In this paper, two approaches were introduced to obtain Stackelberg solutions for two-level linear fractional programming problems with interval coefficients in the objective functions. The approaches were based on the Kth best method and the method for solving linear fractional programming problems with interval coefficients in the objective function. In the first approach, linear fractional programming with interval coefficients in the objective function and linear programming were utilized to obtain Stackelberg solution, but in the second approach only linear programming is used. Since a linear fractional programming with interval coefficients can be equivalently transformed into a linear programming, therefore both of approaches have same results. Numerical examples demonstrate the feasibility and effectiveness of the methods.
In the last few years we have seen a very rapid development on solving generalized geometric programming (GGP) problems, but so far less works has been devoted to MOGP due to the inherent difficulty which may arise in solving such problems. Our aim in this paper was to consider the problem of multi-objective geometric programming (MOGP) and solve the problem via two-level relaxed linear programming problem Yuelin et al. (2005) and that is due to simplicity which occurs through linearization i.e. transforming a GP to LP. In this approach each of the objective functions in multi-objective geometric programming is individually linearized using two-level linear relaxed bound method, which provides a lower bound for the optimal values. Finally our MOGP is transformed to a multi-objective linear programming problem (moLP) which is solved by reference point approach. In the end, a numerical example is given to investigate the feasibility and effectiveness of the proposed approach.
In this present study, microalgal phycobiliproteins were isolated and purified via potential biphasic processing technique for pharmaceutical as well as food applications. The algal pre-treatment techniques were studied to enhance the yield of microalgal phycobiliproteins from the biomass. The proposed methods were optimised to obtain the best recovery yield of phycobiliproteins that can be isolated from the biomass. The phycobiliproteins were further purified using liquid biphasic system. The results showed that microalgal phycobiliproteins of high purity and yield was achieved using sonication treatment (20% power, 50% duty cycle and 7 min of irradiation time) with the biphasic system, where the purification fold of 6.17 and recovery yield of 94.89% was achieved. This work will provide insights towards the effective downstream processing of biomolecules from microalgae.
Swarm intelligence is a discipline which makes use of a number of agents for solving optimization problems by producing low cost, fast and robust solutions. The dragonfly algorithm (DA), a recently proposed swarm intelligence algorithm, is inspired by the dynamic and static swarming behaviors of dragonflies, and it has been found to have a higher performance in comparison to other swarm intelligence and evolutionary algorithms in numerous applications. There are only a few surveys about the dragonfly algorithm, and we have found that they are limited in certain aspects. Hence, in this paper, we present a more comprehensive survey about DA, its applications in various domains, and its performance as compared to other swarm intelligence algorithms. We also analyze the hybrids of DA, the methods they employ to enhance the original DA, their performance as compared to the original DA, and their limitations. Moreover, we categorize the hybrids of DA according to the type of problem that they have been applied to, their objectives, and the methods that they utilize.
Background: Studies have acknowledged that social media enables students to connect with and learn from experts from different ties available in the students' personal learning environment (PLE). Incorporating experts into formal learning activities such as scaffolding problem-solving tasks through social media, allows students to understand how experts solve real-world problems. However, studies that evaluate experts' problem-solving styles on social media in relation to the tie strength of the experts with the students are scarce in the extant literature. This study aimed to explore the problem-solving styles that the experts portrayed based on their ties with the students in problem-based learning (PBL) on Facebook. Methods: This study employed a simultaneous within-subject experimental design which was conducted in three closed Facebook groups with 12 final year management students, six business experts, and one instructor as the participants. The experts were invited by the students from the weak and strong ties in their PLE. Hinging on the Strength of Weak Ties Theory (Granovetter, 1973) and problem-solving styles (Selby et al., 2004), this study employed thematic analysis using the ATLAS.ti qualitative data analysis software to map the experts' comments on Facebook. Results: The experts from strong and weak ties who had a prior relationship with the students showed people preference style by being more sensitive to the students' learning needs and demonstrating firmer scaffolding compared to the weak ties' experts who had no prior relationship with the students. Regardless of the types of ties, all experts applied all manner of processing information and orientation to change but the degree of its applications are correlated with the working experience of the experts. Conclusion: The use of weak or strong ties benefited the students as it expedited their problem-solving tasks since the experts have unique expertise to offer depending on the problem-solving styles that they exhibited.
