Optimization and evaluation of multi-bed adsorbent tube method in collection of volatile organic compounds

The feasibility of using adsorbent tubes to collect volatile organic compounds (VOCs) has been demonstrated since the 1990's and standardized as Compendium Method TO-17 by the U.S. Environmental Protection Agency (U.S EPA). This paper investigates sampling and analytical variables on concentrations of 57 ozone (O-3) precursors (C-2-C-12 aliphatic and aromatic VOCs) specified for the Photochemical Assessment Monitoring Station (PAMS). Laboratory and field tests examined multi-bed adsorbent tubes containing a sorbate combination of Tenax TA, Carbograph 1 TD, and Carboxen 1003. Analyte stabilities were influenced by both collection tube temperature and ambient O-3 concentrations. Analytes degraded during storage, while blank levels were elevated by passive adsorption. Adsorbent tube storage under cold temperatures (- 10 degrees C) in a preservation container filled with solid silica gel and anhydrous calcium sulfate (CaSO4) ensured sample integrity. A high efficiency (> 99%) O-3 scrubber (i.e., copper coil tube filled with saturated potassium iodide [KM removed O-3 (i.e., < 200 ppbv) from the air stream with a sampling capacity of 30 h. Water vapor scrubbers interfered with VOC measurements. The optimal thermal desorption-gas chromatography/mass spectrometry (TD-GC/MS) desorption time of 8 min was found at 330 degrees C. Good linearity (R-2 > 0.995) was achieved for individual analyte calibrations (with the exception of acetylene) for mixing ratios of 0.08-1.96 ppbv. The method detection limits (MDLs) were below 0.055 ppbv for a 3 L sample volume. Replicate analyses showed relative standard deviations (RSDs) of < 10%, with the majority of the analytes within < 5%

Retention in STEM: Factors Influencing Student Persistence and Employment

This study utilizes data from the Baccalaureate and Beyond Longitudinal Study to explore factors associated with the likelihood of students' employment in STEM fields one year after graduation. We examined various factors related to students' individual characteristics (e.g., gender, race, and financial situation), institutional experiences (e.g., major, academic standing, research involvement, internships, extracurricular activities, and undergraduate practicum), and institutional and national trends. The results indicate lower STEM employment likelihood for minority groups and students with academic probation. The findings also highlight the positive impact of undergraduate practicum and job relevance to major on STEM employment likelihood. On the contrary, career services were negatively associated with the likelihood of students' STEM occupation choice, suggesting potential shortcomings in STEM job preparation within these services. The study provides valuable insights and actionable recommendations for policymakers and educators seeking to increase diversity and inclusion in STEM fields, suggesting the need for more efficient and tailored educational interventions and curriculum development

TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding

This work proposes TimeChat, a time-sensitive multimodal large language model specifically designed for long video understanding. Our model incorporates two key architectural contributions: (1) a timestamp-aware frame encoder that binds visual content with the timestamp of each frame, and (2) a sliding video Q-Former that produces a video token sequence of varying lengths to accommodate videos of various durations. Additionally, we construct an instruction-tuning dataset, encompassing 6 tasks and a total of 125K instances, to further enhance TimeChat's instruction-following performance. Experiment results across various video understanding tasks, such as dense captioning, temporal grounding, and highlight detection, demonstrate TimeChat's strong zero-shot temporal localization and reasoning capabilities. For example, it achieves +9.2 F1 score and +2.8 CIDEr on YouCook2, +5.8 HIT@1 on QVHighlights, and +27.5 R@1 (IoU=0.5) on Charades-STA, compared to state-of-the-art video large language models, holding the potential to serve as a versatile video assistant for long-form video comprehension tasks and satisfy realistic user requirements.Comment: CVPR 2024 camera-ready version, code is available at https://github.com/RenShuhuai-Andy/TimeCha

