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The purpose of this paper is to investigate the three-dimensional mixed convection flow of viscoelastic nanofluid induced by an exponentially stretching surface.

Similarity transformations are utilized to reduce the partial differential equations into the ordinary differential equations. The corresponding non-linear problems are solved by homotopy analysis method.

The authors found that an increase in thermophoresis and Brownian motion parameter enhance the temperature. Here thermal conductivity of fluid is enhanced due to which higher temperature and thicker thermal boundary layer thickness is obtained.

Heat and mass transfer effects in mixed convection flow over a stretching surface have numerous applications in the polymer technology and metallurgy. Such flows are encountered in metallurgical processes which involve the cooling of continuous strips or filaments by drawing them through a quiescent fluid and that in the process of drawing, these strips are sometimes stretched.

Three-dimensional flows over an exponentially stretching surface are very rare in the literature. Three-dimensional flow of viscoelastic nanofluid due to an exponentially stretching surface is first time investigated.

Business process re-engineering (BPR) initiatives are complex endeavours which require many factors to ensure success. However, most studies focus on the organisational processes and improvement within the organisation itself and less on the project team and management dynamics. The study aimed to identify factors that enabled the completion of a BPR, in a technical firm, based on reflections on the project management style.

The study entailed a descriptive and interpretive case study with reflections from project team members. Data were analysed using descriptive statistics and content analysis.

Findings revealed that critical success factors for BPR in a technical firm include project leadership and sponsorship, organisational culture and attributes, team dynamics and the nature (activities), and duration of the process.

The findings will benefit project managers in improving their competence and project success through reflective practice. The identified factors could be used in future projects of a similar nature and size to improve how organisations execute BPR projects.

The study used reflections to identify success factors for BPR in a technical firm.

Business Process Reengineering (BPR) eliminates non-value-added (NVA) and essential non-value-added (ENVA) waste through radical process redesign to improve organizational operations. Comprehensive research integrating BPR tools is needed to understand their benefits for manufacturing firms. This research presents an integrated BPR-simulation framework tailored to the manufacturing sector to maximize process improvements and operational excellence.

The BPR design methodology adopts a systematic, multi-stage approach. The first phase involves identifying a specific improvement process aligned with BPR's core objectives. This phase analyses and redesigns workflows to optimize task sequences, roles, and stakeholder interactions while eliminating redundancies and inefficiencies via Workflow Process Reengineering. Visual process mapping tools, including VSM and simulation, pinpoint areas of waste, delay, and potential enhancement. The second phase follows the workflow analysis and aims to improve efficiency and effectiveness by redefining roles, rearranging tasks, and integrating automation and technology solutions. The redesigned process undergoes evaluation against key performance indicators to ensure measurable improvements are achieved. The final phase validates the proposed changes through simulation models, assesses the impact on key performance metrics, and establishes the necessary infrastructure for successful implementation. The proposed model is empirically validated through a case study of a leading apparel company in Vietnam, confirming its effectiveness.

The findings reveal that NVA activities are being eliminated, and ENVA activities in key departments are significantly reduced. This yielded a substantial improvement, reducing 25 out of 186 combined ENVA and NVA operations in the sewing facility, involving a decrease of 15 ENVA operations and the removal of 10 NVA operations. Consequently, this led to an 8.5% reduction in the proportion of ENVA operations, accompanied by a complete 100% elimination of NVA activities.

The single case study limits generalizability; thus, expanded implementation across diverse manufacturing sub-sectors is required to establish validity and broader applicability of the integrated framework.

The experimental results highlight the proposed model's effectiveness in optimizing resource utilization and its practical implementation potential. This structured BPR methodology enables organizations to validate, evaluate, and establish proposed process changes to enhance operational performance and productivity.

Present investigation based on the flow of electrically conducting Williamson nanofluid embedded in a porous medium past a linearly horizontal stretching sheet. In addition to that, the combined effect of thermophoresis, Brownian motion, thermal radiation and chemical reaction is considered in both energy and solutal transfer equation, respectively.

