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The Necessity of Electric School Bus: A Comparative Study of Myanmar and Vietnam
Get PDFPurpose: This research aims to ascertain key factors which influence the necessity of South Korean electric school buses in Myanmar and Vietnam. The interest in business sustainability and the demand for electric or safe school buses are increasing in Myanmar and Vietnam. Design/methodology/approach: As quantitative research, this study relies on a non-probability sampling method to collect data sets from experts in both countries. As this research covers three steps, including 1) literature reviews, 2) preliminary analysis and 3) Consistent Fuzzy Preference Relations (CFPR) analysis, robust outcomes were found and established in this study. Findings: Among five driving factors, including 1) safety, 2) technology, 3) industry, 4) environment and 5) policy reasons, this research demonstrated that while in Vietnam, safety and environmental reasons are the crucial driving factors, in Myanmar, safety and technical reasons are the critical driving elements towards the necessity of electric school buses. Research limitations/implications: Even though this paper lacks an underpinning theory, as an exploratory state, both policymakers and business leaders in South Korea, Vietnam and Myanmar can refer to our findings and analysis to facilitate the necessity of electric school buses and enhance perceived safety. Originality/value: As a first attempt covering the range from business to safety aspects, this research demonstrated that safety is the most crucial factor influencing the necessity of South Korean electric school buses in Myanmar and Vietnam
Developing an Artificial Intelligence Framework for Identifying Fusion Blood-Based Biomarkers in Alzheimer\u27s Disease
Get PDFAlzheimer’s Disease (AD) is an irreversible neurological disorder, a major cause of disability among the elderly, with no effective therapeutic options currently available. It is an asymp-tomatic disease in the prodromal stages and begins many years before clinical appearances. Early diagnosis of AD allows patients to obtain appropriate healthcare assistance, accelerating the development of new medications. A biomarker that evaluates the alterations in the brain cells produced by AD in its preliminary periods might be significant for its early identification. Blood-based biomarkers (BBBMs) facilitate the early detection of AD. The BBBMs detection procedure is cost-efficient and minimally invasive. The aim of this study is to identify the best BBBMs, and machine learning (ML) algorithms play a significant role in identifying people at the high-risk of AD. A total of 146 BBBMs from a database by ADNI, and 12-ML algorithms were investigated. The results show that linear discriminant analysis, Naive Bayes, and support vector machine are the promising ML algorithms for AD detection that integrated into the novel ensemble voting detection model. Furthermore, the four BBBMs i.e., Immunoglobulin M (IGM), Placenta Growth Factor (PLGF), Serum Glutamic Oxaloacetic Transaminase (SGOT), and Alpha-1-Microglobulin (A1Micro) are the significant biomarkers to detect AD in its early stages with performance of 92.86% for sensitivity and 82.35% for specificity. Consequently, BBBMs are the preferred option in clinical practice. In addition, integrating artificial intelli-gence such as ML into healthcare might help with early detection of AD
Vegetation dynamics and land-use history during the Holocene in Corsica (Western Mediterranean): Regional patterns and local landscape transformation in changing coastal ecosystems
No full textThe archaeoecological research conducted in Corsica underscores the critical role of integrating palynological studies from sedimentary deposits to reconstruct past human-environment interactions. This approach sheds new light on evidence of farming activities and redefines archaeological territories and human occupation temporalities. Understanding the influence of human activities on coastal ecosystems requires outlining natural and climatic dynamics at a regional scale while evaluating landscape transformations at local scale, where human imprints are clearly evidenced. To achieve this, we applied quantitative methodologies to disentangle regional pollen loading from local vegetation signals. The Landscape Reconstruction Algorithm (LRA) was employed as an advanced tool to quantify vegetation cover within the source area of pollen sites, enabling the estimation of the relative abundance of key taxa around pollen sites and distinguishing these from the regional vegetation background. Specifically, the REVEALS and LOVE models were used to reconstruct vegetation history at two spatial scales: regional and local.This study reconstructs Holocene vegetation dynamics and human impact in Corsica using quantitative pollen-based models (REVEALS, LOVE) applied to fifteen coastal wetland records and one high-mountain lake. Results reveal that farming activities began around 7400 cal yr BP and intensified during the Roman period, profoundly altering Mediterranean forests and maquis. Coastal landscape evolution was shaped by marine transgression, deltaic progradation, and anthropogenic transformation, with key phases of environmental change aligning with major climatic oscillations and land-use intensification. These findings offer valuable insights into long-term Mediterranean socio-ecological dynamics
