Cooling load prediction can provide reliable data support for the development of low energy consumption building. It is difficult for traditional models to achieve stable prediction accuracy under ...different load mode when the amount of data is limited. To resolve this problem, gradient boosting decision tree (GBDT) model based on selection of input variables was proposed to predict the cooling load of commercial buildings. First, the input variables that affect prediction cooling load were determined through correlation analysis on the original data set. Then, the Pearson analysis was done on the original data to get the optimal combination of input variables. Next, the ensemble learning technology was used to build GBDT model. In order to improve the prediction accuracy of the model, the grid search method was applied to determine the hyperparameter combination of GBDT. Experimental results showed that in the high-load mode, the mean absolute error (MAE) of the GBDT model was reduced by about 24% compared with the SVM, and was reduced by about 37% compared with the DNN model. Under the general load mode, MAE was reduced by about 30% and 53% respectively. Compared with the other models, the prediction accuracy of GBDT model has been significantly improved under different load mode. The proposed cooling load prediction model can provide reliable data support for the predictive control of buildings.
Nitrogen (N) is an essential nutrient for plant growth and development. Plant species respond to N fluctuations and N sources, i.e., ammonium or nitrate, differently. Masson pine (
Lamb.) is one of ...the pioneer plants in the southern forests of China. It shows better growth when grown in medium containing ammonium as compared to nitrate. In this study, we had grown masson pine seedlings in medium containing ammonium, nitrate, and a mixture of both, and performed comparative transcriptome and proteome analyses to observe the differential signatures. Our transcriptome and proteome resulted in the identification of 1593 and 71 differentially expressed genes and proteins, respectively. Overall, the masson pine roots had better performance when fed with a mixture of ammonium and nitrate. The transcriptomic and proteomics results combined with the root morphological responses suggest that when ammonium is supplied as a sole N-source to masson pine seedlings, the expression of ammonium transporters and other non-specific NH
-channels increased, resulting in higher NH
concentrations. This stimulates lateral roots branching as evidenced from increased number of root tips. We discussed the root performance in association with ethylene responsive transcription factors, WRKYs, and MADS-box transcription factors. The differential analysis data suggest that the adaptability of roots to ammonium is possibly through the promotion of TCA cycle, owing to the higher expression of malate synthase and malate dehydrogenase. Masson pine seedlings managed the increased NH
influx by rerouting N resources to asparagine production. Additionally, flavonoid biosynthesis and flavone and flavonol biosynthesis pathways were differentially regulated in response to increased ammonium influx. Finally, changes in the glutathione s-transferase genes suggested the role of glutathione cycle in scavenging the possible stress induced by excess NH
. These results demonstrate that masson pine shows increased growth when grown under ammonium by increased N assimilation. Furthermore, it can tolerate high NH
content by involving asparagine biosynthesis and glutathione cycle.
Explosive thermal spalling behavior during fire exposure is one of the major issues in the design of modern reinforced concrete structures. Previous experience on fire disasters indicates that ...spalling of concrete can have serious structural and economic consequences and must be taken into account in the design for fire. However, spalling mechanisms and their interaction still remain in dispute in the scientific community. In order to shed some light on this phenomenon, a discrete hygro-thermal model of concrete at high temperature called DTemPor3 is proposed and a full coupling scheme between DTemPor3 and the Lattice Discrete Particle Model (LDPM) is performed. The proposed multi-physical coupled model features the effect of pore pressure and temperature on the mechanical response as well as the impact of cracking on moisture mass transport and heat transfer. Simulations of typical spalling experiments show good agreements with data gathered from the literature for both high-performance concrete and ordinary concrete, demonstrating the accuracy of the proposed approach. Cracking localization is found to significantly impede the local pore pressure build-up due to the increase of pore or crack volume. The numerical simulations demonstrate that the spalling phenomenon can be successfully reproduced, only when the effect of thermal stresses is taken in account along with the effect of pore pressure on crack initiation.
