Hybrid Analytical Approach Research Articles

Exploring the adoption of wearable healthcare devices among the Pakistani adults with dual analysis techniques

The technological insurgency benefits human life in numerous ways. Wearable personal mobile devices enable consumers to manage their health while performing daily routine activities at the workplace or home. The current study aimed to assess the effect of health improvement expectancy, effort expectancy, price value, health consciousness, and perceived reliability on adopting wearable health devices (WHDs) among Pakistani adults using the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). The study assumed a quantitative & cross-sectional research design which involved getting feedback from 385 adults through a survey on social media. A hybrid analytic approach was assumed in the current work, where partial least square structural equational (PLS-SEM) modelling and necessary condition analysis (NCA) were employed. The PLS-SEM findings revealed that health improvement expectancy, perceived vulnerability, price value, and health consciousness significantly impact the adoption of WHDs. The NCA analysis indicated that the three most contributing factors forming the adoption of WHDs were perceived reliability, effort expectancy, and perceived price value. The study's theoretical and practical suggestions offer to harness the WHDs adoption. The adoption of WHD can facilitate the consumers and public health system to minimize the physical presence of people looking for health facilities at the hospitals without compromising the effectiveness of healthcare systems. The mass adoption of WHDs may revolutionize the health industry and permit adults to track and manage their health conveniently at their own. The limitations of the study and future research implications are stated at the end of the manuscript.

Factors affecting Industry 4.0 adoption – A hybrid SEM-ANN approach

Industry 4.0 is a technology-driven digital transformation to enable data-driven decision-making based on real-time data to enhance the competitiveness of traditional manufacturing. Moving forward, adopting Industry 4.0 is an evident requirement for manufacturers to remain competitive. Currently, in the early stages of adoption in most industries and countries, there is an evident lack of foundational Industry 4.0 knowledge among decision-makers. There are few studies on the adoption of Industry 4.0; however, they focus on specific domains like cloud computing, virtual reality, IoT, etc., and are primarily set in developed countries. This research proposes a multi-stage hybrid analytic approach whereby the research model was tested using Structural Equation Modelling (SEM). The SEM results were used as inputs for the Artificial Neural Networks (ANN) to determine significant predictors for the adoption of Industry 4.0 in Indian manufacturing industries. A comprehensive sample of 350 responses from various Indian manufacturers was collected and analyzed. Results revealed that the factors viz. Software Infrastructure (SI), System Flexibility (SF), Operational Accuracy (OA), and the Technical Capabilities (TC) play a dominant role in the successful adoption intentions of Industry 4.0 in India. Manufacturers from India and other emerging economies will benefit from the findings of this study by concentrating and improving on the dominant adoption factors of Industry 4.0. Concluding, we discuss the study’s results and derive lessons learned for stakeholders, including managers, consultants, policymakers, and regulatory authorities.

Modeling of Industrial Robot Kinematics Using a Hybrid Analytical and Statistical Approach

Abstract Industrial robots are highly desirable in applications including manufacturing and surgery. However, errors in the modeling of the kinematics of robotic arms limit their positional accuracy in industrial applications. Specifically, analytical kinematic models of the robot arm suffer from errors in coefficient calibrations and the inability to account for effects including gear backlash. However, statistical modeling methods require an extensive amount of points for calibration, which is infeasible in practical industrial environments. Hence, this paper describes, develops, and experimentally validates a hybrid modeling methodology combining both analytical and statistical methods to describe the robot kinematics in an intuitive manner that is easily adaptable for small- and medium-sized industries. By formulating an explicitly described analytical kinematic model as a prior mean distribution of a Gaussian process, the prior distribution can be updated with experimental data using statistical Bayesian Inference, thus enabling more accurate description of the robot kinematics with fewer data points. The hybrid model is demonstrated to outperform an analytical model, a neural network model, and a Gaussian Process Regression model with no prior distribution in predicting both the forward and inverse kinematics of a UR5 and UR10 robot arm. Also, the error propagation of the inverse kinematic solutions is studied. In addition, the testing framework used in this work can be used as a standardized benchmark to evaluate alternative kinematic models.

