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This research aims to measure the public sector comparator (PSC) to reach public–private partnership (PPP) projects' negotiable price range for water and sewage companies in Iran. PSC measurement drives the public sector to make valid decisions about costs.

Around 170 risks were primarily determined through studying numerous articles. Then, risk effects were specified by distributing questionnaires in two steps. The questionnaires are distributed among experts on PPP-related projects and the Monte Carlo simulation method is used for confidence factors of 70, 80 and 90%. PSC is measured based on these results to study cases of Sirjan’s sewerage and sewage purification systems.

11 risks were identified as the main risks that are effective on PSC, and project implementation costs were specified based on the modeling. The corruption of the private and public sectors was identified as the most effective risk in this research. It can affect a project’s cost up to 158% in the construction period and up to 134% in the operation period. Based on the obtained results, 63% of this risk’s cost goes to the public sector.

The originality of this research is the PSC measurement method and appointing the risk share of each private and public sector. The results of this research can be applied to all the infrastructure and PPP projects in Iran and other developing countries as a way for employers to estimate accurate negotiable price ranges.

Students’ academic achievement is a multifaceted phenomenon. While depression can suppress academic performance, academic satisfaction can promote it. This study aims to investigate the relationship between depression and academic satisfaction among students studying at Shahroud University of Medical Sciences.

This cross-sectional study was carried out on 312 undergraduate students of Shahroud University of Medical Sciences. Data collection tools included demographic data form, University Student Depression Inventory and academic satisfaction scale. Data were collected by a simple random sampling method and self-reporting by the participants. Data were analyzed using descriptive and inferential statistics (multivariate multiple regression analysis and multivariate linear regression).

The participants’ mean depression and academic satisfaction scores were 71.92 ± 22.94 and 53.70 ± 9.69, respectively. In addition, the depression score was significantly and inversely correlated with students' academic satisfaction (r = −0.122, p-value = 0.031). Moreover, there was a significant correlation between students’ depression with marital status, level of the semester, interest in the field of study and study topic.

This study emphasizes improving education, spiritual and social support and strengthens strategies to deal with depression and medical science students’ related factors.

Students of medical sciences are exposed to depression during their college years, which is related to their academic satisfaction.

This study aims to investigate the impact of varying the number of minimum quantity lubrication (MQL) nozzles, wind pressure, spindle speed and type of lubrication on surface roughness, fatigue life and tool wear in the drilling of aluminum alloy 6061-T6.

The effect of using different lubricants such as palm oil, graphene/water nanofluid and SiO₂/water in the MQL method was compared with flood and dry methods. The lubricant flow and feed rate were kept constant throughout the drilling, while the number of nozzles, wind pressure and spindle speed varied. After preparing the parts, surface roughness, fatigue life and tool wear were measured, and the results were analyzed by ANOVA.

The results showed that using MQL with four nozzles and graphene/water nanofluid reduced surface roughness by 60%, followed by SiO₂ nanofluid at 56%, and then by palm oil at 50%. Increasing the spindle speed in MQL mode with four nozzles using graphene nanofluid decreased surface roughness by 52% and improved fatigue life by 34% compared to the dry mode. SEM results showed that tool wear and deformation rates significantly decreased. Increasing the number of nozzles caused the fluid particles to penetrate the cutting area, resulting in improved tool cooling with lubrication in all directions.

Numerous attempts have been made worldwide to eliminate industrial lubricants due to environmental pollution. In this research, using nanofluid with wind pressure in MQL reduces environmental impacts and production costs while improving the quality of the final workpiece more than flood and dry methods.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-01-2024-0021/

This paper explores the challenges of conducting qualitative research from ELT (English Language Teaching) Ph.D. candidates' perspectives.

The participants of the study consisted of 30 Iranian Ph.D. students majoring in ELT. The semi-structured interview was employed to investigate the heart of experiences, issues and concerns of participants with regard to conducting qualitative research (QLR) challenges. To analyze the collected data, the recorded interviews were transcribed, and then the grounded theory approach was employed (Charmaz, 2006).

