Asia Pacific Academy of Science Pte. Ltd.
Not a member yet
3612 research outputs found
Sort by
Optimizing tourist flows through operative carrying capacity assessment: The case of Bakkhali coastal tourism, W.B., India
Full text linkCarrying capacity assessment of nature-based tourist destinations is important for keeping the consumption of natural resources and anthropogenic pollution levels within environmentally safe and sustainable limits. With the mostly rural character of such destinations, the local community's well-being also needs to be prioritized. Exposure to natural hazards and climate crises have further exacerbated concerns about the long-term sustainability of these locations. The interrelationship between tourism intensity and its impacts clearly reflects Butler’s Tourism Area Life Cycle model of 1980. The ‘elements of capacity’ and their ‘critical range’ mark a significant threshold in the model that leads us to the concept of carrying capacity. The capacity may be physical, spatial, ecological, environmental, social, economic, management, and governance, among others. This is also linked with the quality of the touristic experience and satisfaction. In this context, aiming to understand the optimum level of tourist traffic flow in Bakkhali, one of the popular beach destinations of the deltaic island system of the Indian Sundarbans, this study assesses its visitor carrying capacity at three levels—physical, real, and effective. It also briefly introduces the idea of ‘operative’ carrying capacity at the fourth level. The study is based on tourist data until 2019 and adopts the well-established methodological framework of carrying capacity assessment applied widely in several settings. The result suggests that tourism operations at Bakkhali may optimally handle 2040 visitors per day, which may be stretched to a maximum of 2267 visitors per day. This may be used as baseline information for sustainable coastal tourism policy framing in the long term while planning for tourism management and infrastructure development in the Sundarban region in immediate terms
A review of the active industrial area at Pasir Gudang area, Johore: Some notes and knowledge gaps
Full text linkThis study aimed to examine the literature on Pasir Gudang, analyse the gathered material, and identify areas lacking knowledge. The literature research reveals that the published studies may be classified into hydrological, monitoring and forecasting, social, economic, and environmental issues. Nevertheless, the subjects of conservation, preservation, pollution recovery, and bioremediation, particularly environmental, social, and governance (ESG) concerns, have been of recent interest. Thus, these information gaps are strongly advised to be addressed in future research in and around the busy Pasir Gudang industrial sector.
ESG and sustainable development
Full text linkPeople’s expectations for businesses’ social responsibility are rising as environmental and social issues are gaining more and more concerns worldwide. Furthermore, many big businesses need to take ESG (Environmental, Social, and Governance) factors into account when making decisions due to the growing trend of influence investing. What is ESG? What’s the advantage of ESG? What’s the relationship between ESG and sustainable development? These questions will be answered in this article
A survey of sustainable development of intelligent transportation system based on urban travel demand
Full text linkThis paper provides a comprehensive exploration of urban travel demand forecasting and its implications for intelligent transportation systems, emphasizing the crucial role of intelligent transportation systems in promoting sustainable urban development. With the increasing challenges posed by traffic congestion, environmental pollution, and diverse travel needs, accurate prediction of urban travel demand becomes essential for optimizing transportation systems, fostering sustainable travel methods, and creating opportunities for business development. However, achieving this goal involves overcoming challenges such as data collection and processing, privacy protection, and information security. To address these challenges, the paper proposes a set of strategic measures, including advancing intelligent transportation technology, integrating intelligent transportation systems with urban planning, enforcing policy guidance and market supervision, promoting sustainable travel methods, and adopting intelligent transportation technology and green energy solutions. Additionally, the study highlights the role of intelligent transportation systems in mitigating traffic congestion and environmental impact through intelligent road condition monitoring, prediction, and traffic optimization. Looking ahead, the paper foresees an increasingly pivotal role for intelligent transportation systems in the future, leveraging advancements in deep learning and information technology to more accurately collect and analyze urban travel-related data for better predictive modeling. By combining data analysis, public transportation promotion, shared travel modes, intelligent transportation technology, and green energy adoption, cities can build more efficient, environmentally friendly transportation systems, enhancing residents’ travel experiences while reducing congestion and pollution to promote sustainable urban development. Furthermore, the study anticipates that intelligent transportation systems will be intricately integrated with urban public services and management, facilitating efficient and coordinated urban functions. Ultimately, the paper envisions intelligent transportation systems playing a vital role in supporting urban traffic management and enhancing the overall well-being of urban construction and residents’ lives. In conclusion, this research not only enhances our understanding of urban travel demand forecasting and the evolving landscape of intelligent transportation systems but also provides valuable insights for future research and practical applications in related fields. The study encourages greater attention and investment from scholars and practitioners in the research and practice of intelligent transportation systems to collectively advance the progress of urban transportation and sustainable development
Prediction model for diabetes mellitus using machine learning algorithms for enhanced diagnosis and prognosis in healthcare
