PROGRAMME OVERVIEW
PROGRAMME OVERVIEW
48th World Conference on Applied Science, Engineering & Technology (WCASET), which will transpire on the 17th & 18th of November 2025, in Kuala Lumpur, Malaysia
Day 1 - 17th November 2025
Registration Desk
Introduction by Moderator
Inaugural Ceremony
Panel Discussion
Group Photo
Networking Break
Keynote Speaker - Assoc Prof Ts. Dr. Goh Wei Wei
Keynote Speaker - Prof. Dr. Sher Afghan Khan
Keynote Speaker - Assoc. Prof. Dr Hassanudin Mohd Thas Thaker
Keynote Speaker - Prof. Ts. Dr. Mardeni Bin Roslee FASc
Keynote Speaker - Dr. Sufian Mousa Ibrahim Mitani
Keynote Speaker - Assoc. Prof. Dr. Farman Ali
Keynote Speaker - Dr. Anand Ramanathan
Keynote Speaker - Prof. Dr. K. Meena Anand
Lunch Break
Keynote Speaker - Prof. Dr. Noor Zaman
Session Speaker - Prof. Dr. C. Ranjeeth Kumar
Networking Break
Valedictory Function
Vote of Thanks
Inaugural ceremony
Welcome & Speech by Guest of Honour – Mr. A. Siddth Kumar Chhajer
Inaugural Speech – Assoc Prof Ts. Dr. Goh Wei Wei
Inaugural Speech – Prof. Dr. Sher Afghan Khan
Inaugural Speech – Assoc. Prof. Dr Hassanudin Mohd Thas Thaker
Inaugural Speech – Prof. Ts. Dr. Mardeni Bin Roslee FASc
Inaugural Speech – Dr. Sufian Mousa Ibrahim Mitani
Inaugural Speech – Assoc. Prof. Dr. Farman Ali
Inaugural Speech – Dr. Anand Ramanathan
Inaugural Speech – Prof. Dr. K. Meena Anand
Inaugural Speech – Prof. Dr. C. Ranjeeth Kumar
Memento Distribution
Proceeding Book Release
Day 2 - 18th November 2025
Introduction by Moderator
Technical Sessions
Paper Title : Efficacy of the antibiotics benzylpenicillin and streptomycin dissolved in ethanol and applied in the form of a mist on non-spore-forming (Staphylococcus aureus) and spore-forming (Bacillus subtilis) microorganisms.
Anna Wawrzyk
Abstract:
The aim of this study was to investigate the antimicrobial efficacy of penicillin and/or streptomycin applied in an ethanol mist and to examine their effect on the surface integrity of model and historical textiles from the collections of the Auschwitz-Birkenau State Museum (A-BSM) in Oświęcim, Poland. Bacillus subtilis and Staphylococcus aureus bacteria, previously found on historical textile objects at the A-BSM, were inoculated onto samples of model and historical textiles. The disk diffusion method was used to determine effective antibiotic doses. Penicillin and streptomycin, either separately or in combination, were applied to the materials suspended in an ethanol mist. Disinfection efficacy was assessed using culture-dependent microbiological methods, and surface changes were analyzed using SEM, confocal microscopy, and XPS. The developed method of applying antibiotics in an ethanol mist proved to be ineffective biocidal properties, resulting only in a temporary inhibition of the growth of the tested bacteria. However, this did not negatively impact the surface morphology of the textiles. In summary, disinfection of historical textile objects in A-BSM using antibiotics applied in an ethanol mist can be used to reduce the total number of microorganisms, but it is not effective in eliminating spore-forming Bacillus. Nevertheless, this study indicates that further research into the use of antibacterial molecules in disinfecting cultural heritage objects would be necessary and valuable.
Paper Title : Smart Health Companion: A Mini Robot for Vital Signs Tracking
Maria Luisa Francisco
Abstract:
This study presents the design and development of a smart health companion robot that can monitor heart rate and body temperature in real time. The system is built using cost-effective components such as the Arduino Uno, MAX30102 heart rate sensor, and DS18B20 temperature sensor. Housed in a mini car platform, the robot is Bluetooth-controlled through a mobile application, allowing it to move to different users for vital signs monitoring. This makes it useful in settings where contactless or mobile health screening is needed, such as schools, clinics, and remote communities. The main goal of the project is to create an accessible, user-friendly device that supports proactive healthcare and early detection of possible health concerns. The robot displays results on an LCD screen, providing quick feedback on a user’s vital signs. Testing was conducted within the Polytechnic University of the Philippines – Lopez Quezon Campus. A total of 50 respondents, including students, faculty members, and a healthcare professional, participated in the evaluation. Results from surveys and interviews showed high levels of satisfaction in terms of functionality, usability, accuracy, and accessibility. Although the system currently does not store data or send information wirelessly to external platforms, it effectively demonstrates the potential of combining robotics and health technology for real-world applications. Users noted its ease of use and portability, which are important for adoption in community and educational settings. The study highlights how simple, mobile robotic tools can assist in monitoring health metrics, reduce manual workload, and improve response time in detecting early signs of illness. With further development—such as adding data storage, wireless connectivity, and AI features—this health companion robot can become an even more powerful tool for modern healthcare systems.
