Napping/siesta during the day is a phenomenon, which is widely practised in the world. However, the timing, frequency, and duration may vary. The basis of napping is also diverse, but it is mainly done for improvement in alertness and general well-being. Neuroscience reveals that midday napping improves memory, enhances alertness, boosts wakefulness and performance, and recovers certain qualities of lost night sleep. Interestingly, Islam, the religion of the Muslims, advocates midday napping primarily because it was a practice preferred by Prophet Muhammad (pbuh). The objectives of this review were to investigate and compare identical key points on focused topic from both neuroscientific and Islamic perspectives and make recommendations for future researches.
What is COVID-19's impact on development? What lessons can be drawn from development studies regarding the effects of and recovery from COVID-19? The unprecedented scale and scope of government interventions carry implications at all levels: global, national, and local. In this introduction, our team of Editors underline the importance of systematic substantive study to further knowledge acquisition, and rigorous global-, national-, or context-specific evaluation to inform evidence-based policymaking. The 12 articles summarised here capture these values and sense of "high quality". In particular, despite early considerations in the first year of the pandemic, they illuminate the need for diverse responses beyond business-as-usual, attention to the multiplicity of impact of policies formulated, and progressive strategies to counteract the impacts of this disaster around the world. The path of future research is clear: studies need to consider and give voice to marginalised groups to counteract the short- and long-term impacts of the pandemic.
Over the past few years, high step-up dc-dc converters have been drawn substantial attention because of their wide-ranging application not only in the renewable energy sector but also in many other applications. To acquire a high voltage gain in photovoltaic (PV) and other renewable energy applications, a high step-up dc-dc converter is proposed in this paper. The proposed converter structure consists of a full-bridge (FB) module along with an input boost inductor and a voltage multiplier based on the Cockcroft-Walton (CW) principle with a parallel inductor. The key features of the proposed converter are: 1) high voltage gain with lower voltage stress on the switches, diodes and other passive elements without affecting the number of cascaded stages, 2) a minimum size of boost inductance and cascaded stage capacitance that ensures its compactness and low cost, and 3) a minimal number of major components. Circuit operation, steady-state analysis and various design parameters of the proposed converter are explained in details. In order to prove the performance of the theoretical analysis, a laboratory prototype is also implemented. The peak voltage gain and the maximum efficiency obtained are 11.9 and 94.6% respectively with very low input current ripple and output voltage ripple generated.
Taking into consideration that the prior CMF detection methods rely on several fixed threshold values in the filtering process, we propose an efficient CMF detection method with an automatic threshold selection, named as CMF-iteMS. The CMF-iteMS recommends a PatchMatch-based CMF detection method that adapts Fourier-Mellin Transform (FMT) as the feature extraction technique while a new automatic threshold selection based on iterative means of regions size (iteMS) procedure is introduced to have flexibility in changing the threshold value for various characteristics (quality, sizes, and attacks) in each input image. To ensure the reliability of the proposed CMF-iteMS, the method is compared with four state-of-the-art CMF detection methods based on Scale Invariant Feature Transform (SIFT), patch matching, multi-scale analysis and symmetry techniques using three available datasets that cover the variety of characteristics in CMF images. The results show that the F-score of the CMF-iteMS outperformed existing CMF detection methods by exceeding an average of 90% F-score values for image-level evaluation and 82% of F-score value for pixel-level evaluation for all datasets in original size. As special attention is given to the image resizing attack, the method is able to maintain the highest performance even if the images in the datasets are resized to 0.25 parameter.
Optimization is central to any problem involving decision making. The area
of optimization has received enormous attention for over 30 years and it is still popular
in research field to this day. In this paper, a global optimization method called Improved
Homotopy with 2-Step Predictor-corrector Method will be introduced. The method in-
troduced is able to identify all local solutions by converting non-convex optimization
problems into piece-wise convex optimization problems. A mechanism which only consid-
ers the convex part where minimizers existed on a function is applied. This mechanism
allows the method to filter out concave parts and some unrelated parts automatically.
The identified convex parts are called trusted intervals. The descent property and the
global convergence of the method was shown in this paper. 15 test problems have been
used to show the ability of the algorithm proposed in locating global minimizer.
