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Mosques are built with dome-shaped ceilings for communal worship with common architectural styles worldwide for prayer. Since the acoustics of worship buildings are just as significant as their aesthetics, they should enhance people’s sense of hearing. This study evaluates the speech intelligibility of a small mosque with multiple domes to determine the space acoustic conditions.

The investigation involved extensive literature reviews to collect relevant data to model the case study. The Enhanced Acoustics Simulator for Engineers (EASE) software program was used to integrate critical parameters such as the absorption coefficient of materials, dome shapes and the number of domes in the simulation. The study employed speech intelligibility parameters such as C50, S.T.I. and %ALcons to assess the acoustic conditions. The assessment model was validated through statistical analysis and a paired t-test.

The study discovered that varying shapes of the multiple domes showed no significant impact on speech intelligibility. However, different multiple domes materials resulted in significant disparities in speech intelligibility. Applying high-absorption materials in multiple dome designs achieved the most effective acoustic performance. Except for C50 in some circumstances and receiver positions, all other alternatives met the optimal value for overall speech intelligibility because the sound was not sufficiently diffused early on, suggesting that the early reflection sounds were either weak or insufficient.

This study not only helps to determine the multiple-dome effect on mosque acoustics but also empowers archaeoacoustics and historic conservation by documenting these significant places of worship. The findings advocate using high-absorption materials in multiple dome designs and offer practical insight into mosque design material selection. By enhancing the understanding of the acoustic conditions in small-scale mosques, this study equips architects, engineers and builders with the knowledge to create spaces prioritizing speech clarity and intelligibility.

This study assesses the relevance of foreign aid to the incidence of capital flight and unemployment in 20 countries in sub-Saharan Africa.

The study is for the period 1996–2018, and the empirical evidence is based on interactive quantile regressions in order to assess the nexuses throughout the conditional distribution of the unemployment outcome variable.

From the findings, capital flight has a positive unconditional incidence on unemployment, while foreign aid dampens the underlying positive unconditional nexus. Moreover, in order for the positive incidence of capital flight to be completely dampened, foreign aid thresholds of 2.230 and 3.964 (% of GDP) are needed at the 10th and 25th quantiles, respectively, of the conditional distribution of unemployment. It follows that the relevance of foreign aid in crowding out the unfavourable incidence of capital flight on unemployment is significantly apparent only in the lowest quantiles or countries with below-median levels of unemployment. The policy implications are discussed.

The study complements the extant literature by assessing the importance of development assistance in how capital flight affects unemployment in sub-Saharan Africa.

The study aims to explore the moderation effect of renewable energy consumption (REC) on the relationship between foreign direct investment (FDI) inflows and carbon emission (CO₂). Furthermore, the study investigates the prevalence of rebound effect in energy efficiency for the top five FDI inbound destinations in the Asia-Pacific region.

The study uses a balanced panel data set spanning from 1995 to 2020 obtained from the World Bank Database. This paper used feasible generalized least squares (FGLS) as the primary method, and to ensure the robustness of the findings, this paper used the panels corrected standard errors (PCSE) model.

The findings reveal a negative relationship between FDI and CO₂ emissions and REC and CO₂ emissions. However, the moderation effect of REC on the relationship between FDI inflows and CO₂ emissions is positive, suggesting that when both FDI and REC increase simultaneously, carbon emissions also increase. This study attributes the observed positive moderation effect to the phenomenon known as the rebound effect.

FDI fosters environmental sustainability. Regions’ FDI policies can be guidelines for other nations aiming for similar outcomes. REC reduces CO₂ emissions, underlining renewable energy’s efficacy. However, positive moderation effect of REC on the relationship between FDI and CO₂ emissions highlights the necessity for balanced policies to prevent unintended consequences like the rebound effect.

The originality of this study lies in examining the prevalence of rebound effect in energy efficiency. Prior empirical studies have explored the relationship between REC and carbon emission and established that increased efficiency in renewable energy creates positive environmental and climate externalities. However, it is constrained by rebound effects and this has been ignored by previous studies.

In current era, retaining the best-performing employees has become essential for businesses to compete in the dynamic technological landscape. Consequently, organizations must ensure that their star performers believe that company’s reward and recognition (R&R) system is fair and equal. This study aims to use an explainable machine learning (eXML) model to develop a prediction algorithm for employee satisfaction with the fairness of R&R systems.

