Evaluation of mixing and mixing rate in a multiple spouted bed by image processing technique

Mixing efficiency is one of the most significant factors, affecting both performance and scale-up of a gas-solid reactor system. This paper presents an experimental investigation on the particle mixing in a multiple spouted bed. Image processing technique was used to extract the real-time information concerning the distribution of particle components (bed materials and tracer particles). A more accurate definition of the tracer concentration was developed to calculate the mixing index. According to the visual observation and image analysis, the mixing mechanism was revealed and the mixing rate was evaluated. Based on these results, the effects of operation parameters on the mixing rate were discussed in terms of the flow patterns. It is found that the detection of the pixel distribution of each component in RGB images is not affected by the interference of air void, thus maintaining good measurement accuracy. Convective transportation controls the particle mixing in the internal jet and spout, while shear dominants the particle mixing in the dense moving region. Global mixing takes place only when the path from one spout cell to the other is open. This path can be formed either by the bubbles or particle circulation flows. The mixing rate is linked to the bubble motion and particle circulation. Provided that there are interactions between the spout cells, any parameters promoting the bubble motion and circulation can increase the mixing rate. Finally, a mixing pattern diagram was constructed to establish the connection between the flow structure and mixing intensity

Strain-induced semiconductor to metal transition in MA2Z4 bilayers

Very recently, a new type of two-dimensional layered material MoSi2N4 has been fabricated, which is semiconducting with weak interlayer interaction, high strength, and excellent stability. We systematically investigate theoretically the effect of vertical strain on the electronic structure of MA2Z4 (M=Ti/Cr/Mo, A=Si, Z=N/P) bilayers. Taking bilayer MoSi2N4 as an example, our first principle calculations show that its indirect band gap decreases monotonically as the vertical compressive strain increases. Under a critical strain around 22%, it undergoes a transition from semiconductor to metal. We attribute this to the opposite energy shift of states in different layers, which originates from the built-in electric field induced by the asymmetric charge transfer between two inner sublayers near the interface. Similar semiconductor to metal transitions are observed in other strained MA2Z4 bilayers, and the estimated critical pressures to realize such transitions are within the same order as semiconducting transition metal dichalcogenides. The semiconductor to metal transitions observed in the family of MA2Z4 bilayers present interesting possibilities for strain-induced engineering of their electronic properties

Rethinking Learning Rate Tuning in the Era of Large Language Models

Large Language Models (LLMs) represent the recent success of deep learning in achieving remarkable human-like predictive performance. It has become a mainstream strategy to leverage fine-tuning to adapt LLMs for various real-world applications due to the prohibitive expenses associated with LLM training. The learning rate is one of the most important hyperparameters in LLM fine-tuning with direct impacts on both fine-tuning efficiency and fine-tuned LLM quality. Existing learning rate policies are primarily designed for training traditional deep neural networks (DNNs), which may not work well for LLM fine-tuning. We reassess the research challenges and opportunities of learning rate tuning in the coming era of Large Language Models. This paper makes three original contributions. First, we revisit existing learning rate policies to analyze the critical challenges of learning rate tuning in the era of LLMs. Second, we present LRBench++ to benchmark learning rate policies and facilitate learning rate tuning for both traditional DNNs and LLMs. Third, our experimental analysis with LRBench++ demonstrates the key differences between LLM fine-tuning and traditional DNN training and validates our analysis

Methylmalonic acid levels in serum, exosomes, and urine and its association with cblC type methylmalonic acidemia-induced cognitive impairment

BackgroundThe cblC type methylmalonic acidemia is the most common methylmalonic acidemia (MMA) in China. The biochemical characteristics of this disease include elevated methylmalonic acid and homocysteine (HCY), increased propionylcarnitine (C3), decreased free carnitine (C0). In this study, we aimed to clarify the roles of these biomarkers in cblC-MMA induced cognitive impairment and evaluate the capacity of methylmalonic acid in different fluids or exosomes to distinguish cblC-MMA induced cognitive impairment.Methods15 non-inherited hyperhomocysteinemia (HHcy) patients, 42 cblC-MMA patients and 57 age- and sex-matched healthy children were recruited in this study. The levels of HCY were detected by an automatic immune analyzer. The levels of acylcarnitines and methylmalonic acid were detected by tandem mass spectrometer.ResultsThe main findings were all biomarkers as HCY, acylcarnitines and methylmalonic acid had capacities for distinguishing patients with cblC-MMA induced cognitive impairment from healthy children. The methylmalonic acid in different fluids or exosomes had good performances for distinguishing patients with cblC-MMA induced cognitive impairment from HHcy patients. The methylmalonic acid in serum exosomes and neuronal-derived exosomes were able to distinguishing cblC-MMA patients with cognitive impairment from patients without cognitive impairment. The methylmalonic acid in neuronal-derived exosomes might be helpful to evaluate the severity of cblC-MMA induced cognitive impairment.DiscussionMethylmalonic acid levels in serum exosomes, especially in serum neuronal-derived exosomes, serve as potential biomarkers for distinguishing cblC-MMA induced cognitive impairment

Nitrogen, Phosphorus, and Potassium Flows through the Manure Management Chain in China

