Search Results (555)

Background/Objectives: Lipid nanoparticles (LNPs) have demonstrated notable clinical success as advanced drug delivery systems. However, the development of novel covalently bonded ionizable lipids faces substantial technical challenges, as their modification is difficult and they have a high molecular weight. To address this issue, we report the use of host–guest complexes in supramolecular chemistry as functional lipid motifs for constructing LNPs. Methods: Ionizable amine β-cyclodextrin (amine β-CD)-derived host–guest amphiphilic lipid molecules (HGLs) were designed for the construction of multi-stage assembly supramolecular LNPs (MSLNPs). The structure–function relationships and stability of MSLNPs were explored by screening eight types of amine β-CDs and varying the ratio of HGL to yolk phosphatidylcholine. Stability screening and molecular dynamics simulations were performed to clarify the self-assembly mechanisms and optimal formulations, followed by a systematic evaluation of delivery performance. Results: MSLNPs showed a high drug-loading efficiency (> 30%), a rapid-response release in acidic environments, and multi-pathway cellular uptake. In vivo delivery experiments using ethylenediamine β-CD-based MSLNPs in mice revealed no significant immunogenicity, no significant abnormalities in organs/tissues or their functions, a unique biodistribution pattern, and pronounced renal targeting. The successful development of MSLNPs with acidic pH-responsive control, a high delivery efficiency, and renal-targeting properties simplifies LNP preparation. Conclusions: This study offers novel insights into the design of simplified LNPs and the optimization of targeted delivery, with potential applications in renal disease therapy. Full article

Karst collapse columns (KCCs) represent key concealed hazard-inducing factors that threaten the safety of coal mines in North China. To clarify their primary controlling geological factors and evolutionary processes, this study focuses on KCCs in the Hancheng Mining Area, situated on the southeastern margin of the Ordos Basin, China. A comprehensive methodological approach—integrating field surveys, petrographic and mineralogical identification, geochemical analysis, and structural interpretation—was employed to investigate the dominant factors controlling KCC development and their evolutionary mechanisms. The results indicate the following: (1) Thick-bedded dolomites of the 5th Member of the Majiagou Formation (Middle Ordovician Series) serve as the material foundation for karstification. These dolomites were deposited in an oxidized shallow-water tidal flat setting, which endowed them with favorable lithological properties for subsequent dissolution. (2) NE-SW trending erosional grooves within the paleogeomorphology of the Ordovician top surface functioned as preferential flow paths for karst water, channeling fluid movement and intensifying localized dissolution. (3) Multi-phase tectonic activities, particularly extensional deformation during the Himalayan orogeny, created the necessary stress conditions to trigger cave collapse. (4) KCCs undergo a multi-stage formation and evolution process: Starting with the Majiagou Formation’s 5th Member dolomites as the primary lithology, initial modification occurred via Caledonian weathering–crust karstification. Subsequently, compressional tectonism during the Yanshanian orogeny generated void spaces that facilitated deep-seated dissolution. Rapid uplift in the Paleogene exacerbated vertical dissolution, leading to extensive cavity development, which ultimately collapsed under the extensional tectonic regime of the Neogene. This study provides theoretical support for predicting and mitigating sudden water inrushes caused by KCCs in the Hancheng Mining Area. Furthermore, it offers novel insights and a scientific basis for advancing understanding of the developmental mechanisms of North China-type KCCs. Full article