Keywords:
Mobile health robot, real-time monitoring, heart rate, body temperature, Arduino, vital signs, smart healthcare
Paper Title : Smart Health Companion: A Mini Robot for Vital Signs Tracking
Amitava Sil
Abstract:
Two Indian bamboo species namely Bambusa balcooa and Bambusa tulda has been graded based on different characteristics like diameter, wall thickness, length and density use for bamboo space frame. The loads which resulted from self-weight, dead load, live load, wind load and seismic load either individually or from their possible combinations has been taken on bamboo members and the supports. Structural design of 3D bamboo space frame and determination of loads on a typical connection has been carried out as per Indian Standards. Cylindrical steel connector with provision of bamboo having diameter ranging from 70mm to 85mm were designed and using LFRD method and STAAD Pro as they are used for 2D and 3D structural frames ensuring modularity and saving of time. They are having a ball and socket which are connected to a circular ring for 360 deg rotation with maximum angle of incidence 32 degree. They are fixed on box type connector of size of 180 mm x 180 mm for bamboo columns. 3D Space frame of grid 6 m x 6 m has been taken as prototype bamboo space frame structure with bamboo members of length (horizontal member of 3m and inclined member of 1.95 m having two connectors at the end and circular ring connector at junction nodes has been constructed. Incremental load upto 88 kg has been vertically hanged at four nodal joints and nodal displacement has been noted at all the incremental load assigned after 12 hrs and 24 hrs of loading. This has been carried out two types of bamboo species as structural member for comparison. It has been observed that Bambusa balcooa sustained more load than Bambusa tulda having nodal displacement within the allowable limit (Span/240). Hence this ensured structural integrity of the bamboo frame using ball socket bamboo connector and preventing the damage that could lead to cracking and breakage due to movement of nodes. By analyzing the displacement behavior of these two bamboo species, it becomes evident that species selection plays a crucial role in the structural integrity and performance of bamboo-based systems. The experiment underscores that Bambusa balcooa offers superior structural efficiency under gradually increasing loads due to its stiffer nature and better dimensional control. These insights are critical for engineers and architects involved in designing bamboo structures, as they guide material choice based on required strength, stiffness, and long-term stability. It is hoped that these innovative connectors could inspire and promote local people, designers, engineers and architects to gain more interest of making bamboo architecture in the future.
Keywords:
Cylindrical bamboo connector, bamboo space frame, LFRD, Analysis of Covariance, nodal displacement, hanging load.
Paper Title : Intelligent Progressive Web Application for sustainable tourism in Mexico
Idalia Maldonado Castillo
Abstract:
This paper presents the design and implementation of an intelligent Progressive Web Application (PWA) for sustainable tourism in Mexico. The proposed system, which utilizes an Ant Colony Optimization (ACO) algorithm to generate the best solution for itineraries, provides personalized and eco-conscious travel recommendations for four key Mexican states, based on the country's Sustainable Development Indicators. The PWA analyzes visitor preferences to generate optimized travel routes and suggest destinations, focusing on sites of interest. A core component of this research is the integration of a sustainability heuristic within the ACO model, which prioritizes accommodation with certified sustainability credentials. The PWA novelty lies in its dual focus on user personalization and environmental responsibility. It offers users a choice between a standard itinerary and a more sustainable one, providing transparency by calculating and displaying the carbon footprint generated by using the application itself. The PWA is designed for seamless cross-platform access, offering an efficient and user-friendly platform. Our results demonstrate that the system effectively delivers tailored recommendations while actively promoting responsible travel practices, making it a valuable tool for advancing sustainable tourism in Mexico.
Keywords:
Progressive Web Application, Sustainable Tourism, Ant Colony Optimization, Itinerary Optimization, Carbon Accounting, Environmental Impact Assessment, Mexico.
Paper Title : Exploring the Sentiment Analysis of Twitter Posts for Decision Support System
Parne Usharani
Abstract:
Social media platforms like Twitter and Face book are the few powerful sources of communication that are booming among people to share their views and opinions on any topic or article. These opinions form a massive amount of unstructured data, which can be used to analyze public sentiment opinions, providing valuable information. These paper pro-videos a comprehensive sentiment analysis on Twitter reviews using Long Short-Term Memory (LSTM).The data collected is the Twitter reviews on the Train's data and their management. Initially, pre-processing techniques were used for text cleaning, removing stop words, and tokenization to enhance data quality. Data augmentation and retrained Glove embeddings are also employed. The proposed system leverages more advanced architectures of deep learning, especially LSTM, which shows superior performance because of its ability to capture long-term dependencies in sequential data. Results obtained from the train-in and testing of models built on the data provide the performance overview that the LSTM model outperformed as compared to Logistic Regression, SVM, and CNN methods, and we evaluated the different performances of the metrics like accuracy, precision, recall, and F-score. These outputs emphasize the robust deep learning approaches for analyzing views and sentiments on social media, providing valuable insights to improve customer satisfaction and maintain service quality in Trains.
