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Rising carbon emissions from cement production have drawn attention to the need for sustainable alternatives, as cement is a widely used building material. This has led to increased research into cement replacements, which can enhance the mechanical properties of mortar composites. In Malaysia, the growing palm oil industry has generated abundant palm waste, such as palm kernel shells and empty fruit bunches, making them viable candidates for biochar production. This study used response surface methodology to optimize the production of biochar from empty fruit bunches for potential use as a cement replacement. The pyrolysis parameters investigated included temperature, heating rate, and residence time, with responses measured in terms of biochar yield and chemical composition (carbon, oxygen, potassium, and silicon content). The optimal parameters were 455 °C, 15 °C/min, and 20 minutes, resulting in a biochar yield of 37%, along with 71.98% carbon, 23.40% oxygen, 0.61% potassium, and 3.68% silicon. These results were compared to biochar produced from palm kernel shells under similar conditions, underscoring the importance of selecting appropriate biomass for effective cement replacement. Ultimately, this research supports waste management in the palm oil industry and promotes sustainable construction practices.

Several factors may influence the exposure of nursery workers to pesticides during their work. This study investigates the influence of crop type on potential exposure and associated health risks among ten nursery workers from ten different nurseries in Johor, Malaysia. Personal interviews and questionnaire surveys were used to collect various information from the selected nursery workers in August and September 2023, together with the collection of pesticide labels. The collected data were input parameters in the appropriate exposure model to quantify their daily exposure and then assessed against the regulatory limit. Overall, individuals treated 1-13 different plant species on 2-9 spray days with 1-4 products containing 1-3 active substances. Ornamental nursery workers had the highest estimated daily exposures (30 applications; mean: 0.05 mg kg-bw-1 day-1), followed by flowers (11 applications; mean: 0.02 mg kg-bw-1 day-1) and fruits (6 applications; mean: 0.01 mg kg-bw-1 day-1). Thirty applications exceeded the respective regulatory limits (ornamentals>flowers>fruits: 20>9>1), indicating a possible health risk. While the use of highly hazardous active substances and inappropriate protective measures were the significant risk factors, pesticide applications were primarily influenced by the crop type, which requires crop species-specific assessment among nursery workers.

Climate is changing at an unprecedented rate, and the increasing demands of the growing population will place further pressures on the climate and environment. Water infrastructures such as water treatment plants often face increased stress during extreme weather events such as heavy rainfall or prolonged drought, disrupting societal needs and prompt urgent upgrades to adapt to the changing climate. The use of climate model projections is increasingly adopted in engineering practices for assessing climate risks and impacts to inform adaptive design and strategy of new infrastructures or upgrades. Using a bias-corrected ensemble member of the fifth phase of the Coupled Model Intercomparison Project (CMIP5) tailored for applications in the water sector, we assessed the variability of projected rainfall from 2021 to 2050 under the moderate warming scenario (RCP 4.5). The findings from this study aim to inform adaptive strategies for upgrading 22 water treatment plants across 18 sites in Sabah, Malaysia, where these plants have experienced numerous disruptions due to high turbidity resulting from heavy rainfall in recent years. The average annual rainfall amount ranges from 1630 mm to 4415 mm, with average total monthly rainfall peaks (>460 mm) in June and August at most sites. The observed shift in June and August is a deviation from historical patterns and is also reflected in the maximum consecutive 1- to 5-day rainfall, ranging from 22 mm to 760 mm across all sites, particularly in the interior and west coast districts. This highlights the need for resilient infrastructure upgrade strategy and adaptive water resource management, such as the introduction of off-site river storage to accommodate the anticipated future rainfall.

Desiccation cracks in soils are important in engineering structures that need hydraulic integrity such as earth dams, and containment facilities. At the element test level, such cracks are a hindrance when the volume change of the soil specimen needs to be measured during the test. One such test is the volumetric shrinkage test where the volume of the soil specimen needs to be continuously determined together with its mass change as the soil specimen dries. The soil specimen may deform non-uniformly. Previously hazardous and cumbersome methods using mercury or wax are needed to determine the volume of soil specimens that deform non-uniformly, but the advent of 3D scanning and photogrammetry enables the volume of non-uniform soil specimens to be measured quite accurately. However, such techniques cannot accurately determine the volume of the deformed soil specimen once cracks start appearing in the soil specimen. Volumetric shrinkage tests are commonly conducted for cylindrical soil specimens. This paper investigates the geometry (shapes and dimensions) of soil specimens other than cylindrical on the formation of desiccation cracks. Bentonite is used as the test soil as it experiences large volume change on drying. The test results provide guidance for the geometry of a soil specimen to be used in the volumetric shrinkage test.

