Augmented Reality Applied in Road Excavation System of Government

As one of the presentative novel technologies in recent years, Augmented Reality (AR) has gotten the ROC government’s attention, and thereby the AR-related applications have been taken into official account in facilitating citizen life. In this research, first, a sort-out of the definitions and the scope of three types of Realities—AR, VR, MR—will be offered to tell the much more realistic dimension of AR. Under the official road excavation context in X city via observation method, second, specific AR applications will be proposed by illustrating it in the pre-excavation, the excavation, and the post-excavation phases, respectively. By cross-referencing between the official road excavation system and public infrastructure pipeline databases, third, the related data of pipeline maps could endow the current AR positioning with better accuracy and directionality

Unusual case of spontaneous uterine rupture in a single gestational primipara

AbstractSpontaneous rupture of the primiparous uterus is a rare but catastrophic obstetrical emergency. It is usually associated with prior uterine surgery, trauma, or placental abnormality. To remind physicians to include this condition in their differential diagnosis of acute abdominal pain in pregnant patients, we describe an interesting case of spontaneous uterine rupture that clinically mimicked bowel perforation. A 27-year-old single primiparous pregnant woman presented with sudden onset of severe abdominal pain and peritoneal signs, with absence of vaginal bleeding at 26 weeks’ gestation. The usual risk factors for uterine rupture, such as advanced maternal age, scarred uterus due to mode of previous delivery, or unusual pregnancy, were not present in our patient. Based on clinical examination, abdominal sonography and magnetic resonance imaging, uterine rupture was suspected and eventually confirmed at exploratory laparotomy. No uterine pathological abnormality was noted on the microscopic examination The preterm newborn expired after surgery. Since surgical intervention is the only definitive treatment, emergency physicians should be aware of this rare complication. Emergency physicians should be aware of spontaneous uterine rupture in pregnant patients, even in the absence of risk factors

Structural and DNA-binding studies on the bovine antimicrobial peptide, indolicidin: evidence for multiple conformations involved in binding to membranes and DNA

Indolicidin, a l3-residue antimicrobial peptide-amide, which is unusually rich in tryptophan and proline, is isolated from the cytoplasmic granules of bovine neutrophils. In this study, the structures of indolicidin in 50% D(3)-trifluoroethanol and in the absence and presence of SDS and D(38)-dodecylphosphocholine were determined using NMR spectroscopy. Multiple conformations were found and were shown to be due to different combinations of contact between the two WPW motifs. Although indolicidin is bactericidal and able to permeabilize bacterial membranes, it does not lead to cell wall lysis, showing that there is more than one mechanism of antimicrobial action. The structure of indolicidin in aqueous solution was a globular and amphipathic conformation, differing from the wedge shape adopted in lipid micelles, and these two structures were predicted to have different functions. Indolicidin, which is known to inhibit DNA synthesis and induce filamentation of bacteria, was shown to bind DNA in gel retardation and fluorescence quenching experiments. Further investigations using surface plasmon resonance confirmed the DNA-binding ability and showed the sequence preference of indolicidin. Based on our biophysical studies and previous results, we present a diagram illustrating the DNA-binding mechanism of the antimicrobial action of indolicidin and explaining the roles of the peptide when interacting with lipid bilayers at different concentrations

AV-SUPERB: A Multi-Task Evaluation Benchmark for Audio-Visual Representation Models

Audio-visual representation learning aims to develop systems with human-like perception by utilizing correlation between auditory and visual information. However, current models often focus on a limited set of tasks, and generalization abilities of learned representations are unclear. To this end, we propose the AV-SUPERB benchmark that enables general-purpose evaluation of unimodal audio/visual and bimodal fusion representations on 7 datasets covering 5 audio-visual tasks in speech and audio processing. We evaluate 5 recent self-supervised models and show that none of these models generalize to all tasks, emphasizing the need for future study on improving universal model performance. In addition, we show that representations may be improved with intermediate-task fine-tuning and audio event classification with AudioSet serves as a strong intermediate task. We release our benchmark with evaluation code and a model submission platform to encourage further research in audio-visual learning.Comment: Submitted to ICASSP 2024; Evaluation Code: https://github.com/roger-tseng/av-superb Submission Platform: https://av.superbbenchmark.or

Tai-Chi-Chuan Exercise Improves Pulmonary Function and Decreases Exhaled Nitric Oxide Level in Both Asthmatic and Nonasthmatic Children and Improves Quality of Life in Children with Asthma

