13474 research outputs found
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Synthesis and characterization of water-soluble polyaniline for hydrazine detection / Hazira Hussin
No full textIn this study, polyaniline (PAni) was synthesized through the chemical oxidation method using aniline (Ani) as a monomer, hydrochloric acid (HCl) as a dopant, and ammonium persulphate (APS) as an initiator. PAni was rendered water soluble through treating with cellulose derivatives. Cellulose derivatives such as methylcellulose (MC), hydroxypropyl cellulose (HPC), and hydroxypropyl methylcellulose (HPMC) were used as a stabilizer to improve the solubility of PAni in an aqueous medium. The water-soluble PAni-cellulose derivative was applied as a chemical sensor for hydrazine detection. The chemical structures and electrical conducting behaviours of PAni-cellulose derivatives were investigated using Fourier transform infrared (FTIR) and Ultraviolet- visible (UV-Vis) spectroscopic analyses. The conductivities of water-soluble PAni-cellulose derivatives were measured using conductivity meter. In calibrating the hydrazine detection, water-soluble PAni-cellulose derivatives were mixed with various concentrations of hydrazine and the sensor responses were measured using the conductivity meter. At the early stage of the application, the range from 10 ppm to 100 ppm of hydrazine concentration was used. There were several parameters that have been studied, these include: (i) different volume ratio of PAni to hydrazine, (ii) different types of cellulose derivatives, (iii) different mass ratio of Ani to MC, and (iv) the detection at different concentration of hydrazine (1 ppm to 10 ppm). From the results obtained, all water-soluble PAni-cellulose derivatives showed a decrease in the electrical conductivities with the increasing of hydrazine concentration. This happens due to the reduction of PAni from conducting emeraldine salt (ES) to non-conducting leucoemeraldine (LE) by hydrazine. The PAni-MC (ratio 2:5) showed the best result with a good linearity of 0.9832, hence was selected to study the sensor performance. The calibration plot was constructed from 0.05 ppm to 5.00 ppm of hydrazine. From the calibration curve, it showed a good linear correlation determination of 0.9953, a good sensitivity of 0.0132 ppm-1, the limit of detection (LOD) of 0.55 ppm, and the limit of quantitation (LOQ) of 1.82 ppm. This study showed that the LOD achieved was lower than the OSHA standard of 1 ppm. Besides, the sensor performance was supported by UV-Vis and FTIR analyses. UV-Vis spectra show a significant decrease in absorbance at the bipolaron band (~821 nm) from 3.27 to 0.63 and at polaron band (~450 nm) from 2.89 to 0.55. A notable change in the chemical structures analyzed by FTIR for the quinoid to benzenoid (IQ / IB) intensity ratio of 1.03 before the addition of hydrazine decreased to 0.97 after the addition of hydrazine. Both UV-Vis and FTIR analyses significantly proved that the ES of water-soluble PAni-MC has turned to LE because LE consists of the more benzenoid units compared to ES. The water-soluble PAni-MC eventually indicated a good recovery range of 96 - 136 % and a good relative standard deviation (RSD) of 2.69 - 5.84 % in the real sample analysis study. As a conclusion, the water-soluble PAni-MC showed good potential as a chemical sensor for hydrazine detection
Efficient vehicle routing optimization for autistic users / Abdulrahman Hasan Mohammed Shabalah
No full textIn recent years, daily life without a vehicle would be impossible. As an inevitable result, the number of vehicles on the road increases day by day in various large cities around the world. The increased number of vehicles is a big concern because it causes a lot of traffic congestions, especially during peak hours. Besides, there has been a rapid rise of on-demand Ride-Hailing Services (RHSs), such as Grab, Uber, EzCab, and MyCar, etc. This allows passengers with smartphones to place trip requests and assign them to drivers according to requester’s location and drivers' availability. In consequence, efficient routing algorithms are needed for the sake of enhancing the availability of car-resources. Even though there is an emerging number of RHS applications, there is a lack in their algorithms to tackle the issue of the special characteristics for the autistic users’ requirements. Therefore, in this research, a routing algorithm for ride-hailing services has been proposed. The new proposed algorithm is called Autistic-Features Ant Colony (AFAC). This proposed algorithm utilizes the Ant Colony Optimization (ACO) with autistic features to enhance the efficiency and performance of the overall system and the autistic user's satisfaction in ride-hailing services. AFAC considers the road