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.
In recent years, frequent and substantial area-wide power outages have underscored the critical need for cities to possess robust backup power sources capable of swift response to prevent prolonged power system disruptions. Electric vehicles can contribute electricity to the power grid using vehicle-to-grid technology. The power delivered by electric vehicles in this context is termed as response capability. However, existing studies have overlooked response capability dynamics during transitions between electric vehicle states-such as the shift from charging or discharging to an idle state, thereby hindering a comprehensive understanding of this aspect. Hence, this paper introduces a multi-timescale response capability prediction model that evaluates the electric vehicle's state of charge to ensure users' requirements are met for upcoming trips. To better assess users' travel demand, the gravity model is employed as a precursor to response capability prediction to further enhance the validity of the prediction outcomes. Three neighborhoods in Los Angeles have been chosen for analysis: Downtown, Lincoln Heights, and Silver Lake. Predictions indicate that neglecting the response capability when electric vehicles undergo state transformation can lead to a differential response capability ranging from 2000 kWh to 4000 kWh, resulting in a loss of prediction accuracy by 20 % to 25 %.•The response capability of EV is non-zero during state transformations•Users' travel demand assessment•Seamless integration of vehicle-to-grid technology into the power grid.
Firefighters encounter numerous complex and ever-changing hazards when carrying out emergency response activities, necessitating the development of effective risk profiling methods to enhance both their safety and operational efficiency. This study outlines a comprehensive approach to constructing risk profiles tailored specifically for firefighters, integrating various methodologies to create a robust and adaptable framework. The methods used incorporating historical incident data, environmental variables, and individual firefighter characteristics to identify and assess potential risks. Additionally, the risk profiling framework include Psychosocial risk factors are also considered, allowing for a holistic understanding of the human element in firefighting risk assessment. By developing risk profiles to the specific needs and characteristics of firefighters, this method aims to significantly improve their safety, ability to make decisions, and overall operational efficiency in the demanding and ever-changing setting of emergency response situations. This article discussed methods•To identify safety cultures using questionnaires•To analyse risk from incident reports using content analysis•To verify and validate risk using thematic analysis from Focus Group Discussion.
The goal of this research is to develop a model employing deep neural networks (DNNs) to predict the effectiveness of mangrove forests in attenuating the impact of tsunami waves. The dataset for the DNN model is obtained by simulating tsunami wave attenuation using the Boussinesq model with a staggered grid approximation. The Boussinesq model for wave attenuation is validated using laboratory experiments exhibiting a mean absolute error (MAE) ranging from 0.003 to 0.01. We employ over 40,000 data points generated from the Boussinesq numerical simulations to train the DNN. Efforts are made to optimize hyperparameters and determine the neural network architecture to attain optimal performance during the training process. The prediction results of the DNN model exhibit a coefficient of determination (R2 ) of 0.99560, an MAE of 0.00118, a root mean squared error (RMSE) of 0.00151, and a mean absolute percentage error (MAPE) of 3 %. When comparing the DNN model with three alternative machine learning models- support vector regression (SVR), multiple linear regression (MLR), and extreme gradient boosting (XGBoost)- the performance of DNN is superior to that of SVR and MLR, but it is similar to XGBoost.•High-accuracy DNN models require hyperparameter optimization and neural network architecture selection.•The error of DNN models in predicting the attenuation of tsunami waves by mangrove forests is less than 3 %.•DNN can serve as an alternate predictive model to empirical formulas or classical numerical models.
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.
Colorectal cancer poses a significant threat to global health, necessitating the development of effective early detection techniques. However, the potential of the fungal microbiome as a putative biomarker for the detection of colorectal adenocarcinoma has not been extensively explored. We analyzed the viability of implementing the fungal mycobiome for this purpose. Biopsies were collected from cancer and polyp patients. The total genomic DNA was extracted from the biopsy samples by utilizing a comprehensive kit to ensure optimal microbial DNA recovery. To characterize the composition and diversity of the fungal mycobiome, high-throughput amplicon sequencing targeting the internal transcribed spacer 1 (ITS1) region was proposed. A comparative analysis revealed discrete fungal profiles among the diseased groups. Here, we also proposed pipelines based on a predictive model using statistical and machine learning algorithms to accurately differentiate colorectal adenocarcinoma and polyp patients from normal individuals. These findings suggest the utility of gut mycobiome as biomarkers for the detection of colorectal adenocarcinoma. Expanding our understanding of the role of the gut mycobiome in disease detection creates novel opportunities for early intervention and personalized therapeutic strategies for colorectal cancer.•Detailed method to identify the gut mycobiome in colorectal cancer patients using ITS-specific amplicon sequencing.•Application of machine learning algorithms to the identification of potential mycobiome biomarkers for non-invasive colorectal cancer screening.•Contribution to the advancement of innovative colorectal cancer diagnostic methods and targeted therapies by applying gut mycobiome knowledge.
