Volume 37 (06) September 2025

Commercial space plays a crucial role in urban environments and is among the most dynamic components of cities. Its development often dictates the vitality and economic prosperity of urban areas. However, with rapid urbanization and shifting consumer behaviours, the spatial organization of these areas faces substantial changes, raising critical challenges for effective urban planning. This study aims to identify and analyse the spatial patterns of commercial spaces in Qingdao, China, using advanced data-driven techniques to address these issues. Qingdao was selected as the case study due to its rapid evolution into a major commercial hub in China’s eastern coastal region, with a diverse and competitive commercial landscape shaped by urbanization and economic reforms. This study uses multisource POI data from Qingdao, collected and processed through the Baidu Maps API, with data cleaning and visualization in R Programming. Applying kernel density estimation, spatial autocorrelation, and Moran’s I with local indicators of spatial association (LISA) indices, it examines the distribution and clustering characteristics of urban commercial centres across the city. Results reveal a high concentration of commercial activity in Shibei and Shinan districts, with significantly lower density in peripheral areas such as Pingdu, Laixi, Jiaozhou, and Laoshan. These insights support urban planning efforts to expand commercial development in outer regions, contributing to balanced economic growth and more liveable urban environments. This research enhances understanding of commercial spatial patterns, providing practical guidance for promoting economic vitality and fostering more liveable urban environments through strategic development.

Keywords: Point of interest (POI) data; Spatial patterns; Kernel density estimation; Autocorrelation analysis; Commercial space

Spasticity, a common sign of upper motor neuron syndrome, affects conditions such as stroke, cerebral palsy, traumatic brain injury, and spinal cord injury. The Modified Ashworth Scale (MAS) is widely used by therapists to evaluate spasticity during passive flexion to the appropriate joints of limbs according to the level of muscle resistance, but its reliance on subjective judgment can lead to inconsistent assessments and impact rehabilitation strategies. This study introduces Mechanomyography (MMG) as a quantitative approach for assessing spasticity in the forearm muscles of 30 patients (29 stroke, 1 cerebral palsy), with ethical approval and informed consent. Before feature extraction, the data underwent thorough pre-processing, yielding a dataset of 48 features derived from the x, y, and z axes in three dimensions, representing the longitudinal, lateral, and transverse orientations of biceps and triceps muscle fibers. The extracted features were subjected to statistical analyses, including linear regression, Pearson correlation, and one-way MANOVA, to examine the relationship between MMG signal features with muscle spasticity levels as quantified through the MAS. Linear regression showed a significant positive association (R = 0.881, F (41,48) = 4.076, p < 0.001), with MMG features contributing 77.7% of MAS variability (R² = 0.777). Pearson correlation revealed strong associations, with Miny1 negatively correlated (r = -0.542) and RMSy1 positively correlated (r = 0.515). Additionally, one-way MANOVA confirmed significant differences in MMG features across MAS levels, validating their relevance in spasticity assessment. These results establish MMG as a reliable, objective tool for spasticity evaluation, advancing beyond traditional subjective methods.

Keywords: Spasticity; Mechanomyography; Modified Ashworth Scale; Linear Regression; Pearson Correlation; MANOVA

Performance upgrade in remanufacturing process is one of the lifecycle extension strategies which provides additional values in a used product. The consideration to upgrade the performance requires thorough investigation at the design stage, by integrating Design for Upgrade (DfU) strategies. One of the factors that need to be considered include the service life of the component to ensure proper upgrade can be accomplished in the next life cycle of the component. This study is aimed to evaluate the service life related criteria that affect the decision making when selecting the suitable and best alternative to upgrade the performance of the automotive component. An automotive turbocharger has been selected as the case exemplary of the study. Hence, this study proposes the multi-criteria decision-making approach by using Analytic Network Process (ANP) to evaluate the best selected alternative for performance upgrade of turbocharger component. There are four (4) alternatives were identified which include Alternative 1-Dual bearing system, Alternative 2-Compressor diameter, Alternative 3-Heat insulator layer on compressor and Alternative 4-Improve lubrication system. Besides, ten criteria were identified through literature surveys and online surveys involving 20 respondents and eight criteria were finalised for evaluation using ANP. The eight criteria involved were arranged according to different levels of criteria and sub-criteria. To complete the evaluation using ANP, three experts were involved to assign the scores in each criterion, sub-criteria and alternatives and thus, the weightage can be further calculated using ANP model. The findings show that Alternative 4 ranked at the first place with priority score of 0.50 compared to other alternatives. The robustness of the ANP model was further verified using sensitivity analysis which depict that Alternative 4 has still remained at the first place as the best selected alternative.

Keywords: Analytic network process; turbocharger; performance upgrade; remanufacturing

An innovative approach using flexible sensors based on carbon nanotubes (CNTs) is rapidly gaining prominence in health monitoring, human motion tracking, and artificial intelligence (AI). These flexible strain sensors, which mimic the properties of skin, function as transducers by generating signals in response to various spatial and impact conditions. In this study, CNT-based flexible strain sensors were developed using polydimethylsiloxane (PDMS), polytetrafluoroethylene (PTFE), and Ecoflex with a 2 wt% CNT concentration. The hybrid CNT nanocomposites were fabricated through a drop casting method, with the objective of optimizing fabrication parameters to produce highly sensitive sensors. The electrical properties, specifically on conductivity and sensitivity, were measured and analysed to evaluate performance. The study found that sonicating the CNT solution at room temperature (23°C) with 80% amplitude for 5 minutes produced a solid but slightly sticky sample that cured properly. Under these fabrication conditions, the CNT/PDMS/PTFE composite demonstrated a satisfactory response, with a relatively high conductivity of 0.05023 S/m and high sensitivity of 44.246. This study provides optimized parameters for synthesizing CNT-based hybrid nanocomposites to enhance sensing performance. The results may contribute to the design of next-generation, advanced strain sensors with enhanced performance, reliability, and potential integration in biomedical and soft robotics technologies.