Full text linkDiabetes mellitus (DM) affects the hormone insulin, which causes improper glucose metabolism and raises the body’s blood sugar levels. With 4.2 million fatalities in 2019, DM is one of the top 10 global causes of mortality. Early detection of DM will aid in its treatment and avert complications. There must be a quick and simple technique to diagnose it. Such diseases can be managed, and human lives can be saved with early diagnosis. Smart prediction techniques like machine learning (ML) have produced encouraging outcomes in predictive classifications. There has been a lot of interest in ML-based decision-support platforms for the prediction of chronic illnesses to provide improved diagnosis and prognosis help to medical professionals and the general population. By building predictive models using diagnostic medical datasets gathered from DM patients, ML algorithms efficiently extract knowledge that helps predict diabetic individuals. The association between DM and a healthy lifestyle is used in the model. In this study, the NHANES (National Health and Nutrition Examination Survey) data set is utilized, along with five ML methods such as Artificial Neural Networks (ANN), CATBoost, XGBoost, XGBoost-histogram, and Light GBM to predict DM. The results of the experiment demonstrate that the XGB-h model outperformed other ML methods regarding area under the receiver operating characteristic curve (AUC-ROC) and accuracy. The most effective XGB-h framework can be used in a mobile app and a website to rapidly forecast DM. Real-time prediction using details delivered by the model at runtime can be developed as a whole bundle as a product. Clinicians can quickly determine who is likely to get diabetes using the proposed strategy, which will facilitate prompt intervention and caring
Analyzing device-to-device communication performance with amplify-and-forward relaying amid co-channel interference
Full text linkCurrently, the pressing issue of spectrum limitation, driven by the increasing demand for wireless communication services, has led to the challenge of co-channel interference (CCI) due to the reuse of frequencies in wireless networks. To address this, non-orthogonal multiple access (NOMA) has emerged as a solution. This report conducts a thorough evaluation of NOMA’s system performance over independent and non-identical Rayleigh fading channels in device-to-device (D2D) communications networks, where CCI is significant. The analysis includes examining the probability density function (PDF) and cumulative distribution function (CDF) of the upper SINR threshold. Communication channels are defined as independent and non-identical Rayleigh fading channels, and probability expressions are formulated to assess system failure likelihood for two users. Additionally, Monte-Carlo simulations are conducted to validate the proposed theoretical mathematical expressions
Construction Technology of Tunnel Consolidation Excavation in Water-rich Soft and Broken Rock Formations
Full text linkCombined with the example of the Junchang Tunnel of Censhui Expressway in the middle and Guangxi section of Baomao Expressway, this paper briefly introduces the four major mud and water inrush disasters that occurred, and expounds the causes of the disasters. Consolidation excavation of the tunnel has made a summary of construction technology
At the frontier of the metaverse: NFTs, artistic expression, and digital immersions
Full text linkThis paper explores the multifaceted landscape of Non-Fungible Tokens beyond their role in digitizing artworks. It delves into the intersection of art, utility, and cultural preservation within the NFT realm. The discussion revolves around the duality of “art for art’s sake” and functional utility, sparking ongoing debates about the essence of art in both the analog and digital realms. The convergence of contemporary aesthetics with practical token utility challenges conventional definitions, offering an alternative to narrative retrieval and contributing to cultural preservation in the virtual sphere. As one enters the NFT ecosystem, a utopian quest for autonomy in healthy production becomes evident, albeit not without formidable obstacles. The paper examines the promise of NFTs to liberate creators from intermediary control and the entwined complexities accompanying their evolution. It sheds light on the cautious and ethical approach required for the mass adoption of these technologies, contrasting this with the current global context characterized by rampant consumerism, individualism, and the relentless pursuit of easy financial gains
The Diagnostic and Prognostic Potentials of TSPANs in Cancer: A Comprehensive Review
No full textTetraspanins are four transmembrane proteins that serve as a platform for interacting with a wide range of different molecules. It plays a crucial role in oncogenesis as well as in the metastasis in various cancer types. Numerous studies have investigated the use of various members of the tetraspanin family as prognostic markers and predictors of metastatic potential according to specific cancer types. This manuscript provides an overview of these advances, and their limitations, and suggests insights into future research areas for their clinical applications. Furthermore, the potential value of tetraspanin as a tumor-associated antigenic target will also be discussed in this review
AP5S1 is a Promising Prognostic and Predictive Biomarker Associated with Tumor Stemness in Lung Squamous Cell Carcinoma
No full textBackground: Lung squamous cell carcinoma (LUSC) is considered the second most prevalent subtype of non-small cell lung cancer (NSCLC), with a high frequency of somatic mutations and limited therapeutic options. Adaptor-associated protein complex 5 subunit sigma 1 (AP5S1) is a component of fifth adaptor proteins complexes. However, the correlation between AP5S1 expression and the occurrence, development, and tumor stemness in LUSC has rarely been studied. Therefore, this study aimed to elucidate the correlation between AP5S1 expression and the stemness of LUSC. Methods: The transcriptomic and functional clinic data of LUSC were accessed from various publicly accessible databases. The data were intensively analyzed using various bioinformatics tools and visualized employing statistical packages. Subsequently, the expression levels were validated through quantitative real-time polymerase chain reaction (qRT-PCR) using LUSC cell lines. The correlations between AP5S1 expression and prognosis, potential pathogenic mechanisms, tumor stemness, immune cell infiltration, DNA methylation, drug sensitivity, and malignant biological behavior in LUSC patients were assessed utilizing both bioinformatics approaches and various assays such as Cell Counting Kit-8 (CCK-8), colony formation, tumor sphere formation, and Transwell assays. Results: This study indicated a significant oncogenic role of AP5S1 in LUSC. Moreover, AP5S1 was confirmed as an independent prognostic risk factor for LUSC. Additionally, there was a strong association between AP5S1 expression and cancer stemness, immunity, DNA methylation, and drug treatment response. Furthermore, the knocking down of AP5S1 inhibited cell proliferation, migration, invasion, and stemness in LUSC in vitro. Conclusions: AP5S1 could be a promising prognostic indicator and a potential therapeutic target closely related to tumor stemness in LUSC patients