Deep Convolution and Correlated Manifold Embedded Distribution Alignment for Forest Fire Smoke Prediction

This paper proposes the deep convolution and correlated manifold embedded distribution alignment (DC-CMEDA) model, which is able to realize the transfer learning classification between and among various small datasets, and greatly shorten the training time. First, pre-trained Resnet50 network is used for feature transfer to extract smoke features because of the difficulty in training small dataset of forest fire smoke; second, a correlated manifold embedded distribution alignment (CMEDA) is proposed to register the smoke features in order to align the input feature distributions of the source and target domains; and finally, a trainable network model is constructed. This model is evaluated in the paper based on satellite remote sensing image and video image datasets. Compared with the deep convolutional integrated long short-term memory (DC-ILSTM) network, DC-CMEDA has increased the accuracy of video images by 1.50 %, and the accuracy of satellite remote sensing images by 4.00 %. Compared the CMEDA algorithm with the ILSTM algorithm, the number of iterations of the former has decreased to 10 times or less, and the algorithm complexity of CMEDA is lower than that of ILSTM. DC-CMEDA has a great advantage in terms of convergence speed. The experimental results show that DC-CMEDA can solve the problem of small sample smoke dataset detection and recognition

Fulminant psittacosis complicated with multiple organ dysfunction syndrome: a case report

The cases of fulminant psittacosis complicated with multiple organ dysfunction syndrome （MODS） have been rarely reported in China. In this article， clinical manifestations and treatment of a patient with fulminant psittacosis complicated with MODS were summarized and analyzed. The 80-year-old male patient developed respiratory failure in Emergency Department and received mechanical ventilation， which rapidly progressed into acute respiratory distress syndrome （ARDS） complicated with MODS. Metagenomic next-generation sequencing （mNGS） using bronchoalveolar lavage fluid （BALF） samples confirmed the pathogen of Chlamydia psittaci. Then， moxifloxacin， doxycycline and omadacycline were given， which yielded favorable efficacy and prognosis. The diagnosis and treatment of this patient suggests that fulminant psittacosis can be manifested with respiratory failure， severe pneumonia with rapid progression and MODS. Imaging manifestations consist of pneumonia， bronchial inflation sign and pleural effusion. mNGS can be performed to identify the rare pathogens during early stage and confirm the diagnosis. Tetracyclines， macrolides and quinolones can be delivered as antibacterial drugs

Trust in AutoML: Exploring Information Needs for Establishing Trust in Automated Machine Learning Systems

We explore trust in a relatively new area of data science: Automated Machine Learning (AutoML). In AutoML, AI methods are used to generate and optimize machine learning models by automatically engineering features, selecting models, and optimizing hyperparameters. In this paper, we seek to understand what kinds of information influence data scientists' trust in the models produced by AutoML? We operationalize trust as a willingness to deploy a model produced using automated methods. We report results from three studies -- qualitative interviews, a controlled experiment, and a card-sorting task -- to understand the information needs of data scientists for establishing trust in AutoML systems. We find that including transparency features in an AutoML tool increased user trust and understandability in the tool; and out of all proposed features, model performance metrics and visualizations are the most important information to data scientists when establishing their trust with an AutoML tool.Comment: IUI 202

C1q complement/tumor necrosis factor-associated proteins in cardiovascular disease and covid-19

With continually improving treatment strategies and patient care, the overall mortality of cardiovascular disease (CVD) has been significantly reduced. However, this success is a double-edged sword, as many patients who survive cardiovascular complications will progress towards a chronic disorder over time. A family of adiponectin paralogs designated as C1q complement/tumor necrosis factor (TNF)-associated proteins (CTRPs) has been found to play a role in the development of CVD. CTRPs, which are comprised of 15 members, CTRP1 to CTRP15, are secreted from different organs/tissues and exhibit diverse functions, have attracted increasing attention because of their roles in maintaining inner homeostasis by regulating metabolism, inflammation, and immune surveillance. In particular, studies indicate that CTRPs participate in the progression of CVD, influencing its prognosis. This review aims to improve understanding of the role of CTRPs in the cardiovascular system by analyzing current knowledge. In particular, we examine the association of CTRPs with endothelial cell dysfunction, inflammation, and diabetes, which are the basis for development of CVD. Additionally, the recently emerged novel coronavirus (COVID-19), officially known as severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), has been found to trigger severe cardiovascular injury in some patients, and evidence indicates that the mortality of COVID-19 is much higher in patients with CVD than without CVD. Understanding the relationship of CTRPs and the SARS-CoV-2-related damage to the cardiovascular system, as well as the potential mechanisms, will achieve a profound insight into a therapeutic strategy to effectively control CVD and reduce the mortality rate

Nicotine aggravates vascular adiponectin resistance via ubiquitin-mediated adiponectin receptor degradation in diabetic Apolipoprotein E knockout mouse