The Upryamoye ore field is located in the Chukotka metallogenic belt in Northeast Russia. The orebodies are hosted within Late Jurassic–Early Cretaceous greenschist-facies metamorphosed rocks and structurally controlled by NW-trending fold-and-thrust dislocations. Based on geological exploration, petrographic, mineralogical, and geochronological studies, new data on the geological structure and composition of gold–quartz mineralization of the Upryamoye ore field are presented. Optical and scanning microscopy were used to study the lithological features of the host rocks and determine the ore textures and the morphology and internal structure of native gold, auriferous pyrite, and arsenopyrite. Qualitative and quantitative characterization of the ore minerals was carried out using SEM-EDS and EPMA. To determine the age of the gold mineralization, Re-Os dating of arsenopyrite and U-Th/He dating of pyrite were performed. The results show that the orebodies comprise carbonate–quartz and sulphide–carbonate–quartz saddle reef veins in both the fold hinge and limbs, as well as mineralized shatter zones and mylonite zones that trace thrust faults. The main ore minerals are arsenopyrite and pyrite, associated with minor amounts of galena, sphalerite, chalcopyrite, tetrahedrite, and bournonite. Native gold is distributed extremely unevenly, forming thin and finely dispersed inclusions in pyrite and arsenopyrite. U-Th/He isotopic analyses of auriferous pyrites suggest that gold mineralization in the Upryamoye ore field occurred at 123 ± 4 Ma. The data obtained by Re–Os dating of auriferous arsenopyrite are inconsistent with direct geological observations but indicate that Os in the arsenopyrite was derived from the crustal source. According to a number of characteristic features of mineralization, the Upryamoye ore field is attributed to a metamorphic genetic type of orogenic low-sulphide gold–quartz deposits. The ore-forming process was long and multi-stage, occurring during the final collisional phase and the beginning of the extensional phase of the Chukotka orogen. Full article

Early plant disease detection is crucial for sustainable crop production and food security. Stem rust, caused by Puccinia graminis f. sp. tritici, poses a major threat to wheat and barley. This study evaluates the feasibility of using hyperspectral imaging and machine learning for early detection of stem rust and examines the cross-crop transferability of diagnostic models. Hyperspectral datasets of wheat (Triticum aestivum L.) and barley (Hordeum vulgare L.) were collected under controlled conditions, before visible symptoms appeared. Multi-stage preprocessing, including spectral normalization and standardization, was applied to enhance data quality. Feature engineering focused on spectral curve morphology using first-order derivatives, categorical transformations, and extrema-based descriptors. Models based on Support Vector Machines, Logistic Regression, and Light Gradient Boosting Machine were optimized through Bayesian search. The best-performing feature set achieved F1-scores up to 0.962 on wheat and 0.94 on barley. Cross-crop transferability was evaluated using zero-shot cross-domain validation. High model transferability was confirmed, with F1 > 0.94 and minimal false negatives (<2%), indicating the universality of spectral patterns of stem rust. Experiments were conducted under controlled laboratory conditions; therefore, direct field transferability may be limited. These findings demonstrate that hyperspectral imaging with robust preprocessing and feature engineering enables early diagnostics of rust diseases in cereal crops. Full article

Background: Comprehensive data on the economic burden of cervical cancer treatment remain scarce in China’s less developed regions, necessitating this study on hospitalization costs and expenditure trends in these areas. Methods: Employing a multi-stage stratified cluster sampling approach, this study enrolled 10,070 cervical cancer inpatients from 72 healthcare facilities in Gansu Province. Clinical and expenditure data were extracted from hospital information systems. Rank sum tests and Spearman correlation analyses were performed for univariate assessment, while quantile regression and random forest models were applied to identify determinant factors. Results: From 2019 to 2023, the average hospitalization duration for cervical cancer patients in Gansu Province was 16.12 days, with an average hospitalization cost of USD 3862.08 (2023 constant prices, converted from CNY at 1:7.0467). During these five years, the average inpatient costs per hospitalization increased from USD 3473.45 to USD 4202.57, and the average daily hospitalization cost rose from USD 230.53 to USD 241.77. The average drug cost decreased from USD 769.06 to USD 640.16. The main factors influencing hospitalization costs included the length of hospital stay, whether cervical cancer surgery was performed, hospital type, hospital level, and the proportion of medications. Conclusions: Our findings indicate that cervical cancer is a considerable economic burden on both families and society. This highlights the need to control the length of hospital stay and optimize the allocation of medical resources, in addition to strengthening cervical cancer screening and HPV vaccination in underdeveloped areas, in order to enhance the efficiency of prevention and treatment and ensure medical equity. Full article