Reinforcement Learning-Based Time-Slotted Protocol: A Reinforcement Learning Approach for Optimizing Long-Range Network Scalability
Get PDFThe Internet of Things (IoT) is revolutionizing communication by connecting everyday objects to the Internet, enabling data exchange and automation. Low-Power Wide-Area networks (LPWANs) provide a wireless communication solution optimized for long-range, low-power IoT devices. LoRa is a prominent LPWAN technology; its ability to provide long-range, low-power wireless connectivity makes it ideal for IoT applications that cover large areas or where battery life is critical. Despite its advantages, LoRa uses a random access mode, which makes it susceptible to increased collisions as the network expands. In addition, the scalability of LoRa is affected by the distribution of its transmission parameters. This paper introduces a Reinforcement Learning-based Time-Slotted (RL-TS) LoRa protocol that incorporates a mechanism for distributing transmission parameters. It leverages a reinforcement learning algorithm, enabling nodes to autonomously select their time slots, thereby optimizing the allocation of transmission parameters and TDMA slots. To evaluate the effectiveness of our approach, we conduct simulations to assess the convergence speed of the reinforcement learning algorithm, as well as its impact on throughput and packet delivery ratio (PDR). The results demonstrate significant improvements, with PDR increasing from 0.45–0.85 in LoRa to 0.88–0.97 in RL-TS, and throughput rising from 80–150 packets to 156–172 packets. Additionally, RL-TS achieves 82% reduction in collisions compared to LoRa, highlighting its effectiveness in enhancing network performance. Moreover, a detailed comparison with conventional LoRa and other existing protocols is provided, highlighting the advantages of the proposed method
Scale Model and Ship Simulator Towing of the IEA 15 MW Wind Turbine on the UMaine VolturnUS-S Platform
Get PDFFloating offshore wind turbines are expected to be deployed in significant numbers across the globe in the coming decades, with wet towing of these structures between ports and farms a key operation in their installation, maintenance, and decommissioning. Platforms are likely to be towed across large distances, and accurate understanding of tow dynamics will be crucial to optimising these journeys to minimise costs, timescales, emissions, and risk. This paper presents experimental and ship simulator modelling of the oceanic towing of the IEA 15 MW turbine on the UMaine VolturnUS-S platform. The work provides indicative data for floating offshore wind turbines under tow, investigates the effect of added wave resistance in head seas, and discusses some of the challenges with traditional modelling and the effectiveness of using a ship simulator to model study offshore wind turbine towing operations
Multiphase modelling of electrical conductivity of concrete with conductive inclusions
Get PDFElectrical conductivity (EC) of concrete can be effectively improved by incorporating conductive materials such as carbon nanotubes in the mix design. However, mechanism for the EC enhancement to which the addition of conductive materials contributes has not yet been well understood. Besides, either sand or coarse aggregate in concrete also affects the EC of concrete, which complicates understanding of the mechanism. This paper presents a multiphase analytical model to investigate the effect of constituents on the effective EC of concrete. This multi-phase model was developed based on Maxwell model which was modified in this research to consider the effects of electrical conductivities and volume fractions of sand, cement and/or binder paste, coarse aggregate, and conductive inclusions on the effective EC of concrete. The model developed is validated against published experimental data. To highlight the features of the model, parametric analysis is further performed
A growing threat of multi-hazard cascades highlighted by the Birch Glacier collapse and Blatten landslide in the Swiss Alps
Get PDFRapid atmospheric warming, especially at high altitude, leads to alpine mountain landscapes becoming more vulnerable to mass movements and consequently unstable. For example, decay of mountain permafrost contributes to rockfalls, landslides and debris flows; glaciers are retreating and losing mass at alarming rates, exposing unstable slopes that are more likely to fail; and meltwater, which collects in a growing number of glacial lakes, can pose an outburst flood hazard, putting communities and infrastructure downstream at risk of damage. Occurring now with increasing frequency, these natural phenomena often combine to create complex multi-hazard cascades that are more powerful and have a greater reach down-valley than a singular isolated event. Combined with increasing population and infrastructure and economic activity in high mountains, there is therefore increased vulnerability of society to natural hazards in high alpine mountains, as has been experienced in the Swiss Alps in 2025, with the collapse of the Birch Glacier and the destruction of the alpine village of Blatten. Here, we review the physical processes of this recent event, their impact on environment, people and economy, and consider what can be learned from them