As the technology for 3D photography has developed rapidly in recent years, an enormous amount of 3D images has been produced, one of the directions of research for which is face recognition. Improving the accuracy of a number of data is crucial in 3D face recognition problems. Traditional machine learning methods can be used to recognize 3D faces, but the face recognition rate has declined rapidly with the increasing number of 3D images. As a result, classifying large amounts of 3D image data is time-consuming, expensive, and inefficient. The deep learning methods have become the focus of attention in the 3D face recognition research. In our experiment, the end-to-end face recognition system based on 3D face texture is proposed, combining the geometric invariants, histogram of oriented gradients and the fine-tuned residual neural networks. The research shows that when the performance is evaluated by the FRGC-v2 dataset, as the fine-tuned ResNet deep neural network layers are increased, the best Top-1 accuracy is up to 98.26% and the Top-2 accuracy is 99.40%. The framework proposed costs less iterations than traditional methods. The analysis suggests that a large number of 3D face data by the proposed recognition framework could significantly improve recognition decisions in realistic 3D face scenarios.
The domain of underwater wireless sensor networks (UWSNs) had received a lot of attention recently due to its significant advanced capabilities in the ocean surveillance, marine monitoring and application deployment for detecting underwater targets. However, the literature have not compiled the state-of-the-art along its direction to discover the recent advancements which were fuelled by the underwater sensor technologies. Hence, this paper offers the newest analysis on the available evidences by reviewing studies in the past five years on various aspects that support network activities and applications in UWSN environments. This work was motivated by the need for robust and flexible solutions that can satisfy the requirements for the rapid development of the underwater wireless sensor networks. This paper identifies the key requirements for achieving essential services as well as common platforms for UWSN. It also contributes a taxonomy of the critical elements in UWSNs by devising a classification on architectural elements, communications, routing protocol and standards, security, and applications of UWSNs. Finally, the major challenges that remain open are presented as a guide for future research directions.
The recent interest to nanotechnology aims not only at device miniaturisation, but also at understanding the effects of quantised structure in materials of reduced dimensions, which exhibit different properties from their bulk counterparts. In particular, quantised metal nanowires made of silver, gold or copper have attracted much attention owing to their unique intrinsic and extrinsic length-dependent mechanical properties. Here we review the current state of art and developments in these nanowires from synthesis to mechanical properties, which make them leading contenders for next-generation nanoelectromechanical systems. We also present theories of interatomic interaction in metallic nanowires, as well as challenges in their synthesis and simulation.
The analysis of atom-to-atom and/or residue-to-residue contacts remains a favoured mode of analysing the mol-ecular packing in crystals. In this contribution, additional tools are highlighted as methods for analysis in order to complement the 'crystallographer's tool', PLATON [Spek (2009). Acta Cryst. D65, 148-155]. Thus, a brief outline of the procedures and what can be learned by using Crystal Explorer [Spackman & Jayatilaka (2009). CrystEngComm11, 19-23] is presented. Attention is then directed towards evaluating the nature, i.e. attractive/weakly attractive/repulsive, of specific contacts employing NCIPLOT [Johnson et al. (2010). J. Am. Chem. Soc. 132, 6498-6506]. This is complemented by a discussion of the calculation of energy frameworks utilizing the latest version of Crystal Explorer. All the mentioned programs are free of charge and straightforward to use. More importantly, they complement each other to give a more complete picture of how mol-ecules assemble in mol-ecular crystals.
Paraphrase identification serves as an important topic in natural language processing while sequence alignment and matching underlie the principle of this task. Traditional alignment methods take advantage of attention mechanism. Attention mechanism, i.e. weighting technique, could pick out the most similar/dissimilar parts, but is weak in modeling the aligned unmatched parts, which are the crucial evidence to identify paraphrases. In this paper, we empower neural architecture with Hungarian algorithm to extract the aligned unmatched parts. Specifically, first, our model applies BiLSTM/BERT to encode the input sentences into hidden representations. Then, Hungarian layer leverages the hidden representations to extract the aligned unmatched parts. Last, we apply cosine similarity to metric the aligned unmatched parts for a final discrimination. Extensive experiments show that our model outperforms other baselines, substantially and significantly.
Parser is aprocess of classifying sentence structuresof a language. Parser receives a sentence and breaks it up into correct phrases. The purpose of this research is to develop a Malay single sentence parser that can help primary school studentsto learn Malay language according to the correct phrases. Thisis because research in Malay sentenceparsinghasnot gottenenough attention from researchers tothe extent ofbuildingparserprototypes. This research used top-down parsing technique,and grammar chosen was context-free grammar (CFG) for Malay language. However, to parse a sentence with correct phrase was a difficult task due to lack of resourcesfor obtainingMalay lexicon. Malay lexicon is a database that storesthousands of words with their correct phrases. Therefore, this research developeda Malay lexicon based on an articlefrom Dewan Masyarakatmagazine. In conclusion, this research can providehelpto the primaryschoolstudentsto organize correct Malay single sentences.
