineering, side-chain functionalization, and microenvironmental regulation. Representative applications are discussed, including non-small cell lung cancer, breast cancer, bacterial and viral infections, Alzheimer’s disease, and diabetic nephropathy, highlighting the remarkable diagnostic capabilities achieved through tailored CP materials. Finally, future perspectives are focused on novel material designs and device integration to advance this vibrant field. Full article

Disputes over episcopal temporalities provide a crucial lens through which to view complex Church–Crown relations in medieval England. The secular estates endowed by the Crown formed the backbone of ecclesiastical wealth, sustaining diocesan administration and judicial operations. As the principal patron, the king frequently intervened in episcopal property disputes. By drawing them into the royal courts, the Crown was able to diminish episcopal authority within bishops’ temporal jurisdiction and to consolidate control over ecclesiastical lands and benefice appointments, thereby contributing to the institutionalization of secular judicial interventions in ecclesiastical affairs. Confiscation of temporalities functioned as a key royal instrument for disciplining recalcitrant bishops, carrying both fiscal and political implications. A micro-level analysis of three major fourteenth-century seizures of episcopal temporalities reveals that the process of “seizure—negotiation—conditional restitution” fostered a pattern of “negotiated obedience” between the king and the episcopate. At the same time, conflicts between bishops and lay magnates over landholdings expose the multi-layered configuration of Church–Crown relations. In these contests, both sides petitioned the king and offered strategic concessions to seek royal support, generating a tripartite form of “competitive compromise” that further entrenched royal supremacy in the adjudication of ecclesiastical and secular property. Full article

Smart-city last-mile rail access, referred to in this entry simply as last-mile access, captures how travelers connect to and from rail stations during the first or last leg of a journey. It encompasses both the design of multimodal connections and the experience of accessibility that results from them. On the supply side, last-mile access involves the coordination of walking, cycling, micromobility, and feeder transit with rail services, supported by digital systems that unify planning, ticketing, and payment. On the demand side, it reflects how efficiently and equitably travelers can reach stations within these coordinated networks. Together, these physical and institutional dimensions extend the functional reach of rail, reduce transfer barriers, and reinforce its role as the backbone of sustainable urban mobility. As cities strive to reduce car dependency while promoting inclusivity and accessibility, last-mile access has become a key indicator of how infrastructure, technology, and governance intersect to deliver more equitable transportation systems. Full article

The clinical durability of SARS-CoV-2 main protease (M^pro) inhibitors depends on their resilience to emerging resistance mutations. Recent genomic surveillance and functional reports have highlighted substitutions at positions 49, 165, and 301, raising questions about the robustness of the noncovalent inhibitor WU-04 in variant backgrounds. Here, we combined μs-scale, triplicate molecular dynamics simulations with end-state binding free energy estimates and a network-rewiring inference (NRI) framework that maps long-range dynamical communication across the full protease dimer. We evaluated wild type (WT), single mutants M49K, M165V, S301P, and selected double mutants (M49K & M165V, M49K & S301P). Relative to WT, single substitutions produced reductions in computed binding affinity of up to ~12kcal/mol, accompanied by loss or reshaping of the S2 subsite and altered ligand burial. Notably, the M49K/S301P double mutant partially restored WU-04 engagement, narrowing the ΔΔG_restore gap to within ΔΔG_restore of WT and re-establishing key hydrophobic and hydrogen-bond contacts. NRI analysis revealed that distal residue 301 participates in a communication corridor linking the C-terminal helical domain to the active-site cleft; its substitution rewires inter-domain coupling that can compensate for local disruptions at residue 49. Together, these results identify structural hotspots and network pathways that may inform the design of next-generation M^pro inhibitors with improved mutation tolerance—specifically by strengthening interactions that do not rely solely on the mutable S2 pocket and by engaging conserved backbone features near the 165–166 region. Full article

