On the Evolution Rule for Regional Industrial Structure Based on the Stochastic Process Theory

This paper studied the regional industrial structure evolution rules by the theory of stochastic process. The regional industrial structure changes depend on market demands, technological progress and production factors flow, policy value orientation and other factors which are random variables (RVs). Regional industrial structure evolves as a stochastic process and this process is influenced by the market demand, technological progress and production factors flow, the policy value orientation and other RVs. Key words: Regional industrial structure; Evolution rule; Stochastic proces

IMECE2002-34487 STRESS ANALYSES AND STRUCTURAL MODIFICATIONS OF FABRIC COMPOSITE SEAMS

ABSTRACT An adhesively bonded seam is a common method of joining coated fabrics in the manufacturing of inflatables. In this paper, Nylon and Polyester seams are studied both experimentally and numerically. In the numerical analyses, the seam components are described with layered models containing fabric composite layers. The in-plane and out-of-plane elastic constants of the fabric composite layers are derived using the crimp model and a stacked model respectively. An existing finite element code, ANSYS 5.7 is used to perform twodimensional stress analyses of the seams under tension. In the analyses, a stress concentration factor is defined to evaluate the strength of the seams in comparison with their base fabric laminates. Numerical data show that Nylon seams are almost as strong as their base laminate but there is strength degradation in Polyester seams, which agrees well with test results. Finally, two structural modifications are proposed to improve the strength of the Polyester seams. The modifications are evaluated by both simulations and tests. Keywords: Coated Fabrics, Composites, Modeling, Stress Analysis INTRODUCTION Inflatables such as inflatable habitats, airships and aerostats are manufactured from flexible composite materials (coated fabrics) that are made structural via internal inflation pressure Inflatables are made up of pieces of coated fabrics. One common method of joining coated fabrics is to use an adhesively bonded seam. A good seam should not be the weak link in a structure. Three common types of adhesively bonded seams in use are overlap seam, single tape seam, and double tape seam. In this paper, a Nylon double tape seam and a Polyester double tape seam are investigated

Biomedical Image Splicing Detection using Uncertainty-Guided Refinement

Recently, a surge in biomedical academic publications suspected of image manipulation has led to numerous retractions, turning biomedical image forensics into a research hotspot. While manipulation detectors are concerning, the specific detection of splicing traces in biomedical images remains underexplored. The disruptive factors within biomedical images, such as artifacts, abnormal patterns, and noises, show misleading features like the splicing traces, greatly increasing the challenge for this task. Moreover, the scarcity of high-quality spliced biomedical images also limits potential advancements in this field. In this work, we propose an Uncertainty-guided Refinement Network (URN) to mitigate the effects of these disruptive factors. Our URN can explicitly suppress the propagation of unreliable information flow caused by disruptive factors among regions, thereby obtaining robust features. Moreover, URN enables a concentration on the refinement of uncertainly predicted regions during the decoding phase. Besides, we construct a dataset for Biomedical image Splicing (BioSp) detection, which consists of 1,290 spliced images. Compared with existing datasets, BioSp comprises the largest number of spliced images and the most diverse sources. Comprehensive experiments on three benchmark datasets demonstrate the superiority of the proposed method. Meanwhile, we verify the generalizability of URN when against cross-dataset domain shifts and its robustness to resist post-processing approaches. Our BioSp dataset will be released upon acceptance

Familial multinodular goiter syndrome with papillary thyroid carcinomas: mutational analysis of the associated genes in 5 cases from 1 Chinese family

Background: Familial papillary thyroid cancer (fPTC) is recognized as a distinct entity only recently and no fPTC predisposing genes have been identified. Several potential regions and susceptibility loci for sporadic PTC have been reported. We aimed to evaluate the role of the reported susceptibility loci and potential risk genomic region in a Chinese familial multinodular goiter (fMNG) with PTC family. Methods: We sequenced the related risk genomic regions and analyzed the known PTC susceptibility loci in the Chinese family members who consented to join the study. These loci included (1) the point mutations of the BRAF and RET; (2) the possible susceptibility loci to sporadic PTC; and (3) the suggested potential fMNG syndrome with PTC risk region. Results: The members showed no mutations in the common susceptible BRAF and RET genomic region, although contained several different heterozygous alleles in the RET introns. All the members were homozygous for PTC risk alleles of rs966423 (C) at chromosome 2q35, rs2910164 (C) at chromosome 5q24 and rs2439302 (G) at chromosome 8p12; while carried no risk allele of rs4733616 (T) at chromosome 8q24, rs965513 (A) or rs1867277 (A) at chromosome 9q22 which were associated with radiation-related PTC. The frequency of the risk allele of rs944289 (T) but not that of rs116909374 (T) at chromosome 14q13 was increased in the MNG or PTC family members. Conclusions: Our work provided additional evidence to the genetic predisposition to a Chinese familial form of MNG with PTC. The family members carried quite a few risk alleles found in sporadic PTC; particularly, homozygous rs944289 (T) at chromosome 14q13 which was previously shown to be linked to a form of fMNG with PTC. Moreover, the genetic determinants of radiation-related PTC were not presented in this family

KL-6 levels in the connective tissue disease population: typical values and potential confounders–a retrospective, real-world study