Detection of Behavioral Patterns Employing a Hybrid Approach of Computational Techniques

As far as the present state is concerned in detecting the behavioral pattern of humans (subject) using morphological image processing, a considerable portion of the study has been conducted utilizing frontal vision data of human faces. The present research work had used a side vision of human-face data to develop a theoretical framework via a hybrid analytical model approach. In this example, hybridization includes an artificial neural network (ANN) with a genetic algorithm (GA). We researched the geometrical properties extracted from side-vision human-face data. An additional study was conducted to determine the ideal number of geometrical characteristics to pick while clustering. The close vicinity of minimum distance measurements is done for these clusters, mapped for proper classification and decision process of behavioral pattern. To identify the data acquired, support vector machines and artificial neural networks are utilized. A method known as an adaptive-unidirectional associative memory (AUTAM) was used to map one side of a human face to the other side of the same subject. The behavioral pattern has been detected based on two-class problem classification, and the decision process has been done using a genetic algorithm with best-fit measurements. The developed algorithm in the present work has been tested by considering a dataset of 100 subjects and tested using standard databases like FERET, Multi-PIE, Yale Face database, RTR, CASIA, etc. The complexity measures have also been calculated under worst-case and best-case situations.

On the development of a hybrid analytical approach to modeling complex systems containing both weakly structured and well-structured subsystems

On the development of a hybrid analytical approach to modeling complex systems containing both weakly structured and well-structured subsystems

Identification of Inter-Turn Short-Circuits in Induction Motor Stator Winding Using Simulated Annealing

This paper presents a method of inter-turn short-circuit identification in induction motors during load current variations based on a hybrid analytic approach that combines the genetic algorithm and simulated annealing. With this approach, the essence of the method relies on determining the reference matrices and calculating the distance between the reference matric values and the test matrix. As a whole, it is a novel approach to the process of identifying faults in induction motors. Moreover, applying a discrete optimization algorithm to search for alternative solutions makes it possible to obtain the true minimal values of the matrices in the identification process. The effectiveness of the applied method in the monitoring and identification processes of the inter-turn short-circuit in the early stage of its creation was confirmed in tests carried out for several significant state variables describing physical magnitudes of the selected induction motor model. The need for identification of a particular fault is related to a gradual increase in its magnitude in the process of the induction motor’s exploitation. The occurrence of short-circuits complicates the dynamic properties of the measured diagnostic signals of the system to a great extent.

Solicitation of Knowledge Graph Enhanced Neural Network Objects Detection by Sentiment Analysis

In the machine learning technique, the knowledge graph is advancing swiftly; however, the basic models are not able to grasp all the affluence of the script that comes from the different personal web graphics, social media, ads, and diaries, etc., ignoring the semantic of the basic text identification. The knowledge graph provides a real way to extract structured knowledge from the texts and desire images of neural network, to expedite their semantics examination. In this study, we propose a new hybrid analytic approach for sentiment evaluation based on knowledge graphs, to identify the polarity of sentiment with positive and negative attitudes in short documents, particularly in 4 chirps. We used the tweets graphs, then the similarity of graph highlighted metrics and algorithm classification pertain sentimentality pre-dictions. This technique facilitates the explicability and clarifies the results in the knowledge graph. Also, we compare our differentiate the embeddings n-gram based on sentiment analysis and the result is indicated that our study can outperform classical n-gram models, with an F1-score of 89% and recall up to 90%.