Keywords:
Social media, Sentiment analysis, Glove embeddings, Twitter reviews
Paper Title :Improvement of the Methodology of Processing Digitalized Figure Images for 3D Clothing Design
Mukhtabar Shomansurova
Abstract:
Three-dimensional clothing modeling has been on the market for many years. At first, most users found it difficult to transfer traditional analog processes to the digital world. The advent of technical systems for contactless measurement and high-speed digitization of human figures – body scanners – is the first stage of a revolutionary change in the content of the initial base of the clothing design process. The experience of equipping specialized automated clothing design systems with various modifications of three-dimensional design modules has shown that the user tools of some programs need to be improved. Virtual fittings may be unreliable if the system base is insufficiently filled with three dimensional avatars or if their personalized correction is impossible. The article presents a study on parametric and 3D scanned digital twins of typical figures for clothing design.
Keywords:
Three-dimensional, digital, 3D scanning, CAD systems, body scan, digital avatar, free rotation.
Paper Title : Perception of Knowledge about Smart Agriculture among Technical Agricultural Students in the Amazon
Carmen Rosa Cruzalegui Cachique
Abstract:
This work comprises a descriptive study of the perception that students in the Technical Agriculture program have regarding the use of solutions related to Smart Agriculture within the context of their academic training. Smart Agriculture is an approach that integrates the use of emerging technologies (drones, Artificial Intelligence, etc.) with the aim of improving productivity and environmental sustainability. It is important to note that agricultural activities in the Peruvian Amazon face a series of limitations (climate, soil quality, pests, scarce cold chain systems, etc.) that restrict productivity. To date, there are only a few and discontinuous successful experiences. Agricultural activities in the Amazon are of great importance to ensure food security for both urban and indigenous populations, as well as to catalyze economic dynamics. The study revealed the limited knowledge and use of technologies related to Smart Agriculture, despite the fact that all students use the Internet, mobile technology, and even Artificial Intelligence on a daily basis. At the same time, it is important to highlight their interest and demand for experimenting with technologies related to Smart Agriculture.
Paper Title : Perception of the use of information technologies as educational tools among university students in Loreto, Peru
Lelis Antony Saravia Llaja
Abstract:
This study provides a descriptive analysis of the perception held by students from the Faculty of Education and Humanities at the National University of the Peruvian Amazon (UNAP) in Iquitos, Peru, regarding the use of information technologies as educational tools. Prior to 2023, the quality of internet services in the region was highly deficient, which limited the use of various digital platforms and tools that could have supported teaching and learning processes across different educational levels. It is also worth noting that, before the Covid-19 crisis of 2020, the use of online systems for educational purposes in the Peruvian Amazon was minimal. At present, improvements in internet services have brought about changes in how technologies are perceived as tools that can be integrated into curricula and classroom activities. Among the most relevant findings, the study highlights a notable increase in the use of digital tools and computational processes in recent years. However, despite this progress, the actual content and mechanisms applied in classrooms have scarcely been updated, underscoring the urgent need to modernise curricular plans.
Paper Title : Data-Driven Query Execution and Resource Provisioning in Dynamic Distributed Cloud Environments
Chakradhar Bandla
Abstract:
This paper introduces a novel AI-driven framework for autonomous performance optimization in dynamic distributed cloud database environments. Traditional database management systems, relying on static configurations and manual tuning, are ill-equipped to handle the fluctuating workloads and unpredictable resource availability of modern cloud ecosystems. Our proposed approach leverages machine learning (ML) algorithms to enable data-driven query execution and resource provisioning. The core of our framework is a multi-agent reinforcement learning (RL) system. One agent, the Query Optimizer Agent, learns from a vast historical dataset of query plans to predict and select the most efficient execution strategy in real time, factoring in current system load and data distribution. Concurrently, a separate Resource Provisioning Agent uses time-series forecasting models (e.g., LSTM networks) to predict future resource demands and dynamically scale database instances, CPU, and memory allocations. These agents operate in a feedback loop, where the Query Optimizer Agent's decisions inform the Resource Provisioning Agent's actions, and vice versa, creating a continuously self-tuning system. Our experimental validation on a representative distributed database benchmark shows that this integrated approach achieves a 30% reduction in average query latency and a 25% improvement in resource utilization efficiency compared to state-of-the-art static and rule-based methods.
