Search Results (394)

Supramolecular inclusion complexes are widely used in drug delivery and other fields, with the advantages of controllable structures, high stability, excellent biocompatibility, and the ability to improve drug solubility and achieve controlled release. However, traditional screening methods rely on experimental trial and error, which suffer from long cycles, high costs, and low throughput, limiting research and development efficiency. In recent years, the development of artificial intelligence has provided new solutions for the screening of inclusion complexes. This paper systematically reviewed the core technological system of AI in the screening of inclusion complexes, focusing on two aspects: prediction and optimization of key properties and rational design of host molecules, summarizing their specific application progress. At the same time, we analyzed the current core challenges, including data scarcity, insufficient model interpretability, and limited generalization capabilities, and propose future development directions such as building standardized databases, integrating physicochemical principles (e.g., molecular mechanics and thermodynamics), and establishing closed-loop research and development platforms. This review aims to provide a systematic reference for the in-depth application of artificial intelligence in the field of supramolecular inclusion complexes. Full article

Polyacrylamide (PAM), as a widely used effective soil conditioner, can decrease sandy soil infiltration, but its function may decline significantly in a short time. Previous study results showed that the annual degradation rate of PAM in soil is about 10%, and the migration ability of PAM in soil is fairly weak; thus we hypothesized that the functional group of PAM is prone to aging caused by physical and biological factors, which is different from degradation caused by the breaking of long main chains into short ones. The sandy loam soil was selected to conduct column infiltration experiments to (1) determine the effects of PAM application and drying and wetting intensity on infiltration and (2) identify the causes of the aging effect. Soil samples were treated with three doses of PAM (0, 1, and 2 g·kg⁻¹) and incubated in three soil water conditions (constant wetting, moderate and strong drying/wetting cycles). Under constant wetting condition, the stable infiltration rates of soils were decreased by PAM. However, after two strong drying and wetting cycles, the decrement of infiltration rates of PAM-treated soils was reversed. The results of FTIR suggested that drying and wetting cycles led to the hydrolysis of amide groups in PAM, resulting in the weakening of PAM’s function on soil infiltration characteristics. The leaching amounts of NH₄⁺-N generated by the amide group hydrolysis increased through the drying/wetting alternation and the application of PAM. Therefore, based on the findings of this column study using a specific sandy loam soil under controlled intense drying–wetting cycles, reapplication of polyacrylamide (PAM) after two cycles may facilitate the sustained lowering of infiltration. However, this recommendation should be confined to analogous experimental conditions and necessitates further validation under field scenarios or for alternative soil types. Full article

RNA viruses encode multiple layers of regulatory information within their genomes, extending beyond their protein-coding sequences. Through local secondary structures and long-range RNA–RNA interactions, viral RNAs control essential steps of the viral life cycle, including translation, replication, genome cyclization, packaging, and evasion of host defenses. Over the last two decades, chemical probing approaches—particularly Selective 2′-Hydroxyl Acylation analyzed by a primer extension (SHAPE) and its high-throughput derivatives—have transformed our ability to investigate these structures at a single nucleotide resolution and on a genome-wide scale. These technologies have revealed that viral genomes are highly structured and contain numerous functional RNA elements within untranslated regions as well as coding sequences. In this review, we summarize the main experimental strategies used to profile viral RNA architecture, with a focus on SHAPE-based methodologies and complementary approaches. We then discuss the major classes of functional RNA structures identified across diverse viral families, focusing on elements involved in translation and replication, such as internal ribosome entry sites (IRES) and cyclization elements, as well as other functional structures, including XRN1-resistant and frameshifting elements. Finally, we examine how structure-guided analyses are opening new avenues for antiviral intervention, including antisense oligonucleotides, small molecules, and RNA-degrading chimeras. Together, these advances highlight the viral RNA structure as both a key determinant of virus biology and a promising target for therapeutic innovation. Full article

