A Generalized Solow-Swan Model

We set up a generalized Solow-Swan model to study the exogenous impact of population, saving rate, technological change, and labor participation rate on economic growth. By introducing generalized exogenous variables into the classical Solow-Swan model, we obtain a nonautomatic differential equation. It is proved that the solution of the differential equation is asymptotically stable if the generalized exogenous variables converge and does not converge when one of the generalized exogenous variables persistently oscillates

Riemann Boundary Value Problem for Triharmonic Equation in Higher Space

We mainly deal with the boundary value problem for triharmonic function with value in a universal Clifford algebra: Δ3[u](x)=0, x∈Rn∖∂Ω, u+(x)=u-(x)G(x)+g(x), x∈∂Ω, (Dju)+(x)=(Dju)-(x)Aj+fj(x), x∈∂Ω, u(∞)=0, where (j=1,…,5)  ∂Ω is a Lyapunov surface in Rn, D=∑k=1nek(∂/∂xk) is the Dirac operator, and u(x)=∑AeAuA(x) are unknown functions with values in a universal Clifford algebra Cl(Vn,n). Under some hypotheses, it is proved that the boundary value problem has a unique solution

Internet-of-things enabled supply chain planning and coordination with big data services: certain theoretic implications

Recent advances in information technology have led to profound changes in global manufacturing. This study focuses on the theoretical and practical challenges and opportunities arising from the Internet of Things (IoT) as it enables new ways of supply-chain operations partially based on big-data analytics and changes in the nature of industries. We intend to reveal the acting principle of the IoT and its implications for big-data analytics on the supply chain operational performance, particularly with regard to dynamics of operational coordination and optimization for supply chains by leveraging big data obtained from smart connected products (SCPs), and the governance mechanism of big-data sharing. Building on literature closely related to our focal topic, we analyze and deduce the substantial influence of disruptive technologies and emerging business models including the IoT, big data analytics and SCPs on many aspects of supply chains, such as consumers value judgment, products development, resources allocation, operations optimization, revenue management and network governance. Furthermore, we propose several research directions and corresponding research schemes in the new situations. This study aims to promote future researches in the field of big data-driven supply chain management with the IoT, help firms improve data-driven operational decisions, and provide government a reference to advance and regulate the development of the IoT and big data industry.Published versio

Microdialysis Determination of Cefquinome Pharmacokinetics in Murine Thigh From Healthy, Neutropenic, and Actinobacillus pleuropneumoniae-Infected Mice

This study was aimed at applying microdialysis to explore cefquinome pharmacokinetics in thigh and plasma of healthy, neutropenic, and Actinobacillus pleuropneumoniae-infected mice. The relative recoveries (RRs) were tested in vitro by dialysis and retrodialysis and in vivo by retrodialysis. ICR mice were randomly divided into four groups: H-40 (healthy mice receiving cefquinome at 40 mg/kg), H-160, N-40 (neutropenic mice), and I-40 mg/kg (thigh infected-mice with A. pleuropneumoniae). After cefquinome administration, plasma was collected by retro-orbital puncture and thigh dialysate was collected by using a microdialysis probe with Ringer’s solution at a perfusion rate of 1.5 μL/min. Plasma and thigh dialysate samples were assessed by HPLC–MS/MS and analyzed by a non-compartment model. The mean in vivo recoveries in the thigh were 39.35, 38.59, and 37.29% for healthy, neutropenic, and infected mice, respectively. The mean plasma protein-binding level was 16.40% and was independent of drug concentrations. For all groups, the mean values of the free AUCinf in plasma were higher than those in murine thigh, while the elimination T1/2β for plasma were lower than those for murine thigh. Cefquinome penetration (AUCthigh/AUCplasma) from the plasma to thigh was 0.76, 0.88, 0.47, and 0.98 for H-40, N-40, I-40, and H-160 mg/kg, respectively. These results indicated that infection significantly affected cefquinome pharmacokinetics in murine thigh. In conclusion, we successfully applied a microdialysis method to evaluate the pharmacokinetics of cefquinome in murine thigh of healthy, neutropenic, and A. pleuropneumonia-infected mice and the pharmacokinetics of cefquinome was obviously affected by infection in thigh