High-voltage operation is essential for the energy and power densities of battery cathode materials, but its stabilization remains a universal challenge. To date, the degradation origin has been ...mostly attributed to cycling-initiated structural deformation while the effect of native crystallographic defects induced during the sophisticated synthesis process has been significantly overlooked. Here, using in situ synchrotron X-ray probes and advanced transmission electron microscopy to probe the solid-state synthesis and charge/discharge process of sodium layered oxide cathodes, we reveal that quenching-induced native lattice strain plays an overwhelming role in the catastrophic capacity degradation of sodium layered cathodes, which runs counter to conventional perception-phase transition and cathode interfacial reactions. We observe that the spontaneous relaxation of native lattice strain is responsible for the structural earthquake (e.g., dislocation, stacking faults and fragmentation) of sodium layered cathodes during cycling, which is unexpectedly not regulated by the voltage window but is strongly coupled with charge/discharge temperature and rate. Our findings resolve the controversial understanding on the degradation origin of cathode materials and highlight the importance of eliminating intrinsic crystallographic defects to guarantee superior cycling stability at high voltages.
Semantic segmentation of high-resolution remote sensing images holds paramount importance in the field of remote sensing. To better excavate and fully fuse the features in high-resolution remote ...sensing images, this paper introduces a novel Global and Local Feature Fusion Network, abbreviated as GLF-Net, by incorporating the extensive contextual information and refined fine-grained features. The proposed GLF-Net, devised as an encoder–decoder network, employs the powerful ResNet50 as its baseline model. It incorporates two pivotal components within the encoder phase: a Covariance Attention Module (CAM) and a Local Fine-Grained Extraction Module (LFM). And an additional wavelet self-attention module (WST) is integrated into the decoder stage. The CAM effectively extracts the features of different scales from various stages of the ResNet and then encodes them with graph convolutions. In this way, the proposed GLF-Net model can well capture the global contextual information with both universality and consistency. Additionally, the local feature extraction module refines the feature map by encoding the semantic and spatial information, thereby capturing the local fine-grained features in images. Furthermore, the WST maximizes the synergy between the high-frequency and the low-frequency information, facilitating the fusion of global and local features for better performance in semantic segmentation. The effectiveness of the proposed GLF-Net model is validated through experiments conducted on the ISPRS Potsdam and Vaihingen datasets. The results verify that it can greatly improve segmentation accuracy.
Purpose:
Anti-tumour necrosis factor-alpha (TNF-α) agents are often used for Behçet’s disease (BD) in clinical practice, but they have not been validated by a high level of evidence. We ...systematically reviewed published controlled trials to investigate the efficacy and safety of anti-TNF-α therapy and summarize the efficacy of anti-TNF-α therapy relative to the available therapeutic options.
Methods:
A systematic database search was conducted (PubMed, Embase and Cochrane) using specific search terms. All controlled studies of anti-TNF-α treatment of BD patients prior to December 2021 were included. Single-arm studies were excluded. The decision of whether to incorporate data into the meta-analysis or summarize the data by qualitative synthesis was based on the results of the literature screening.
Results:
Of 4389 screened studies, 13 (total 778 patients) were included in accordance with our retrieval strategy, comprising 1 randomized controlled trial, 1 prospective study, 10 retrospective studies, and 1 multicentre open-label study. Ten studies (76.9%) involved Behçet’s uveitis (BU), 1 involved intestinal BD, and the other studies had undefined subtypes. Subgroup reviews were conducted according to the control drug. Four studies involving 167 participants reported relapse rates. Meta-analysis of three of these studies demonstrated that, compared with traditional immunosuppressant (TIS) therapy, anti-TNF-α therapy reduced the relapse rates in patients with BU. In targeted drug comparison studies, the efficacy appeared to be similar between the anti-TNF-α agent and interferon in BU patients. The rates of adverse events were comparable between a variety of different therapeutic controls. Serious adverse events were not observed in 53.8% (7/13) of the studies.
Conclusions:
Compared with TIS therapy, anti-TNF-a therapy reduces the relapse of uveitis in patients with BD. However, the evidence regarding anti-TNF-α therapy is very limited for the full spectrum of BD subtypes, which calls for caution.
