Coastal Disasters and Remote Sensing Monitoring Methods

Coastal disaster is abnormal changes caused by climate change, human activities, geological movement or natural environment changes. According to formation cause, marine disasters as storm surges, waves, Tsunami coastal erosion, sea-level rise, red tide, seawater intrusion, marine oil spill and soil salinization. Remote sensing technology has real-time and large-area advantages in promoting the monitoring and forecast ability of coastal disaster. Relative to natural disasters, ones caused by human factors are more likely to be monitored and prevented. In this paper, we use several remote sensing methods to monitor or forecast three kinds of coastal disaster cause by human factors including red tide, sea-level rise and oil spilling, and make proposals for infrastructure based on the research results. The chosen method of monitoring red tide by inversing chlorophyll-a concentration is improved OC3M Model, which is more suitable for the coastal zone and higher spatial resolution than the MODIS chlorophyll-a production. We monitor the sea-level rise in coastal zone through coastline changes without artificial modifications. The improved Lagrangian model can simulate the trajectory of oil slick efficiently. Making the infrastructure planning according the coastal disasters and features of coastline contributes to prevent coastal disaster and coastal ecosystem protection. Multi-source remote sensing data can effectively monitor and prevent coastal disaster, and provide planning advices for coastal infrastructure construction

Feature-Based Matrix Factorization

Recommender system has been more and more popular and widely used in many applications recently. The increasing information available, not only in quantities but also in types, leads to a big challenge for recommender system that how to leverage these rich information to get a better performance. Most traditional approaches try to design a specific model for each scenario, which demands great efforts in developing and modifying models. In this technical report, we describe our implementation of feature-based matrix factorization. This model is an abstract of many variants of matrix factorization models, and new types of information can be utilized by simply defining new features, without modifying any lines of code. Using the toolkit, we built the best single model reported on track 1 of KDDCup'11.Comment: Minor update, add some related work

A Parallel and Efficient Algorithm for Learning to Match

Many tasks in data mining and related fields can be formalized as matching between objects in two heterogeneous domains, including collaborative filtering, link prediction, image tagging, and web search. Machine learning techniques, referred to as learning-to-match in this paper, have been successfully applied to the problems. Among them, a class of state-of-the-art methods, named feature-based matrix factorization, formalize the task as an extension to matrix factorization by incorporating auxiliary features into the model. Unfortunately, making those algorithms scale to real world problems is challenging, and simple parallelization strategies fail due to the complex cross talking patterns between sub-tasks. In this paper, we tackle this challenge with a novel parallel and efficient algorithm for feature-based matrix factorization. Our algorithm, based on coordinate descent, can easily handle hundreds of millions of instances and features on a single machine. The key recipe of this algorithm is an iterative relaxation of the objective to facilitate parallel updates of parameters, with guaranteed convergence on minimizing the original objective function. Experimental results demonstrate that the proposed method is effective on a wide range of matching problems, with efficiency significantly improved upon the baselines while accuracy retained unchanged.Comment: 10 pages, short version was published in ICDM 201

The AGE-RAGE axis associates with chronic pulmonary diseases and smoking in the Rotterdam study

Background: Chronic obstructive pulmonary disease (COPD) and asthma associate with high morbidity and mortality. High levels of advanced glycation end products (AGEs) were found in tissue and plasma of COPD patients but their role in COPD and asthma is unclear. Methods: In the Rotterdam Study (n = 2577), AGEs (by skin autofluorescence (SAF)), FEV1 and lung diffusing capacity (DLCOc and DLCOc /alveolar volume [VA]) were measured. Associations of SAF with asthma, COPD, GOLD stage, and lung function were analyzed using logistic and linear regression adjusted for covariates, followed by interaction and stratification analyses. sRAGE and EN-RAGE associations with COPD prevalence were analyzed by logistic regression. Results: SAF associated with COPD prevalence (OR = 1.299 [1.060, 1.591]) but not when adjusted for smoking (OR = 1.106 [0.89, 1.363]). SAF associated with FEV1% predicted (β=-3.384 [-4.877, -1.892]), DLCOc (β=-0.212 [-0.327, -0.097]) and GOLD stage (OR = 4.073, p = 0.001, stage 3&amp;4 versus 1). Stratified, the association between SAF and FEV1%predicted was stronger in COPD (β=-6.362 [-9.055, -3.670]) than non-COPD (β=-1.712 [-3.306, -0.118]). Association of SAF with DLCOc and DLCOc/VA were confined to COPD (β=-0.550 [-0.909, -0.191]; β=-0.065 [-0.117, -0.014] respectively). SAF interacted with former smoking and COPD prevalence for associations with lung function. Lower sRAGE and higher EN-RAGE associated with COPD prevalence (OR = 0.575[0.354, 0.931]; OR = 1.778[1.142, 2.768], respectively). Conclusions: Associations between SAF, lung function and COPD prevalence were strongly influenced by smoking. SAF associated with COPD severity and its association with lung function was more prominent within COPD. These results fuel further research into interrelations and causality between SAF, smoking and COPD. Take-home message: Skin AGEs associated with prevalence and severity of COPD and lung function in the general population with a stronger effect in COPD, calling for further research into interrelations and causality between SAF, smoking and COPD.</p

