Factors Affecting Purchase Intention of Air Purifier as Green Product among Consumers during the Air Pollution Crisis

The purpose of this study is to determine the awareness of people towards air purifiers during the pollution crisis in Bangkok. In addition, it is important for people in Bangkok to breathe good air as recently, the air is getting worse and so this would affect the lungs of young children and old people living at homes and may cause certain diseases. Therefore, the purpose of this research is to spread the awareness of purifier so that PM Level in Bangkok gets better and people can live a healthy lifestyle. The sample (400 respondents) was collected from online questionnaires by using convenience sampling technique and snowball sampling technique. The data were analyzed by using simple linear regression and multiple linear regression to confirm the hypotheses testing. The results revealed perceived consumer effectiveness, environmental consciousness, and environmental attitude have a significant effect on air purifier purchase intention. Meanwhile, on the other framework environmental knowledge and environmental consciousness are both significant for having an environmental attitude. The researchers’ study also stated that few people do not know about the air purifier. The findings of this study showed that all variables (perceived consumer effectiveness, environmental consciousness, and environmental attitude) have significant influences on purchase intention of air purifier as a green product. This study helps to increase the awareness of air purifiers among consumers as some of them do not really know the advantages of keeping the air purifier and how the air purifier acts as a green product in protecting the environment

SocNavGym: A Reinforcement Learning Gym for Social Navigation

It is essential for autonomous robots to be socially compliant while navigating in human-populated environments. Machine Learning and, especially, Deep Reinforcement Learning have recently gained considerable traction in the field of Social Navigation. This can be partially attributed to the resulting policies not being bound by human limitations in terms of code complexity or the number of variables that are handled. Unfortunately, the lack of safety guarantees and the large data requirements by DRL algorithms make learning in the real world unfeasible. To bridge this gap, simulation environments are frequently used. We propose SocNavGym, an advanced simulation environment for social navigation that can generate a wide variety of social navigation scenarios and facilitates the development of intelligent social agents. SocNavGym is light-weight, fast, easy-to-use, and can be effortlessly configured to generate different types of social navigation scenarios. It can also be configured to work with different hand-crafted and data-driven social reward signals and to yield a variety of evaluation metrics to benchmark agents' performance. Further, we also provide a case study where a Dueling-DQN agent is trained to learn social-navigation policies using SocNavGym. The results provides evidence that SocNavGym can be used to train an agent from scratch to navigate in simple as well as complex social scenarios. Our experiments also show that the agents trained using the data-driven reward function displays more advanced social compliance in comparison to the heuristic-based reward function.Comment: IEEE RO-MA

Kinetics of Ce(IV) Oxidation of Monothioglycerol in Carbonate Medium

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Duration of antiplatelet therapy cessation before coronary artery bypass surgery: Relation with platelet count

AbstractAs of now, no study or data is available to determine the period of discontinuation of antiplatelet therapy and the timing of elective surgery in clopidogrel treated patients. The 2011 ACCF/AHA Guidelines have a Class-I recommendation for withdrawing clopidogrel for 5 days before elective coronary artery bypass grafting. However, 5 days period may not suit all patients as platelet count varies from 150 × 109/L to 450 × 109/L. Based on our retrospective data analysis, we have proposed a hypothesis to determine no of days of discontinuation of antiplatelet therapy while taking in consideration the basal count and life-span of platelets

Auto-TransRL: Autonomous Composition of Vision Pipelines for Robotic Perception

Creating a vision pipeline for different datasets to solve a computer vision task is a complex and time consuming process. Currently, these pipelines are developed with the help of domain experts. Moreover, there is no systematic structure to construct a vision pipeline apart from relying on experience, trial and error or using template-based approaches. As the search space for choosing suitable algorithms for achieving a particular vision task is large, human exploration for finding a good solution requires time and effort. To address the following issues, we propose a dynamic and data-driven way to identify an appropriate set of algorithms that would be fit for building the vision pipeline in order to achieve the goal task. We introduce a Transformer Architecture complemented with Deep Reinforcement Learning to recommend algorithms that can be incorporated at different stages of the vision workflow. This system is both robust and adaptive to dynamic changes in the environment. Experimental results further show that our method also generalizes well to recommend algorithms that have not been used while training and hence alleviates the need of retraining the system on a new set of algorithms introduced during test time.Comment: Presented at the IEEE ICRA 2022 Workshop in Robotic Perception and Mapping: Emerging Technique