Tai-Chi-Chuan (TCC) is an exercise of low-to-moderate intensity which is suitable for asthmatic patients. The aim of our study is to investigate improvements of the lung function, airway inflammation, and quality of life of asthmatic children after TCC. Participants included sixty-one elementary school students and they were divided into asthmatic (n=29) and nonasthmatic (n=32) groups by the International Study of Asthma and Allergies in Childhood (ISAAC) questionnaire. Among them, 20 asthmatic and 18 nonasthmatic children volunteered to participate in a 60-minute TCC exercise weekly for 12 weeks. Baseline and postintervention assessments included forced expiratory volume in one second (FEV1), forced vital capacity (FVC), peak expiratory flow rate (PEFR), fractional exhaled nitric oxide (FeNO) level, and Standardised Pediatric Asthma Quality of Life Questionnaire (PAQLQ(S)). After intervention, the level of FeNO decreased significantly; PEFR and the FEV1/FVC also improved significantly in both asthmatic group and nonasthmatic group after TCC. The asthmatic children also had improved quality of life after TCC. The results indicated that TCC could improve the pulmonary function and decrease airway inflammation in both children with mild asthma and those without asthma. It also improves quality of life in mild asthmatic children. Nevertheless, further studies are required to determine the effect of TCC on children with moderate-to-severe asthma

Assessing trends and predictors of tuberculosis in Taiwan

<p>Abstract</p> <p>Background</p> <p>Variety of environmental and individual factors can cause tuberculosis (TB) incidence change. The purpose of this study was to assess the characteristics of TB trends in the period 2004 - 2008 in Taiwan by month, year, gender, age, temperature, seasonality, and aborigines.</p> <p>Methods</p> <p>The generalized regression models were used to examine the potential predictors for the monthly TB incidence in regional and national scales.</p> <p>Results</p> <p>We found that (<it>i</it>) in Taiwan the average TB incidence was 68 per 100,000 population with mortality rate of 0.036 person^-1yr^-1, (<it>ii</it>) the highest TB incidence rate was found in eastern Taiwan (116 per 100,000 population) with the largest proportion of TB relapse cases (8.17%), (<it>iii</it>) seasonality, aborigines, gender, and age had a consistent and dominant role in constructing TB incidence patterns in Taiwan, and (<it>iv</it>) gender, time trend, and 2-month lag maximum temperature showed strong association with TB trends in aboriginal subpopulations.</p> <p>Conclusions</p> <p>The proposed Poisson regression model is capable of forecasting patterns of TB incidence at regional and national scales. This study suggested that assessment of TB trends in eastern Taiwan presents an important opportunity for understanding the time-series dynamics and control of TB infections, given that this is the typical host demography in regions where these infections remain major public health problems.</p

Near Optimal Protection Strategies against Targeted Attacks on the Core Node of a Network