The AGE-RAGE axis associates with chronic pulmonary diseases and smoking in the Rotterdam study

Background: Chronic obstructive pulmonary disease (COPD) and asthma associate with high morbidity and mortality. High levels of advanced glycation end products (AGEs) were found in tissue and plasma of COPD patients but their role in COPD and asthma is unclear. Methods: In the Rotterdam Study (n = 2577), AGEs (by skin autofluorescence (SAF)), FEV1 and lung diffusing capacity (DLCOc and DLCOc /alveolar volume [VA]) were measured. Associations of SAF with asthma, COPD, GOLD stage, and lung function were analyzed using logistic and linear regression adjusted for covariates, followed by interaction and stratification analyses. sRAGE and EN-RAGE associations with COPD prevalence were analyzed by logistic regression. Results: SAF associated with COPD prevalence (OR = 1.299 [1.060, 1.591]) but not when adjusted for smoking (OR = 1.106 [0.89, 1.363]). SAF associated with FEV1% predicted (β=-3.384 [-4.877, -1.892]), DLCOc (β=-0.212 [-0.327, -0.097]) and GOLD stage (OR = 4.073, p = 0.001, stage 3&amp;4 versus 1). Stratified, the association between SAF and FEV1%predicted was stronger in COPD (β=-6.362 [-9.055, -3.670]) than non-COPD (β=-1.712 [-3.306, -0.118]). Association of SAF with DLCOc and DLCOc/VA were confined to COPD (β=-0.550 [-0.909, -0.191]; β=-0.065 [-0.117, -0.014] respectively). SAF interacted with former smoking and COPD prevalence for associations with lung function. Lower sRAGE and higher EN-RAGE associated with COPD prevalence (OR = 0.575[0.354, 0.931]; OR = 1.778[1.142, 2.768], respectively). Conclusions: Associations between SAF, lung function and COPD prevalence were strongly influenced by smoking. SAF associated with COPD severity and its association with lung function was more prominent within COPD. These results fuel further research into interrelations and causality between SAF, smoking and COPD. Take-home message: Skin AGEs associated with prevalence and severity of COPD and lung function in the general population with a stronger effect in COPD, calling for further research into interrelations and causality between SAF, smoking and COPD.</p

A bio-inspired multi-functional tendon-driven tactile sensor and application in obstacle avoidance using reinforcement learning

This paper presents a new bio-inspired tactile sensor that is multi-functional and has different sensitivity contact areas. The TacTop area is sensitive and is used for object classification when there is a direct contact. On the other hand, the TacSide area is less sensitive and is used to localize the side contact areas. By connecting tendons from the TacSide area to the TacTop area, the sensor is able to perform multiple detection functions using the same expression region. For the mixed contacting signals collected from the expression region with numerous markers and pins, we build a modified DenseNet121 network which specifically removes all fully connected layers and keeps the rest as a sub-network. The proposed model also contains a global average pooling layer with two branching networks to handle different functions and provide accurate spatial translation of the extracted features. The experimental results demonstrate a high prediction accuracy of 98% for object perception and localization. Furthermore, the new tactile sensor is utilized for obstacle avoidance, where action skills are extracted from human demonstrations and then an action dataset is generated for reinforcement learning to guide robots towards correct responses after contact detection. To evaluate the effectiveness of the proposed framework, several simulations are performed in the MuJoCo environment

Skin autofluorescence, reflecting accumulation of advanced glycation end products, and the risk of dementia in a population-based cohort