and the autistic features simultaneously to find the optimum route for the autistic user. While the road features play a vital role in finding the optimum route from nearby car-resources to the autistic user, the autistic features help to make a better selection of car-resources in terms of providing autistic users with specialized drivers who can deal with them. Simulation experiments have been conducted using the Unity game engine to analyse the effect of these features on the performance of the overall system. AFAC is designed based on client-server architecture, which includes both the server-side and the client-side parts. The communication between the client and the server is made by requesting the Hypertext Transfer Protocol (HTTP). The Simulation results were obtained by measuring the performance of three algorithms named Ant Colony, A Multiple Parameter control for Ant Colony (MPAC), and the proposed AFAC. The design and analysis of the comparison have been done using MATLAB tool. The experimental results showed that the performance of the proposed AFAC algorithm outperformed the classical Ant Colony and the recent MPAC algorithms
An enhancement of age and gender classification accuracy with hybrid handcrafted and deep features using hierarchical extreme learning machine / Mohammad Javidan Darugar
No full textAge and gender classification are some of the essential algorithms that have many use cases in our everyday life. For example, in robotics, field robots can interact with a human base on their gender in data analysis, to have statistics about age and gender of audiences in social events, YouTube video analysis, and many other applications. In this research, we have addressed limitations in deep neural networks, which by overcoming this limitation, we can gain better accuracy and performance. Our study has several other possible applications which are not limited only to age and gender classification. This dissertation is about a high-performance method for age and gender classification in captured facial photos, which is employing deep network architectures as the primary basis of our architecture. We have employed branches of deep learning, such as convolutional neural networks and autoencoders. We have also used Hierarchical Extreme Learning Machines to avoid significant time consumption and to overcome limitations that most conventional deep networks have. We have addressed the problem of using Softmax as the classifier, which usually leads to less performance and accuracy due to its limitation, which is only able to classify linearly separable data. Our proposed architecture consists of two main categories of feature extraction and learning methods which are namely supervised and unsupervised. We have investigated two supervised feature extraction techniques and a deep feature extraction technique to extract unsupervised features to judge the influence of each one on the accuracy of our proposed model. The supervised methods that are examined in this study are Histogram of Oriented Gradients or HOG and Action Units (or AUs). For unsupervised feature extraction techniques, we have employed a deep neural network which is known as convolutional neural network (CNN). CNNs use shift-invariant filters to make discriminative features inside neural networks. One of the very potent tools is convolutional neural networks, which is used for obtaining useful features that also are useful for unknown classes. In our research, we figured out that some of our features are high, and some are very low in dimension. So to combine these groups of supervised and unsupervised features with different dimensions, we have used multiple autoencoder neural networks to join, reduce, and encode all employed feature maps into a single feature vector. Hierarchical-ELM, which is a branch of Extreme Learning Machines, is adopted to classify the final feature vector. Toward this research, we have analyzed the result of our proposed work with state of the art, and also related works are explained to illustrate our significant improvement for age and gender classification in facial images. Our gains are in both accuracy and performance. Regarding performance, we have achieved faster training and testing process. Since we are dealing with large datasets of facial images, therefore, speeding up these steps can influence a more reliable solution
Political religious extremism and its impact on social security: A study at Gaza Strip, Palestine / Mona J.M Abumarzouq
No full textThis study, named 'political religious extremism and its impact on societal security: A Study
at Gaza Strip, Palestine ', approaches its types, causes, manifestations, effects, attitude of
interpreters and how university students in Gaza Strip perceive its reality and impact.
