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Fulltext Coffee and Tea Consumption and the Contribution of Their Added Ingredients to Total Energy and Nutrient Intakes in 10 European Countries: Benchmark Data from the Late 1990s

Landais E, Moskal A, Mullee A, Nicolas G, Gunter MJ, Huybrechts I, et al.

Nutrients, 2018 Jun 05;10(6).
PMID: 29874819 DOI: 10.3390/nu10060725

BACKGROUND: Coffee and tea are among the most commonly consumed nonalcoholic beverages worldwide, but methodological differences in assessing intake often hamper comparisons across populations. We aimed to (i) describe coffee and tea intakes and (ii) assess their contribution to intakes of selected nutrients in adults across 10 European countries.
METHOD: Between 1995 and 2000, a standardized 24-h dietary recall was conducted among 36,018 men and women from 27 European Prospective Investigation into Cancer and Nutrition (EPIC) study centres. Adjusted arithmetic means of intakes were estimated in grams (=volume) per day by sex and centre. Means of intake across centres were compared by sociodemographic characteristics and lifestyle factors.
RESULTS: In women, the mean daily intake of coffee ranged from 94 g/day (~0.6 cups) in Greece to 781 g/day (~4.4 cups) in Aarhus (Denmark), and tea from 14 g/day (~0.1 cups) in Navarra (Spain) to 788 g/day (~4.3 cups) in the UK general population. Similar geographical patterns for mean daily intakes of both coffee and tea were observed in men. Current smokers as compared with those who reported never smoking tended to drink on average up to 500 g/day more coffee and tea combined, but with substantial variation across centres. Other individuals' characteristics such as educational attainment or age were less predictive. In all centres, coffee and tea contributed to less than 10% of the energy intake. The greatest contribution to total sugar intakes was observed in Southern European centres (up to ~20%).
CONCLUSION: Coffee and tea intake and their contribution to energy and sugar intake differed greatly among European adults. Variation in consumption was mostly driven by geographical region.

Matched MeSH terms: Benchmarking*
Fulltext Quality indicators in pediatric colonoscopy in a low-volume center: Implications for training

Lee WS, Tee CW, Koay ZL, Wong TS, Zahraq F, Foo HW, et al.

World J Gastroenterol, 2018 Mar 07;24(9):1013-1021.
PMID: 29531465 DOI: 10.3748/wjg.v24.i9.1013

AIM: To study implications of measuring quality indicators on training and trainees' performance in pediatric colonoscopy in a low-volume training center.
METHODS: We reviewed retrospectively the performance of pediatric colonoscopies in a training center in Malaysia over 5 years (January 2010-December 2015), benchmarked against five quality indicators: appropriateness of indications, bowel preparations, cecum and ileal examination rates, and complications. The European Society of Gastrointestinal Endoscopy guideline for pediatric endoscopy and North American Society for Pediatric Gastroenterology, Hepatology and Nutrition training guidelines were used as benchmarks.
RESULTS: Median (± SD) age of 121 children [males = 74 (61.2%)] who had 177 colonoscopies was 7.0 (± 4.6) years. On average, 30 colonoscopies were performed each year (range: 19-58). Except for investigations of abdominal pain (21/177, 17%), indications for colonoscopies were appropriate in the remaining 83%. Bowel preparation was good in 87%. One patient (0.6%) with severe Crohn's disease had bowel perforation. Cecum examination and ileal intubation rate was 95% and 68.1%. Ileal intubation rate was significantly higher in diagnosing or assessing inflammatory bowel disease (IBD) than non-IBD (72.9% vs 50.0% P = 0.016). Performance of four trainees was consistent throughout the study period. Average cecum and ileal examination rate among trainees were 97% and 77%.
CONCLUSION: Benchmarking against established guidelines helps units with a low-volume of colonoscopies to identify area for further improvement.

