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Even though it is widely used, reinforced concrete (RC) is susceptible to damage from various environmental factors. The hazard of a fire attack is particularly severe because it may cause the whole structure to collapse. Furthermore, repairing and strengthening existing structures with high-performance concrete (HPC) has become essential from both technical and financial points of view. In particular, studying the postfire behavior of HPC with normal strength concrete substrate requires experimental and numerical investigations. Accordingly, this study aims to numerically investigate the post-fire behavior of reinforced composite RC slabs.

Consequently, in this study, a numerical analysis was carried out to ascertain the flexural behavior of simply supported RC slabs strengthened with HPC and exposed to a particularly high temperature of 600°C for 2 h. This behavior was investigated and analyzed in the presence of a number of parameters, such as HPC types (fiber-reinforced, 0.5% steel, polypropylene fibers [PPF], hybrid fibers), strengthening side (tension or compression), strengthening layer thickness, slab thickness, boundary conditions, reinforcement ratio and yield strength of reinforcement.

The results showed that traction-separation and full-bond models can achieve accuracy compared with experimental results. Also, the fiber type significantly affects the postfire performance of RC slab strengthened with HPC, where the inclusion of hybrid fiber recorded the highest ultimate load. While adding PPF to HPC showed a rapid decrease in the load-deflection curve after reaching the ultimate load.

The proposed model accurately predicted the thermomechanical behavior of RC slabs strengthened with HPC after being exposed to the fire regarding load-deflection response, crack pattern and failure mode. Moreover, the considered independent parametric variables significantly affect the composite slabs’ behavior.

The successful implementation of hospital projects (HPs) tends to confront sundry challenges in the planning and construction (P&C) phases due to their complexity and particularity. Employing key performance indicators (KPIs) facilitates the monitoring of HPs to advance their successful delivery. This study aims to comprehensively investigate the KPIs for hospital planning and construction (HPC).

The KPIs for HPC were identified through a systematic review. Then a comprehensive assessment of these KPIs was performed utilizing a meta-analysis method. In this process, basic statistical analysis, subgroup analysis, sensitive analysis and publication bias analysis were performed.

Results indicate that all 27 KPIs identified from the literature are significant for executing HPs in P&C phases. Also, some unconventional performance indicators are crucial for implementing HPs, such as “Project monitoring effectiveness” and “Industry innovation and synergy,” as their high significance is reflected in this study. Despite the fact that the findings of meta-analysis are more trustworthy than those of individual studies, a high heterogeneity still exists in the findings. It highlights the inherent uncertainty in the construction industry. Hence, this study applied subgroup analysis to explore the underlying factors causing the high level of heterogeneity and used sensitive analysis to assess the robustness of the findings.

There is no consensus among the prior studies on KPIs for HPC specifically and their degree of significance. Additionally, few reviews in this field have focused on the reliability of the results. This study comprehensively assesses the KPIs for HPC and explores the variability and robustness of the results, which provides a multi-dimensional perspective for practitioners and the research community to investigate the performance of HPs during the P&C stages.

This study aims to focus on the main materials used in consolidation processes of illuminated paper manuscripts and leather binding.

For each material, chemical structure, chemical composition, molecular formula, solubility, advantages, disadvantages and its role in treatment process are presented.

This study concluded that carboxy methyl cellulose, hydroxy propyl cellulose, methyl cellulose, cellulose acetate, nanocrystalline cellulose, funori, sturgeon glue, poly vinyl alcohol, chitosan, chitosan nanoparticles (NPs), gelatin, aquazol, paraloid B72 and hydroxyapatite NPs were the most common and important materials used for the consolidation of illuminated paper manuscripts. For the leather bindings, hydroxy propyl cellulose, polyethylene glycol, oligomeric melamine-formaldehyde resin, acrylic wax SC6000, pliantex, paraloid B67 and B72, silicone oil and collagen NPs are the most consolidants used.

Illuminated paper manuscripts with leather binding are considered one of the most important objects in libraries, museums and storehouses. The uncontrolled conditions and other deterioration factors inside the libraries and storehouses lead to degradation of these artifacts. The brittleness, fragility and weakness are considered the most common deterioration aspects of illuminated paper manuscripts and leather binding. Therefore, the consolidation process became vital and important to solve this problem. This study presents the main materials used for consolidation process of illuminated paper manuscripts and leather bindings.