Conditions such as hyperglycemia and oxidative stress lead to the formation of advanced glycation end products (AGEs), which are harmful compounds that have been implicated in dementia. Within the Rotterdam Study, we measured skin AGEs as skin autofluorescence, reflecting long-term accumulation of AGEs, and determined their association with the risk of dementia and with brain magnetic resonance imaging (MRI) measures. Skin autofluorescence was measured between 2013 and 2016 in 2922 participants without dementia. Of these, 1504 also underwent brain MRI, on which measures of brain atrophy and cerebral small vessel disease were assessed. All participants were followed for the incidence of dementia until 2020. Of 2922 participants (mean age 72.6 years, 57% women), 123 developed dementia. Higher skin autofluorescence (per standard deviation) was associated with an increased risk of dementia (hazard ratio 1.21 [95% confidence interval 1.01–1.46]) and Alzheimer’s disease (1.19 [0.97–1.47]), independently of age and other studied potential confounders. Stronger effects were seen in apolipoprotein E (APOE) ε4 carriers (1.34 [0.98–1.82]) and in participants with diabetes (1.35 [0.94–1.94]). Participants with higher skin autofluorescence levels also had smaller total brain volumes and smaller hippocampus volumes on MRI, and they had more often lacunes. These results suggest that AGEs may be involved in dementia pathophysiology.</p

Advanced glycation end products measured by skin autofluorescence and subclinical cardiovascular disease:the Rotterdam Study

Background: Advanced glycation end products (AGEs) have been linked to cardiovascular disease (CVD), especially coronary heart disease (CHD), but their role in CVD pathogenesis remains unclear. Therefore, we investigated cross-sectional associations of skin AGEs with subclinical atherosclerosis, arterial stiffness, and hypertension after confirming their relation with CHD. Methods: In the population-based Rotterdam Study, skin AGEs were measured as skin autofluorescence (SAF). Prevalent MI was obtained from digital medical records. Carotid plaques, carotid intima-media thickness (IMT), coronary artery calcification (CAC), pulse wave velocity (PWV), and hypertension were assessed. Associations of SAF with endophenotypes were investigated in logistic and linear regression models adjusting for common cardiovascular risk factors. Effect modification by sex, diabetes mellitus, and chronic kidney disease (CKD) was tested. Results: 3001 participants were included (mean age 73 (SD 9) years, 57% women). One unit higher SAF was associated with the presence of carotid plaques (OR 1.2 (0.92, 1.57)), a higher max IMT (0.08 SD (0.01, 0.15)), higher CAC (OR 2.2 (1.39, 3.48)), and PWV (0.09 SD (0.01, 0.16)), but not with hypertension (OR 0.99 (0.81, 1.21)). The associations with endophenotypes were more pronounced in men and participants with diabetes or CKD with significant interactions. Conclusions: Previously documented associations between SAF and CVD, also found in our study, may be explained by the endophenotypes atherosclerosis and arterial stiffness, especially in men and individuals with diabetes or CKD, but not by hypertension. Longitudinal studies are needed to replicate these findings and to test if SAF is an independent risk factor or biomarker of CVD. Trial registration: The Rotterdam Study has been entered into the Netherlands National Trial Register (NTR; www.trialregister.nl) and the WHO International Clinical Trials Registry Platform (ICTRP; www.who.int/ictrp/network/primary/en/) under shared catalogue number NTR6831.</p

Skin autofluorescence, reflecting accumulation of advanced glycation end products, and the risk of dementia in a population-based cohort

Conditions such as hyperglycemia and oxidative stress lead to the formation of advanced glycation end products (AGEs), which are harmful compounds that have been implicated in dementia. Within the Rotterdam Study, we measured skin AGEs as skin autofluorescence, reflecting long-term accumulation of AGEs, and determined their association with the risk of dementia and with brain magnetic resonance imaging (MRI) measures. Skin autofluorescence was measured between 2013 and 2016 in 2922 participants without dementia. Of these, 1504 also underwent brain MRI, on which measures of brain atrophy and cerebral small vessel disease were assessed. All participants were followed for the incidence of dementia until 2020. Of 2922 participants (mean age 72.6 years, 57% women), 123 developed dementia. Higher skin autofluorescence (per standard deviation) was associated with an increased risk of dementia (hazard ratio 1.21 [95% confidence interval 1.01–1.46]) and Alzheimer’s disease (1.19 [0.97–1.47]), independently of age and other studied potential confounders. Stronger effects were seen in apolipoprotein E (APOE) ε4 carriers (1.34 [0.98–1.82]) and in participants with diabetes (1.35 [0.94–1.94]). Participants with higher skin autofluorescence levels also had smaller total brain volumes and smaller hippocampus volumes on MRI, and they had more often lacunes. These results suggest that AGEs may be involved in dementia pathophysiology.</p