A natural language processing-based approach: mapping human perception by understanding deep semantic features in street view images

In the past decade, using Street View images and machine learning to measure human perception has become a mainstream research approach in urban science. However, this approach using only image-shallow information makes it difficult to comprehensively understand the deep semantic features of human perception of a scene. In this study, we proposed a new framework based on a pre-train natural language model to understand the relationship between human perception and the sense of a scene. Firstly, Place Pulse 2.0 was used as our base dataset, which contains a variety of human-perceived labels, namely, beautiful, safe, wealthy, depressing, boring, and lively. An image captioning network was used to extract the description information of each street view image. Secondly, a pre-trained BERT model was finetuning and added a regression function for six human perceptual dimensions. Furthermore, we compared the performance of five traditional regression methods with our approach and conducted a migration experiment in Hong Kong. Our results show that human perception scoring by deep semantic features performed better than previous studies by machine learning methods with shallow features. The use of deep scene semantic features provides new ideas for subsequent human perception research, as well as better explanatory power in the face of spatial heterogeneity.Comment: 11 pages, 8 figure

Effect of Carbon Dioxide-Sustained Adsorption Using Ion Exchange Resin on Mixed-Acid Fermentation

The carboxylate platform is a biomass-to-energy process that converts biomass into hydrocarbon fuels or chemicals. The mixed-acid fermentation is the essential unit of the process that uses mixed cultures of microorganisms to anaerobically produce carboxylic acids; however, high acid concentrations in fermentation broth inhibit the microorganisms and negatively affect fermentation performance. This study employed a weak-base anion-exchange resin (Amberlite IRA-67) to recover inhibitory acid products from countercurrent and propagated fixed-bed mixed-acid fermentations. The ion-exchange resins were employed in a novel fluidized bed that was purged with COv2. Compared with traditional plug-flow ion-exchange adsorption, fluidized-bed COv2-sustained ion-exchange resin adsorption increased carboxylic acids recoveries by up to a factor of 4.58 times. Four countercurrent fermentation trains with an average 1.4 L total liquid volume were established under identical conditions. Different amounts of IRA-67 resin (10–40 g wet resinvFB) were employed to adsorb the acids produced from fermentation trains in the presence of COv2. The increases of biomass conversion and acid yield were found out to be 34–128% and 45–107%, respectively. The optimal normalized resin loading for biomass conversion was the 10.9 g wet resinvFB/ (Lvliq·d). One train of propagated fixed-bed fermentation with 1.45 L total liquid volume was run under the same conditions as the countercurrent trains. With COv2, 30 g wet resinvFB was employed to adsorb the acids produced from this train, which caused acid yield to increase by 24%

Condition-based Inspection and Maintenance of Medical Devices

Inspection and Maintenance of medical devices are essential for modern health services, but the low availability of devices or unnecessary maintenance can cause major problems. A proper maintenance program can signi cantly reduce operational costs and increase device availability. For any maintenance program, two questions arise: 1) What kinds of devices should be included? and 2) How and when should they be inspected and maintained? This thesis proposes methods to solve those two problems. For the rst question, numerous classi cation and prioritization models have been suggested to evaluate medical devices, but most are empirical scoring systems, which can not be widely used. To build a generalized scoring system, we propose a risk level classi cation model. More speci cally, we select three important risk factors (Equipment function, Location of use and Frequency of use), then use provided data to nd the relationship between risk factors and risk levels. Four di erent classi cation models (Linear regression, Logistic regression, Classi cation tree and Random forest) are used to analyze the problem, and all of them are effective. For the second question, some inspection and maintenance models have been developed and widely used to assure the performance of medical devices. However, those models are restricted to a few speci c kind of problems. In contrast, our model provides a more comprehensive response to current maintenance problems in the healthcare industry, by introducing a condition-based multi-component inspection and maintenance model. We rst present a parameter estimation method to predict the deterioration rate of a system. We use provided data and expectation-maximization algorithm to estimate the transition matrix of system conditions. Then, we use Markov decision processes to solve the decision model, which consists of two decisions: the next inspection time and whether to repair the devices. The inspection interval is non-periodic in our model, and this flexibility of non-periodic inspection model can avoid unnecessary inspections. We use relative value iteration to nd the optimal inspection and maintenance strategies and the long-run average cost. Changing the parameter of cost and the structure of the system clarified the influence of these parameters. Our model achieves lower minimal average costs for complex systems than previous periodic inspection models

Analysis of Stochastic Models through Multi-Layer Markov Modulated Fluid Flow Processes