Keywords:
Distributed Systems, Cloud Computing, Query Optimization, Resource Provisioning, Reinforcement Learning, Autonomous Systems, Performance Tuning
Paper Title : Comprehensive Characterizations and Comparative Performance Analysis of Crude Oil Grades Processed at Iraqi Oil Refining
Zheno Kareem Ahmed
Abstract:
This study analyses and compares the physicochemical properties of two Iraqi crude oils—Crude A (a blend of Hassira and Sarsang) and Crude B (By Hassan)—to assess their refining potential and operational challenges. Samples were analyzed at the Department of Chemistry, University of Sulaimani, employing conventional American Society for Testing and Materials (ASTM) and Institute of Petroleum (IP) methodologies. The primary parameters examined included water content, sulfur percentage, flash point, density, API gravity, pour point, viscosity, total acid number (TAN), and trace metals such as nickel and vanadium. The findings indicated that Crude A is a lighter crude oil (API 41.48) with a reduced sulfur content (1.08 wt%) and excellent cold-flow characteristics. However, it contains a higher concentration of vanadium and nickel. Conversely, Crude B possesses a higher density (API 25.7) and contains greater sulfur content (3.25 wt%) and water, necessitating enhanced procedures for sulfur and salt removal. It was also found that light crude oil has a high percentage of light fracture and that the pour point of light crude oil is higher than that of heavy crude oil. An increase in the boiling point of the distillate was observed with an increase in the percentage of the fraction volume. Moreover, Diesel has a higher boiling point than kerosene, which in turn has a higher boiling point than naphtha for all of the fractions. Sulfur content is low in light ends; however, it is increasing with increasing carbon number of refineries’ products. These findings offer critical insights for selecting refinery feedstocks, blending techniques, and policy development, underscoring the importance of crude-specific characterization for efficient and sustainable refining processes.
Paper Title : Food Quality Evaluation of Manihot esculenta Crantz Gluten-Free Noodles
Farhan Mohd Said
Abstract:
Gluten-free noodles often have less acceptable cooking behavior and appearance compared to gluten-containing counterparts. In this study, the cooking and color characteristics of Manihot esculenta Crantz flour–based GF noodles fortified with xanthan gum, guar gum, whey protein, and egg were investigated. The effects and interactions among the ingredients were estimated using a fractional factorial design. The optimized formulation demonstrated good cooking characteristics, with rapid optimum cooking time (3.33 min), minimum cooking loss (1.53%), a high yield (103.05%), and acceptable swelling index (2.47). Yellowness (b* 10.13) was an acceptable value in color measurements. Egg was the main contributor to the yellow colour. Validation of the model confirmed good predictability performance and is comparable to commercial noodles, which contributes to sustainable food production and diversity in diet.
Paper Title : Architecture After Intuition: Traditional vs. AI-Driven Processes in Architectural Education in Bangladesh
Ashik Vaskor Mannan
Abstract:
The upsurge of AI is beginning to convert architectural education, even in contexts such as Bangladesh where traditions of studio practice remain deeply rooted. This paper offers a critical comparison between conventional modes of design—rooted in intuition, precedent, and hand-drawing—and the emergent AI-driven approaches that assemble neural networks, generative platforms, and prompt engineering. What emerges is not a simple technological upgrade, but a thoughtful reconfiguration of the design process. The answers suggest that AI enlarges the horizon of creativity, extending the capacity of students to imagine and problem-solve by generating outcomes beyond the limits of individual intuition. In doing so, AI not only hastens efficiency but also introduces new possibilities of innovation, aesthetics, and authorship. Crucially, it reframes the role of the architect from maker to curator, from one who produces singular visions to one who composes and filters among a production of machine-generated alternatives. Grounded in literature and experimental studio projects, this paper argues that the integration of AI into architectural pedagogy in Bangladesh is not optional, but inevitable. The question is not whether AI should enter the studio, but how critically and creatively it can be integrated. The conclusion points toward a future in which design education must evolve into a dialogue between human and machine intelligence—an association that reshapes not only the tools we use, but the very meaning of design itself.
Keywords:
Artificial Intelligence (AI), Architectural Design Process, Architectural Education, Design Studio, Prompt.
Paper Title : Mobile App for Personalized Tourist Itinerary Generation Based on a Genetic Algorithm
Idalia Maldonado Castillo
Abstract:
Mexico has established itself as the leading tourist destination in Latin America, recognized for its vast cultural heritage, natural diversity, and wide range of modern attractions. However, this richness poses a challenge for travelers when planning itineraries that efficiently integrate the most relevant destinations according to their personal preferences, available time, and travel objectives. This paper presents the design and implementation of a mobile application capable of generating personalized travel itineraries in Mexico through the use of a Genetic Algorithm (GA). The algorithm evaluates multiple combinations of destinations, optimizing factors such as place diversity and user interests to produce balanced and coherent itineraries. The system architecture integrates a modular backend and a cross-platform mobile application (iOS and Android), ensuring scalability and a seamless user experience. Experimental results demonstrate the effectiveness of the Genetic Algorithm in automatically generating optimized itineraries that assist travelers in decision-making, highlighting the potential of artificial intelligence —particularly the use of genetic algorithms— in enhancing the functionality and personalization of tourism recommendation systems
Keywords:
Tourism recommendation system, Personalized itineraries, Smart tourism, Mobile app, Genetic algorithm.
