Classical “Zeno” and “Anti-Zeno” effect?

If one continuously measures a decaying system, the system will appear to never decay that was called quantum Zeno effect. The continuous measurement is defined by a sequence of measurements whose time interval t between measurements approaches zero. Later many works chose the time interval t as finite (and greater than the Zeno time) which corresponds to making equal spaced measurements over a discrete time interval. With the discrete variable formulism one can derive the so-called Anti-Zeno effect. Our study is trying to contrast the results between continuous time interval measurement versus discrete time interval measurement. We demonstrate that we can obtain so-called “Zeno” and “Anti-Zeno” in a classical system if we apply the definition of non-ideal measurement.Physic

Including Urban Heat Island in Bioclimatic Early-Design Phases: A Simplified Methodology and Sample Applications

Urban heat island and urban‐driven climate variations are recognized issues and may considerably affect the local climatic potential of free‐running technologies. Nevertheless, green design and bioclimatic early‐design analyses are generally based on typical rural climate data, without including urban effects. This paper aims to define a simple approach to considering urban shapes and expected effects on local bioclimatic potential indicators to support early‐design choices. Furthermore, the proposed approach is based on simplifying urban shapes to simplify analyses in early‐design phases. The proposed approach was applied to a sample location (Turin, temperate climate) and five other climate conditions representative of Eurasian climates. The results show that the inclusion of the urban climate dimension considerably reduced rural HDD (heating degree‐days) from 10% to 30% and increased CDD (cooling degree‐days) from 70% to 95%. The results reveal the importance of including the urban climate dimension in early‐design phases, such as building programming in which specific design actions are not yet defined, to support the correct definition of early‐design bioclimatic analyse

Progress in Application of CNTs in Lithium-Ion Batteries

The lithium-ion battery is widely used in the fields of portable devices and electric cars with its superior performance and promising energy storage applications. The unique one-dimensional structure formed by the graphene layer makes carbon nanotubes possess excellent mechanical, electrical, and electrochemical properties and becomes a hot material in the research of lithium-ion battery. In this paper, the applicable research progress of carbon nanotubes in lithium-ion battery is described, and its future development is put forward from its two aspects of being not only the anodic conductive reinforcing material and the cathodic energy storage material but also the electrically conductive framework material

WeakTr: Exploring Plain Vision Transformer for Weakly-supervised Semantic Segmentation

This paper explores the properties of the plain Vision Transformer (ViT) for Weakly-supervised Semantic Segmentation (WSSS). The class activation map (CAM) is of critical importance for understanding a classification network and launching WSSS. We observe that different attention heads of ViT focus on different image areas. Thus a novel weight-based method is proposed to end-to-end estimate the importance of attention heads, while the self-attention maps are adaptively fused for high-quality CAM results that tend to have more complete objects. Besides, we propose a ViT-based gradient clipping decoder for online retraining with the CAM results to complete the WSSS task. We name this plain Transformer-based Weakly-supervised learning framework WeakTr. It achieves the state-of-the-art WSSS performance on standard benchmarks, i.e., 78.4% mIoU on the val set of PASCAL VOC 2012 and 50.3% mIoU on the val set of COCO 2014. Code is available at https://github.com/hustvl/WeakTr.Comment: 20 pages, 11 figure

LaFFi: Leveraging Hybrid Natural Language Feedback for Fine-tuning Language Models

Fine-tuning Large Language Models (LLMs) adapts a trained model to specific downstream tasks, significantly improving task-specific performance. Supervised Fine-Tuning (SFT) is a common approach, where an LLM is trained to produce desired answers. However, LLMs trained with SFT sometimes make simple mistakes and result in hallucinations on reasoning tasks such as question-answering. Without external feedback, it is difficult for SFT to learn a good mapping between the question and the desired answer, especially with a small dataset. This paper introduces an alternative to SFT called Natural Language Feedback for Finetuning LLMs (LaFFi). LaFFi has LLMs directly predict the feedback they will receive from an annotator. We find that requiring such reflection can significantly improve the accuracy in in-domain question-answering tasks, providing a promising direction for the application of natural language feedback in the realm of SFT LLMs. Additional ablation studies show that the portion of human-annotated data in the annotated datasets affects the fine-tuning performance.Comment: Paper accepted in Human-Centric Representation Learning workshop at AAAI 2024 (https://hcrl-workshop.github.io/2024/