Euler method is a numerical order process for solving problems with the Ordinary Differential Equation (ODE). It is a fast and easy way. While Euler offers a simple procedure for solving ODEs, problems such as complexity, processing time and accuracy have driven others to use more sophisticated methods. Improvements to the Euler method have attracted much attention resulting in numerous modified Euler methods. This paper proposes Cube Polygon, a modified Euler method with improved accuracy and complexity. In order to demonstrate the accuracy and easy implementation of the proposed method, several examples are presented. Cube Polygon’s performance was compared to Polygon’s scheme and evaluated against exact solutions using SCILAB. Results indicate that not only Cube Polygon has produced solutions that are close to identical solutions for small step sizes, but also for higher step sizes, thus generating more accurate results and decrease complexity. Also known in this paper is the general of the RL circuit due to the ODE problem.
The pursuit for higher degrees is accelerating in the country. With mushrooming foreign and local graduates from non-university and university status institutions, it is critical to explore the types of qualification awarded and the existing platform for recognition and accreditation purposes. The objectives of this study are: (i) to gather information with regard to current policies and practices pertaining to recognition and accreditation systems of the higher education sector, with specific reference to Malaysia and china (ii) to review the existing policy between accreditation and recognition agencies/providers and (iii) to recommend best practices, guidelines and strategies for practical implementation in Malaysia. The methodology pursuit in Malaysia and china involved inspection of documents and purposive interviews. The research was implemented from May 2009 to november 2009. The results of the research revealed that though the worldview of mutual recognition agreement is to liberalise the education sector, the authentic situations prevailing in the country requires the purposive liberalization of the education sector, with periodic reviews for its appropriateness and relevance for the needs of the country (provisional and conditional), thereby ensuring regulatory, review and quality sustainability. The customized regulatory framework would be a prerequisite (conditional), with due attention be given to either implicit or explicit conditions in the recognition of academic degrees. In deliberating the mutual recognition agreement with jurisdiction including those which are more educationally advanced, selective emerging 'niche' areas and/or supportive (conditional) have been proposed. Finally, to strengthen the existing regulatory frame work, innovative provision in this legal framework is recommended.
Audio forgery is any act of tampering, illegal copy and fake quality in the audio in a criminal way. In the last decade, there has been increasing attention to the audio forgery detection due to a significant increase in the number of forge in different type of audio. There are a number of methods for forgery detection, which electric network frequency (ENF) is one of the powerful methods in this area for forgery detection in terms of accuracy. In spite of suitable accuracy of ENF in a majority of plug-in powered devices, the weak accuracy of ENF in audio forgery detection for battery-powered devices, especially in laptop and mobile phone, can be consider as one of the main obstacles of the ENF. To solve the ENF problem in terms of accuracy in battery-powered devices, a combination method of ENF and phase feature is proposed. From experiment conducted, ENF alone give 50% and 60% accuracy for forgery detection in mobile phone and laptop respectively, while the proposed method shows 88% and 92% accuracy respectively, for forgery detection in battery-powered devices. The results lead to higher accuracy for forgery detection with the combination of ENF and phase feature.
As the amount of document increases, automation of classification that aids the analysis and management of documents receive focal attention. Classification, based on association rules that are generated from a collection of documents, is a recent data mining approach that integrates association rule mining and classification. The existing approaches produces either high accuracy with large number of rules or a small number of association rules that generate low accuracy. This work presents an association rule mining that employs a new item production algorithm that generates a small number of rules and produces an acceptable accuracy rate. The proposed method is evaluated on UCI datasets and measured based on prediction accuracy and the number of generated association rules. Comparison is later made against an existing classifier, Multi-class Classification based on Association Rule (MCAR). From the undertaken experiments, it is learned that the proposed method produces similar accuracy rate as MCAR but yet uses lesser number of rules.
Sports coaching and especially high performance coaching has long existed in some sort of duality. On one hand, sport coaching has been regarded by many as a prestigious and rewarding job, whereas on the other, sport coaching still lacks a reputation as a career opportunity mostly due to the fact that coaching is yet to receive its full professional recognition in the society. Given the vast variety of coaching qualifications, coaching roles and coaching occupations available within sport infrastructure in the society, the situation has got progressively complicated with the recognition of coaching qualifications. In addition, the growing popularity of high performance and participation sports in the society started drawing more attention from the public to the issues of coach education, competence and qualifications. Malaysian scenario on the issue is quite complicated as well, and growing demand to uplift the country’s performance in SEA, Asian, Commonwealth and Olympic Games requires interference from the higher education institutions and NGOs.
