Readiness and Influencing Factors for Disruptive Technologies Application in Malaysian Highway Maintenance: A Qualitative Study

The Fourth Industrial Revolution (IR 4.0) is transforming the construction industry through digitalisation and automation, offering opportunities for cost reduction and improved efficiency in infrastructure projects. This study investigates the readiness of Malaysian highway operators to adopt disruptive technologies during the operation and maintenance phases, identifying key influencing factors. A qualitative approach was employed, with semi-structured interviews conducted between June 2023 and March 2024 involving five experienced professionals from various highway concessionaires. Data analysis using NVivo 14 revealed that although operators show readiness for certain technologies, adoption levels vary across maintenance processes. Thematic analysis identified four critical factors influencing implementation: Discomfort, Innovativeness, Insecurity, and Optimism. The findings suggest that Innovativeness and Optimism drive adoption by potentially reducing labour dependency while enhancing efficiency and safety. In contrast, addressing Discomfort and Insecurity could lead to long-term time and cost savings. The study concludes that overcoming these barriers may accelerate technology integration in highway maintenance. Future research should examine emerging technologies such as machine learning, blockchain, and big data analytics to further enhance highway infrastructure management. This research contributes to a deeper understanding of the human factors influencing technology adoption in transportation infrastructure maintenance

The Effect of Mass Fraction of Rice Straw Fiber on The Mechanical Properties and Water Absorption of Cassava Starch Biocomposite

The massive use of plastic packaging has a detrimental impact on theenvironment. Alternative solutions are needed, such as replacing plasticwith biocomposites. In this study, biocomposites were made from ricestraw fiber and cassava starch using the solution casting method. Themass fraction of rice straw fiber was varied at 74%, 78%, 82%, 86%, and90%. To determine the characteristics of the biocomposites, tensiletesting, bending testing, water absorption testing, macro observation, andScanning Electron Microscope (SEM) observation were conducted. Theresults showed that as the mass fraction of rice straw fiber increased, themechanical properties of the biocomposites decreased and waterabsorption increased. The ultimate tensile strength, tensile modulus, andelongation at break in the tensile test decreased by 79.95%, 66.65%, and40.45%, respectively. The bending test results showed a decrease inflexural strength and flexural modulus by 78.53% and 70.37%,respectively. The water absorption test results showed an increase inwater absorption by 49.1%. Macro and SEM fracture morphologyobservations revealed the presence of voids, agglomeration, and fiberspulled out of the matrix due to weak interfacial bonding, which caused thelow mechanical properties and high water absorption. This study alsocompared rice straw fiber/cassava starch biocomposites with commercialegg tray samples. The results indicated that rice straw fiber/cassavastarch biocomposites have superior mechanical properties and lowerwater absorption, suggesting their potential use as biocompositepackaging products

Gain Enhancement of E-Shaped Microstrip Patch Antenna for Underground RFID Reader Application

Radio Frequency Identification or (RFID) is a wireless communication technology that uses radio waves and consists of a reader, tag, and antenna. This paper introduces an innovative concept: an E-shaped microstrip patch antenna utilizing an air gap substrate. The proposed design is tailored for the specific application of underground RFID readers, aiming to optimize the antenna\u27s performance within this unique context. The study encompassed the execution of a parameter analysis to thoroughly explore and optimize the impact of alterations in the antenna dimension on the performance of S11. The antenna design uses coaxial probe feeding technique with aluminum patch. Then, the performance of the final antenna design is analyzed and evaluated. The gain obtained for the proposed design is 9.121 dBi, while the S11 value at the desired resonant frequency 921 MHz is -11.99 dB. The simulation is performed with CST suite software

Metal Film-Based Flexible Sensor for Omnidirectional Airflow Measurement

The previous study on airflow sensors were fabricated using a flap device printed using polylactic acid (PLA) plastic, which had high stiffness, preventing the sensor from bending and returning to its original shape. The used aluminium (Al) strips exhibited relatively higher resistance values compared to copper (Cu), resulting in inconsistent resistance readings at various angles of bending measurement. This paper presents a new development of an enhanced metal film-based flexible sensor for application on omnidirectional 360-degree airflow measurement. The sensor was fabricated using copper film and velostat, a material made of polymeric foil (polyolefins) infused with carbon black to make it electrically conductive. The flapping device was modelled in SolidWorks (3D CAD) and printed using TPE 83A (Thermoplastic Elastomer) filament on a 3D printing machine. An Arduino Mega was used as a controller, data collector, and for evaluating the results. The copper film and TPE 83A material demonstrated significant potential in developing a new flexible sensor for achieving high-accuracy airflow measurement in omnidirectional