Bolt loosening is a critical precursor to structural failure in major industrial and transportation equipment. Although acoustic non-destructive testing (NDT) offers a cost-effective diagnostic solution, its practical deployment is often hindered by low signal-to-noise ratios (SNRs) and the limited ability of conventional models to isolate fine-grained transient acoustic signatures from complex background interference. To address these challenges, this paper proposes PCT-Net, a multi-scenario noise-adaptive fusion network for bolt-state recognition. First, an Adaptive Spectral Masking mechanism is introduced as a data augmentation strategy. Instead of rigid zero-padding, it dynamically blends local spectral energies to encourage the learning of more robust and noise-invariant representations. Furthermore, rather than simply concatenating multiple modules, PCT-Net adopts a synergistic feature extraction framework to decouple complex acoustic signatures. A perceptual frontend is used to establish acoustically meaningful representation priors. To handle the highly dispersed characteristics of loosening signals, cascaded convolutional modules progressively suppress redundant environmental interference while capturing high-frequency local transient impacts. Meanwhile, to overcome the limited receptive field of convolutional operations, an embedded Transformer mechanism is introduced to model long-range temporal dependencies and low-frequency structural variations throughout the tapping cycle. By integrating local fine-grained transient modeling with global structural dependency modeling, the proposed network can better distinguish subtle decision boundaries among different loosening states. Extensive experiments show that PCT-Net achieves a classification accuracy of 97.12% under standard conditions and maintains stable performance under severe noise scenarios. These results demonstrate the effectiveness of the proposed method and highlight its potential for intelligent industrial safety monitoring. Full article

The Fenton reaction remains one of the most widely investigated advanced oxidation processes for wastewater treatment due to its ability to generate highly reactive oxygen species capable of degrading persistent organic pollutants. However, classical homogeneous Fenton systems suffer from significant limitations, including narrow pH applicability, iron sludge generation, and poor catalyst reusability. In response, extensive research has focused on the development of heterogeneous and advanced Fenton-like catalysts aimed at overcoming these challenges while enhancing catalytic efficiency and operational stability. This review provides a comprehensive and critical analysis of the evolution of Fenton catalysis, from classical homogeneous systems to advanced materials, including nanostructured catalysts, carbon-based Fe–N–C systems, metal–organic frameworks, and single-atom catalysts. A unified evaluation framework is proposed, integrating key performance parameters such as catalytic activity, manufacturability, stability, and catalyst lifespan. Comparative analysis reveals that improvements in activity are often accompanied by trade-offs in cost and scalability, indicating that the most advanced materials do not necessarily provide the best practical performance. A life cycle-oriented perspective is incorporated, emphasizing catalyst reuse, lifespan, and iron leaching, and providing quantitative insight into cumulative catalytic performance. The results demonstrate that long-term efficiency is governed not only by intrinsic activity but also by durability and operational stability under realistic conditions. Finally, current challenges and future directions are discussed, including scalable synthesis, improved mechanistic understanding, and integration into hybrid treatment systems. This review bridges the gap between fundamental research and practical application by highlighting the importance of balancing performance, stability, and sustainability in the design of next-generation Fenton catalysts. Full article

As the number of gears in automatic transmissions (AT) increases, the calibration parameters in the gear shift control process of the transmission control unit (TCU) increase exponentially, significantly increasing the calibration workload during engineering development. To address the challenges of high cost and long cycle times associated with traditional manual calibration, this paper proposes an automatic calibration strategy for shift control based on artificial neural networks (ANNs). The core of this method lies in utilizing an ANN to establish a non-linear mapping relationship between shift characteristics and calibration parameters, thereby simulating and replacing the analysis and adjustment process of engineers. In this research, a vehicle simulation model based on a 9-speed automatic transmission (9AT) was first constructed. A large-scale dataset of shift characteristics was obtained by traversing various parameter combinations, and key features were extracted for model training. Simulation results demonstrate that the trained ANN model performs excellently in the automatic calibration process, requiring only 4 to 5 iterations to adjust shift quality to a level comparable to manual calibration. Its convergence speed and efficiency are significantly superior to traditional rule-based calibration methods. Furthermore, the model exhibits a certain degree of generalization ability and robustness across different throttle openings and gear-shifting conditions. The proposed automatic calibration method does not rely on high-precision physical models, effectively shortening the development cycle and improving calibration efficiency, which holds significant application value in the field of automatic transmission engineering development. Full article

Colorectal cancer (CRC) is a digestive tract malignant tumor with a relatively high incidence and mortality rate worldwide. The occurrence and development of CRC are closely associated with disturbances in the gut microbiota. Paraclostridium tenue (synonym Eubacterium tenue) is generally considered a harmless commensal and can be isolated from fecal samples of healthy adults. However, whether this bacterium is a beneficial organism with an antitumor effect is unknown. This study systematically evaluated the anti-CRC effects of P. tenue strain Pt517 on CRC cells in vitro and in the CT26 syngeneic mouse model. Pt517 culture supernatant (Pt517CS) inhibited the proliferation, colony formation, and migration ability of CRC cells; induced cell apoptosis; and altered cell cycle distribution. Daily intragastric administration of Pt517 significantly inhibited tumor growth in mice; increased the expression levels of TNF-α, INF-γ, and CD8 in tumor tissues; and decreased the levels of IL-6, IL-10, and TGF-β. Pt517 intervention significantly modulated the gut microbiota composition with increased relative abundance of Parabacteroides goldsteinii, Lachnospiraceae, and Enterorhabdus caecimuris B7. The long-chain fatty acids (LCFAs), stearic acid and palmitic acid, were increased in the serum of treatment group mice and detected in Pt517CS. Functional verification indicated that stearic acid and palmitic acid directly inhibited the proliferation of CT26 cells in a dose-dependent manner, suggesting that Pt517 might exert its anti-CRC effect by secreting LCFAs. These findings indicate that P. tenue Pt517 is a potential new candidate for the microbial treatment of CRC, which warrants further validation for its safety and efficacy before clinical translation. Full article