Constructing ring roads is a traditional approach to alleviating traffic congestion in urban areas and enhancing road capacity. However, this solution is frequently inefficient in the long run. This study investigates different strategies for managing traffic congestion in urban areas and evaluates their efficacy. The text examines three situations where various methods were employed to address traffic congestion issues. These strategies are categorized into three distinct categories. The initial concept encompasses substantial engineering interventions, such as the establishment of ring roads, the creation of new roadways, and the enlargement of preexisting roads. Many cities are implementing push-and-pull strategies to optimize existing capacities and promote environmentally friendly transportation alternatives, thereby reducing car usage. These strategies include promoting public transportation, cycling, and walking, as well as modifying urban land use patterns to minimize the need for people to travel. These methods are successful in delivering long-lasting transportation solutions. Nevertheless, more than push-pull strategies are needed to adequately address traffic congestion, necessitating extensive engineering interventions to expand road capacity, particularly in cases of severe traffic in urban areas. The integration of these methods is categorized as the third principle. The utilization of ring roads also gives rise to numerous novel challenges, particularly in metropolitan areas. The predominant issue lies in the unregulated expansion of houses (urban sprawl) and inadequate public transportation. The problems identified of significant magnitude include Interconnectivity between different regions, the point where ring roads intersect, accessibility to housing, settlement amenities, and social challenges. The study determined that the third concept to tackle traffic congestion could be the most efficient approach for constructing ring roads in urban areas. To address the difficulties involved in the construction and development of circular roads, it would be beneficial to establish an organization that facilitates coordination among all stakeholders involved in the development of ring roads right from the outset. Urban Planning Directorates at the governorate level are responsible for carrying out this work in urban cities. In addition, rigorous enforcement of laws is also crucial for achieving desired goal.

Drone and the Internet of Things are different technologies, actively utilised and implemented across various fields. Connectivity is the difference between the IoT drone and the regular drone. IoT drones can receive and transmit data and command signals in real-time while being controlled through the IoT network. By implementing both drone and IoT, the flying IoT (IoT-Drone) is built as a new form of IoT device. This work improves how to use the drone effectively in many aspects; data transferring, data collecting, real-time monitoring, and control. With the emergence of the Internet of Things, Artificial Intelligence, and advanced communication, the IoT drone technology has been utilized in many fields, especially in agriculture and inspection, and discussed in smart cities, logistic delivery, and the military. This paper reviews the IoT drone implementation in those fields and reviews the researchers’ discussion and studies on IoT drone optimization.

AbstractLittle is known about the use or misuse of cleaning products during the COVID-19 pandemic. We compiled data from January to June in 2019 and 2020 from Canadian poison centres, and report on calls regarding selected cleaning products and present year-overyear percentage change. There were 3408 (42%) calls related to bleaches; 2015 (25%) to hand sanitizers; 1667 (21%) to disinfectants; 949 (12%) to chlorine gas; and 148 (2%) to chloramine gas. An increase in calls occurred in conjunction with the onset of COVID-19, with the largest increase occurring in March. Timely access to Canadian poison centre data facilitated early communication of safety messaging for dissemination to the public.

RésuméOn sait peu de choses sur l’utilisation, correcte ou incorrecte, des produits de nettoyage pendant la pandémie de COVID-19. Nous avons compilé des données provenant de centres antipoison canadiens pour janvier à juin 2019 et janvier à juin 2020 et nous rendons compte ici des appels relatifs à certains produits de nettoyage et de l’évolution en pourcentages entre ces deux périodes. Il y a eu 3 408 appels (42 %) portant sur des agents de blanchiment, 2015 (25 %) sur des désinfectants pour les mains, 1667 (21 %) sur des désinfectants, 949 (12 %) sur le chlore gazeux et 148 (2 %) sur la chloramine gazeuse. On a observé une augmentation du nombre d’appels en concomitance avec l’apparition de la COVID-19, avec un pic en mars. L’accès rapide aux données des centres antipoison canadiens a permis une communication précoce de messages de sécurité au public.

It is recommended that violence prevention interventions start early for students and include conflict resolution education and social-emotional skills training components. Although school-based programs have shown some promise, community-based or out-of-school time programs require more study. A social-emotional learning-focused conflict resolution intervention using role-play and puppetry was implemented in a small afterschool program as an exploratory study. Student participants’ conflict resolution knowledge and after-school teacher observation of their pro-social skill behaviors were assessed pre- and post- program. Although many participants scored high in conflict resolution knowledge pre-program, they appeared to gain some additional knowledge, specifically on disagreements between friends and empathy for other’s feelings. After-school teachers, however, observed no significant overall differences in their pro-social behaviors pre- and post- program. All in all, as an exploratory study, the slight positive changes in knowledge provide data to suggest continuing the curriculum with more emphasis on the weakest topics as well as more role-play or puppet play about friendship and sharing behaviors.

Arisaema filiforme and A. brichangense are a perennial herbaceous plant (family Araceae) found distributed in mossy forest, Cameron Highlands, with the elevation of 1,900 meters above sea level (a.s.l). The unique inflorescence formation resembling cobra has given this plant the name Cobra lilies, and suitable to be planted as ornamental plant. In addition, it has been used traditionally as a herb. However, the population of these two species are very limited, only thrive in higher elevation and also considered as an endangered. Therefore, realizing its potential in the future as one of the new ornamental plant and materials for the herb bioindustry, a micropropagation approach was employed to produce these species in mass production. Seeds, rhizomes, and petioles were used as the explant materials, cultured onto Murashige and Skoog (MS) media supplemented with different concentrations (0, 0.5, 1, 2 mg L-1) of 6-Benzylaminopurine (BAP). The findings revealed rhizomes and seeds to be significant explants for micropropagation, where the survival rate for these two are more than 80%. Petioles had 0% of survivability after week eight of culture due to the fungi infection and tissue necrosis. This study provides an insight into explant selection, where different plant organs have different survival rate due to the tissue mechanical strength. Also, optimum surface sterilization process is very critical in micropropagation to avoid the contamination of the culture and also necrotizing.