Keywords: Carbon nanotube sensors; flexible electronics; hybrid nanocomposite; strain sensor; fabrication method

Multiplication is an important operation in digital systems, and is critical in fields like microprocessor design, communication systems, application-specific integration circuit (ASIC). Since speed is frequently important, the selection of a multiplication algorithm may have a substantial impact on the performance of the system as a whole. The comparison is done between Booth-Wallace Multiplier and Vedic Multiplier in terms of delay, area and power. Booth-Wallace’s algorithm employs modified Booth encoding so as to minimize the quantity of partial products, thus allowing computation to be faster. On the other hand, The Vedic Multiplier is an efficient multiplier inspired by the Indian ancient mathematical principles and it based on parallel methods of multiplication. In order to compare performance of the two architectures, both multipliers were coded in Verilog HDL and operated under Intel Quartus Prime and ModelSim. Different input conditions were applied to address real world situations and timing study was carried out. Results indicated that the Booth-Wallace Multiplier presented faster delay on average17.863 ns compared with the Vedic 19.667 ns. The difference isn’t large, but it does ostensibly imply some superiority depending on the application. As an outcome, this comparison provides useful know-how for engineers and researchers who are making a decision on selecting suitable multiplications for digital design, especially when the trade-off between performance and hardware constraint is considered.

Keywords: Booth-Wallace Tree Multiplier; Vedic Multiplier; efficient multiplier; Verilog HDL

It is a top priority to reduce the underreport and misreport rates and make the early warning system more effective. This study employed methods including early dynamic identification of debris flows, rainfall pattern analysis, geotechnical testing, and triggering tests further to optimize this gully’s critical rainfall early warning indicators. The study proposed a multi-stage, multi-process comprehensive early warning system to reduce debris flow’s under report and misreport rate. First, the adequate peak acceleration and Standardized Precipitation Index before the 2015 Jintou Gully debris flow outbreak revealed that earthquakes and wet/dry cycles often influence the debris flow of loose and bare soil, so debris flow is more likely to occur under the effect of rainfall. Therefore, the implementation of the early dynamic prediction of debris flow plays a crucial part in debris flow early warning. Second, the critical rainfall indicators need further optimization. The critical rainfall threshold should be put forward comprehensively with rainfall patterns. Third, the debris flow is a typical viscous debris flow formed by the liquefaction of loose bare soil after shear contraction. Dual fractal dimensions, small initiation scale, rapid confluence process, and destructive solid power characterize it. Fourth, the scientific training of new researches for
monitoring and early warning decision maker is the important chain in the system. They can initiate the early warning system immediately based on technical defence signals to reduce underreporting and misreporting rates only if they fully understand the research results on the debris flow triggering mechanism.

Keywords: Debris flow; early warning system; triggering mechanism; science popularization training

The assessment in evaluating a stroke patient’s ability done by the doctor and therapist provides an important information that is valuable in guiding treatment plans, determining the appropriate levels of stroke severity, and identifying areas where interventions are needed to improve a patient’s functional ability. However, the current manual method for assessing stroke patients in occupational therapy is subjective and inconsistent, as it relies on the therapist’s individual expertise and experience. To address this issue, this paper presents a pilot study on a quantitative
measure of Functional Ability Index (FAI) utilising activities of daily living (ADL) based on the Motor Activity Log (MAL) clinical assessment. The Pearson correlation technique is implemented to measure the relationship between variables and normalisation function is adopted for stroke patient’s FAI estimation to describe their capability. Ten ADLs from MAL assessment have been selected for this study. Data on force exerted, arm movement, equipment motion during ADLs, and the time duration to execute the ADL task have been collected from 30 healthy subjects and 56 stroke patients. Sensors of force, IMU, encoder, and distance have been employed for this purpose. The estimated FAI’s are then compared to the manually scored given by the therapists. The results indicate that the highest achieved accuracy is 77% for the ADL ‘Fan Regulator’. These results demonstrate the feasibility of the method for establishing a quantitative measure of FAI.

Keywords: Activity of Daily Living; Motor Activity Log; Occupational Therapy; Statistical Analysis; Stroke Rehabilitation

Advancements in process modelling are unlocking new opportunities for the application of reactive distillation (RD) in various chemical industries. Despite its inherent complexities, RD is gaining recognition as an effective method for enhancing both the efficiency and safety of chemical production. This study presents a new approach for synthesizing high-purity mono ethylene glycol (MEG) through RD, aiming to uncover non-ideal behaviors and to correlate equilibrium models with rate-based models pertinent to this process. The methodology incorporates an increased packing factor to enhance separation efficiency and offers a more streamlined control mechanism, complemented by further model validation through experimental data. The results demonstrate that within a comprehensive modeling and RD rating simulation framework, it is feasible to produce a high-purity MEG product with a concentration of 99.8% using a single RD column setup. This strategy enables the achievement of several improved process attributes. When compared to traditional methods, the RD process results in a 3.4% increase in MEG yield and a 53.2% decrease in energy consumption. Additional advantages include a reduction of 0.1 kPa in pressure drop, the elimination of demineralized water usage during operation, and a lower water-to-ethylene oxide feed ratio, which collectively contribute to decreased capital costs. The outcomes of the plant test have led to the adjustment of the reactive stages into kinetic reactors to improve accuracy and predict the conversion and yield of the glycol reaction. In the subsequent phase of experimental validation, critical design and operational parameters will be defined alongside the RD column configuration.

Keywords: Reactive distillation; mono ethylene glycol; process intensification; ethylene oxide

In designing the engineered timber products such as glued laminated timber (glulam) and cross-laminated timber (CLT), the finger-jointed strength data for lamellae are required and mentioned in BS EN 14080:2013 and BS EN 16351: 2015, respectively. However, the strength data presented in these standards are primarily derived from European timber and their applicability to tropical plantation timber remains uncertain. Thus, this paper presented the flatwise and edgewise finger-jointed bending strength of Malaysian plantation species namely Laran (Neolamarckia Cadamba). Four-point bending tests were conducted in accordance with BS EN 408:2010 on finger-jointed specimens. A total of 100 specimens were prepared for flatwise and edgewise tests with dimensions of 40mm x 135mm x 950mm and 40mm x 135mm x 2700mm, respectively. The finger joints with 12mm finger length located at the centre of each lamellae were bonded using phenol-resorcinol formaldehyde (PRF) adhesive. The characteristic bending strength was calculated according to BS EN 384:2016. The results indicate a mean modulus of rupture (MOR) of 60.42 N/mm² for flatwise and 29.52 N/mm² for edgewise bending.
The characteristic finger-joint bending strength were determined to be 21.56 N/mm² for flatwise and 17.44 N/mm² for edgewise. The predominant failure modes observed were splitting at the finger base (57% for flatwise) and shearing (78% for edgewise). These findings provide valuable data for early classification of finger-jointed bending strength in flatwise and edgewise direction, contributing to the design of glulam beams and CLT panels.