The results revealed that the major challenges of the participants consist of the credibility of QLR in ELT contexts, hermeneutic and fuzzy nature of QLR, qualitative data analysis and interpretation, publishing qualitative findings and the system of measuring professors' productivity.

The findings may help professors, mainly EFL ones, in research mentoring and developing research syllabi for graduate students. In addition, it may motivate Ph.D. candidates to employ QLR methods in their research studies. The pedagogical and theoretical implications of the study are discussed at the end of the paper.

This chapter briefly explores selected English and general education policy documents, curricula, and textbooks within the context of Kingdom of Saudi Arabia (KSA) from a Critical Discourse Analysis perspective and examines how they have changed pre- and post-21st century. First, a policy document related to education in KSA in general (pre-21st century) is analyzed along with an English language teaching (ELT) policy document of the same period. Next, two general policy documents post-21st century are explored, followed by one related to ELT policy. Finally, one post-21st century document related to higher education is discussed. The “network of practices” within which these documents are situated are first detailed, as well as the structural order of the discourse, and some linguistic analysis of the choice of vocabulary and grammatical structures (Meyer, 2001). Issues which might be problematic to the learning and teaching identities of the students and teachers interpreting these documents are also highlighted. Finally, we consider whether the network of practices at this institution and KSA in general “needs” the problems identified in the analysis and critically reflect on the analysis.

Previous research indicates that vitamin D and omega-3 co-supplementation may benefit overall health, but current evidence regarding its effects on lipid profile remains unclear. The present systematic review and meta-analysis aimed to examine the effects of vitamin D and omega-3 co-supplementation on lipid profile (total cholesterol [TC], low-density lipoprotein [LDL], triglyceride [TG] and high-density lipoprotein [HDL]) in adults.

In this systematic review and meta-analysis, relevant studies were obtained by searching the PubMed, Scopus and Web of Science databases (from inception to January 2022). Weighted mean differences and 95% confidence intervals were estimated via a random-effects model. Heterogeneity, sensitivity analysis and publication bias were reported using standard methods.

Pooled analysis of six randomized controlled trials (RCTs) revealed that vitamin D and omega-3 co-supplementation yielded significant reductions in TG (p = 0.631). A pooled analysis of five trials indicated a significant association between omega-3 and vitamin D treatment and reductions in TC (p = 0.001) and LDL (p = 0.001). Although, pooled analyses of omega-3 and vitamin D did not significantly affect HDL.

The findings suggest that vitamin D and omega-3 co-supplementation lowers TG, TC and LDL in adults. Future, large-scale, RCTs on various populations are needed to elucidate further beneficial effects of vitamin D and omega-3 co-supplementation on lipid profile and establish guidelines for clinical practice.

Looking for ways to gain competitive advantage (CA) is one of the most challenging issues for today's businesses. Although previous research considered several aspects in this regard, the literature has largely overlooked the process of gaining CA via strategic intangibles, regarding business type and context. This paper aims to examine how to gain CA through strategic intangibles such as intellectual capital (IC).

Building on the concept of IC, and using data gathered from both the manufacturing/service and public/private firms, the authors tested a moderated mediation model to determine if the effect of IC on CA was conditioned on business type, competitive intensity and managerial support.

Among the factors in the relationship between IC and CA, the results discovered the role of business intelligence (BIN) and brand image (IM), as two key mediators. Furthermore, it was revealed that managerial support and competitive intensity moderate the relationship between IC, the mediators and CA. Finally, the authors provide academics and practitioners with some implications.

Previous research did not fully address the aforementioned antecedents (i.e., IC, BIN and IM) toward CA in a comprehensive model. Developing the path toward CA by focusing on the role of intangibles, the authors proposed a moderated mediation model, which has hitherto received scant attention in the field of competition.

The purpose of this paper is to forecast wind power generation in an area through different methods, and then, recommend the most suitable one using some performance criteria.

The Box–Jenkins modeling and the Neural network modeling approaches are applied to perform forecasting for the last 12 months.