A prescriptive model approach in decision making could help achieve better diagnostic accuracy in clinical practice through methods that are less reliant on probabilistic assessments. Various prescriptive measures aimed at regulating factors that influence heuristics and clinical reasoning could support clinical decision-making process. Clinicians could avoid time-consuming decision-making methods that require probabilistic calculations. Intuitively, they could rely on heuristics to obtain an accurate diagnosis in a given clinical setting. An extensive literature review of cognitive psychology and medical decision-making theory was performed to illustrate how heuristics could be effectively utilized in daily practice. Since physicians often rely on heuristics in realistic situations, probabilistic estimation might not be a useful tool in everyday clinical practice. Improvements in the descriptive model of decision making (heuristics) may allow for greater diagnostic accuracy.
Network function virtualization (NFV) is a rapidly growing technology that enables the virtualization of traditional network hardware components, offering benefits such as cost reduction, increased flexibility, and efficient resource utilization. Moreover, NFV plays a crucial role in sensor and IoT networks by ensuring optimal resource usage and effective network management. However, adopting NFV in these networks also brings security challenges that must promptly and effectively address. This survey paper focuses on exploring the security challenges associated with NFV. It proposes the utilization of anomaly detection techniques as a means to mitigate the potential risks of cyber attacks. The research evaluates the strengths and weaknesses of various machine learning-based algorithms for detecting network-based anomalies in NFV networks. By providing insights into the most efficient algorithm for timely and effective anomaly detection in NFV networks, this study aims to assist network administrators and security professionals in enhancing the security of NFV deployments, thus safeguarding the integrity and performance of sensors and IoT systems.
Neuroimaging is a new technique used to create images of the structure and function of the nervous system in the human brain. Currently, it is crucial in scientific fields. Neuroimaging data are becoming of more interest among the circle of neuroimaging experts. Therefore, it is necessary to develop a large amount of neuroimaging tools. This paper gives an overview of the tools that have been used to image the structure and function of the nervous system. This information can help developers, experts, and users gain insight and a better understanding of the neuroimaging tools available, enabling better decision making in choosing tools of particular research interest. Sources, links, and descriptions of the application of each tool are provided in this paper as well. Lastly, this paper presents the language implemented, system requirements, strengths, and weaknesses of the tools that have been widely used to image the structure and function of the nervous system.
Previous studies on individual differences in mathematical abilities have shown that working memory contributes to early arithmetic performance. In this study, we extended the investigation to algebraic word problem solving. A total of 151 10-year-olds were administered algebraic word problems and measures of working memory, intelligence quotient (IQ), and reading ability. Regression results were consistent with findings from the arithmetic literature showing that a literacy composite measure provided greater contribution than did executive function capacity. However, a series of path analyses showed that the overall contribution of executive function was comparable to that of literacy; the effect of executive function was mediated by that of literacy. Both the phonological loop and the visual spatial sketchpad failed to contribute directly; they contributed only indirectly by way of literacy and performance IQ, respectively.
This paper proposes the African Buffalo Optimization (ABO) which is a new metaheuristic algorithm that is derived from careful observation of the African buffalos, a species of wild cows, in the African forests and savannahs. This animal displays uncommon intelligence, strategic organizational skills, and exceptional navigational ingenuity in its traversal of the African landscape in search for food. The African Buffalo Optimization builds a mathematical model from the behavior of this animal and uses the model to solve 33 benchmark symmetric Traveling Salesman's Problem and six difficult asymmetric instances from the TSPLIB. This study shows that buffalos are able to ensure excellent exploration and exploitation of the search space through regular communication, cooperation, and good memory of its previous personal exploits as well as tapping from the herd's collective exploits. The results obtained by using the ABO to solve these TSP cases were benchmarked against the results obtained by using other popular algorithms. The results obtained using the African Buffalo Optimization algorithm are very competitive.
