Theoretical analysis of a membrane-based cross-flow liquid desiccant system

Liquid desiccant air dehumidification has become one of the most widely used dehumidification technologies with advantages of high efficiency, no liquid condensate droplets and capability of energy storage. In this paper a cross-flow mathematical model is developed for a single layer membrane unit. The governing equations are solved iteratively by finite difference method. The performance analysis is carried out for a small-scale membrane-based dehumidification module consisting of 8 air channels and 8 solution channels. The influences of main design parameters on system effectiveness are evaluated. These include air flow rate (NTU), solution to air mass flow rate ration (m*) and solution inlet temperature and concentration. It is revealed that higher sensible and latent effectiveness can be achieved with larger NTU and m*. Increasing solution concentration can also improve the dehumidification effect

Theoretical analysis of a membrane-based cross-flow liquid desiccant system

Liquid desiccant air dehumidification has become one of the most widely used dehumidification technologies with advantages of high efficiency, no liquid condensate droplets and capability of energy storage. In this paper a cross-flow mathematical model is developed for a single layer membrane unit. The governing equations are solved iteratively by finite difference method. The performance analysis is carried out for a small-scale membrane-based dehumidification module consisting of 8 air channels and 8 solution channels. The influences of main design parameters on system effectiveness are evaluated. These include air flow rate (NTU), solution to air mass flow rate ration (m*) and solution inlet temperature and concentration. It is revealed that higher sensible and latent effectiveness can be achieved with larger NTU and m*. Increasing solution concentration can also improve the dehumidification effect

Preparation and properties of antistatic high-strength aramid III/MWCNTs-OH fibers

Composite fibers made from aramid III and hydroxylated multiwalled carbon nanotubes (MWCNTs-OH) combine the excellent mechanical and electrical properties of both components, resulting in strong antistatic performance. However, it is of paramount importance to ensure the homogeneous dispersion of multi-walled carbon nanotubes functionalized with hydroxyl groups (MWCNTs-OH) within the aramid III spinning solution and optimize the compatibility between the two constituents to augment the overall performance of the composite fibers. To this end, this investigation successfully accomplished the dispersion of MWCNTs-OH in the spinning solution and probed the dispersion mechanism using molecular dynamics simulations. Moreover, composite fibers, comprising 2.4 weight percent MWCNTs-OH, were initially fabricated using the wet spinning method. These fibers displayed a uniform texture and a tensile strength of 1.210 GPa, signifying a noteworthy enhancement of 113.25% in comparison to the strength prior to modification. With respect to thermal behavior, the fibers exhibited a mass reduction of 21.24% within the temperature range of 0°C–538°C. In the temperature interval from 538°C to 800°C, the mass loss diminished to 10.31%, representing a substantial 71.03% reduction when compared to the unmodified state. Remarkably, even when subjected to temperatures exceeding 800°C, the composite fibers retained a residual mass of 68.45%, indicating a notable 61.17% increase from their initial condition. In terms of electrical properties, the fibers exhibited a specific resistance (ρ) of 3.330 × 109 Ω cm, demonstrating effective antistatic behavior. In summary, the antistatic composite fibers studied in this paper can effectively mitigate the hazards of static electricity in various applications, including military protection and engineering equipment in both military and civilian fields

Landslide susceptibility mapping using hybrid random forest with GeoDetector and RFE for factor optimization

The present study aims to develop two hybrid models to optimize the factors and enhance the predictive ability of the landslide susceptibility models. For this, a landslide inventory map was created with 406 historical landslides and 2030 non-landslide points, which was randomly divided into two datasets for model training (70%) and model testing (30%). 22 factors were initially selected to establish a landslide factor database. We applied the GeoDetector and recursive feature elimination method (RFE) to address factor optimization to reduce information redundancy and collinearity in the data. Thereafter, the frequency ratio method, multicollinearity test, and interactive detector were used to analyze and evaluate the optimized factors. Subsequently, the random forest (RF) model was used to create a landslide susceptibility map with original and optimized factors. The resultant hybrid models GeoDetector-RF and RFE-RF were evaluated and compared by the area under the receiver operating characteristic curve (AUC) and accuracy. The accuracy of the two hybrid models (0.868 for GeoDetector-RF and 0.869 for RFE-RF) were higher than that of the RF model (0.860), indicating that the hybrid models with factor optimization have high reliability and predictability. Both RFE-RF GeoDetector-RF had higher AUC values, respectively 0.863 and 0.860, than RF (0.853). These results confirm the ability of factor optimization methods to improve the performance of landslide susceptibility models

