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The multi-granular concept involves many parts of a complex system or model. Since the late 1990s, many researchers have started to incorporate multi-granular concepts in their research areas. This paper focuses on the use of multi-granular linguistic (MGL) term sets in the decision-making method. The use of the MGL method in decision-making may impose a high level of complexity since it offers flexibility to the decision-maker. The flexibility given is by determining the output of the cardinality. Complexity in a method may impose disadvantages in terms of, for example, inaccuracy of outcomes, loss of information, and time consumption. Hence, a simplified multi-granular linguistic term sets (SMM) method is proposed with a lower complexity level to overcome the disadvantages of complexity. This was achieved by introducing a parallel process of cardinality (PPC) into the simplified multi-granular linguistic term sets method (SMM). After proposing the simplified multi-granular linguistic term sets method, the complexity level of this method is compared with other methods based on the relative complexity index (RCI).

•A unique perspective to review BEGs-based biosensors for agenda goals 2030.•Different designs of wearable BEGs-based biosensors are summarized.•Employment of BEGs-based biosensor for health monitoring.

The fast proliferation of wearable electronics has ushered in a new era of technological convergence with daily living, necessitating sustainable power solutions. Wearable microbial fuel cells (MFCs), which use microbe’s ability to produce energy, have emerged as an innovative solution to this demand. Recent advances in electrode materials, including as conductive polymers, graphene, and carbon nanotubes, are the focus of this overview on wearable MFCs. Hybrid systems, which combine wearable MFCs with technologies like piezoelectric generators, offer comprehensive energy solutions; miniaturization techniques allow for smooth integration into clothing, accessories, and implants. Despite advancements, difficulties in scaling and electrode stability exist, necessitating focused approaches. This review article focuses on how wearable MFCs are changing the energy landscape and ushering in a new era of inventive electronics by advancing sustainable energy and environmental sensing.

The combustion of bituminous coal, bio-oil, and their slurry mixtures were performed under air atmosphere using Thermogravimetric Analyzer (TGA). All samples were run from room temperature to 110°C and held for 10 minutes before the temperature was ramped to 1100°C and held again for 10 minutes at 1100°C at the heating rate of 10°C/min and gas flow rate of 50mL/min. Kinetic evaluation was conducted using a simple Arrhenius-type kinetic model with first-order decomposition reaction. Apparent activation energy, Ea, and pre-exponential factor, A, were calculated from the modelling equation. Results reveal that the reactivity of CBS fuel is higher than a single coal fuel to which the addition of bio-oil helps to increase the combustion performance of the blends. The optimum fuel ratio appears at 50:50 ratio with equal contribution of coal and bio-oil properties that contribute to the increase in volatile matter causing maximum combustion rate achievable at much lower temperature compared to single coal fuel.

Combustion behaviour study of coal-biooil slurry (CBS) fuel prepared at different ratios was conducted using Thermogravimetric Analyzer (TGA). The materials comprised of Clermont bituminous coal (Australia) and bio-oil, derived from Empty Fruit Bunch (EFB) by means of fast pyrolysis. The samples were heated from room temperature to 110°C and held for 10 minutes before the temperature was ramped to 1100°C and held for another 10 minutes at constant heating rate of 10°C/min under air atmosphere at constant flow rate of 50 mL/min. The proportions of CBS fuel at 50:50 blends were observed to have significant influence on the combustibility of the slurry blends. The addition of bio-oil will shifted the ignition temperature towards early devolatilization. Meanwhile, the DTG profiles of the blends, showed decomposition of VM and char indicating bio-oil to be more reactive than coal with highest degradation rate appears at the blend ratio of 50:50 biooil/coal. These findings could be useful to introduce a new application of bio-oil in energy mix generation into coal mixture to cater for coal consumption reduction strategy for future application in vast existing conventional power plants.

Density and viscosity are two major rheological properties that determine the performance of an isopropyl laurate (IPL) ester based drilling fluid. In this study, the effect of barite particle size on the IPL drilling fluid system settling behaviour was investigated using five particle sizes namely-32, 32-45, 45-75, +75μm and the unsieved barite. Stokes’ law of separation theory was employed in the analysis. It was found that smaller barite particle sizes (<75μm) enhances the settling resistance. An ideal barite particle size of-32μm was chosen and used in the subsequent formulations to obtain optimum rheological properties by varying the amount of viscosifier used at 4.0, 10.0 and 20.0g marked as sample (a), (b) and (c). These samples were analysed for their rheology by using the conventional Fann viscometer calibrated using Bingham model. The results showed that sample (b) exhibited the most promising performance based on the plastic viscosity, yield point and gel strength rheological analyses. Moreover, IPL drilling fluid sample (b) showed improve particle suspension ability for more than 78 hours when compared to IPL drilling fluid system prepared using unsieved barite particles. The results suggested that palm oil based isopropyl laurate ester has the potential to be used as based fluid in the ester based drilling fluid system.