This study was designed to examine the relationships between problem-solving skills, hardiness, and perceived stress and to test the moderating role of hardiness in the relationship between problem-solving skills and perceived stress among 500 undergraduates from Malaysian public universities. The analyses showed that undergraduates with poor problem-solving confidence, external personal control of emotion, and approach-avoidance style were more likely to report perceived stress. Hardiness moderated the relationships between problem-solving skills and perceived stress. These findings reinforce the importance of moderating role of hardiness as an influencing factor that explains how problem-solving skills affect perceived stress among undergraduates.
Problem-based learning (PBL) is a student-centred learning system that involves multidisciplinary fields focused on problem solving. Facilitators of PBL are not necessarily content experts but little is known on how this concept has affected the outcomes of PBL sessions in learning Medical Biochemistry. We aimed to evaluate the impact of having the content expert as a facilitator in conducting PBL.
The purpose of this article is to discuss the strengths and limitations of two teaching strategies currently utilized
in Diploma in Nursing, in Malaysia. The diploma was started in 1994 with a 3 years duration of study. It also
requires certificates of qualification from the Malaysian Quality Agency (MQA) and the Malaysian Board of
Nursing.
Teaching strategies of individual teachers differ according to their teaching styles and generalized lesson plans,
which include structures, instructional objectives, outlines of teaching and learning tactics, and other
accessories needed to implement the strategies. A strategy does not necessarily follow a single track all the
time, but changes according to the demands of the situations such as the age, level, needs, interests and abilities
of the students. Thus, strategy is a method that is more comprehensive. It is directional in nature and refers to
the goal oriented activities of the teacher. Hence, it resembles science rather than arts.
Lecture and simulation methods are the best teaching strategies for nursing students in Malaysia. The lecture
method allows clarification on difficult concepts, organizes thinking, and promotes problem solving attitudes,
whereas simulation provides students with the opportunity for proper social, emotional and intellectual
development. Moreover, students are highly motivated by educational simulation, for they enjoy the learning
process while participating in it.
This study aims to examine key attributes affecting Airbnb users' satisfaction and dissatisfaction through the analysis of online reviews. A corpus that comprises 59,766 Airbnb reviews form 27,980 listings located in 12 different cities is analyzed by using both Latent Dirichlet Allocation (LDA) and supervised LDA (sLDA) approach. Unlike previous LDA based Airbnb studies, this study examines positive and negative Airbnb reviews separately, and results reveal the heterogeneity of satisfaction and dissatisfaction attributes in Airbnb accommodation. In particular, the emergence of the topic "guest conflicts" in this study leads to a new direction in future sharing economy accommodation research, which is to study the interactions of different guests in a highly shared environment. The results of topic distribution analysis show that in different types of Airbnb properties, Airbnb users attach different importance to the same service attributes. The topic correlation analysis reveals that home like experience and help from the host are associated with Airbnb users' revisit intention. We determine attributes that have the strongest predictive power to Airbnb users' satisfaction and dissatisfaction through the sLDA analysis, which provides valuable managerial insights into priority setting when developing strategies to increase Airbnb users' satisfaction. Methodologically, this study contributes by illustrating how to employ novel approaches to transform social media data into useful knowledge about customer satisfaction, and the findings can provide valuable managerial implications for Airbnb practitioners.
The collaborative and competitive nature of multi-agent systems (MAS) is visible through the simple social mode of communication that emerges between human-agent interactions or agent-to-agent interactions. A simple mode of communication involves the fundamental actions carried out by individual agents in achieving their desired goal. The sum of these achievements contribute to the overall group goal. Comparatively, the collective intelligence (CI) of a MAS simply means that these agents should work together to produce better solutions than those made possible when using the traditional approach. In designing MAS with CI properties, formalisation of a higher level deliberation process is essential. A high level deliberation process refers to the judgement comprehension of tasks, reasoning and problem solving and planning. In this paper, we propose our Collective Intelligence Model, CIM, which has the potential to control and coordinate a high-level deliberation process of a MAS. CIM is inspired by the emerging processes of controlled discussion, argumentation and negotiation between two or more intelligent human agents. These processes screen and validate the deliberation process through a crossfertilisation approach. The emergent property of the cross-fertilised ideas results in an intelligent solution that solves optimisation-related tasks.
