Optimisation of Microwave Pretreatment for Biogas Enhancement through Anaerobic Digestion of Microalgal Biomass

In this study, optimization of microwave (MW) pretreatment conditions for anaerobic digestion of green microalgae (Enteromorpha) is carried out by using response surface methodology (RSM). MW power, pretreatment time and liquid-solid ratio were selected as independent variables for optimization. The optimum conditions were achieved at MW power, pretreatment time and liquid-solid ratio of 656.92 W, 5.10 min and 33.63:1, respectively. From these optimum conditions, it was found that MW pretreatment power of about 600 W had better effect. An anaerobic digestion was carried out batch-wise with working volume, operating temperature and mixing rate as 250 ml, 37 °C and 150 rpm, respectively. Optimum conditions provide highest amount of COD and reducing sugar increase of 10,420 mg/L and 0.77-0.79 g/L respectively. The increase in COD and reducing sugar showed that the pretreatment has improved anaerobic digestion of microalgae. The peak biogas production amount of MW pretreated 20:1, 6 min group reached 244 mL whereas the control group only reached 188 mL in total

New cooperation between Belarus and China: new pedagogical reforms

The article reflects the ways of improvement of educational process for Chinese in Belarus according to the cultural specific

Effect of Autoclave Pretreatment on Biogas Production through Anaerobic Digestion of Green Algae

Anaerobic Digestion (AD) is one of the most widely used methods in the field of sustainable bioenergy production from various feedstock. One such feedstock is algae waste which has become an increasingly serious environmental problem. AD of algal biomass is hindered by the presence of resistant cell walls; hence a pretreatment step is usually required to decompose the cell wall structure. This study uses green algae (Enteromorpha) and anaerobic sludge as raw materials to explore the impact of autoclave (AC) pretreatment on biogas production. AC pretreatment was performed at 120 °C and 80 °C. The cumulative biogas production of the 120 °C AC pretreatment, 80 °C AC pretreatment and control group were 600 mL, 450 mL and 400 mL, respectively. The results showed that AC pretreatment improved the biodegradability of biomass as 120 °C AC pretreatment group achieved higher degradation rate of cells (95.99 %). The energy evaluation showed that the net energy ratio of the 120 °C AC pretreatment group was 1.07, indicating high overall energy gain via AD process. The experimental data is further modeled by using Modified Gompertz Model (MGM) and Logistic Function Model (LFM). To check the applicability of better model for this AD process, an Akaike Information Criteria (AIC) test was performed. AIC showed that the MGM is basically consistent with the experimental data and more reliable for prediction modeling of Enteromorpha AD

Unleashing the Power of Pre-trained Language Models for Offline Reinforcement Learning

Offline reinforcement learning (RL) aims to find a near-optimal policy using pre-collected datasets. In real-world scenarios, data collection could be costly and risky; therefore, offline RL becomes particularly challenging when the in-domain data is limited. Given recent advances in Large Language Models (LLMs) and their few-shot learning prowess, this paper introduces $\textbf{La}$nguage Models for $\textbf{Mo}$tion Control ($\textbf{LaMo}$), a general framework based on Decision Transformers to effectively use pre-trained Language Models (LMs) for offline RL. Our framework highlights four crucial components: (1) Initializing Decision Transformers with sequentially pre-trained LMs, (2) employing the LoRA fine-tuning method, in contrast to full-weight fine-tuning, to combine the pre-trained knowledge from LMs and in-domain knowledge effectively, (3) using the non-linear MLP transformation instead of linear projections, to generate embeddings, and (4) integrating an auxiliary language prediction loss during fine-tuning to stabilize the LMs and retain their original abilities on languages. Empirical results indicate $\textbf{LaMo}$ achieves state-of-the-art performance in sparse-reward tasks and closes the gap between value-based offline RL methods and decision transformers in dense-reward tasks. In particular, our method demonstrates superior performance in scenarios with limited data samples.Comment: 24 pages, 16 table

China’s accelerated development of AI and possibilities of future cooperation in Belarus

The continuous advancement of AI technology is continuously developing China's comprehensive AI infrastructure. Local tech companies represented by Tencent, Baidu, Alibaba and Huawei are emerging as flagship entities for this development. This thesis analyze these advances provides a framework for understanding possible paths for mutual development and cooperation in AI in China and Belarus

NADPH oxidase 4 mediates insulin-stimulated HIF-1α and VEGF expression, and angiogenesis in vitro

