LLM-Rec: Personalized Recommendation via Prompting Large Language Models

We investigate various prompting strategies for enhancing personalized recommendation performance with large language models (LLMs) through input augmentation. Our proposed approach, termed LLM-Rec, encompasses four distinct prompting strategies: (1) basic prompting, (2) recommendation-driven prompting, (3) engagement-guided prompting, and (4) recommendation-driven + engagement-guided prompting. Our empirical experiments show that incorporating the augmented input text generated by LLM leads to improved recommendation performance. Recommendation-driven and engagement-guided prompting strategies are found to elicit LLM's understanding of global and local item characteristics. This finding highlights the importance of leveraging diverse prompts and input augmentation techniques to enhance the recommendation capabilities with LLMs

Alterations of Sub-cortical Gray Matter Volume and Their Associations With Disease Duration in Patients With Restless Legs Syndrome

Object: The purpose of this study was to uncover the pathology of restless legs syndrome (RLS) by exploring brain structural alterations and their corresponding functional abnormality.Method: Surface-based morphometry (SBM) and voxel-based morphometry (VBM) were performed to explore the alterations in cortical and sub-cortical gray matter volume (GMV) in a cohort of 20 RLS and 18 normal controls (NC). Furthermore, resting-state functional connectivity (RSFC) was also performed to identify the functional alterations in patients with RLS.Results: We found significant alterations of sub-cortical GMV, especially the bilateral putamen (PUT), rather than alterations of cortical GMV in patients with RLS compared to NC using both SBM and VBM. Further sub-regional analysis revealed that GMV alterations of PUT was mostly located in the left dorsal caudal PUT in patients with RLS. In addition, altered RSFC patterns of PUT were identified in patients with RLS compared to NC. Moreover, correlation analyses showed that the GMV of the left caudate and the left ventral rostral PUT were positively correlated with disease duration in patients with RLS.Conclusions: The alterations of subcortical GMV might imply that the primarily affected areas are located in sub-cortical areas especially in the sub-region of PUT by the pathologic process of RLS, which might be used as potential biomarkers for the early diagnosis of RLS

Alterations in White Matter Integrity in Young Adults with Smartphone Dependence

Smartphone dependence (SPD) is increasingly regarded as a psychological problem, however, the underlying neural substrates of SPD is still not clear. High resolution magnetic resonance imaging provides a useful tool to help understand and manage the disorder. In this study, a tract-based spatial statistics (TBSS) analysis on diffusion tensor imaging (DTI) was used to measure white matter integrity in young adults with SPD. A total of 49 subjects were recruited and categorized into SPD and control group based on their clinical behavioral tests. To localize regions with abnormal white matter integrity in SPD, the voxel-wise analysis of fractional anisotropy (FA) and mean diffusivity (MD) on the whole brain was performed by TBSS. The correlation between the quantitative variables of brain structures and the behavior measures were performed. Our result demonstrated that SPD had significantly lower white matter integrity than controls in superior longitudinal fasciculus (SLF), superior corona radiata (SCR), internal capsule, external capsule, sagittal stratum, fornix/stria terminalis and midbrain structures. Correlation analysis showed that the observed abnormalities in internal capsule and stria terminalis were correlated with the severity of dependence and behavioral assessments. Our finding facilitated a primary understanding of white matter characteristics in SPD and indicated that the structural deficits might link to behavioral impairments

An Online-SPE/SEC/LCMS Method for the Detection of N-Nitrosamine Disinfection Byproducts in Wastewater Plant Tailwater

