Community-Based Behavioral Understanding of Mobility Trends and Public Attitude through Transportation User and Agency Interactions on Social Media in the Emergence of Covid-19

The increased availability of technology-enabled transportation options and modern communication devices (smartphones, in particular) is transforming travel-related decision-making in the population differently at different places, points in time, modes of transportation, and socio-economic groups. The emergence of COVID-19 made the dynamics of passenger travel behavior more complex, forcing a worldwide, unparalleled change in human travel behavior and introducing a new normal into their existence. This dissertation explores the potential of social media platforms (SMPs) as a viable alternative to traditional approaches (e.g., travel surveys) to understand the complex dynamics of people’s mobility patterns in the emergence of COVID-19. In this dissertation, we focus on three objectives. First, a novel approach to developing comparative infographics of emerging transportation trends is introduced by natural language processing and data-driven techniques using large-scale social media data. Second, a methodology has been developed to model community-based travel behavior under different socioeconomic and demographic factors at the community level in the emergence of COVID-19 on Twitter, inferring users’ demographics to overcome sampling bias. Third, the communication patterns of different transportation agencies on Twitter regarding message kinds, communication sufficiency, consistency, and coordination were examined by applying text mining techniques and dynamic network analysis. The methodologies and findings of the dissertation will allow real-time monitoring of transportation trends by agencies, researchers, and professionals. Potential applications of the work may include: (1) identifying spatial diversity of public mobility needs and concerns through social media platforms; (2) developing new policies that would satisfy the diverse needs at different locations; (3) introducing new plans to support and celebrate equity, diversity, and inclusion in the transportation sector that would improve the efficient flow of goods and services; (4) designing new methods to model community-based travel behavior at different scales (e.g., census block, zip code, etc.) using social media data inferring users’ socio-economic and demographic properties; and (5) implementing efficient policies to improve existing communication plans, critical information dissemination efficacy, and coordination of different transportation actors to raise awareness among passengers in general and during unprecedented health crises in the fragmented communication world

Multi Wall Carbon Nanotube (MWCNT) Laminar Composite Structures Reinforced with Titanium Carbide (TIC)

Laminated composites have been widely used in weight critical structures, such as aircraft, spacecraft, bullet-proof vests, radiation protection suits, spacesuits etc., owing to their high stiffness, strength, and thermal stability. Multi Wall Carbon Nanotube (MWCNT) laminar composite have attracted a good attention for these laminar composites due to its outstanding physical and mechanical properties as well as extraordinary electrical, optical, and thermal properties. Laminar composite structures based on twisted MWCNT yarn were crafted by integrating titanium and graphene mixture at (80:20) wt. %, respectively into multi wall carbon nanotube sheets. Titanium and Graphene mixture addition has been used to improve the mechanical properties of MWCNTs based composites structures through in situ formation of TiC. We produced this homogeneous powder mixture by using MSK-SFM-3 high speed vibrating ball miller. Afterwards the twisted yarn incorporated with powder mixture was heated up to 800 °C in Differential Scanning Calorimetry (DSC) to initiate the reaction between titanium and graphene, and an exothermic reaction has been observed at 642 °C. The formation mechanisms of TiC production inside yarn were studied by using differential scanning calorimetry (DSC), XRD, and SEM to identify the reaction products in different temperature ranges. The characteristics X-ray peaks of the TiC phase were well observed on 2θ =34, 36, 41. We have also investigated MWCNT/TiC based composite morphology, TiC particles size and elemental atomic by using the thermal field emission Scanning Electron Microscope (SEM, JOEL) equipped with an Electron Dispersive X-ray Spectroscopy. Form the experimental results, it has been shown that the as-prepared laminar composites possess a better comprehensive performance. Our synthesized laminar composite structures exhibit the tensile strength of 320 MPa which is worthy of comparison to the results available in literature

A review on the charging station planning and fleet operation for electric freight vehicles

Freight electrification introduces new opportunities and challenges for planning and operation. Although research on charging infrastructure planning and operation is widely available for general electric vehicles, unique physical and operational characteristics of EFVs coupled with specific patterns of logistics require dedicated research. This paper presents a comprehensive literature review to gain a better understanding of the state-of-the-art research efforts related to planning (charging station siting and sizing) and operation (routing, charge scheduling, platoon scheduling, and fleet sizing) for EFVs. We classified the existing literature based on the research topics, innovations, methodologies, and solution approaches, and future research directions are identified. Different types of methodologies, such as heuristic, simulation, and mathematical programming approaches, were applied in the reviewed literature where mathematical models account for the majority. We further narrated the specific modeling considerations for different logistic patterns and research goals with proper reasoning. To solve the proposed models, different solution approaches, including exact algorithms, metaheuristic algorithms, and software simulation, were evaluated in terms of applicability, advantages, and disadvantages. This paper helps to draw more attention to the planning and operation issues and solutions for freight electrification and facilitates future studies on EFV to ensure a smooth transition to a clean freight system.Comment: 43 pages, 4 figures, 2 table