The exposure of the air microbiome in indoor air posed a detrimental health effect to the building occupants compared to the outdoor air. Indoor air in hospitals has been identified as a reservoir for various pathogenic microbes. The conventional culture-dependent method has been widely used to access the microbial community in the air. However, it has limited capability in enumerating the complex air microbiome communities, as some of the air microbiomes are uncultivable, slow-growers, and require specific media for cultivation. Here, we utilized a culture-independent method via amplicon sequencing to target the V3 region of 16S rRNA from the pool of total genomic DNA extracted from the dust samples taken from hospital interiors. This method will help occupational health practitioners, researchers, and health authorities to efficiently and comprehensively monitor the presence of harmful air microbiome thus take appropriate action in controlling and minimizing the health risks to the hospital occupants. Key features;•Culture-independent methods offer fast, comprehensive, and unbias profiles of pathogenic and non-pathogenic bacteria from the air microbiomes.•Unlike the culture-dependent method, amplicon sequencing allows bacteria identification to the lowest taxonomy levels.
This study introduces a hybrid model for an advanced medical chatbot addressing crucial healthcare communication challenges. Leveraging a hybrid ML model, the chatbot aims to provide accurate and prompt responses to users' health-related queries. The proposed model will overcome limitations observed in previous medical chatbots by integrating a dual-stemming approach, P-Stemmer and NLTK-Stemmer, accommodating both semitic and non-semitic languages. The system prioritizes the analysis of cognates, identification of symptoms, doctor recommendations, and prescription generation. It integrates an automatic translation module to facilitate a smooth multilingual diagnostic experience. Following the Scrum methodology for agile development, the framework ensures adaptability to evolving research needs and stays current with recent medical discoveries. This groundbreaking idea aims to improve the effectiveness and availability of healthcare services by introducing an intelligent, multilingual chatbot. This technology enables patients to communicate with doctors from diverse linguistic backgrounds through an automated language translation model, eliminating language barriers and extending healthcare access to rural regions worldwide.•A simple but efficient hybrid conceptual model for advancement in smart medical assistance.•This conceptual model can be applied to implement a medical chatbot that can understand multiple languages.•This method can be utilized to address medical chatbot limitations and enhance accuracy in response generation.
Mosquito-borne diseases pose a significant threat in many Southeast Asian countries, particularly through the sylvatic cycle, which has a wildlife reservoir in forests and rural areas. Studying the composition and diversity of vectors and pathogen transmission is especially challenging in forests and rural areas due to their remoteness, limited accessibility, lack of power, and underdeveloped infrastructure. This study is based on the WHO mosquito sampling protocol, modifies technical details to support mosquito collection in difficult-to-access and resource-limited areas. Specifically, we describe the procedure for using rechargeable lithium batteries and solar panels to power the mosquito traps, demonstrate a workflow for processing and storing the mosquitoes in a -20 °C freezer, data management tools including microclimate data, and quality assurance processes to ensure the validity and reliability of the results. A pre- and post-test was utilized to measure participant knowledge levels. Additional research is needed to validate this protocol for monitoring vector-borne diseases in hard-to-reach areas within other countries and settings.
Biochemical oxygen demand (BOD) serves as an important indicator in water quality monitoring. It provides valuable information for studying biology and conducting environmental impact assessments, making it the preferred method for environmental applications. Currently, the most common approach for BOD monitoring is the BOD5 standard detection method. However, this method has several drawbacks, like a long 5-day culture time, extended detection duration, complex operations, and low reproducibility of results. To address these issues, our study introduces a rapid BOD detection method, that focused on optimizing microbial immobilized particles and their detection capabilities. The method demonstrated better detection accuracy, stability, and reproducibility, with results available in less than 8 min. Our customization includes: •Prepared the particles using the cross-linking-embedding method by adding specific modifiers which are Polyvinyl Alcohol (PVA) and diatomite.•Improved the detection results, reducing the overall detection error by over 10%.•Confirmed our method' effectiveness in rapidly detecting BOD solution prepared in the lab, outsourced BOD standard solution and actual waste water samples with high accuracy.