This study aims to identify the role of mathematical representation as a mediator between mathematical belief and problem solving. A quasi-experimental design was developed that included 426 Form 1 secondary school students. Respondents comprised 209 and 217 students in the treatment and control groups, respectively. SPSS 23.0, ANATES 4 and Amos 18 were used for data analysis. Findings indicated that mathematical representation plays a significant role as mediator between mathematical belief and arithmetic problem solving. The Realistic Mathematics Education (RME) approach successfully increased the arithmetic problem-solving ability of students.
There has been a growing concern on the rising of environmental issues in Malaysia over the last decade. Many environmental studies conducted in this country began to utilise the chemometrics techniques to overcome the limitation in the environmental monitoring studies. Chemometrics becomes an important tool in environmental fields to evaluate the relationship of various environmental variables particularly in a large and complex database. The review aimed to analyse and summarize the current evidences and limitations on the application of chemometrics techniques in the environmental studies in Malaysia. The study performed a comprehensive review of relevant scientific journals concerning on the major environmental issues in the country, published between 2013 and 2017. A total of 29 papers which focused on the environmental issues were reviewed. Available evidences suggested that chemometrics techniques have a greater accuracy, flexibility and efficiency to be applied in environmental modelling. It also reported that chemometrics techniques are more practical for cost effective and time management in sampling and monitoring purposes. However, chemometrics is relatively new in environmental field in Malaysia and various scopes need to be considered in the future as the current studies focused on very limited number of major environmental issues. Overall, chemometrics techniques have a lot of advantages in solving environmental problems. The development of chemometrics in environmental studies in the country is necessary to advance understanding, thus able to produce more significant impacts towards the effective environmental management.
Carbon spheres (CSs) were synthesized from sucrose by hydrothermal reaction. The synthesized materials were further
activated with potassium hydroxide (KOH) at different concentrations. The effects of KOH concentration on the surface area
and morphology were investigated. The route for pore formation and structural deformation in carbon spheres during
activation has been proposed and discussed based on micrographs and porosity trends. It was suggested that the pore
formation and structural deformation phenomena were due to the intercalating power of energized K+ into the carbon.
This work provides an insight of the pore formation in carbon spheres for the development of adsorbents as well as for
the understanding of the structural deformation of such materials at higher KOH concentrations.
This program was conducted to analyze the effect of a robotic program in assessing technological problem solving among primary school children. The content in the learning module which contain technological problem solving and visible thinking activities has been going through expert validation before it were applied in this study. The instrument used to measure the technological problem solving is Technological Problem Solving Inventory (PSI-TECH). Quasi-experiments was implemented in this study, involving experimental and control group which were equal and homogeneous in selected characteristics. The robotic and basic visual coding program was conducted for 5 months, with an hour of lesson each week, consistent with the school syllabus and activities. Result obtained by collecting the data before and after the program, and quantitative analysis of t-test and MANOVA were used. Result had shown a significance positive value for the experimental group after the program. This study contributes in the field of education, in investigating the technological problem-solving skills among students. In addition, help to diversify the studies in the field of robotics.
Multicriteria decision making (MCDM) is one of the methods that popularly has been used in solving personnel selection problem. Alternatives, criteria, and weights are some of the fundamental aspects in MCDM that need to be defined clearly in order to achieve a good result. Apart from these aspects, fuzzy data has to take into consideration that it may arise from unobtainable and incomplete information. In this paper, we propose a new approach for personnel selection problem. The proposed approach is based on Hamming distance method with subjective and objective weights (HDMSOW's). In case of vagueness situation, fuzzy set theory is then incorporated onto the HDMSOW's. To determine the objective weight for each attribute, the fuzzy Shannon's entropy is considered. While for the subjective weight, it is aggregated into a comparable scale. A numerical example is presented to illustrate the HDMSOW's.