Moreover, the role of lecturers in approaching it, and detecting of statistical differences in
how the sample’s individuals realize reality of the phenomenon, attributed to the variables:
(gender - university - governorate). The researcher follows the descriptive, applied, analytical
approach, by extrapolating texts related to the topic comprehensively, extracting a base from
contemporary interpretations, an applied study that combines interviews and questionnaire,
describing the results, analyzing and interpreting them, and explaining ways of prevention
and treatment. The most important results are that religious extremism is a product of
religious, political, social, economic or educational conditions that created its types; the most
influential factor is the boldness and misunderstanding of God’s rulings, and the influence of
deviant beliefs that are contrary to the moderate approach. Its impacts are also clear, but the
misleading focus of the media on Sunni groups as an example of extremism, and its serious
consequences from the emergence of misguided ideas contrary to the nation culture, and
contradictory groups in the principles and beliefs. The attitudes of interpreters are parallel in
that the basic principle is establishing of Al-Sharia in all fields and relations between
Muslims and others, and refusing to acknowledge its validity obliges the person the features
of immorality and disbelief and injustice, if scholars do not persevere in the opposition and
avoid the Sharia, they would betray what they believed in, the students' awareness of problem
has an average degree). The lecturers 'role in approaching it is limited by the lack of
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methodology, and the problem is spread in a varying degree, and there are no statistical
differences at (α ≥.0.0) between the averages of students' awareness of the problem due to
the gender variable in addition to statistical differences due to the variable of residence and
university for the Rafah region and Al-Quds University, the most important
recommendations is to activate the role of technology to explain the danger of the problem
and spread of moderation and advancement of the culture of society in line with the religious
teachings, conduct training courses for lecturers and speakers to approach in thoughtful ways
with extremism, and adopt planning for societal political security
Pembinaan model penerimagunaan persekitaran pembelajaran maya dalam kalangan guru bahasa Melayu sekolah menengah di Selangor / Zanariah Hamid
No full textKajian ini bertujuan untuk mengenal pasti tahap penerimagunaan Persekitaran Pembelajaran Maya serta menguji model cadangan yang menjelaskan faktor-faktor yang mempengaruhi penerimagunaan Persekitaran Pembelajaran Maya dalam kalangan guru Bahasa Melayu sekolah menengah di Selangor. Selain itu, kajian mengenai pemboleh ubah perantara (keinginan untuk penerimagunaan) dan pemboleh ubah penyederhana (umur, pengalaman mengajar dan jantina) juga dikaji memandangkan kajian berkaitan perantara dan penyederhana masih kurang dijalankan di negara ini. Walaupun terdapat kajian mengenai Persekitaran Pembelajaran Maya di Malaysia, namun kajian tersebut hanya memfokuskan kepada cabaran yang dihadapi oleh guru, manfaat aplikasi tersebut dalam pengajaran serta penerimaan aplikasi tersebut daripada pihak murid dan guru pelatih serta tidak berkaitan dengan guru Bahasa Melayu. Oleh itu, kajian ini cuba menghuraikan isu penerimagunaan guru Bahasa Melayu dengan menggunakan pemboleh ubah yang terdapat dalam Model Penerimaan Teknologi (TAM), Teori Bersepadu Penerimaan dan Penggunaan Teknologi (UTAUT), Konsep Kepercayaan Guru dalam Pengajaran dan Model Syarat Perubahan Ely. Kajian ini ialah kajian tinjauan dengan menggunakan soal selidik. Sebanyak 400 orang guru yang mengajar Bahasa Melayu sekolah menengah harian di Selangor telah dipilih secara rawak sebagai responden. Kajian ini menggunakan instrumen soal selidik yang diadaptasi daripada penyelidik terdahulu dengan tahap kesahan dan kebolehpercayaan yang baik. Pemodelan Persamaan Struktur Kuasa Dua Terkecil Separa (PLS-SEM) telah digunakan untuk mengenal pasti kesahan, kebolehpercayaan dan menganalisis data struktural. Dapatan kajian mendapati tahap penerimagunaan guru Bahasa Melayu adalah sederhana. Tujuh daripada lapan pemboleh ubah kajian menjadi peramal kepada penerimagunaan Persekitaran Pembelajaran Maya iaitu sokongan pentadbir (β = .230, p .05). Selain itu, pemboleh ubah keinginan untuk penerimagunaan berfungsi sebagai perantara separa (β = .061, p < .05) dalam hubungan antara sikap terhadap penerimagunaan dengan