With suitable choice of nondimensional variables the governing equations for the velocity, temperature, species concentration fields, as well as rate shear stress at the plate, rate of heat and mass transfer are expressed in the nondimensional form. These transformed coupled nonlinear differential equations are solved semi-analytically using variation parameter method.

The behavior of characterizing parameters such as magnetic parameter, melting parameter, porous matrix, Brownian motion, thermophoretic parameter, radiation, Lewis number and chemical particular case present result validates with earlier established results and found to be in good agreement. Finally reaction parameter is demonstrated via graphs and numerical results are presented in tabular form.

The said work is an original work of the authors.

Nanofluid flow which is squeezed between parallel plates is studied using differential transformation method (DTM). The fluid in the enclosure is water containing different types of nanoparticles: Al₂O₃ and CuO. The effective thermal conductivity and viscosity of nanofluid are calculated by Koo–Kleinstreuer–Li (KKL) correlation. The comparison between the results from DTM and numerical method are in well agreement which proofs the capability of this method for solving such problems. Effects of the squeeze number and nanofluid volume fraction on flow and heat transfer are examined. Results indicate that Nusselt number augment with increase of the nanoparticle volume fraction. Also, it can be found that heat transfer enhancement of CuO is higher than Al₂O₃.

The problem of nanofluid flow which is squeezed between parallel plates is investigated analytically using DTM. The fluid in the enclosure is water containing different types of nanoparticles: Al₂O₃ and CuO. The effective thermal conductivity and viscosity of nanofluid are calculated by KKL correlation. In this model, effect of Brownian motion on the effective thermal conductivity is considered. The comparison between the results from DTM and numerical method are in well agreement which proves the capability of this method for solving such problems. The effect of the squeeze number and the nanofluid volume fraction on flow and heat transfer is investigated. The results show that Nusselt number increase with increase of the nanoparticle volume fraction. Also, it can be found that heat transfer enhancement of CuO is higher than Al₂O₃.

The effect of the squeeze number and the nanofluid volume fraction on flow and heat transfer is investigated. The results show that Nusselt number increase with increase of the nanoparticle volume fraction. Also, it can be found that heat transfer enhancement of CuO is higher than Al₂O₃.

This paper is original.

The purpose of this paper is to research on CuO-water nanofluid Non-Darcy flow because of magnetic field. Porous cavity has circular heat source and filled with nanofluid. Lattice Boltzmann Method (LBM) has been used to simulate this problem.

In this research, LBM has been applied as mesoscopic approach to simulate water-based nanofluid free convection. Koo–Kleinstreuer–Li model is used to consider Brownian motion impact on nanofluid properties. Impacts of Rayleigh number, Darcy number, nanofluid volume fraction and Hartmann number on heat transfer treatment are illustrated.

It is found that temperature gradient decreases with rise of while it enhances with augment of Ha. Darcy number can enhance the convective flow.

The originality of this work is to analyze the to investigate magnetic field impact on water based CuO-H₂O nanofluid natural convection inside a porous cavity with elliptic heat source.

Currently, many experimental studies on the properties and behavior of rubberized concrete are available in the literature. These findings have motivated scholars to propose models for estimating some properties of rubberized concrete using traditional and advanced techniques. However, with the advancement of computational techniques and new estimation models, selecting a model that best estimates concrete's property is becoming challenging.

In this study, over 1,000 different experimental findings were obtained from the literature and used to investigate the capabilities of ten different machine learning algorithms in modeling the hardened density, compressive, splitting tensile, and flexural strengths, static and dynamic moduli, and damping ratio of rubberized concrete through adopting three different prediction approaches with respect to the inputs of the model.

In general, the study's findings have shown that XGBoosting and FFBP models result in the best performances compared to other techniques.

Previous studies have focused on the compressive strength of rubberized concrete as the main parameter to be estimated and rarely went into other characteristics of the material. In this study, the capabilities of different machine learning algorithms in predicting the properties of rubberized concrete were investigated and compared. Additionally, most of the studies adopted the direct estimation approach in which the concrete constituent materials are used as inputs to the prediction model. In contrast, this study evaluates three different prediction approaches based on the input parameters used, referred to as direct, generalized, and nondestructive methods.