Paper Title : An Enhanced Ensemble Model For Cervical Cancer Prediction Using Machine Learning With XGBoost Integration
Kalaipriya S M
Abstract:
Cervical cancer is among the main causes of death for women all over the world, particularly in areas with low access to frequent screening by medical specialists. Early detection by using machine learning (ML) has been found to have favourable outcomes in determining high-risk patients from non-invasive characteristics. This project suggests a better ensemble model for the prediction of cervical cancer through the incorporation of XGBoost into a current multi-classifier ensemble. The conventional model, which utilized equal-weight majority voting among classifiers such as SVM, DT, NB, KNN, LR, J48, Multi-layer Perceptron, and RF, is enhanced by using a weighted voting system. This modification favors top-performing classifiers, enhancing overall model accuracy and dependability. The UCI Cervical Cancer Risk Factors dataset was employed, and preprocessing involved missing value handling, data balancing, and feature selection. The developed system attained higher accuracy, precision, recall, and F1-score than the baseline. This paper shows the usage of the state-of-the-art ML techniques assistance in the prompt diagnosis of cancer and improve healthcare decision-making.
Keywords:
Cervical Cancer, Machine Learning, Ensemble Models, XGBoost, Weighted Voting, Healthcare Analytics.
Paper Title : Has Walking Improved? Behavioural and Perceptual Evidence from Jakarta’s Complete Street Implementation
Anastasia Maria Delfiera Setyo
Abstract:
Since 2022, several key pedestrian corridors in Jakarta have undergone major revitalisation using the Complete Street concept. Originally developed in North America, the approach was formally adopted by Jakarta through Governor Regulation No. 58/2022. It aims to create more pedestrian-friendly urban spaces that support walking and public transport use. However, limited evidence exists on whether such design interventions effectively influence travel behaviour and user perceptions, particularly in cities like Jakarta, where car dependency and informal street practices reduce walkability. This study investigates the behavioural and perceptual impacts of the Complete Street implementation in Blok M, one of Jakarta’s most active mixed-use and transit-oriented districts. A questionnaire survey of 120 respondents was conducted using a retrospective approach to compare travel modes before and after the intervention and assess walking environment quality via a Likert scale. The results then are analysed through descriptive and inferential statistical methods. The results reveal a substantial increase in walking trips to both local destinations and nearby transport hubs, accompanied by a decline in private vehicle and ride-hailing use. Positive perceptions of safety and connectivity were found to be strongly associated with the likelihood of walking, indicating that improved perceived quality of the environment can encourage behavioural change. Despite challenges such as limited parking management, most respondents viewed the redesigned street environment positively. These findings demonstrate that well-planned street interventions can successfully encourage behavioural change and improve user perceptions on walking. The study also highlights the importance of incorporating behavioural understanding and context-sensitive design into adopting Complete Street policies developing cities.
Paper Title : Estimation of Land Value Uplift Impacts of Thorpe Park Station Using Evidence from the North of England Model
Anitya Putri
Abstract:
This research examines the potential land and property value uplift resulting from the proposed Thorpe Park Station in East Leeds, UK. The study identifies the key variables influencing land and property value changes, evaluate how the station will affect these factors, and estimates the magnitude of value uplift in the surrounding area. Using the North of England Model, an ex-ante quantitative analysis was conducted based on five variables: accessibility, place quality, building and plot characteristics, neighborhood socio-economic context and supply-demand balance. The finding indicate that Thorpe Park Station will generate significant spatial and economic impacts. Improved accessibility to major employment centers nearby, particularly Leeds and York, is projected to reduce travel times by up to 10.4 minutes, leading to an estimated 6.15% property value uplift within 400 meters of the station. Additional factor such as enhanced place quality from proximity to The Springs retail hub (10%), new-build housing premiums (18.1%), and generation effects (2.76%) are expected to further enhance the value growth. Collectively, these factors suggest a potential property value increase of approximately 40% in the closest catchment zone. This research contributes to the understanding of transport-triggered land value uplift in emerging suburban areas and provides insights for applying land value capture mechanisms to support sustainable transport financing.
