Mining temporal and spatial travel regularity for transit planning

Smart Card data from Automated Fare Collection system has been considered as a promising source of information for transit planning. However, literature has been limited to mining travel patterns from transit users and suggesting the potential of using this information. This paper proposes a method for mining spatial regular origins-destinations and temporal habitual travelling time from transit users. These travel regularity are discussed as being useful for transit planning. After reconstructing the travel itineraries, three levels of Density-Based Spatial Clustering of Application with Noise (DBSCAN) have been utilised to retrieve travel regularity of each of each frequent transit users. Analyses of passenger classifications and personal travel time variability estimation are performed as the examples of using travel regularity in transit planning. The methodology introduced in this paper is of interest for transit authorities in planning and management

BaAl1.4Si0.6O3.4N0.6:Eu2+ green phosphors’ application for improving luminous performance

The molten salt synthesis (MSS) method was used to effectively prepare green phosphors BaAl1.4Si0.6O3.4N0.6:Eu2+ (or BSON:Eu2+) via one homogeneous sphere-like morphology utilizing NaNO3 in the form of the reacting agent. The phosphors produced one wide stimulation spectrum between 250 and 460 nm, as well as a significant green emission has a maximum point at 510 nm owing to the 4f65d1-4f7 (8S7/2) shifts for Eu2+ ions. With illumination under 365 as well as 450 nm, the ideal discharge strengths for the specimen prepared utilizing melted salt would receive a boost of 17% and 13%, surpassing the specimen prepared utilizing the traditional solid-state reaction (SSR) approach. The abatement of concentration for the ions of Eu2+ from BSON:Eu2+ is 5 mol%. In addition, the interactivity of dipole-dipole would be the cause of said abatement. Heat abatement would be studied utilizing the formation coordinate method with abatement temperature reaching ∼200 oC. Elemental mapping as well as power-dispersing X-ray spectroscopy (EDS) spectra demonstrated that the expected BaAl1.4Si0.6O3.4N0.6:Eu2+ materials were formed

Experiences of Housing Adapted to Sea Level Rise and Applicability for Houses in the Can Gio District, Ho Chi Minh City, Vietnam

As a coastal district located in the Southeast of Ho Chi Minh City (HCMC), Vietnam, Can Gio is characterized by low average terrains ranging up to only 1.5m above the sea level. Impacted by climate change and sea level rise in recent years, certain neighborhoods in the Can Gio District have been facing the loss of their residential and arable lands, as well as undesired relocations. Together with riverbank and coastal erosion, this phenomenon has several negative impacts on the lives of people in residential areas and on their economic activities. This research uses a literature review and observation as the main methods to explore the experiences of sea level rise adaptive housing and thereby suggests certain solutions for the Can Gio District. The solutions include saving space for water, elevating floors, constructing with floating floors, and creating biological ditches and osmotic lines to help quickly drain flooded water. These solutions aim to protect people’s lives and houses against the rising sea level and ensure the sustainable development of the neighborhoods

RMDM: A Multilabel Fakenews Dataset for Vietnamese Evidence Verification

In this study, we present a novel and challenging multilabel Vietnamese dataset (RMDM) designed to assess the performance of large language models (LLMs), in verifying electronic information related to legal contexts, focusing on fake news as potential input for electronic evidence. The RMDM dataset comprises four labels: real, mis, dis, and mal, representing real information, misinformation, disinformation, and mal-information, respectively. By including these diverse labels, RMDM captures the complexities of differing fake news categories and offers insights into the abilities of different language models to handle various types of information that could be part of electronic evidence. The dataset consists of a total of 1,556 samples, with 389 samples for each label. Preliminary tests on the dataset using GPT-based and BERT-based models reveal variations in the models' performance across different labels, indicating that the dataset effectively challenges the ability of various language models to verify the authenticity of such information. Our findings suggest that verifying electronic information related to legal contexts, including fake news, remains a difficult problem for language models, warranting further attention from the research community to advance toward more reliable AI models for potential legal applications.Comment: ISAILD@KSE 202

Data assimilation and agent-based modelling: towards the incorporation of categorical agent parameters

This paper explores the use of a particle filter—a data assimilation method—to incorporate real-time data into an agent-based model. We apply the method to a simulation of real pedestrians moving through the concourse of Grand Central Terminal in New York City (USA). The results show that the particle filter does not perform well due to (i) the unpredictable behaviour of some pedestrians and (ii) because the filter does not optimise the categorical agent parameters that are characteristic of this type of model. This problem only arises because the experiments use real-world pedestrian movement data, rather than simulated, hypothetical data, as is more common. We point to a potential solution that involves resampling some of the variables in a particle, such as the locations of the agents in space, but keeps other variables such as the agents’ choice of destination. This research illustrates the importance of including real-world data and provides a proof of concept for the application of an improved particle filter to an agent-based model. The obstacles and solutions discussed have important implications for future work that is focused on building large-scale real-time agent-based models

Environmental allergen reduction in asthma management: an overview

Asthma is a prevalent non-communicable disease that affects both children and adults. Many patients with severe, uncontrolled asthma could not achieve total control despite using anti-asthmatic drugs. There is increasing evidence that allergy to environmental allergens, including both indoor and outdoor allergens, is associated with asthma symptoms and severe asthma. Frequently reported sensitized allergens were dust mites, cockroaches, grass pollens, molds, pets, and rodents in allergic asthma patients, although the patterns of widespread allergens differed from each country. Allergen avoidance is the cornerstone of asthma management, especially in sensitized subjects. This review summarizes environmental allergen avoidance and clarifies their effects on asthma control. Despite contrasting results about the impact of allergen exposure reduction on asthma control, several studies supported the beneficial effects of reducing asthma-related symptoms or risk of exacerbations as a nondrug therapy. Identifying environmental allergens is helpful for asthma patients, and further studies on clinically effective avoidance methods are required