The use of hybrid analytics to establish effective strategies for household energy conservation

Taiwan highly relies on foreign countries' supply in various types of energy because of the shortage of energy resources. Taiwanese government encourages industrial and academic research organizations to invest research in energy conservation that could be applicable to countries with similar settings. This research analyzed energy conservation and demographic data of households in Taiwan and developed a novel method for best setting household energy conservation strategies. Through the analysis of high volume and high dimensional data, it showed a hybrid analytical method is required, to predict and identify target households and refine their energy conservation strategies. The hybrid method includes a Logistic Regression based predictive model and a Rough Set Theory-based decision-making model. A hybrid analytical approach is used to identify target households and the critical energy conservation factors by households, such as the use of air conditioning in terms of time length and time of the day, age of the house, energy conservation of the electrical appliances, and price promotion. References show that no study was conducted with such a hybrid approach that can refine strategies for the households that were false-positive in achieving the conservation goal. Besides the above-mentioned purpose, this novel method intended to lower the promotion cost, as promotion is a part of the strategy refinement. In overall, the following problems were considered for decision-making: 1. how to identify the target households that require strategy refinement, 2. how to identify key variables and determine the change of these variables to increase the likelihood of achieving conservation goal, 3. what options can be provided to the decision-makers for feasible strategy refinements. Some studies with qualitative approaches and judgmental decision-making methods were found in this area. However, with the available large dataset, the proposed quantitative method achieved better decision-making efficiency and result optimality.

Initial loss to follow up of tuberculosis patients in South Africa: perspectives of program managers

BackgroundTuberculosis (TB) remains a serious public health problem in South Africa. Initial loss to follow up (LTFU) rates among TB patients are high, varying between 14.9 and 22.5%. From the perspective of patients, documented reasons for this include poor communication between patient and staff after testing, not being aware that results are ready and other competing priorities such as preference to go to work as opposed to seeking healthcare. Ward-based Outreach Teams (WBOTs) routinely conduct home visits to ensure adherence to medication for various conditions including TB. We explored reasons for TB initial loss to follow up from the perspectives of TB program managers and WBOT program managers, with a focus on the WBOT’s (potential) role in reducing initial LTFU, in particular.MethodsKey informant interviews with five WBOT program managers and four TB program managers were conducted. The interviews were audio-recorded, then transcribed and exported to NVivo 11 software for coding. A hybrid analytic approach consisting of both inductive and deductive coding was used to identify themes.ResultsThe age of the nine managers ranged between 28 and 52 years old, of which two were male. They had been in their current position for between 2 to 12 years. Prior to treatment initiation, WBOTs screen household members for TB and refer them for TB testing if need be, but integration of the two programs is emphasized only after TB treatment has been initiated. Counseling of patients testing for TB is not guaranteed due to frequent staff rotations and staff shortages. Participants reported that possible dissatisfaction with services as well as stigma associated with the TB diagnosis could explain loss to follow up prior to treatment initiation.ConclusionProgram managers view health system related factors such as staff rotations, poor communication with patients and lack of counseling as contributing to the problem of initial LTFU among TB patients. The integration of the WBOT and TB programs is limited to referring suspected cases for testing and patients already on treatment.

Two-dimensional hybrid analytical approach for the investigation of thermal aspects in human tissue undergoing regional hyperthermia therapy

The formation of the present work is based on the development of the exact analytical solution of two-dimensional temperature response by employing the hyperbolic heat conduction bioheat model in a single-layered human skin tissue subjected to the regional hyperthermia therapy (RHT) for cancer treatment. The mathematical approach has been utilized as a hybrid form of ‘separation of variables’ and ‘finite integral transform’ method. Three kinds of surface heat fluxes (constant, sinusoidal and cosine) have been employed as an external heat source on the therapeutic surface of the square-shaped skin tissue of 100 mm × 100 mm. An innovative form of initial condition (spatially dependent) has been implemented in the present mathematical formulation as skin tissues are highly non-homogeneous and non-uniform in structure. The present research outcome indicates that cosine heat flux would be a suitable alternative for the sinusoidal heat flux. The impact of the relaxation time lag has been clearly noted in the thermal response with the waveform-like behaviour and it justifies the postulate of hyperbolic heat conduction. The two-dimensional temperature of the skin tissue has been observed in the range of 48.1 ℃–40 ℃ (in decreasing order). Estimated peak temperatures are in the proposed spectrum of hyperthermia therapy for an exposure time of 100 s, and this fact is true in an agreement with the medical protocol of the cancer treatment. The accuracy of the mathematical modelling and in-house computer codes are justified with the published numerical models and the maximum deviation of the thermal response has been noticed in order of 1.5–3%. The two-dimensional surface thermal contours have provided a glimpse of heat flow in the physical domain of skin tissue under different heating conditions and this research output may be beneficial to establish the theoretical standard of the regional hyperthermia treatment for cancer eradication.