Keywords: Bending strength; characteristic value; engineered timber products, finger jointed; tropical plantation timber

Intense international competition has led the construction industry to embrace technological digitalisation, including Building Information Modelling (BIM). Despite the well-established benefits of BIM’s collaborative nature, it remains constrained by various challenges, notably contractual ambiguity, throughout a project’s lifecycle. As a result, industry stakeholders remain cautious in adopting the technology, leading to slow adoption rates. Legal and contractual factors remain interdependent, with legal issues arising from current contractual practice that hinder the accommodation towards BIM’s roles, procedures, and outcomes. This paper aims to define the contractual determinants relevant to BIM projects within the construction industry. The paper adopts a comprehensive and systematic literature review to shed light in uncovering prevailing doubts and lack of enthusiasm towards its adoption. A four-stage process of identification, screening, eligibility, and inclusion through Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) of two significant peer-reviewed online databases, Scopus and Web of Science (WOS), has been used to uncover the research topic. The findings indicate unclear contractual documents, misinterpretation of contractual terms and conditions, and inadequate contract management from the review of BIM contractual factors. By clarifying these factors, this paper aims to clarify the contractual aspects of BIM for industry stakeholders, thereby facilitating wider adoption and overcoming the challenges associated with the technology.

Keywords: Building Information Modelling; BIM; construction industry; technological digitalisation; contractual determinants

Project delays are a common challenge in the construction industry and often result in increased costs and inefficiencies. As a solution to these ongoing problems, 4D Building Information Modelling (BIM) has emerged as a powerful tool for improving project planning and project management. However, despite the growing interest in 4D BIM, there is a lack of comprehensive analysis of its development and integration with new technologies in construction. This study aims to address this gap by conducting a scientometric review of 4D BIM, focusing on growth patterns, prominent subject categories, keywords, highly cited studies, influential authors and emerging themes. A total of 558 bibliographic records from the Web of Science core collection database (2007-2024) were analysed. CiteSpace was employed to conduct four scientometric analyses, which included exploring coauthorship, co-word relationships, co-citation patterns and clusters. The study found that the emerging themes include technology integration, construction management, safety and risk management, spatial and urban modelling and digital twin reality capture planning. The study also emphasises the importance of integrating technologies such as artificial intelligence (AI), the Internet of Things (IoT) and augmented reality (AR) with 4D BIM to enhance predictive analytics and support smarter construction management practices. This review not only provides valuable insights into the future of 4D BIM in construction digitalisation but also offers practical guidance for countries looking to adopt 4D BIM and emerging technologies in their construction industries.

Keywords: Scientometric analysis; building information modelling; 4D BIM; construction digitalisation

El Nino frequently results in protracted droughts through decreased rainfall concentration, which may cause rivers, lakes, and reservoirs to become depleted. One of the mitigation plans to reduce this impact is by applying the sustainable water management solution. Rainwater Harvesting system has been proposed where the system can directly collect rainfall. This water can be stored for later use or returned into the groundwater system. With additional financial advantages, rainwater harvesting systems (RWHS) can increase water supply to suit urban and rural demands. However, the application of RWHS for the large scale of water demand such as educational institution have been scanty, therefore the aims of the study are to evaluate the suitability of rainwater harvesting installation in educational institutions, thus able to propose the suitable number of tanks. The analysis using Tangki NAHRIM 2.0 software was conducted to determine the suitability of the implementation. Rainfall at location, roof information, water demand, and tank capacity were the data that needed to be input in the software. Then, the data were analysed to evaluate the suitable rainwater harvesting tank for the selected building, and results indicate the RWH is suitable to be placed with the minimum tank provided being 2 tanks and the maximum tank being 3 tanks. In a nutshell, the Tangki NAHRIM 2.0 successfully identified the optimum tank size, indicates the accuracy and cost-effectiveness thus able to benefit the management in order to provide a suitable volume of tank and be able to reduce the consumption of treated water.

Keywords: Rainwater harvesting; water resources; sustainable water; water scarcity; water demand

This study examines the influence of filament color on optimizing FDM process parameters for PLA parts using the Taguchi method. Parameters such as layer thickness, print speed, and printing temperature were varied to identify optimal settings for white and black PLA filaments. The results demonstrate that the optimal parameters vary based on color: for white PLA, the best configuration involves a layer thickness of 0.35 mm, print speed of 50 mm/s, and a printing temperature of 210°C. For black PLA, the same layer thickness and print speed are optimal, but the printing temperature is lower at 200°C. Layer thickness was identified as the most significant factor affecting tensile strength across both filament types. However, the ideal printing temperature depended on the color of the filament. Notably, white PLA exhibited higher tensile strength than black PLA, with an increase ranging from 1.33% to 15.54%, attributed to the thermal properties of color pigments. These findings highlight the critical role of filament color in determining mechanical performance during FDM printing. Incorporating filament color into the optimization of FDM parameters can enhance the quality, strength, and reliability of 3D-printed components. This research provides valuable insights for improving additive manufacturing outcomes across a range of applications.

Keywords: FDM; PLA; Taguchi method; tensile strength; process optimization; material color

This study examines the effect of incorporating treated crumb rubber (CR) as a partial substitute for sand on the engineering properties of rubberized geopolymer concrete (RGC) formulated with Ground Granulated Blast Furnace Slag (GGBS) and Waste Clay Tiles (WCT). The primary aim is to assess the impact of different CR content levels (0%, 5%, 10%, 15%, and 20%) on the physical and mechanical properties of RGC. The experimental methodologies encompass water absorption testing, surface abrasion resistance assessment, and compressive strength evaluation. The findings indicate that elevated CR content typically results in greater water absorption due to the porous characteristics of the rubber particles; yet, all measurements were within permissible durability thresholds. The abrasion resistance is enhanced with increased CR content, with 10% and 15% exhibiting optimal resistance attributable to the elastic properties of rubber. The compressive strength diminished with increased CR percentages; however, the 10% CR mixture maintained a strength exceeding 42 MPa, rendering it appropriate for structural application. In summary, a 10% CR replacement provides the optimal equilibrium of strength, durability, and sustainability. This supports the use of recycled rubber and industrial by-products in the production of eco-friendly concrete.