Unmanned Aerial Vehicle (UAV)-based inspection is crucial for the maintenance and monitoring of high-voltage transmission lines, but detecting small objects in inspection images presents significant challenges, especially under complex backgrounds and varying lighting. These challenges are particularly evident when detecting the wire features of optical fiber composite overhead ground wire and conventional ground wires. Optical fiber composite overhead ground wire (OPGW) is a specialized cable designed to replace conventional shield wires on power utility towers. It contains one or more optical fibers housed in a protective tube, surrounded by layers of aluminum-clad steel and/or aluminum alloy wires, ensuring robust mechanical strength for grounding and high-bandwidth capabilities for remote sensing and control. Existing detection methods often struggle with low accuracy, insufficient performance, and high computational demands when dealing with small objects. To address these issues, this paper proposes an energy-efficient OPGW feature detection model for UAV-based inspection. The model incorporates a Feature Enhancement Module (FEM) to replace the C3K2 module in the sixth layer of the YOLO11 backbone, improving multi-scale feature extraction. A P2 shallow detection head is added to enhance the perception of small and edge features. Additionally, the traditional Intersection over Union (IoU) loss is replaced with Normalized Wasserstein Distance (NWD) loss function, which improves boundary regression accuracy for small objects. Experimental results show that the proposed method achieves a ${\mathrm{mAP}}_{50}$ of 78.3% and ${\mathrm{mAP}}_{50–95}$ of 52.0%, surpassing the baseline by 2.3% and 1.1%, respectively. The proposed model offers the advantages of high detection accuracy and low computational resource requirements, providing a practical solution for sustainable UAV-based inspections. Full article

Tomatoes are an important economic crop in China, and crop diseases often lead to a decline in their yield. Deep learning-based visual recognition methods have become an approach for disease identification; however, challenges remain due to complex background interference in the field and the diversity of disease manifestations. To address these issues, this paper proposes the SXA-YOLO (an improvement based on YOLO, where S stands for the SAAPAN architecture, X represents the XIoU loss function, and A denotes the AsDDet module) symmetric perception recognition model. First, a comprehensive symmetry architecture system is established. The backbone network creates a hierarchical feature foundation through C3k2 (Cross-stage Partial Concatenated Bottleneck Convolution with Dual-kernel Design) and SPPF (the Fast Pyramid Pooling module) modules; the neck employs a SAAPAN (Symmetry-Aware Adaptive Path Aggregation Architecture) bidirectional feature pyramid architecture, utilizing multiple modules to achieve equal fusion of multi-scale features; and the detection head is based on the AsDDet (Adaptive Symmetry-aware Decoupled Detection Head) module for functional decoupling, combining dynamic label assignment and the XIoU (Extended Intersection over Union) loss function to collaboratively optimize classification, regression, and confidence prediction. Ultimately, a complete recognition framework is formed through triple symmetric optimization of “feature hierarchy, fusion path, and task functionality.” Experimental results indicate that this method effectively enhances the model’s recognition performance, achieving a P (Precision) value of 0.992 and an mAP50 (mean Average Precision at 50% IoU threshold) of 0.993. Furthermore, for ten categories of diseases, the SXA-YOLO symmetric perception recognition model outperforms other comparative models in both p value and mAP50. The improved algorithm enhances the recognition of foliar diseases in tomatoes, achieving a high level of accuracy. Full article

Obesity is closely linked to dyslipidemia, hepatic injury, and chronic inflammation through disturbances in the gut–liver axis. Here, we evaluated the anti-obesity effects of L. rhamnosus (Lacticaseibacillus rhamnosus) CU262 in a high-fat diet (HFD) mouse model and elucidated mechanisms using an integrated multi-omics strategy. Male C57BL/6 mice received CU262 during 12 weeks of HFD feeding. Phenotypes, serum/liver biochemistry, gut microbiota (16S rRNA sequencing), fecal short-chain fatty acids (SCFAs), and hepatic transcriptomes (RNA-seq) were assessed. CU262 significantly attenuated weight gain and adiposity; improved serum TC, TG, LDL-C and HDL-C; lowered ALT/AST and FFA; and mitigated oxidative stress and inflammatory imbalance (↓ IL-6/TNF-α, ↑ IL-10). CU262 restored alpha diversity, reduced the Firmicutes/Bacteroidetes ratio, enriched beneficial taxa (e.g., Akkermansia), and increased acetate and butyrate. Liver transcriptomics showed CU262 reversed HFD-induced activation of cholesterol/steroid biosynthesis and endoplasmic reticulum stress, with downregulation of key genes (Mvk, Mvd, Fdps, Nsdhl, and Dhcr7) and Pcsk9, yielding negative enrichment of steroid and terpenoid backbone pathways and enhancement of oxidative phosphorylation and glutathione metabolism. Correlation analyses linked Akkermansia and SCFAs with improved lipid/inflammatory indices and repression of cholesterol-synthetic and stress-response genes. These findings demonstrate that CU262 alleviates HFD-induced metabolic derangements via microbiota-SCFA-hepatic gene network reprogramming along the gut–liver axis, supporting its potential as a functional probiotic for obesity management. Full article