penerimagunaan. Kajian ini mendapati umur berperanan sebagai penyederhana (β = .065, p < .05) dalam hubungan antara pemboleh ubah sikap terhadap penerimagunaan dengan penerimagunaan Persekitaran Pembelajaran Maya. Nilai pekali penentuan (R2) kajian ini adalah sebanyak 81.5% yang memberi maksud sebarang perubahan penerimagunaan Persekitaran Pembelajaran Maya disumbangkan oleh gabungan perubahan oleh ketujuh-tujuh faktor kajian yang signifikan secara statistik manakala 18.5% oleh faktor-faktor lain yang tidak dikaji. Model kajian ini mempunyai ramalan yang relevan (Q2) pada peringkat yang tinggi dengan nilai catatan sebanyak .465. Kesimpulannya, model penerimagunaan Persekitaran Pembelajaran Maya dalam kalangan guru Bahasa Melayu diharap dapat membantu kepada kejayaan penggunaan Persekitaran Pembelajaran Maya dalam pengajaran Bahasa Melayu
The evolving fuzzy clustering approach for discriminating neutron and gamma-ray pulses / Ali Seyed Shirkhorshidi
No full textHaving a significant amount of data is not useful unless the data can be processed for extracting knowledge and information. One of the elementary steps in crunching data is to break it down into groups. When the data is small and collected in a controlled manner, and when the training data is appropriately labelled, the trivial approach is to use supervised learning to perform the grouping. Supervised methods need training data and information about groups beforehand; however, in the current reality, with an avalanche of data, this information is not available. Nevertheless, the need for grouping data remains. Clustering, as an unsupervised method, helps in these situations to group the data. However, unsupervised methods are usually less accurate than their supervised counterparts. To solve this drawback, unsupervised methods are often used as a pre-processing step, along with human judgment, to prune the data to create a reliable training set for the supervised process. One reason that clustering approaches do not yield desirable accuracy is that they will attempt to perform the procedure on all data, which may contain noise or outliers, and they do not have any mechanism by which to set aside the problematic data. Pulse-shape discrimination (PSD) for neutron and gamma-ray pulses that is addressed in this research is one example of a real-world case study that faces the same issues. Although the data utilised for this study is from a liquid scintillator, it can be applied to other signal detectors as well. Aside from this particular dataset, the proposed approach has been applied to a set of publicly available multivariate and time series datasets to prove the performance of the presented approach through an exploratory study. The evolving fuzzy clustering approach (EFCA) proposed in this study utilises a fuzzy membership matrix in fuzzy clustering to propose a new approach for clustering that embeds a heuristic post-pruning solution to address the aforementioned drawback. The method is an EFCA that attempts to find clusters of similar shapes with better accuracy. It introduces an approach for post-pruning that is examined not only on neutron and gamma-ray discrimination but also on various datasets. The outcomes of the proposed method are evaluated against the traditional fuzzy C-means method and another well-known crisp clustering method, namely, K-means. For neutron and gamma-ray discrimination, the EFCA improved the Rand index (RI) accuracy by almost 8%. For other multivariate and time series datasets utilised in this study, results demonstrate the achievement of significant accuracy improvements for some of these datasets after heuristic post-pruning, resulting in 100% RI accuracy for some of them
Metacognition in problem solving process of one-dimensional kinematics among physics pre-service teachers / Suci Rizkina Tari
No full textIssue about low physics problem solving performance in Indonesia due to lack of metacognitive skills urged the development of explicit instruction. This urgency has led to the need of deep understanding about students’ metacognitive behaviour and how these behaviour affect the problem solving process. This study aims to identify metacognitive behaviour that students exhibited while solving one-dimensional kinematics problem and examined the role of metacognition in problem solving process of one-dimensional kinematics. A qualitative study was employed. Six students from Physics Education programme at a university was chosen as the participants in this study. Data were collected through think aloud method, observation, retrospective interview and answer sheet. The think aloud activity