Mineral carbon dioxide sequestration provides a leakage free and permanent method of CO2 disposal to produce environmentally benign and stable solid carbonates. FGD gypsum as a source of calcium was proposed as the potential feedstock in this study. The purpose of this laboratory study was to investigate the effects of reaction parameters such as CO2 pressure, reaction temperature, particle size, and ammonia solution concentration on calcium carbonate purity through Merseburg process. Increasing the reaction temperature as well as the pressure was very effective in improving the calcium carbonate purity. High purity calcium carbonate was produced when reaction temperature and CO2 was increased to 400 °C and 70 bar, resulting in 93% and 94% purity, respectively. Experimental results showed that reducing particle size was also effective in enhancing the calcium carbonate purity in which the smallest particles produced higher purity calcium carbonates rather than larger particles. The role of ammonia solution on calcium carbonate purity was found to be beneficial in improving the calcium carbonate purity in which increasing the ammonia solution increased calcium carbonate purity significantly in all experiments.

Four samples of covalent organic framework-1 (COF-1) assigned as S1 to S4 were prepared by varying the initial mass of 1,4-benzene diboronic acid (BDBA) used and heating condition. The samples were physically characterized using Brunauer–Emmett–Teller (BET) surface area analysis and FESEM analysis. The BET surface area value showed an increasing trend with increasing mass of BDBA used. The highest achievable BET surface area is recorded by COF-1 (S3) with a value of 107.9 m2/g. The low surface area obtained is likely due to the distribution of particles with large pore sizes. This is confirmed by the Field Emission Scanning Electron Microscopy (FESEM) images which correlate well with the surface area obtained. The presence of dendrites phase within the COF-1 structure also indicates incomplete formation of a crystalline structure, hence contributed to the low surface area achieved. It was also found that the use of ramping heating did not significantly influence the formation of COF-1 crystalline structure which promotes the surface area.

Lignocellulosic biomass (LB) sources which are readily available in abundance are widely considered as a potential future sustainable raw materials for biofuel production. Typically, biofuel production involved several chemical and mechanical steps consisting of pretreatment, hydrolysis, fermentation and separation. The pretreatment step is considered as one of the most vital part of the whole processing scheme due to the impact it had on the efficiency of the subsequent processing steps. In this study we reviewed the mechanical pretreatment of LB focusing mainly on the size reduction technique by grinding process. Grinding is one of the proven preliminary pretreatment techniques employed in biomass conversion to liquid biofuel. However, this technique is known to be costly due to high energy consumption. In view of this, an efficient and cost effective pretreatment technology is required in order for the biofuel to be produced at a competitive level. At the same time, the impact on environment caused by the conventional pretreatment processes can be minimized. Thus, a new combined chemical-mechanical pretreatment is considered whereby a green ionic liquid (IL) solvent is introduced.

This paper presents an evaluation of the critical factors influencing local tourists choice of destinations in Kedah and determination of the tourists preference for destinations with respect to these factors using Fuzzy Hierarchical TOPSIS (FHTOPSIS) method. This study focuses on the internal factors that motivate tourists to choose their preference of destination. The result shows the purpose of visiting friends and relatives is the most important factors that motivate their visit to Kedah, while novelty seeking is the least motivating factors influencing the choice of destinations. The best destination to be selected among five destinations under consideration in this study is Langkawi, followed by Alor Setar, Sedim River, Bujang Valley and Bukit Kayu Hitam. This study can assist relevant authorities and travel agencies to plan and promote the places of attraction in Kedah with effective marketing strategies besides assisting tourists to decide where to go to main attractions in Kedah.

Both ratings and weight aspects have recently received great consideration in teaching performance evaluation. Unfortunately, in some cases, the trend of adopting merely the rating aspect has created concern as it can lead to questionable outcome. Besides that, the existence of outliers in the data will also affect the evaluations’ results. Evaluating teaching performance is also not an easy task as it involves human decision making which is imprecise, vague and uncertain. In this paper, the fuzzy evaluation method with fuzzy Jaccard ranking index is applied in evaluating teaching performance at one of the public universities in the East Coast of Malaysia. The outliers data which are detected by using the standard score concept were trimmed off and thus had minimized the variation within the data. Findings conclude that teaching is the key factor in evaluating the teaching performance. The proposed approach gives a promising prospect in teaching performance evaluation where it provides a more reasonable and intelligent evaluation with accurate results.

The aim of this research is to predict the ozone concentration level for the next three days. Linear regression model and a nonlinear regression model are used to measure the air pollution data and the result was compared. The performance indicator used to evaluate the accuracy of the methods is Index of Agreement (IA), Prediction Accuracy (PA) and Coefficient of Determination (R2). While Normalized Absolute Error (NAE) and Root Mean Square Error (RMSE) are for error measured. The results show that the prediction for the next three days. The highest ozone concentration of the linear regression model is 0.085ppm at Petaling Jaya, Selangor. While the lowest concentration for the linear regression model is 0.015 ppm at Klang, Selangor. Besides, the highest ozone concentration for the nonlinear regression model is 0.1 ppm at Petaling Jaya, Selangor for the second-day prediction. Comparison between the linear regression model and a nonlinear regression model indicates that nonlinear regression model can as an alternative method to the linear regression model.

This study is concerning phosphorus distribution in Putrajaya Wetland. Phosphorus is one of the important component in nutrients for living things be it aquatic or non - aquatic organisms. Total phosphorus (TP) results will give some information on the trophic status of surface water in water bodies. The focus of this study is to determine the total phosphorus concentration in Putrajaya Wetland which is in the inlet of the wetland then outlet of the wetland (Central Wetland Lake). The water sample is taken from Putrajaya Wetland and the test was conducted in the laboratory. The result from this study shows the results for total phosphorus according to month, sampling station and cells. Lowest total phosphate at the Central Wetland compare with all the wetland arms cells.