N-nitrosamines have recently attracted attention as a class of disinfection byproducts and are also a hot spot in environmental studies. Current N-nitrosamine analytical methods typically involve manual solid phase extraction (SPE) of samples followed by quantitative analysis using liquid chromatography-mass spectrometry (LCMS), which is time-consuming and may also fail to eliminate complex matrix effects. Size exclusion chromatography (SEC) is a technique that can separate compounds according to their molecular size. For the first time, this study developed an Online-SPE/SEC/LCMS quantitative analysis method to detect and analyze nine common N-nitrosamine disinfection byproducts in wastewater plant tailwater, including N-dimethylnitrosamine (NDMA) and N-nitrosodiethylamine (NDEA), etc. The samples of 1.0 mL can be directly injected after the simple 0.22 μm membrane filtration. This method reports the combination of SPE, SEC, and RP C18 columns to achieve several functions in a processing time of 20 min, including online enrichment, desalination, and matrix separation for the first time. The method provides good linearity (R2 > 0.999), recoveries ranging from 91.67% to 105.88%, relative standard deviation (RSD) lower than 4.17%, and the limits of detection (LOD) are 0.12–6.60 ng/L. This method alleviates tedious human labor and can effectively overcome the matrix effect (ME < 20%). This method allows for the accurate quantitative analysis of N-nitrosamines with high compatibility in wastewater plant tailwater, rivers, and lakes with a high background matrix. Interested researchers can also use this method as a reference in the online analysis of other specific pollutants after necessary optimization. It can also be utilized for non-targeted screening and targeted analysis of contaminants in water with a wide range of applications, giving valuable information for environmental monitoring

An Online-SPE/SEC/LCMS Method for the Detection of N-Nitrosamine Disinfection Byproducts in Wastewater Plant Tailwater

N-nitrosamines have recently attracted attention as a class of disinfection byproducts and are also a hot spot in environmental studies. Current N-nitrosamine analytical methods typically involve manual solid phase extraction (SPE) of samples followed by quantitative analysis using liquid chromatography-mass spectrometry (LCMS), which is time-consuming and may also fail to eliminate complex matrix effects. Size exclusion chromatography (SEC) is a technique that can separate compounds according to their molecular size. For the first time, this study developed an Online-SPE/SEC/LCMS quantitative analysis method to detect and analyze nine common N-nitrosamine disinfection byproducts in wastewater plant tailwater, including N-dimethylnitrosamine (NDMA) and N-nitrosodiethylamine (NDEA), etc. The samples of 1.0 mL can be directly injected after the simple 0.22 μm membrane filtration. This method reports the combination of SPE, SEC, and RP C18 columns to achieve several functions in a processing time of 20 min, including online enrichment, desalination, and matrix separation for the first time. The method provides good linearity (R2 > 0.999), recoveries ranging from 91.67% to 105.88%, relative standard deviation (RSD) lower than 4.17%, and the limits of detection (LOD) are 0.12–6.60 ng/L. This method alleviates tedious human labor and can effectively overcome the matrix effect (ME < 20%). This method allows for the accurate quantitative analysis of N-nitrosamines with high compatibility in wastewater plant tailwater, rivers, and lakes with a high background matrix. Interested researchers can also use this method as a reference in the online analysis of other specific pollutants after necessary optimization. It can also be utilized for non-targeted screening and targeted analysis of contaminants in water with a wide range of applications, giving valuable information for environmental monitoring

The Thermal Infrared Visual Object Tracking VOT-TIR2015 Challenge Results

The Thermal Infrared Visual Object Tracking challenge 2015, VOTTIR2015, aims at comparing short-term single-object visual trackers that work on thermal infrared (TIR) sequences and do not apply prelearned models of object appearance. VOT-TIR2015 is the first benchmark on short-term tracking in TIR sequences. Results of 24 trackers are presented. For each participating tracker, a short description is provided in the appendix. The VOT-TIR2015 challenge is based on the VOT2013 challenge, but introduces the following novelties: (i) the newly collected LTIR (Linköping TIR) dataset is used, (ii) the VOT2013 attributes are adapted to TIR data, (iii) the evaluation is performed using insights gained during VOT2013 and VOT2014 and is similar to VOT2015

Учебная программа по учебной дисциплине "Химия"

Учебная программа " Химия" кафедры "Материаловедение в машиностроении" для дневной формы получения образования: общее количество часов – 184, трудоемкость учебной дисциплины – 5 з.е., форма контроля знаний – экзамен