Hybrid Analytical and Numerical Approach for Modeling Fluid Flow in Simplified Three-Dimensional Fracture Networks

Modeling fluid flow in three-dimensional fracture networks is required in a wide variety of applications related to fractured rocks. Numerical approaches developed for this purpose rely on either simplified representations of the physics of the considered problem using mesh-free methods at the fracture scale or complex meshing of the studied systems resulting in considerable computational costs. Here, we derive an alternative approach that does not rely on a full meshing of the fracture network yet maintains an accurate representation of the modeled physical processes. This is done by considering simplified fracture networks in which the fractures are represented as rectangles that are divided into rectangular subfractures such that the fracture intersections are defined on the borders of these subfractures. Two-dimensional analytical solutions for the Darcy-scale flow problem are utilized at the subfracture scale and coupled at the fracture-network scale through discretization nodes located on the subfracture borders. We investigate the impact of parameters related to the location and number of the discretization nodes on the results obtained, and we compare our results with those calculated using reference solutions, which are an analytical solution for simple configurations and a standard finite-element modeling approach for complex configurations. This work represents a first step towards the development of 3D hybrid analytical and numerical approaches where the impact of the surrounding matrix will be eventually considered.

A novel hybrid micro extraction for the sensitive determination of 17β-estradiol in water samples.

A novel and green hybrid of pipette tip micro solid-phase extraction (PT-μSPE) and supported liquid extraction (SLE) was tailored and established for efficient and sensitive determination of 17β-estradiol (E2) in water samples. The hybrid design used chitin as a solid phase in PT-μSPE and applies the SLE principle on it for micro extraction. The analytical setup was termed as chitin-supported pipette tip-micro liquid extraction (CPT-μLE). E2 is a highly active environmental endocrine disruptor, widely spread in the water environment, and thus it is relevant to develop a novel and noble method for its monitoring at trace concentrations in water. The detection and quantification of E2 were performed via HPLC-UV. Under the optimal conditions, E2 showed excellent linearity in the lower scale range of 0.02-20 ng mL-1, and the correlation coefficient (R2) value was 0.9958. The limit of quantitation (LOQ) and limit of detection (LOD) were 0.01 ng mL-1 and 0.008 ng mL-1, respectively, with inter-day and intra-day precision in the range of 3.2-5.1% and 3.6-4.8%, respectively. The method was successfully applied for the sensitive determination of E2 in real water samples. Overall, the method is simple, rapid, technically innovative, cost-effective, highly sensitive, environment friendly, and highly efficient. Furthermore, this method should allow easy expansion to pharmaceutical and biological samples. Finally, this novel hybrid analytical approach proposes a new direction in sample pre-treatment advancements and should act as a reference for other noble analytical advancements in the trace analysis of various emerging contaminants in environmental and biological matrices.

Homogenization theory with multiscale perturbation analysis for supervised learning of complex adsorption-desorption process in porous-media systems