Matched MeSH terms: Benchmarking/standards
Fulltext Evidence on Digital Mental Health Interventions for Adolescents and Young People: Systematic Overview

Lehtimaki S, Martic J, Wahl B, Foster KT, Schwalbe N

JMIR Ment Health, 2021 Apr 29;8(4):e25847.
PMID: 33913817 DOI: 10.2196/25847

BACKGROUND: An estimated 1 in 5 adolescents experience a mental health disorder each year; yet because of barriers to accessing and seeking care, most remain undiagnosed and untreated. Furthermore, the early emergence of psychopathology contributes to a lifelong course of challenges across a broad set of functional domains, so addressing this early in the life course is essential. With increasing digital connectivity, including in low- and middle-income countries, digital health technologies are considered promising for addressing mental health among adolescents and young people. In recent years, a growing number of digital health interventions, including more than 2 million web-based mental health apps, have been developed to address a range of mental health issues.
OBJECTIVE: This review aims to synthesize the current evidence on digital health interventions targeting adolescents and young people with mental health conditions, aged between 10-24 years, with a focus on effectiveness, cost-effectiveness, and generalizability to low-resource settings (eg, low- and middle-income countries).
METHODS: We searched MEDLINE, PubMed, PsycINFO, and Cochrane databases between January 2010 and June 2020 for systematic reviews and meta-analyses on digital mental health interventions targeting adolescents and young people aged between 10-24 years. Two authors independently screened the studies, extracted data, and assessed the quality of the reviews.
RESULTS: In this systematic overview, we included 18 systematic reviews and meta-analyses. We found evidence on the effectiveness of computerized cognitive behavioral therapy on anxiety and depression, whereas the effectiveness of other digital mental health interventions remains inconclusive. Interventions with an in-person element with a professional, peer, or parent were associated with greater effectiveness, adherence, and lower dropout than fully automatized or self-administered interventions. Despite the proposed utility of digital interventions for increasing accessibility of treatment across settings, no study has reported sample-specific metrics of social context (eg, socioeconomic background) or focused on low-resource settings.
CONCLUSIONS: Although digital interventions for mental health can be effective for both supplementing and supplanting traditional mental health treatment, only a small proportion of existing digital platforms are evidence based. Furthermore, their cost-effectiveness and effectiveness, including in low- and middle-income countries, have been understudied. Widespread adoption and scale-up of digital mental health interventions, especially in settings with limited resources for health, will require more rigorous and consistent demonstrations of effectiveness and cost-effectiveness vis-à-vis the type of service provided, target population, and the current standard of care.

Matched MeSH terms: Benchmarking
Fulltext Distance-Based and Low Energy Adaptive Clustering Protocol for Wireless Sensor Networks

Liaqat M, Gani A, Anisi MH, Ab Hamid SH, Akhunzada A, Khan MK, et al.

PLoS One, 2016 Sep 22;11(9):e0161340.
PMID: 27658194 DOI: 10.1371/journal.pone.0161340

A wireless sensor network (WSN) comprises small sensor nodes with limited energy capabilities. The power constraints of WSNs necessitate efficient energy utilization to extend the overall network lifetime of these networks. We propose a distance-based and low-energy adaptive clustering (DISCPLN) protocol to streamline the green issue of efficient energy utilization in WSNs. We also enhance our proposed protocol into the multi-hop-DISCPLN protocol to increase the lifetime of the network in terms of high throughput with minimum delay time and packet loss. We also propose the mobile-DISCPLN protocol to maintain the stability of the network. The modelling and comparison of these protocols with their corresponding benchmarks exhibit promising results.

Matched MeSH terms: Benchmarking
SCL: Self-supervised contrastive learning for few-shot image classification

Lim JY, Lim KM, Lee CP, Tan YX

Neural Netw, 2023 Aug;165:19-30.
PMID: 37263089 DOI: 10.1016/j.neunet.2023.05.037

Few-shot learning aims to train a model with a limited number of base class samples to classify the novel class samples. However, to attain generalization with a limited number of samples is not a trivial task. This paper proposed a novel few-shot learning approach named Self-supervised Contrastive Learning (SCL) that enriched the model representation with multiple self-supervision objectives. Given the base class samples, the model is trained with the base class loss. Subsequently, contrastive-based self-supervision is introduced to minimize the distance between each training sample with their augmented variants to improve the sample discrimination. To recognize the distant sample, rotation-based self-supervision is proposed to enable the model to learn to recognize the rotation degree of the samples for better sample diversity. The multitask environment is introduced where each training sample is assigned with two class labels: base class label and rotation class label. Complex augmentation is put forth to help the model learn a deeper understanding of the object. The image structure of the training samples are augmented independent of the base class information. The proposed SCL is trained to minimize the base class loss, contrastive distance loss, and rotation class loss simultaneously to learn the generic features and improve the novel class performance. With the multiple self-supervision objectives, the proposed SCL outperforms state-of-the-art few-shot approaches on few-shot image classification benchmark datasets.

