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

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

Break-taking behaviour pattern of long-distance freight vehicles based on GPS trajectory data

This paper focuses on the break-taking behaviour pattern of long-distance freight vehicles, providing a new perspective on the study of behaviour patterns and simultaneously providing a reference for transport management departments and related enterprises. Based on Global Positioning System (GPS) trajectory data, we select stopping points as break-taking sites of long-distance freight vehicles and then classify the stopping points into three different classes based on the break-taking duration. We then explore the relationship of the distribution of the break-taking frequency between the three single classifications and their combinations, on the basis of the break-taking duration distribution. We find that the combination is a Gaussian distribution when each of the three individual classes is a Gaussian distribution, contrasting with the power-law distribution of the break-taking duration. Then we experimental analysis the distribution of the break-taking durations and frequencies, and find that, for the durations, the three single classifications can be fitted individually by an Exponential distribution and together by a Power-law distribution, for the frequencies, both the three single classifications and together can be fitted by a Gaussian distribution,so that can validate the above theoretical analysis. Key words: break-taking behaviour, long-distance freight vehicle, statistical analysi