Royal Belum-Temenggor Rainforest Complex (RBTRC) is renowned as one of the world’s oldest tropical rainforests and biodiversity hotspots. Its extensive forest coverage serves as a crucial carbon sink that stock more than 185 Mg ha-1 carbon, thereby playing a crucial role in regulating Earth’s temperature. Despite its significance as a major carbon sink and biodiversity ecosystem, studies on RBTRC have been limited primarily to biological and ecological studies. Baseline climate studies for this region are non-existent to-date. Baseline climate assessment is crucial for assessments of future climate change impact in this region. This study fills this critical research gap by investigating the climatological trends of annual-averaged temperature, surface pressure, specific humidity, wind speed and rainfall in RBTRC from 1991 to 2020 using the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) reanalysis dataset. All atmospheric variables exhibit a normal distribution, and increasing trends are observed for all variables except for windspeed. Pearson’s correlations indicate positive correlation between pressure and temperature (0.5), specific humidity and rainfall (0.45), and negative correlation in pressure and rainfall (-0.59), specific humidity and windspeed (-0.55), and temperature and windspeed (-0.42). This complements the fundamental physical laws, indicating the reliability of the MERRA-2 reanalysis datasets for further climatological evaluation.

Entrepreneurship has been considered one of the major universal drivers of economic development, and sustainable development while also contributing to the growth of a country’s economy. This study explored the perspectives of business entrepreneurs on the contributions of entrepreneurship to economic development in Liberia. A qualitative research approach while an in-depth interview guided by a semi-structured interview was conducted among Liberian entrepreneurs to explore whether entrepreneurship contributes to a country’s economic development based on their views, opinions, and experiences. The findings of the study show that the lack of proper incentives, government support, infrastructure, cost of utilities, entrepreneurship awareness, and lack of entrepreneurship education in schools and universities curriculum is considered to be one of the core factors that is hindering the promotion of entrepreneurship in Liberia. As a result, the study recommends the Liberian government's need to implement policies that will enhance the development of entrepreneurship, provide basic infrastructure, and introduce entrepreneurship education in secondary and tertiary institutions as a core module. This study contributed to the entrepreneurship literature and confirmed that entrepreneurship has played a key role in providing solutions for Liberia’s economy while generating a greater number of employment and enhancing society's living standards.

Internship is critical as it allows hands-on experience for what they have learned in classroom in real settings. It meant to test their career interests and acts as a personal development opportunity. However, little is known about the work environment they were in, as well as their internship experience had on their personal development. In this paper, we present a descriptive analysis from a survey where the responses were collected from 104 students who had underwent a 16 weeks paid internship. The purpose is to assess the work environment factors especially the amount work stress and workplace bullying they have had experience. Later we conducted an inferential analysis to determine the effect of work stress and workplace bullying have on their personal development. The outcomes from this study have the potential to bridge the expectation-reality gap on the career that they will be joining, to strengthen the bonds between university-industry and to fill the internship literature gap on personal development.

The effect of matric suction on S-wave velocity (Vs) of unsaturated soils has been observed to show different trends from the Vs relationship with effective confining pressure. In some soils, the increase in S-wave velocity per unit stress change in matric suction (ua-uw) is greater than the increase in S-wave velocity per unit stress change in effective confining pressure (σ’3) for saturated and dry soils and vice versa for other soils. In this study, the difference was examined through the stress state variables for unsaturated soils on similar soil samples. Experiments were conducted on sand and kaolin specimens using a triaxial cell modified to include bender elements and a high-air-entry disk for the testing of unsaturated soils. The soil specimens were first fully saturated and Vs at various effective confining pressures were measured. The tests were repeated with similar soil specimens subjected to net confining pressure and matric suction for similar stress range as the previous tests. Data was also collated from the literature. Comparisons were made by plotting on two-dimensional and three-dimensional plots to understand the differences. It was found that soil type and soil fabric play important role in the differences. Soil fabric plays a more important role for compacted soils where soil fabric is affected by the compaction water content.

Constant suction direct shear test enables the understanding of the failure mechanism in rainfallinduced landslides. It can be conducted using a conventional direct shear apparatus with some modifications. The constant suction direct shear test is carried out in two stages. In the first stage, the unsaturated soil specimen is consolidated to the target net normal stress and matric suction then sheared in the second stage. Matric suction is usually controlled using the axis-translation principle. It is commonly observed that the shear stress of an unsaturated soil sheared in the direct shear shows a strain-hardening behaviour at large displacements making the determination of the failure stress difficult. Hence, the objective of this study is to critically examine the constant suction direct shear tests and the analysis of the test results to obtain the shear strength parameters for unsaturated soils. Constant suction direct shear test data were collated from the literature. It was found that the interpretation of the direct shear test has two inconsistencies: (1) taking failure shear stress at arbitrary displacement strain or limit, dependent on the size of the direct shear apparatus, and (2) correcting only shear stress for contact area. The effect of these two consequences on the interpretation of the direct shear test range from negligible to significant. The study shows that arbitrary determination of failure shear stress can be resolved by plotting the direct shear test results using a stresspath plot. The effects of area correction are shown to be almost negligible for small horizontal displacements of less than 2 mm for both square and circular shear boxes. A more consistent interpretation of the constant suction direct shear test is demonstrated where both these inconsistencies are considered.