Paper Title : The Potentials of Telraam Systems for Cycling Infrastructure Planning and Studies
Dendy Hardian
Abstract:
Active travel data remains sparse in many monitoring regimes, limiting evidence-based cycling planning. Telraam an affordable, citizen science–based, AI-enabled image counter offers a scalable alternative, yet independent validations in mixed-traffic settings are limited. This study assesses Telraam’s bicycle-count accuracy against manual ground truth on an urban mixed-traffic corridor and observed in 15-minute intervals for 3 hours in morning peak hour time from 7am to 10am in Leeds, UK. Linear regression between Telraam and manual counts shows strong agreement (R² = 0.974, p < 0.001), with slope [β̂ ± 1,007] and intercept [α̂ ± 0,25]. Error analysis indicates MAE = 0.75 bicycles per hour (≈ 98% of the mean flow) and RMSE = 1,041; Bland Altman plots display solid concurrence with bias/LoA [-0,17 ± 2]. Accuracy is robust across daytime conditions. However, further examinations should be performed for different situations such as low-light, rain and seasons which this study does not cover. These results suggest Telraam can reliably complement traditional manual counts and enable large-scale, cost-efficient bicycle monitoring where permanent counters are infeasible. We discuss deployment guidelines (camera height/angle, siting away from occlusions) and data governance considerations. Further work should examine night-time, seasonal effects, and class misclassification (bicycle vs motorcycle) to define boundaries for operational use.
Paper Title : An Econometric Analysis of Airport Performance: Modelling the Determinants of Flight Cancellations in the United Kingdom
Edy Ariyanto
Abstract:
This dissertation provides a comprehensive econometric analysis of the primary determinants of flight cancellations at UK airports, utilizing a longitudinal panel dataset covering the period from 2020 to 2024. Amidst a modern aviation ecosystem characterized by high demand and constrained capacity, flight cancellations have become a critical indicator of operational performance and system resilience. Moving beyond conventional analyses that often examine causal factors in isolation, this research adopts a holistic Input-Output framework to investigate the complex interplay between an airport's resources (inputs), its workload (outputs), and uncontrollable external factors such as weather. The study employs a multi-stage analytical strategy, progressing from an exploratory Pooled Ordinary Least Squares (OLS) model to a definitive Fixed Effects (FE) panel data model to ensure the validity and reliability of its findings. The results reveal that the primary drivers of flight cancellations are aggregate operational utilization pressure and specific weather conditions related to visibility. The final comprehensive model demonstrates that aircraft movements and cargo volume are the most significant components of utilization, highlighting that principal operational bottlenecks are located on the airside and apron, rather than the landside passenger terminal. Furthermore, the analysis identifies a significant and non-linear (inverted U-shaped) relationship between cloud cover (a proxy for visibility) and cancellations. Conversely, the study finds no statistically significant evidence that marginal annual changes in operational inputs, such as staff numbers or physical capacity, directly reduce cancellation rates. These findings carry significant implications, suggesting that strategies to enhance operational resilience should pivot from a singular focus on static infrastructure expansion towards a greater emphasis on dynamic efficiency, sophisticated air traffic flow management (ATFM), and targeted investment in technologies that mitigate the impact of low visibility.
Paper Title : Implementation of School Streets Programme and Its Impact on Traffic and Air Emissions – Case Study in Leeds using SATURN
Fikri Rachmad Ardi
Abstract:
Air emissions from motor vehicles adversely impact public health, especially among students who are vulnerable to pollutants when commuting to and from schools located near busy roads. Various policies have been implemented to address this issue, including the School Streets programme, which restricts motor vehicle access around schools’ areas at the beginning and end of the school day. This programme has been adopted in several cities across the UK, including Leeds, to improve safety and air quality in school environments. The SATURN traffic model and the DEFRA Emission Factor Toolkit were used to evaluate the impact of School Streets on private vehicle route changes and air emissions at four school corridors in Leeds, which located in Armley, Chapel Allerton, Weetwood, and Woodhouse. Moreover, five time-segment scenarios were simulated to analyse the effects of road closures on traffic flow and pollutant emissions. The results show that road closures successfully diverted traffic but increased congestion and travel costs on alternative routes. In School Streets’ scenario, emissions of four pollutants: CO₂, NOx, PM10, and PM2.5 were significantly decreased at school located in Chapel Allerton and Weetwood but increased in Armley and Woodhouse. The study concludes that the effectiveness of School Streets is highly context-dependent and recommends micro-level evaluations and integration with complementary policies within local context, such as 20 miles-per-hour zones and park-and-stride schemes
Paper Title : Analysing Public Preferences in Bringing Non-Folding Bicycles onto the Palembang LRT
Ghina Dwi Aqila
Abstract:
Integrating cycling with public transport is increasingly recognised as a key strategy for achieving sustainable urban mobility. This study investigates the feasibility of allowing non-folding bicycles on board the Palembang Light Rail Transit (LRT), Indonesia’s first LRT system, which continues to face low ridership levels. A stated preference survey was conducted among 162 non-folding bicycle owners in Palembang to assess user preferences across three policy attributes: fare surcharge, reservation mechanism, and on-train storage provision. Using the Multinomial Logit (MNL) model, this study found that fare surcharges have a significant negative effect, while the availability of dedicated bicycle storage space has the strongest positive influence on users’ willingness to bring their bicycles. Reservation mechanisms produce mixed responses; meanwhile, the no-reservation system has the highest preference among respondents. The estimated willingness to pay ranges between IDR 5,000 and 8,000 for improved storage and simpler procedures. Overall, the findings highlight that policies promoting affordability, convenience, and security are most effective, and that integrating non-folding bicycles into the Palembang LRT could play an important role in advancing sustainable urban mobility in Indonesian cities.