Mainstreaming Climate Change Adaptation into Regional Planning of Least Developed Countries: Strategy Implications for Regions in Bangladesh

The purpose of the study is to assess the search for mainstreaming climate change adaptation into regional planning of least developed countries (LDCs) and draw strategy implications for regions in Bangladesh. The findings of the study revealed that national adaptation programme of action (NAPAs) in least developed countries were being gender-blind and failed to be properly implemented. Least developed countries should therefore do more to prepare for ongoing and future climate changes focusing on actions that are no-regrets, multi-sectoral and multi-level, and that improve the management of current climate variability. Strengthening capacities to use climate information, enabling locally appropriate responses, screening climate risks, assessing risks and adaptation options, starting with existing policies and plans, broadening constituencies beyond environment agencies, managing strategy conflicts, learning from projects and recognizing their limitations, monitoring and learning are the foreseen strategic actions by regions in Bangladesh for effective mainstreaming of climate change adaptation into regional development planning in the years to come. Keywords: Climate Change, Adaptation, Bangladesh, Least Developed Countries, Mainstreamin

Fabric Defect Detection using Image Processing

Fabric defect is one of the most important and serious matters of quality control in textile industry in Bangladesh. This task takes a lot of time and money. For this reason we have introduced a simple process to find defects on fabric based on edge detection. This process is mainly focused on image processing which can be integrated with fabric defect detection automation system. In this paper we have tried a new approach using the filter method with edge detection and found good results. Our algorithm can detect defected fabric area successfully. It can be also used in real-time defect detection considering light intensity, zoom, fabric width, camera resolution etc. As our algorithm mainly works on the principle of edge detection, it cannot detect defect on multicoloured or patterned fabric. It works well on single coloured fabric without any fold or edg

Pavement life cycle assessment: from case study to machine learning modeling

Climate change is a global challenge with long-term implications. Human activities are changing the global climate system, and the warming of the climate system is undeniable. According to a roadway construction study, the construction of the surface layer of an asphalt pavement alone generates a carbon footprint of 65.8 kg of CO₂ per km. Therefore, a sensible approach to study environmental impact from road pavement is crucial. Pavement life cycle assessment (LCA) is a comprehensive method to evaluate the environmental impacts of a pavement section. It features a cradle-to-grave approach assessing critical stages of the pavement’s life. Material production, initial construction, maintenance, use and end of life phases exist in an entire pavement life cycle. The thesis consists of three components, which started with finding the environmental impact for different pavement maintenance and rehabilitation (M&R) techniques in the maintenance phase. The second component evaluated the environmental impact due to pavement vehicle interaction (PVI) in the use phase. Finally, the goal of the third component was to develop a set of pavement LCA models. To evaluate environmental impact for four major M&R techniques: rout and sealing, patching, hot in-place recycling (HIR) and cold in-place recycling (CIR), initially a fractional factorial design approach was applied to determine which factors were significant. Considering those significant factors and other necessary data, a hypothetical LCA case study was performed for the city of St. John’s. It was found that the global warming potential (GWP) held the highest values among four M&R techniques. CIR technique produced the lowest percentage of GWP (83.87%), and for asphalt patching, the CO₂ emission resulted in the highest percentage (92.22%) which became the least suitable option. To understand the PVI effect, the required data and information are collected from the Long-Term Pavement Performance (LTPP) program. Out of 141 Canadian road sections, 22 sections were selected. Several climatic parameters, including annual precipitation, annual temperature, and annual freezing index data, were collected from these 22 sections and further processed for developing clusters using a hierarchical clustering approach. Finally, the Athena Pavement LCA tool was used to measure the environmental impact from the PVI effect for each cluster. It was found that cluster 2 (high annual precipitation, high annual freezing index, and medium annual temperature) experienced the highest rate of IRI increase and therefore, high GWP value. The LCA result also indicated a relatively higher GWP due to pavement roughness from heavy vehicle traffic compared with light vehicle traffic. For the PVI effect due to pavement deflection, cluster 4 (maximum vehicle load and the minimum subgrade stiffness) emitted the highest GWP among all the clusters. Pavement LCA tools require an extensive amount of data to estimate the environmental impact. In the first and second studies, all Canadian road pavement sections were not possible to consider because of the large quantity of time consumption for LCA of each section. Therefore, a database management software, Microsoft SQL Server Management Studio, was used for filtering and data manipulation of the LTPP database considering all Canadian road sections. The manipulated data were further used to develop the LCA models using machine learning algorithms: multiple linear regression, polynomial regression, decision tree regression and support vector regression. The models determined the significant contributors and quantified the CO₂ emission in pavement material production, initial construction, maintenance and use phase. Model validation was also performed. The study also revealed the contribution of Canadian provinces’ CO₂ emission. The proposed LCA models will help the decision-makers in the pavement management system

