University of Technology Malaysia
Universiti Teknologi Malaysia Institutional RepositoryNot a member yet
70456 research outputs found
Sort by
Application of growth strategies on the performance of Eatalian Express
Full text linkEatalian Express is a 100% locally owned company based in Setia Alam, Selangor which specialize in making vegetarian pasta. Their product is also suitable for babies transitioning from solid to liquid. By 2020, demand for Eatalian Express’ product had exceed the maximum production capacity of the machineries. This had caused the company’s sales growth to plateau. In order to buy new machineries, the company is in need of proper source of capital. Additionally, the company’s customer perception in terms of brand awareness, brand impression and purchasing are still lacking. New growth strategies need to be formulated to address the issue and its effectiveness shall be evaluated. The research is important as an additional input on the still small amount of literature talking about the application of growth strategies on the sales and growth of a company particularly the SMEs. The study was performed with the help of interviews with the owner, production and sales growth data, as well as a survey on customer’s perception of the brand pre- and post-intervention. Result from the study indicates that the application of growth strategies such as by increasing production capacity, offering discount and bundle promotions, obtaining Halal certification and offering new product had a significant impact on the sales performance of Eatalian Express as well as its growth. The customers perception in term of brand awareness, brand impression as well as purchasing intention also improves as a result. This study had resulted in the improvement of sales performance and growth of Eatalian Express and shall serve as a case study reference for future entrepreneurs on the application of growth strategies particularly in the F&B industries
Biodegradable poly (ethylene glycol) diacrylate filled aramid nanofiber hydrogel three dimensional printed tissue engineering scaffold
Full text linkDigital Light Processing (DLP) process is one of the additive manufacturing techniques and has been widely used to fabricate tissue engineering scaffold based on Poly (ethylene glycol) diacrylate (PEGDA) material. However, the existing PEGDA scaffold via DLP 3D printing commonly exhibits poor mechanical and biocompatible properties. The PEGDA 3D scaffolds also have low cells viability which can cause tissue engineering failure. Therefore, this study aims to develop a novel soft tissue engineering scaffold biomaterial, using PEGDA filled with Aramid nanofibers (ANFs), with enhanced mechanical strength and biocompatible properties via DLP 3D printing technique. ANFs was first synthesized from macro size Kevlar fibre (0.2 %wt.) prior to crosslinking with Diphenyl (2,4,6-trimethylbenzoyl) phosphine oxide (TPO) photo initiator. The mixing ratio of PEGDA resin to ANFs was fixed to 9:1. During the mixing, the concentration of TPO was varied at 0.5, 1.0 and 1.7% wt., while the resin concentration was fixed at 30% wt. to produce three sets of biomaterials. Preliminary study was conducted prior to the actual printing for the purpose of eliminating unprintable TPO concentration. The final scaffold was printed using a FEMTO3D DLP machine at two different curing times; 70s and 80s to obtain good shape and printable 3D structure. It was found that 1.7%wt of TPO failed to produce a 3D profile shape. It was observed the printed 3D scaffold of 1%wt TPO at 70s curing time produced the most discernible shape of the compression specimen (ASTM D695). Based on the printable photo initiator results, the experiments were expanded further by taking into account the PEGDA concentration, resin to ANFs ratio and DLP curing time. At this stage, both resin-PEGDA/TPO ratio and TPO concentration were fixed at 8:2 and 1.0 %wt. respectively. A two level factorial design involving three factors was used to determine the feasible printing parameter where the response is the Young's Modulus. The resin to ANFs ratio (9:1, 8:2, 7:3), PEGDA concentrations (30, 40, 50 %wt.) and curing time (70, 80, 90s) were varied during the experiments. Response surface method was used to determine the optimum setting for maximizing the Young’s Modulus. The synthesized ANFs have shown a nano diameter size distributions ranging from 20 nm to 80 nm. The optimum condition was found at 7:3 resin to ANFs ratio, PEGDA concentration at 50 %wt. and at 100s curing time, which recorded the highest Young’s modulus (0.55 MPa). 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide assay showed a weak condition of the cells viability at a ratio of 10:0 (61.4 %) after 3 days of incubation. Increased ratio of ANFs enhanced the cell viability where 81.6%, 89.3 % and 96.3 % of cells viability were recorded at the ratios of 9:1, 8:2 and 7:3, respectively. Fourier Transform Infrared Spectroscopy and Diffraction Scanning Calorimetry analyses also proved that the presence of Aramid functional group in the printed PEGDA/ANFs scaffold. The optimized dried sample after freeze-drying process for 24 hours confirmed that their physical reliability with minimal volume shrinkage (30%) and 80% water content remained in the final scaffold with high interconnected internal porous structure. The mechanical strength of the optimized printed scaffold also increased at 69.1% (0.93 MPa) after the freeze dried. Overall, the mechanical and biocompatibility properties of the fabricated PEGDA filled with ANFs exhibits significant improvement as compared to PEGDA without ANFs. It has proved that the newly developed PEGDA-ANFs scaffold has a great potential to be used as an articular cartilage in soft tissue engineering applications
