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Normal-weight obesity (NWO), characterized by normal body mass index (BMI) but excess body fat, is a potential contributor to chronic diseases. This study aims to assess the relationship between this phenomenon and some metabolic factors in a population of Iranian employees.

This cross-sectional study was conducted on Iranian employees from the baseline data of Employees Health Cohort Study, Shiraz, Iran. Anthropometric measures, including weight, height, waist circumference and percentage of body fat, were obtained from the cohort database. The participants were divided into three groups: healthy, normal-weight obese and overweight/obese. Metabolic variables including blood pressure, fasting blood sugar, lipid profile, liver function enzymes and metabolic syndrome were assessed in relation to the study groups.

A total of 985 participants aged 25–64 years were included. Males with NWO had significantly higher alanine aminotransferase (ALT) levels compared to the healthy group in the fully adjusted model. Also, high-density lipoprotein (HDL) was significantly lower among females with overweight/obesity than healthy group when adjusted for age and energy intake. Furthermore, after adjusting for age and energy intake, both genders in the overweight/obese group showed significantly elevated systolic and diastolic blood pressure, while this was not observed for the NWO group. Lastly, metabolic syndrome was more prevalent in NWO as well as overweight/obesity.

These findings further encourage identification of excess body fat, even in normal-weight individuals, to prevent chronic metabolic diseases. Special attention should be paid to subgroups with sedentary occupations, as they may be at increased risk for NWO-related health issues.

This study aims to investigate the complexity factors associated with BIM-enabled projects. BIM has been widely promoted as a potential solution to numerous challenges that hinder productivity in construction projects, owing to its numerous advantages. Nevertheless, it is crucial to acknowledge the heightened complexity it introduces to project workflows, stakeholder coordination and information management.

This study employs the Delphi method to identify and extract complexity factors specific to BIM-enabled projects. A panel of industry and academic experts is engaged to discern and prioritise these factors based on their expertise and knowledge.

The study reveals a comprehensive list of 34 complexity factors that significantly impact BIM-enabled projects. Among the most influential factors are laws and regulations, variety of procurement methods, technical capabilities of teams, project manager competence, information transfer capacity, range of project deliverables and diversity of project locations. The findings highlight the importance of these factors and emphasise the need for proactive and adaptive management to navigate their impact and achieve positive project outcomes.

This study introduces the DEBACCS framework, a metric-based model designed to understand and evaluate complexity within BIM-enabled projects. DEBACCS stands for seven key dimensions: diversity, emergence, belonging, autonomy, connectivity, context and size. These dimensions represent essential aspects for gauging project complexity. By applying the concept of complexity from project management to BIM, the study offers valuable insights for practitioners and researchers. It provides a unique perspective on the challenges and considerations associated with implementing and managing BIM in construction projects. The findings have practical value for practitioners, enabling them to better understand and address the implications of complexity in BIM-enabled projects, ultimately leading to improved project outcomes.

This study aims to enhance singularity detection in non-stationary signals by introducing the frugal wavelet transform (FrugWT), a novel variation of the wavelet transform.

The frugal wavelet transform, based on a modified first-level discrete wavelet transform decomposition, is compared with traditional discrete wavelet transform. The performance of these transforms is evaluated using signals derived from finite element analysis of a functionally graded tapered beam made of porous material.

The frugal wavelet transform significantly outperforms the discrete wavelet transform in detecting singularities within the analyzed signals. It offers more accurate detection of singularities and local abrupt changes, demonstrating its effectiveness for signal analysis.

This paper contributes to the field by proposing the relative frugal wavelet transform as a novel enhancement of the frugal wavelet transform. It provides a significant improvement in detecting subtle singularities in one-dimensional signals, with potential applications in advanced signal processing and analysis across various scientific domains such as electrical engineering, automotive, aerospace engineering, civil engineering, marine engineering and medical signal processing.

This article identifies hybrid nanofluids and industrial thermal engineering devices as significant sources of solar energy. In this study, various nanoparticles suspended in base fluids such as water (H2O) and carboxymethyl cellulose (CMC), along with nanoparticles like graphene oxide (GO) and molybdenum disulfide (MoS2), are examined. The model also incorporates permeability and the effects of angled magnetic fields to provide a comprehensive analysis.