TRAPping the effects of tobacco smoking: the regulation and function of Acp5 expression in lung macrophages
Get PDFTartrate-resistant acid phosphatase [TRAP, gene acid phosphatase 5 (Acp5; gene name for TRAP)] is highly expressed in alveolar macrophages with proposed roles in lung inflammation and lung fibrosis development. We previously showed that its expression and activity are higher in lung macrophages of smokers and patients with chronic obstructive pulmonary disease (COPD), suggesting involvement in smoke-induced lung damage. In this study, we explored the function of TRAP and regulation of its different mRNA transcripts (Acp5 201-206) in lung tissue exposed to cigarette smoke to elucidate its function in alveolar macrophages. In mice exposed to cigarette smoke or air for 4–6 wk, higher Acp5 mRNA expression in lung tissue after smoking was mainly driven by transcript Acp5-202, which originates from macrophages. The expression of Acp5-202 correlated with transcription factors previously found to drive proliferation of macrophages. Treating fetal liver progenitor-derived alveolar-like macrophages [Max Planck Institute (MPI; macrophages derived from fetal liver progenitors) macrophages] with cigarette smoke extract resulted in more proliferation compared with nontreated cells. In contrast, Acp5-deficient MPI macrophages and MPI macrophages treated with a TRAP inhibitor proliferated significantly less than control macrophages. Mechanistically, this lack of proliferation after TRAP inhibition was associated with higher presence of phosphorylated Beta-catenin (b-catenin; a signaling protein) compared with nontreated controls. Phosphorylation of b-catenin is known to mark it for ubiquitination and degradation by the proteasome, preventing its activity in promoting cell proliferation. In conclusion, our findings provide strong evidence for TRAP stimulating alveolar macrophage proliferation by dephosphorylating b-catenin. By driving proliferation, TRAP likely helps sustain alveolar macrophage populations during smoke exposure, either compensating for their loss due to smoking or increasing their numbers to better manage smoke-induced damage
Consensus-based research priorities for post-collision care in the Western Cape province of South Africa
Get PDFIntroduction: Road traffic injuries constitute a significant global health burden, causing 1.3 million deaths and 50million injuries annually, with 92 % of fatalities occurring in low-and middle-income countries (LMICs). Despitethis disproportionate impact, research priorities in post-collision care often reflect high-income country contexts,creating a critical misalignment between evidence generation and contextual realities in LMICs.Methods: This mixed-methods study employed a three-phase approach to identify research priorities for postcollision care in the Western Cape, South Africa. Phase 1 consisted of a comprehensive literature review toidentify preliminary thematic areas and research questions. Phase 2 involved a stakeholder engagement workshop using modified nominal group techniques (NGT) with purposively sampled participants representingemergency medical services, fire services, law enforcement, community members and academia. Phase 3 entailedsystematic prioritisation, where participants independently scored each theme and associated research questionson a Likert scale.Results: Eight thematic domains were identified and ranked in order of priority. EMS safety (highest priority),communication and coordination, public awareness and prevention, transportation and access to care, firstresponder capabilities, training implementation, resource optimisation, disaster and mass casualty management, and specialised care accessibility. The highest-ranked individual research question concerned the minimum set of practical skills and resources required by first responders to effectively provide immediate postcollision care. Technological integration emerged as a cross-cutting priority across multiple themes.Conclusion: The study represents the first published systematic approach to identifying post-collision careresearch priorities in South Africa. Diverging from previous exercises that emphasise advanced interventions orsystem integration, this study highlights foundational challenges of EMS safety and communication as top priorities, reflecting the contextual realities of emergency service delivery in South Africa. The findings provide astrategic roadmap for researchers, funders, and policymakers to direct resources toward questions with maximalpotential to improve post-collision care and strengthen health systems in similar LMIC contexts