A comparative analysis of generative neural attention-based service chatbot
Full text linkCompanies constantly rely on customer support to deliver pre-and post-sale services to their clients through websites, mobile devices or social media platforms such as Twitter. In assisting customers, companies employ virtual service agents (chatbots) to provide support via communication devices. The primary focus is to automate the generation of conversational chat between a computer and a human by constructing virtual service agents that can predict appropriate and automatic responses to customers’ queries. This paper aims to present and implement a seq2seq-based learning task model based on encoder-decoder architectural solutions by training generative chatbots on customer support Twitter datasets. The model is based on deep Recurrent Neural Networks (RNNs) structures which are uni-directional and bi-directional encoder types of Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU). The RNNs are augmented with an attention layer to focus on important information between input and output sequences. Word level embedding such as Word2Vec, GloVe, and FastText are employed as input to the model. Incorporating the base architecture, a comparative analysis is applied where baseline models are compared with and without the use of attention as well as different types of input embedding for each experiment. Bilingual Evaluation Understudy (BLEU) was employed to evaluate the model’s performance. Results revealed that while biLSTM performs better with Glove, biGRU operates better with FastText. Thus, the finding significantly indicated that the attention-based, bi-directional RNNs (LSTM or GRU) model significantly outperformed baseline approaches in their BLEU score as a promising use in future works
An enhancement of succinate production using a hybrid of bacterial foraging optimization algorithm
No full textGenetic modifications, such as gene knockout technique, have become mainstream in metabolic engineering to produce desired amount of targeted metabolites through reconstruction of the metabolic networks. The production, however, does not often achieve desirable outcome. To this end, in-silico methods have been applied to predict potential metabolic network response and optimise production. Previous methods working on relational modelling framework, such as OptKnock and OptGene, however, failed at handling its multivariable and multimodal functions optimization algorithms. This paper proposes hybridising bacterial foraging optimizationg algorithm (BFO) and dynamic flux balance analysis (DFBA) to overcome problems in OptKnock and OptGene with a nature-inspired algorithm and also to couple kinematic variables in the model to predict production of succinate in E.coli model. In-silico results showed that by knocking out genes identifed by BFODFBA, production rate of succinate is better as when compared to OptKnock and OptGene
Designing of triple-band, quad-band, and super wideband microstrip antennas for THz application
No full textIn this paper microstrip ring antenna is designed for triple, quad, and super wideband terahertz application. Three antenna configurations are discussed here with application in upcoming 6G technology, Health monitoring systems, wireless sensor network, and the Internet of Things (IoT). A square ring resonator antenna designed on 710 × 910 × 10 µm3 Rogers R03003 substrate. The first antenna is working on 374, 444, and 510 GHz with maximum bandwidth of 40 GHz. Further ground structure of ring antenna defected for super wide bandwidth of 192 GHz. Third antennas show quad-band performance with fractal rings. The result and performance indicate that the recommended antenna will be well-suited with compact future wireless devices. An soft High Frequency Structure Simulator (HFSS) is used for all simulation work
Exploring the factors influencing employee awareness of social engineering threats: A review
No full textPhishing is a dynamic threat, making it much more dangerous because a lack of awareness among employees makes it much more difficult for the business to detect and respond to Phishing situations. Social engineering is a process that essentially involves human interactions that can be exploited to the point where typical security methods become defective. The most dangerous risk of social engineering is that the criminals understand human nature and may exploit vulnerabilities without the victims realizing it. This study examines relevant research to determine the factors that may influence employee awareness of social engineering threats. Three demographic factors and seven other factors, according to the literature, may influence employee awareness of social engineering threats
Fabrication of mesoporous CeO2–MgO adsorbent with diverse active sites via eggshell membrane-templating for CO2 capture