We have utilized the fractal fractional operator definition, the quickest and most advanced fractional approach, to address the problems with the hybrid nanofluid suspension. The integral transform scheme, i.e. the Laplace transform, converts the governing equations into a fractional form before various numerical methods are applied to solve the problem. Further, some numerical schemes to address the Laplace inverse are also utilized.

The fractional effects on flow rate and heat transfer are evident at varying time intervals. Consequently, we conclude that as the fractal constraints increase, the momentum and heat profiles decelerate. Furthermore, all necessary conditions are satisfied, resulting in the momentum and temperature fields decreasing near the plate and increasing over time. Additionally, the water-based (H2O + Go + MoS2) solution exhibits a more pronounced impact compared to the CMC-based (CMC + Go + MoS2) hybrid suspension.

The findings could be very useful in enhancing the efficiency of thermal systems. These findings align more accurately with conventional solutions and can be used to build and optimize various heat management strategies.

The primary goals of this research are to examine the thermal and flow properties of hybrid nanofluids for manufacturing purposes of thermal engineering equipment utilizing fractal fractional definition. Further, to improve thermal system productivity by applying sophisticated fractional techniques to better and maximize heat and momentum transmission in these hybrid nanofluid solutions

This paper aims to facilitate reliable online diagnosis of early faults in the stator winding inter-turn short circuits of induction motors (IMs) under various operating conditions.

A novel fault characteristic component, the characteristic current amplitude, is proposed for the fault. Defined as the product of short-circuit coefficient and short-circuit current, the characteristic current is derived from the positive and negative-sequence components of the stator-side current and voltage.

Simulation models of the IMs pre- and postfault, along with an experimental platform for the motor’s inter-turn short circuit, were established. The characteristic current amplitude proves more robust against voltage unbalance and load variations, which offers enhanced reliability and sensitivity for early fault diagnosis of inter-turn short circuit in IMs stator windings.

A novel feature is proposed. Compared with negative-sequence current, which is considered as a traditional fault feature, the characteristic current amplitude exhibits a greater robustness against the imbalanced conditions, which simultaneously possesses the attributes of both reliability and expeditiousness in fault detection.

This study aims to develop a neurocomputational approach using the Levenberg–Marquardt artificial neural network (LM-ANN) to analyze flow and heat transfer characteristics in mixed convection involving radiative magnetohydrodynamic hybrid nanofluids. The focus is on the influence of morphological nanolayers at the fluid–nanoparticle interface, which significantly impacts coupled heat and mass transfer processes.

This research simplifies a complex system of higher-order nonlinear coupled partial differential equations governing the flow between orthogonal coaxially porous disks into ordinary differential equations via similarity transformations. These equations are solved using the shooting method, and parametric studies are conducted to observe the impact of varying important parameters. The resulting data sets are used to train, validate and test the LM-ANN model, which ensures high predictive accuracy. Machine learning and curve-fitting techniques further enhance the model’s capability to generate detailed visualizations.

The findings of this study indicate that increased nanolayer thickness (0.4–1.6) significantly improves thermal performance, while changes in the chemical reaction parameter (0.2–1) have a notable effect on enhancing the Sherwood number. These results highlight the critical role of morphological nanolayers in optimizing thermal and mass transfer efficiency in MHD nanofluids.

This research provides a novel neurocomputational framework for understanding the thermal and mass transfer dynamics in MHD nanofluids by incorporating the effects of interfacial nanolayers, an aspect often overlooked in conventional studies. The use of LM-ANN trained on computational data sets enables high-fidelity predictive analysis, offering new insights into the enhancement of thermal and mass transfer efficiency in hybrid nanofluid systems.

Following the job demands-resources theory, this study investigates the role of female managers in enhancing employee well-being in terms of psychological health via workplace resources.

To accomplish this objective, we conducted a comprehensive literature review to identify key IPS. Subsequently, a fuzzy-based algorithm was employed to prioritize these skills. Following this, we developed an algorithm based on Extreme Gradient Boosting (XGBoost) to predict the quality of workers’ IC. The efficacy of the XGBoost model was assessed by applying it to three real-life construction projects.

Upon application of the model to the case studies, we made the following conclusions: (1) “Leadership Style,” “Listening,” “Team Building” and “Clarifying Expectations” emerged as significant skills and (2) the model accurately predicted workers’ IC quality in over 78% of the cases. This algorithm has the potential to preempt interpersonal conflicts, enhancing job-site productivity, team development and human resources management. Furthermore, it can guide construction managers in designing IPS training programs.