Keywords: Geopolymer concrete; water absorption; abrasion resistance; compressive strength; treated crumb rubber

Access to safe water remains a global concern, particularly in developing countries facing issues of fecal contamination in drinking water supplies. This research investigates the potential of Arachis hypogaea (peanut seeds) as a natural coagulant for treating water in the Tasik Titiwangsa recreational lake. The study aligns with the United Nation’s Sustainable Development Goals (SDGs), focusing on SDG 6 to ensure safe and affordable drinking water for all. By addressing the limitations of chemical coagulants like aluminium and ferric chloride, the research aims to contribute to sustainable and eco-friendly water treatment methods. The main objective is to evaluate the effectiveness of peanut seeds in improving water quality parameters such as pH, turbidity, total suspended solids (TSS), and colour. However, challenges such as seed quality and the study’s timeframe limit the scope of experimentation. The study successfully characterized raw lake water, established the optimum dosage of 0.75 mL of Arachis hypogaea, and found it effective in reducing turbidity by 74.7%, though slightly less effective than alum, which achieved 89.8% removal at 4 mL. Both coagulants maintained stable pH levels post-treatment. In conclusion, while alum is more effective, Arachis hypogaea is a promising natural alternative for sustainable water treatment, potentially reducing reliance on synthetic chemicals.

Keywords: Natural coagulant; water treatment; arachis hypogaea; recreational lake; environmental sustainability

Due to the global warming effect, the application of shading device in current building design is increased to enhance thermal comfort and daylight in the building. This research focuses on office space for industrial building, which is the most significant space due to it’s high usage. Therefore, this research is conducted to investigate the indoor thermal and daylight conditions, and to determine the potential shading device to enhance thermal comfort and daylight in the office space of the selected industrial building. The methods employed by the research are field measurement and computer simulation using Sefaira software. This study identified that after the shading device are added, the average indoor operative temperature of studied space decreased by 6.3% to 8.72% and achieved the required temperature for thermal comfort, which is 30.17°C to 30.34°C. The suggested optimum size of shading device is M4, which is the combination of both horizontal shading device and vertical shading device, and sized between 0.5m width and 2.0m height for each panel. This study suggests that the width of shading device should be 0.5m minimum and 1.0m maximum because this study identified the size that beyond this range can affect the quality of daylight inside the studied space. This research suggestions might help the building industries to provide the optimum size of shading device for reducing the average indoor operative temperature of the office space, in turn, the indoor thermal comfort can be enhanced and the optimum daylight factor can be achieved.

Keywords: Shading device; office space; thermal comfort; indoor operative temperature; daylighting

Shophouses in Pekan Bangi Lama are analyzed as the typo-morphological transformation of plots and buildings because they had gone through a time-crossing period process starting from 1900 until now and were gradually devasted by development that destroys the genius loci of the town. The typological transformation of shophouses along with the morphology of development in Southeast Asian cities has also been observed through the study of existing literature. The Muratori Typo-Morphology method is used as the objective in identifying the typomorphological transformation of the plots and shophouse buildings of Pekan Bangi Lama and then analysing the socio-spatial morphological changes of Pekan Bangi Lama. Cartography, images of past and present were examined as a comparative basis for transformation. At the same time, a field survey was conducted to find out the changes in facade typology, function, and the latest physical condition of shophouses in Pekan Bangi Lama.

Keywords: Typo-morphological transformation; shophouse; Bangi Lama

In the industry, demand for lightweight vehicle components is skyrocketing. The majority of these components are made using metal forming techniques to generate components that are lightweight, strong, and stiff. The majority of rolling procedures are used to increase the ductility and strength of the material. To change the rolling surface, an additional cross-rolling step is added during the deformation. This paper has reviewed the mechanical properties of al alloy sheet. The article focuses on the current situation, recent changes, and anticipated future orientations on the subjects. This article attempts to compare how well various rolling processes—such as room temperature rolling, cryogenic rolling perform on various materials in the literature. It has been discovered that cryorolling of aluminium alloys increases strength and hardness more than cold rolling because it effectively suppresses dynamic recovery, which raises the density of dislocations. Nonetheless, post-cryorolling techniques like warm rolling and low temperature annealing have been undertaken in an attempt to produce sheets with the best possible balance of ductility and strength because of the low ductility of the sheets in their as-cryorolled state.

Keywords: Aluminum alloys; cold rolling; cryorolling; hardness; tensile properties

One of the main causes of railway track failure is fatigue fracture, particularly in steel materials. Frequent use can compromise the structural integrity of the tracks, making regular monitoring and daily maintenance essential to ensure safe operation. To support effective maintenance, an efficient system must be established to detect early signs of deformation on the railway tracks. The objective of this study is to develop an understanding of the strainstress behaviour in railway tracks. A Fibre Bragg Grating (FBG) sensor with a Rosette arrangement is used in this investigation to measure strain under both static and dynamic conditions. The Rosette configuration is chosen because it groups two or more FBG sensors together, allowing for the measurement of strain in multiple directions. The FBG sensors are installed 120 metres from the station, specifically at a critical point, to collect strain data. Once the data is obtained, a strain-stress analysis is conducted to assess the strain behaviour of the track. The results show that the principal strain and stress recorded by the sensor mounted at 90° are higher compared to those at 45° and 0°. The average principal strain is 37.3540 µƐ, while the average principal stress is 16.1339 MPa. Implementing an efficient system of this nature can significantly reduce the risk of railway track failure by enabling early prediction and timely intervention.

Keywords: Structural integrity; railway track; FBG Sensors; strain-stress

The advantages of optical fibers that are immune to electromagnetic waves, high sensitivity, resistance to temperature and pressure, and resistant to corrosions, broaden the reach of this field, especially after the pandemic era. One of the sensor designs that can be explored is the tapered optical fiber sensor because of its advantages of being more sensitive, easy to develop, wider applications and having a larger contact surface between the optical fiber and the surrounding conditions. In this study, research on tapered fiber optic sensors through simulation and experiment was done to detect five refractive index readings using different refractive index liquids. The main objective of this study is to analyze the absorption readings of refractive index liquids through simulation and experiment. The simulation of the tapered fiber optic sensor was executed using COMSOL MULTIPHYSICS 6.0 software. An experimental study was made using a Deuterium Tungsten Halogen light source and a FLAME spectrometer. A total of five refractive indices were used namely 1.46, 1.50, 1.54, 1.58 and 1.62. While a total of 6 wavelengths has been selected, namely 365 nm, 406 nm, 473 nm, 532 nm, 589 nm and 632 nm. The results of the sensitivity and limit of detection analysis of tapered optical fiber sensors through simulation and experiment methods exhibited similar pattern. Despite a minor difference in the sensitivity value data, the simulation results can still predict the optical properties of the refractive index liquid because the data difference is too small, which is only between 0.69% to 5.91%.

