A short-term hybrid wind speed prediction model based on decomposition and improved optimization algorithm

Introduction: In the field of wind power generation, short-term wind speed prediction plays an increasingly important role as the foundation for effective utilization of wind energy. However, accurately predicting wind speed is highly challenging due to its complexity and randomness in practical applications. Currently, single algorithms exhibit poor accuracy in short-term wind speed prediction, leading to the widespread adoption of hybrid wind speed prediction models based on deep learning techniques. To comprehensively enhance the predictive performance of short-term wind speed models, this study proposes a hybrid model, VMDAttention LSTM-ASSA, which consists of three stages: decomposition of the original wind speed sequence, prediction of each mode component, and weight optimization.Methods: To comprehensively enhance the predictive performance of short-term wind speed models, this study proposes a hybrid model, VMDAttention LSTM-ASSA, which consists of three stages: decomposition of the original wind speed sequence, prediction of each mode component, and weight optimization. Firstly, the model incorporates an attention mechanism into the LSTM model to extract important temporal slices from each mode component, effectively improving the slice prediction accuracy. Secondly, two different search operators are introduced to enhance the original Salp Swarm Algorithm, addressing the issue of getting trapped in local optima and achieving globally optimal short-term wind speed predictions.Result: Through comparative experiments using multiple-site short-term wind speed datasets, this study demonstrates that the proposed VMD-AtLSTM-ASSA model outperforms other hybrid prediction models (VMD-RNN, VMD-BPNN, VMD-GRU, VMD-LSTM) with a maximum reduction of 80.33% in MAPE values. The experimental results validate the high accuracy and stability of the VMD-AtLSTM-ASSA model.Discussion: Short-term wind speed prediction is of paramount importance for the effective utilization of wind power generation, and our research provides strong support for enhancing the efficiency and reliability of wind power generation systems. Future research directions may include further improvements in model performance and extension into other meteorological and environmental application domains

Deciphering molecular details in the assembly of alpha-type carboxysome

Bacterial microcompartments (BMCs) are promising natural protein structures for applications that require the segregation of certain metabolic functions or molecular species in a defined microenvironment. To understand how endogenous cargos are packaged inside the protein shell is key for using BMCs as nano-scale reactors or delivery vesicles. In this report, we studied the encapsulation of RuBisCO into the α-type carboxysome from Halothiobacillus neapolitan. Our experimental data revealed that the CsoS2 scaffold proteins engage RuBisCO enzyme through an interaction with the small subunit (CbbS). In addition, the N domain of the large subunit (CbbL) of RuBisCO interacts with all shell proteins that can form the hexamers. The binding affinity between the N domain of CbbL and one of the major shell proteins, CsoS1C, is within the submicromolar range. The absence of the N domain also prevented the encapsulation of the rest of the RuBisCO subunits. Our findings complete the picture of how RuBisCOs are encapsulated into the α-type carboxysome and provide insights for future studies and engineering of carboxysome as a protein shell

Unlocking the Potential: Amino Acids’ Role in Predicting and Exploring Therapeutic Avenues for Type 2 Diabetes Mellitus

Diabetes mellitus, particularly type 2 diabetes mellitus (T2DM), imposes a significant global burden with adverse clinical outcomes and escalating healthcare expenditures. Early identification of biomarkers can facilitate better screening, earlier diagnosis, and the prevention of diabetes. However, current clinical predictors often fail to detect abnormalities during the prediabetic state. Emerging studies have identified specific amino acids as potential biomarkers for predicting the onset and progression of diabetes. Understanding the underlying pathophysiological mechanisms can offer valuable insights into disease prevention and therapeutic interventions. This review provides a comprehensive summary of evidence supporting the use of amino acids and metabolites as clinical biomarkers for insulin resistance and diabetes. We discuss promising combinations of amino acids, including branched-chain amino acids, aromatic amino acids, glycine, asparagine and aspartate, in the prediction of T2DM. Furthermore, we delve into the mechanisms involving various signaling pathways and the metabolism underlying the role of amino acids in disease development. Finally, we highlight the potential of targeting predictive amino acids for preventive and therapeutic interventions, aiming to inspire further clinical investigations and mitigate the progression of T2DM, particularly in the prediabetic stage

Deciphering molecular details in the assembly of alpha-type carboxysome

Bacterial microcompartments (BMCs) are promising natural protein structures for applications that require the segregation of certain metabolic functions or molecular species in a defined microenvironment. To understand how endogenous cargos are packaged inside the protein shell is key for using BMCs as nano-scale reactors or delivery vesicles. In this report, we studied the encapsulation of RuBisCO into the α-type carboxysome from Halothiobacillus neapolitan. Our experimental data revealed that the CsoS2 scaffold proteins engage RuBisCO enzyme through an interaction with the small subunit (CbbS). In addition, the N domain of the large subunit (CbbL) of RuBisCO interacts with all shell proteins that can form the hexamers. The binding affinity between the N domain of CbbL and one of the major shell proteins, CsoS1C, is within the submicromolar range. The absence of the N domain also prevented the encapsulation of the rest of the RuBisCO subunits. Our findings complete the picture of how RuBisCOs are encapsulated into the α-type carboxysome and provide insights for future studies and engineering of carboxysome as a protein shell