Higher energy conversion efficiency of internal combustion engine can be achieved with optimal control of unsteady in-cylinder flow fields inside a direct-injection (DI) engine. However, it remains a daunting task to predict the nonlinear and transient in-cylinder flow motion because they are highly complex which change both in space and time. Recently, machine learning methods have demonstrated great promises to infer relatively simple temporal flow field development. This paper aims to feature a physics-guided machine learning approach to realize high accuracy and generalization prediction for complex swirl-induced flow field motions.

To achieve high-fidelity time-series prediction of unsteady engine flow fields, this work features an automated machine learning framework with the following objectives: (1) The spatiotemporal physical constraint of the flow field structure is transferred to machine learning structure. (2) The ML inputs and targets are efficiently designed that ensure high model convergence with limited sets of experiments. (3) The prediction results are optimized by ensemble learning mechanism within the automated machine learning framework.

The proposed data-driven framework is proven effective in different time periods and different extent of unsteadiness of the flow dynamics, and the predicted flow fields are highly similar to the target field under various complex flow patterns. Among the described framework designs, the utilization of spatial flow field structure is the featured improvement to the time-series flow field prediction process.

The proposed flow field prediction framework could be generalized to different crank angle periods, cycles and swirl ratio conditions, which could greatly promote real-time flow control and reduce experiments on in-cylinder flow field measurement and diagnostics.

This study aims to investigate how early and late adopters of Quality 4.0 (Q4.0) differ in terms of organizational performance.

The authors employed a grounded theory approach for interviewing 15 senior managers from diverse organizational contexts throughout the globe as part of their qualitative research methodology.

The research's findings were analyzed based on four types of performance: operational, financial, environmental and social. It was clear that early adopters of Q4.0 were sustaining superior performance in quality over time, even though their investment was significantly higher than that of late adopters. From a financial viewpoint, it was evident that early adopters had a competitive edge over their rivals compared to late adopters. Late adopters have utilized the notion of the circular economy (CE) more effectively than many early adopters in the context of environmental performance in order to establish a green economy and sustainable development.

Although the results of the interview indicate that Q4.0 is having some positive effects on social performance, in the authors' view, it is still least understood from an empirical standpoint.

The study's findings assist organizations in comprehending the performance differences between Q4.0 early adopters and late adopters.

Artificial intelligence (AI) is a powerful and promising technology that can foster the performance, and competitiveness of micro, small and medium enterprises (MSMEs). However, the adoption of AI among MSMEs is still low and slow, especially in developing countries like Jordan. This study aims to explore the elements that influence the intention to adopt AI among MSMEs in Jordan and examines the roles of firm innovativeness and government support within the context.

The study develops a conceptual framework based on the integration of the technology acceptance model, the resource-based view, the uncertainty reduction theory and the communication privacy management. Using partial least squares structural equation modeling – through AMOS and R studio – and the importance–performance map analysis techniques, the responses of 471 MSME founders were analyzed.

The findings reveal that perceived usefulness, perceived ease of use and facilitating conditions are significant drivers of AI adoption, while perceived risks act as a barrier. AI autonomy positively influences both firm innovativeness and AI adoption intention. Firm innovativeness mediates the relationship between AI autonomy and AI adoption intention, and government support moderates the relationship between facilitating conditions and AI adoption intention.

The findings provide valuable insights for policy formulation and strategy development aimed at promoting AI adoption among MSMEs. They highlight the need to address perceived risks and enhance facilitating conditions and underscore the potential of AI autonomy and firm innovativeness as drivers of AI adoption. The study also emphasizes the role of government support in fostering a conducive environment for AI adoption.

As in many emerging nations, the AI adoption research for MSMEs in Jordan (which constitute 99.5% of businesses), is under-researched. In addition, the study adds value to the entrepreneurship literature and integrates four theories to explore other significant factors such as firm innovativeness and AI autonomy.

This paper aims to investigate the ability of traditional biopolymers, such as funori or the nanoscale form of cellulose nanocrystals, to consolidate fragile paper and preserve it for as long as possible.

Degraded papers dating back two centuries were separated into paper samples for consolidation processes. Funori – a marine spleen – was used as a traditional consolidation material and a mixture with ZnO NPs compared with modern materials, such as cellulose nanocrystals. The samples were aged for 25 years, examinations and analyses were performed using scanning electron microscopy and color change was assessed using the CIELAB system, X-ray diffraction and Fourier-transform infrared spectroscopy.

According to the results, using traditional materials to consolidate damage, such as funori, after aging resulted in glossiness on the surface, a color change and increased water content and oxidation. Furthermore, samples treated with a mixture of ZnO NPs and funori revealed that the mixture improved the sample properties and increased the degree of crystallization. Cellulose nanocrystals improved the surface, filled gaps, formed bridges between the fibers and acted as a protector from aging effects.