Keywords: Taper optical fiber; refractive index; spectrometer; sensitivity; COMSOL 6.0

The symmetrized implicit trapezoidal rule (SITR) is a novel finite difference method that involves symmetrization of the calculated numerical solutions using the implicit trapezoidal rule (ITR). By solving differential equations using large space and time steps, compared with conventional methods, the SITR method can generate more accurate results by preventing oscillations in the numerical solutions. In this study, the effect of parameters such as the space step and time step on the accuracy and computational efficiency of the active one-step symmetrized implicit trapezoidal rule (1SITR), the active two-step symmetrized implicit trapezoidal rule (2SITR) and the generalized trapezoidal differencing time-marching (GTDTM) method is investigated in solving aeroacoustics problem involving a linear advection equation. The results of the study show that compared to the conventional GTDTM method, both the enhanced 1SITR and 2SITR methods possess damping characteristics that reduce the oscillatory behaviour of the numerical solution, leading to better solution accuracy at large spatial and temporal step sizes. At smaller temporal step sizes, the increase in the dissipative characteristics of 1SITR and 2SITR reduces their accuracy. The computational time for both 1SITR and 2SITR is greater than that for GTDTM due to the extra symmetrization calculations. This study presents a unique contribution through the development of two symmetrized implicit trapezoidal rule methods which provide improved damping characteristics, allowing for more stable and accurate solutions even under coarse discretization settings.

Keywords: Numerical method; finite difference method; acoustic analysis; symmetrization; implicit trapezoidal rule

Accurate numerical prediction of fatigue crack growth under cyclic loading is crucial for ensuring the safety and structural integrity of engineering structures. However, classical numerical integration schemes, such as the fourth-order Runge–Kutta (RK4) method, offer high accuracy but can fail to produce conservative crack size predictions when larger integration step sizes are used; as a result, they may underestimate crack growth and compromise safety margins. To address this issue, modified RK4 schemes have been proposed to enforce conservatism, but these approaches often introduce additional computational overhead or involve complex correction terms. In this study, a simplified conservative RK4 algorithm is proposed to enhance computational efficiency without sacrificing the conservative bias (overestimation) in crack growth prediction. The method integrates Paris law for fatigue crack growth with a reduced-order correction mechanism applied at each stage of the standard RK4 process. The performance of the proposed scheme is evaluated by analysing convergence behaviour, relative error, and conservative prediction regions across a range of Paris law exponents (m = 2 to 5). Simulation results demonstrate that the proposed scheme consistently yields upper-bound crack growth predictions across various step sizes, thereby providing an effective balance between structural safety and computational efficiency. This approach is particularly well-suited for high-cycle fatigue analysis, where the large number of load cycles demands efficient computation and a high level of prediction reliability.

Keywords: Fatigue crack growth; Paris law; Runge–Kutta method; conservative integration; numerical fatigue analysis

Ozone (O₃) is a significant air contaminant that poses severe health risks, particularly in urban areas. Accurate prediction of the ozone concentration level is crucial for increasing public consciousness and giving important data to governments for public health alerts and air quality management. This study explores the application of machine learning techniques, the K-Nearest Neighbors (KNN) method, for predicting ozone (O₃) concentrations based on meteorological variables collected from three monitoring stations in the Klang Valley region. The research involves data preprocessing procedure that includes handling missing values through imputation and applying the KNN algorithm to predict ozone concentrations. The model was trained and tested using cross-validation and its performance was assessed using evaluation metrics, such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The KNN model achieved the most accurate predictions at Petaling Jaya station with an MAE of 0.00350 and RMSE of 0.00447, followed by Cheras station (MAE: 0.00402, RMSE: 0.00520) and Batu Muda station (MAE: 0.00406, RMSE: 0.00527). These results indicate that the KNN model achieved relatively low prediction error in this study, demonstrating its potential reliability in forecasting ozone concentrations. These findings suggest that the KNN model can be applied to predict ozone concentrations across different urban locations, offering valuable insights for public health planning and supporting strategies for mitigating ozone pollution.

Keywords: Ozone concentration; machine learning; K-Nearest Neighbors (KNN); air quality prediction

Despite the common practice of converting post-closure landfill sites into agricultural areas in the UK, the effectiveness of this approach in supporting species diversity remains unclear. The study provides a long monitoring of butterflies and passerine birds on selected post-closure disposal and landfill sites in England. Results show that the Shannon Diversity Index was relatively higher at many study sites. There are two butterfly species known as the Gatekeeper (Pyronia tithonus) and the Small White (Pieris rapae) and a bird species known as the Carrion crow(Corvus corone)showed significant associations with other species at the sites. Among the priority species, one butterfly species known as the Small Heath (Coenonympha pamphilus) and two bird species known as the Dunnock (Prunella modularis) and Song Thrush (Turdus philomelos) also exhibited significant correlations. These findings suggest that post-closure disposal and landfill sites in England play an important role as habitats for various species, aligning with the current biodiversity and environmental policies. Countries such as Malaysia should explore the potential of such sites to support their native and species with important priority status.

Keywords: Butterfly and bird distribution; lepidoptera; passerines; waste management

The escalating global water quality crisis necessitates sustainable and efficient treatment solutions, particularly for challenging raw water sources. This study investigates the potential of coconut husk, an abundant agricultural byproduct, as a natural coagulant for treating raw water from the Mendana Strait River in Johor, Malaysia. A systematic jar test approach was employed to comprehensively optimize key coagulation parameters, revealing an optimal coagulant dosage of 1.2 g, a contact time of 40 minutes, an agitation speed of 80 rpm, and a remarkably high optimal pH of 11.13. This unique alkaline preference, distinct from conventional coagulants like alum (optimal pH 6–8), suggests novel underlying mechanisms, potentially involving surface modification and the release of active components from the lignocellulosic husk. The optimized process achieved an exceptional turbidity removal efficiency of up to 95.16%, demonstrating coconut husk’s strong potential in clarifying highly turbid water. Furthermore, detailed characterization using Fourier Transform Infrared (FTIR) spectroscopy, Scanning Electron Microscopy (SEM), and Elemental Microanalysis (CHNS) provided crucial insights into the material’s physicochemical changes and its active role in contaminant removal, including nitrogenous compounds. Beyond its technical performance, coconut husk offers significant environmental and economic advantages by minimizing chemical usage, valorizing agro-waste, and reducing treatment costs, making it a highly promising and sustainable solution for water purification, particularly in resource-constrained and rural settings.

