Integrated Model Research Articles

Assessing carbon capture and storage technology in industrial joint venture

Carbon Capture and Storage (CCS) technology can effectively reduce carbon dioxide emissions from industrial and energy production processes. Yet the commercialization of CCS technology is hampered by financial requirements. Existing research compares the costs and benefits of business models for individual CCS projects, no studies assess the industry and national levels. Here we use a dynamic computable general equilibrium model, constructure the vertical integration and joint venture business models for CCS. This study assesses the emission and economic impacts of joint ventures between China's high-emission industries and their upstream and downstream sectors. We find that adopting the joint venture model in the coal power sector, compared to the vertical integration model, initially exerts a negative economic impact but can mitigate 0.04 % of GDP by 2060. The chemical industry consistently benefits from the joint venture model both economically and in emission reductions. Downstream sectors of the steel and cement industries are unsuitable for participation in joint ventures. In addition, the joint venture model has significant industry linkage effects, affecting energy consumption, the scale and cost of CCS deployment. This work provides important information for the large-scale commercialization of CCS technology.

Exploring the impact of heterocyclic bridges for tuning the amplitudes of Nonlinear optical properties under Gas, IEFPCM and COSMO solvent models

Nonlinear optical (NLO) materials are a distinctive class of materials due to their NLO response and wide range of applications. We present a systematic quantum chemical study for designing triphenylamine based derivatives (1Ph to 3PhDTDZ) by modifying central bridge with various size and types of heterocyclic rings. Density functional theory (DFT) method is used to calculate the NLO properties including linear and third-order NLO polarizabilities (α and γ) by using M06-2X functional coupled with the 6-311G** basis set. The calculation results predict that among the designed compounds, 1PhDTDZ has the highest third-order NLO polarizability of 2347.7 x 10−36 esu and lowest transition energy of 1.828 eV. On the other hand, 3PhDTDZ has a greater amplitude (108.5 x 10−24 esu) of static linear polarizability (αiso). We also computed frequency-dependent third-order NLO polarizability values at various laser wavelengths (1500 nm to 1970 nm). This provides valuable information about the complex interaction between light and matter. Dynamic second hyperpolarizability of electro-optic Pockels effect (EOPE effect, (γ (−ω; ω,0,0)) is calculated at 1500 nm laser wavelength with a larger γ-amplitude of 36708.4 x 10−36 esu. The effect of solvent on α and γ has also been the primary focus of this research. An extensive solvation impact has been studied using integral equation formalism Polarizable Continuum Model (IEFPCM) and Conductor-like Screening Model (COSMO). Magnitudes of αiso and 〈γ〉 have shown variation from gas to solvent methods. While among solvents, amplitudes are seen 3.87 % to 11.29 % greater under the effect of COSMO-methanol than PCM-methanol. The Frontier molecular orbital (FMO) studies found a significant decrease in energy band gaps from 5.75 eV to 2.98 eV. Since our systems exhibit remarkable photovoltaic properties, they can be used as dye-sensitized solar cells (DSSCs). Their open circuit voltage (VOC) ranging from 1 eV to 4 eV. Thus, our designed triphenylamine based derivatives with distinct central heterocyclic bridges showcase remarkable efficiency as NLO materials, with additional benefits of exhibiting good photovoltaic properties.

3D coupled vibrations of asymmetric nano-particle mass sensors: Two-phase integral nonlocal strain gradient model and MD simulation

Since it is possible for the nano-particle to have any asymmetric shape and may attach to every position of the resonator-based mass sensors, any type of mass eccentricity and subsequently coupled vibrations are possible. So, in the present work, the 3D coupled axial-torsional-flexural vibrations of the mass nano-sensors are investigated considering both in-plane and out-of-plane bending as well as axial and torsional vibrations. In addition, the integral form of the two-phase local/nonlocal strain gradient (LNSG), as the consistent type of the common nonlocal strain gradient (NSG) elasticity theory, is employed for the first time to consider the size effects in the mass nano-sensors. A quasi-3D coupled FE model is constructed with no shear-locking within the complicated environment of the integral LNSG. The impacts of the LNSG elasticity and diverse types of possible coupling in changing the vibrational behavior of the mass nano-sensor are scrutinized and their crucial role in modeling of the nano-sensors is indicated. Furthermore, molecular dynamic (MD) simulations are implemented to identify and analyze the coupled vibrations of a mass nano-sensor consisting of a fixed-free carbon nanotube. Comparison between the MD results with those of the present coupled FE model reveals that considering the coupling effects reduces the modeling error, especially in the cases in which the coupling influences intensify. The present work can help to provide more accurate models of mechanical resonance-based nanosensors to detect nanoparticles with different shapes, larger sizes, and high mass ratios, which can lead to achieving sensors with higher sensitivity.