Engineered and natural adsorbers, which undergo both adsorption and desorption mechanisms during operations, are dominant treatment technologies to remove difficult contaminants from influent sources to safeguard supplies of potable water to local communities. The slow net adsorption, i.e. adsorption rate greater than desorption rate, of contaminants over long periods of operational time contributes to chemical clogging inside an operating adsorber which complexity continues to be difficult for engineers to quantify, particularly on the adsorption and desorption processes coexisting during their near-equilibrium concentration state which builds towards its exhaustion stage for maintenance purpose.In this study, we leverage on the homogenization theory with the multiscale perturbation analysis to develop an engineering model which encapsulates the complex adsorption-desorption mechanics in adsorbers. The desired model contributes towards the primary objective of having the required capabilities to perform predictive maintenance of adsorbers to garner operational benefits, especially for large-scale systems. The hybrid analytical approach systematically derives a unique homogenized representation which contains an unknown reaction rate parameter responsible for the adsorption-desorption processes taking place over a significantly long period of time leading to the adsorber’s exhaustion stage. Dimensional analysis is then carried out to express the reaction rate parameter as a function of the known physical parameters to predict the transient variations in an adsorber’s effluent concentration during its gradual build-up towards exhaustion.Measured data from both the literature and our own adsorber experimental runs are then acquired to train and validate the model’s predictive capability, via supervised learning methods, which yields an average error deviation of 10 % or less for the optimal training period determined. Finally, we demonstrate quantitatively how the model can be useful to engineers to estimate: (a) the timing for an operating adsorber to reach its exhaustion stage; and (b) the associated Damköhler number, adsorption and desorption coefficients and etc., responsible for the concerned adsorber’s effluent concentration profile for the varying types of contaminants removed.

Hybrid analytical and simulation optimization approach for production and distribution supply chain planning

Supply chain planning consists of designing an optimal and feasible production and distribution plan for the whole supply chain. Traditionally, two common methods of optimization are analytical and simulation-based optimization, and each of them has pros and cons. In this paper, both methods are combined to consolidate the strengths of each, also known as the hybrid analytical and simulation approach. A case study of a multi-period, multi-echelon, and multi-product production and distribution problem that maximizes the whole supply chain’s profit is introduced, to demonstrate the proposed hybrid approach. The analytical model is solved to find the optimal production-distribution plan, and then the plan is inputted into a simulation model, where uncertainties are incorporated. The proposed algorithm is then applied to identify a feasible plan that meets makespan limitation and service level requirements. Safety stock is incorporated to satisfy the service level requirements and maximize the supply chain’s profit. This procedure continues iteratively until the production-distribution plan is feasible and optimized. The results show that the proposed approach can solve for a near or possibly optimal as well as feasible solution with relatively fast computational time.

From Playboy to Prison: When Pornography Use Becomes a Crime

ABSTRACT This study explores pornography use across the lifespan for a sample of registrants convicted of child pornography related offenses. We conducted qualitative life history interviews with nine offenders to examine how offender, victim, and situational factors interact to produce pornography-related criminal events. Using a hybrid analytic approach, themes related to persistence in pornography use and the social acceptability of pornography are identified. These themes relate to transitions from legal pornography use to illegal child pornography possession.

Covering Protest at the Vancouver 2010 Olympics: A “Peace Journalism” Inspired Analysis

This paper reports findings from a study of Canadian mainstream media coverage of anti-Olympic protests around the 2010 Vancouver Olympics. The study included an experiment with a hybrid analytic approach, as we drew together Galtung’s “peace journalism” (PJ) framework with a more critically and contextually-oriented strategy. We found that articles written about the anti-Olympic protests commonly fell into the ‘war/violence’ sub-categories proposed by Galtung, which are: propaganda oriented, elite oriented, and ‘us vs them’ oriented. We also identified segments of articles that were ‘peace-oriented’ in that they offered nuanced contextual information and included perspectives of traditionally marginalized groups. We conclude by comparing our findings with other work on coverage of sport-related protests and reflect on the value of a PJ approach for the study of conflict in sport media.

A Hybrid Process Mining Approach for Business Processes in Financial Organizations

Business process management is an integral part of many organizations in doing their day-to-day business operations considering that they help organizations transfer their business critical information from one organizational entity to another in the form of automated state transitions. The data (or event logs) that have been generated during these transitions hold valuable insights for improving organizational business processes, highlighting problem areas and visualizing the actual vs the formal procedures. The goal of this paper is to summarize the development of a hybrid analytical approach that utilizes both SQL and NO-SQL based back-end platforms in harmony in order to carry out process mining for a participation bank in Turkey. For this purpose, first we have developed a hybrid analytical software infrastructure that is backed by MS SQL Server and Hadoop platform components in order to discover key business processes of the organization based on event data. We then established a process mining framework that visualizes the process performance indicators and proposes workflow design changes and carries out statistical tests for identifying performance fluctuations by particularly using an in-memory parallel processing framework, named Apache Spark.

