Mosquito identification and classification are the most important steps in a surveillance program of mosquito-borne diseases. With conventional approach of data collection, the process of sorting and classification are laborious and time-consuming. The advancement of computer vision with transfer learning provides excellent alternative to the challenge. Transfer learning is a type of machine learning that is viable and durable in image classification with limited training images. This protocol aims to develop step-by-step procedure in developing a classification system with transfer learning algorithm for mosquito, we demonstrate the protocol to classify two species of Aedes mosquito - Aedes aegypti L. and Aedes albopitus L, but user can adopt the protocol for higher number of species classification. We demonstrated the way of start from the scratch, fine-tuning two pre-trained model performance by using different combination of hyperparameters - batch size and learning rate, and explain the terminology in the Appendix. This protocol target on the domain expert such as entomologist and public health practices to develop their own model to solve the task of mosquito/insect classification.
Laser cutting is a non-contact machining employed for producing small, intricate shapes. The acrylic materials are widely used in many applications. The parametric and heat affected zone study of acrylic materials by using CO2 laser machining is attempted in this research to evaluate the process variables, laser scanning speed, current, and nozzle-work material gap.•Research result indicate that the higher the current and the higher the cutting speed, result in higher the material removal rate•Other parameter such as current and nozzle, work material gap are also significant impact on the cutting process of the acrylic material.•In addition, heat affect zone increase with laser scanning speed.
A one-step wet chemical approach or seedless growth process has several advantages compared to the traditional seed-mediated growth method (SMGM), such as being simpler and not requiring a multistep growth of seeds. This study had introduced a one-step wet chemical method to synthesis gold nanoplates on a solid substrate. The synthesis was carried out by simply immersing clean ITO substrate into a solution, which was made from mixing of gold chloride (precursor), cetyltrimethylammonium bromide or CTAB (stabilizing agent), and poly-l-lysine or PLL (reducing agent). Consequently, the size of the nanoplates in the range of (0.40 - 0.89) μm and a surface density within the range (21.89-57.19) % can be easily controlled by changing the concentration of PLL from 0.050 to 0.100 w/v % in H2O. At low PLL concentrations, the reduction of the gold precursor by PLL is limited, leading to the formation of gold nanoplates with a smaller size and surface density. The study on the sample by using energy-dispersive x-ray spectroscopy (EDS) confirmed that gold peaks occurred. The optical properties of the samples were examined by a UV-vis Spectrophotometer and showed that there was no strong surface plasmon resonance band observed at UV-vis and infrared regions, which agreed to micron-sized gold nanoplates. •Gold nanoplates synthesized on the substrate using a simple one-step wet chemical synthesis approach with poly-l-lysine (PLL) as a reducing agent and CTAB as a stabilizing agent.•The nanoplate's size and surface density was strongly dependent on the concentration of PLL.•Gold nanoplates synthesized using PLL with a concentration 0.050% showed perfect triangular shape, less by-products and more homogenous in size.
Financial literacy is an essential lifelong skill that should be taught to children at any age. It holds the key to develop a generation of adults who are knowledgeable about money and the economy. Additionally, OECD (2018) suggests that using digital tools could significantly enhance financial literacy and well-being. Therefore, this paper aims to:(i)assess the financial literacy level of primary school children in the northern region of Malaysia and(ii)explore interactive and engaging methods for teaching financial literacy.The sample size was determined using Krejcie and Morgan's (1970) approach, resulting in 419 primary school students aged 7 to 12 and their parents. An online questionnaire was employed, and multi-regression analysis was conducted. The findings highlighted those primary students displayed a high level of financial literacy, scoring above 80 % on the questionnaire. Furthermore, parents expressed a preference for their children to enroll in personal finance subjects offered by schools, have financial assignments or activities at school, and engage in online financial games. The study emphasized the crucial roles of schools, teachers, and active parental involvements to enhance financial literacy. This study recommends incorporating interactive and attractive teaching methods through in-class and online activities at the school level.