Exploration on the Integration Model of “Post–Course–Competition– Certificate” in Culinary Major under the “1+X” Certificate System: Taking Cantonese Cuisine and Cantonese Dimsum Vocational Skill Level Certificates as Examples

Exploration on the Integration Model of “Post–Course–Competition– Certificate” in Culinary Major under the “1+X” Certificate System: Taking Cantonese Cuisine and Cantonese Dimsum Vocational Skill Level Certificates as Examples

A novel multiscale cGAN approach for enhanced salient object detection in single haze images

In computer vision, image dehazing is a low-level task that employs algorithms to analyze and remove haze from images, resulting in haze-free visuals. The aim of Salient Object Detection (SOD) is to locate the most visually prominent areas in images. However, most SOD techniques applied to visible images struggle in complex scenarios characterized by similarities between the foreground and background, cluttered backgrounds, adverse weather conditions, and low lighting. Identifying objects in hazy images is challenging due to the degradation of visibility caused by atmospheric conditions, leading to diminished visibility and reduced contrast. This paper introduces an innovative approach called Dehaze-SOD, a unique integrated model that addresses two vital tasks: dehazing and salient object detection. The key novelty of Dehaze-SOD lies in its dual functionality, seamlessly integrating dehazing and salient object identification into a unified framework. This is achieved using a conditional Generative Adversarial Network (cGAN) comprising two distinct subnetworks: one for image dehazing and another for salient object detection. The first module, designed with residual blocks, Dark Channel Prior (DCP), total variation, and the multiscale Retinex algorithm, processes the input hazy images. The second module employs an enhanced EfficientNet architecture with added attention mechanisms and pixel-wise refinement to further improve the dehazing process. The outputs from these subnetworks are combined to produce dehazed images, which are then fed into our proposed encoder–decoder framework for salient object detection. The cGAN is trained with two modules working together: the generator aims to produce haze-free images, whereas the discriminator distinguishes between the generated haze-free images and real haze-free images. Dehaze-SOD demonstrates superior performance compared to state-of-the-art dehazing methods in terms of color fidelity, visibility enhancement, and haze removal. The proposed method effectively produces high-quality, haze-free images from various hazy inputs and accurately detects salient objects within them. This makes Dehaze-SOD a promising tool for improving salient object detection in challenging hazy conditions. The effectiveness of our approach has been validated using benchmark evaluation metrics such as mean absolute error (MAE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM).

Multiparametric MRI-based radiomics combined with 3D deep transfer learning to predict cervical stromal invasion in patients with endometrial carcinoma.

To develop and compare various preoperative cervical stromal invasion (CSI) prediction models, including radiomics, three-dimensional (3D) deep transfer learning (DTL), and integrated models, using single-sequence and multiparametric MRI. Data from 466 early-stage endometrial carcinoma (EC) patients from three centers were collected. Radiomics models were constructed based on T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), apparent diffusion coefficient (ADC) mapping, contrast-enhanced T1-weighted imaging (CE-T1WI), and four combined sequences as well as 3D DTL models. Two integrated models were created using ensemble and stacking algorithms based on optimal radiomics and DTL models. Model performance and clinical benefits were assessed using area under the curve (AUC), decision curve analysis (DCA), net reclassification index (NRI), integrated discrimination index (IDI), and the Delong test for model comparisons. Multiparametric MRI models were superior to single-sequence models for radiomics or DTL models. Ensemble and stacking integrated models displayed excellent performance. The stacking model had the highest average AUC (0.908) and accuracy (0.883) in external validation groups 1 and 2 (AUC = 0.965 and 0.851, respectively) and emerged as the best predictive model for CSI. All models significantly outperformed the radiologist (P < 0.05). In terms of net benefits, all models demonstrated favorable outcomes in DCA, NRI, and IDI, with the stacking model yielding the highest net benefit. Multiparametric MRI-based radiomics combined with 3D DTL can be used to noninvasively predict CSI in EC patients with greater diagnostic accuracy than the radiologist. Stacking integrated models showed significant potential utility in predicting CSI. Which helps to provide new treatment strategy for clinicians to treat early-stage EC patients.

