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Dysarthria is a neuromotor speech disorder caused by neuromuscular disturbances that affect one or more articulators resulting in unintelligible speech. Though inter-phoneme articulatory variations are well captured by formant frequency-based acoustic features, these variations are expected to be much higher for dysarthric speakers than normal. These substantial variations can be well captured by placing sensors in appropriate articulatory position. This study focuses to determine a set of articulatory sensors and parameters in order to assess articulatory dysfunctions in dysarthric speech.

The current work aims to determine significant sensors and parameters associated using motion path and correlation analyzes on the TORGO database of dysarthric speech. Among eight informative sensor channels and six parameters per channel in positional data, the sensors such as tongue middle, back and tip, lower and upper lips and parameters (y, z, φ) are found to contribute significantly toward capturing the articulatory information. Acoustic and positional data analyzes are performed to validate these identified significant sensors. Furthermore, a convolutional neural network-based classifier is developed for both phone-and word-level classification of dysarthric speech using acoustic and positional data.

The average phone error rate is observed to be lower, up to 15.54% for positional data when compared with acoustic-only data. Further, word-level classification using a combination of both acoustic and positional information is performed to study that the positional data acquired using significant sensors will boost the performance of classification even for severe dysarthric speakers.

The proposed work shows that the significant sensors and parameters can be used to assess dysfunctions in dysarthric speech effectively. The articulatory sensor data helps in better assessment than the acoustic data even for severe dysarthric speakers.

The analysis of fluid flow and thermal transport performance inside the cavity has found numerous applications in various engineering fields, such as nuclear reactors and solar collectors. Nowadays, researchers are concentrating on improving heat transfer by using ternary nanofluids. With this motivation, the present study analyzes the natural convective flow and heat transfer efficiency of ternary nanofluids in different types of porous square cavities.

The cavity inclination angle is fixed ω = 0 in case (I) and ω=π4 in case (II). The traditional fluid is water, and Fe3O4+MWCNT+Cu/H2O is treated as a working fluid. Ternary nanofluid's thermophysical properties are considered, according to the Tiwari–Das model. The marker-and-cell numerical scheme is adopted to solve the transformed dimensionless mathematical model with associated initial–boundary conditions.

The average heat transfer rate is computed for four combinations of ternary nanofluids: Fe3O4(25%)+MWCNT(25%)+Cu(50%),Fe3O4(50%)+MWCNT(25%)+Cu(25%),Fe3O4(33.3%)+MWCNT(33.3%)+Cu(33.3%) and Fe3O4(25%)+MWCNT(50%)+Cu(25%) under the influence of various physical factors such as volume fraction of nanoparticles, inclined magnetic field, cavity inclination angle, porous medium, internal heat generation/absorption and thermal radiation. The transport phenomena within the square cavity are graphically displayed via streamlines, isotherms, local and average Nusselt number profiles with adequate physical interpretations.

The purpose of this study is to determine whether the ternary nanofluids may be used to achieve the high thermal transmission in nuclear power systems, generators and electronic device applications.

The current analysis is useful to improve the thermal features of nuclear reactors, solar collectors, energy storage and hybrid fuel cells.

To the best of the authors’ knowledge, no research has been carried out related to the magneto-hydrodynamic natural convective Fe3O4+MWCNT+Cu/H2O ternary nanofluid flow and heat transmission filled in porous square cavities with an inclined cavity angle. The computational outcomes revealed that the average heat transfer depends not only on the nanoparticle’s volume concentration but also on the existence of heat source and sink.

This study aims to investigate entropy generation through natural convection and examine heat transfer properties within a partially heated and cooled enclosure influenced by an angled magnetic field. The enclosure, subjected to consistent heat production or absorption, contains a porous medium saturated with a hybrid nanofluid blend of Cu-Fe₃O₄ and MoS₂-Fe₃O₄.

The temperature and velocity equations are converted to a dimensionless form using suitable non-dimensional quantities, adhering to the imposed constraints. To solve these transformed dimensionless equations, the finite-difference method, based on the MAC (Marker and Cell) technique, is used. Comprehensive numerical simulations address various control parameters, including nanoparticle volume fraction, Rayleigh number, heat source or sink, Darcy number, Hartmann number and slit position. The results are illustrated through streamlines, isotherms, average Nusselt numbers and entropy generation plots, offering a clear visualization of the impact of these parameters across different scenarios.