Biomarker study of symptomatic intracranial atherosclerotic stenosis in patients with acute ischemic stroke

ObjectiveAcute ischemic stroke (AIS) is characterized by high rates of morbidity, disability, mortality, and recurrence, often leaving patients with varying degrees of sequelae. Symptomatic intracranial atherosclerotic stenosis (sICAS) is a significant contributor to AIS pathogenesis and recurrence. The formation and progression of sICAS are influenced by pathways such as lipid metabolism and inflammatory response. Given its high risk of clinical recurrence, timely assessment of intracranial vascular stenosis in AIS is crucial for diagnosing sICAS, treating stroke, and preventing stroke recurrence.MethodsFourteen AIS patients were divided into stenosis and control groups based on the presence or absence of intracranial vessel stenosis. Initially, 4D Label-free proteome quantification technology was employed for mass spectrometry analysis to identify differential proteins between the groups. Subsequently, functional enrichment analysis, including GO classification, KEGG pathway, and Domain, revealed trends related to differential proteins. The STRING (v.11.5) protein interaction network database was used to identify differential protein interactions and target proteins. Finally, parallel reaction monitoring (PRM) validated the selected target proteins.ResultsMass spectrometry identified 1,096 proteins, with 991 being quantitatively comparable. Using a p-value <0.05 and differential expression change thresholds of >1.3 for significant up-regulation and < 1/1.3 for significant down-regulation, 46 differential proteins were identified: 24 significantly up-regulated and 22 significantly down-regulated. PRM experiments validated five proteins related to lipid metabolism and inflammatory response: namely alpha-2-macroglobulin (A2M), lipopolysaccharide-binding protein (LBP), cathepsin G (CTSG), cystatin (CST)3, and fatty acid-binding protein (FABP)1.ConclusionThe detection of changes in these five proteins in AIS patients can aid in the diagnosis of sICAS, inform stroke treatment, and assist in preventing stroke recurrence. Moreover, it can contribute to the development of drugs for preventing AIS recurrence by integrating traditional Chinese and Western medicine

Construction of a prognostic model of lung adenocarcinoma based on machine learning

In order to more accurately predict the prognosis and survival of lung adenocarcinoma patients, this paper used the gene expression and clinical information data of lung adenocarcinoma patients in the open database of TCGA to jointly construct a prognosis model of lung adenocarcinoma. Three difference analysis methods and univariate cox regression analysis were used as the preliminary screening method. By comparing the variable selection ability of lasso regression and random survival forest, comparing the performance of cox proportional risk regression model and random survival forest model, and integrating clinical data, a model that can more accurately predict the prognosis of lung adenocarcinoma patients was constructed. After comparison and selection, lasso regression was used to select variables and cox proportional risk model was used as the prediction model. The consistency index of the model reached 0.712. The AUC for 1-year, 3-year and 5-year survival of lung adenocarcinoma patients in the validation set were 0.808, 0.816 and 0.754, respectively. After the fusion of clinical data, the 1-year, 3-year and 5-year survival prediction AUC in the validation set were 0.840, 0.836 and 0.865, respectively, indicating that the model had good predictive performance

Macerals of lignite and the effect of alkali treatment on the structure and combustion performance of lignite

Suppressing the spontaneous combustion of lignite is of great significance for safe transportation and efficient utilization of lignite. Taking the Shengli lignite as the research object, two different macerals, inertinite and huminite, were selected by optical microscope, and treated with NaOH respectively to study the relationship between the structure and combustion reaction performance of different macerals and lignite treated with NaOH. The structure of the prepared coal samples was characterized by SEM-EDS, XPS, FT-IR, XRD and Raman, and the changes of the main functional groups were analyzed. The effect of NaOH treatment on the combustion performance of different maceral lignite was investigated by TGA. The results showed that the ignition temperature of huminite lignite was about 10 ℃ earlier than that of inertinite, but the comprehensive combustion characteristic index of inertinite lignite was slightly higher than that of huminite. After the NaOH treatment, the lignite of different macerals showed a hysteresis of combustion, there were two obvious weight losses in the range of 200−500 ℃ and 650−800 ℃, respectively, and the mass loss was mainly concentrated in the second weight loss, in particular, the effect of huminite lignite was more significant, and the temperature corresponding to the maximum combustion reaction rate was about 60 ℃ behind that of inertinite. The kinetic analysis of the combustion process of the coal samples showed that the activation energy of combustion reaction of lignite with different macerals significantly increased after the NaOH treatment, and the huminite lignite was higher than that of inertinite lignite. The XPS/FT-IR results revealed that the contents of carboxylic oxygen-containing functional groups in different macerals of lignite treated by NaOH decreased, the main reason is that in the process of NaOH treatment, Na+ interacted with the carboxylic oxygen-containing functional groups in lignite to form the sodium carboxylate structure, and the relative amount of the sodium carboxylate structure in huminite coal was relatively large. It is believed that the inhibitory effect on the combustion of lignite with different macerals is attributed to the stability of the sodium carboxylate structure, and the number of the sodium carboxylate structure formed by combining with Na is the main reason for the difference in its combustion performance. The XRD/Raman analysis indicates that the formation of the sodium carboxylate structure in lignite leads to the increase of the order degree of carbon microcrystalline structure, and the order degree of huminite lignite is higher than that in inertinite