Paper Title : Evaluating the Passenger Demand Forecasting Handbook (PDFH) Model for Great Britain Rail Demand Before and After the COVID-19 Pandemic
Mohamad Agus Jidni
Abstract:
The COVID-19 pandemic has fundamentally altered travel patterns, raising critical questions about the validity of pre-existing forecasting models. This study evaluates the post-pandemic accuracy of Great Britain's Passenger Demand Forecasting Handbook (PDFH) model, which is widely utilised in the UK to predict rail demand. Utilising open-source rail and socio-economic data from 2018 to 2023, the model's performance was first evaluated by comparing backcasted demand with actual journeys using a Mann-Whitney U (MWU) test. A fixed-effects panel regression was then employed to quantify 'elasticity gaps' (the difference between the PDFH's assumed elasticities and empirically observed values) for fare and socio-economic variables. The results demonstrate a significant deterioration in the PDFH's accuracy, with the model consistently overestimating post-pandemic demand. Crucially, the model was found to be statistically inaccurate for most non-London routes even before the pandemic began, highlighting pre-existing weaknesses. The regression analysis reveals a profound structural shift in passenger behaviour. Fare elasticity for non-London travel has become almost perfectly inelastic, a finding which contradicts conventional demand theory. Furthermore, socio-economic elasticities have significantly weakened and, in some cases, present theoretically implausible negative coefficients for employment and GDP, which might suggest underlying model misspecification. A diagnostic model confirmed this structural break in passenger response to fares. The study concludes that continued reliance on the current framework for policy and investment decisions is untenable. A comprehensive recalibration of the national rail demand model is therefore urgently required to ensure future transport strategies are based on robust, evidence-based forecasts.
Paper Title : Understanding the Impacts of Psycho-social Constructs On the Readiness to Adopt Autonomous Vehicles in the United Kingdom
Mohammad Afwan
Abstract:
Grounded in established technology adoption theory, this study investigates autonomous-vehicle (AV) adoption readiness (AAR) by emphasizing the roles of perceived safety (AVS), perceived attractiveness (AVA), and mode-related attitudes pro-car (PCP) and pro-public transport (PPT). A theory-driven structural approach is employed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to assess measurement validity and relations among latent constructs, complemented by an ordered logit model applied to categorical readiness levels as a robustness check. Findings indicate that stronger perceptions of AV safety enhance perceived attractiveness, and that attractiveness serves as a principal pathway to readiness for adoption. Results remain consistent across analytical approaches, supporting a safety-driven mechanism by which perceived benefits translate into heightened early-adoption propensity. Practical implications point to communication strategies and feature design that credibly improve safety perceptions and highlight tangible advantages, while acknowledging boundary conditions linked to user attitudes toward competing modes. Limitations include reliance on self-reported data and a single-context sample, motivating external validation and longitudinal designs to examine stability over time and across settings. Overall, the evidence positions perceived safety and attractiveness as actionable levers for policy and product development aimed at accelerating responsible AV uptake.
Paper Title : Spatial Prioritisation Framework for Enhancing Pedestrian and Cyclist Safety: A Data-Driven Approach
Rifki Wijaya
Abstract:
The rising road fatality involving vulnerable road users, especially pedestrians and cyclists, poses significant threat to active travel uptake. UK statistics recently highlighted the average of annual rise of 2% in pedestrian and cyclist fatalities during 2024. Safe system approach introduces new philosophy in road safety intervention, which accommodates human error in road collisions. In identifying hotspots, conventional road safety analysis often relies on casualty severity frequency, without incorporating contextual risk factors. To minimise this gap, this study aimed to develop an integrated framework that quantify severity and contextual risk. The research employed DBSCAN analysis to understand spatial pattern of road collisions, then by adapting Eisenhower matrix to prioritise clusters within limited road safety investment. Therefore, this approach is expected to help policymaker in deciding road safety enhancement. The research utilised UK road safety STATS19 dataset over last five years from 2020 to 2024 and vehicle safety rating protection provided by Euro NCAP. Furthermore, to obtain road characteristics, this study used OpenStreetMap package. The result of this study showed that pedestrian and cyclist collision formed 107 clusters across Leeds, capturing 24.8% or 651 casualties. These clusters were classified into four quadrants, where generated 18 Immediate Action, 53 Planned Improvement, 9 Alternate Intervention, and 27 Monitoring zone. The findings suggest that in high-risk score, especially immediate action clusters, priority intervention should focus on engineering updates to close infrastructure gaps such as providing signalized pedestrian crossings and segregated lanes. On the contrary, alternate intervention clusters have low risk-score, however, these clusters resulted high frequency of severity. Thus, these clusters need human factor intervention such as enforcement and road safety education due to the risk score could not captured human factor risk. Therefore, the proposed framework enables evidence-based to support more targeted strategies in enhancing pedestrian and cyclist safety in urban transport planning.