There is limited and discordant evidence on the role of nicotine in diabetic vascular disease. Exacerbated endothelial cell dysregulation in smokers with diabetes is associated with the disrupted adipose function. Adipokines possess vascular protective, anti-inflammatory, and anti-diabetic properties. However, whether and how nicotine primes and aggravates diabetic vascular disorders remain uncertain. In this study, we evaluated the alteration of adiponectin (APN) level in high-fat diet (HFD) mice with nicotine (NIC) administration. The vascular pathophysiological response was evaluated with vascular ring assay. Confocal and co-immunoprecipitation analysis were applied to identify the signal interaction and transduction. These results indicated that the circulating APN level in nicotine-administrated diabetic Apolipoprotein E-deficient (ApoE−/−) mice was elevated in advance of 2 weeks of diabetic ApoE−/− mice. NIC and NIC addition in HFD groups (NIC + HFD) reduced the vascular relaxation and signaling response to APN at 6 weeks. Mechanistically, APN receptor 1 (AdipoR1) level was decreased in NIC and further significantly reduced in NIC + HFD group at 6 weeks, while elevated suppressor of cytokine signaling 3 (SOCS3) expression was induced by NIC and further augmented in NIC + HFD group. Additionally, nicotine provoked SOCS3, degraded AdipoR1, and attenuated APN-activated ERK1/2 in the presence of high glucose and high lipid (HG/HL) in human umbilical vein endothelial cells (HUVECs). MG132 (proteasome inhibitor) administration manifested that AdipoR1 was ubiquitinated, while inhibited SOCS3 rescued the reduced AdipoR1. In summary, this study demonstrated for the first time that nicotine primed vascular APN resistance via SOCS3-mediated degradation of ubiquitinated AdipoR1, accelerating diabetic endothelial dysfunction. This discovery provides a potential therapeutic target for preventing nicotine-accelerated diabetic vascular dysfunction

Electro-Superplastic Solid State Welding of 40Cr/QCr0.5

Hot-squeezed chrome bronze QCr0.5 and ultra-fine treated 40Cr steel have been successfully welded using an electro-superplastic solid-state welding technique. Results have shown that the tensile strength of a 40Cr/QCr0.5 weld joint can be greatly increased, up to or exceeding that of QCr0.5 base metal. The weld interface between 40Cr and QCr0.5 has achieved metallurgical bonding and there are less micro-gaps, thicker transition regions and more copper convexes and dimples on the fracture surface of the 40Cr side when applying an external electrical field of E = 3 kV/cm, as well as with other welding parameters, including no vacuum, no shield gas, a pre-pressure of 56.6 MPa, an initial strain rate of 1.5 × 10−4 s−1, a pressure welding temperature of 710–800 °C, and a pressure welding time of 0–8 min

Collaborative activity- based ridesharing: linking human movement, social network, and platial semantics for future transportation

© 2018 Dr. Yaoli WangThis thesis presents a collaborative activity space model and the implemented prototype for ridesharing under the umbrella of this model. The ridesharing prototype accommodates socio-psychological barriers and travel flexibilities in multi-person travel behaviours. Despite the positiveness of ridesharing, such as reducing private car usage for social and environmental sakes, people find it uncomfortable or inconvenient to share rides. The two issues underneath are social preference and travel flexibility. If people are prioritised to match with those they prefer, and if an efficient strategy exists to reduce detour cost or the probability of no-ride, commitment to ridesharing might be increased. Current software or algorithms on the market, however, mostly overlook the socio-psychological aspects of ridesharing, and thus cannot better exploit the full potential of this new travel mode. This work provides the solutions incorporating social network preference and travel flexibility from three perspectives: 1) A method called Social Network based Ridesharing allows travellers to prioritise their closer acquaintances as ride partners while still considering local strangers of low detour cost. 2) The Activity-based Ridesharing Algorithm involves travel aims and functions of places to provide alternative destination choices. 3) A combination of the social network based and the activity-based methods, called Collaborative Activity-based Ridesharing, is suggested where social network preference is not only satisfied but also used as a space search heuristic for fast retrieval of alternative destinations and potential ride partners. The implementation of the model is experimented with agent-based simulation in a real study area with travel survey datasets. The outcomes justify that the proposed collaborative activity model and algorithms are capable of significantly increasing match rates and reducing socio-psychological obstacles for ridesharing

Reducing Parking Pressure by Sharing Resources

This chapter describes a scenario where ridesharing is introduced in urban parking to relieve the pressure of finding a parking site in the city center. A significant amount of time is wasted in cruising for a parking lot according to both life experience and research findings. Although a few policies and strategies have been tested, the middle ground between individual flexibility and reduced travel demand is not yet well accommodated. Therefore, I report of a joint model of ridesharing and parking: people drive from their front doors to a satellite parking site to share rides, and travel to a similar destination in the city center so that parking demand is reduced.1741831