ORGANISATIONAL SUPPORT AND TEACHERS' ENGAGEMENT FOR PROFESSIONAL DEVELOPMENT IN SECONDARY SCHOOLS OF NEPAL

The teacher professional development program in Nepal is an ambitious initiative designed to support teachers' professional growth. Organizational support and teacher engagement are of the utmost importance to ensure professional development. This study signifies the role of organizational support in enabling professional development and engaging teachers in teacher growth and development. This study aims to propose and rest an integrative model that views organizational supoort practices as a synergistic component that influences the sustainbility of teacher engagement. Survey-based cross-sectional data were collected from secondary schools in Kathmandu Valley using a structured questionnaire to teachers for this non-experimental correlational research design. Likert scales were used to express the perception-based data quantitatively and analyzed using regression models. This study confirms that organizational support procedures strongly influence professional development, highlighting organizational support as a moderator between teacher engagement and professional development. Organizational support also contributes to improving organizational performance. These practices are standard in Nepal schools but at a moderate level. However, schools are run for the benefit of one person only, so schools must be institutionalized with standard norms and values of participatory practices.

Competitive integration of time and reward explains value-sensitive foraging decisions and frontal cortex ramping dynamics.

Patch foraging presents a ubiquitous decision-making process in which animals decide when to abandon a resource patch of diminishing value to pursue an alternative. We developed a virtual foraging task in which mouse behavior varied systematically with patch value. Mouse behavior could be explained by a model integrating time and rewards antagonistically, scaled by a latent patience state. The model accounted for deviations from predictions of optimal foraging theory. Neural recordings throughout frontal areas revealed encoding of decision variables from the integrator model, most robustly in frontal cortex. Regression modeling followed by unsupervised clustering identified a subset of ramping neurons. These neurons' firing rates ramped up gradually (up to tens of seconds), were inhibited by rewards, and were better described as a continuous ramp than a discrete stepping process. Together, these results identify integration via frontal cortex ramping dynamics as a candidate mechanism for solving patch foraging problems.

Metastable zone width and nucleation kinetics of vanillyl alcohol crystallization in various solvents

In this study, linear cooling batch crystallization of vanillyl alcohol using a laser power system was carried out to experimentally measure the metastable zone widths of vanillyl alcohol in selected solvents (ethanol, 2-propanol, and methyl cyanide) at various saturated temperatures (40, 50, and 60 °C), cooling rates (0.5, 1, 1.5, and 2 °C/min), and agitation speeds (300, 400 rpm). Besides, the sonocrystallization of vanillyl alcohol in ethanol was conducted for measuring MSZW at a various ultrasonic amplitudes (0 %, 25 %, 50 %, and 100 %) at a fixed temperature of 40 °C, cooling rate of 1 °C/min, and agitation speed of 300 rpm. For all three solvents, the MSZW decreases with saturation temperature, while it increases with cooling rate, and this trend doesn’t change for different agitation speeds. Three models—the self-consistent Nývlt-like model, the classical 3D nucleation theory model, and the simplified linear integral model based on classical nucleation theory (CNT)—were employed to estimate the nucleation kinetic parameters for vanillyl alcohol in three solvents. The goodness fit of the models were checked by the coefficient of determination (R-squared). The R-squared values reflected a very good fit between the experimental and predicted values and implied that the models are reliable to estimate nucleation kinetic parameters. Additionally, the interfacial energy between vanillyl alcohol and the solvents was observed to decrease with increasing temperature. Overall, the results indicate that a low nucleation order and low interfacial energy suggest weak solute–solvent interactions, making nucleation easier in the following order: methyl cyanide > ethanol > 2-propanol.

Centering the role of community health workers in social risk screening, referral, and follow-up within the primary care setting

BackgroundCommunity health workers (CHWs) remain an underutilized resource in social risk diagnostics in the primary care setting. This process evaluation study seeks to assess the role of CHWs in social risk screening, referral, and follow-up through process mapping to identify barriers to the process for future quality improvement efforts.MethodsResearchers at the Arizona Prevention Research Center (AzPRC) engaged with two Federally Qualified Health Centers (FQHCs) in two of Arizona’s major urban areas to evaluate their internal processes for social risk screening and intervention. The Consolidated Framework for Implementation Research (CFIR) was used to direct a process mapping exercise to visually describe the workflow, gaps, and barriers to identifying and addressing social risk.ResultsThe process unveiled key areas for health system improvements in the community setting, the organizational setting, and in the implementation of social risk screening, referral, and follow-up. Further, process maps highlight the potential resources needed for effective CHW integration to address social risk in the primary care setting.ConclusionsOur findings demonstrate the importance of organizational tools, such as process mapping, to assist primary care settings in evaluating internal processes for quality improvement in addressing social risk and in effectively integrating the CHW workforce. Subsequent research will evaluate rates of social risk screening, referral, and follow-up within all of Arizona’s FQHCs and propose models for CHW integration to address social risk in primary care and strengthen social risk screening reach and effectiveness.