Al-Zn-Mg-Cu alloys are widely used as heat-treatable ultra-high-strength materials in aerospace structural applications. While conventional single-stage aging enables high strength, advanced performance demands call for precise microstructural control via multi-stage aging. In this study, we employ a combination of scanning transmission electron microscopy (STEM), energy-dispersive X-ray spectroscopy (EDS), and X-ray diffraction (XRD) to investigate the microstructural evolution and its correlation with mechanical properties of AA7150 aluminum alloy subjected to two-step aging treatments, following a 6 h pre-aging at 120 °C. Through atomic-scale STEM imaging along the [110]_Al zone axis, we systematically characterize the precipitation behavior of GPII zones, η′ phases, and equilibrium η phases both within the grains and at grain boundaries under varying secondary aging (SA) conditions. Our results reveal that increasing the SA temperature from 140 °C to 180 °C leads to coarsening and reduced number density of intragranular precipitates, while promoting the continuous and coarse precipitation of η phases along grain boundaries, accompanied by a widening of the precipitation-free zone (PFZ). Notably, SA at 160 °C induces the formation of fine, uniformly dispersed nanoscale η′ precipitates in the alloy, as confirmed by XRD phase analysis. Aging at this temperature markedly enhances the mechanical properties, achieving an ultimate tensile strength (UTS) of 613 MPa and a yield strength (YS) of 598 MPa, while presenting an exceptionally broad peak-aging plateau. Owing to this feature, a moderate extension of the SA duration does not reduce strength and can further improve ductility, increasing the elongation (EL) to 14.26%. These results demonstrate a novel two-step heat-treatment strategy that simultaneously achieves ultra-high strength and excellent ductility, highlighting the critical role of advanced electron microscopy in elucidating phase-transformation pathways that inform microstructure-guided alloy design and processing. Full article

Based on the perspective of multi-stage dynamic competition, this study constructs a discrete dynamic model of price competition between the “direct sales” and “resale” channels in cross-border e-commerce (CBEC) under three blockchain deployment modes. Drawing on nonlinear dynamics theory, the Nash equilibrium of the system and its stability conditions are examined. Using numerical simulations, the effects of factors such as the channel price adjustment speed, tariff rate, and commission ratio on the dynamic evolution, entropy, and stability of the system under the empowerment of blockchain technology are investigated. Furthermore, the impact of noise factors on system stability and the corresponding chaos control strategies are further analyzed. This study finds that a single-channel deployment tends to induce asymmetric system responses, whereas dual-channel collaborative deployment helps enhance strategic coordination. An increase in price adjustment speed, tariffs, and commission rates can drive the system’s pricing dynamics from a stable state into chaos, thereby raising its entropy, while the adoption of blockchain technology tends to weaken dynamic stability. Therefore, after deploying blockchain technology, each channel should make its pricing decisions more cautiously. Moderate noise can exert a stabilizing effect, whereas excessive disturbances may cause the system to diverge. Hence, enterprises should carefully assess the magnitude of disturbances and capitalize on the positive effects brought about by moderate fluctuations. In addition, the delayed feedback control method can effectively suppress chaotic fluctuations and enhance system stability, demonstrating strong adaptability across different blockchain deployment modes. Full article

Triboelectric nanogenerators (TENGs) have emerged as efficient mechanical-energy harvesters with advantages—simple architectures, broad material compatibility, low cost, and strong environmental tolerance—positioning them as key enablers of self-powered systems. This review synthesizes recent progress in energy-storage interfaces, power management, and system-level integration for TENGs. We analyze how intrinsic source characteristics—high output voltage, low current, large internal impedance, and pulsed waveforms—complicate efficient charge extraction and utilization. Accordingly, this work highlights a variety of power-conditioning approaches, including advanced rectification, multistage buffering, impedance transformation/matching, and voltage regulation. Moreover, recent developments in the integration of TENGs with storage elements, cover hybrid topologies and flexible architectures. Application case studies in wearable electronics, environmental monitoring, smart-home security, and human–machine interfaces illustrate the dual roles of TENGs as power sources and self-driven sensors. Finally, we outline research priorities: miniaturized and integrated power-management circuits, AI-assisted adaptive control, multimodal hybrid storage platforms, load-adaptive power delivery, and flexible, biocompatible encapsulation. Overall, this review provides a consolidated view of state-of-the-art TENG-based self-powered systems and practical guidance toward real-world deployment. Full article