Dexamethasone-Activated MSCs Release MVs for Stimulating Osteogenic Response

The extracellular microvesicles (MVs) are attracting much attention because they are found to be the key paracrine mediator participating in tissue regeneration. Dexamethasone (DXM) is widely accepted as an important regulator in tailoring the differentiation potential of mesenchymal stem cells (MSCs). However, the effect of DXM on the paracrine signaling of MSCs remains unknown. To this point, we aimed to explore the role of DXM in regulating the paracrine activity of MSCs through evaluating the release and function of MSC-MVs, based on their physicochemical characteristics and support on osteogenic response. Results showed that DXM had no evident impact on the release of MSC-MVs but played a pivotal role in regulating the function of MSC-MVs. MVs obtained from the DXM-stimulated MSCs (DXM-MVs) increased MC3T3 cell proliferation and migration and upregulated Runt-related transcription factor 2 (Runx2), alkaline phosphatase (ALP), and osteopontin (OPN) expression. The repair efficiency of DXM-MVs for femur defects was further investigated in an established rat model. It was found that DXM-MVs accelerated the healing process of bone formation in the defect area. Thus, we conclude that using DXM as stimuli to obtain functional MSCs-MVs could become a valuable tool for promoting bone regeneration

Essential insights into decision mechanism of landslide susceptibility mapping based on different machine learning models

This work aims to discuss and compare the inherent essence of different machine learning algorithms for landslide susceptibility models (LSMs), which is of great significance for accurate prevention and detection of landslides. A geospatial database was established in GIS based on various factors of topography, geological conditions, environmental conditions and human activities, including 22 conditioning factors and 866 historical landslides. As for model algorithms, ANN is an operation model composed of a large number of interconnected nodes, and RF refers to an ensemble method of separately trained binary decision trees. Two algorithms were adopted in this paper for landslide susceptibility models. Meantime, an interpretable algorithm SHAP was used to gain insight into essential decision mechanism of LSMs. The result showed that RF model exhibits better stability and robustness. The global interpretation shows that the same landslide moderators play different roles in different models. The local interpretation shows that for the same evaluation unit different models give different decision mechanisms, and the local interpretation can be combined with the field survey, which can provide a comprehensive framework for assessing the assigned landslide

Aptamer Technology and Its Applications in Bone Diseases

Aptamers are single-stranded nucleic acids (DNA, short RNA, or other artificial molecules) produced by the Systematic Evolution of Ligands by Exponential Enrichment (SELEX) technology, which can be tightly and specifically combined with desired targets. As a comparable alternative to antibodies, aptamers have many advantages over traditional antibodies such as a strong chemical stability and rapid bulk production. In addition, aptamers can bind targets in various ways, and are not limited like the antigen–antibody combination. Studies have shown that aptamers have tremendous potential to diagnose and treat clinical diseases. However, only a few aptamer-based drugs have been used because of limitations of the aptamers and SELEX technology. To promote the development and applications of aptamers, we present a review of the methods optimizing the SELEX technology and modifying aptamers to boost the selection success rate and improve aptamer characteristics. In addition, we review the application of aptamers to treat bone diseases

An analysis of 60 years of autopsy data from Zhejiang university in Hangzhou, China.

CONTEXT: The autopsy rate gradually decreased during 1950-1999, and increased during the most recent decade (2000-2009). The diagnostic inaccuracy rate was continuously high during the 60 years. OBJECTIVE: To investigate disagreement between the pathological and clinical diagnosis during 60 years (1950-2009). DATA SOURCES: A 60-year retrospective study was carried out on the 4140 autopsy cases performed in Zhejiang University School of Medicine. RESULTS: The highest number of cases was 1037 during 1960-1969, while the lowest was 102 during 1990-1999. During the 1999-2009 period, 978 cases were completed, which ranked second within the 60 years. The total clinical misdiagnosis rate was 46.38%, while the highest was 73.82% in 2000-2009. During the 60 years, the diseases associated with highest diagnostic inaccuracy rates were circulatory diseases (76.97%), cancer (60.99%), and brain diseases (54.48%). The invasive fungal infection rate was 1.84% of the 4140 cases, and the diagnostic inaccuracy rate for this condition reached as high as 86.10%. In the autopsied disease spectrum over the 60 years, the most common diseases were respiratory (1349, 32.58%), circulatory (495, 11.96%), and brain diseases (424, 10.24%). CONCLUSION: Although the number of autopsies decreased from 1950 to 1999, it increased from 2000 to 2009, while the discordance rate between clinical and autopsy diagnosis remained high throughout