Improved SVD + + Recommendation Algorithm Based on Fusion Time Factor

Collaborative filtering algorithm is widely used in recommendation system. Aiming at the problems of data sparsity and low recommendation accuracy in traditional collaborative filtering algorithm, an improved recommendation algorithm is proposed PT _ SVD++. Firstly, the attribute information of users and the implicit feedback information of items are introduced to improve the SVD++ algorithm, which solves the insufficient utilization of information and alleviates the problem of sparse data；Secondly the time effect model is established to further improve the accuracy of the prediction results. The experimental results on MovieLens dataset show that compared with other algorithms, the average absolute error and root mean square error of this algorithm are lower, and its recommendation accuracy is higher

ControlLLM: Augment Language Models with Tools by Searching on Graphs

We present ControlLLM, a novel framework that enables large language models (LLMs) to utilize multi-modal tools for solving complex real-world tasks. Despite the remarkable performance of LLMs, they still struggle with tool invocation due to ambiguous user prompts, inaccurate tool selection and parameterization, and inefficient tool scheduling. To overcome these challenges, our framework comprises three key components: (1) a \textit{task decomposer} that breaks down a complex task into clear subtasks with well-defined inputs and outputs; (2) a \textit{Thoughts-on-Graph (ToG) paradigm} that searches the optimal solution path on a pre-built tool graph, which specifies the parameter and dependency relations among different tools; and (3) an \textit{execution engine with a rich toolbox} that interprets the solution path and runs the tools efficiently on different computational devices. We evaluate our framework on diverse tasks involving image, audio, and video processing, demonstrating its superior accuracy, efficiency, and versatility compared to existing methods. The code is at https://github.com/OpenGVLab/ControlLLM.Comment: 24 pages, 9 figures, 12 table

A Quantum Mechanism Study of the C-C Bond Cleavage to Predict the Bio-Catalytic Polyethylene Degradation

The growing amount of plastic solid waste (PSW) is a global concern. Despite increasing efforts to reduce the residual amounts of PSW to be disposed off through segregated collection and recycling, a considerable amount of PSW is still landfilled and the extent of PSW ocean pollution has become a worldwide issue. Particularly, polyethylene (PE) and polystyrene (PS) are considered as notably recalcitrant to biodegradation due to the carbon-carbon backbone that is highly resistant to enzymatic degradation via oxidative reactions. The present research investigated the catalytic mechanism of P450 monooxygenases by quantum mechanics to determine the bio-catalytic degradation of PE or PS. The findings indicated that the oxygenase-induced free radical transition caused the carbon-carbon backbone cleavage of aliphatic compounds. This work provides a fundamental knowledge of the biodegradation process of PE or PS at the atomic level and facilitates predicting the pathway of plastics’ biodegradation by microbial enzymes

Feasible Cluster Model Method for Simulating the Redox Potentials of Laccase CueO and Its Variant

Laccases are regarded as versatile green biocatalysts, and recent scientific research has focused on improving their redox potential for broader industrial and environmental applications. The density functional theory (DFT) quantum mechanics approach, sufficiently rigorous and efficient for the calculation of electronic structures, is conducted to better comprehend the connection between the redox potential and the atomic structural feature of laccases. According to the crystal structure of wild type laccase CueO and its variant, a truncated miniature cluster model method was established in this research. On the basic of thermodynamic cycle, the overall Gibbs free energy variations before and after the one-electron reduction were calculated. It turned out that the trends of redox potentials to increase after variant predicted by the theoretical calculations correlated well with those obtained by experiments, thereby validating the feasibility of this cluster model method for simulating the redox potentials of laccases

Modeling Coordination-Directed Self-Assembly of M₂L₄ Nanocapsule Featuring Competitive Guest Encapsulation

Exploring the mechanism of self-assembly and guest encapsulation of nanocapsules is highly imperative for the design of sophisticated molecular containers and multistimuli-responsive functional materials. Here we present a molecular dynamics simulation protocol with implicit solvent and simulated annealing techniques to investigate the self-assembly and competitive guest (C₆₀ and C₇₀ fullerenes) encapsulation of a M₂L₄ nanocapsule that is self-assembled by the coordination of mercury cations and bent bidentate ligands. Stepwise formation of the nanocapsule and competitive fullerene encapsulation during dynamic structural changes in the self-assembly were detected successfully. Such processes were driven by coordination bonding and π–π stacking and obey the minimum total potential energy principle. Potential of mean force calculations for guest binding to the M₂L₄ nanocapsule explained the mechanism underlying the competitive encapsulations of C₆₀ and C₇₀. This work helps design new functional nanomaterials capable of guest encapsulation and release

Quantum Mechanical Investigation of the Oxidative Cleavage of the C−C Backbone Bonds in Polyethylene Model Molecules