Background: Peste des petits ruminants (PPR) is an acute or subacute contagious trans-boundary viral disease causing high morbidity and mortality in domestic and wild small ruminants. The national UAE-PPR control and eradication plan follows the PPR Global Control and Eradication Strategy (PPR GCES) and relies on the annual mass vaccination of small ruminants to eradicate the disease from the country by 2030. Despite the immunization effort against PPR, the vaccination coverage reached 65% at maximum, which necessitates conducting a post-vaccination evaluation (PVE) study at the national level. Methods: Using multistage random sampling to assess the PPR vaccine and vaccination effectiveness, protocol (2) of the PPR GCES, using two serosurveys; serosurvey (1) (pre-vaccination) at day 0 before vaccination, to assess the primary PPR serological investigation, and serosurvey (2) at (30–90) days post-PPR vaccination, to evaluate the immune response, were carried out from September to December 2024 across the seven Emirates of the UAE. The nucleoprotein-based competitive enzyme-linked immunosorbent assay (c-ELISA) was used to detect PPR antibodies in a total of 1592 and 1589 sera samples collected, respectively, before and after vaccination from different (n = 163) sheep and goats holdings (epi-unit) distributed in the different Emirates of the UAE. Results: In serosurvey (1). prior to vaccination, out of the total 1592 samples tested (839 goats and 753 sheep), 833 animals (52.32%) were found to be seropositive for PPR antibodies. In contrast, in serosurvey (2), after vaccination, 1490 (93.77%) animals were found to be seropositive out of the total 1589 small ruminants (825 goats and 764 sheep) tested by c-ELISA. A statistically significant increase (41.45%) in the overall seroprevalence from (52.32%) pre-vaccination to (93.77%) post-vaccination was observed. Post-vaccination, 93.87% (n = 153) of the vaccinated epi-units achieved more than 70% seroprevalence compared to 43.56% (n = 71) before vaccination. Prediction analysis showed that all the seven UAE Emirates require 1.2 years maximum to reach 100% immune-protection levels. Conclusions: An efficient PPR vaccine was used to immunize small ruminants in the UAE. Higher (89.47–100%) post-vaccination herd immunity than the threshold recommended by the PPR GCES (>80% immunity) was attained, which can efficiently break the spread of PPRV within the UAE. To enhance the eradication of PPR I the UAE, conducting mass vaccination campaigns targeting over the (95%) immunization coverage of eligible animals for the next three years is recommended to attain the requested sustained (>80%) immunity at the animals holding level. Full article

A stable atmosphere is very important for long-term life science experiments with small mammals in sealed habitats. In this study, we constructed a multi-stage, solid-state purification system to regulate and control the supply of oxygen and remove carbon dioxide, humidity, and trace contaminants in a sealed metal box. Lithium hydroxide was used as the material to absorb CO₂; molecular sieves and humidity-indicating silica gel were used as dehumidification material; and activated carbon-based sorbent was used to remove ammonia and hydrogen sulfide. We evaluated the performance of the system by implementing a sealed culture test with mice for 7 days. The pressure, relative humidity, oxygen concentration, carbon dioxide concentration, and ammonia/hydrogen sulfide concentration in the sealed box were maintained at 101–103 kPa, 30–36%, 19–21%, <1000 ppm, and the main goal of this study was to test for single gas absorption. In the future, we will investigate the competitive effect of multi-gas mixtures. Full article

The digitization of healthcare has enabled the application of advanced analytics, such as process mining and machine learning, to electronic health records (EHRs). This study aims to identify workflow inefficiencies, temporal bottlenecks, and risk factors for delayed recovery in surgical pathways using the open-access MOVER dataset. A multi-stage framework was implemented, including heuristic control-flow discovery, Petri net-based conformance checking, temporal performance analysis, unsupervised clustering, and Random Forest-based classification. All analyses were simulated on pre-discharge (“preliminary”) patient records to enhance real-time applicability. Control-flow models revealed deviations from expected pathways and issues with data quality. Conformance checking yielded perfect fitness (1.0) and moderate precision (0.46), indicating that the model generalizes despite clinical variability. Stratified performance analysis exposed duration differences across ASA scores and age groups. Clustering revealed latent patient subgroups with distinct perioperative timelines. The predictive model achieved 90.33% accuracy, though recall for delayed recovery cases was limited (24.23%), reflecting class imbalance challenges. Key features included procedural delays, ICU status, and ASA classification. This study highlights the translational potential of integrating process mining and predictive modeling to optimize perioperative workflows, stratify recovery risk, and plan resources. Full article