The Surviving Sepsis Campaign (SCC) and the American College of Critical Care Medicine (ACCM) guidelines recommend blood transfusion in sepsis when the haemoglobin concentration drops below 7.0 g/dL and 10.0 g/dL respectively, while the World Health Organisation (WHO) guideline recommends transfusion in septic shock 'if intravenous (IV) fluids do not maintain adequate circulation', as a supportive measure of last resort. Volume expansion using crystalloid and colloid fluid boluses for haemodynamic resuscitation in severe illness/sepsis, has been associated with adverse outcomes in recent literature. However, the volume expansion effect(s) following blood transfusion for haemodynamic circulatory support, in severe illness remain unclear with most previous studies having focused on evaluating effects of either different RBC storage durations (short versus long duration) or haemoglobin thresholds (low versus high threshold) pre-transfusion. •We describe the protocol for a pre-clinical randomised controlled trial designed to examine haemodynamic effect(s) of early volume expansion using packed RBCs (PRBCs) transfusion (before any crystalloids or colloids) in a validated ovine-model of hyperdynamic endotoxaemic shock.•Additional exploration of mechanisms underlying any physiological, haemodynamic, haematological, immunologic and tissue specific-effects of blood transfusion will be undertaken including comparison of effects of short (≤5 days) versus long (≥30 days) storage duration of PRBCs prior to transfusion.
Currently, the available indices to measure mangrove health are not comprehensive. An integrative ecological-socio economic index could give a better picture of the mangrove ecosystem health. This method explored all key biological, hydrological, ecological and socio-economic variables to form a comprehensive mangrove quality index. A total of 10 out of 43 variables were selected based on principal component analysis (PCA). They are aboveground biomass, crab abundance, soil carbon, soil nitrogen, number of phytoplankton species, number of diatom species, dissolved oxygen, turbidity, education level and fishing time spent by fishers. Two types of indices were successfully developed to indicate the health status viz., (1) Mangrove quality index for a specific category (MQISi ) and, (2) Overall mangrove quality index (MQI) to reflect the overall health status of the ecosystem. The indices for the five different categories were mangrove biotic integrity index ( M Q I S 1 ), mangrove soil index ( M Q I S 2 ), marine-mangrove index ( M Q I S 3 ), mangrove-hydrology index ( M Q I S 4 ) and mangrove socio-economic index ( M Q I S 5 ). The quality of the mangroves was classified from 1 to 5 viz. 1 (worst), 2 (bad), 3 (moderate), 4 (good), 5 (excellent). These MQI class could reflect the quality of mangrove forest which could be managed with the objective of improving its quality. Advantages of this method include: •PCA to select metrics from ecological-socioeconomic variables•Formulation of MQI based on selected metrics•Comprehensive index to classify mangrove ecosystem health.
Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is a popular multi-criteria decision-making method that ranks the available alternatives by examining the ideal-positive and ideal-negative solutions for each decision criterion. The first step of using TOPSIS is to normalize the presence of incommensurable data in the decision matrix. There are several normalization methods, and the choice of these methods does affect TOPSIS results. As such, some efforts were made in the past to compare and recommend suitable normalization methods for TOPSIS. However, such studies merely compared a limited collection of normalization methods or used a noncomprehensive procedure to evaluate each method's suitability, leading to equivocal recommendations. This study, therefore, employed an alternate, comprehensive procedure to evaluate and recommend suitable benefit/cost criteria-based normalization methods for TOPSIS (out of ten methods extracted from past literature). The procedure was devised based on three evaluation metrics: the average Spearman's rank correlation, average Pearson correlation, and standard deviation metrics, combined with the Borda count technique.•The first study examined the suitability of ten benefit/cost criteria-based normalization methods over TOPSIS.•Users should combine the sum-based method and vector method into the TOPSIS application for safer decision-making.•The maximum method (version I) or Jüttler's-Körth's method has an identical effect on TOPSIS results.
The primary objective of the study was to examine the distribution of various elements, namely Cadmium (Cd), Copper (Cu), Iron (Fe), Nickel (Ni), and Lead (Pb), in the soft tissues, shells, and associated surface sediments of Cerithidea obtusa (C. obtusa) mangrove snails collected from Sungai Besar Sepang. To conduct the analysis, the preferred and most convenient methods employed were Instrumental Neutron Activation Analysis (INAA) and Atomic absorption spectrometry (AAS). The results showed that the mean concentration of elements in the sediments and soft tissues followed the order Fe > Cu > Ni > Pb > Cd, while for the shell of C. obtusa, it was Fe > Ni > Cu > Pb > Cd.•Iron (Fe) showed the highest concentration among all elements monitored in sediments, soft tissues, and shells of C. obtusa.•The PF results indicated higher incorporation of Pb and Ni into shells.•BSAF results showed that C. obtusa shells accumulated more Cu and Cd from sediments, making them effective biomonitors.
