This paper has applied a simple model (H2CrO4 + Aliquat 336 ↔ complex) to the mass transfer mechanism of Cr(VI) with Aliquat 336 in a batch-type, supported liquid membrane module. Concentrations at pH 3 are as follows: 0.019 kmol/m3 ≤ Aliquat 336 ≤ 0.035 kmol/m3 and 20 g/m3 ≤ Cr (VI) ≤ 500 g/m3. The measured values of forward- and backward-reaction rate constants between Cr(VI) and Aliquat 336 were used to simulate the model with the mass conservation equation and associated boundary conditions. Comparison between the experimental and simulated facilitated factors of Cr(VI) transport led to classification of reaction regions.

Conversational recommender systems (CRSs) have garnered increasing attention for their ability to provide personalized recommendations through natural language interactions. Although large language models (LLMs) have shown potential in recommendation systems owing to their superior language understanding and reasoning capabilities, extracting and utilizing implicit user preferences from conversations remains a formidable challenge. This paper proposes a method that leverages LLMs to extract implicit preferences and explicitly incorporate them into the recommendation process. Initially, LLMs identify implicit user preferences from conversations, which are then refined into fine-grained numerical values using a BERT-based multi-label classifier to enhance recommendation precision. The proposed approach is validated through experiments on three comprehensive datasets: the Reddit Movie Dataset (8413 dialogues), Inspired (825 dialogues), and ReDial (2311 dialogues). Results show that our approach considerably outperforms traditional CRS methods, achieving a 23.3% improvement in Recall@20 on the ReDial dataset and a 7.2% average improvement in recommendation accuracy across all datasets with GPT-3.5-turbo and GPT-4. These findings highlight the potential of using LLMs to extract and utilize implicit conversational information, effectively enhancing the quality of recommendations in CRSs.

The rapid expansion of medical information has resulted in named entity recognition (NER) and relation extraction (RE) essential for clinical decision support systems. Medical texts often contain specialized vocabulary, ambiguous abbreviations, synonyms, polysemous terms, and overlapping entities, which introduce significant challenges to the extraction process. Existing approaches, which typically rely on single models such as BiLSTM or BERT, often struggle with these complexities. Although large language models (LLMs) have shown promise in various NLP tasks, they still face limitations in handling token-level tasks critical for medical NER and RE. To address these challenges, we propose COMCARE, a collaborative ensemble framework for context-aware medical NER and RE that integrates multiple pre-trained language models through a collaborative decision strategy. For NER, we combined PubMedBERT and PubMed-T5, leveraging PubMedBERT’s contextual understanding and PubMed-T5’s generative capabilities to handle diverse forms of medical terminology, from standard domain-specific jargon to nonstandard representations, such as uncommon abbreviations and out-of-vocabulary (OOV) terms. For RE, we integrated general-domain BERT with biomedical-specific BERT and PubMed-T5, utilizing token-level information from the NER module to enhance the context-aware entity-based relation extraction. To effectively handle long-range dependencies and maintain consistent performance across diverse texts, we implemented a semantic chunking approach and combined the model outputs through a majority voting mechanism. We evaluated COMCARE on several biomedical datasets, including BioRED, ADE, RDD, and DIANN Corpus. For BioRED, COMCARE achieved F1 scores of 93.76% for NER and 68.73% for RE, outperforming BioBERT by 1.25% and 1.74%, respectively. On the RDD Corpus, COMCARE showed F1 scores of 77.86% for NER and 86.79% for RE while achieving 82.48% for NER on ADE and 99.36% for NER on DIANN. These results demonstrate the effectiveness of our approach in handling complex medical terminology and overlapping entities, highlighting its potential to improve clinical decision support systems.

During outbreaks of infectious diseases, such as COVID-19, it is critical to rapidly determine treatment priorities and identify patients requiring hospitalization based on clinical severity. Although various machine learning models have been developed to predict COVID-19 severity, most have limitations, such as small dataset sizes, the limited availability of clinical variables, or a constrained classification of severity levels by a single classifier. In this paper, we propose an adaptive stacking ensemble technique that identifies various COVID-19 patient severity levels and separates them into three formats: Type 1 (low or high severity), Type 2 (mild, severe, critical), and Type 3 (asymptomatic, mild, moderate, severe, fatal). To enhance the model’s generalizability, we utilized a nationwide dataset from the South Korean government, comprising data from 5644 patients across over 100 hospitals. To address the limited availability of clinical variables, our technique employs data-driven strategies and a proposed feature selection method. This ensures the availability of clinical variables across diverse hospital environments. To construct optimal stacking ensemble models, our technique adaptively selects candidate base classifiers by analyzing the correlation between their predicted outcomes and performance. It then automatically determines the optimal multi-layer combination of base and meta-classifiers using a greedy search algorithm. To further improve the performance, we applied various techniques, including imputation of missing values and oversampling. The experimental results demonstrate that our stacking ensemble models significantly outperform existing single classifiers and AutoML approaches, with improvements of 6.42% and 8.86% in F1 and AUC scores for Type 1, 9.59% and 6.68% for Type 2, and 11.94% and 9.24% for Type 3, respectively. Consequently, our approach improves the prediction of COVID-19 severity levels and potentially assists frontline healthcare providers in making informed decisions.

We propose an intention-related long short-term memory (Ir-LSTM) model based on deep learning to realize web prediction. This model draws on an LSTM model and skip-gram embedding method, and we expand the input features with user information. To maximize its potential, we propose a real-time dynamic allocation module that detects traffic bursts in real time and ensures better utilization of server resources. Experiments demonstrated that Ir-LSTM can improve the hit ratio by approximately 27% rather than hidden Markov model (HMM) and pure LSTM.