Mechanical Properties of σ-Phase and Its Effect on the Mechanical Properties of Austenitic Stainless Steel

In this present paper, the mechanical properties of σ-phase and its effect on the mechanical properties of 304H austenitic stainless steel after servicing for about 8 years at 680–720 °C were studied by nano-indentation test, uniaxial tensile test, and impact test. The results showed that the nano-hardness (H), Young’s modulus (E), strain hardening exponent (n), and yield strength (σy) of σ-phase were 14.95 GPa, 263 GPa, 0.78, and 2.42 GPa, respectively. The presence of σ-phase increased the hardness, yield strength, and tensile strength, but greatly reduced the elongation and impact toughness of the material

Identification and validation of diagnostic biomarkers for intrahepatic cholestasis of pregnancy based on untargeted and targeted metabolomics analyses of urine metabolite profiles

Abstract Background Intrahepatic cholestasis of pregnancy (ICP) is a prevalent pregnancy-specific complication that presents with maternal itching and elevated serum bile acid levels. ICP is associated with unfavorable pregnancy outcomes, severely decreasing the pregnant woman’s quality of life. Timely identification of ICP is crucial for effective management and improved outcomes. Methods We collected urine samples from 8 patients with ICP and 8 healthy individuals. We used Liquid Chromatography-Mass Spectrometry (LC-MS) to detect metabolite expression levels, then conducted a series of bioinformatic analyses to explore the potential biological meanings of differentially expressed metabolites, and preliminarily discovered several candidate biomarkers. To validate these candidate biomarkers, we performed Gas Chromatography-Mass Spectrometry (GC-MS) detection and analyzed their diagnostic values using receiver operating characteristic (ROC) curve. Results Untargeted metabolomics data showed that 6129 positive peaks and 6218 negative peaks were extracted from each specimen. OPLS-DA analysis and the heat map for cluster analysis showed satisfactory capability in discriminating ICP specimens from controls. Subsequent analysis extracted 64 significantly differentially expressed metabolites, which could be potential biomarkers for diagnosis of ICP. Based on the KEGG enrichment analyses, six candidate biomarkers were preliminarily identified. Two most promising biomarkers (3-hydroxypropionic acid and uracil) were validated by targeted metabolomics analyses with the area under the curve (AUC) of 0.920 and 0.850 respectively. Conclusion Based on preliminary screening from untargeted metabolomics and subsequent validation through targeted metabolomics, 3-hydroxypropionic acid and uracil were identified as promising diagnostic biomarkers for ICP

Stabilising cobalt sulphide nanocapsules with nitrogen-doped carbon for high-performance sodium-ion storage

Abstract: Conversion-type anode materials with a high charge storage capability generally suffer from large volume expansion, poor electron conductivity, and sluggish metal ion transport kinetics. The electrode material described in this paper, namely cobalt sulphide nanoparticles encapsulated in carbon cages (CoS@NC), can circumvent these problems. This electrode material exhibited a reversible sodium-ion storage capacity of 705\ua0mAh\ua0g at 100\ua0mA\ua0g with an extraordinary rate capability and good cycling stability. Mechanistic study using the in situ transmission electron microscope technique revealed that the volumetric expansion of the CoS nanoparticles is buffered by the carbon cages, enabling a stable electrode–electrolyte interface. In addition, the carbon shell with high-content doped nitrogen significantly enhances the electron conductivity of the CoS@NC electrode material and provides doping-induced active sites to accommodate sodium ions. By integrating the CoS@NC as negative electrode with a cellulose-derived porous hard carbon/graphene oxide composite as positive electrode and 1\ua0M NaPF in diglyme as the electrolyte, the sodium-ion capacitor full cell can achieve energy densities of 101.4 and 45.8\ua0Wh\ua0kg at power densities of 200 and 10,000\ua0W\ua0kg, respectively

Assessing the cost-effectiveness of carbon neutrality for light-duty vehicle sector in China

Summary: China’s progress in decarbonizing its transportation, particularly vehicle electrification, is notable. However, the economically effective pathways are underexplored. To find out how much cost is necessary for carbon neutrality for the light-duty vehicle (LDV) sector, this study examines twenty decarbonization pathways, combining the New Energy and Oil Consumption Credit model and the China-Fleet model. We find that the 2060 zero-greenhouse gas (GHG) emission goal for LDVs is achievable via electrification if the battery pack cost is under CNY483/kWh by 2050. However, an extra of CNY8.86 trillion internal subsidies is needed under pessimistic battery cost scenarios (CNY759/kWh in 2050) to eliminate 246 million tonnes of CO2-eq by 2050 ensuring over 80% market penetration of battery electric vehicles (BEVs) in 2050. Moreover, the promotion of fuel cell electric vehicles is synergy with BEVs to mitigate the carbon abatement difficulties, decreasing up to 34% of the maximum marginal abatement internal investment