Bentonite is commonly used in geosynthetic clay liner (GCL) as well as compacted clay liner (CCL) for containment facilities such as sanitary landfills due to its low permeability. Bentonite experiences significant shrinkage on drying, leading to the formation of desiccation cracks and increasing the likelihood of contaminant leachate from the landfill. Adding granular materials such as fine sand to bentonite canreduce shrinkage and hence shrinkage cracks. Recently, zeolite has been suggested as another possible additive to reduce be shrinkage cracks in bentonite. Zeolite has additional benefits of being able to adsorb heavy metals and when enriched with silver, zinc, or copper, it can function as a biocide. The objective of this paper is to present the results of a study on shrinkage crack formation of bentonite-zeolite mixtures during drying. The evolution of shrinkage cracks of the bentonite-zeolite mixtures on drying was studiedusing image processing. The results show that desiccation cracks are at a minimum when the zeolite content is between 15% and 20%. The finding is useful to design more efficient GCL or CCL when zeolite is incorporated into the bentonite layer.

In a gedankenexperiment, we pose the philosophical question as to whether human breast cancer cells or tissues can still be considered human after transplantation into another species. Alongside medical research images illustrating xenotransplantation, we provide descriptions of how tissues were prepared for imaging. In addition, we discuss how such models enable further understanding of cancer and provide invaluable tools for testing new therapies.

Tensegrity structures are spatial structures made up of an assembly of pin-jointed, prestressed continuous tension members and discontinuous compressive members, arranged in different complex shapes. Limited research has been carried out to analyse the dynamic response of these structures under seismic loading. This paper presents a parametric study to identify the suitability of different forms of these structures under combined vertical gravity and lateral seismic loadings. Different tensegrity shapes were analysed, as simplex and complex structures, under wind and earthquake loadings. These responses of the structures were also compared to that of conventional steel structures of the same shapes. Different parameters such as the effect of varying bar diameter, bar thickness, cable diameter, and tower height, were studied, and behavioural trends were identified. It was found that if a suitable tensegrity shape is chosen and optimized, its behavioural trends can be very similar to that of the conventional steel structures, while saving materials and therefore cost. Modal frequencies of the structure were also identified and found to be low, which is beneficial under earthquake loading. Thus, these systems can prove to be a valuable replacement to conventional systems under adverse dynamic loading.

Lightweight foamed concrete (LFC) is normally used on the exterior of buildings such as walls and roof slabs, which causes it to be often exposed to natural weathering such as rain. Since water is an agent of deterioration towards the durability of LFC in long term, calcium stearate, is introduced into LFC. Calcium stearate (CS) is a type of water repellent agent that reduces the penetration of water into LFC. This research aims to investigate the effect of incorporating CS into the LFC mix in terms of its strength performance. Four types of LFC containing different percentages of CS ranging from 0 % to 0.6 % of cement weight were cast and water cured for 7, 28 and 56 days before being tested for its strength properties. It was found that adding CS into the LFC does not affect the workability of fresh concrete. The finding also showed that CS only retards the strength development rate of LFC during the early ages of LFC instead of reducing its overall strength. Under continuous curing, LFC with CS can achieve strength similar to the strength obtained by the control mix. If the early strength of LFC is not a major concern, incorporating CS into LFC will have an added advantage of lower water absorption which improves the durability of LFC.

In recent years, the manufacturing sector has been pressured with global warming and resource scarcity. This issue has triggered the industry to seek for solutions to improve the sustainability of their production. Based on literature study, the main components of an organisation consists of manpower, machine, money, material and environment. Thus, the fundamentals need to be addressed to improve the production performance. In this study, an adaptive lean and green approach is presented to identify the priority areas that can improve the organisation performance. Backpropagation algorithm is incorporated into the adaptive model to analyse the dynamic performance of the organisation. However, the input of industry expert is required to prioritise the initial input of the main components. This is relatively important as prioritisation of main components defer from sectors. A case study will be illustrated with the adaptive lean and green model. Operation improvements shall be observed through the implementation of the proposed method.

Wireless sensor networks are solely dependent on battery limits the operation as well as maintenance effort. Vibroelectric nano generator able to prolong the operation by converting ambient vibration and power the equipment. The working principles includes Piezoelectric, Electromagnetic, Triboelectric, Magnetostrictive and Flexoelectric. This paper discussed the principle operations and key function materials, the advantages and disadvantages are specified. Hybrid incorporated with solar, thermal or radio frequency is considered as current trend of nano generator with further improvement as future work.

Blade fault diagnosis had become more significant and impactful for rotating machinery operators in the industry. Many works had been carried out using different signal processing techniques and artificial intelligence approaches for blade fault diagnosis. Frequency and wavelet based features are usually used as the input to the artificial neural network for blade fault diagnosis. However, the application of others time-frequency based feature extraction technique and artificial intelligence approach for blade fault diagnosis is still lacking. In this study, a novel blade fault diagnosis method based on ensemble empirical mode decomposition and extreme learning machine was developed. Bandpass filtering was applied to the raw vibration signals and integrated with the high pass filter to obtain the velocity signal. Synchronous time averaging was then applied to the velocity signals. Three ensemble empirical mode decomposition based feature extraction methods were proposed: direct statistical parameters extraction, intrinsic mode functions averaging statistical parameters extraction and features averaging statistical parameters extraction. The effectiveness of different feature vector sets for blade fault diagnosis was examined. Feature vector set of intrinsic mode functions averaging statistical parameters extraction was found to be more effective for blade fault diagnosis. With the novel proposed method, blade fault diagnosis could be more accurate and precise.