Results obtained show that the Cu-Fe₃O₄ hybrid nanofluid exhibits higher entropy generation than the MoS₂-Fe₃O₄ hybrid nanofluid when comparing them at a Rayleigh number of 10⁶ and a Darcy number of 10^–1. The MoS₂ hybrid nanofluid demonstrates a low permeability, as evidenced by an average Darcy number of 10^–3, in comparison to the Cu hybrid nanofluid. The isothermal contours for a Rayleigh number of 10⁴are positioned parallel to the vertical walls. Additionally, the quantity of each isotherm contour adjacent to the hot wall is being monitored. The Cu and MoS₂ nanoparticles exhibit the highest average entropy generation at a Rayleigh number of 10⁵ and a Darcy number of 10^–1, respectively. When a uniform heat sink is present, the temperature gradient in the central part of the cavity decreases. In contrast, the absence of a heat source or sink leads to a more intense temperature distribution within the cavity. This differs significantly from the scenario where a uniform heat sink regulates the temperature.

The originality of this study is to examine the generation of entropy in natural convection within a partially heated and cooled enclosure that contains hybrid nanofluids. Partially heated corners are essential for optimizing heat transfer in a wide range of industrial applications. This enhancement is achieved by increasing the surface area, which improves convective heat transfer. These diverse applications encompass fields such as chemical engineering, mechanical engineering, surface research, energy production and heat recovery processes. Researchers have been working on improving the precision of heated and cold corners using various methods, such as numerical, experimental and analytical approaches. These efforts aim to enhance the broad utility of these corners further.

Thermal energy storage systems use thermal energy to elevate the temperature of a storage substance, enabling the release of energy during a discharge cycle. The storage or retrieval of energy occurs through the heating or cooling of either a liquid or a solid, without undergoing a phase change, within a sensible heat storage system. In a sensible packed bed thermal energy storage system, the structure comprises porous media that form the packed solid material, while fluid occupies the voids. Thus, a cavity, partially filled with a fluid layer and partially with a saturated porous layer, has become important in the investigation of natural convection heat transfer, carrying significant relevance within thermal energy storage systems. Motivated by these insights, the current investigation delves into the convection heat transfer driven by buoyancy and entropy generation within a partially porous cavity that is differentially heated, vertically layered and filled with a hybrid nanofluid.

The investigation encompasses two distinct scenarios. In the first instance, the porous layer is positioned next to the heated wall, while the opposite region consists of a fluid layer. In the second case, the layers switch places, with the fluid layer adjacent to the heated wall. The system of equations for fluid and porous media, along with appropriate initial and boundary conditions, is addressed using the finite difference method. The Tiwari–Das model is used in this investigation, and the viscosity and thermal conductivity are determined using correlations specific to spherical nanoparticles.

Comprehensive numerical simulations have been performed, considering controlling factors such as the Darcy number, nanoparticle volume fraction, Rayleigh number, bottom slit position and Hartmann number. The visual representation of the numerical findings includes streamlines, isotherms and entropy lines, as well as plots illustrating average entropy generation and the average Nusselt number. These representations aim to provide insight into the influence of these parameters across a spectrum of scenarios.

The computational outcomes indicate that with an increase in the Darcy number, the addition of 2.5% magnetite nanoparticles to the GO nanofluid results in an enhanced heat transfer rate, showing increases of 0.567% in Case 1 and 3.894% in Case 2. Compared with Case 2, Case 1 exhibits a 59.90% enhancement in heat transfer within the enclosure. Positioning the porous layer next to the partially cooled wall significantly boosts the average total entropy production, showing a substantial increase of 11.36% at an elevated Rayleigh number value. Positioning the hot slit near the bottom wall leads to a reduction in total entropy generation by 33.20% compared to its placement at the center and by 33.32% in comparison to its proximity to the top wall.

The purpose of this paper is to synthesise new fatty dicarboxylic acid half ester (NFAHE) C25, which can be used as substitute to dimer/trimer acids commonly used (C36, 54) as basic raw materials for manufacture of polyamides for printing inks or as curing agents for epoxy paints and adhesives. This could be an economically viable synthesis by which the user could manufacture the finished products from relatively low cost raw materials.