The integration of Selective Laser Melting (SLM) into high-end manufacturing necessitates robust machine-condition monitoring and subsystem fault classification to navigate the intricate coupling and dynamic transients inherent in these systems. Current diagnostic frameworks often struggle to decouple high-dimensional state variables or track their underlying temporal evolution. To overcome these bottlenecks, this paper develops a spatiotemporal deep learning architecture by coupling Convolutional Neural Networks (CNNs) with Long Short-Term Memory (LSTM) units. This hybrid approach leverages CNNs to distill multi-dimensional spatial features from subsystem sensor arrays, while LSTMs interpret the sequential dependencies critical for identifying systemic drifts. The proposed framework was validated using an extensive industrial dataset comprising over 310,000 temporal samples across seven critical SLM subsystems, including optical, cooling, and energy units. We systematically investigated three data-handling strategies—feature weighting, balancing, and distribution-based synthesis—to optimize the model’s sensitivity to rare-event anomalies. Benchmarking across six architectural variants reveals that a specific CNN × 3 + LSTM × 1 configuration yields superior diagnostic fidelity, achieving a classification accuracy of 98.81%. Visualization of the feature space confirms high inter-class separability, demonstrating the model’s ability to isolate faults within complex manufacturing cycles. This research offers a scalable paradigm for the intelligent monitoring of SLM equipment and provides a technical benchmark for equipment health management and predictive maintenance in advanced additive manufacturing. Full article

The retinal pigment epithelium (RPE) is a long-lived, highly polarised epithelial monolayer that performs essential functions in retinal homeostasis, including outer blood–retina barrier maintenance, visual cycle activity, metabolic exchange, phagocytic clearance of photoreceptor outer segments, and regulation of oxidative and immune balance. Because RPE cells persist for decades under conditions of sustained oxidative, metabolic, and phagocytic stress, this tissue provides a valuable model for examining how long-lived post-mitotic cells preserve function over time and how age-related dysfunction emerges when that balance weakens. Although much of the current literature on RPE ageing has been shaped by age-related macular degeneration (AMD), age-dependent change in the RPE should not be understood solely as a preclinical stage of disease. Rather, the ageing RPE offers a broader framework for studying cellular maintenance under chronic physiological load. In this review, we synthesise current evidence on RPE ageing across four interrelated domains: structural remodelling, mitochondrial and metabolic imbalance, proteostatic and lysosomal burden, and chronic inflammatory dysregulation. Across these processes, ageing in the RPE is expressed less as widespread cell loss than as progressive decline in cellular organisation, buffering capacity, and functional precision. Structural irregularity, altered mitochondrial regulation, incomplete degradative clearance, and persistent low-grade inflammatory signalling together reduce the ability of the RPE to maintain long-term homeostasis and increase vulnerability to age-related retinal dysfunction. We further argue that ageing in the RPE is best understood not as abrupt failure of isolated pathways, but as gradual loss of system coherence among interacting homeostatic systems that remain active while operating under increasing constraint. This view helps integrate diverse cellular and molecular findings and highlights the RPE as an informative model for understanding ageing in long-lived post-mitotic tissues. Full article