Extreme learning machine (ELM) is a fast and quick learning algorithm with better generalization performance. However, the randomness of input weight and hidden layer bias may affect the overall performance of ELM. This paper proposed a new approach to determine the optimized values of input weight and hidden layer bias for ELM using whale optimization algorithm (WOA), which we call WOA-ELM. An online gearbox vibration signals is used in this study. Empirical mode decomposition (EMD) and complementary mode decomposition (CEEMD) are used to decompose the signals into sub-signals known as intrinsic mode functions (IMFs). Then, statistical features are extracted from selected IMFs. WOA-ELM is used for classification of healthy and faulty condition of gearbox. The result shows that WOA-ELM provide better classification result as compared with conventional ELM. Therefore, this study provide a new diagnosis approach for gearbox application.

Recent developments in the field of modal-based damage detection and vibration-based monitoring have led to a renewed interest in automated procedures for the operational modal analysis (OMA). The development of automated operational modal analysis (OMA) procedures marked a fundamental step towards the elimination of any user intervention since traditional modal identification requires a lot of interaction by an expert user. A key for effective automation of OMA is depended on well- defined modal indicators for a clear indication about which modes are to be selected as the physical modes. In all modal analysis, the construction of stabilization diagrams is necessary in order to illustrate, and decide, if a mode is physical or not for predefined range of the model order. On the other hand, the use of stabilization diagram tools involves a large amount of user interaction, costly, time-consuming process and certainly unsuited for online applications. Therefore, the development of automatic procedures for the analysis of stabilization diagrams by resembling decision-making process of a human has been carried out in recent years. For the sake of clearness, the automation of the interpretation of stabilization diagrams can generally be divided into two steps in order to speed up the process: a) elimination of noise modes and b) clustering of physical modes in order to obtain the most representative values of the estimated parameters of each clustered mode. In recent years, several alternative procedures have been proposed for clustering techniques. Therefore, this review aims to provide relevant essential information on the recent developments of cluster analysis in automated OMA. A literature review of existing clustering algorithm has been carried out to find best practice criteria for automated modal parameter identification which involving the general concepts of these techniques as well as the pro and cons of applying these clustering techniques are also discussed and summarised.

Bentonite is one of the most commonly used materials in geotechnical engineering applications especially for isolation purposes due its low permeability and its highly expansive nature. For instance, Geosynthetic Clay Liners (GCL) self-healing ability relies on the swelling properties of the bentonite to close any holes in the GCL while the low permeability of the bentonite serves to reduce the infiltration of leachate to the surrounding soil. However, different types of bentonite have different shrinkage or swelling properties and hence affects the effectiveness of the GCL. A method to assess the wetting/drying induced volume change of the bentonite is through its shrinkage curve. This paper focuses on the shrinkage behaviour of reconstituted granular and powder bentonites which are used in GCL. Photogrammetry method is used to measure the volume change of specimen during drying. The shrinkage curves of the bentonites are then compared with other bentonites from the literature.

Rolling element bearing (REB) is a well-known component that most extensively used in the industry. They operate in extreme condition (high temperature, dirty environment) which may lead to unexpected failure after the certain operation. Faulty on bearing cause severe equipment damage, financial loss and threaten people's life. Development of proper fault diagnosis system of REB capable of preventing unexpected failure from occurs and maintain the machine work in the healthy state. Over a few decades, machine learning is introduced to provide a consistent fault diagnosis result. Hence, this paper reviewed the development of bearing diagnosis method using machine learning models.

Reciprocating compressor is one of the most popular classes of machines use with wide applications in the industry. However, valve failures in this machine often results unplanned shutdown. Therefore, the effective valve fault detection technique is very necessary to ensure safe operation and to reduce the unplanned shutdown. This paper propose an artificial intelligence (AI) model to detect valve condition in reciprocating compressor based on acoustic emission (AE) parameters measurement and artificial neural network (ANN). A set of experiments were conducted on an industrial reciprocating air compressor with several operational conditions including good valve and faulty valve to acquire AE signal. A fault detection model was then developed from the combination of healthy-faulty data using ANN tool box available in MATLAB. The results of the model validation demonstrated accuracy of valves condition classification exceeding 97%. Eventually, the authors intend to do more efforts for programming this model in smart portable device which can be one of the innovative engineering technologies in the field of machinery condition monitoring in the near future.

Signal processing method is very important in most diagnosis approach for rotating machinery due to non-linearity, non-stationary and noise signals. Recently, a new adaptive signal decomposition method has been proposed by Dragomiretskiy and Zosso known as variational mode decomposition (VMD). The VMD method has merit in solving mode mixing problem in most conventional signal decomposition method. This paper aims to review the applications of the VMD method in rotating machinery diagnosis. The advantages and limitations of the VMD method are discussed. Current solution on VMD limitation also have been review and discussed. Lastly, the future research suggestion has been pointed out in order to enhance the performance of the VMD method on rotating machinery diagnosis.