Matched MeSH terms: Benchmarking
Review article: Use of renal registry data for research, health-care planning and quality improvement: what can we learn from registry data in the Asia-Pacific region?

Lim TO, Goh A, Lim YN, Morad Z

Nephrology (Carlton), 2008 Dec;13(8):745-52.
PMID: 19154324 DOI: 10.1111/j.1440-1797.2008.01044.x

We review renal registry data from the Asia-Pacific region with an emphasis on their uses in health care and in dialysis care in particular. The review aims to demonstrate the information value of registry data. While renal registry provides a useful data resource for epidemiological research, there are severe methodological limitations in its application for analytical or therapeutic research. However, it is the use of renal registry data for public health and health-care management purposes that registry really comes into its own, and it is primarily for these that governments have invested in national patient and disease registries. We apply data from several renal registries in the Asia-Pacific region to illustrate its wide application for planning dialysis services, for evaluating dialysis practices and health outcomes, with a view to improving the quality of dialysis care. In the course of preparing the review, we have found that the quality and accessibility of renal registry data were highly variable across the region. Given the value of renal registry, every country in the Asia-Pacific region should establish one or should ensure that their current registries are better resourced and developed. Greater data sharing and collaboration among registries in the region could help advance the nephrology to serve our patients better.

Matched MeSH terms: Benchmarking
Fulltext Creating Personalized Recommendations in a Smart Community by Performing User Trajectory Analysis through Social Internet of Things Deployment

Lye GX, Cheng WK, Tan TB, Hung CW, Chen YL

Sensors (Basel), 2020 Apr 08;20(7).
PMID: 32276431 DOI: 10.3390/s20072098

Despite advancements in the Internet of Things (IoT) and social networks, developing an intelligent service discovery and composition framework in the Social IoT (SIoT) domain remains a challenge. In the IoT, a large number of things are connected together according to the different objectives of their owners. Due to this extensive connection of heterogeneous objects, generating a suitable recommendation for users becomes very difficult. The complexity of this problem exponentially increases when additional issues, such as user preferences, autonomous settings, and a chaotic IoT environment, must be considered. For the aforementioned reasons, this paper presents an SIoT architecture with a personalized recommendation framework to enhance service discovery and composition. The novel contribution of this study is the development of a unique personalized recommender engine that is based on the knowledge-desire-intention model and is suitable for service discovery in a smart community. Our algorithm provides service recommendations with high satisfaction by analyzing data concerning users' beliefs and surroundings. Moreover, the algorithm eliminates the prevalent cold start problem in the early stage of recommendation generation. Several experiments and benchmarking on different datasets are conducted to investigate the performance of the proposed personalized recommender engine. The experimental precision and recall results indicate that the proposed approach can achieve up to an approximately 28% higher F-score than conventional approaches. In general, the proposed hybrid approach outperforms other methods.

Matched MeSH terms: Benchmarking
Fulltext Risk prediction analysis for classifying type 2 diabetes occurrence using local dataset

M. Hafiz Fazren Abd Rahman, Wan Wardatul Amani Wan Salim, M. Firdaus Abd-Wahab

Biological and Natural Resources Engineering Journal, 2020;3(1):48-61.
MyJurnal

The steep rise of cases pertaining to Diabetes Mellitus (DM) condition among global population has encouraged extensive researches on DM, which led to exhaustive accumulation of data related to DM. In this case, data mining and machine learning applications prove to be a powerful tool in transforming data into meaningful deductions. Several machine learning tools have shown great promise in diabetes classification. However, challenges remain in obtaining an accurate model suitable for real world application. Most disease risk-prediction modelling are found to be specific to a local population. Moreover, real-world data are likely to be complex, incomplete and unorganized, thus, convoluting efforts to develop models around it. This research aims to develop a robust prediction model for classification of type 2 diabetes mellitus (T2DM), with the interest of a Malaysian population, using three different machine learning algorithms; Decision Tree, Support Vector Machine and Naïve Bayes. Data pre-processing methods are utilised to the raw data to improve model performance. This study uses datasets obtained from the IIUM Medical Centre for classification and modelling. Ultimately, the performance of each model is validated, evaluated and compared based on several statistical metrics that measures accuracy, precision, sensitivity and efficiency. This study shows that the random forest model provides the best overall prediction performance in terms of accuracy (0.87), sensitivity (0.9), specificity (0.8), precision (0.9), F1-score (0.9) and AUC value (0.93) (Normal).