Designing Social Networking Mobile App for Diabetes Management

The knowledge required for diabetes prevention and management among the rural people in developing countries vastly remains in the state of non-existence. To address this, a diabetic knowledge sharing platform, as an effective means for diabetes prevention, control, and treatment, can play role in increasing diabetes awareness and literacy. Currently researchers have emphasized the scope of peer-led learning by knowledge sharing on social media platforms in healthcare context. Therefore, by identifying this scope, we have prototyped a mobile app integrated with social media features to enable diabetic patients for cost-effective peer-led learning, knowledge sharing, and awareness building. In this process, we resorted to follow the cycles and guidelines as proposed in the Information System Research (ISR) framework for identifying users\u27 needs and preferences as well as building the theoretical foundation for the design of an app. This study demonstrates that the users had positive response and well acceptance to this prototype app as a medium for peer-led for diabetes management. Based on the findings, the researchers are optimistic about the potentiality of the app for a wider scale adoption by diabetic patients as a cost-effective peer-led learning platform

Effects of Dietary Vitamin C on the Growth Performance, Antioxidant Activity and Disease Resistance of Fish: A Review

Vitamin C is an essential micronutrient that cannot be synthesized by fish and must be present in fish diets for proper functioning of the physiological conditions. It is required for the biosynthesis of the collagen which is a pre-requisite for the formation of connective tissue and increases the absorption of iron in fish. It prevents various diseases; it is soluble in water and is easily oxidated by heat, light and metal. Most animals can generate vitamin C in sufficient quantities for normal growth and function, but many fish cannot because they lack the enzyme L-gulonolactone oxidase for its manufacture. Vitamin C facilitates the absorption of iron and is necessary for a maximum rate of immune responses and enables a good response to stressors. This updated review presents a general outline of the possible physiological function of vitamin C for fish, with an emphasis on the information on growth performance, antioxidant activity, immune response and disease prevention of fish as well as the synergistic effects of vitamin C with other micronutrients. The diets supplemented with vitamin C promote the growth performance, improve the structure of the intestinal mucosal epithelium, and have a positive impact on the hematological parameter. The addition of different dietary vitamin C to the basal diets significantly improved the growth performance, antioxidant activity, immune response and disease resistance of fish. vitamin C in the aquaculture, having a solid understanding of the positive functions and mechanisms that vitamin C possesses is of the utmost significance

Current Status and Development Trend of Aquaculture: Prospects and Future Potentials

In recent years world aquaculture production has been increased with innovative and technological developments within fisheries sector and scaled up in world total fisheries production. This increasing aquaculture production depends on innovative production systems and technologies, biotechnological developments. The advancements in these cutting-edge technologies have been focused on promoting sustainable aquaculture production, mitigating the risk of disease outbreaks, and contributing to eco-friendly environmental initiatives. This review paper highlights the cutting-edge technologies that have emerged in the field of aquaculture in recent years, up until the present time, with a focus on advancements in fish nutrition. The advancements in aquaculture technology have been instrumental in promoting the achievement of sustainable development goals. As the aquaculture industry continues to evolve, it is expected that there will be further advancements in technology, sustainability practices, and innovative approaches to meet the increasing demand for seafood while minimizing environmental impact. Overall, the future of aquaculture is likely to be characterized by a combination of technological innovation, sustainable practices, and increased focus on environmental and social responsibility. In the arena of aquaculture, this review paper has the potential to nourish the minds of aquaculturists and aquafarmers with a bountiful feast of knowledge. It unveils the latest technologies and developments in the realm of aquaculture, serving as a nutritious resource that can enhance the operation of cultures and promote a fruitful increase in production in the not-so-distant future