Chromatographic Data Segmentation Method: A Hybrid Analytical Approach for the Investigation of Antiviral Substances in Medicinal Plant Extracts.

The methodology described in this article will significantly reduce the time required for understanding the relations between chromatographic data and bioactivity assays. The methodology is a hybrid of hypothesis-based and data-driven scientific approaches. In this work, a novel chromatographic data segmentation method is proposed, which demonstrates the capability of finding what volatile substances are responsible for antiviral and cytotoxic effects in the medicinal plant extracts. Up until now, the full potential of the separation methods has not been exploited in the life sciences. This was due to the lack of data ordering methods capable of adequately preparing the chromatographic information. Furthermore, the data analysis methods suffer from multidimensionality, requiring a large number of investigated data points. A new method is described for processing any chromatographic information into a vector. The obtained vectors of highly complex and different origin samples can be compared mathematically. The proposed method, efficient with relatively small sized data sets, does not suffer from multidimensionality. In this novel analytical approach, the samples did not need fractionation and purification, which is typically used in hypothesis-based scientific research. All investigations were performed using crude extracts possessing hundreds of phyto-substances. The antiviral properties of medicinal plant extracts were investigated using gas chromatography-mass spectrometry, antiviral tests, and proposed data analysis methods. The findings suggested that (i) β- cis-caryophyllene, linalool, and eucalyptol possess antiviral activity, while (ii) thujones do not, and (iii) α-thujone, β-thujone, cis- p-menthan-3-one, and estragole show cytotoxic effects.

Application of hybrid analytical hierarchy process and complex proportional assessment approach for optimal design of brake friction materials

In this article, hybrid analytical hierarchy process (AHP) and complex proportional assessment (COPRAS) approach was proposed to optimize automotive brake friction materials. Sixteen formulations based on phenolic resin, barium sulfate, Kevlar fiber, Lapinus fiber, multiwall carbon nanotube (MWCNT), nanoclay and graphite were formulated and evaluated for tribological properties. The tribo‐evaluation parameters such as coefficient of friction, friction‐fluctuations, friction‐variability, friction‐fade, wear, friction‐recovery, friction‐stability and disc temperature rise were considered as criterion for the performance optimization. The optimization of formulations was carry out in two steps: (1) finding the weight of each criterion by AHP method and (2) ranking the formulations with COPRAS method. The AHP‐COPRAS results show that the formulation of 50 wt% barium sulfate, 15 wt% phenolic resin, 2.5 wt% graphite, 5 wt% Kevlar fiber, 25 wt% Lapinus fiber with 2.5 wt% MWCNT exhibits the optimal tribological properties. POLYM. COMPOS., 40:1602–1608, 2019. © 2018 Society of Plastics Engineers

A hybrid analytic approach for understanding patient demand for mental health services

The increase in digital/data resources available in the healthcare sector has heightened the emphasis of applying analytics to extract information to provide solutions to problems. However, the process of providing analytic-based healthcare solutions may introduce factors that require multiple analytic techniques or a hybrid approach. Data resources can involve complexities including formatting and volume issues or multiplicity of sub-tasks in achieving a full problem solution. This work extends the previous research on AI in forecasting patient demand and adds clustering methods to identify the types of ailments that need to be treated according to diagnostic codes. The hybrid approach is applied to data from a US-based psychiatry/behavioral health center and the results indicate clustering can add value to demand forecasts established by AI by identifying the type of ailments that patients require treatment for. With this information, care providers can better optimize staffing resources to meet demand in a cost-effective and efficient way by better understanding not only the amount of patient demand, but also the type of treatment that is required for select ailments.