Acute intensive insulin therapy causes a transient worsening of diabetic retinopathy in type 1 diabetes patients and is related to VEGF expression. Reactive oxygen species (ROS) have been shown to be involved in HIF-1α and VEGF expression induced by insulin, but the role of specific ROS sources has not been fully elucidated. In this study we examined the role of NADPH oxidase subunit 4 (Nox4) in insulin-stimulated HIF-1α and VEGF expression, and angiogenic responses in human microvascular endothelial cells (HMVECs). Here we demonstrate that knockdown of Nox4 by siRNA reduced insulin-stimulated ROS generation, the tyrosine phosphorylation of IR-β and IRS-1, but did not change the serine phosphorylation of IRS-1. Nox4 gene silencing had a much greater inhibitory effect on insulin-induced AKT activation than ERK1/2 activation, whereas it had little effect on the expression of the phosphatases such as MKP-1 and SHIP. Inhibition of Nox4 expression inhibited the transcriptional activity of VEGF through HIF-1. Overexpression of wild-type Nox4 was sufficient to increase VEGF transcriptional activity, and further enhanced insulin-stimulated the activation of VEGF. Downregulation of Nox4 expression decreased insulin-stimulated mRNA and protein expression of HIF-1α, but did not change the rate of HIF-1α degradation. Inhibition of Nox4 impaired insulin-stimulated VEGF expression, cell migration, cell proliferation, and tube formation in HMVECs. Our data indicate that Nox4-derived ROS are essential for HIF-1α-dependent VEGF expression, and angiogenesis in vitro induced by insulin. Nox4 may be an attractive therapeutic target for diabetic retinopathy caused by intensive insulin treatment

H-InDex: Visual Reinforcement Learning with Hand-Informed Representations for Dexterous Manipulation

Human hands possess remarkable dexterity and have long served as a source of inspiration for robotic manipulation. In this work, we propose a human $\textbf{H}$and$\textbf{-In}$formed visual representation learning framework to solve difficult $\textbf{Dex}$terous manipulation tasks ($\textbf{H-InDex}$) with reinforcement learning. Our framework consists of three stages: (i) pre-training representations with 3D human hand pose estimation, (ii) offline adapting representations with self-supervised keypoint detection, and (iii) reinforcement learning with exponential moving average BatchNorm. The last two stages only modify $0.36\%$ parameters of the pre-trained representation in total, ensuring the knowledge from pre-training is maintained to the full extent. We empirically study 12 challenging dexterous manipulation tasks and find that H-InDex largely surpasses strong baseline methods and the recent visual foundation models for motor control. Code is available at https://yanjieze.com/H-InDex .Comment: NeurIPS 2023. Code and videos: https://yanjieze.com/H-InDe

Hearing Lips in Noise : Universal Viseme-Phoneme Mapping and Transfer for Robust Audio-Visual Speech Recognition

PreprintPublisher PD

The clinical predictive value of geriatric nutritional risk index in elderly rectal cancer patients received surgical treatment after neoadjuvant therapy

ObjectiveThe assessment of nutritional status has been recognized as crucial in the treatment of geriatric cancer patients. The objective of this study is to determine the clinical predictive value of the geriatric nutritional risk index (GNRI) in predicting the short-term and long-term prognosis of elderly rectal cancer (RC) patients who undergo surgical treatment after neoadjuvant therapy.MethodsBetween January 2014 and December 2020, the clinical materials of 639 RC patients aged ≥70 years who underwent surgical treatment after neoadjuvant therapy were retrospectively analysed. Propensity score matching was performed to adjust for baseline potential confounders. Logistic regression analysis and competing risk analysis were conducted to evaluate the correlation between the GNRI and the risk of postoperative major complications and cumulative incidence of cancer-specific survival (CSS). Nomograms were then constructed for postoperative major complications and CSS. Additionally, 203 elderly RC patients were enrolled between January 2021 and December 2022 as an external validation cohort.ResultsMultivariate logistic regression analysis showed that GNRI [odds ratio = 1.903, 95% confidence intervals (CI): 1.120–3.233, p = 0.017] was an independent risk factor for postoperative major complications. In competing risk analysis, the GNRI was also identified as an independent prognostic factor for CSS (subdistribution hazard ratio = 3.90, 95% CI: 2.46–6.19, p < 0.001). The postoperative major complication nomogram showed excellent performance internally and externally in the area under the receiver operating characteristic curve (AUC), calibration plots and decision curve analysis (DCA). When compared with other models, the competing risk prognosis nomogram incorporating the GNRI achieved the highest outcomes in terms of the C-index, AUC, calibration plots, and DCA.ConclusionThe GNRI is a simple and effective tool for predicting the risk of postoperative major complications and the long-term prognosis of elderly RC patients who undergo surgical treatment after neoadjuvant therapy