Using data analytics to properly extracting insights that are in-line to the enterprises strategic goals is crucial for the business sustainability. Developing the most fitting context as a knowledge graph that answer related businesses questions and queries at scale. Data analytics is an integral main part of smart manufacturing for monitoring the production processes and identifying the potentials for automated operations for improved manufacturing performance. This paper reviews and investigates the best development practices to be followed for industrial enterprise knowledge-graph development that support smart manufacturing in the following aspects:•Decision for intelligent business processes, data collection from multiple sources, competitive advantage graph ontology, ensuring data quality, improved data analytics, human-friendly interaction, rapid and scalable enterprise's architectures.•Successful digital-transformation adoption for smart manufacturing as an enterprise knowledge-graph development with the capability to be transformed to data fabric supporting scalability of smart manufacturing processes in industrial enterprises.
Machine learning has been very promising in solving real problems, but the implementation involved difficulties mainly for the inexpert data scientists. Therefore, this paper presents an automated machine learning (AutoML) to simplify and accelerate the modeling tasks. Focused on Python and RapidMiner rapid modeling tools, Tree-based Pipeline Optimization Tool (TPOT) and AutoModel were used. This paper presents a comprehensive comparison between these tools with regard to the prediction accuracy and Area Under Curve (AUC) in classifying real cases of whistleblowing academic dishonesty among undergraduate students of two universities in Indonesia. Additionally, the correlations weight from demographic and Theory of Planned Behavior (TOB) attributes in the different machine learning models are also discussed. All the machine learning algorithms from TPOT and AutoModel are considerable powerful to generate good accuracy level (between 70-93% of AUC) in classifying both cases of whistleblowing and non-whistleblowing on the hold-out samples from the testing process. Generally, based on the validation results of the prediction models, demographic attributes presented more importance than the TBP attributes. The findings of this study will be a great interest of many research scholars to conduct a more in-depth analysis on AutoML for many domains mainly in education and academic misconduct fields.•AutoML is the first of its kind to be empirically compared between TPOT and AutoModel in an application to predict academic dishonesty whistleblowing.•Besides accuracy performances of the AutoML, the proportion of the variance of each attribute from demographic and Theory of Planned Behavior (TPB) is also presented in the prediction models of academic dishonesty whistleblowing.•AutoML is a convenient and reproducible rapid modeling method of machine learning to be used in many kinds of prediction problem.
The need for technical support for data handling and visualization solutions has increased in tandem with the complexity of today's data and information, that is of multiple sources, huge in size and of different formats. This study focuses on handling and analyzing text-based data. Despite many available text analysis tools, there is a high demand among researchers for easy- to-use tools yet scalable and with incomparable visualization features. Of recent, there has been a significant focus on utilizing VOSviewer, an open-source software for bibliometric analysis. This software is able to analyze a significant amount of data and provide excellent network data mapping. However, there is a lack of existing work in evaluating this sophisticated tool for text analysis. Thus, this article explores the capability of VOSviewer and presents evidence-based implementation of this software for text analysis. Specifically, this study demonstrates the usage of VOSviewer to analyze text based on YouTube interviews related to ChatGPT. Hence, this study significantly contributes by processing textual data and producing visualization network maps that are different from bibliometric data. The study recognizes VOSviewer as a powerful tool for data visualization in mapping text data and illustrates the potential of this software for analyzing text networks in various fields. •The study illustrates how text analysis and visualization can be realized using VOSviewer, an open-source software mostly used for biblio- metric analysis.•The study presents the workflow indicating how the dataset can be prepared as input for VOSviewer for text analysis.•The study proves that VOSviewer is a powerful tool for data visualization and network mapping for any type of network data including transcripts from social media.