Predictive Prioritization of Enhancers Associated with Pancreas Disease Risk.

Genetic and epigenetic variations in regulatory enhancer elements increase susceptibility to a range of pathologies. Despite recent advances, linking enhancer elements to target genes and predicting transcriptional outcomes of enhancer dysfunction remain significant challenges. Using 3D chromatin conformation assays, we generated an extensive enhancer interaction dataset for the human pancreas, encompassing more than 20 donors and five major cell types, including both exocrine and endocrine compartments. We employed a network approach to parse chromatin interactions into enhancer-promoter tree models, facilitating a quantitative, genome-wide analysis of enhancer connectivity. With these tree models, we developed a machine learning algorithm to estimate the impact of enhancer perturbations on cell type-specific gene expression in the human pancreas. Orthogonal to our computational approach, we perturbed enhancer function in primary human pancreas cells using CRISPR interference and quantified the effects at the single-cell level through RNA FISH coupled with high-throughput imaging. Our enhancer tree models enabled the annotation of common germline risk variants associated with pancreas diseases, linking them to putative target genes in specific cell types. For pancreatic ductal adenocarcinoma, we found a stronger enrichment of disease susceptibility variants within acinar cell regulatory elements, despite ductal cells historically being assumed as the primary cell-of-origin. Our integrative approach-combining cell type-specific enhancer-promoter interaction mapping, computational models, and single-cell enhancer perturbation assays-produced a robust resource for studying the genetic basis of pancreas disorders.

Optimal economic configuration by sharing hydrogen storage while considering distributed demand response in hydrogen-based renewable microgrid

With the issues of the greenhouse effect and environmental pollution globally, a high proportion of renewable energy has been integrated into microgrids. Its interconnection brings many challenges to the electric power industry. This paper proposes a new distributed response strategy through sharing hydrogen storage resources, aiming to solve the supply-demand imbalance in microgrids. First, the uneven power distribution from shared energy storage stations necessitates ensuring a fair power dispatch among users. Next, a microgrid demand response model with multi-constraint conditions and high penetration of wind and solar power is established, incorporating constraints like user power consumption, energy storage capacity, and charging/discharging power while considering differences in user electricity behaviors. Finally, the optimal economic configuration uses Lagrange duality theory to process the integrated models, including the demand response, hydrogen sharing, and power generation models, to make the optimization goals fairer. Results demonstrate that our strategy can adjust user electricity usage according to real-time supply-demand conditions, reducing users' total costs by 39.4 %. Compared with the existing configurations of sharing hydrogen storage configurations, our model uses more accurate utility functions, demonstrating better economic advantages, with users' total costs reduced by 4.89 % and revenue of the shared storage station increased by 4.02 %.

FMCF: A fusing multiple code features approach based on Transformer for Solidity smart contracts source code summarization

A smart contract is a software program executed on a blockchain, designed to facilitate functionalities such as contract execution, asset administration, and identity validation within a secure and decentralized ecosystem. Summarizing the code of Solidity smart contracts aids developers in promptly grasping essential functionalities, thereby enhancing the security posture of Ethereum-based projects. Existing smart contract code summarization works mainly use traditional information retrieval and single code features, resulting in suboptimal performance. In this study, we propose a fusing multiple code features (FMCF) approach based on Transformer for Solidity summarization. First, FMCF created contract integrity modeling and state immutability modeling in the data preprocessing stage to process and filter data that meets security conditions. At the same time, FMCF retains the self-attention mechanism to construct the Graph Attention Network (GAT) encoder and CodeBERT encoder, which respectively extract multiple feature vectors of the code to ensure the integrity of the source code information. Furthermore, the FMCF uses a weighted summation method to input these two types of feature vectors into the feature fusion module for fusion and inputs the fused feature vectors into the Transformer decoder to obtain the final smart contract code summarization. The experimental results show that FMCF outperforms the standard baseline methods by 12.45% in the BLEU score and maximally preserves the semantic information and syntax structures of the source code. The results demonstrate that the FMCF can provide a good direction for future research on smart contract code summarization, thereby helping developers enhance the security of development projects.