This article aims to provide a comprehensive review on the condition monitoring techniques of underground storage tanks (UST). Generally, the UST has long been a favourite toxic substance reservation apparatus, thanks to its large capacity and minimum floor space requirement. Recently, attention has been drawn to the safety risks of the complex cylindrical-shaped system and its surrounding environment due to contamination resulting from unwanted subsurface leakage. Studies on related countermeasures shows that numerous efforts have been focused on the damage remediation process and fault detection practice; however, it has also been observed that there are uncertainties in present technical complications involving the effectiveness of corrective actions and the robustness of condition monitoring techniques. As an alternative means to deliver spatial information on structural integrity, the feasibility of integrating non- destructive evaluation (NDE) techniques with machine learning algorithms, on observing the degradation process of UST, so as to enhance condition monitoring competency, is discussed.

Industrial practise typically applies pre-set original equipment manufacturers (OEMs) limits to turbomachinery online condition monitoring. However, aforementioned technique which considers sensor readings within range as normal state often get overlooked in the developments of degradation process. Thus, turbomachinery application in dire need of a responsive monitoring analysis in order to avoid machine breakdown before leading to a more disastrous event. A feasible machine learning algorithm consists of k-means and Gaussian Mixture Model (GMM) is proposed to observe the existence of signal trend or anomaly over machine active period. The aim of the unsupervised k-means is to determine the number of clusters, k according to the total trend detected from the processed dataset. Next, the designated k is input into the supervised GMM algorithm to initialize the number of components. Experiment results showed that the k-means-GMM model set up not only capable of statistically define machine state conditions, but also yield a time-dependent clustering image in reflecting degradation severity, as a mean to achieve predictive maintenance.

Monitoring pipeline wall is an important issue in oil and gas industries. Over time, the defect can occur in the pipeline and can impact surrounding population, environment and may result in injuries or fatalities. While flaws in the pipeline could be detected by ultrasonic testing and monitoring the severity of the flaw. The limitation of ultrasonic testing is the signal contaminate with backscattering noise, which masks flaw echoes in the measured signal. Signal processing take place in the recent year to de-noising for improving signal-to-noise ratio and extract the feature for flaws classification. This paper presents a comprehensive overview of signal processing techniques used to improve ultrasonic detection method with and without intelligent classifier. Finally, the advantages and disadvantages feature extraction provided for classifications process.

Intelligent machinery fault diagnosis commonly utilises statistical features of sensor signals as the inputs for its machine learning algorithm. Due to the abundance of statistical features that can be extracted from raw signals and the accuracy of inserting all the available features into the machine learning algorithm for machinery fault classification, less accurate fault classification may inadvertently result due to overfitting issues. It is therefore only by selecting the most representative features that overfitting outcomes can be avoided and classification accuracy be improved. Currently, the genetic algorithm (GA) is regarded as the most commonly used and reliable feature selection tool for the improvement of accuracy for any machine learning algorithm. However, the greatest challenge for GA is that it may fall into a local optima and be computationally demanding. To overcome this limitation, a feature selection tree (FST) is here proposed. Numerous experimental dataset feature selections were executed using FST and GA; their performance is compared and discussed. Analysis showed that the proposed FST resulted in identical or superior optimal feature subsets when compared to the renowned GA method, but with a 20-time faster simulation period. The proposed FST is therefore more efficient in performing feature selection task than GA.

This paper studies the diagnosis of twisted blade in a multi stages rotor system using adapted wavelet transform and casing vibration. The common detection method (FFT) is effective only if sever blade faults occurred while the minor faults usually remain undetected. Wavelet analysis as alternative technique is still unable to fulfill the fault detection and diagnosis accurately due to its inadequate time-frequency resolution. In this paper, wavelet is adapted and its time-frequency is improved. Experimental study was undertaken to simulate multi stages rotor system. Results showed that the adapted wavelet analysis is effective in twisted blade diagnosis compared to the conventional one.

Compacted soils constitute most engineering projects such as earth dams, embankments, pavements, and engineered slopes because of their high shear strength and low compressibility. The shear strength of compacted soils is a key soil parameter in the design of earth structures but it is seldom determined correctly due to their unsaturated state. The shear strength of compacted soils can be better evaluated under the framework of unsaturated soil mechanics. Saturated and unsaturated tests were conducted on compacted specimens using conventional direct shear apparatus under constant water content condition. Tests were conducted at different water contents and net normal stresses. The main objective of this study is to develop a shear strength model for compacted soils. Initial matric suction was measured before the test using the filter paper method. The two-stress state variables together with the extended Mohr-Coulomb failure criterion for unsaturated soils were used to obtain a lower bound model of the shear strength. The model was demonstrated using published data.

Biodiesel is one of the effective alternative fuels to overcome the problems associated with environmental issues and energy crisis. However, the production of biodiesel from edible oil has provoked the food versus fuel dispute. Thus, a non-edible crop, Annona muricata, is selected as the raw material to produce oil for biodiesel production. In this study, A. muricata oil was extracted with n-hexane at 70 °C for 6 h. Subsequently, the oil was transesterified with methanol and potassium hydroxide (KOH) to produce biodiesel. The significance of transesterification parameters including temperature, time, catalyst concentration, and oil-to-methanol ratio on biodiesel yield (%) was established through two-level factorial design. The factorial design shows that all parameters are significant. Besides, the high content of monounsaturated fatty acid (oleic acid) in the resultant biodiesel suggests that A. muricata biodiesel could display good fuel quality. This hypothesis is proven by the excellent calorific value (39.21 MJ kg-1).