Clustering a set of objects into homogeneous groups is a fundamental operation in data mining. Recently, many attentions have been put on categorical data clustering, where data objects are made up of non-numerical attributes. For categorical data clustering the rough set based approaches such as Maximum Dependency Attribute (MDA) and Maximum Significance Attribute (MSA) has outperformed their predecessor approaches like Bi-Clustering (BC), Total Roughness (TR) and Min-Min Roughness(MMR). This paper presents the limitations and issues of MDA and MSA techniques on special type of data sets where both techniques fails to select or faces difficulty in selecting their best clustering attribute. Therefore, this analysis motivates the need to come up with better and more generalize rough set theory approach that can cope the issues with MDA and MSA. Hence, an alternative technique named Maximum Indiscernible Attribute (MIA) for clustering categorical data using rough set indiscernible relations is proposed. The novelty of the proposed approach is that, unlike other rough set theory techniques, it uses the domain knowledge of the data set. It is based on the concept of indiscernibility relation combined with a number of clusters. To show the significance of proposed approach, the effect of number of clusters on rough accuracy, purity and entropy are described in the form of propositions. Moreover, ten different data sets from previously utilized research cases and UCI repository are used for experiments. The results produced in tabular and graphical forms shows that the proposed MIA technique provides better performance in selecting the clustering attribute in terms of purity, entropy, iterations, time, accuracy and rough accuracy.
Thinking is something that we do all through our lives - an activity thcit possibly antedates our very birth itself Yet our children and we are not told about thinking or taught about the thinking process that dominates our lives, possibly, because of our own limited under-standing. Consequently, children are told to be logical and are discouraged from thinking differently, because it is the only type of think-ing we know and can understand. Methods of assessing their performance based on logical thinking underestimate their true potentials. The creative potentials of these children, 40% of who are right-brained need to be harnessed by approaches to learning that utilize methods of teaching and assessment, appropriate for their style of thinking. Another group of children, who need special attention, are those with learning disabilities that have been ignored, but can be corrected with appropriate programmes that provide a comprehensive approach to regular and special education.
In this paper, we introduce T-DepExp system to simulate the transitive dependence based coalition formation (CF). It is a multi-agent based simulation (MABS) tool that aims to enhance cooperation between agents through transitive dependence. Previously, the transitive dependence was introduced by An and his colleagues for expressing the indirect dependence between agents in their cooperation. However, it did not receive much attention. Although it has a few problems need to be addressed, we try to propose our own mechanism to increase the efficiency of the transitive dependence based CF. To simulate MAS dependence relationship, we have included two fundamental dependence relationships in this MABS tool, which are AND-Dependence and OR-Dependence. In addition, the architecture of the T-DepExp system is presented and discussed. It allows possible integration of other features such as budget mechanism and trust model. Subsequently, hypothesis for the experiments and experimental setup are explained. The overall system will be demonstrated for its functionality and the experimental results will also be discussed.
Surface sediments were collected from the north western intertidal area (14 sites), drainage (3 sites), and rivers (3 sites) of Peninsular Malaysia in April 2005. The samples were analyzed for their concentrations of Cd, Ni, and Zn. The ranges for the total concentrations (µg/g dry weight) of Cd, Ni, and Zn were found to be 0.79-2.48, 6.46-73.92, and 33.6-484.14, respectively. Factory drainage site at Juru exceeded the established sediment quality values (Effect Range Median-ERM) for Zn and Ni, while the concentrations of Zn were also found to have exceeded the ERM at drainages at Kuala Kurau Town and Sg. Juru sites. The geochemical study, based on the sequential extraction technique on the sediments, revealed that the metal percentages of non-resistant fractions of the drainage at Kuala Kurau Town (drainage), Sg. Juru (river), Kuala Juru (intertidal), and factory drainage site at Juru were higher than the resistant fractions of the metals. These indicated that the sites (intertidal, river, and drainages) received anthropogenic inputs of these metals. Therefore, the point source of anthropogenic input in these sites should be given attention in future in order to mitigate the environmental problem on the living resources in the north western of Peninsular Malaysia. The present monitoring data are useful for future establishment of sediment quality guideline for Malaysian aquatic environment.