was recorded, transcribed and coded. Findings from think aloud analysis was supported by findings from analyses of observation, interview, and students’ answer sheets. The findings reveals twelve metacognitive behaviours that exhibited by the students while solving one-dimensional kinematics problem which were rereading, reading with strategy, arrange the information, draw a sketch, making sense of the problem, relating the concept, speculating the answer, formulating the plan, breaking down the plan, assessing the plan, reflecting on the plan, and assessing the computation. The data findings also suggest three roles of metacognition in problem solving process of one-dimensional kinematics which were metacognition enables student to construct their understanding of the problem, regulate their action, and monitor their progress during problem solving. The findings of this study provided information to design the explicit metacognitive instruction which aimed to improve problem solving performance of students
Expression of stress related genes of Penaeus varnamei in biofloc closed breeding system / Nguyen Truong An
No full textThe Pacific shrimp Penaeus vannamei, which is also known as the whiteleg shrimp, is fast becoming a key species of shrimp farming worldwide since this species has been cultured widely in many countries, such as Mexico, America, especially in Southeast Asian countries including Thailand, Vietnam, and Malaysia. In addition, the shrimp aquaculture industry has applied many new cultural techniques, especially biofloc technology – which is an eco-friendly culture technique and has many advantages over other traditional methods, such as high productivity, the stable environment of the culture, and low levels of feed conversion. Unfortunately, for so many years, shrimp farming has been constantly hounded by stress, which is caused by several factors of the culture environment including the temperature of the water, dissolved oxygen, salinity, and pH. Stress can give negative effects on cultured shrimps, such as slow growth or high mortality, subsequently economic losses. Therefore, it is important to identify whether shrimp farming using biofloc technology causes any stresses to the Pacific white shrimp. It has been investigated that when the shrimps being stressed, they can generate various physiological as well as genomic reactions to modify the mechanism of compensation or adaptation, thus allowing the mitigation of stressors as well as the maintenance of their hemostasis that help them to survive under the pressure of stress. Therefore, environmental stressors can be determined by detecting those genes associated with the stress response. In the current study, three different sets of primers were used for the amplification of catalase, Na+-K+ATPase α-subunit, and heat shock protein 70 – these are the genes that respond to hypoxia stress, salinity stress, and heat shock stress, respectively. Sequences of those primers that were used for the amplification of catalase and Na+-K+ATPase α-subunit were designed according to the published mRNA sequences of L. vannamemi available, while sequences of the primers used for amplification of heat shock protein 70 were retrieved from literature. qPCR was then applied for determining the expression of those three genes in the shrimps which were reared in four different biofloc culture conditions over a six-week period. The four conditions included microbubble aeration, a mixture of microbubble and macro bubble, macro bubble with bioballs, and macro bubble without bioballs. The up-regulated of catalase expression was found in the condition of microbubble and the condition of macro bubble with bioballs during the last two weeks of the cultivation and in the condition of macro bubble without bioballs in the 3rd, 4th, and 5th week of the cultivation. Meanwhile, the expression of Na+-K+-ATPase α-subunit was up-regulated in the condition of microbubble during the last two weeks of the cultivation, and in the condition of macro bubble with bioballs at the end of the cultivation. Lastly, the up-regulated of hsp70 was found in all four conditions throughout the cultivation. A negative correlation was found between temperature and hsp70 (n = 3; R2 = 0.454; p < 0.05). These findings make catalase, Na+-K+-ATPase α-subunit, and hsp70 genes a suitable predictive biomarker for penaeid shrimps
Energy-efficient communications in wireless powered cognitive radio networks based on game theory / Fadhil Mukhlif Aswad Al-Obaidy
