The study of holomorphic functions has been recently extended through the application of diverse techniques, among which quantum calculus stands out due to its wide-ranging applications across various scientific disciplines. In this context, we introduce a novel q-differential operator defined via the generalized binomial series, which leads to the derivation of new classes of quantum-convex (q-convex) functions. Several specific instances within these classes were explored in detail. Consequently, the boundary values of the Hankel determinants associated with these functions were analyzed. All graphical representations and computational analyses were performed using Mathematica 12.0.•These classes are defined by utilizing a new q-differential operator.•The coefficient values | a i | ( i = 2 , 3 , 4 ) are investigated.•Toeplitz determinants, such as the second T 2 ( 2 ) and the third T 3 ( 1 ) order inequalities, are calculated.
The use of technology in healthcare is one of the most critical application areas today. With the development of medical applications, people's quality of life has improved. However, it is impractical and unnecessary for medium-risk people to receive specialized daily hospital monitoring. Due to their health status, they will be exposed to a high risk of severe health damage or even life-threatening conditions without monitoring. Therefore, remote, real-time, low-cost, wearable, and effective monitoring is ideal for this problem. Many researchers mentioned that their studies could use electrocardiogram (ECG) detection to discover emergencies. However, how to respond to discovered emergencies in household life is still a research gap in this field.•This paper proposes a real-time monitoring of ECG signals and sending them to the cloud for Sudden Cardiac Death (SCD) prediction.•Unlike previous studies, the proposed system has an additional emergency response mechanism to alert nearby community healthcare workers when SCD is predicted to occur.
Forecasting COVID-19 cases is challenging, and inaccurate forecast values will lead to poor decision-making by the authorities. Conversely, accurate forecasts aid Malaysian government authorities and agencies (National Security Council, Ministry of Health, Ministry of Finance, Ministry of Education, and Ministry of International Trade and Industry) and financial institutions in formulating action plans, regulations, and legal acts to control COVID-19 spread in the country. Therefore, this study proposes Repeated Time-Series Cross-Validation, a new data-splitting strategy to identify the best forecasting model that is capable of producing the lowest error measures value and a high percentage of forecast accuracy for COVID-19 prediction in Malaysia. Some of the highlights of the proposed method are:•A total of 21 models, five data partitioning sets, and four error measures to improve the forecast accuracy of daily COVID-19 cases in Malaysia.•The best model selected produces the lowest error measure value for the Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Mean Absolute Scaled Error (MASE).•The average 8-day forecast accuracy is 90.2 %. The lowest and highest forecast accuracy was 83.7 % and 98.7 %.
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.
Parkinson's Disease (PD) is a common disorder of the central nervous system. The Unified Parkinson's Disease Rating Scale or UPDRS is commonly used to track PD symptom progression because it displays the presence and severity of symptoms. To model the relationship between speech signal properties and UPDRS scores, this study develops a new method using Neuro-Fuzzy (ANFIS) and Optimized Learning Rate Learning Vector Quantization (OLVQ1). ANFIS is developed for different Membership Functions (MFs). The method is evaluated using Parkinson's telemonitoring dataset which includes a total of 5875 voice recordings from 42 individuals in the early stages of PD which comprises 28 men and 14 women. The dataset is comprised of 16 vocal features and Motor-UPDRS, and Total-UPDRS. The method is compared with other learning techniques. The results show that OLVQ1 combined with the ANFIS has provided the best results in predicting Motor-UPDRS and Total-UPDRS. The lowest Root Mean Square Error (RMSE) values (UPDRS (Total)=0.5732; UPDRS (Motor)=0.5645) and highest R-squared values (UPDRS (Total)=0.9876; UPDRS (Motor)=0.9911) are obtained by this method. The results are discussed and directions for future studies are presented.i.ANFIS and OLVQ1 are combined to predict UPDRS.ii.OLVQ1 is used for PD data segmentation.iii.ANFIS is developed for different MFs to predict Motor-UPDRS and Total-UPDRS.