This paper aims to investigate numerically the free convective heat transfer efficiency inside a rectotrapezoidal enclosure filled with Al₂O₃–Cu/water hybrid fluid. The bottom wall of the cavity is uniformly heated, the upper horizontal wall is insulated, and the remaining walls are considered cold. A new thermophysical relation determining the thermal conductivity of the hybrid nanofluid has been established, which produced results those match with experimental ones.

The governing partial differential equations are solved using the finite element method of Galerkin type. The simulated results in terms of streamlines, heat lines and isotherms are displayed for various values of the model parameters, which govern the flow.

The Nusselt number, friction factor and the thermal efficiency index are also determined for the pertinent parameters varying different ratios of the hybrid nanoparticles. The simulated results showed that thermal buoyancy significantly controls the heat transfer, friction factor and thermal efficiency index. The highest thermal efficiency is obtained for the lowest Rayleigh number.

This theoretical study is significantly relevant to the applications of the hybrid nanofluids electronic devices cooled by fans, manufacturing process, renewable energies, nuclear reactors, electronic cooling, lubrication, refrigeration, combustion, medicine, thermal storage, etc.

The results showed that nanoparticle loading intensified the rate of heat transfer and thermal efficiency index at the expense of the higher friction factor or higher pumping power. The results further show that the heat transmission in Al₂O₃–Cu/water hybrid nanofluid at a fixed value of intensified $\phi_{hnf}$ compared to the Al₂O₃/water nanofluid when an amount of higher conductivity nanoparticles (Cu) added to it. Besides, the rate of heat transfer in Cu/water nanofluid declines when the lower thermal conductivity Al₂O₃ nanoparticles are added to the mixture.

This study aims to clarify the relationship between inclination angle of hot surface of CPU and its temperature in absence and presence of aluminum foam as a cooling system. It proposes application of the artificial neural [multi-layer perceptron (MLP) and radial basis function] networks and adaptive neuron-fuzzy inference system (ANFIS) to predict interface temperature of central processing unit (CPU)/metal foam heat sink.

To provide a consistent set of data, the surface of an aluminum cone with and without installing Duocel aluminum foam was heated in a natural convection using an electrical resistor. The hot surface temperature was measured using five K-type thermocouples (±0.1°C). To develop the predictive models, ambient temperature, input power and inclination angle are taken as input which varied from 23°C to 32°C, 4 to 20 W and 0° to 90°, respectively. The hot surface temperature is taken as the output.

The results show that in the presence of foam, the hot surface temperature was less sensitive to the variations of angle, and the maximum enhancement of the heat transfer coefficient was 23 per cent at the vertical position. Both MLP network and ANFIS are comparable, but the values predicted by MLP network are in more conformity with the measured values.

The effect of metal foam on the inclination angle/hot surface temperature dependence is identified. The optimum angle is clarified. The applicability of the MLP networks to predict interface temperature of CPU/heat sink is approved.

Topic segmentation is one of the active research fields in natural language processing. Also, many topic segmenters have been proposed. However, the current challenge of researchers is the improvement of these segmenters by using external resources. Therefore, the purpose of this paper is to integrate study and evaluate a new external semantic resource in topic segmentation.

New topic segmenters (TSS-Onto and TSB-Onto) are proposed based on the two well-known segmenters C99 and TextTiling. The proposed segmenters integrate semantic knowledge to the segmentation process by using a domain ontology as an external resource. Subsequently, an evaluation is made to study the effect of this resource on the quality of topic segmentation along with a comparative study with related works.

Based on this study, the authors showed that adding semantic knowledge, which is extracted from a domain ontology, improves the quality of topic segmentation. Moreover, TSS-Ont outperforms TSB-Ont in terms of quality of topic segmentation.

The main limitation of this study is the used test corpus for the evaluation which is not a benchmark. However, we used a collection of scientific papers from well-known digital libraries (ArXiv and ACM).

The proposed topic segmenters can be useful in different NLP applications such as information retrieval and text summarizing.

The primary original contribution of this paper is the improvement of topic segmentation based on semantic knowledge. This knowledge is extracted from an ontological external resource.