Recalcitrant plastic waste has caused serious global ecological problems. There is an urgent need to develop environmentally friendly and efficient methods for degrading the highly stable carbon skeleton structure of plastics. To that end, we used a quantum mechanical calculation to thoroughly investigate the oxidative scission of the carbon-carbon (C–C) backbone in polyethylene (PE). Here, we studied the reaction path of C–C bond oxidation via hydroxyl radical in PE. The flexible force constants and fuzzy bond orders of the C–C bonds were calculated in the presence of one or more carbocations in the same PE carbon chain. By comparison, the strength of the C–C bond decreased when carbocation density increased. However, the higher the density of carbocations, the higher the total energy of the molecule and the more difficult it was to be generated. The results revealed that PE oxidized to alcohol and other products, such as carboxylic acid, aldehyde and ketone, etc. Moreover, the presence of carbocations was seen to promote the cleavage of C–C backbones in the absence of oxygen

Enzymatic Synthesis of Structured Lipids Enriched with Medium- and Long-Chain Triacylglycerols via Pickering Emulsion-Assisted Interfacial Catalysis: A Preliminary Exploration

Medium- and long-chain triacylglycerol (MLCT), as a novel functional lipid, is valuable due to its special nutritional properties. Its low content in natural resources and inefficient synthesis during preparation have limited its practical applications. In this study, we developed an effective Pickering emulsion interfacial catalysis system (PE system) for the enzymatic synthesis of MLCT by trans-esterification. Lipase NS 40086 served simultaneously as a catalyst and a solid emulsifier to stabilize the Pickering emulsion. Benefitting from the sufficient oil–water interface, the obtained PE system exhibited outstanding catalytic efficiency, achieving 77.5% of MLCT content within 30 min, 26% higher than that of a water-free system. The Km value (0.259 mM) and activation energy (14.45 kJ mol−1) were 6.8-fold and 1.6-fold lower than those of the water-free system, respectively. The kinetic parameters as well as the molecular dynamics simulation and the tunnel analysis implied that the oil–water interface enhanced the binding between substrate and lipase and thus boosted catalytic efficiency. The conformational changes in the lipase were further explored by FT-IR. This method could give a novel strategy for enhancing lipase activity and the design of efficient catalytic systems to produce added-value lipids. This work will open a new methodology for the enzymatic synthesis of structured lipids

Prediction of Plasticizer Property Based on an Improved Genetic Algorithm

Different plasticizers have obvious differences in plasticizing properties. As one of the important indicators for evaluating plasticization performance, the substitution factor (SF) has great significance for product cost accounting. In this research, a genetic algorithm with “variable mutation probability” was developed to screen the key molecular descriptors of plasticizers that are highly correlated with the SF, and a SF prediction model was established based on these filtered molecular descriptors. The results show that the improved genetic algorithm greatly improved the prediction accuracy in different regression models. The coefficient of determination (R2) for the test set and the cross-validation both reached 0.92, which is at least 0.15 higher than the R2 of the unimproved genetic algorithm. From the results of the selected descriptors, most of the descriptors focused on describing the branching of the molecule, which is consistent with the view that the branching chain plays an important role in the plasticization process. As the first study to establish the relationship between plasticizer SF and plasticizer molecular structure, this work provides a basis for subsequent plasticizer performance and evaluation system modeling

Comparative Assessment of Computational Methods for Free Energy Calculations of Ionic Hydration

Experimental observations for ionic hydration free energies are highly debated mainly due to the ambiguous absolute hydration free energy of proton, Δ<i>G</i>_hyd^*(H⁺). Hydration free energies (HFEs) of the 112 singly charged ions in the Minnesota solvation database were predicted by six methods with explicit and implicit solvent models, namely, thermodynamic integration (TI), energy representation module (ERmod), three-dimensional reference interaction site model (3D-RISM), and continuum solvation models based on the quantum mechanical charge density (SMD) and on the Poisson–Boltzmann (PB) and generalized Born (GB) theories. Taking the solvent Galvani potential of water into account, the resulting real HFEs from TI calculations for the generalized Amber force field (GAFF) modeled ions best match the experiments based on Δ<i>G</i>_hyd^*(H⁺) = −262.4 kcal/mol (Randles Trans. Faraday Soc. 1956, 52, 1573–1581), in agreement with our previous work on charged amino acids (Zhang et al. J. Phys. Chem. Lett. 2017, 8, 2705–2712). The examined computational methods show an accuracy of ∼7 kcal/mol for the GAFF-modeled ions, except for SMD with a higher accuracy of ∼4 kcal/mol. A biased deficiency in modeling anionic compounds by GAFF is observed with a larger standard deviation (SD) of 9 kcal/mol than that for cations (SD ∼ 4 kcal/mol). The relatively cheap ERmod and 3D-RISM methods reproduce TI results with good accuracy, although ERmod yields a systematic underestimation for cations by 9 kcal/mol; PB and GB generate relative (but not absolute) HFEs comparable to the TI predictions. Computational accuracy is found to be more limited by the accuracy of force fields rather than the models themselves