Syncope is a transient loss of consciousness with rapid onset. The aims of the study were to systematically evaluate available machine learning (ML) algorithm for supporting syncope diagnosis to determine their performance compared to existing point scoring protocols. We systematically searched IEEE Xplore, Web of Science, and Elsevier for English articles (Jan 2011 - Sep 2021) on individuals aged five and above, employing ML algorithms in syncope detection with Head-up titl table test (HUTT)-monitored hemodynamic parameters and reported metrics. Extracted data encompassed subject count, age range, syncope protocols, ML type, hemodynamic parameters, and performance metrics. Of the 6301 studies initially identified, 10 studies, involving 1205 participants aged 5 to 82 years, met the inclusion criteria, and formed the basis for it. Selected studies must use ML algorithms in syncope detection with hemodynamic parameters recorded throughout HUTT. The overall ML algorithm performance achieved a sensitivity of 88.8% (95% CI: 79.4-96.1%), specificity of 81.5% (95% CI: 69.8-92.8%) and accuracy of 85.8% (95% CI: 78.6-92.8%). Machine learning improves syncope diagnosis compared to traditional scoring, requiring fewer parameters. Future enhancements with larger databases are anticipated. Integrating ML can curb needless admissions, refine diagnostics, and enhance the quality of life for syncope patients.
Color-blind is a generic disability whereby the affected individuals are not given the opportunity to benefit from the various functions provided by color that would impact humans physically and psychologically. Although this disability is not fatal, it brought plenty of turbulence in the affected individuals' daily activities. This paper aims to develop a system for recognizing and detecting colors of clothes in images, improve accuracy by using advanced algorithms to handle lighting variations, and provide color matching recommendations to assist color-blind individuals in making informed choices when purchasing shirts. The proposed methodology for color recognition involves:•retrieving the RGB values of a given point from the input image and converting them into HSV values.•creating web application integrated with a machine learning model to classify and predict the corresponding color based on the HSV values.•predicting the color name with suggestions of matching colors will be displayed on the interface.
An efficient trigonometrical-fitted two-derivative multistep collocation (TF-TDMC) method using Legendre polynomials up to order five as the basis functions, has been developed for solving second-order ordinary differential equations with oscillatory solution effectively. Interpolation method of approximated power series and collocation technique of its second and third derivative are implemented in the construction of the methods. Two-derivative multistep collocation methods are developed in predictor and corrector form with varying collocation and interpolation points. Later, trigonometrically-fitting technique is implemented into TF-TDMC method, using the linear combination of trigonometrical functions, to produce frequency-dependent coefficients in TF-TDMC method. The stability of the TF-TDMC method, with fitted parameters, is thoroughly analyzed and has been proven to achieve zero stability. Stability polynomials and regions for predictor and corrector of TF-TDMC method are developed and plotted. In the operation of the TF-TDMC method, initial conditions and the frequency for each problem (based on the exact solutions) are identified. The frequency-dependent coefficients are then adjusted according to the identified frequency. Predictor and corrector steps are implemented to estimate and refine the values of the dependent variable and its derivative, ensuring that convergence is achieved. A numerical experiment demonstrates that the proposed method significantly outperforms other existing methods in the literature, achieving the lowest maximum global error with moderate computational time across all step sizes for solving second-order ordinary differential equations with oscillatory solutions. Additionally, it effectively addresses real-world perturbed Kepler problems. The results include a detailed discussion and analysis of the numerical performance.•An efficient two-derivative multistep collocation method in predictor-corrector mode with trigonometrically-fitting technique (TF-TDMC) is developed for direct solving second-order ordinary differential equations with oscillatory solution.•TF-TDMC method has been proved to acquire zero-stability and its stability region is analyzed.•TF-TDMC method is the best among all selected methods in solving second-order ordinary differential equations with oscillatory solution, including perturbed Kepler problem.