Keywords: coagulation; coconut husk; turbidity; dosage; pH

Engineering education is undergoing a paradigm shift in response to Industry 5.0, emphasizing the need for higher-order cognitive competencies beyond mere technical skills. This study investigates the effectiveness of Scaffolding Project-Based Learning (SPjBL), an instructional approach integrating Phased Guidance (PG), Timely Feedback (TF), and Reflective Activities (RA), in enhancing engineering students’ metacognitive regulation—particularly in planning, monitoring, and evaluation. Utilizing a mixed-methods design, this research involved firstyear electrical engineering students at a Malaysian university participating in a 12-week “Mini Home Getaway Electrical Design” project. Quantitative results from the Metacognitive Awareness Inventory (MAI) indicated significant improvements in information management (p < 0.000), evaluation skills (p < 0.015), and procedural knowledge (p < 0.049). Qualitative analysis of 132 reflective entries showed predominantly positive, objective responses highlighting effective knowledge and task management, though students exhibited limited reflective depth in evaluation-related aspects. Among scaffolding strategies, Timely Feedback was particularly effective in supporting real-time monitoring and self-adjustments, while Phased Guidance facilitated early project planning but showed decreased utility in later stages. Reflective Activities demonstrated potential yet required optimization to enhance deeper evaluative reflection. The findings underscore the practical significance of scaffolding interventions in fostering self-regulated learning within engineering education contexts. Future research is recommended to refine reflective activities, possibly extending the intervention duration, to further validate and enhance the long-term efficacy of the SPjBL model for diverse educational environments.

Keywords: Scaffolding Project-Based Learning (SPjBL); metacognitive regulation; reflective activities; selfregulated learning; engineering education

Establishing criteria for rubric development, particularly within the context of engineering education, is not a straightforward task. This challenge is further compounded by the availability of various types of rubrics, as well as the emergence of numerous AI-based tools and applications that can be used to construct rubrics for assessing student competencies. Each of these options has its own advantages and disadvantages, especially in terms of the validity and reliability of the assessment tool which refer to the extent to which the tool accurately measures the intended learning outcomes and reflects what actually occurs in the course. Therefore, the main objective of this study is to inductively develop rubric criteria based on qualitative data collected directly from the implementation of Project-Based Learning (PjBL) in an engineering laboratory setting. The step-by-step approach adopted in this study involves qualitative data collection and validation at each stage of analysis to identify the key criteria that should be included in the student rubric. By the end of the study, a clear process will be demonstrated on how rubric criteria can be developed and qualitatively evaluated based on actual learning experiences within the classroom.

Keywords: Rubrics Assessment; project-based learning; laboratory; engineering education

The Fourth Industrial Revolution (IR4.0) is driving significant technological transformations across industries, requiring engineers to adapt quickly. In Malaysia, policies such as Industry4WRD and the Malaysia Productivity Blueprint aim to facilitate industrial transformation at a macro level. However, their implementation at the micro level, particularly in Small and Medium Enterprises (SMEs), remains limited. Engineers in SMEs often struggle to keep pace with rapid changes due to limited exposure, training opportunities, and financial constraints. This study examines learning agility as a key factor influencing engineers’ adaptability, focusing on four dimensions: people agility, change agility, result agility, and mental agility. A quantitative survey conducted among 207 engineers across Malaysia revealed that 55.56 percent exhibited high learning agility, while 44.44 percent fell into the moderate category. Among the four dimensions, people agility scored the highest, indicating strong teamwork, collaboration, and communication skills. Conversely, mental agility scored the lowest, highlighting challenges in critical thinking, problem-solving, and learning from mistakes. These findings suggest that while Malaysian engineers excel in interpersonal adaptability, they may require additional support in developing cognitive flexibility and analytical skills. To enhance engineers’ adaptability in SMEs, targeted interventions such as specialized training programs, mentorship initiatives, and continuous learning opportunities should be prioritized. Strengthening learning agility, particularly mental agility, will be crucial for ensuring engineers are well-equipped to navigate the complexities of IR4.0. This study provides valuable insights for policymakers, industry leaders, and educators in fostering a resilient and agile engineering workforce in Malaysia.

Keywords: Industrial Revolution 4.0 (IR4.0); learning agility; SMEs

Nature journaling fosters environmental awareness by encouraging structured observation and documentation of natural landscapes. This study examines its role in enhancing environmental sensitivity and spatial awareness among 86 participants, including university students from design backgrounds, school students and youth workers from non-design backgrounds. A mixed-method approach was employed, with surveys conducted among university students and youth workers (46 responses), capturing changes in environmental awareness and their connection to natural landscapes. For all participants, including school students, engagement was assessed through observation and analysis of their journaling pages. Findings indicate that the workshop improved spatial observation skills and ecological literacy, equipping participants with an improved understanding of natural elements and their integration into the built environment. Participants reported heightened mindfulness, appreciation for environmental details, and a calming and engaging learning experience that fostered creative exploration. Statistical analyses revealed a significant increase in participants’ confidence in documenting nature (p=.00007) and their sense of connectedness to the natural environment (p= .005) following the workshop. These insights highlight the potential of nature journaling as an accessible tool for integrating environmental awareness into design and sustainability education. While the study is exploratory, it underscores the need for creative, community-based approaches in built environment education. Future research should explore its application in built environment and sustainable design education to bridge the gap between nature engagement and the built environment.

Keywords: Nature journaling; environmental education; built environment education; spatial observation skills; design and sustainability education

This quasi-experimental study explores the impact of authentic task-based learning on Omani university female students’ willingness to communicate (WTC) in English. The research examines key factors such as self-perceived communication competence (SPCC), communication anxiety, motivation, and the practical applicability of learning experiences. A sequential mixed-methods design was employed, beginning with quantitative data collection via questionnaires completed by 145 students, followed by qualitative interviews with nine selected participants. Results demonstrate that over 66% preferred familiar topics; 79% favoured pair work; WhatsApp (80%) and MS Teams (48.30%) were top communication tools. However, barriers such as unfamiliar topics, fear of making mistakes, and limited language proficiency hinder effective communication. The study highlights a strong correlation between WTC and confidence levels, emphasizing the role of authentic content and assessment in fostering student confidence, increasing practice opportunities, and promoting collaboration. These findings offer pedagogical insights into effective language instruction strategies aimed at enhancing learners’ WTC in EFL settings—crucial for preparing students for academic, professional, and societal participation.