This study contributes to the existing knowledge by addressing the crucial connection between IPS and IC quality in construction projects. Additionally, our novel approach, integrating fuzzy logic and XGBoost, provides a valuable tool for IC prediction. By identifying significant IPS and offering predictive insights, this research facilitates improved communication and collaboration in the construction industry, ultimately enhancing project outcomes.

The construction sector plays a vital role in the economic progress of every nation, including Tanzania. Notwithstanding its significance, the industry experiences poor performance. This study aims to assess the impact of procurement contract risk management on the performance of force account (FA) construction projects, using a regulatory framework as a moderator.

The cross-sectional research design was used, and data was collected by using a structured questionnaire. The study employed a sample size of 318 respondents to analyse data by using partial least square structural modelling (PLS-SEM).

The findings revealed that supply risk management, procurement internal control system and procurement contract administration influence the performance of FA construction projects. Additionally, the study confirms that the regulatory framework strengthens the relationship between the procurement internal control system and procurement contract administration with the performance of FA construction projects. Therefore, procurement contract risk management, as moderated by the regulatory framework, plays a significant role towards the performance of FA construction projects.

These findings have significant implications for practitioners and policymakers involved in FA construction projects in the public sector, as they highlight the importance of procurement contract risk management in achieving the successful performance of FA construction projects.

This study contributes to the ongoing discussion about the performance of construction projects, especially those under the FA procurement. It also contributes to the literature on public construction projects in developing countries.

This study aims to observe the coloring efficacy of graphite (G) and nano bentonite clay (BCNPs) on the adsorption of Basic Blue 5 dye from residual dye bath solution.

Some factors that affected the adsorption processes were examined and found to have significant impacts on the adsorption capacity such as the initial concentration of G and/or BCNPs (Co: 40–2,320 mg/L), adsorbent bath pH (4–9), shaking time (30–150 min.) and initial dye concentration (40–200 mg/L). The adsorption mechanism of dye by using G and/or BCNPs was studied using two different models (first-pseudo order and second-pseudo order diffusion models). The equilibrium adsorption data for the dye understudy was analyzed by using four different models (Langmuir, Freundlich, Temkin modle and Dubinin–Radushkevich) models.

It has been found that the adsorption kinetics follow rather a pseudo-first-order kinetic model with a determination coefficient (R²) of 0.99117 for G and 0.98665 for BCNPs. The results indicate that the Freundlich model provides the best correlation for G with capacities q_max = 2.33116535 mg/g and R² = 0.99588, while the Langmuir model provides the best correlation for BCNPs with R² = 0.99074. The adsorbent elaborated from BCNPs was found to be efficient and suitable for removing basic dyes rather than G from aqueous solutions due to its availability, good adsorption capability, as well as low-cost preparation.

There is no research limitation for this work. Basic Blue 5 dye graphite (G) and nano bentonite clay (BCNPs) were used.

This work has practical applications for the textile industry. It is concluded that using graphite and nano bentonite clay can be a possible alternative to adsorb residual dye from dye bath solution and can make the process greener.

Socially, it has a good impact on the ecosystem and global community because the residual dye does not contain any carcinogenic materials.

The work is original and contains value-added products for the textile industry and other confederate fields.

The purpose of this paper is to assess the perceived level of Jordanian nurses’ competencies in offering care to the community during a disaster.

A correlational descriptive design was used to assess nurses’ competencies in offering care for the community during a disaster.

A total of 370 nurses (55 % males) aged 25−55 agreed to participate. The mean score of competencies of nurses who offer care to the community during the disaster was 2.11 (SD = 0.59) points. The results of correlation coefficient tests revealed a significant positive correlation between stated competencies level and nurses’ sex, receiving disaster education and training with rpb (371) = 0.13, p < 0.01; rpb (598) = 0.15, p = 0.004; rpb (598) = 0.21, p < 0.001, respectively. Furthermore, the “care of communities” subscale had a weak positive correlation with the.

Nurses play a critical role in disaster response. However, there was a gap in nurses’ competencies for disaster, which shows there is a crucial need to include disaster management courses in the nursing curriculum and update disaster management courses in hospitals based on nurses’ needs to improve their competencies during disasters.