Exploring the integration of culturally and linguistically diverse nurses and nursing students in healthcare: A cross-sectional study

AimTo investigate the factors that influence the integration of CALD nurses and nursing students into the healthcare system and to explore their perception of integration. BackgroundIn many countries, strengthening the nursing workforce requires a ramping up of international recruitment. Culturally and linguistically diverse (CALD) nurses and nursing students experience significant challenges when integrating into new healthcare systems. DesignData for this cross-sectional observational study were collected electronically from CALD nurses (n = 15) and from nursing students (n = 87). MethodsThree new instruments were developed and used to measure nurses’ own role at work, cultural and linguistic diversity at work, and the professional competence development. Likert scale from 1 to 4 was used to evaluate the agreement rate. The study data were analysed using K-means cluster analysis to determine the integration profiles. ResultsThree integration profiles (A, B, and C) were identified based on the participants' self-assessed perceptions of integration. Perceptions of each profile ranged from low (≤2.49) to high (≥3.50), with most being intermediate (2.50–3.49). Further, the perceptions of integration were lowest in Profile A and highest in Profile C. The profiles differed statistically significantly in all other measured integration areas, except CALD nurses’ language skills and in the nurses’ ability to develop their skills. Individuals educated in Finland, who have clinical practice experience and assessed their language proficiency at least at an intermediate level, demonstrated better performance, a deeper understanding of the nurse's role in healthcare, greater confidence in their skills, and fewer experiences of discrimination. ConclusionsIntegration is weakest among those with lower language skills and who completed their education outside of Finland. Healthcare organisations should develop integration models and mentoring programmes informed by new insights to support the integration of CALD nurses into the healthcare system. These models and programmes could help CALD nurses and nursing students to better understand their roles in healthcare.

GIS-based Modelling of Hydrogen Integration in Urban Energy Systems – a Systematic Review

GIS-based Modelling of Hydrogen Integration in Urban Energy Systems – a Systematic Review

Reclaiming identities: exploring the influence of simulation on refugee doctors’ workforce integration

BackgroundHealthcare professionals are a precious resource, however, if they fail to integrate into the workforce, they are likely to relocate. Refugee doctors face workforce integration challenges including differences in language and culture, educational background, reduced confidence, and sense of identity. It has been proposed that simulation programmes may have the power to influence workforce integration. This study aimed to explore how an immersive simulation programme influenced workforce integration for refugee doctors joining a new healthcare system.MethodsDoctors were referred to a six-day immersive simulation programme by a refugee doctor charity. Following the simulation programme, they were invited to participate in the study. Semi-structured interviews, based on the ‘pillars’ conceptual model of workforce integration, were undertaken. Data were analysed using template analysis, with the workforce integration conceptual model forming the initial coding template. Themes and sub-themes were modified according to the data, and new codes were constructed. Data were presented as an elaborated pillars model, exploring the relationship between simulation and workforce integration.ResultsFourteen doctors participated. The ‘learning pillar’ comprised communication, culture, clinical skills and knowledge, healthcare systems and assessment, with a new sub-theme of role expectations. The ‘connecting pillar’ comprised bonds and bridges, which were strengthened by the simulation programme. The ‘being pillar’ encompassed the reclaiming of the doctor’s identity and the formation of a new social identity as an international medical graduate. Simulation opportunities sometimes provided ‘building blocks’ for the pillars, but at other times opportunities were missed. There was also an example of the simulation programme threatening one of the integration pillars.ConclusionsOpportunities provided within simulation programmes may help refugee doctors form social connections and aid learning in a variety of domains. Learning, social connections, and skills application in simulation may help doctors to reclaim their professional identities, and forge new identities as international medical graduates. Fundamentally, simulation experiences allow newcomers to understand what is expected of them. These processes are key to successful workforce integration. The simulation community should be curious about the potential of simulation experiences to influence integration, whilst also considering the possibility of unintentional ‘othering’ between faculty and participants.