Paper Title : Who Gets Distracted? Exploring Psychosocial and Demographic Factors in Driving Behaviour in Greater Jakarta
Rizka Amalia
Abstract:
Distracted driving has become a critical safety issue in Indonesia’s rapidly growing urban areas, particularly in Greater Jakarta. This study examines how psychosocial and demographic factors relate to distraction engagement (DE) among private car drivers, drawing on the Theory of Planned Behaviour (TPB). The psychosocial factors include Attitude Toward Distraction (ATD), Perceived Behavioural Control (PBC), Descriptive Norm (DN), and Injunctive Norm (IN), while demographic factors include gender, age, education, occupation, driving experience, and driving frequency. Data from 321 respondents were collected through an online survey and analysed using multiple linear regression in R Studio to assess moderation effects. The author found that most psychosocial factors are positively associated with distraction engagement. Drivers with more permissive attitudes toward distraction and higher perceived behavioural control tend to engage more frequently in distraction-related behaviours. Gender was identified as a significant moderating factor, revealing that the effects of ATD (β = 0.111, p = .041) and PBC (β = 0.155, p = .004) on distraction engagement were stronger among male drivers than female drivers. Other demographic characteristics such as age, education, occupation, driving experience, and driving frequency did not show significant moderating influences. These results highlight the importance of designing gender-sensitive road safety interventions. Addressing male drivers’ overconfidence and challenging social norms that normalise distraction could contribute to safer road behaviour in Indonesia’s increasingly congested urban transport system.
Keywords:
Distracted driving, psychosocial factors, demographic factors, Theory of Planned Behaviour, Indonesia.
Paper Title :The Impact of Digital Transformation Disclosure Features in Annual Reports on the Cost of Debt Capital of A-Share Listed Companies in China
Yuxin Ma
Abstract:
With the rapid development of digital technologies, corporate digital transformation has become a key strategy for enhancing competitiveness and optimizing the financing environment. This paper explores the impact of digital transformation disclosure characteristics in annual reports on the debt capital costs of Chinese A-share listed companies. The aim is to quantify the disclosure of digital transformation information in annual reports in terms of quantity, quality, tone, and extent, and to reveal how these disclosure characteristics, under the moderating effects of the degree of digitalization in analyst reports, media reports, and digital strategy consistency, influence the company's debt capital cost. This study employs text analysis techniques combined with deep learning and natural language processing (NLP) methods. It extracts data from the annual reports of Chinese listed companies from 2013 to 2024 using Python, and utilizes Stata software for data processing, quantifying digital transformation disclosure information in the annual reports and conducting regression analysis. The empirical results show that the quality, tone, and extent of digital transformation disclosure in annual reports have a significant impact on the company's debt capital costs, with the degree of digitalization in analyst reports, media reports, and digital strategy consistency playing a moderating role. Robustness tests further validate the effectiveness of these results. The study finds that improvements in the quality and extent of digital transformation disclosures significantly reduce the company's debt capital costs, especially when digital strategy consistency within the company moderates this effect. This suggests that when a company's digital strategy is highly consistent across goal setting, resource allocation, and execution, the credibility and transparency of its disclosed information are enhanced, thereby further reducing investors' risk expectations and optimizing debt financing conditions. Furthermore, external information sources such as media reports and the degree of digitalization in analyst reports also play a key moderating role in this process. Specifically, the level of attention and depth of interpretation given to digital transformation by these external sources can influence market perceptions of the company's digital transformation and further amplify or strengthen the impact of digital transformation disclosure on debt capital costs. In summary, the empirical results of this paper reveal how internal and external factors interact to affect the company's debt financing environment and capital costs through the quality and transparency of digital transformation information disclosures. This paper not only provides empirical evidence for the relationship between digital transformation and debt financing costs, but also offers theoretical support and practical guidance for policymakers and business managers in optimizing information disclosure and reducing cost of debt capital.
Keywords:
Digital Transformation, Disclosure Characteristics, Debt Capital Cost, Text Analysis, Natural Language Processing (NLP).
whatsapp Community
Join IFERP whatsapp Community
Join Now