Matched MeSH terms: Benchmarking
Fulltext A new hybrid algorithm for intelligent detection of sudden decline syndrome of date palm disease

Magsi A, Mahar JA, Maitlo A, Ahmad M, Razzaq MA, Bhuiyan MAS, et al.

Sci Rep, 2023 Sep 16;13(1):15381.
PMID: 37717081 DOI: 10.1038/s41598-023-41727-9

Date palm is an important domestic cash crop in most countries. Sudden Decline Syndrome (SDS) causes a huge loss to the crop both in quality and quantity. The literature reports the significance of early detection of disease towards preventive measures to improve the quality of the crop. The number of prevailing detection methods limits to consideration of a certain aspect of disease identification. This study proposes a new hybrid fuzzy fast multi-Otsu K-Means (FFMKO) algorithm integrating the date palm image enhancement, robust thresholding, and optimal clustering for significant disease identification. The algorithm adopts a multi-operator image resizing cost function based on image energy and the dominant color descriptor, the adaptive Fuzzy noise filter, and Otsu image thresholding combined with K-Means clustering enhancements. Besides, we validate the process with histogram equalization and threshold transformation towards enhanced color feature extraction of date palm images. The algorithm authenticates findings on a local dataset of 3293 date palm images and, on a benchmarked data set as well. It achieves an accuracy of 94.175% for successful detection of SDS that outperforms the existing similar algorithms. The impactful findings of this study assure the fast and authentic detection of the disease at an earlier stage to uplift the quality and quantity of the date palm and boost the agriculture-based economy.

Matched MeSH terms: Benchmarking
Fulltext Data on the effects of imidazo[1,2-a]benzimidazole and pyrimido[1,2-a]benzimidazole compounds on intraocular pressure of ocular normotensive rats

Marcus AJ, Iezhitsa I, Agarwal R, Vassiliev P, Spasov A, Zhukovskaya O, et al.

Data Brief, 2018 Jun;18:523-554.
PMID: 29896529 DOI: 10.1016/j.dib.2018.03.019

This data is to document the intraocular pressure (IOP) lowering activity of imidazo[1,2-a]benzimidazole and pyrimido[1,2-a]benzimidazole compounds in ocular normotensive rats. Effects of single drop application of imidazo[1,2-a]benzimidazole and pyrimido[1,2-a]benzimidazole compounds on IOP in ocular normotensive rats are presented at 3 different concentrations (0.1%, 0.2% and 0.4%). Time course of changes in IOP is presented over 6 h period post-instillation. The IOP lowering activities of test compounds were determined by assessing maximum decrease in IOP from baseline and corresponding control, duration of IOP lowering and area under curve (AUC) of time versus response curve. Data shown here may serve as benchmarks for other researchers studying IOP-lowering effect of imidazo[1,2-a]benzimidazole and pyrimido[1,2-a]benzimidazole compounds and would be useful in determining therapeutic potential of these test compounds as IOP lowering agents.

Matched MeSH terms: Benchmarking
Fulltext Microclimatic performance of courtyard residential buildings in Kafanchan-Nigeria

Markus Bulus, Lim, Yaik-Wah, Malsiah Hamid

Journal of Advanced Research Design, 2017;37(1):16-25.
MyJurnal

The Climatic performance of courtyard residential buildings needs to be
investigated if the assertion that courtyard is a microclimate modifier is to be
accepted. Therefore, this study seeks to examine the microclimatic performance
of two existing courtyard residential buildings with similar characteristics in
Kafanchan-Kaduna Nigeria, -the fully enclosed courtyard residential building and
the semi-enclosed courtyard residential building. The purpose of this research is
to investigate their microclimatic performances in other to establish the best
courtyard house. This study uses measurement to achieve its aim. The tool
employed for data collection is the Hobo Weather Data Loggers (HWDL). Three
HWDL were used to collect data in the two case-study, and the third one was
placed in the outside area as a benchmark. Only air temperature and relative
humidity were measured. This study revealed a tangible difference in the
microclimatic performance of the two case-study. The fully enclosed courtyard
residential building is seen to have air temperature difference of 1 oC to 3 oC, and
the relative humidity difference of 4 % to 8 %. In conclusion, the fully enclosed
courtyard house demonstrated a more favorable microclimatic performance than
the semi-enclosed, and further simulation studies towards its optimization are
required.

