Improving the Cellulose Enzymatic Digestibility of Sugarcane Bagasse by Atmospheric Acetic Acid Pretreatment and Peracetic Acid Post-Treatment

Pretreatment of sugarcane bagasse (SCB) by aqueous acetic acid (AA), with the addition of sulfuric acid (SA) as a catalyst under mild condition (<110 °C), was investigated. A response surface methodology (central composite design) was employed to study the effects of temperature, AA concentration, time, and SA concentration, as well as their interactive effects, on several response variables. Kinetic modeling was further investigated for AA pretreatment using both Saeman’s model and the Potential Degree of Reaction (PDR) model. It was found that Saeman’s model showed a great deviation from the experimental results, while the PDR model fitted the experimental data very well, with determination coefficients of 0.95–0.99. However, poor enzymatic digestibility of the AA-pretreated substrates was observed, mainly due to the relatively low degree of delignification and acetylation of cellulose. Post-treatment of the pretreated cellulosic solid well improved the cellulose digestibly by further selectively removing 50–60% of the residual linin and acetyl group. The enzymatic polysaccharide conversion increased from <30% for AA-pretreatment to about 70% for PAA post-treatment

An Enhanced Differential Grouping Method for Large-Scale Overlapping Problems

Tian M, Chen M, Du W, Tang Y, Jin Y. An Enhanced Differential Grouping Method for Large-Scale Overlapping Problems. IEEE Transactions on Evolutionary Computation. 2024:1-1.Large-scale overlapping problems are prevalent in practical engineering applications, and the optimization challenge is significantly amplified due to the existence of shared variables. Decomposition-based cooperative coevolution (CC) algorithms have demonstrated promising performance in addressing large-scale overlapping problems. However, current CC frameworks designed for overlapping problems rely on grouping methods for the identification of overlapping problem structures and the current grouping methods for large-scale overlapping problems fail to consider both accuracy and efficiency simultaneously. In this article, we propose a two-stage enhanced grouping method for large-scale overlapping problems, called OEDG, which achieves accurate grouping while significantly reducing computational resource consumption. In the first stage, OEDG employs a grouping method based on the finite differences principle to identify all subcomponents and shared variables. In the second stage, we propose two grouping refinement methods, called subcomponent union detection (SUD) and subcomponent detection (SD), to enhance and refine the grouping results. SUD examines the information of the subcomponents and shared variables obtained in the previous stage, and SD corrects inaccurate grouping results. To better verify the performance of the proposed OEDG, we propose a series of novel benchmarks that consider various properties of large-scale overlapping problems, including the topology structure, overlapping degree, and separability. Extensive experimental results demonstrate that OEDG is capable of accurately grouping different types of large-scale overlapping problems while consuming fewer computational resources. Finally, we empirically verify that the proposed OEDG can effectively improve the optimization performance of diverse large-scale overlapping problems

Prediction of lymph node metastasis in operable cervical cancer using clinical parameters and deep learning with MRI data: a multicentre study

Abstract Objectives To develop and validate a magnetic resonance imaging-based (MRI) deep multiple instance learning (D-MIL) model and combine it with clinical parameters for preoperative prediction of lymph node metastasis (LNM) in operable cervical cancer. Methods A total of 392 patients with cervical cancer were retrospectively enrolled. Clinical parameters were analysed by logistical regression to construct a clinical model (M1). A ResNet50 structure is applied to extract features at the instance level without using manual annotations about the tumour region and then construct a D-MIL model (M2). A hybrid model (M3) was constructed by M1 and M2 scores. The diagnostic performance of each model was evaluated by the area under the receiver operating characteristic curve (AUC) and compared using the Delong method. Disease-free survival (DFS) was evaluated by the Kaplan‒Meier method. Results SCC-Ag, maximum lymph node short diameter (LNmax), and tumour volume were found to be independent predictors of M1 model. For the diagnosis of LNM, the AUC of the training/internal/external cohort of M1 was 0.736/0.690/0.732, the AUC of the training/internal/external cohort of M2 was 0.757/0.714/0.765, and the AUC of the training/internal/external cohort of M3 was 0.838/0.764/0.835. M3 showed better performance than M1 and M2. Through the survival analysis, patients with higher hybrid model scores had a shorter time to reach DFS. Conclusion The proposed hybrid model could be used as a personalised non-invasive tool, which is helpful for predicting LNM in operable cervical cancer. The score of the hybrid model could also reflect the DFS of operable cervical cancer. Critical relevance statement Lymph node metastasis is an important factor affecting the prognosis of cervical cancer. Preoperative prediction of lymph node status is helpful to make treatment decisions, improve prognosis, and prolong survival time. Key points • The MRI-based deep-learning model can predict the LNM in operable cervical cancer. • The hybrid model has the highest diagnostic efficiency for the LNM prediction. • The score of the hybrid model can reflect the DFS of operable cervical cancer. Graphical Abstrac

Field test and preliminary analysis of a combined diurnal solar heating and nocturnal radiative cooling system

© 2016 A type of composite surface was manufactured for trial to achieve integrated solar heating and radiative cooling functions. The spectral properties of the composite surface present a relatively clear selectivity in the spectra of solar heating and radiation cooling wavelengths. A combined system for both solar heating and radiative cooling (named SH-RC system) based on the composite surface was mounted together with a traditional flat-plate solar heating system. Comparative experiments were carried out to investigate their thermal performances both at daytime and nighttime. Results showed that the composite surface has a relatively evident spectral selectivity. In diurnal collector testing mode, the thermal efficiency of the SH-RC collector was 62.7% at zero-reduced temperature, which was about 86.4% of that of the traditional flat-plate solar heating collector. In nocturnal collector testing mode, the SH-RC collector had net radiative cooling powers of 50.3 W/m2 on a clear night and 23.4 W/m2 on an overcast night; by contrast, the traditional flat-plate solar heating collector exhibited very little radiative cooling capacity. In diurnal system testing mode, the daily average thermal efficiency of the SH-RC system and the traditional flat-plate solar heating system at zero-reduced temperature was 38.6% and 48.4%, respectively. Based on experimental results, the SH-RC system showed a considerable performance for both diurnal solar heating and nocturnal radiative cooling