A three-dimensional multiphysics lattice discrete particle model (M-LDPM) framework is formulated to investigate the fracture permeability behavior of shale. The framework features a dual lattice ...system mimicking the mesostructure of the material and simulates coupled mechanical and flow behavior. The mechanical lattice model simulates the granular internal structure of shale, and describes heterogeneous deformation by means of discrete compatibility and equilibrium equations. The network of flow lattice elements constitutes a dual graph of the mechanical lattice system. A discrete formulation of mass balance for the flow elements is presented to model fluid flow along cracks and intact materials. The overall computational framework is implemented with a mixed explicit–implicit integration scheme and a staggered coupling method that makes use of the dual lattice topology enabling the seamless two-way coupling of the mechanical and flow behaviors. The proposed model is used for the computational analysis of shale fracture permeability behavior by simulating triaxial direct shear tests on Marcellus shale specimens under various confining pressures. The simulated mechanical response is calibrated against the experimental data, and the predicted permeability values are also compared with the experimental measurements. Furthermore, the paper presents the scaling analysis of both the mechanical response and permeability measurements based on simulations performed on geometrically similar specimens with increasing size. The simulated stress strain curves show a significant size effect in the post-peak due to the presence of localized fractures. The scaling analysis of permeability measurements enables prediction of permeability for large specimens by extrapolating the numerical results of small ones.
The spatial distribution patterns of soil organic carbon (SOC) and its relationship with various environmental factors in different topographic features are of great significance for evaluating SOC ...reserves and carbon pool management in karst areas. In this paper, SOC from the non-karst landforms and four typical karst landforms (karst canyon, karst peak cluster, karst plateau, and karst trough valley) was selected as the research object. The distribution patterns and the driving factors of SOC in the typical landforms were investigated from the topographical and environmental perspectives. The results showed that the average content of organic carbon in non-karst region was 9.39 g/kg, while that in karst areas was 16.31 g/kg. The vertical changes in SOC content showed different patterns in different topographic features, and each layer of the soil profile showed that SOC content in karst regions was greater than that in the non-karst one. The driving factors for SOC varied in different landforms. Specifically, in non-karst and karst peak cluster areas, slope gradient was the key factor influencing SOC. In karst canyon areas, soil thickness was the key factor influencing SOC. In karst plateau areas, land utilization was the key factor influencing superficial SOC. In karst trough valley areas, rock exposure was the major driving factor for karst SOC. These findings could provide an excellent explanation for the factors influencing SOC in karst and non-karst regions in Southwest China.
Clear cell renal cell carcinoma (ccRCC) is a highly invasive and metastatic subtype of kidney malignancy and is correlated with metabolic reprogramming for adaptation to the tumor microenvironment ...comprising infiltrated immune cells and immunomodulatory molecules. The role of immune cells in the tumor microenvironment (TME) and their association with abnormal fatty acids metabolism in ccRCC remains poorly understood.
RNA-seq and clinical data of KIRC from The Cancer Genome Atlas (TCGA) and E-MTAB-1980 from the ArrayExpress dataset. The Nivolumab group and Everolimus group of the CheckMate 025 study, the Atezolizumab arm of IMmotion150 and the Atezolizumab plus Bevacizumab group of IMmotion151 cohort were obtained for subsequent analysis. After differential expression genes identification, the signature was constructed through univariate Cox proportional hazard regression and simultaneously the least absolute shrinkage and selection operator (Lasso) analysis and the predictive performance of our signature was assessed by using receiver operating characteristic (ROC), Kaplan-Meier (KM) survival analysis, nomogram, drug sensitivity analysis, immunotherapeutic effect analysis and enrichment analysis. Immunohistochemistry (IHC), qPCR and western blot were performed to measure related mRNA or protein expression. Biological features were evaluated by wound healing, cell migration and invasion assays and colony formation test and analyzed using coculture assay and flow cytometry.
Twenty fatty acids metabolism-related mRNA signatures were constructed in TCGA and possessed a strong predictive performance demonstrated through time-dependent ROC and KM survival analysis. Notably, the high-risk group exhibited an impaired response to anti-PD-1/PD-L1 (Programmed death-1 receptor/Programmed death-1 receptor-ligand) therapy compared to the low-risk group. The overall levels of the immune score were higher in the high-risk group. Additionally, drug sensitivity analysis observed that the model could effectively predict efficacy and sensitivity to chemotherapy. Enrichment analysis revealed that the IL6-JAK-STAT3 signaling pathway was a major pathway. IL4I1 could promote ccRCC cells' malignant features through JAK1/STAT3 signaling pathway and M2-like macrophage polarization.
The study elucidates that targeting fatty acids metabolism can affect the therapeutic effect of PD-1/PD-L1 in TME and related signal pathways. The model can effectively predict the response to several treatment options, underscoring its potential clinical utility.
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