BackgroundKrebs von den Lungen 6 (KL-6) is a potential biomarker for determining the severity of interstitial lung disease (ILD) in patients with connective tissue disease (CTD). Whether KL-6 levels can be affected by potential confounders such as underlying CTD patterns, patient-associated demographics, and comorbidities needs further investigation.MethodsFrom the database created by Xiangya Hospital, 524 patients with CTD, with or without ILD, were recruited for this retrospective analysis. Recorded data included demographic information, comorbidities, inflammatory biomarkers, autoimmune antibodies, and the KL-6 level at admission. Results of CT and pulmonary function tests were collected one week before or after KL-6 measurements. The percent of predicted diffusing capacity of the lung for carbon monoxide (DLCO%) and computed tomography (CT) scans were used to determine the severity of ILD.ResultsUnivariate linear regression analysis showed that BMI, lung cancer, TB, lung infections, underlying CTD type, white blood cell (WBC) counts, neutrophil (Neu) counts, and hemoglobin (Hb) were related to KL-6 levels. Multiple linear regression confirmed that Hb and lung infections could affect KL-6 levels independently; the β were 9.64 and 315.93, and the P values were 0.015 and 0.039, respectively. CTD-ILD patients had higher levels of KL-6 (864.9 vs 463.9, P < 0.001) than those without ILD. KL-6 levels were closely correlated to the severity of ILD assessed both by CT and DLCO%. Additionally, we found that KL-6 level was an independent predictive factor for the presence of ILD and further constructed a decision tree model to rapidly determine the risk of developing ILD among CTD patients.ConclusionKL-6 is a potential biomarker for gauging the incidence and severity of ILD in CTD patients. To use this typical value of KL-6, however, doctors should take Hb and the presence of lung infections into account

Safeguarding Medical Image Segmentation Datasets against Unauthorized Training via Contour- and Texture-Aware Perturbations

The widespread availability of publicly accessible medical images has significantly propelled advancements in various research and clinical fields. Nonetheless, concerns regarding unauthorized training of AI systems for commercial purposes and the duties of patient privacy protection have led numerous institutions to hesitate to share their images. This is particularly true for medical image segmentation (MIS) datasets, where the processes of collection and fine-grained annotation are time-intensive and laborious. Recently, Unlearnable Examples (UEs) methods have shown the potential to protect images by adding invisible shortcuts. These shortcuts can prevent unauthorized deep neural networks from generalizing. However, existing UEs are designed for natural image classification and fail to protect MIS datasets imperceptibly as their protective perturbations are less learnable than important prior knowledge in MIS, e.g., contour and texture features. To this end, we propose an Unlearnable Medical image generation method, termed UMed. UMed integrates the prior knowledge of MIS by injecting contour- and texture-aware perturbations to protect images. Given that our target is to only poison features critical to MIS, UMed requires only minimal perturbations within the ROI and its contour to achieve greater imperceptibility (average PSNR is 50.03) and protective performance (clean average DSC degrades from 82.18% to 6.80%)

Augmenting Pathologists with NaviPath: Design and Evaluation of a Human-AI Collaborative Navigation System

Artificial Intelligence (AI) brings advancements to support pathologists in navigating high-resolution tumor images to search for pathology patterns of interest. However, existing AI-assisted tools have not realized this promised potential due to a lack of insight into pathology and HCI considerations for pathologists' navigation workflows in practice. We first conducted a formative study with six medical professionals in pathology to capture their navigation strategies. By incorporating our observations along with the pathologists' domain knowledge, we designed NaviPath -- a human-AI collaborative navigation system. An evaluation study with 15 medical professionals in pathology indicated that: (i) compared to the manual navigation, participants saw more than twice the number of pathological patterns in unit time with NaviPath, and (ii) participants achieved higher precision and recall against the AI and the manual navigation on average. Further qualitative analysis revealed that navigation was more consistent with NaviPath, which can improve the overall examination quality.Comment: Accepted ACM CHI Conference on Human Factors in Computing Systems (CHI '23

A study of latent profile analysis of empathic competence and factors influencing it in nursing interns: a multicenter cross-sectional study

BackgroundEmpathy, as one of the fundamental principles of nursing professionalism, plays a pivotal role in the formation and advancement of the nursing team. Nursing interns, as a reserve force within the nursing team, are of significant importance in terms of their ability to empathize. This quality is not only directly related to the degree of harmony in the nurse–patient relationship and the enhancement of patient satisfaction, but also plays a pivotal role in the promotion of the quality of nursing services to a new level.AimThe objective of this study was to gain a deeper understanding of the current state of nursing interns’ empathic abilities. To this end, we sought to examine empathic performance under different profile models and to identify the key factors influencing these profile models.MethodsThe study utilized 444 nursing interns from 11 tertiary general hospitals in Inner Mongolia as research subjects. The study employed a number of research tools, including demographic characteristics, the Jefferson Scale of Empathy, and the Professional Quality of Life Scale. A latent profile model of nursing interns’ empathy ability was analyzed using Mplus 8.3. The test of variability of intergroup variables was performed using the chi-square test. Finally, the influencing factors of each profile model were analyzed by unordered multi-categorical logistic regression analysis.ResultsThe overall level of empathy among nursing interns was found to be low, with 45% belonging to the humanistic care group, 43% exhibiting low empathy, and 12% demonstrating high empathy. The internship duration, empathy satisfaction, secondary traumatic stress, only child, place of birth, and satisfaction with nursing were identified as factors influencing the latent profiles of empathy in nursing interns (p < 0.05).ConclusionThere is considerable heterogeneity in nursing interns’ ability to empathize. Consequently, nursing educators and administrators should direct greater attention to interns with lower empathy and develop targeted intervention strategies based on the influences of the different underlying profiles