Vegetable oils have several double bonds that undergo large number of reactions. Diels‐Alder addition is one of them. Dimer acids have been produced by using these double bonds by reaction of two fatty acid molecules. Maleic acid, acrylic acid has also been used for this purpose. Sorbic acid is a derivative of alcohol and hence a renewable raw material. It is relatively less used by the coating chemists due to its relatively limited availability due to restricted uses.

It was found that sorbic acid reacts easily with unsaturated fatty acids. Its solubility in fatty acids and esters is limited. A common solvent that can be removed easily after the reaction was necessary. Cyclohexanone was found to meet this requirement. The resultant half ester of dicarboxylic acid could be easily converted to polyamides for curing epoxies.

The user can manufacture his own dibasic/tribasic acid as a first step. As a source of methyl esters of fatty acids with iodine value about 110 to 130, vegetable oils such as soyabean oil can be used. Low value acid oils obtained from vegetable oil refining are also suitable. Bio diesel could be used directly. To account for large saturated fatty acids in bio diesel, corresponding trimer may be produced by appropriate addition of sorbic acid to fatty acid.

The process allows a manufacturer to develop low cost formulations for bulk products using simple chemistry that can be integrated in the existing process.

The purpose of this paper was to assess and compare beliefs about mental illness among pharmacy and non-pharmacy students and to explore its associated factors.

This research was a cross-sectional survey conducted among undergraduate pharmacy and non-pharmacy students of a Nigerian university. The selected participants completed the Belief toward Mental Illness questionnaires after usual class lectures. Descriptive statistics, χ² test, and t-test were used for data analysis. The level of significance was set at p<0.05.

Overall, pharmacy and non-pharmacy undergraduate students demonstrated negative beliefs about mental illness. There were no substantial differences in beliefs about mental illness among both groups of students. Students’ class, age, visit to a mental hospital and personal experience of mental disorder were significantly associated with beliefs about mental illness.

The generalization of the study findings to other schools of pharmacy in Nigeria is uncertain because this study used convenience sampling technique and was conducted in a single public university. However, the study provides relevant educational opportunities to guide policy makers and university administrators on mental health literacy. Therefore, educational interventions addressing observed gaps in students’ opinions regarding mental illness are recommended.

There appears to be little or no data on the beliefs of undergraduate pharmacy trainees about mental disorders in Nigeria and Sub-Saharan Africa.

This paper tests whether Bayesian A/B testing yields better decisions that traditional Neyman-Pearson hypothesis testing. It proposes a model and tests it using a large, multiyear Google Analytics (GA) dataset.

This paper is an empirical study. Competing A/B testing models were used to analyze a large, multiyear dataset of GA dataset for a firm that relies entirely on their website and online transactions for customer engagement and sales.

Bayesian A/B tests of the data not only yielded a clear delineation of the timing and impact of the intellectual property fraud, but calculated the loss of sales dollars, traffic and time on the firm’s website, with precise confidence limits. Frequentist A/B testing identified fraud in bounce rate at 5% significance, and bounces at 10% significance, but was unable to ascertain fraud at the standard significance cutoffs for scientific studies.

None within the scope of the research plan.

Bayesian A/B tests of the data not only yielded a clear delineation of the timing and impact of the IP fraud, but calculated the loss of sales dollars, traffic and time on the firm’s website, with precise confidence limits.

Bayesian A/B testing can derive economically meaningful statistics, whereas frequentist A/B testing only provide p-value’s whose meaning may be hard to grasp, and where misuse is widespread and has been a major topic in metascience. While misuse of p-values in scholarly articles may simply be grist for academic debate, the uncertainty surrounding the meaning of p-values in business analytics actually can cost firms money.

There is very little empirical research in e-commerce that uses Bayesian A/B testing. Almost all corporate testing is done via frequentist Neyman-Pearson methods.

Despite an enormous body of literature on conflict management, intra-group conflicts vis-à-vis team performance, there is currently no study investigating the conflict prevention approach to handling innovation-induced conflicts that may hinder smooth implementation of big data technology in project teams.