The Water Quality Issue: A Study of Tasik Kemajuan’s Water Quality Status

Lakes are crucial for irrigation, drinkable water, transportation, and power generation. Tasik Kemajuan, located on UTHM\u27s main campus, is used for kayaking and flood mitigation. However, pollution in the lake, through various contaminants, poses risks to human health and the aquatic environment. This study aims to monitor water quality and determine the Water Quality Index (WQI) of Tasik Kemajuan. Six water parameters were evaluated to calculate the WQI: BOD5, COD, TSS, pH, AN, and DO. These parameters contributed to determining the water quality subindex and, ultimately, the WQI based on the formula used in Malaysia. An average water quality status was determined for each parameter: AN at 0.56 mg/L (Class III), BOD5 at 9.75 mg/L (Class IV), COD at 21.93 mg/L (Class IIB), DO at 4.02 mg/L (Class III), pH at 6.45 (Class II), and TSS at 20.33 mg/L (Class I), with the water temperature recorded at 28.50°C (Class IIB). The findings reveal that the WQI for Tasik Kemajuan is on the upper edge of being polluted, with a score of 74, placing it in the Class III category. According to the NWQS, Tasik Kemajuan fails to meet the standards for recreational activities involving direct contact with water and shows that significant purification efforts are necessary to make the water suitable for such purposes

Integrating TOE, TAM, and UTAUT to Analyze E-Bidding Effectiveness in Vietnam

This study develops an integrated conceptual framework to assess the effectiveness of electronic construction tendering (ECT) in Vietnam’s public procurement sector. Although ECT is legally mandated, implementation remains uneven, particularly among small and medium-sized enterprises (SMEs). To analyze this complexity, the framework combines three established models - Technology-Organization-Environment (TOE), Technology Acceptance Model (TAM), and Unified Theory of Acceptance and Use of Technology (UTAUT) - capturing key dimensions such as infrastructure readiness, organizational capacity, institutional environment, and user behavior. Notably, the study redefines “attitude toward technology” as an organization’s adaptive capacity, rather than individual intention, to reflect Vietnam’s semi-coercive digital transition. The proposed model lays the groundwork for empirical testing and offers concrete policy directions, including subsidized training and mobile-based access for rural SMEs, integration incentives for larger firms, and legal reforms to stabilize the regulatory environment. These insights aim to inform more effective and inclusive digital transformation strategies in the construction sector

Non-Destructive Method for Moisture Content Sensing Inside a Rice Storage

Rice grains represent the majority of worldwide consumed daily food, especially for most countries in Asia, where rice crops symbolize the feature of the local culture. However, as rice grains are naturally hygroscopic, the total values (quality and quantity) are degrading due to their varying level of moisture content. Currently, a sampling moisture sensing based on a single-point measurement is employed to monitor the moisture content level. In this scenario, the conventional method needs to be revised because it is very localized and only represents part of the moisture distribution inside the bulk grains. Besides, implementing several high-end technologies is considerably expensive for small-scale industries in developing countries. Therefore, this study has developed an RTI system in a prototype scale for a constructive moisture sensing method. RTI is a unique approach that reconstructs an image across the monitored WSN area by exploiting the attenuation of RF signals caused by the presence of targeted subjects. Five rice moisture profiles at the percentage of 15%, 20% and 25% were reconstructed using image reconstruction algorithms, LBP, FBP, NOSER and TR. This study analyses the effectiveness of the proposed method in both simulation and experimental studies. The results positively support the possibility of engaging the RTI technique to localize the moisture distribution in rice storage

Shaping Futures for the Hotel Industry: Certified Internships and Commitment Toward Career Development