To address challenges such as sparse feature representation difficulties and poor robustness in detecting weak targets against sea clutter backgrounds, this study investigates the adaptability of channel modeling and sparse reconstruction techniques for target recognition. It proposes a method for detecting small sea targets that integrates OTFS with deep unfolding. Using OTFS modulation to map signals from the time domain to the Delay-Doppler domain, a sparse recovery model is constructed. Deep unfolding is employed to transform the FISTA iterative process into a trainable network architecture. A GAN model is employed for adaptive parameter optimization across layers, while the CBAM mechanism enhances response to critical regions. A multi-stage loss function design and false alarm rate control mechanism improve detection accuracy and interference resistance. Validation using the IPIX dataset yields average detection rates of 88.2%, 91.5%, 90.0%, and 83.3% across four polarization modes, demonstrating the proposed method’s robust performance. Full article

Amid the increasing frequency and severity of major disasters, the rapid spread of online misinformation poses substantial risks to public safety, effective crisis management, and long-term societal sustainability. Current methods for managing disaster-related rumors rely on static, rule-based approaches that lack scalability, fail to capture nuanced misinformation, and are limited to reactive responses, hindering effective disaster management. To address this gap, this study proposes a novel framework that leverages large language models (LLMs) and event knowledge graphs (EKGs) to facilitate the sustainable agile identification and adaptive control of disaster-related online rumors. The framework follows a multi-stage process, which includes the collection and preprocessing of disaster-related online data, the application of Gaussian Mixture Wasserstein Autoencoders (GMWAEs) for sentiment and rumor analysis, and the development of EKGs to enrich the understanding and reasoning of disaster events. Additionally, an enhanced model for rumor identification and risk control is introduced, utilizing Graph Attention Networks (GATs) to extract node features for accurate rumor detection and prediction of rumor propagation paths. Extensive experimental validation confirms the efficacy of the proposed methodology in improving disaster response. This study contributes novel theoretical insights and presents practical, scalable solutions for rumor control and risk management during crises. Full article

To address the challenge of long-term stiffness retention of subgrades in humid–hot climates, this study evaluates expansive soil stabilized with construction and demolition waste (CDW), focusing on the resilient modulus (M_r) under coupled stress states and wetting–drying histories. Basic physical and swelling tests identified an optimal CDW incorporation of about 40%, which was then used to prepare specimens subjected to controlled. Wetting–drying cycles (0, 1, 3, 6, 10) and multistage cyclic triaxial loading across confining and deviatoric stress combinations. M_r increased monotonically with both stresses, with stronger confinement hardening at higher deviatoric levels; with cycling, M_r exhibited a rapid then gradual degradation, and for most stress combinations, the ten-cycle loss was 20%–30%, slightly mitigated by higher confinement. Grey relational analysis ranked influence as follows: the number of wetting–drying cycles > deviatoric stress > confining pressure. A Lytton model, based on a modified prediction method, accurately predicted M_r across conditions (R² ≈ 0.95–0.98). These results integrate stress dependence with environmental degradation, offering guidance on material selection (approximately 40% incorporation), construction (adequate compaction), and maintenance (priority control of early moisture fluctuations), and provide theoretical support for durable expansive soil subgrades in humid–hot regions. Full article

Modern societies are increasingly experiencing limited contact with nature, a phenomenon referred to as the “nature deficit.” The article presents a systematic review of the literature on this issue, with particular emphasis on the role of forests in mitigating its effects. The analysis, based on the Scopus and Web of Science databases, synthesizes the current state of knowledge on the consequences of nature deficit for physical, mental, and social health, while also highlighting the potential of forests as spaces supporting human well-being. The review process followed a systematic methodology, using precisely defined keyword combinations and multi-stage screening. From an initial pool of 88 publications, a critical selection process led to 11 articles that met the inclusion criteria and were analyzed in depth. The findings show that regular contact with nature reduces stress, anxiety, and ADHD symptoms, supports cognitive development, and im-proves concentration, creativity, and social skills. At the same time, there is a lack of consistent tools for clearly diagnosing nature deficit, and existing studies face significant methodological limitations (small samples, subjective measurements, lack of laboratory control). The article also identifies research gaps, particularly in the context of sustainable forest management, cultural differences, and the long-term health effects of exposure to nature. Full article