The current study uses state-of-the-art machine learning models such as Naive Bayes, Decision Tree C5.0, Random Forest and support vector machine-RBF to predict employee satisfaction towards fairness in R&R. The primary data used in the study has been collected from the employees of a large public sector undertaking from an emerging economy. This study also proposes a novel improved Naïve Bayes (INB) algorithm, the efficiency of which is compared with the state-of-the-art algorithms.

It is seen that the proposed INB model outperforms the state-of-the-art algorithms in many scenarios. Further, the proposed model and feature interaction are explained using the explainable machine learning (XML) concept. In addition, this study incorporates text mining techniques to corroborate the results from XML and suggests that “Transparency”, “Recognition”, “Unbiasedness”, “Appreciation” and “Timeliness in reward” are the most important features that impact employee satisfaction.

To the best of the authors’ knowledge, this is one of the first studies to use INB algorithm and mixed method research (text mining along with machine learning algorithms) for the prediction of employee satisfaction with respect to the R&R system.

This paper aims to optimize cable-stayed force in asymmetric one-tower cable-stayed bridge formation using an improved particle swarm algorithm. It compares results with the traditional unconstrained minimum bending energy method.

This paper proposes an improved particle swarm algorithm to optimize cable-stayed force in bridge formation. It formulates a quadratic programming mathematical model considering the sum of bending energies of the main girder and bridge tower as the objective function. Constraints include displacements, stresses, cable-stayed force, and uniformity. The algorithm is applied to optimize the formation of an asymmetrical single-tower cable-stayed bridge, combining it with the finite element method.

The study’s findings reveal significant improvements over the minimum bending energy method. Results show that the structural displacement and internal force are within constraints, the maximum bending moment of the main girder decreases, resulting in smoother linear shape and more even internal force distribution. Additionally, the tower top offset decreases, and the bending moment change at the tower-beam junction is reduced. Moreover, diagonal cable force and cable force increase uniformly with cable length growth.

The improved particle swarm algorithm offers simplicity, effectiveness, and practicality in optimizing bridge-forming cable-staying force. It eliminates the need for arbitrary manual cable adjustments seen in traditional methods and effectively addresses the optimization challenge in asymmetric cable-stayed bridges.

Wearing clothes requires specifications for feeling comfortable, derived from the fibres, fabrics and finishing properties. This study aims to deal with the effect of economic blends containing hollow fibres, bamboo and cotton/polyester waste on the mechanical properties of the produced fabrics and the appropriate end use.

This research included two blends: one consisted of cotton/polyester wastes blended with bamboo and the other to which Chorisia fibres were added. Two weft counts 10,6/1 Ne were made from each blend, which were used to produce four fabric samples (S1 Chorisia-free and S2 with Chorisia); additionally, another two samples were dyed that contain Chorisia (S3) from each count. The six samples were tested by Kawabata Evaluation System (KES).

The samples gave a good total hand value (THV) for use as men's winter suits, where the thicker count 6/1, with and without Chorisia had better properties, also both counts 6, 10/1 with dye. The hollow fibres affected the fabrics’ properties, including thickness, shear, bending, thermal conductivity and weight. Both blends had a positive effect on THV.

Cotton/polyester waste, Chorisia and bamboo fibres were tested, and 2% Remazol Yellow GNL dye was used.

The ratio of blending, weft counts and dye affected the fabric’s properties, with consequences for the use of the Kawabata system and its applications.

The fabrics used in this research may be considered to be economical and have good THV.

The study proved the usefulness of fabrics made of two blends. The Chorisia component may be seen as a good alternative to cotton fibres to reduce the cost of producing high-consumption winter suit fabrics.

This study aims to investigate the interrelationship between critical design criteria (CDC) that affect health, well-being and productivity (i.e. WELL) for residential buildings in developing countries, using Malaysia as a case study. To achieve the aim, the objectives are to identify CDC that affect WELL collectively; determine CDC that affect health, well-being, and productivity simultaneously; and analyze the interrelationship between the CDC.

Data from the semi-structured interviews and a systematic review of the existing literature were gathered for survey development. Next, survey data was collected from 114 professionals living in multistory buildings. Finally, normalized mean analysis, analytic hierarchy process (AHP), agreement analysis and Spearman correlation analysis were used to analyze the collected data.