This scholarly investigation explores the impact of hotel industry hallmarks, internship service quality, and the mediating role of mentorship on undergraduate hotel management students’ commitment toward careers in the hospitality industry. A quantitative, cross-sectional design utilizing a self-administered survey questionnaire was employed. The sample and unit of analysis comprised hotel management students from Universitas Negeri Padang, West Sumatera, Indonesia, who had completed certified internship programs. Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to test the proposed hypotheses. The findings revealed that the hallmarks of the hotel industry such as demanding work environments, extensive responsibilities, limited career advancement, and minimal recognition did not exert a negative influence on students\u27 commitment toward career. Conversely, the quality of internship service experience exhibited a strong positive effect on students’ professional commitment. The study also established a causal relationship between industry hallmarks, service quality, and mentorship. Notably, mentorship was found to significantly mediate the relationships between both hallmarks and service quality with students’ commitment toward career. These findings offer critical insights and carry significant implications for students, academic institutions, industry practitioners, and policymakers

A Multi-Agent-Based Deep Learning Model for Protecting Cloud Computing Environment Against Distributed Denial of Service Flooding Attacks

Distributed Denial of Service (DDoS) flooding attacks pose a significant threat to the resilience of modern cloud computing infrastructures and their ability to sustain operational stability. Unlike traditional DoS and DDoS attacks, it is the legitimate user who inadvertently causes the damage; the applications exploit the architecture, latency limits, and resource bottlenecks, rendering the service unavailable to genuine users. The current mitigation strategies struggle to keep pace with the diverse attack vectors and fluctuating traffic patterns, particularly in a cloud-native environment where a more intelligent and distributed approach is necessary. A high-fidelity detection system that employs deep learning and self-driving agents offers an effective defensive mechanism. This paper proposes a deep learning model based on a multi-agent and Dynamic Ensemble Selection (DES) strategy, combined with five separately trained Long Short-Term Memory (LSTM) models, to form a DES-LSTM model for identifying and mitigating DDoS flooding attacks in real-time. TS allocates intelligent agents across multiple nodes in the cloud infrastructure, allowing each node to conduct local traffic analysis and contribute to collective threat detection. The system employs DES to facilitate context-based model selection through dynamically evaluated accuracy values, enabling adaptive decision-making. The CIC-DDoS2019 dataset, which encompasses the full spectrum of DDoS attack types, is utilized to train, validate, and evaluate the model\u27s performance. This paper provides a detailed description of the architecture, integration methodology, and simulation, as well as model training, traffic modeling equations, and visualizations. Evaluations against a baseline LSTM model demonstrate that the proposed ensemble achieves superior detection accuracy, reduced false-positive rates, and enhanced robustness in varying attack conditions. The DES-LSTM architecture effectively works based on the experimental outcomes. It possesses real time feasibility, as the classification accuracies (97.8%), precision (96.6%) and recall (97.2%) were in all likelihood enhanced, meanwhile the false alarm rate (2.1%) and the detection latency (19 ms) were tremendously diminished. The agent-based, decentralized structure is both scalable and delivers low latency, making it suitable for deployment in existing cloud security systems

Evaluation of Hyperparameter Optimization Techniques in Deep Learning considering Accuracy, Runtime and Computational Efficiency Metrics

Hyperparameter optimization is considered one of the most important steps in training deep learning models, since the performance metrics of the models, such as accuracy, generalizability, and computational efficiency, are closely related to this. The following five hyperparameter optimization techniques have been explored in this work: Grid Search, Random Search, Genetic Algorithm, Particle Swarm Optimization, and Simulated Annealing, on a feedforward neural network (FFNN) trained with the MNIST dataset. It considers two main configurations of 20 epochs and 50 epochs, focusing on three key metrics: accuracy, runtime, and computational efficiency. Results show that the approximation algorithms, like Genetic Algorithm and Simulated Annealing, can achieve a fantastic trade-off between accuracy and runtime, which allows them to perform much better in terms of computational practicality than the classical methods like Grid Search and Random Search. As a simple example, the highest Genetic Algorithm accuracy is 98.60% within 50 epochs, while Simulated Annealing performed better with the fastest run in 357.52 seconds. These results are bound to show how much flexibility and efficiency there is by the approximation algorithms when searching high-dimensional hyperparameter spaces under scarce resources. This work also presents a trade-off analysis between exhaustive classic techniques and adaptive approximation techniques. The Python implementation used-which is modular in architecture-provides a basic structure that can be extended to complex datasets and architectures. By bridging computational efficiency with practical efficacy, this work provides actionable guidance by both practitioners and researchers in the use of deep learning, offering a possible direction for choosing hyperparameter optimization methodologies most appropriate to constraints versus objectives