This paper highlights the ability of nanomaterials to enhance the properties of materials as additives and treat the paper manuscripts from weaknesses.

Study on knowledge-based systems for scientific publications is growing very broadly. However, most of these studies do not explicitly discuss the knowledge management (KM) component as knowledge management system (KMS) implementation. This background causes academic institutions to face challenges in developing KMS to support scholarly publication cycle (SPC). Therefore, this study aims to develop a new KMS conceptual model, Identify critical components and provide research gap opportunities for future KM studies on SPC.

This study used a systematic literature review (SLR) method with the procedure from Kitchenham et al. Then, the SLR results are compiled into a conceptual model design based on a framework on KM foundations and KM solutions. Finally, the model design was validated through interviews with related field experts.

The KMS for SPC focuses on the discovery, sharing and application of knowledge. The majority of KMS use recommendation systems technology with content-based filtering and collaborative filtering personalization approaches. The characteristics data used in KMS for SPC are structured and unstructured. Metadata and article abstracts are considered sufficiently representative of the entire article content to be used as a search tool and can provide recommendations. The KMS model for SPC has layers of KM infrastructure, processes, systems, strategies, outputs and outcomes.

This study has limitations in discussing tacit knowledge. In contrast, tacit knowledge for SPC is essential for scientific publication performance. The tacit knowledge includes experience in searching, writing, submitting, publishing and disseminating scientific publications. Tacit knowledge plays a vital role in the development of knowledge sharing system (KSS) and KCS. Therefore, KSS and KCS for SPC are still very challenging to be researched in the future. KMS opportunities that might be developed further are lessons learned databases and interactive forums that capture tacit knowledge about SPC. Future work potential could identify other types of KMS in academia and focus more on SPC.

This study proposes a novel comprehensive KMS model to support scientific publication performance. This model has a critical path as a KMS implementation solution for SPC. This model proposes and recommends appropriate components for SPC requirements (KM processes, technology, methods/techniques and data). This study also proposes novel research gaps as KMS research opportunities for SPC in the future.

Forests are negatively affected from rapid world population increase and industrialization that create intense pressures on natural resources and the possibility of an achieving circular economy. Forests can be considered as essential resources for providing sustainable society and meeting the requirements of future generations and circular economy. Therefore sustainable production tools as part of circular economy can be handled as one of the basic indicators for achieving circular economy. Accordingly the main purpose of this study is developing a novel rough – fuzzy multi-criteria decision-making model (MCDM) for evaluation sustainable production for forestry firms in Eastern Black Sea Region.

For determining 18 criteria weights a novel Rough PIPRECIA (PIvot Pairwise RElative Criteria Importance Assessment) method is developed. Eight decision-makers (DMs) participated in the research, and to obtain group rough decision matrix, rough Dombi weighted geometric averaging (RNDWGA) operator has been applied. For evaluation forestry firms fuzzy MARCOS (Measurement of alternatives and ranking according to COmpromise solution) method was utilized.

After application developed model the fourth alternative was found as the best. Sensitivity analysis and comparison were made to present the applicability of this method.

Development of novel integrated Rough PIPRECIA-Fuzzy MARCOS model with emphasis on developing new Rough PIPRECIA method.

Despite the enormous need to succeed in the urban model, scientists and policymakers should work consistently to create blueprints to regulate urbanization. The absence of coordination between the crucial requirements and the regional strategies of the local authorities leads to a lack of conformance in urban development. The purpose of this paper is to address this issue.

This study intends to manage future urban growth patterns using integrated methods and then employ the results in the genetic algorithm (GA) model to considerably improve growth behavior. Multi-temporal land-use datasets have been derived from remotely sensed images for the years 1990, 2000, 2010 and 2020. Urban growth patterns and processes were then analyzed with land-use-and-land-cover dynamics. Results were examined for simulation and utilization of the GA.

Model parameters were derived and evaluated, and a preliminary assessment of the effective coefficient in the formation of urbanization is analyzed, showing the city's urbanization pattern has followed along with the transportation infrastructure and outward growth, and the scattering rates are high, with an increase of 5.64% in building area associated with a decrease in agricultural lands and rangelands.

The research achieved a considerable improvement over the growth behavior. The conducted research design was the first of its type in that field to be executed to any specific growth pattern parameters in terms of regulating and policymaking. The method has integrated various artificial intelligence models to monitor, measure and optimize the projected growth by applying this design. Other research on the area was limited to projecting the future of Amman as it is an urbanized distressed city.