Full text linkThere is an increasing demand for the development of highly efficient CO2 capture techniques to address global warming and climate change. Although adsorption is an effective approach towards capturing CO2, conventional adsorbents possess limited adsorption capacities and exhibit low adsorption rates. In this study, we successfully fabricated mesoporous composite CeO2–MgO adsorbents (CM-BT) with diverse active sites via the eggshell membrane (ESM)-templating method, for CO2 capture applications. The utilisation of ESM-templating produced a CM-BT with better structural and textural properties. The CM-BT possessed a higher surface area (42 m2/g) and pore volume (0.185 cm3/g) than those of the composite prepared using a thermal decomposition method (CM-TD). In addition, the CM-BT possessed more diverse base sites of various strong base site strengths (O2-) and abundant hydroxyl groups, and metal–oxygen pair base sites than CM-TD. The diverse strengths of the strong base sites were correlated with the coordination of O2- and the electronegativity of metal ions. With these excellent physicochemical properties, the CM-BT composite exhibited a high CO2 uptake capacity of 5.7 mmol/g under CO2 flow and ambient conditions, which is 2.5 times higher than that of CM-TD
Smart piezoelectric-based wearable system for calorie intake estimation using machine learning
Full text linkEating an appropriate food volume, maintaining the required calorie count, and making good nutritional choices are key factors for reducing the risk of obesity, which has many consequences such as Osteoarthritis (OA) that affects the patient’s knee. In this paper, we present a wearable sensor in the form of a necklace embedded with a piezoelectric sensor, that detects skin movement from the lower trachea while eating. In contrast to the previous state-of-the-art piezoelectric sensor-based system that used spectral features, our system fully exploits temporal amplitude-varying signals for optimal features, and thus classifies foods more accurately. Through evaluation of the frame length and the position of swallowing in the frame, we found the best performance was with a frame length of 30 samples (1.5 s), with swallowing located towards the end of the frame. This demonstrates that the chewing sequence carries important information for classification. Additionally, we present a new approach in which the weight of solid food can be estimated from the swallow count, and the calorie count of food can be calculated from their estimated weight. Our system based on a smartphone app helps users live healthily by providing them with real-time feedback about their ingested food types, volume, and calorie count
A novel natural active coagulant agent extracted from the sugarcane bagasse for wastewater treatment
Full text linkThe performance of extracted coagulant from the sugarcane bagasse was tested using synthetic wastewater for turbidity removal. Sugarcane bagasse was selected because it is available in abundance as a waste. This study was carried out to analyze the effect of the extraction process in optimizing the active coagulant agent of bagasse as a natural coagulant for optimum turbidity removal. Bagasse was characterized in terms of physical, chemical and morphological properties. The results showed bagasse has very high polysaccharide content which can act as an active coagulant agent together with hemicellulose and lignin. The extraction process for degradation of lignin and hemicellulose was run based on two different solvents (NaOH and H2SO4) with varying concentrations from 2% to 10% at different extraction temperatures varied from 60 °C to 180 °C for various extraction times (0.5 h to 3 h). The optimum polysaccharide content extracted from bagasse was 697.5 mg/mL by using 2% NaOH at 120 °C for 2 h extraction. The coagulation process using extracted bagasse showed the removal of suspended solids up to 95.9% under optimum conditions. The concentration of polysaccharides as the active coagulant agent plays a vital role where high polysaccharides content removes most turbidity at a lower dosage. Bagasse has the potential to be an alternative coagulating agent due to its efficiency, and eco-friendly properties for the treatment of wastewater
Predicting the Young’s modulus of rock material based on petrographic and rock index tests using boosting and bagging intelligence techniques
Full text linkRock deformation is considered one of the essential rock properties used in designing and constructing rock-based structures, such as tunnels and slopes. This study applied two well-established ensemble techniques, including boosting and bagging, to the artificial neural networks and decision tree methods for predicting the Young’s modulus of rock material. These techniques were applied to a dataset comprising 45 data samples from a mountain range in Malaysia. The final input variables of these models, including p-wave velocity, interlocking coarse-grained crystals of quartz, dry density, and Mica, were selected through a likelihood ratio test. In total, six models were developed: standard artificial neural networks, boosted artificial neural networks, bagged artificial neural networks, classification and regression trees, extreme gradient boosting trees (as a boosted decision tree), and random forest (as a bagging decision tree). The performance of these models was appraised utilizing correlation coefficient (R), mean absolute error (MAE), and lift chart. The findings of this study showed that, firstly, extreme gradient boosting trees outperformed all models developed in this study, secondly, boosting models outperformed the bagging models