Molecular mechanism of the treatment of lung adenocarcinoma by Hedyotis Diffusa: an integrative study with real-world clinical data and experimental validation

BackgroundWith a variety of active ingredients, Hedyotis Diffusa (H. diffusa) can treat a variety of tumors. The purpose of our study is based on real-world data and experimental level, to double demonstrate the efficacy and possible molecular mechanism of H. diffusa in the treatment of lung adenocarcinom (LUAD).MethodsPhenotype-genotype and herbal-target associations were extracted from the SymMap database. Disease-gene associations were extracted from the MalaCards database. A molecular network-based correlation analysis was further conducted on the collection of genes associated with TCM and the collection of genes associated with diseases and symptoms. Then, the network separation SAB metrics were applied to evaluate the network proximity relationship between TCM and symptoms. Finally, cell apoptosis experiment, Western blot, and Real-time PCR were used for biological experimental level validation analysis.ResultsIncluded in the study were 85,437 electronic medical records (318 patients with LUAD). The proportion of prescriptions containing H. diffusa in the LUAD group was much higher than that in the non-LUAD group (p < 0.005). We counted the symptom relief of patients in the group and the group without the use of H. diffusa: except for symptoms such as fatigue, palpitations, and dizziness, the improvement rate of symptoms in the user group was higher than that in the non-use group. We selected the five most frequently occurring symptoms in the use group, namely, cough, expectoration, fatigue, chest tightness and wheezing. We combined the above five symptom genes into one group. The overlapping genes obtained were CTNNB1, STAT3, CASP8, and APC. The selection of CTNNB1 target for biological experiments showed that the proliferation rate of LUAD A549 cells in the drug intervention group was significantly lower than that in the control group, and it was concentration-dependent. H. diffusa can promote the apoptosis of A549 cells, and the apoptosis rate of the high-concentration drug group is significantly higher than that of the low-concentration drug group. The transcription and expression level of CTNNB1 gene in the drug intervention group were significantly decreased.ConclusionH. diffusa inhibits the proliferation and promotes apoptosis of LUAD A549 cells, which may be related to the fact that H. diffusa can regulate the expression of CTNNB1

Research Progress on Spatiotemporal Distribution Characteristics of Carbon Stable Isotopes in Wines from New and Old World Countries

With the continuous development of economic globalization, the production and consumption of wine in New and Old World countries have shown a steady growth trend. Wines of different grades and styles have been developed, which are related to geographical origin. Carbon stable isotopes are important indicators for geographical origin identification and have been of great concern to researchers due to their stability and objectivity. This paper summarizes the spatiotemporal distribution characteristics of carbon stable isotopes in ethanol, glycerol, total carbon and other components in wines from the Old (France, Italy, Spain, Germany, Switzerland, Croatia, and Romania) and New Worlds (Australia, South Africa, Chile, Argentina, Brazil, and China). In general, there are small differences in the carbon stable isotope ratio of various components between New and Old World wines, so it is impossible to achieve reliable results when only carbon stable isotope data is used for geographical origin identification. The combined use of carbon stable isotope data with other isotope data and mineral elements can improve the accuracy of geographical origin identification. Furthermore, the application of carbon stable isotope technology is also summarized over the past 15 years with the aim to provide a reference for the establishment of database