Acoustic-based anomaly detection in elevators is crucial for predictive maintenance and operational safety, yet it faces significant challenges in real-world settings, including pervasive multi-source acoustic interference within confined spaces and severe class imbalance in collected data, which critically degrades the detection performance for minority yet critical acoustic events. To address these issues, this study proposes a novel hierarchical self-distillation framework. The method embeds auxiliary classifiers into the intermediate layers of a backbone network, creating a deep teacher–shallow student knowledge transfer paradigm optimized jointly via Kullback–Leibler divergence and feature alignment losses. A self-attentive temporal pooling layer is introduced to adaptively weigh discriminative time-frequency features, thereby mitigating temporal overlap interference, while a focal loss function is employed specifically in the teacher model to recalibrate the learning focus towards hard-to-classify minority samples. Extensive evaluations on the public UrbanSound8K dataset and a proprietary industrial elevator audio dataset demonstrate that the proposed model achieves superior performance, exceeding 90% in both accuracy and F1-score. Notably, it yields substantial improvements in recognizing rare events, validating its robustness for elevator acoustic monitoring. Full article

Tremella mesenterica, commonly known as the yellow brain or golden jelly fungus, has been traditionally used for its medicinal properties. In this study, we elucidated the structural characteristics of T. mesenterica polysaccharide (TMP) and evaluated its potential moisturizing mechanism in vitro, comparing it to Tremella fuciformis polysaccharide (TFP) and hyaluronic acid (HA). TMP was isolated through enzyme assisted extraction and it has a molecular weight (MW) of approximately 143 kDa. We investigated the composition of mannose, xylose, glucuronic acid, and glucose as a ratio of 59.8 ± 0.3, 24.0 ± 1.2, 11.0 ± 0.8, 5.2 ± 0.0, respectively. Through methylation and GC-MS analysis, we discovered TMP was composed of a main chain of β-(1→3)-linked mannopyranoside, substituted with various side chains such as xylopyranoside, glucuronopyranoside, glucopyranoside at the C-2 or C-4 positions of the backbone. TMP upregulated the expression of key moisturizing-related factors compared to TFP and HA, such as aquaporin-3 (AQP3) with 55% and 57% at 25 and 50 μg/mL and hyaluronic acid synthase-2 (HAS2) with 22% at 25 μg/mL, as confirmed through qRT-PCR analysis. Additionally, TMP significantly enhanced the expression of filaggrin (FLG), a critical protein involved in skin barrier function, with 22% at 25 μg/mL. Immunocytochemistry (ICC) analysis further revealed that TMP achieved the highest improvement in hyaluronic acid synthase-3 (HAS3) protein levels by 475% at 50 μg/mL. While further in vivo studies are required to substantiate its functional moisturizing efficacy, these findings suggest that TMP serves as a promising moisturizing agent. The structural and functional properties of TMP provide a potential foundation for its application in diverse industries, including cosmetics, food, biopolymers, and pharmaceuticals. Full article

The green-making stage is crucial for forming the distinctive aroma and flavor of Tieguanyin tea. Current green-making stage recognition relies on tea makers’ sensory experience, which is labor-intensive and time-consuming. To address these issues, this paper proposes a lightweight automatic recognition model named T-GSR for the accurate and objective identification of Tieguanyin tea green-making stages. First, an extensive set of Tieguanyin tea images at different green-making stages was collected. Subsequently, preprocessing techniques, i.e., multi-color-space fusion and morphological filtering, were applied to enhance the representation of target tea features. Furthermore, three targeted improvements were implemented based on the MobileNet V3 backbone network: (1) an adaptive residual branch was introduced to strengthen feature propagation; (2) the Rectified Linear Unit (ReLU) activation function was replaced with the Gaussian Error Linear Unit (GELU) to improve gradient propagation efficiency; and (3) an Improved Coordinate Attention (ICA) mechanism was adopted to replace the original Squeeze-and-Excitation (SE) module, enabling more accurate capture of complex tea features. Experimental results demonstrate that the T-GSR model outperforms the original MobileNet V3 in both classification performance and model complexity, achieving a recognition accuracy of 93.38%, an F1-score of 93.33%, with only 3.025 M parameters and 0.242 G FLOPs. The proposed model offers an effective solution for the intelligent recognition of Tieguanyin tea green-making stages, facilitating online monitoring and supporting automated tea production. Full article