隨著近年來網路科技的蓬勃發展，網際網路已成為21世紀最重要的傳播媒體，伴隨而來，資訊安全的議題也越形重要。我們發現，在網路攻防下，攻防雙方都會依據對方的策略而改變自己的對策，就如矛與盾一般地相互抗衡。 在本篇論文中，我們以防守方的角度來思考，在有限的防禦資源限制下，提出一個有效的防禦資源配置策略，來最大化攻擊者的攻擊成本，以提高核心節點的防護能力。分析此問題，為一非線性混合整數規劃的數學最佳化問題，由於問題本身高度的複雜性與困難度，所以我們以格拉蘭日鬆弛法為基礎的演算法來處理此問題，並針對與真實網路環境相似之無尺度網路，進行其存活度分析與探討。With the rapid growth of network technologies, the Internet may well become the single most important medium of the 21st century. Therefore, the issue of information security has drawn increasing attention. In network attack and defense, attackers and defenders constantly change their respective strategies. The situation is like the balance between a lance and a targe. In this thesis, we view the problem of security from the defender’s perspective. Given that defense resources are limited, we propose an effective defense resource allocation strategy that maximizes the attackers’ costs, and improves the protection of the core node. The problem is analyzed as a mixed nonlinear integer programming optimization problem. The solution approach is based on the Lagrangean relaxation method, which effectively solves this complicated problem. Furthermore, we evaluate the survivability of real network environment-like scale-free networks.謝 詞.......................................................................................................................I 論文摘要....................................................................................................................III THESIS ABSTRACT.................................................................................................V Contents....................................................................................................................VII List of Figures............................................................................................................IX List of Tables..............................................................................................................XI Chapter 1 Introduction................................................................................................1 1.1 Background......................................................................................................1 1.2 Motivation........................................................................................................3 1.3 Literature Survey.............................................................................................4 1.3.1 Survivability..........................................................................................4 1.3.2 Scale-Free Networks.............................................................................7 1.4 Proposed Approach..........................................................................................9 Chapter 2 Problem Formulation..............................................................................11 2.1 Protection Strategy for Defenders (PSD) Model...........................................11 2.1.1 Problem Description and Assumptions...............................................11 2.1.2 Notations.............................................................................................15 2.1.3 Problem Formulation..........................................................................16 2.1.4 Problem Reformulation.......................................................................17 2.2 Probabilistic Protection Strategy for Defenders (PPSD) Model....................18 2.2.1 Problem Description and Assumptions...............................................18 2.2.2 Notations.............................................................................................21 2.2.3 Problem Formulation..........................................................................22 2.2.4 Problem Reformulation.......................................................................23 Chapter 3 Solution Approach...................................................................................25 3.1 Lagrangean Relaxation Method.....................................................................25 3.2 PSD Model.....................................................................................................28 3.2.1 Solution Approach..............................................................................28 3.2.2 Lagrangean Relaxation.......................................................................28 3.2.3 The Dual Problem and the Subgradient Method.................................31 3.2.4 Getting Primal Feasible Solution........................................................32 3.3 PPSD Model...................................................................................................34 3.3.1 Solution Approach..............................................................................34 3.3.2 Lagrangean Relaxation.......................................................................34 3.3.3 The Dual Problem and the Subgradient Method.................................37 3.3.4 Getting Primal Feasible Solution........................................................38 Chapter 4 Computational Experiments...................................................................41 4.1 Computational Experiments on the PSD Model............................................41 4.1.1 Experiment Environments..................................................................41 4.1.2 Experiment Results.............................................................................42 4.2 Computational Experiments on the PPSD Model..........................................46 4.2.1 Experiment Environments..................................................................46 4.2.2 Experiment Results.............................................................................48 Chapter 5 Conclusion................................................................................................63 5-1 Summary........................................................................................................63 5-2 Future Work...................................................................................................64 References...................................................................................................................66 List of Figures Figure 1-1 Trend of Incidents......................................................................................1 Figure 1-2 Attack Tree Example.................................................................................5 Figure 1-3 Random Network Example......................................................................7 Figure 1-4 Scale-Free Network Example...................................................................9 Figure 2-1 Network Attack and Defense Behavior.................................................12 Figure 3-1 Illustration of the Lagrangean Relaxation Method.............................27 Figure 3-2 Procedures of the Lagrangean Relaxation Method..............................28 Figure 4-1 Experiment Results for Grid Networks.................................................43 Figure 4-2 Survivability of Scale-Free Networks....................................................43 Figure 4-3 Experiment Results for Different Network Topologies........................44 Figure 4-4 Average Number of Nodes Must be Compromised Distribution........44 Figure 4-5 Experiment Results for the PPSD Model Scenario 1 in Grid Networks (λ1=0.1, λ2=0.2)............................................................................................................48 Figure 4-6 Survivability of the PPSD Model Scenario 1 in Random Networks (λ1=0.1, λ2=0.2)............................................................................................................48 Figure 4-7 Experiment Results for the PPSD Model Scenario 1 in Different Network Topologies (λ1=0.1, λ2=0.2).........................................................................49 Figure 4-8 Experiment Results for the PPSD Model Scenario 1 in Different Network Topologies (λ1=0.1, λ2=0.4).........................................................................50 Figure 4-9 Experiment Results for the PPSD Model Scenario 1 in Different Network Topologies (λ1=0.2, λ2=0.4).........................................................................50 Figure 4-10 Experiment Results for the PPSD Model Scenario 1 in Different Network Topologies (λ1=0.2, λ2=0.8).........................................................................51 Figure 4-11 Experiment Results for the PPSD Model Scenario 2 in Scale-Free Networks.....................................................................................................................52 Figure 4-12 Survivability of the PPSD Model Scenario 2 in Random Networks.52 Figure 4-13 Experiment Results for the PPSD Model Scenario 2 in Different Network Topologies....................................................................................................52 Figure 5-1 Choke Point Example..............................................................................65 List of Tables Table 2-1 Problem Description of the PSD Model..................................................13 Table 2-2 Problem Assumptions of the PSD Model................................................14 Table 2-3 Problem Description of the PPSD Model................................................19 Table 2-4 Problem Assumptions of the PPSD Model..............................................20 Table 3-1 Heuristic for the PSD Model....................................................................33 Table 3-2 Heuristic for the PPSD Model..................................................................39 Table 4-1 Experimental Parameter Settings for the PSD Model...........................42 Table 4-2 Experiment Results for the PSD Model..................................................45 Table 4-3 Experimental Parameter Settings for the PPSD Model........................47 Table 4-4 Experiment Results for the PPSD Model Scenario 1 (λ1=0.1, λ2=0.2)...53 Table 4-5 Experiment Results for the PPSD Model Scenario 1 (λ1=0.1, λ2=0.3)...54 Table 4-6 Experiment Results for the PPSD Model Scenario 1 (λ1=0.1, λ2=0.4)...55 Table 4-7 Experiment Results for the PPSD Model Scenario 1 (λ1=0.2, λ2=0.4)..56 Table 4-8 Experiment Results for the PPSD Model Scenario 1 (λ1=0.2, λ2=0.6)..57 Table 4-9 Experiment Results for the PPSD Model Scenario 1 (λ1=0.2, λ2=0.8)..58 Table 4-10 Experiment Results for the PPSD Model Scenario 1 (λ1=0.3, λ2=0.5)59 Table 4-11 Experiment Results for the PPSD Model Scenario 1 (λ1=0.3, λ2=0.7)60 Table 4-12 Experiment Results for the PPSD Model Scenario 1 (λ1=0.3, λ2=0.9)61 Table 4-13 Experiment Results for the PPSD Model Scenario 2...........................6