Video In-context Learning

In-context learning for vision data has been underexplored compared with that in natural language. Previous works studied image in-context learning, urging models to generate a single image guided by demonstrations. In this paper, we propose and study video in-context learning, where the model starts from an existing video clip and generates diverse potential future sequences, each semantically guided by the prompted video demonstrations. To achieve this, we provide a clear definition of the task, and train an autoregressive Transformer on video datasets. We thoroughly analyze the effect of different datasets and represent frames as discrete tokens, and then model them by next token predictions. We design various evaluation metrics, including both objective and subjective measures, to demonstrate the visual quality and semantic accuracy of generation results. Our model follows the scaling law and generates high-quality video clips that accurately align with the semantic guidance provided by in-context examples

DeCo: Decoupling Token Compression from Semantic Abstraction in Multimodal Large Language Models

The visual projector, which bridges the vision and language modalities and facilitates cross-modal alignment, serves as a crucial component in MLLMs. However, measuring the effectiveness of projectors in vision-language alignment remains under-explored, which currently can only be inferred from the performance of MLLMs on downstream tasks. Motivated by the problem, this study examines the projector module by interpreting the vision-language semantic flow within MLLMs. Specifically, we trace back the semantic relevance flow from generated language tokens to raw visual encoder patches and the intermediate outputs produced by projectors. Our findings reveal that compressive projectors (e.g., QFormer), abstract visual patches into a limited set of semantic concepts, such as objects or attributes, resulting in a 'double abstraction' phenomenon. This involves a first visual semantic abstraction by the projector referring to pre-defined query tokens, and a second extraction by the LLM based on text instructions. The double abstraction is inefficient in training and will result in cumulative vision semantics deficiency. To mitigate this issue, we propose the key insight of 'Decouple Compression from Abstraction (DeCo), that is compressing the visual token number at the patch level by projectors and allowing the LLM to handle visual semantic abstraction entirely. Consequently, we adopt a simple compressor, i.e., 2D Adaptive Pooling, to downsample visual patches in a parameter-free manner. Empirical evaluation demonstrates that DeCo surpasses traditional compressive projectors regarding both performance and efficiency. It achieves performance gains of 0.9%, 7.1%, and 2.9% across the MLLM Benchmarks, Visual Localization, and Open-ended VQA tasks with fewer trainable parameters and faster convergence speed

Experimental study on influence of fault dip angle on acoustic emission sinal propagation

Natural rock mass is not a uniform continuum due to discontinuous interfaces such as faults and bedding planes between rock layers, and the propagation law of acoustic emission signals will inevitably change when they pass through faults and bedding planes. Therefore, studying the propagation law of acoustic emission signals in faults has become one of the key topics in rock mechanics. Based on the Huygens principle, the wave front equation under the condition of heterogeneous media containing faults was derived, and 45°, 60°, 75° and other types of fault specimens were made through laboratory similar simulation model tests. The acoustic emission signals across faults were monitored and recorded by combining ultrasonic tachymeter and DS5-16B full-information acoustic emission signal analyzer. Nonlinear fitting and numerical calculation of Matlab software are used to study the influence of faults and the number of bedding planes of different inclination angles on the propagation speed and signal characteristics of acoustic emission signals. The results show that the propagation speed of acoustic emission signals increases gradually with the increase of fault dip angle, and the propagation speed is positively correlated with fault dip angle. The larger the fault dip angle is, the faster the signal propagation speed will be, and the propagation speed will attenuate after the signal passes through the fault, and the larger the fault dip angle is, the smaller the proportion of velocity attenuation will be. The propagation velocity is attenuated by the bedding plane, and the single bedding plane has little influence on the velocity, while the two bedding planes have great influence on the velocity. The fault will make the maximum value of the signal decrease, the main frequency decrease, and the frequency interval move to the low frequency direction. The larger the fault inclination, the larger the maximum value, the main frequency and the frequency interval. One layer has little influence on the signal, which is basically the same as the time-frequency characteristics of the signal without stratification, while the two layers have a greater influence on the signal, which will greatly reduce the maximum value, main frequency and frequency interval of the signal. The existence of fault will cause the instantaneous energy of acoustic emission signal to decrease greatly, and the smaller the inclination angle, the more serious the attenuation is. The research results can provide theoretical basis for the establishment of wave velocity model under the ray theory