Keywords: Authentic foreign language teaching; communication anxiety; higher education; motivation; self-perceived communication competence

This research explores the characterization and development of a circuit capable of powering low-power electronic devices using thermoelectric generators (TEG). As global interest in renewable energy sources grows, alternatives such as hydroelectric and solar power have gained attention for their environmental benefits and availability. However, the challenge of energy loss due to insufficient heat persists. TEGs are electronic devices that can convert various forms of thermal energy, including waste heat, into electrical energy. Utilizing the Seebeck effect, TEGs generate voltage when there is a temperature difference between their hot and cold plates. The greater the temperature difference between the two sides, the higher the voltage production. Several factors influence TEG performance, including the construction materials, such as Bi₂Te₃, and the surface area available for heating. This research aims to identify the best TEG and its characterization that can increase the temperature difference range to produce higher output voltage. Furthermore, it seeks to determine the most suitable type of TEG and the optimal circuit construction for generating sufficient output voltage to power low-power electronic devices. This includes determining the appropriate number of TEG units, the use of a boost-type DC-DC converter, and the number of filter circuits required to smooth the output voltage. Finally, the research aims to identify the optimal voltage, current, and power required to operate low-power electronic devices, such as fans, buzzers, and LEDs. Experimental results show that three TEG units produce a maximum voltage of 0.927V, a current as high as 71.2mA and a highest power of 50.694mW at a surface temperature of 80°C. The efficient energy conversion and voltage stability observed affirm the viability of TEGs as alternative power sources for small-scale sensing systems and IoT applications.

Keywords: Thermoelectric Generator; boost convertor DC-DC; low electronic device; waste heat; renewable energy

This paper proposes the development of tapered optical fiber for porcine DNA detection based on the evanescent field sensing method. DNA detection is most common method compared with protein detection due to complexity and denaturation of protein. Optical single mode fiber (SMF) of 125 μm diameter is tapered to produce 12 μm waist diameter and 15 mm length. Probe DNA is immobilized onto the bare tapered region and subsequently hybridized by its complementary DNA (cDNA). The experiment starts with dropping an optimum concentration probe (6 μM) and target DNA (4 μM) on the tapered region. The transmission spectra of the DNA-based optical fiber sensor are measured in the 1500 to 1600 nm wavelength range. It is discovered that the shift of the wavelength in the SMF sensor is linearly proportional with the increase in the cDNA concentrations from 1 x10^-15 to 1 x10^-6 M. The linear range of the sensor in the direction of DNA is measured as 1 x10^-10 to 1 x10^-6 M. This sensor has a sensitivity level of 0.224nm/log [cDNA]M with a detection limit (LOD) of 6.16875×,10⁻¹¹. M. From the result obtained, porcine DNA is detected using tapered optical fiber without any surface modification involving chemical reagents is successfully proposed and developed in this project. The probe DNA immobilization and target DNA hybridization without any surface modification from the functionalized process, it will avoid time consuming and tedious process which involved several chemicals reagents. From the result obtained, the proposed tapered optical fiber is the potential for DNA hybridization detection.

Keywords: Tapered optical fiber; Evanescent wave; refractive index; DNA biosensor

The design of vernacular mosques is enriched with passive elements such as numerous windows, wide openings, lattice screens, ventilation blocks, and tiered roof openings. These strategies reflect the initiatives of earlier designers to achieve user comfort through efficient natural ventilation while preserving heritage values and the local context. The Uniform Building By-Laws (UBBL) stipulate a minimum natural ventilation requirement of 10% of the floor area, with at least 5% unobstructed openings. However, this provision is often treated as a basic limit by designers without further exploration of more efficient design potentials. This limitation may result in designs that fall short of delivering optimal comfort for users, particularly in terms of natural ventilation needs. This study evaluates the impact of varying opening percentages on the performance of natural ventilation in mosques, in comparison to the minimum standard set by the UBBL. The research approach includes a literature review, observation, and field study of the Masjid Lama Linggi as a case study. The findings reveal that increasing the opening percentage from 10% to 60%, combined with additional elements such as lattice screens, ventilation blocks, and roof openings, can significantly improve ventilation performance and enhance user comfort. These findings can serve as a guide for contemporary designers in formulating more efficient and sustainable mosque design strategies without compromising traditional vernacular values.

Keywords: Masjid Lama Linggi; vernacular mosque; natural ventilation; opening percentage

Solar energy is radiant energy produced by the sun, which radiates and emits enormous amounts of energy every day. Many technologies are used today to harness the energy of the sun, including photovoltaic system. It has promising potential for building large-scale solar power plants. Solar energy forecasts have a significant impact on decisions related to the operation and management of energy systems. Accurately predicting renewable energy performance is critical in ensuring grid reliability, resilience, reducing energy market and system risks. Deep learning (DL) recent success in many applications has attracted researchers implementing DL in solar energy forecasting tasks. Graph Neural Network (GNN) is one of the DL methods that perform inference on data described by graphs. The proposed work implemented GNN method to forecast the solar energy generation of LargeScale Solar (LSS) farm specifically for LSS Sepang plant. In GNN, the variables in the dataset were described by a self-learning graph structure. Four performance metrics were used to assess the model performance, such as root mean squared error (RMSE), mean squared error (MSE), mean absolute error (MAE) and accuracy. GNN model has demonstrated an effective solar energy generation forecasting model with the lowest value of MAE (537.28), MSE (0.07), RMSE (0.26) and highest accuracy (93.42%), that shows the great potential of the GNN method in forecasting the solar energy generation of LSS farm.

Keywords: Deep learning; graph neural network; large-scale solar plant; solar energy generation forecasting; solar energy

Urban heat islands (UHIs) are localized areas within cities that exhibit significantly higher temperatures than surrounding rural regions, primarily because of anthropogenic activities and urban infrastructure. Understanding thermal comfort (TC) is essential, as it directly influences the well-being and quality of life of urban populations. This study investigates the presence of UHIs in Putrajaya and evaluates the TC levels experienced by its residents. TC is assessed using the Predicted Mean Vote (PMV) and Predicted Percentage of Dissatisfied (PPD) indices, following internationally recognized standards ISO 7730 and ASHRAE 55-1992. Field measurements were conducted with the EK-H4 data logger as well as Thermal Comfort Station (TCS), which records key environmental parameters including air temperature, mean radiant temperature, relative humidity, and air velocity. Data collected were retrieved using EK-H4 V1.0 software and analysed with Microsoft Excel. A psychrometric chart was employed to visualize environmental conditions and identify TC zones. The results indicate that PMV values ranged from -1.0 to +1.4, reflecting considerable variability in thermal conditions across different locations and times. Recorded air temperatures ranged from 28.4°C to 36.9°C, relative humidity varied between 48.1% and 72%, and air velocity ranged from 0 to 2.32 m/s. These findings confirm the presence of localized UHIs in Putrajaya and reveal varying levels of thermal discomfort, highlighting the need for urban planning interventions aimed at mitigating heat stress and enhancing urban liveability.