In response to the complex erosion environment caused by periodic water level fluctuations, dry–wet cycles, and long-term water flow scouring on the Yangtze River bank, three typical soil-fixing and bank-protecting plants, Cynodon dactylon, Carex breviculmis, and Digitaria sanguinalis, which can adapt to both aquatic and terrestrial conditions, were selected for planting experiments. Tests on root–soil composite shear strength, disintegration, and water flow scouring were conducted to investigate the effects of different bank-protecting plants on bank stabilization. The results show that: 1. The root systems of the three plants significantly enhance the soil shear strength at various soil depths, but the reinforcing effect decreases with increasing soil depth. The cohesion strength of the root–soil composites ranks as Carex breviculmis > Digitaria sanguinalis > Cynodon dactylon, with maximum increases of 54.83 kPa, 20.66 kPa, and 6.5 kPa, respectively, equivalent to 3.16, 1.82, and 1.26 times that of bare soil. 2. Under dry–wet cycling, the water stability of the root–soil composites is significantly higher than that of bare soil. The disintegration residual rate of Cynodon dactylon and Digitaria sanguinalis decreased from 81.76% to 38.23% and from 80.18% to 34.34%, respectively, whereas Carex breviculmis showed only a slight decrease from 80.41% to 75.1%. Carex breviculmis exhibits the strongest stability and is least affected by dry–wet cycles, while the water stability of Cynodon dactylon and Digitaria sanguinalis declines noticeably with increasing cycle numbers. The plants’ ability to improve soil water stability ranks as Carex breviculmis > Cynodon dactylon > Digitaria sanguinalis. 3. The enhancement of bank erosion resistance is mainly attributed to the formation of a root-reinforced network, which strengthens the soil through root–soil interlocking and anchorage, thereby increasing resistance to flow-induced shear stress and reducing particle detachment under hydraulic action. The bank erosion resistance index ranks as Carex breviculmis > Cynodon dactylon > Digitaria sanguinalis, and decreasing with increasing runoff velocity. Compared to bare soil slopes, the maximum enhancement effects on bank erosion resistance are 75.1%, 63.3%, and 54.2% respectively. Full article

Methanogens, or methanogenic archaea (MA), are among the most ancient and widely distributed microorganisms, characterized by a unique metabolism that generates methane (CH₄) as the terminal product of anaerobic respiration. Their ability to grow and/or survive across a wide range of environmental conditions has made methanogens key contributors to biogeochemical cycles throughout most of Earth’s history. Most importantly, these oxygen-sensitive microorganisms have regulated the climate since the early Archean and impacted biogeochemical cycles throughout Earth’s history by producing the potent greenhouse gas, CH₄, while consuming H₂, CO₂, and small organic molecules. Hence, methanogens are attributed a key role in the start and end of several Proterozoic glaciations and mass extinction events. Their specific roles in the long-term carbon cycle that focus on CH₄ production are well-established, but, in contrast, only very few studies report on interactions with CaCO₃ and long-term carbon storage. Methanogens evolved early during Earth’s history, likely during the Archaean Eon, in layered benthic microbial communities called microbial mats. When lithified, these mats form microbialites that represent some of the earliest evidence of life in the fossil record, dating back >3.5 Gy. Methanogens are an integral part of contemporary microbial mats and have been identified both in the anoxic and oxic zones of these sedimentary ecosystems; however, their adaptations to apparently unfavorable oxic conditions and their role in the precipitation of carbonate in mats are unclear. In addition to an important role in the evolution of our planet by producing CH₄, methanogens may also produce a biosignature that could be relevant for astrobiology research. This review will discuss the diversity, physiology, and ecology of methanogens in detail to clarify their role in some of the major biogeochemical processes and ecological climatic events through the fluctuating environmental conditions on Earth through geologic time. Full article

In cold-region engineering, freeze–thaw (F–T) cycles act as a critical stressor on soil stability, where the recurring transition between frost heave and thaw settlement can drastically alter geotechnical properties and threaten long-term structural integrity. Yet, while the static characteristics of frozen soils are well documented, the dynamic impact of repetitive thermal cycling on long-term soil behavior remains a significant and relatively underexplored challenge in the field. This study investigates the effectiveness of polypropylene fiber (FPP) as a sustainable and environmentally benign reinforcement for high-plasticity clay. The research examines FPP’s influence on stress–axial strain relationships (unconsolidated undrained (UU) compressive strength) and its ability to mitigate frost heave and volumetric changes during F–T cycles. Laboratory-prepared FPP–clay samples were subjected to ten closed-system F–T cycles and tested using a UU triaxial machine. Results showed a 51% decrease in UU strength for unreinforced samples after ten cycles, while samples reinforced with 1% FPP exhibited only an 18.4% reduction. FPP reinforcement reduced frost heave and thaw settlement by 30% and significantly enhanced UU strength, increasing it by 60% before F–T cycles and 167% after exposure. The findings highlight FPP’s effectiveness in improving soil strength, minimizing volumetric changes, and mitigating frost-related damage, making it a viable solution for enhancing soil performance in cold regions. Full article