The present paper deals with the novel approach of filtering technique using hybrid of empirical mode decomposition technique with stabilization diagram, that autonomously implemented within Matlab. Noise or unwanted signal is always present in the data and a bad signal-to-noise can cause a severe error in modal parameter extraction. With the recent developments of automated procedures without user interaction for the operational modal analysis (OMA), the corrupted input signals turn out to be a big issue in obtaining reliable results of automated modal parameter identification. The appearance of noise or unwanted modes due to environmental effects could affect the actual structural modes selection. There is a significant issue regarding “noise” (or spurious) modes and eliminating them from the raw signal remains to be solved and requires a lot of interaction with an expert user. In the parametric modal analysis, oversizing of a modal model is usually performed to minimize the bias on modal estimates by getting all physical modes in the frequency range of interest and help to obtain a good model fit to the data. However, this will introduce noise modes. Thus, authors take advantage of tools, such as the stabilization diagram, to illustrate, and decide, if a mode is physical or not. This selection is not a trivial task, but it may be difficult and time consuming depending on the quality of data, the performance of the estimator and the experience of the user. Since the extensive interaction between tools and user is inappropriate for monitoring purposes, image clustering tool is introduced to separate the physical poles from the others with short response time and low computational efforts compared to the available clustering algorithm. Meanwhile, Empirical mode decomposition (EMD) is then introduced to break down a signal into various components without leaving the time domain and purposely used for filtering. These are a great combination as well as an effective procedure in producing a good input signal that free from unwanted modes which can cause disruptive decision making for the actual modes selection.

Air-conditioning systems consumed the most energy usage nearly 45% of the total energy used in commercial-building. Where AHU is one of the most extensively operated equipment and this device is typical customize and complex which can results in hardwire failure and controller errors. The efficiency of the system is very much depending on the proper functioning of sensors. Faults arising from the sensors and control systems are a major contribution to the energy wastage. As such faults often go unnoticed for extended periods of time until the deterioration in performance becomes great enough to trigger comfort complaints or total equipment failure. Energy could be reduced if those faults can be detected and identified at early stage. This paper aims to review of various existing automated fault detection and diagnosis (AFDD) methods for an Air Handling Unit. The background of AHU system, general fault detection and diagnosis framework and typical faults in AHU is described. Comparison and evaluation of the various methodologies will be reviewed in this paper. This comparative study also reveals the strengths and weaknesses of the different approaches. The important role of fault diagnosis in the broader context of air- conditioning is also outlined. By identifying and diagnosing faults to be repaired, these techniques can benefits building owners by reducing energy consumption, improving indoor air quality and operations and maintenance.

Safety is the first priority during the process of transportation as an accident occurs will cause a person’s life or long-term injury. This study identifies the awareness of road safety for trucks transportation in Peninsular Malaysia that highlight the factor of causing drivers not aware about the road safety for trucks transportation in Peninsular Malaysia which include driver behaviour, attitude and human error, surrounding environment, government legislative and distracted driving. Qualitative research approach is adopted in this study and in-depth interview was conducted to gather the data among the selected trucks operations company in Peninsular Malaysia. The finding show that speed performance results the safety procedure, the company greediness for small gains or profits results the awareness of safety their employees, the government’s legislation punishment is not very effective and also the employees lack of understanding on safety precaution results in their own risk. Therefore, Driver Behaviour, Attitude and Human Error, surrounding Environment, Government Legislative as well as Distracted Driving and Tracking are the important factor that cannot be neglected.

This paper details results from a three-year study investigating how to help students in Singapore write vivid compositions in Mandarin, the Chinese ‘mother tongue’. Mastery of the mother tongue by Singaporean students has become an important government priority in recent years. The strategies employed by this study included the use of information and communications technologies (ICT) mediated scaffolds such as collaborative mind maps and online peer editing to help students learn micro-writing strategies. This paper outlines the process of using various scaffolds to support students to learn and apply the action chain micro-writing strategy. A class of 31 Primary 4 from a neighbourhood school participated in this study. Findings indicated that students were very enthusiastic about writing in the ICT-mediated environment. Contrary to the teacher’s initial belief, students could be scaffolded to write action chains quickly. Findings highlighted the potential of scaffolding students in learning small chunks of writing strategy in an ICT-mediated environment that enabled them to practice these strategies in their composition writing until they could master and apply them. These micro-writing strategies gradually built up to a complex set of skills, including expressive writing. Moreover, students enjoyed working in groups and editing their peers’ work online. This showed that peers could be engaged as scaffolders in the classroom to free up the teacher’ time, allowing the teacher more time to spend with students who were not performing well.

Oil Palm Shell (OPS) is the solid waste product from the palm oil sector of the agricultural industry. The substitution of coarse aggregate in concrete with OPS to produce lightweight concrete (LWC) had been researched since two decades ago. The author has discovered fluctuation on the performance of OPSLWC. One of the factors is the workability. As an initiative to enhance the performance of OPSLWC, the author proposes a new mixing method (NMM) modified from the mix design of self-compacting concrete (SCC). The effects of the NMM on the workability, uniformity, compressive strength and splitting tensile strength are investigated. The workability of the NMM is 25.5% higher than the conventional method (CM). The compressive strength shows an improvement of 5.76%; while the splitting tensile strength is increased by 22.35%. The new findings of this research have shown a positive impact on the concrete industry.