The article presents a secure edge computing model that utilizes machine learning for intrusion detection and isolation. It addresses the security challenges arising from the rapid expansion of IoT and edge computing. The proposed Intrusion Detection System (IDS) combines Linear Discriminant Analysis (LDA) and Logistic Regression (LR) to swiftly and accurately identify intrusions without alerting neighboring devices. The model outperforms existing solutions with an accuracy of 96.56%, precision of 95.78%, and quick training time (0.04 s). It is effective against various types of attacks, enhancing the security of edge networks for IoT applications. •The methodology employs a hybrid model that combines LDA and LR for intrusion detection.•Machine learning techniques are used to analyze and identify intrusive activities during data acquisition by edge nodes.•The methodology includes a mechanism to isolate suspected devices and data without notifying neighboring edge nodes to prevent intruders from gaining control over the edge network.
In recent years, there has been a rise in research on sensorium in various academic disciplines. Olfaction is recognized as a sense that is most closely linked to cognition, memory and emotion. Due to this unique feature, studies on various aspects of human olfaction are steadily gaining prominence in the humanities and social sciences. In order to understand how the olfactory modality is marked, several taxonomies and semantic spaces of olfactory terms have been developed. However, the focus has been on the general olfaction lexicon and there is a lack of systematic and comprehensive lexicons for fragrant smells. This article addresses this gap. It adopts a multilingual perspective and describes the process of developing a fragrance lexicon in two languages, Russian and English. A fragrance lexicon refers to a list of words that people might use to describe a perfume. The steps in the lexicon development included •sourcing the lexical items in the two languages•translating and cleaning the word lists•revising and refining the lexiconThe fragrance lexicon presented in this article can be used to aid linguistic analyses of naturally occurring communications about perfumes, such as computational analyses of consumer-generated perfume reviews.
This research attempts to explore the total of 21 potential internal and external shocks to the European market during the Covid-19 Crisis. Using the time series of 1 Jan 2020 to 26 June 2020, I employ a machine learning technique, i.e. Least Absolute Shrinkage and Selection Operator (LASSO) to examine the research question for its benefits over the traditional regression methods. This further allows me to cater to the issue of limited data during the crisis and at the same time, allows both variable selection and regularization in the analysis. Additionally, LASSO is not susceptible to and sensitive to outliers and multi-collinearity. The European market is mostly affected by indices belonging to Singapore, Switzerland, Spain, France, Germany, and the S&P500 index. There is a significant difference in the predictors before and after the pandemic announcement by WHO. Before the Pandemic period announcement by WHO, Europe was hit by the gold market, EUR/USD exchange rate, Dow Jones index, Switzerland, Spain, France, Italy, Germany, and Turkey and after the announcement by WHO, only France and Germany were selected by the lasso approach. It is found that Germany and France are the most predictors in the European market.•A LASSO approach is used to predict the European stock market index during COVID-19•European market is mostly affected by the indices belonging to Singapore, Switzerland, Spain, France, Germany, and the S&P500 index.•There is a significant difference in the predictors before and after the pandemic announcement by WHO.
This article encompasses the method related to image segmentation of the Field Emission Scanning Electron Microscope (FESEM) images of Acacia Mangium Wood derived Activated Carbons under different conditions. Image segmentation using Hue-Saturation-Value (HSV) thresholding method was adapted to identify the different pattern composition in the grayscale images by varying the intensity Value (V) and keeping Hue (H) and Saturation (S) to zero, and each pattern was considered as one type of element that constituted the Activated Carbon. The algorithm was developed to compute the percentage of each pattern using non-zero pixels, and on the basis of different patterns, different elements having certain percentage of composition were recorded. Later, these results were compared with the Energy Dispersive X-ray Spectroscopy (EDS) to cross check the difference in percentage of each element present at the surface of the Activated Carbon. Part of this result is published in the article [1], "Comparison of surface properties of wood biomass Activated Carbons and their application against rhodamine B and methylene blue dye" Surfaces and Interfaces vol. 11 (2018) pp1-13.•The methods involved will be useful for characterization of Activated Carbon materials.•Image segmentation using HSV thresholding will inspire other researchers to apply similar concept on other materials.•Different patterns obtained for FESEM images using HSV thresholding was able to determine the presence of multiple elements present in the prepared Activated Carbon samples.
Hybrid methodologies have become popular in many fields of research as they allow researchers to explore various methods, understand their strengths and weaknesses and combine them into new frameworks. Thus, the combination of different methods into a hybrid methodology allows to overcome the shortcomings of each singular method. This paper presents the methodology for two hybrid methods that can be used for time series forecasting. The first combines singular spectrum analysis with linear recurrent formula (SSA-LRF) and neural networks (NN), while the second combines the SSA-LRF and weighted fuzzy time series (WFTS). Some of the highlights of these proposed methodologies are:•The two hybrid methods proposed here are applicable to load data series and other time series data.•The two hybrid methods handle the deterministic and the nonlinear stochastic pattern in the data.•The two hybrid methods show a significant improvement to the single methods used separately and to other hybrid methods.
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.