Flypaper Effect is a public finance term that indicates a government grant given to recipient cities increases the local community spending level more than an increase in local income of equivalent size. This paper analyzed the Flypaper Effect Assessment Method in the Expansion of Regional Autonomy. It employed 210 New Autonomous Regions (NARs) in Indonesia during 1999-2021 as a case study, where Indonesia became the country with the highest number of new autonomies in the world. Panel Data Regression was utilized to determine the Flypaper Effect. Flypaper Effect analysis was carried out using the BLUE model selection method. The selected models in this study were Pooled Least Square (PLS), Fixed Effect Model (FEM), and Random Effect Model (REM). Several tests, such as Chow Test, Lagrange Multiplier Test, and Hausman Test, were conducted. Furthermore, the procedures to get the data in BLUE were carried out, such as Heteroscedasticity and Autocorrelation Test. Koenker-Bassett test was used for ascertaining Heterocedascity.•Panel Data Regression is used as a method to determine the Flypaper Effect in the autonomous region.•Each stage in this method is discussed with a calculation/process example.•The method utilized in this paper is recommended to determine the Flypaper Effect of New Autonomous Regions (NARs) for various parties.
The evolution of shear key design for bridges is accompanied by research on structural earthquake resistance. However, the vast majority of pounding forces, responses, and corresponding data for the study and design of shear keys have been based on expensive experimentalism and imprecise empiricism approaches for decades. Hence, strengthening theoretical study on seismic performance of shear key is essential. In this paper, a "Beam-Spring-Beam + Concentrated Mass" continuum dynamic model is proposed. Meanwhile, the transient wave function expansion method and the mode superposition method are applied to determine the analytical expression of the dynamic response from the girder and pier system (pier and cap beam). Furthermore, the combined transient internal force method and Duhamel integration method are introduced to assess the elastic pounding process. Through programming and numerical analysis, a series of pounding response data related to the shear key under various working circumstances will be explored. As mentioned above, the proposed theoretical method can optimize shear key design and boost the reliability of seismic limiting devices in the future. •Establishing a feasible "Beam-Spring-Beam + Concentrated Mass" continuum model of girders and piers based on a two-span continuous girder bridge.•Deriving the analytical solutions of responses by conducting the response equations under horizontal seismic excitation (containing orthonormality verification).•Simulating the pounding process by embedding elastic pounding calculation methods into Continuum Model.
Polyhydroxyalkanoate (PHA)-producing bacteria represent a powerful synthetic biology chassis for waste bioconversion and bio-upcycling where PHAs can be produced as the final products. In this study, we present a seamless plasmid construction for orthogonal expression of recombinant PET hydrolase (PETase) in model PHA-producing bacteria P. putida and C. necator. To this end, this study described seamless cloning and expression methods utilizing SureVector (SV) system for generating pSV-Ortho-PHA (pSVOP) expression platform in bioengineered P. putida and C. necator. Genetic parts specifically Trc promoter, pBBR1 origin of replication, anchoring proteins and signal sequences were utilized for the transformation of pSVOP-based plasmid in electrocompetent cells and orthogonal expression of PETase in both P. putida and C. necator. Validation steps in confirming functional expression of PETase activity in corresponding PETase-expressing strains were also described to demonstrate seamless and detailed methods in establishing bioengineered P. putida and C. necator as whole-cell biocatalysts tailored for plastic bio-upcycling.•Seamless plasmid construction for orthogonal expression in PHA-producing bacteria.•Step-by-step guide for high-efficiency generation of electrotransformants of P. putida and C. necator.•Adaptable methods for rapid strain development (Design, Build, Test and Learn) for whole-cell biocatalysis.
In Malaysia, the increasing frequency and severity of disasters emphasize the urgent need for enhancing disaster management. Given their significant impact on public health and healthcare, effective disaster management becomes a top priority. This study focuses on urban disasters and aims to identify health needs, assess multi-sectorial response gaps, and propose civil-military coordination mechanisms. To achieve this, a qualitative single-case approach will be employed, involving document reviews, in-depth interviews, and focus group discussions with representatives from key governmental agencies responsible for disaster management. The study will specifically concentrate on Kuala Lumpur, the densely populated and commercially active city. Thematic analysis will be used to systematize and verify the collected data, providing comprehensive insights into the current state of civil-military coordination in disaster response and management from stakeholders' perspectives. By examining their perceptions and experiences, the study will identify existing gaps and challenges in civil-military coordination. Ultimately, the findings will contribute to evidence-based policies and strategies aimed at improving disaster management coordination throughout Malaysia.