No full textThere are challenging and prevalent problems related to spectrum resources with the interference of battery-based devices in future wireless networks. To address such challenges, this thesis proposes a theoretical framework for designing and analyzing the distributed power control algorithms in modern 5G cognitive networks. Previous experiments have shown that game theory tools can be used as a suitable and efficient technique to build scalable, balanced, and energy efficient for the distributed power control schemes in order to use it practically in battery-based devices in wireless networks. In reality, the power control issue is constructed as a non-cooperative game for which the user selects its transmission energy to increase or decrease its own utility. The ratio of throughput to transmit power is defined as the utility that is used to signify the power efficiency scheme, on the other hand, the cost can be presented as the sum of the sigmoid weighting of transmit power and the square of the signal to interference ratio error that can be used to signify the signal to interference balancing scheme. This work proposes a novel utility function to derive an efficient distributed power algorithm. Moreover, this thesis proposes a pricing technique which guides users to an effective Nash Equilibrium point to encourage users to use network resources efficiently. Such frameworks are considered as general when applied on candidates of cognitive scenarios which is Cognitive Radio (CR), Cognitive Sensor Networks (CSN) and Unmanned Aerial Vehicle (UAV) because of the critical and challenging issue of interference. In order to prove the effectiveness of such algorithms, numerical solutions are used in comparison with current power control algorithms. The findings of this thesis indicate that the simulated analytical and numerical results of the proposed algorithms can achieve a substantial reduction in transmit power of users which in turn minimizes the overall interference. Furthermore, the convergence rate of these algorithms is relatively fast which can help in guaranteeing that all users achieve their required QoS
Role of technological capability on firm performance among palm oil mills in Malaysia / Sima Parvand
No full textIn spite of its strong economic performance since the 1970s, oil palm mills in Malaysia has faced low technological upgrading. Although the import of capital goods has acted as an effective channel for technology transfer (TT), the lack of strong firm-level in-house upgrading and the presence of barriers has affected mill efficiency levels. Hence, this thesis seeks to examine how firm-level technological capability development (TC) has impacted on firm performance. A quantitative research design using simple random sampling procedure was deployed to gather responses from 54 palm oil mills in Malaysia. The multiple regression analyses revealed that innovation strategy, TT, government support, and size of mills impacted on TC development very strongly, while organization learning, strategy alliance, and type of ownership were insignificant. In addition, innovation types and R&D capability show significant relationships with firm performance. Also, R&D capability and innovation performance influences significantly marketing performance. The findings also highlight the complexity and cost of new technologies as technological barriers. The lack of financial resources and technical skills have posed as organizational barriers to TC development. The lack of government policy as also affected the shift to greening environment practices and the adoption of advanced processing technologies. The results also show that top management support and increasing the size of mills positively raise the adoption of advanced processing technologies and firm performance. However, the findings indicate that all significant factors have been critical to non-adopters, whereas complexity, top management support, technical skill, and size of mills were the most critical factors for adopters. This thesis complements past works by showing evidence that TC development has a strong impact on firm performance in oil palm mills. Secondly, it complements the resource-based view (RBV) and evolutionary theories that effective links between firm resources and capabilities are critical to drive firm performance. Policy-wise, firstly, the thesis makes the point that new technologies development and diffusion is important to quicken technological upgrading in palm oil mills. Secondly, it calls for governments to review their incentives and policies to improve TC development. Thirdly, there is a need for top management to support greening and new technology development strategies in the oil palm mills