This thesis is concerned with the multi-layer Markov modulated fluid flow (MMFF) processes and their applications to queueing systems with customer abandonment. For the multi-layer MMFF processes, we review and refine the theory on the joint distribution of the multi-layer MMFF processes and develop an easy to implement algorithm to calculate the joint distribution. Then, we apply the theory to three quite general queueing systems with customer abandonment to show the applicability of this approach and obtain a variety of queueing quantities, such as the customer abandonment probabilities, waiting times distributions and mean queue lengths. The first application is the MAP/PH/K+GI queue. The MMFF approach and the count-server-for-phase (CSFP) method are combined to analyze this multi-server queueing system with a moderately large number of servers. An efficient and easy-to-implement algorithm is developed for the performance evaluation of the MAP/PH/K +GI queueing model. Some of the queueing quantities such as waiting time distributions of the customers abandoning the queue at the head of the waiting queue are difficult to derive through other methods. Then the double-sided queues with marked Markovian arrival processes (MMAP) and abandonment are studied. Multiple types of inputs and finite discrete abandonment times make this queueing model fairly general. Three age processes related to the inputs are defined and then converted into a multi-layer MMFF process. A number of aggregate queueing quantities and quantities for individual types of inputs are obtained by the MMFF approach, which can be useful for practitioners to design stochastic systems such as ride-hailing platforms and organ transplantation systems. The last queueing model is the double-sided queues with batch Markovian arrival processes (BMAP) and abandonment, which arise in various stochastic systems such as perishable inventory systems and financial markets. Customers arrive at the system with a batch of orders to be matched by counterparts. The abandonment time of a customer depends on the batch size and the position in the queue of the customer. Similar to the previous double-sided queueing model, a multi-layer MMFF process related to some age processes is constructed. A number of queueing quantities including matching rates, fill rates, sojourn times and queue length for both sides of the system are derived. This queueing model is used to analyze a vaccine inventory system as a case study in the thesis. Overall, this thesis studies the joint stationary distribution of the multi-layer MMFF processes and shows the power of this approach in dealing with complex queueing systems. Four algorithms are presented to help practitioners to design stochastic systems and researchers do numerical experiments

Multi-Prompt Alignment for Multi-source Unsupervised Domain Adaptation

Most existing methods for multi-source unsupervised domain adaptation (UDA) rely on a common feature encoder to extract domain-invariant features. However, learning such an encoder involves updating the parameters of the entire network, which makes the optimization computationally expensive, particularly when coupled with min-max objectives. Inspired by recent advances in prompt learning that adapts high-capacity deep models for downstream tasks in a computationally economic way, we introduce Multi-Prompt Alignment (MPA), a simple yet efficient two-stage framework for multi-source UDA. Given a source and target domain pair, MPA first trains an individual prompt to minimize the domain gap through a contrastive loss, while tuning only a small set of parameters. Then, MPA derives a low-dimensional latent space through an auto-encoding process that maximizes the agreement of multiple learned prompts. The resulting embedding further facilitates generalization to unseen domains. Extensive experiments show that our method achieves state-of-the-art results on popular benchmark datasets while requiring substantially fewer tunable parameters. To the best of our knowledge, we are the first to apply prompt learning to the multi-source UDA problem and our method achieves the highest reported average accuracy of 54.1% on DomainNet, the most challenging UDA dataset to date, with only 15.9M parameters trained. More importantly, we demonstrate that the learned embedding space can be easily adapted to novel unseen domains

Geometry of power flows and convex-relaxed power flows in distribution networks with high penetration of renewables

AbstractRenewable energies are increasingly integrated in electric distribution networks and will cause severe overvoltage issues. Smart grid technologies make it possible to use coordinated control to mitigate the overvoltage issues and the optimal power flow (OPF) method is proven to be efficient in the applications such as curtailment management and reactive power control. Nonconvex nature of the OPF makes it difficult to solve and convex relaxation is a promising method to solve the OPF very efficiently. This paper investigates the geometry of the power flows and the convex-relaxed power flows when high penetration level of renewables is present in the distribution networks. The geometry study helps understand the fundamental nature of the OPF and its convex-relaxed problem, such as the second-order cone programming (SOCP) problem. A case study based on a three-node system is used to illustrate the geometry profile of the feasible sub-injection (injection of nodes excluding the root/substation node) region

Rapid Determination of Saponins in the Honey-Fried Processing of Rhizoma Cimicifugae by Near Infrared Diffuse Reflectance Spectroscopy.

ObjectiveA model of Near Infrared Diffuse Reflectance Spectroscopy (NIR-DRS) was established for the first time to determine the content of Shengmaxinside I in the honey-fried processing of Rhizoma Cimicifugae.MethodsShengmaxinside I content was determined by high-performance liquid chromatography (HPLC), and the data of the honey-fried processing of Rhizoma Cimicifugae samples from different batches of different origins by NIR-DRS were collected by TQ Analyst 8.0. Partial Least Squares (PLS) analysis was used to establish a near-infrared quantitative model.ResultsThe determination coefficient R² was 0.9878. The Cross-Validation Root Mean Square Error (RMSECV) was 0.0193%, validating the model with a validation set. The Root Mean Square Error of Prediction (RMSEP) was 0.1064%. The ratio of the standard deviation for the validation samples to the standard error of prediction (RPD) was 5.5130.ConclusionThis method is convenient and efficient, and the experimentally established model has good prediction ability, and can be used for the rapid determination of Shengmaxinside I content in the honey-fried processing of Rhizoma Cimicifugae