The Partially Observable Markov Decision Process (POMDP), a mathematical framework for decision-making in uncertain environments suffers from the curse of dimensionality. There are various methods that can handle huge sizes of POMDP matrices to create approximate solutions, but no serious effort has been reported to effectively control the size of the POMDP matrices. Manually creating the high-dimension matrices of a POMDP model is a cumbersome and sometimes even impossible task. The PCMRPP (POMDP file Creator for Mobile Robot Path Planning) software package implements a novel algorithm to programmatically generate these matrices such that: •The sizes of the matrices can be controlled by configuring the granularity of discretization of the components of the state and•The sparseness of the matrices can be controlled by configuring the spread of the observation probability distribution. This kind of flexibility allows one to achieve a trade-off between time complexity and the level of robustness of the POMDP solution.
Digitization created a demand for highly efficient handwritten document recognition systems. A handwritten document consists of digits, text, symbols, diagrams, etc. Digits are an essential element of handwritten documents. Accurate recognition of handwritten digits is vital for effective communication and data analysis. Various researchers have attempted to address this issue with modern convolutional neural network (CNN) techniques. Even after training, CNN filter weights remain unchanged despite the high identification accuracy. As a result, the process cannot flexibly adapt to input changes. Hence computer vision researchers have recently become interested in Vision Transformers (ViTs) and Multilayer Perceptrons (MLPs). The shortcomings of CNNs gave rise to a hybrid model revolution that combines the best elements of the two fields. This paper analyzes how the hybrid convolutional ViT model affects the ability to recognize handwritten digits. Also, the real-time data contains noise, distortions, and varying writing styles. Hence, cleaned and uncleaned handwritten digit images are used for evaluation in this paper. The accuracy of the proposed method is compared with the state-of-the-art techniques, and the result shows that the proposed model achieves the highest recognition accuracy. Also, the probable solutions for recognizing other aspects of handwritten documents are discussed in this paper.•Analyzed the effect of convolutional vision transformer on cleaned and real-time handwritten digit images.•The model's performance improved with the implication of cross-validation and hyper-parameter tuning.•The results show that the proposed model is robust, feasible, and effective on cleaned and uncleaned handwritten digits.
In vivo extracellular field potential recording is a commonly used technique in modern neuroscience research. The success of long-term electrophysiological recordings often depends on the quality of the implantation surgery. However, there is limited use of visually guided stereotaxic neurosurgery and the application of the eLab/ePulse electrophysiology system in rodent models. This study presents a practical and functional manual guide for surgical electrode implantation in rodent models using the eLab/ePulse electrophysiology system for recording and stimulation purposes to assess neuronal functionality and synaptic plasticity. The evaluation parameters included the input/output function (IO), paired-pulse facilitation or depression (PPF/PPD), long-term potentiation (LTP), and long-term depression (LTD).•Provides a detailed picture-guided procedure for conducting in vivo stereotaxic neurosurgery.•Specifically covers the insertion of hippocampal electrodes and the recording of evoked extracellular field potentials.
Over the last decade, the notion of community resilience, which encompasses planning for, opposing, absorbing, and quickly recovering from disruptive occurrences, has gained momentum across the world. Critical Infrastructures (CI) are seen as critical to attaining success in today's densely populated countries. Such infrastructures must be robust in the face of multi-hazard catastrophes by implementing appropriate disaster management and recovery plans. Given these facts, it is critical to establish a new methodological perspective with an integrated system for effective disaster management of CI, as well as an intelligent application that will aid in the construction of more resilient and sustainable cities and communities. This perspective proposes a holistic gaming scenario application for assessing the vulnerability and accessibility of critical infrastructures during multi-hazard events, with a primary focus on conducting an integrated assessment for critical infrastructures and their assets. Mainly, the perspective includes a holistic gaming scenario application that will aid in accurately quantifying geographical spatial information and integrating big data into predictive and prescriptive management tools using virtual reality.•Conducting Integrated Assessment Models for evaluating vulnerability of Critical Infrastructures.•Inducing Digital Technologies during Multi-Hazard Incidents for improving Natural hazard assessment models.•Developing an open-world gaming scenario that is considered with high visual motion pictures and scenes.