Matched MeSH terms: Benchmarking
Fulltext A study on the microclimatic performance of courtyard and non-courtyard residential buildings in hot-arid climate

Markus Bulus, Lim, Yaik-Wah, Malsiah Hamid

Journal of Advanced Research in Applied Sciences and Engineering Technology, 2017;9(1):1-13.
MyJurnal

Scholars have opined that the courtyard is a passive architectural design element and
that it can act as a microclimate modifier provided that its design requirements are not
ignored. But despite the assertions, empirical studies on the microclimatic
performance of a fully enclosed courtyard house and the non-courtyard house seems
to be deficient, and the assumption that the Courtyard is a passive architectural design
element needs to be substantiated. Therefore, the purpose of this study is to
investigate the microclimatic performance of a fully enclosed courtyard and noncourtyard
residential buildings. The main objective is to compare their microclimatic
performances in other to draw a conclusion on the best option. Three Hobo Weather
Data Loggers were used to collect climatic data in the buildings, and the third one was
situated in the outdoor area as a benchmark. The climatic variables investigated are;
air temperature and relative humidity. The fully enclosed courtyard residential building
is seen to have a better air temperature difference of 2 oC to 4 oC and the relative
humidity of 2 % to 6 %. In conclusion, the fully enclosed courtyard residential building
has confirmed a more favorable microclimatic performance, and future studies
towards its optimization are recommended.

Matched MeSH terms: Benchmarking
Fulltext Content-based image retrieval using colour and shape fused features

Mas Rina Mustaffa, Fatimah Ahmad, Ramlan Mahmod, Shyamala Doraisamy

Pertanika Journal of Science & Technology, 2013;21(1):161-168.
MyJurnal

Multi-feature methods are able to contribute to a more effective method compared to single-feature
methods since feature fusion methods will be able to close the gap that exists in the single-feature
methods. This paper presents a feature fusion method, which focuses on extracting colour and shape features for content-based image retrieval (CBIR). The colour feature is extracted based on the proposed Multi-resolution Joint Auto Correlograms (MJAC), while the shape information is obtained through the proposed Extended Generalised Ridgelet-Fourier (EGRF). These features are fused together through a proposed integrated scheme. The feature fusion method has been tested on the SIMPLIcity image database, where several retrieval measurements are utilised to compare the effectiveness of the proposed method with few other comparable methods. The retrieval results show that the proposed Integrated Colour-shape (ICS) descriptor has successfully obtained the best overall retrieval performance in all the retrieval measurements as compared to the benchmark methods, which include precision (53.50%), precision at 11 standard recall levels (52.48%), and rank (17.40).

Matched MeSH terms: Benchmarking
Fulltext IntFOLD: an integrated web resource for high performance protein structure and function prediction

McGuffin LJ, Adiyaman R, Maghrabi AHA, Shuid AN, Brackenridge DA, Nealon JO, et al.

Nucleic Acids Res, 2019 07 02;47(W1):W408-W413.
PMID: 31045208 DOI: 10.1093/nar/gkz322

The IntFOLD server provides a unified resource for the automated prediction of: protein tertiary structures with built-in estimates of model accuracy (EMA), protein structural domain boundaries, natively unstructured or disordered regions in proteins, and protein-ligand interactions. The component methods have been independently evaluated via the successive blind CASP experiments and the continual CAMEO benchmarking project. The IntFOLD server has established its ranking as one of the best performing publicly available servers, based on independent official evaluation metrics. Here, we describe significant updates to the server back end, where we have focused on performance improvements in tertiary structure predictions, in terms of global 3D model quality and accuracy self-estimates (ASE), which we achieve using our newly improved ModFOLD7_rank algorithm. We also report on various upgrades to the front end including: a streamlined submission process, enhanced visualization of models, new confidence scores for ranking, and links for accessing all annotated model data. Furthermore, we now include an option for users to submit selected models for further refinement via convenient push buttons. The IntFOLD server is freely available at: http://www.reading.ac.uk/bioinf/IntFOLD/.