In this study, using global liner shipping schedules, UNCTAD’s Port Liner Shipping Connectivity Index and Liner Shipping Bilateral Connectivity Index, together with bilateral trade-value data for 2022–2024, we construct a multilayer weighted port-to-port network that explicitly embeds port-level cargo-handling and service organisation capabilities, as well as demand-side routing pressure, into node and edge weights. Building on this network, we apply CONCOR-based structural-equivalence analysis to delineate functionally homogeneous port clusters, and adopt a structural role identification framework that combines multi-indicator connectivity metrics with Rank-Sum Ratio–entropy weighting and Probit-based binning to classify ports into high-efficiency core, bridge-control, and free-form bridge roles, thereby tracing the reconfiguration of cluster-level functional structures before and after the Red Sea crisis. Empirically, the clustering identifies four persistent communities—the Intertropical Maritime Hub Corridor (IMHC), Pacific Rim Mega-Port Agglomeration (PRMPA), Southern Commodity Export Gateway (SCEG), and Euro-Asian Intermodal Chokepoints (EAIC)—and reveals a marked spatial and functional reorganisation between 2022 and 2024. IMHC expands from 96 to 113 ports and SCEG from 33 to 56, whereas EAIC contracts from 27 to 10 nodes as gateway functions are reallocated across clusters, and the combined share of bridge-control and free-form bridge ports increases from 9.6% to 15.5% of all nodes, demonstrating a thicker functional backbone under rerouting pressures. Spatially, IMHC extends from a Mediterranean-centred configuration into tropical, trans-equatorial routes; PRMPA consolidates its role as the densest trans-Pacific belt; SCEG evolves from a commodity-based export gateway into a cross-regional Southern Hemisphere hub; and EAIC reorients from an Atlantic-dominated structure towards Eurasian corridors and emerging bypass routes. Functionally, Singapore, Rotterdam, and Shanghai remain dominant high-efficiency cores, while several Mediterranean and Red Sea ports (e.g., Jeddah, Alexandria) lose centrality as East and Southeast Asian nodes gain prominence; bridge-control functions are increasingly taken up by European and East Asian hubs (e.g., Antwerp, Hamburg, Busan, Kobe), acting as secondary transshipment buffers; and free-form bridge ports such as Manila, Haiphong, and Genoa strengthen their roles as elastic connectors that enhance intra-cluster cohesion and provide redundancy for inter-cluster rerouting. Overall, these patterns show that resilience under the Red Sea crisis is expressed through the cluster-level rebalancing of core–control–bridge roles, suggesting that port managers should prioritise parallel gateways, short-sea and coastal buffers, and sea–land intermodality within clusters when designing capacity expansion, hinterland access, and rerouting strategies. Full article

Ship detection serves as a core foundational task for marine environmental perception. However, in real marine scenarios, dense vessel traffic often causes severe target occlusion while multi-scale targets, asymmetric vessel geometries, and harsh conditions (e.g., haze, low illumination) further degrade image quality. These factors pose significant challenges to vision-based ship detection methods. To address these issues, we propose ${\mathrm{M}}^2\mathrm{S}\text{-}\mathrm{YOLOv}8$, an improved framework based on YOLOv8, which integrates three key enhancements: First, a Multi-Scale Asymmetry-aware Parallelized Patch-wise Attention (MSA-PPA) module is designed in the backbone to strengthen the perception of multi-scale and geometrically asymmetric vessel targets. Second, a Deformable Convolutional Upsampling (DCNUpsample) operator is introduced in the Neck network to enable adaptive feature fusion with high computational efficiency. Third, a Wasserstein-Distance-Based Weighted Normalized CIoU (WA-CIoU) loss function is developed to alleviate gradient imbalance in small-target regression, thereby improving localization stability. Experimental results on the Unmanned Vessel Zhoushan Perception Dataset (UZPD) and the open-source Singapore Maritime Dataset (SMD) demonstrate that ${\mathrm{M}}^2\mathrm{S}\text{-}\mathrm{YOLOv}8$ achieves a balanced performance between lightweight design and real-time inference, showcasing strong potential for reliable deployment on edge devices of unmanned marine platforms. Full article