The largest livestock production and greatest fertilizer use in the world occurs in China. However, quantification of the nutrient flows through the manure management chain and their interactions with management-related measures is lacking. Herein, we present a detailed analysis of the nutrient flows and losses in the “feed intake–excretion–housing–storage–treatment–application” manure chain, while considering differences among livestock production systems. We estimated the environmental loss from the manure chain in 2010 to be up to 78% of the excreted nitrogen and over 50% of the excreted phosphorus and potassium. The greatest losses occurred from housing and storage stages through NH₃ emissions (39% of total nitrogen losses) and direct discharge of manure into water bodies or landfill (30–73% of total nutrient losses). There are large differences among animal production systems, where the landless system has the lowest manure recycling. Scenario analyses for the year 2020 suggest that significant reductions of fertilizer use (27–100%) and nutrient losses (27–56%) can be achieved through a combination of prohibiting manure discharge, improving manure collection and storages infrastructures, and improving manure application to cropland. We recommend that current policies and subsidies targeted at the fertilizer industry should shift to reduce the costs of manure storage, transport, and application

Predicting Axillary Lymph Node Metastasis in Early Breast Cancer Using Deep Learning on Primary Tumor Biopsy Slides

Objectives: To develop and validate a deep learning (DL)-based primary tumor biopsy signature for predicting axillary lymph node (ALN) metastasis preoperatively in early breast cancer (EBC) patients with clinically negative ALN. Methods: A total of 1,058 EBC patients with pathologically confirmed ALN status were enrolled from May 2010 to August 2020. A DL core-needle biopsy (DL-CNB) model was built on the attention-based multiple instance-learning (AMIL) framework to predict ALN status utilizing the DL features, which were extracted from the cancer areas of digitized whole-slide images (WSIs) of breast CNB specimens annotated by two pathologists. Accuracy, sensitivity, specificity, receiver operating characteristic (ROC) curves, and areas under the ROC curve (AUCs) were analyzed to evaluate our model. Results: The best-performing DL-CNB model with VGG16_BN as the feature extractor achieved an AUC of 0.816 (95% confidence interval (CI): 0.758, 0.865) in predicting positive ALN metastasis in the independent test cohort. Furthermore, our model incorporating the clinical data, which was called DL-CNB+C, yielded the best accuracy of 0.831 (95%CI: 0.775, 0.878), especially for patients younger than 50 years (AUC: 0.918, 95%CI: 0.825, 0.971). The interpretation of DL-CNB model showed that the top signatures most predictive of ALN metastasis were characterized by the nucleus features including density ($p$ = 0.015), circumference ($p$ = 0.009), circularity ($p$ = 0.010), and orientation ($p$ = 0.012). Conclusion: Our study provides a novel DL-based biomarker on primary tumor CNB slides to predict the metastatic status of ALN preoperatively for patients with EBC. The codes and dataset are available at https://github.com/bupt-ai-cz/BALNMPComment: Update Table 1 and corresponding description

Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs

Learning on Graphs has attracted immense attention due to its wide real-world applications. The most popular pipeline for learning on graphs with textual node attributes primarily relies on Graph Neural Networks (GNNs), and utilizes shallow text embedding as initial node representations, which has limitations in general knowledge and profound semantic understanding. In recent years, Large Language Models (LLMs) have been proven to possess extensive common knowledge and powerful semantic comprehension abilities that have revolutionized existing workflows to handle text data. In this paper, we aim to explore the potential of LLMs in graph machine learning, especially the node classification task, and investigate two possible pipelines: LLMs-as-Enhancers and LLMs-as-Predictors. The former leverages LLMs to enhance nodes' text attributes with their massive knowledge and then generate predictions through GNNs. The latter attempts to directly employ LLMs as standalone predictors. We conduct comprehensive and systematical studies on these two pipelines under various settings. From comprehensive empirical results, we make original observations and find new insights that open new possibilities and suggest promising directions to leverage LLMs for learning on graphs.Comment: fix some minor typos and error

Orthogonal printing of uniform nanocomposite monolayer and oriented organic semiconductor crystals for high-performance nano-crystal floating gate memory

Inkjet printing is of great interest in the preparation of optoelectronic and microelectronic devices due to its low cost, low process temperature, versatile material compatibility, and ability to precisely manufacture multi-layer devices on demand. However, interlayer solvent erosion is a typical problem that limits the printing of organic semiconductor devices with multi-layer structures. In this study, we proposed a solution to address this erosion problem by designing polystyrene-block-poly(4-vinyl pyridine)-grafted Au nanoparticles (Au@PS-b-P4VP NPs). With a colloidal ink containing the Au@PS-b-P4VP NPs, we obtained a uniform monolayer of Au nano-crystal floating gates (NCFGs) embedded in the PS-b-P4VP tunneling dielectric (TD) layer using direct-ink-writing (DIW). Significantly, PS-b-P4VP has high erosion resistance against the semiconductor ink solvent, which enables multi-layer printing. An active layer of semiconductor crystals with high crystallinity and well-orientation was obtained by DIW. Moreover, we developed a strategy to improve the quality of the TD/semiconductor interface by introducing a polystyrene intermediate layer. We show that the NCFG memory devices exhibit a low threshold voltage (100 cycles), and long-term retention (>10 years). This study provides universal guidance for printing functional coatings and multi-layer devices