In a solid-state hydrogen storage tank, the storage medium is most often in the form of an intermetallic alloy powder. With each cycle of hydrogen absorption/desorption, the particles swell, move, fragment, and segregate. Understanding and modeling these phenomena are essential in order to guide engineers during the tank design process. However, there are little data in the literature on the mechanical behavior of powders for storage applications. This study focuses on the flowability and compression behavior of an intermetallic powder, with the aim of analyzing particle mobility in a confined environment as well as the transmission of forces to the tank walls. In order to represent the evolution of particle size through fragmentation during cycles, five ${\mathrm{T}\mathrm{i}\mathrm{F}\mathrm{e}}_{\left(1-\mathrm{x}\right)}{\mathrm{M}\mathrm{n}}_{\mathrm{x}}\left(\mathrm{x}\approx 0.1\right)$ powders, differing in their average particle size and polydispersity, are studied. Flowability tests on Granutools^® (Awans, Belgium) instruments show that behaviors differ. Fine-grained samples exhibit rheo-thickening behavior, while coarser samples are quasi-Newtonian. These tests highlight variations in cohesion and internal friction, particularly for polydisperse samples. Stepwise cyclic compression tests (in stages 0-10-20-30 kN) were performed to study the elastic response of the powder under confinement and its ability to transfer stresses to the walls. This work highlights the impact of particle size and polydispersity on stress transfer in a confined space. This work therefore presents the mechanical effects of changes in particle size and polydispersity during absorption/desorption cycles on the overall behavior of the powder storage bed, in terms of flowability, cohesion, and stress transmission, in order to better understand, in the long term, its impact on tank deformation. Full article

The steady operation of underground gas storage (UGS) is significant for securing national energy. However, long-term cyclic injection-production operation causes the dynamic changes in formation stress, potentially leading to fault reactivation and slippage. This could affect the seal performance of the fault zone and cause disastrous consequences. In this paper, a mechanical analysis model for fault slip is constructed to study the dynamic seal performance in response to long-term injection-production cycles. An intelligent approach is proposed to predicate the fault slip value based on machine learning algorithms. It can realize long-term prediction of fault slip value under a new condition of injection-production operation. The study shows that (1) formation pressure tends to accumulate near the fault zone due to the low permeability, and the interface of the reservoir layer, cap layer, and fault zone is the seal weak position of UGS; (2) the response of fault slip is driven by the injection-production rate and the reservoir pressure. There is a significant coupling relationship between the fault slip value and the accumulated injection gas volume; (3) the intelligent prediction approach can capture the nonlinear dynamic characteristics of slip tendency accurately, and it exhibits good prediction performance and generalization ability under the new operating condition. This study effectively assesses the dynamic risk for fault slip of depleted hydrocarbon reservoir UGS during the long-term injection-production procedure. It provides an effective technical approach for fault slip tendency analysis and injection-production process optimization, which is important for the sustainable operation of UGS reducing the risk of seal failure and supporting gas storage security. Full article

In China, a majority of the proven crude oil reserves are found in clastic rock reservoirs, which typically exhibit low natural energy levels. Water injection has become the most widely adopted technique for maintaining reservoir pressure and enhancing oil recovery in such formations. However, conventional water injection strategies heavily rely on empirical knowledge, often failing to accurately characterize the dynamic inter-well connectivity between injection and production wells. This limitation hinders the effective management of fluid injection and production processes. To address this challenge, we propose an intelligent optimization method for water allocation in high-water cut, low-permeability reservoirs. Our approach employs a Bidirectional Long Short-Term Memory (BiLSTM) neural network to learn the complex patterns from historical injection data in a data-driven manner. Furthermore, we design a well distance and time joint attention mechanism, which is integrated after the dual BiLSTM layers to enhance the model’s ability to capture the critical dynamic relationships among wells. This mechanism decouples temporal pattern recognition and the spatial physical constraints, laying the foundation for interpretable injection strategy optimization. We name this architecture “AttBiLSTM”, which is designed for optimizing injection strategies for individual layers in separate-layer water injection wells (The layer refers to the basic geological unit or flow unit within a vertically heterogeneous reservoir that is delineated and requires independent water injection regulation). Using field data from the Xinjiang Oilfield, we validate the proposed method and compare its performance against traditional water injection schemes and mainstream data-driven models. The experimental results demonstrate that the AttBiLSTM model effectively establishes a nonlinear mapping between the injection volumes and oil production rates, showing strong performance in both production prediction and injection optimization. An independent numerical reservoir simulation verification confirms that the optimized scheme increases well group oil production by over 3.6%, with no premature water breakthrough risk in a 5-year development cycle. This study provides a novel and practical technical framework for efficiently developing low-porosity, low-permeability, and highly heterogeneous reservoirs. Full article