Momentum and Mass Fluxes in a Gas Confined between Periodically Structured Surfaces at Different Temperatures

It is well known that in a gas-filled duct or channel along which a temperature gradient is applied, a thermal creep flow is created. Here we show that a mass and momentum flux can also be induced in a gas confined between two parallel structured surfaces at different temperatures, i.e. \textit{orthogonal} to the temperature gradient. We use both analytical and numerical methods to compute the resulting fluxes. The momentum flux assumes its maximum value in the free-molecular flow regime, the (normalized) mass flux in the transition flow regime. The discovered phenomena could find applications in novel methods for energy-conversion and thermal pumping of gases.Comment: 6 pages, 5 figures, updated fig.5, updated text for the numerical metho

StratMed: Relevance Stratification for Low-resource Medication Recommendation

With the growing imbalance between limited medical resources and escalating demands, AI-based clinical tasks have become paramount. Medication recommendation, as a sub-domain, aims to amalgamate longitudinal patient history with medical knowledge, assisting physicians in prescribing safer and more accurate medication combinations. Existing methods overlook the inherent long-tail distribution in medical data, lacking balanced representation between head and tail data, which leads to sub-optimal model performance. To address this challenge, we introduce StratMed, a model that incorporates an innovative relevance stratification mechanism. It harmonizes discrepancies in data long-tail distribution and strikes a balance between the safety and accuracy of medication combinations. Specifically, we first construct a pre-training method using deep learning networks to obtain entity representation. After that, we design a pyramid-like data stratification method to obtain more generalized entity relationships by reinforcing the features of unpopular entities. Based on this relationship, we designed two graph structures to express medication precision and safety at the same level to obtain visit representations. Finally, the patient's historical clinical information is fitted to generate medication combinations for the current health condition. Experiments on the MIMIC-III dataset demonstrate that our method has outperformed current state-of-the-art methods in four evaluation metrics (including safety and accuracy)

CMTR: Cross-modality Transformer for Visible-infrared Person Re-identification

Visible-infrared cross-modality person re-identification is a challenging ReID task, which aims to retrieve and match the same identity's images between the heterogeneous visible and infrared modalities. Thus, the core of this task is to bridge the huge gap between these two modalities. The existing convolutional neural network-based methods mainly face the problem of insufficient perception of modalities' information, and can not learn good discriminative modality-invariant embeddings for identities, which limits their performance. To solve these problems, we propose a cross-modality transformer-based method (CMTR) for the visible-infrared person re-identification task, which can explicitly mine the information of each modality and generate better discriminative features based on it. Specifically, to capture modalities' characteristics, we design the novel modality embeddings, which are fused with token embeddings to encode modalities' information. Furthermore, to enhance representation of modality embeddings and adjust matching embeddings' distribution, we propose a modality-aware enhancement loss based on the learned modalities' information, reducing intra-class distance and enlarging inter-class distance. To our knowledge, this is the first work of applying transformer network to the cross-modality re-identification task. We implement extensive experiments on the public SYSU-MM01 and RegDB datasets, and our proposed CMTR model's performance significantly surpasses existing outstanding CNN-based methods.Comment: 11 pages, 7 figures, 7 table

Evaluation of petrophysical classification of strongly heterogeneous reservoirs based on the MRGC algorithm

The target formation in the study area of the Pearl River Mouth Basin is characterized by complex lithology and thin interbedded layers, with a large pore-permeability distribution range and strongly heterogeneous characteristics, which makes the reservoir pore structure and production capacity significantly different and brings research difficulties for reservoir logging evaluation and desert identification. The conventional reservoir classification method is mainly based on physical research, which requires developing extremely accurate formulas for calculating porosity and permeability; the calculation accuracy of pore permeability of low-porosity and low-permeability reservoirs is difficult to guarantee; and the conventional logging data cannot be comprehensively applied in reservoir classification. In this paper, taking Zhujiang and Zhuhai Formation reservoirs in the Huizhou M oilfield as an example, we integrated core analysis data such as core cast thin section, pore permeability data, rock electrical parameters, grain size, and relative permeability curves and combined with petrophysical parameters and pore structure characteristics to classify the reservoirs. The artificial neural network is used to predict the resistivity of saturated pure water (R0) to remove the influence of oil and gas on reservoir resistivity. The natural gamma ray (GR) “fluctuation” is used to calculate the variance root of variation (GS) to reflect the lithological variability and sedimentary heterogeneity of the reservoir, and then the conventional logging preferences, R0 and Gs (based on GR), are classified based on the automatic clustering MRGC algorithm to classify the logging facies. To classify the petrophysical phase reservoirs under the constraint of pore structure classification, we proposed a petrophysical classification logging model based on the natural gamma curve “fluctuation” intensity for strongly heterogeneous reservoirs. The learning model is extended to the whole area for training and prediction of desert identification, and the prediction results of the model are in good agreement with the actual results, which is important for determining favorable reservoirs in the area and the adjustment of oilfield development measures

Poor-prognosis disclosure preference in cancer patient-caregiver dyads and its association with their quality of life and perceived stress: a cross-sectional survey in mainland China

Background This study attempted to examine the discordance between family caregivers and cancer patients in their poor-prognosis disclosure preferences in mainland China and then ascertained the associations between quality of life (QoL), perceived stress, and poor-prognosis disclosure preferences. Methods Six hundred fifty-one pairs of inpatients and their matched caregivers (participation rate = 92.2%) were recruited in this cross-sectional survey. A set of paired self-administered questionnaires were completed independently by patient–caregiver dyads. Results Fewer family caregivers than cancer patients felt that poor prognosis should be disclosed to patients (61.2% vs. 90.0%, p < 0.001). Patients' positive poor-prognosis disclosure preference was associated with patients' better QoL (p < 0.05) and caregivers' reduced perceived stress levels (p = 0.013). However, caregivers' poor-prognosis disclosure preference correlated only with their own physical state (p = 0.028). Moreover, the caregivers who concurred with patients in positive poor-prognosis disclosure preference were more likely to experience a better QoL (p < 0.05) and lower perceived stress levels (p = 0.048) in the III–IV stage subgroup. Conclusions There was a significant discrepancy in poor-prognosis disclosure preference between cancer patients and caregivers in China. The caregivers' preference of concealing poor prognosis from patients was not related to cancer patients' QoL or perceived stress. In addition, caregivers had better QoL and lower stress levels when they held the same positive poor-prognosis disclosure preference as the patients

Disorder in Mn+1AXn phases at the atomic scale.

Atomic disordering in materials alters their physical and chemical properties and can subsequently affect their performance. In complex ceramic materials, it is a challenge to understand the nature of structural disordering, due to the difficulty of direct, atomic-scale experimental observations. Here we report the direct imaging of ion irradiation-induced antisite defects in Mn+1AXn phases using double CS-corrected scanning transmission electron microscopy and provide compelling evidence of order-to-disorder phase transformations, overturning the conventional view that irradiation causes phase decomposition to binary fcc-structured Mn+1Xn. With the formation of uniformly distributed cation antisite defects and the rearrangement of X anions, disordered solid solution γ-(Mn+1A)Xn phases are formed at low ion fluences, followed by gradual transitions to solid solution fcc-structured (Mn+1A)Xn phases. This study provides a comprehensive understanding of the order-to-disorder transformations in Mn+1AXn phases and proposes a method for the synthesis of new solid solution (Mn+1A)Xn phases by tailoring the disorder