ClimaX: A foundation model for weather and climate

Most state-of-the-art approaches for weather and climate modeling are based on physics-informed numerical models of the atmosphere. These approaches aim to model the non-linear dynamics and complex interactions between multiple variables, which are challenging to approximate. Additionally, many such numerical models are computationally intensive, especially when modeling the atmospheric phenomenon at a fine-grained spatial and temporal resolution. Recent data-driven approaches based on machine learning instead aim to directly solve a downstream forecasting or projection task by learning a data-driven functional mapping using deep neural networks. However, these networks are trained using curated and homogeneous climate datasets for specific spatiotemporal tasks, and thus lack the generality of numerical models. We develop and demonstrate ClimaX, a flexible and generalizable deep learning model for weather and climate science that can be trained using heterogeneous datasets spanning different variables, spatio-temporal coverage, and physical groundings. ClimaX extends the Transformer architecture with novel encoding and aggregation blocks that allow effective use of available compute while maintaining general utility. ClimaX is pre-trained with a self-supervised learning objective on climate datasets derived from CMIP6. The pre-trained ClimaX can then be fine-tuned to address a breadth of climate and weather tasks, including those that involve atmospheric variables and spatio-temporal scales unseen during pretraining. Compared to existing data-driven baselines, we show that this generality in ClimaX results in superior performance on benchmarks for weather forecasting and climate projections, even when pretrained at lower resolutions and compute budgets. The source code is available at https://github.com/microsoft/ClimaX.Comment: International Conference on Machine Learning 202

Concept-based Anomaly Detection in Retail Stores for Automatic Correction using Mobile Robots

Tracking of inventory and rearrangement of misplaced items are some of the most labor-intensive tasks in a retail environment. While there have been attempts at using vision-based techniques for these tasks, they mostly use planogram compliance for detection of any anomalies, a technique that has been found lacking in robustness and scalability. Moreover, existing systems rely on human intervention to perform corrective actions after detection. In this paper, we present Co-AD, a Concept-based Anomaly Detection approach using a Vision Transformer (ViT) that is able to flag misplaced objects without using a prior knowledge base such as a planogram. It uses an auto-encoder architecture followed by outlier detection in the latent space. Co-AD has a peak success rate of 89.90% on anomaly detection image sets of retail objects drawn from the RP2K dataset, compared to 80.81% on the best-performing baseline of a standard ViT auto-encoder. To demonstrate its utility, we describe a robotic mobile manipulation pipeline to autonomously correct the anomalies flagged by Co-AD. This work is ultimately aimed towards developing autonomous mobile robot solutions that reduce the need for human intervention in retail store management.Comment: 8 pages, 9 figures, 2 tables, IEEE Transactions on Systems, Man and Cybernetic

Cathodoluminescence Studies of Nanoindented CdZnTe Single Crystal Substrates for Analysis of Residual Stresses and Deformation Behaviour

Nanoindentation-induced residual stresses were analysed on (111) Te face CdZnTe single-crystal substrates in this study. CdZnTe substrates were subjected to nanoindentation using cube corner indenter geometry with a peak load of 10 mN. Loading rates of 1 mN/s and 5 mN/s were used in the experiments, with a holding time of 10 s at peak load. Residual stresses on the indented region were analysed from load-displacement curves and explained using dislocation generation and elastic recovery mechanisms. Residual stresses were found to be of compressive type, just on the indented surface. The slip lines along the slip directions of this material were clearly visible in the FE-SEM images of the indents. Indents and surrounding surfaces were characterized using the Cathodoluminescence (CL) technique. CL mapping of the indented surface revealed the dislocation generation and their propagation behaviour just beneath the indenter as well as in the surrounding surfaces. The dislocations act as non-radiative recombination centres and quench the CL intensity locally. Dark lines were explained as the presence of dislocations in the material. CL mapping analysis shows that both the rosette glide and tetrahedral glide of dislocations are the primary deformation mechanisms present in CdZnTe. A rosette structure was observed in the CL mapping. CL spectra at 300 K of un-deformed CdZnTe show a peak at 810 nm wavelength, which corresponds to near-band-edge emission. After indentation, the CL spectra show the peak intensity at 814 nm and 823 nm wavelengths at the edge of the indents created with a loading rate of 1 mN/s and 5 mN/s, respectively. These peak shifts from 810 nm were attributed to the tensile residual stresses present in the indented material