Matched MeSH terms: Benchmarking
Fulltext Forecasting electricity demand from daily log sheet with correlated variables

Mohamad Syamim Hilm, Sofianita Mutalib, Sarifah Radiah Shari, Siti Nur Kamaliah Kamarudin

ESTEEM Academic Journal, 2020;16(2):31-40.
MyJurnal

Electricity is one of the most important resources and fundamental infrastructure for every nation. Its milestone shows a significant contribution to world development that brought forth new technological breakthroughs throughout the centuries. Electricity demand constantly fluctuates, which affects the supply. Suppliers need to generate more electrical energy when demand is high, and less when demand is low. It is a common practice in power markets to have a reserve margin for unexpected fluctuation of demand. This research paper investigates regression techniques: multiple linear regression (MLR) and vector autoregression (VAR) to forecast demand with predictors of economic growth, population growth, and climate change as well as the demand itself. Auto-Regressive Integrated Moving Average (Auto-ARIMA) was used in benchmarking the forecasting. The results from MLR and VAR (lag-values=20) and Auto-ARIMA are monitored for five months from June to October of 2019. Using the root mean square error (RMSE) as an indicator for accuracy, Auto-ARIMA has the lowest RMSE for four months except in June 2019. VAR (lag-values=20) shows good forecasting capabilities for all five months, considering it uses the same lag values (20) for each month. Three different techniques have been successfully examined in order to find the best model for the prediction of the demand.

Matched MeSH terms: Benchmarking
Fulltext Employer roles and responsibilities in occupational safety and health (OSH) implementation in Malaysia

Mohd Esa Baruji, Sudin, Sham Xahary, Siti Zainatul Arafah, Nuraida Waslee, Siti Nasyrah Ibrahim, Nur Hidayana Abdullah, et al.

Journal of Occupational Safety and Health, 2018;15(1):49-55.
MyJurnal

communication, full compliance and good behavior) as among the crucial elements required to be possessed by the principal employers in three sectors, namely manufacturing, public services and construction. In relation to this, this paper will describe the development and validation of instruments prior to the measurement of principal employers’ roles and responsibilities in the implementation of OSH. Three assessment tools were developed, namely the Benchmarking Interview, Questionnaire and Workplace Inspection. Fifteen companies were selected for the benchmarking interview, 50 employers conveniently selected for the survey interview (covering three sectors) and 90 employers selected for the workplace inspection (30 respondents for each sector). The development of benchmarking interview and workplace inspection scores are briefly discussed while the main focus is on the validation of the survey constructs (or items). The reliability check on 53 items representing four elements (i.e., Commitment, Communication, Compliance, Behaviour) of employers’ roles and responsibilities in the implementation of OSH showed that the Cronbach’s Alpha coefficient is more than 0.90 which indicates that the internal consistency is extremely reliable. It also indicates that the set of items in each element are closely related and well understood by the respondents. Validity check on the items based on the Rasch measurement infit and outfit mean square statistics and standardized z-score found that nine items had misfitting values and finally corrected for further analysis. This study had shown that a valid and reliable instruments are important in ensuring that accurate and precise findings are obtained in measuring the roles and responsibilities of principal employer in the implementation of OSH.

Matched MeSH terms: Benchmarking
Fulltext Occupational safety and health management system in Malaysia's southern region: small and medium enterprise case study

Muhammad Hasbullah, M.A., Leman, A.M., Baba, I.

Journal of Occupational Safety and Health, 2015;12(2):81-86.
MyJurnal

Occupational safety and health (OSH) in Small and Medium Enterprises (SMEs) have not received the proper
attention not only in Malaysia, but in most of the countries globally, in terms of research or support for implementation.
This research focuses on the implementation of the occupational safety and health in small and medium industries in
the southern region of peninsular of Malaysia. The objective of this research is to determine the level of awareness of
SME owners and also their employers towards the importance of implementing OSH in their daily tasks. This study
will be based on conducting a survey to 200 SMEs owners and workers throughout the southern region of Malaysia.
The results from this research can be use as a benchmark for other researchers to further enhance the research in this
area.