Keywords: Heat island; thermal comfort; temperature; relative humidity

The growing demand for high-quality metal components has exposed the limitations of conventional manufacturing techniques. Direct Laser Metal Deposition (DLMD), a key metal additive manufacturing method, offers promising capabilities but its reliability is often compromised by dimensional inconsistencies linked to melt pool anomalies. Although prior studies have explored general process optimization, there remains a critical gap in understanding how specific parameters impact geometrical stability. Hence, this study addresses these issues by investigating the effects of process parameters specifically overlap ratio and travel speed under an oscillation scanning strategy to enhance geometrical reliability in DLMD deposits specifically for single layer track. Nine experimental runs were conducted using a Taguchi design to evaluate dimensional accuracy, focusing on absolute deviation from the target deposition height (1.5 mm) and standard deviation of deposition width (3.0 mm). Surface geometry metrics were analysed using signal-to-noise (SN) ratios and ANOVA analyses. Results reveal that travel speed significantly influences both height and width consistency, with optimal values identified at 450 mm/min and 800 mm/min, respectively. Overlap ratio also affects deposition geometry, with 70% producing the most stable outcomes across both metrics. Revised ANOVA confirms the statistical significance of travel speed, while overlap ratio exhibits a weaker but notable effect. Pareto analysis further underscores travel speed as the dominant factor governing melt pool dynamics and thermal input. The findings demonstrate that precise control of travel speed is essential for achieving consistent deposition profiles, while overlap ratio serves as a secondary tuning parameter. These insights support improved process control in DLMD, enabling enhanced dimensional accuracy and repeatability in advanced manufacturing applications.

Keywords: Direct Energy Deposition (DED); Direct Laser Metal Deposition (DLMD); Additive manufacturing(AM); Ti-6Al-4V; parameters optimization

Sound is a vital means of expression and communication. However, individuals with hearing loss face considerable challenges, particularly in emergency situations. During such events, alerts and critical information are typically conveyed through sirens or verbal commands, which are ineffective for those with hearing loss. To overcome this limitation, an attachable or wearable assistive device is necessary. The proposed wearable system in this work consists of (i) microphone arrays embedded in a headband, and (ii) a wristband with a motor vibrator and display attached to it. The neural network algorithm, specifically the multi-layer perceptron model, is used for selective emergency sound (dog bark, car honk and siren) recognition and classification. Once an emergency sound is detected by the microphone arrays and classified by the algorithm, a vibration signal is transmitted to the motor, while the direction of the sound is simultaneously indicated on the wristband display. The trained model and real-time implementation achieved an accuracy of 90% and 77%, respectively.

Keywords: Machine learning; neural network; assistive technology; emegency sound; hearing loss

Inadequate or faulty test equipment and machinery in engineering laboratories pose significant safety risks and hinder both teaching and learning experiences. This compromises compliance with educational standards and quality assurance. As technological advancements continue to reshape laboratory management, there is a growing demand for increased transparency and trust from both regulators and stakeholders. An IoTenabled RFID-based system can significantly improve compliance management in engineering labs, moving away from error-prone manual documentation processes. This study presents an automated compliance management system driven by RFID technology, designed to track and monitor three key compliance processes: maintenance, calibration, and asset status (e.g., outdated equipment). The system operates at frequencies between 902 MHz and features an integrated graphical user interface (GUI) that displays the real-time compliance status of equipment. Compliance is visually indicated using a color-coded system: green for compliant, yellow for near non-compliance, and red for non-compliance. The system also generates diagnostic reports for enhanced tracking and recordkeeping. Additionally, the system sends email notifications to supervisors when equipment approaches non-compliance thresholds, ensuring timely corrective actions. For example, as the compliance status approaches 180 days since the last service, the system shifts from green to yellow, signaling a minor issue. When the status reaches 90 days, the indicator turns red, marking a critical non-compliance. This proactive approach to compliance management, powered by RFID and IoT technologies, offers significant potential for improving operational efficiency and supporting long-term strategic planning in engineering laboratories.

Keywords: Engineering laboratory; process management; IoT enabled RFID technology; graphical user interface; compliance status

Air conditioning systems release significant waste heat that can reach up to 90°C during operation presenting an opportunity for energy recovery. Thermoelectric generators (TEGs), leveraging the Seebeck effect potentially to convert the heat into electricity with efficiency enhanced by maintaining a large temperature gradient. This research explores the potential of generating electricity from the waste heat of air-conditioners, addressing the growing demand for sustainable energy solutions. Utilizing thermoelectric generators (TEGs), the study evaluates the feasibility of converting waste heat into electricity using Seebeck effect. An experimental setup was developed to measure waste heat output, with simulations validating the concept. Two prototypes were constructed which is aircooled model and water-cooled model and equipped with commercial Bi₂Te₃-based TEG modules. The water-cooled prototype based on water pump circulation demonstrated superior performance, generating a net power output of 1.45 W, compared to 0.05 W for the air-cooled prototyped that made of DC fan and supplied with the exhaust air temperature ranges 85 – 90° C on the hot side and maintained at 20 – 23° C on the cold side. The net power generation was calculated by accounting for the energy consumed by the cooling systems. Scaling the water-cooled design to the dimensions of a typical household air-conditioner resulted in an estimated efficiency improvement of 2.8%, significantly reducing the electricity consumption and promoting environmental sustainability. This study highlights the potential of integrating waste heat recovery into air-conditioning systems, offering a promising pathway toward energy-efficient and eco-friendly solutions.

Keywords: Thermoelectric generator; seebeck effect; waste heat recovery system; air conditioning unit

Indoor fire incidents pose a significant threat to both life and property, particularly in areas that are not regularly monitored or are isolated. Traditional fire detection systems, which typically rely on smoke, temperature, or gas sensors, tend to be passive, prone to false alarms, and incapable of providing intelligent, real-time alerts to users in remote locations. This paper addresses these challenges by introducing the development of an autonomous Blazebot for fire detection and alert systems using GSM technology. The proposed system integrates a lightweight YOLOv8n (You Only Look Once version 8 nano) deep learning model, deployed on a Raspberry Pi 4, to continuously recognize flames visually through a USB camera. Once a flame is confirmed, the system sends an alert signal to an Arduino Mega 2560, which then activates a SIM900 GSM module to send SMS notifications to designated recipients. The system successfully identified flame sources with a minimum size of 2500 pixels at distances up to 350 cm, achieving optimal accuracy between 200 and 250 cm. The average delay for SMS transmission was recorded at 10.01 seconds after detection. These findings demonstrate the viability of a costeffective, real-time, vision-based fire detection and communication system suitable for settings without internet access or constant human oversight.

Keywords: Fire-fighting robot; SMS alert; YOLOv8n; real-time vision; hazard response; fire detection