Out of the 51 potential design criteria, 16 are critically affecting WELL collectively. Furthermore, six are critically affecting WELL collectively as well as health, well-being and productivity simultaneously: property price, water flow and supply, water treatment, pest management, management services and waste management. Finally, “water treatment” is highly correlated to “water management” and “water flow and supply.” In addition, “waste management” and “management services,” as well as “fire safety” and “emergency evacuation plans,” are highly correlated.

This study's originality includes investigating the CDC of residential buildings for the first time, to the best of the authors’ knowledge, in a developing country. As a result, this study uncovers holistic design criteria for policymakers to establish holistic building assessment tools for residential buildings.

This study evaluates how well Victorian local councils’ procurement policies align with the sustainable and circular economy (CE) approach that prioritises sustainable and regenerative practices. It proposes a set of criteria designed to effectively integrate environmental sustainability issues into purchasing policies.

Employing the Specific, Measurable, Assignable, Realistic and Time-bound (SMART) framework, a multi-dimensional content analysis guided by the goal-setting theory was applied to evaluate all 79 Victorian local councils’ procurement policies. This approach provided an assessment of policy specificity, measurability, assignability, realism and time sensitivity in promoting environmental sustainability through purchasing policies.

The findings underscored a significant deficiency in policy adherence to all SMART criteria concerning environmental sustainability, hindering the effective green purchasing decisions within government entities. This lack of integration of greening in purchasing policy poses challenges for manufacturers of waste-derived goods, obscuring the procurement objectives of these critical public sector customers.

The paper contributes to the sustainable procurement (SP) discourse by proposing guidelines aimed at improving the efficacy of governmental purchasing of sustainable products. These guidelines address the broader imperative to mitigate the environmental impacts of governmental spending on less sustainable goods, thereby fostering ecological sustainability and promoting responsible consumption.

While past studies have often relied on subjective content analysis methods, the SMART assessment used to develop the environmental sustainability criteria for purchasing policies, which distinguishes this study from previous governmental policy evaluation studies. This approach marks a departure from traditional governmental policy evaluation studies, offering a more structured analysis of policy effectiveness in promoting SP practices.

This research highlights the ongoing concern about organizational productivity and the lack of focus on designing an optimal model. The authors aim to create a comprehensive model for managing organizational productivity, considering its impact on profitability, customer satisfaction, and employee morale. They use qualitative research methods, including Systematic Literature Review and Interpretive Structural Modeling (ISM).

In this research using the qualitative research method of Systematic Literature Review, 57 variables affecting productivity were identified. These variables were placed in 16 layers by using the ISM method, which were classified analytically in four sections: INPUTS, OUTPUTS, OUTCOMES and IMPACTS. By determining the relationship between the sections, the research model was designed.

The potential model for organizational productivity management provides a comprehensive framework addressing critical factors like technology adoption, employee empowerment, organizational culture, and more. It identifies Linkage, Dependent, and independent variables. The lower layers consist of INPUTS such as Technological Tools, Organizational Values, and more. In the highest layer, impactful variables like Enhanced competitiveness, Improved decision-making, and Improved organizational culture are labeled as IMPACTS. Middle layer variables are categorized as OUTPUTS and OUTCOMES.

In this study, the concept of productivity management was redefined for the first time, and a multi-layered model for productivity management was creatively explicated using the structural equation modeling method.

Lean manufacturing (LM) concepts have been widely adopted in diverse industrial sectors. However, no literature review focusing on case studies describing LM implementation is available. Case studies represent the actual implementation and provide secondary data for further analysis. This study aims to review the same to understand the pathways of LM implementation. In addition, it aims to analyse other related review questions, such as how implementing LM impacts manufacturing capabilities and the maturity level of manufacturing organisations that implemented LM, to name a few.

A literature review of case studies that discuss the implementation of LM during the last decade (from 2010 to 2020) is carried out. These studies were synthesised, and content analyses were performed to reveal critical insights.

The implementation pattern of LM significantly varies across manufacturing organisations. The findings show simultaneous improvement in manufacturing capabilities. Towards the end of the last decade, organisations implemented LM with radio frequency identification, e-kanban, simulation, etc.

Reviewing the case studies documenting LM implementation to comprehend the various nuances is a novel attempt. Furthermore, potential future research directions are identified for advancing the research in the domain of LM.