Leveraging Large Language Models for Pre-trained Recommender Systems

Recent advancements in recommendation systems have shifted towards more comprehensive and personalized recommendations by utilizing large language models (LLM). However, effectively integrating LLM's commonsense knowledge and reasoning abilities into recommendation systems remains a challenging problem. In this paper, we propose RecSysLLM, a novel pre-trained recommendation model based on LLMs. RecSysLLM retains LLM reasoning and knowledge while integrating recommendation domain knowledge through unique designs of data, training, and inference. This allows RecSysLLM to leverage LLMs' capabilities for recommendation tasks in an efficient, unified framework. We demonstrate the effectiveness of RecSysLLM on benchmarks and real-world scenarios. RecSysLLM provides a promising approach to developing unified recommendation systems by fully exploiting the power of pre-trained language models.Comment: 13 pages, 4 figure

Enhancing Recommender Systems with Large Language Model Reasoning Graphs

Recommendation systems aim to provide users with relevant suggestions, but often lack interpretability and fail to capture higher-level semantic relationships between user behaviors and profiles. In this paper, we propose a novel approach that leverages large language models (LLMs) to construct personalized reasoning graphs. These graphs link a user's profile and behavioral sequences through causal and logical inferences, representing the user's interests in an interpretable way. Our approach, LLM reasoning graphs (LLMRG), has four components: chained graph reasoning, divergent extension, self-verification and scoring, and knowledge base self-improvement. The resulting reasoning graph is encoded using graph neural networks, which serves as additional input to improve conventional recommender systems, without requiring extra user or item information. Our approach demonstrates how LLMs can enable more logical and interpretable recommender systems through personalized reasoning graphs. LLMRG allows recommendations to benefit from both engineered recommendation systems and LLM-derived reasoning graphs. We demonstrate the effectiveness of LLMRG on benchmarks and real-world scenarios in enhancing base recommendation models.Comment: 12 pages, 6 figure

Pharmacokinetic/Pharmacodynamic Integration to Evaluate the Changes in Susceptibility of Actinobacillus pleuropneumoniae After Repeated Administration of Danofloxacin

To evaluate the relationship between pharmacokinetic/pharmacodynamic (PK/PD) parameters and changes in susceptibility and resistance frequency of Actinobacillus pleuropneumoniae CVCC 259, a piglet tissue cage (TC) infection model was established. After A. pleuropneumoniae populations maintained at 108 CFU/mL in TCs, piglets were treated with various doses of danofloxacin once daily for 5 consecutive days by intramuscular injection. Both the concentrations of danofloxacin and the population of vial cells were determined. Changes in susceptibility and resistance frequency were monitored. Polymerase chain reaction (PCR) amplification of quinolone resistance-determining regions (QRDRs) and DNA sequencing were performed to identify point mutations in gyrA, gyrB, parC, and parE genes. Furthermore, the susceptibility of mutants to danofloxacin and enrofloxacin was determined in the presence or absence of reserpine to assess whether the mutants were caused by efflux pumps. The MICs and resistant frequency of A. pleuropneumoniae both increased when danofloxacin concentrations fluctuated between MIC99 (0.05 μg/mL) and MPC (mutant prevention concentration, 0.4 μg/mL). As for PK/PD parameters, the resistant mutants were selected and enriched when AUC24h/MIC99 ranged from 34.68 to 148.65 h or AUC24h/MPC ranged from 4.33 to 18.58 h. Substitutions of Ser-83→Tyr or Ser-83→Phe in gyrA and Lys-53→Glu in parC were observed. The susceptibility of mutants obtained via danofloxacin treatment at 1.25 and 2.5 mg/kg were less affected by reserpine. These results demonstrate that maintaining the value of AUC24h/MPC above 18.58 h may produce a desirable antibacterial effect and protect against A. pleuropneumoniae resistance to danofloxacin