Angiotensin II mediates the high-glucose-induced endothelial-to-mesenchymal transition in human aortic endothelial cells

<p>Abstract</p> <p>Background</p> <p>Substantial evidence suggests that high glucose (HG) causes endothelial cell damage; however, the potential mechanism therein has yet to be clarified. The aim of this study was to investigate the influence of HG on the endothelial-to-mesenchymal transition (EndMT) and its relevance to the activation of the renin-angiotensin system.</p> <p>Methods</p> <p>Primary human aortic endothelial cells (HAECs) were divided into three groups: a normal glucose (NG) group, HG group, and irbesartan (1 μM)-treated (HG+irbesartan) group. The concentration of angiotensin II in the supernatant was detected by radioimmunoassay. Pathological changes were investigated using fluorescence microscopy and electron microscopy. Immunofluorescence staining was performed to detect the co-expression of CD31 and fibroblast markers, such as fibroblast-specific protein 1 (FSP1). The expressions of FSP1 and α-SMA were detected by RT-PCR and Western blot.</p> <p>Results</p> <p>The treatment of HAECs in the HG group resulted in significant increases in the expressions of FSP1 and angiotensin II in dose-and time-dependent manners. The incubation of HAECs exposure to HG resulted in a fibroblast-like phenotype, wherein increased microfilamentation and a roughened endoplasmic reticulum structure were observed in the cytoplasm. The expressions of FSP1 and α-SMA were significantly increased in the HG group, and these changes were inhibited by irbesartan treatment (<it>P </it>< 0.05). Double staining of the HAECs indicated a co-localization of CD31 and FSP1 and that some cells acquired spindle-shaped morphologies and a loss of CD31 staining; however, treatment with irbesartan attenuated the expression of EndMT (<it>P </it>< 0.05).</p> <p>Conclusions</p> <p>These findings suggest a novel mechanism in HG-induced endothelial damage via the mediation of the EndMT by angiotensin II, which was inhibited by Irbesartan.</p

Distribution and expression of SLC45A2 in the skin of sheep with different coat colors

Introduction. To investigate whether the membrane-associated transporter protein SLC45A2 is differentially expressed in the skin of sheep with different coat colors and to determine its correlation with coat color establishment in sheep. Material and methods. The expression of SLC45A2 in sheep skin samples with different coat colors was qualitatively and quantitatively analyzed by PCR amplification, RT-PCR, immunohistochemical staining and Western blotting. Results. A 193-bp SLC45A2 CDS sequence was successfully amplified from sheep skin samples with diverse coat colors. RT-PCR analysis revealed that SLC45A2 mRNA was expressed in all sheep skin samples tested, with relative expression levels of 512.74 ± 121.51 in black skin, 143.38 ± 119.31 and 1.36 ± 0.09 in black dots and white dots of piebald skin, respectively, and 1.02 ± 0.23 in white skin (p &lt; 0.01**). Positive SLC45A2 protein bands were also detected in all skin samples by Western blot analysis, with relative expression levels of 0.85 ± ± 0.17** in black skin, 0.60 ± 0.05** and 0.34 ± 0.07 in black dots and white dots of piebald skin, respectively, and 0.20 ± 0.05 in white skin (p &lt; 0.01**). Immunohistochemical assays revealed that SLC45A2 was expressed in the hair follicle matrix, the inner and outer root sheath, and the dermal papilla in the skin tissues with different coat colors. These patterns were quantified by optical density (OD) analysis, which yielded relative expression levels of 0.23 ± 0.11 in black skin, 0.19 ± 0.09 and 0.10 ± 0.03 in black dots and white dots of piebald skin, respectively, and 0.08 ± 0.01 in white skin (p &lt; 0.05*). Conclusion. SLC45A2 is detectably expressed in sheep skin of all coat colors, though at significantly different levels. SLC45A2 may participate in the establishment of coat color by regulating the synthesis and trafficking of melanin.