The results indicated that among the tested artificial neural network (ANN) model and its improved model, artificial neural network-genetic algorithm (ANN-GA) with RMSE of 0.4213 and R2 of 0.9212 gains the best performance in prediction of wind power generation values. Finally, a comparison between ANN-GA and ARIMA method confirmed a far superior power generation prediction performance for ARIMA with RMSE of 0.3443 and R2 of 0.9480.

Performance of the ARIMA method is evaluated in comparison to several types of ANN models including ANN, and its improved model using GA as ANN-GA and particle swarm optimization (PSO) as ANN-PSO.

Evaluating project success within the construction industry presents challenges due to the unique characteristics of the sector, the complexity of projects, and the involvement of diverse stakeholders. Conducting a bibliometric analysis, this paper aims to unravel the major research themes and methodologies utilised by researchers in studying the critical success criteria for construction projects, as well as extracting these success criteria.

The researchers systematically searched and screened 95 papers from Scopus and Web of Science (WoS) databases. This study conducted research focus parallelship network (RFPN) analysis and keywords co-occurrence network (KCON) analysis using BibExcel and Gephi to cluster the papers, illuminate the relationships among keywords within each cluster, and identify the primary research directions.

Using the RFPN analysis, this study classified the papers into three distinct clusters: infrastructure and public projects success, risk and knowledge management, and contractors and procurement management. Statistical techniques such as structural equation modelling (SEM) and multi-criteria decision-making methods such as analytic hierarchy process (AHP) have been used to analyse project success in the construction industry.

Considering the intensified demand for streamlined digital interactions and the increasing emphasis on sustainability and safety performance, construction companies are recommended to allocate greater investments toward the automation and digitisation of their products and processes. Prioritising modular construction and embracing transformative technologies alongside data science is crucial for enabling well-informed decision-making, and enhancing project success.

This study contributes to the existing body of knowledge by conducting a quantitative and systematic evaluation of the literature on project success criteria in the construction industry and uncovering key research areas. It addresses the pressing need to understand the complexities of construction projects amidst evolving industry dynamics and emerging disruptions. Moreover, by highlighting the implications of digital innovations and modular construction, this study urges deeper exploration into their impact on project performance and stakeholder satisfaction. This research sets a comprehensive framework for investigating the interplay between project complexity, technological advancements, and sustainable practices in the construction sector, paving the way for strategic advancements in the field.

The purpose of this study was to validate the feasibility of the proposed microstructure-based model by comparing the simulation results with experimental data. The study also aimed to investigate the relationship between the orientation of graphite flakes and the failure behavior of the material under compressive loads as well as the effect of image size on the accuracy of stress–strain behavior predictions.

This paper presents a microstructure-based model that utilizes the finite element method (FEM) combined with representative volume elements (RVE) to simulate the hardening and failure behavior of ferrite-pearlite matrix gray cast iron under uniaxial loading conditions. The material was first analyzed using optical microscopy, scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS) and X-ray diffraction (XRD) to identify the different phases and their characteristics. High-resolution SEM images of the undeformed material microstructure were then converted into finite element meshes using OOF2 software. The Johnson–Cook (J–C) model, along with a damage model, was employed in Abaqus FEA software to estimate the elastic and elastoplastic behavior under assumed plane stress conditions.

The findings indicate that crack initiation and propagation in gray cast iron begin at the interface between graphite particles and the pearlitic matrix, with microcrack networks extending into the metal matrix, eventually coalescing to cause material failure. The ferritic phase within the material contributes some ductility, thereby delaying crack initiation.

This study introduces a novel approach by integrating microstructural analysis with FEM and RVE techniques to accurately model the hardening and failure behavior of gray cast iron under uniaxial loading. The incorporation of high-resolution SEM images into finite element meshes, combined with the J–C model and damage assessment in Abaqus, provides a comprehensive method for predicting material performance. This approach enhances the understanding of the microstructural influences on crack initiation and propagation, offering valuable insights for improving the design and durability of gray cast iron components.