Matched MeSH terms: Benchmarking
Fulltext Game Theory-Based Cooperation for Underwater Acoustic Sensor Networks: Taxonomy, Review, Research Challenges and Directions

Muhammed D, Anisi MH, Zareei M, Vargas-Rosales C, Khan A

Sensors (Basel), 2018 Feb 01;18(2).
PMID: 29389874 DOI: 10.3390/s18020425

Exploring and monitoring the underwater world using underwater sensors is drawing a lot of attention these days. In this field cooperation between acoustic sensor nodes has been a critical problem due to the challenging features such as acoustic channel failure (sound signal), long propagation delay of acoustic signal, limited bandwidth and loss of connectivity. There are several proposed methods to improve cooperation between the nodes by incorporating information/game theory in the node's cooperation. However, there is a need to classify the existing works and demonstrate their performance in addressing the cooperation issue. In this paper, we have conducted a review to investigate various factors affecting cooperation in underwater acoustic sensor networks. We study various cooperation techniques used for underwater acoustic sensor networks from different perspectives, with a concentration on communication reliability, energy consumption, and security and present a taxonomy for underwater cooperation. Moreover, we further review how the game theory can be applied to make the nodes cooperate with each other. We further analyze different cooperative game methods, where their performance on different metrics is compared. Finally, open issues and future research direction in underwater acoustic sensor networks are highlighted.

Matched MeSH terms: Benchmarking
Fulltext Solving text clustering problem using a memetic differential evolution algorithm

Mustafa HMJ, Ayob M, Albashish D, Abu-Taleb S

PLoS One, 2020;15(6):e0232816.
PMID: 32525869 DOI: 10.1371/journal.pone.0232816

The text clustering is considered as one of the most effective text document analysis methods, which is applied to cluster documents as a consequence of the expanded big data and online information. Based on the review of the related work of the text clustering algorithms, these algorithms achieved reasonable clustering results for some datasets, while they failed on a wide variety of benchmark datasets. Furthermore, the performance of these algorithms was not robust due to the inefficient balance between the exploitation and exploration capabilities of the clustering algorithm. Accordingly, this research proposes a Memetic Differential Evolution algorithm (MDETC) to solve the text clustering problem, which aims to address the effect of the hybridization between the differential evolution (DE) mutation strategy with the memetic algorithm (MA). This hybridization intends to enhance the quality of text clustering and improve the exploitation and exploration capabilities of the algorithm. Our experimental results based on six standard text clustering benchmark datasets (i.e. the Laboratory of Computational Intelligence (LABIC)) have shown that the MDETC algorithm outperformed other compared clustering algorithms based on AUC metric, F-measure, and the statistical analysis. Furthermore, the MDETC is compared with the state of art text clustering algorithms and obtained almost the best results for the standard benchmark datasets.

Matched MeSH terms: Benchmarking
Fulltext TOURIST PERCEPTION TOWARDS RECREATION CENTRE IN MALAYSIA

NURUL SYUHADA SHARIFF, YUSNITA YUSOF, NOOR ZATUL IFFAH HUSSIN

UMT Journal of Undergraduate Research, 2020;2(3):103-114.
MyJurnal

Recreation centre become one of the centres for a family to bring their children for recreation and leisure activity. Moreover, the recreation centre is the place for education, research, and awareness to the public. The main objective of this study is to investigate factors that relate to tourist perception in their reference to their interest, expectations, satisfaction, and a general understanding of the recreation centre. The antecedent factors are awareness of the surrounding environment, visitor experiences, and destination image. This research using a quantitative method via a survey questionnaire and a domestic tourist as a sample. A sample is consist of 384 respondent of domestic tourists who visited the recreation centre in Malaysia. This survey has been done in Zoo Negara, Aquaria KLCC, and FRIM, Kepong. The results show the majority of respondents are female, age below 26 years old, single, obtained higher education, working, and had an income below RM1000. The respondents are mostly from Selangor and their purpose of visit to the recreation centre is for leisure and recreation. The major source of information to visit the recreation centre was from the internet. There were have a significant relationship between an antecedent factor with tourist perception towards the recreation centre in Malaysia. The result of this study will help marketers and management of recreation centres to understand the perceptions of their future visitors. Based on the study, it is should be used as an initial benchmark for the future study, however, they may execute a depth analysis on the tourism that related to the recreation centre in Malaysia.

Matched MeSH terms: Benchmarking

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