This study uses constructs from conflict theory, and team power relations to develop an explanatory framework. The study proceeded to formulate theoretical hypotheses from task-conflict, process-conflict, relationship and team power conflict. The hypotheses were tested using Partial Least Square Structural Equation Model (PLS-SEM) to understand key preventive measures that can encourage conflict prevention in project teams when implementing big data technology.

Results from the structural model validated six out of seven theoretical hypotheses and identified Relationship Conflict Prevention as the most important factor for promoting smooth implementation of Big Data Analytics technology in project teams. This is followed by power-conflict prevention, prevention of task disputes and prevention of Process conflicts respectively. Results also show that relationship and power conflicts interact on the one hand, while task and relationship conflict prevention also interact on the other hand, thus, suggesting the prevention of one of the conflicts could minimise the outbreak of the other.

The study has been conducted within the context of big data adoption in a project-based work environment and the need to prevent innovation-induced conflicts in teams. Similarly, the research participants examined are stakeholders within UK projected-based organisations.

The study urges organisations wishing to embrace big data innovation to evolve a multipronged approach for facilitating smooth implementation through prevention of conflicts among project frontlines. This study urges organisations to anticipate both subtle and overt frictions that can undermine relationships and team dynamics, effective task performance, derail processes and create unhealthy rivalry that undermines cooperation and collaboration in the team.

The study also addresses the uncertainty and disruption that big data technology presents to employees in teams and explore conflict prevention measure which can be used to mitigate such in project teams.

The study proposes a Structural Model for establishing conflict prevention strategies in project teams through a multidimensional framework that combines constructs like team power conflict, process, relationship and task conflicts; to encourage Big Data implementation.

The purpose of this study was to examine the relationship between online banking fraud experience and fear of cybercrime and distrust of online banking services, and to understand how perceived usefulness of online banking moderates the relationship.

The number of respondents involved in this study was 271 people from the Central Java region, Indonesia. Statistical analysis was performed using Jeffreys’s Amazing Statistics Program software to examine the relationships and interactions between the variables studied.

Experience of online banking fraud is positively related to fear of cybercrime and distrust of online banking services. Perceived usefulness of online banking moderates the relationship between online banking fraud experience and fear of cybercrime and distrust of digital payments. Perceived usefulness is negatively related to the level of distrust of online banking services.

Overall, the implications of this study underscore the importance of dealing with the risks of cybercrime in online banking services. By focusing on security, user awareness and the role of perceived usefulness, banking service providers can create a safer and more trusting environment for users of online banking services. This also contributes to the development of more innovative services and can increase customer satisfaction and trust.

The practical application of these findings is important for financial institutions and online banking service providers. Companies must improve cybersecurity with the latest technology and provide education about online security practices. Transparent communication and better customer service will help overcome customer fears. Compliance with security regulations and technological innovation is also important to protect online banking services. With these steps, customer security and trust can be improved, and the adoption of online banking services will increase widely.

The social implications of this research are increasing public awareness about cybersecurity, consumer protection and strengthening trust in online banking services. With joint efforts, a safer and more trusting environment in using online banking services can be realized.

The originality of this research lies in the use of perceived usefulness of online banking as a moderating variable to reduce the negative impact of online banking fraud experience. With a focus on the psychological effects of customers experiencing fraud, this research seeks to rebuild trust and improve the security of online banking services.

The purpose of this paper is to minimize corrosion-related pollution in the environment. From the lemongrass extract (LGE), the authors selected one of the best green inhibitors.

The corrosion and inhibition of mild steel in traditional acidification solutions were estimated by electrochemical measurements. The corrosion appearance was observed with scanning electron microscopy, atomic force microscopy micrographs and attenuated total reflectance infrared spectroscopy spectrum. The correlation was formed between the gained inhibition efficiency (IE)% from electrochemical measurements and certain quantum chemical parameters.

The results displayed that the IE was up to 90% when the LGE concentration was 300 ppm. The results confirmed that the theoretical experiments are very similar to the experimental observations.

For the first time, LGE was used as a cheap and safe corrosion inhibitor for mild steel corrosion in the acidification process. The mechanism of mild steel corrosion and anti-corrosion in acid solution has been suggested.