The National Pingtung University of Science and Technology was established as an agricultural institution in 1924. Since then, this educational institution has been one of the key role players in developing the agricultural advancement in Taiwan. For years, the university has seriously planned and carried out projects that strengthen her identity, “being green”. Under the scope of university’s uniqueness and responsibility, measures taken in the university directly involves administrative affairs, such as teaching, research, university development, student welfare, etc. As a matter of fact, initiating new policies, measures, and projects is only the beginning of everything. Without proper planning, enforcement, and funding, any one of these policies, measures, and projects may end up going nowhere. In other words, sustainability is absolutely the most critical element that dictates the success of the policies, measures, and projects. This sustainability covers financial allocation, manpower, negotiation, evaluation, and adjustments. The university has invested a lot of funding and effort to achieve the aforementioned goals through various means in the aspects of administration, education, research and development.

Energy consumption in developing countries is sharply increasing due to higher economic growth of industrialization along with population growth and urbanization. This paper provides a multiple linear regression evidence to illustrate the association between final energy consumption and three economic variables. Multiple linear regression analysis was used to obtain a predictive equation and to check for linearity assumption. Three input variables, viz. growth domestic product, population, and tourism are the predictors for final energy consumption. Time series yearly data of final energy consumption and the three input variables for the year 2001 until 2012 was retrieved from various databases. It is found that there was a significant variation in the final energy consumption explained by the three variables. The multiple linear regression equation indicates that the ‘population’ is the most influential variable in predicting final energy consumption.

This study reports a fast and green preparative strategy to synthesize water soluble and fluorescent carbon quantum dots (CQDs) through hydrothermal method by using low cost organic waste of human fingernails as the carbon precursor for the first time. The coupling of CQD with pure carbon nitride (g-C3N4) was further explored to enhance the latter’s performance in photocatalysis of 2,4-dicholorophenol (2,4-DCP), a toxic and recalcitrant compound mostly released from industrial effluent. Such coupling overcame the weakness of pure g-C3N4 in photocatalysis process by broadening its visible light absorption and promoting the charge separation. As a result, the removal rate of CQD/ g-C3N4(10) was up to 71.53%, which was approximately 1.5 times higher than that of pure g-C3N4 under sunlight irradiation. The morphological structure, optical properties and chemical compositions of CQDs/g-C3N4 composites were characterized using various spectroscopic techniques including field emission scanning electron microscopy (FESEM), Energy Dispersive X-Ray (EDX) and Ultraviolet-visible diffuse reflectance spectra (UV-DRS).

N co-doped graphene quantum dots (N-GQDs) incorporated with nanocubic likes titanium dioxide (TiO2) are synthesized via facile hydrothermal method. The morphology features of the synthesized N-GQDs/TiO2 nanocomposites are seen to exhibited in cubic shape. The average lateral size of the synthesized nanocomposites is found to be ~5.1 nm. UV-Vis absorption spectra show vastly improvement in the visible light range when N-GQDs is introduced to TiO2. The photocatalytic activity clearly demonstrated its solar light harvesting capability of prepared nanocomposites by photodegrading 4-chlorophenol (4-CP) under sunlight irradiation.

This study investigates the development of biochar catalyst from bamboo applied for biodiesel synthesis. A non-conventional biodiesel feedstock was used in the in-situ transesterification reaction. This non-conventional feedstock is obtained from an insect’s fly, the Hermetia illucens fly. Biochar derived from bamboo has been investigated as a promising catalyst for biodiesel synthesis. The biochar acid catalysts were prepared by sulphonation via impregnation with concentrated sulphuric acid. The prepared catalysts were investigated for their performance to catalyse in-situ transesterification via ultra-sonication of Hermetia illucens bio-oil. The effects of carbonisation time (1 hour and 2 hour) and temperature (400°C, 500°C and 600°C) as well as catalyst loading (5-20 wt% on oil basis) on the transesterification yield were studied. Result showed that the highest yield of FAME obtained was 95.6% with catalyst loading of 15 wt% carbonized at 500°C for 2 hours. Sharp band of methyl ester functional groups were observed in the FTIR spectra at 1735-1750cm-1. The composition of this methyl ester was further deduced using gas chromatography and the fatty acid was predominantly lauric acid.

Post-tensioning (PT) system is widely used not only in bridge construction, but also used as a replacement of reinforced concrete (RC) structure for slab, beam, and wall. Nowadays, many countries around the world are focusing on sustainable development in the construction industry. PT system is one of a system which able to meet the standard of sustainable building construction. Its advantages are able to save more materials, construction time and maintenance cost. To ensure the safety of PT systems during construction, some methods are implemented to evaluate the concrete condition. In this study, the Rayleigh wave (R-wave) method is used to evaluate the grouting condition of the post-tensioned concrete structure. Useful parameters of R-wave such as amplitude, time, frequency and wave velocity are identified and obtained through a series of numerical simulations and experimental tests. Different frequencies are excited to examine the behavior of R-wave propagated through a concrete medium with different grouting conditions in the simulation. In experimental testing, different diameters of steel balls are used as an impactor to generate waveform and the generated waveforms were recorded by the data logger. Three equidistance receivers are placed on top of the tested concrete specimen with different duct materials of aluminium duct and PVC duct embedded in it. As a conclusion, when voids are present in the concrete specimen, high readings of amplitude and dominant frequency are recorded and the travelling wave velocity is slow.