Superconvergent scheme for a system of green Fredholm integral equations

In this study, a numerical scheme to a system of second-kind linear Fredholm integral equations featuring a Green's kernel function is proposed. This involves introducing Galerkin and iterated Galerkin (IG) methods based on piecewise polynomials to tackle the integral model. A thorough analysis of convergence and error for these proposed methods is carried out. Firstly, the existence and uniqueness of solutions for the Galerkin and iterated Galerkin methods are established. Later, the order of convergence is derived using tools from functional analysis and the boundedness property of Green's kernel. The Galerkin scheme has O(hα) order of convergence. Next, the superconvergence of the iterated Galerkin (IG) method is established. The IG method exhibits O(hα+α⁎) order of convergence. Theoretical findings are validated through extensive numerical experiments.

How CSR communication and value co-creation shape consumer well-being and brand love in the post-COVID-19 pandemic: evidence from the UK

PurposeDrawing upon the service-dominant logic (S-D logic) and elaboration likelihood model (ELM), this paper provides an integrative model to investigate how argument quality and source credibility of CSR communication affects customer value co-creation behavior, resulting in increased brand trust. Additionally, it unveils how brand trust and the perception of COVID-19 risk influence both brand love and subjective well-being.Design/methodology/approachThe data of this study were collected through survey questionnaire from 304 coffee shop customers using convenience sampling and tested using the partial least squares structural equation modeling (PLS-SEM) technique to validate its model.FindingsThe research findings confirm the positive association between source credibility and customer value co-creation (i.e. customer participation and citizenship behavior). By contrast, argument quality is revealed to have a significantly positive effect on customer citizenship behavior (CCB) but a non-significant effect on customer participation behavior (CPB). Additionally, the study identifies that customer value co-creation behavior significantly promotes brand trust. Finally, findings indicate that brand trust and the perception of COVID-19 risk significantly influence brand love and subjective well-being.Originality/valueThis study contributes to the literature on CSR communication and S-D logic and provides new insights for marketers and advertisers to manage brands in the post-pandemic scenario through CSR communications. Furthermore, this study theoretically extends the ELM model to the CSR communication research. Finally, this study expands the relevant literature by clarifying the relationships between the perception of COVID-19 risk, brand love and subjective well-being.

Influencing factors and paths of public support for carbon emission reduction policies—An integration model

Research has shown that understanding the fundamental of public support for carbon emission reduction policies may undermine policy formulation and implementation, yet the direction of influence and the transmission mechanism remain unclear. Using data from using data from 1482 questionnaires conducted in Hangzhou, China, this paper has examined a comprehensive model of the factors and paths influencing public support for carbon emission reduction policies, and evaluated the determinants and predictors of policy support regarding individual psychological perceptions, social-contextual perceptions, and perceptions of policy features. The results show that the variables in both the individual psychological perception and social contextual perception dimensions have no significant effect on carbon tax, however, be important constructure in carbon trading; in the policy characteristics perception dimension, both variables have a significant positive effect on both carbon tax and carbon trading, and are also the strongest predictors of policy support for carbon policies. Further evidence suggests that future policies could be more acceptable to residents by strengthening their environmental values, social norms can further arouse residents’ social responsibility to care about climate, and whether the policy is effective or fair to help residents realize the importance of the policy as well as the need for their participation and willingness to dedicate themselves to the mitigation of climate change.

Unified, Integral Approach to Modeling and Design of High-Pressure Pipelines: Assessment of Various Flow and Temperature Models for Supercritical Fluids

Abstract A unified one-dimensional, steady-state flow and heat transfer model is presented for the pipeline transport of fluids at high pressures, including the supercritical conditions. The model includes a generalized temperature equation, presented here for the first time, and accounts for all of the important effects, including the property variation, viscous dissipation, Joule-Thomson (J-T) cooling, and heat exchange with the surrounding. With appropriate approximations, this model can yield all isothermal and non-isothermal pipe flow solutions reported thus far. A generalized multi-zone integral method is developed which solves the two resulting algebraic equations for pressure and temperature in conjunction with a property database, such as the National Institute of Standard and Technology (NIST) Reference Fluid Thermodynamic and Transport Properties (REFPROP). With appropriately selected number and size of the zones and using property values at the mean temperature and pressure within each zone, this integral method can accurately predict the complex effects of the governing parameters, such as the pipe diameter and length, inlet and exit pressures, mass flow rate, J-T cooling, and inlet and surrounding temperatures. Its accuracy for small-to-large diameter pipes has been ascertained by a comparison with the numerical solutions of the differential form of governing equations that requires a large number of small grids along the pipe and the values of mean properties within each grid. Indeed, this integral model can be used for the pipeline transport at both subcritical and supercritical pressures as long as the fluid does not encounter its anomalous states and the phase-change.