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The purpose of the study is to analytically as well as numerically investigate the weight of throughflow on the onset of Casson nanofluid layer in a permeable matrix. This study examines both the marginal and over stable kind of convective movement in the system.

A double-phase model is used for Casson nanofluid, which integrates the impacts of thermophoresis and Brownian wave, whereas for flow in the porous matrix the altered Darcy model is occupied under the statement that nanoparticle flux is disappear on the boundaries. The resultant eigenvalue problem is resolved analytically as well as numerically with the help of Galerkin process with the Casson nanofluid Rayleigh–Darcy number as the eigenvalue.

The findings revealed that the throughflow factor postpones the arrival of convective flow and reduces the extent of convective cells, whereas the Casson factor, the Casson nanoparticle Rayleigh–Darcy number and the reformed diffusivity ratio promote convective motion and also decrease the extent of convective cells.

Controlling the convective movement in heat transfer systems that generate high heat flux is a real mechanical challenge. The proposed framework proved that the use of throughflow is one of the most important ways to control the convective movement in Casson nanofluid. To the best of the authors’ knowledge, no inspection has been established in the literature that studies the outcome of throughflow on the Casson nanofluid convective flow in a porous medium layer. However, the convective flow of Casson nanofluid finds many applications in improving heat transmission and energy efficiency in a range of thermal systems, such as the cooling of heat-generating elements in electronic devices, heat exchangers, pharmaceutical practices and hybrid-powered engines, where throughflow can play a significant role in controlling the convective motion.

Designing a sustainable bituminous concrete with long-term performance is a challenging problem. In addition, strength of the subgrade has a crucial impact on pavement design. This paper aims to concentrate on subgrade soil stabilization with granite dust powder (GDP) and crumb rubber powder (CRP) to improve the engineering properties of the soil. Further design of bituminous concrete pavement with cement-treated layers in base and subbase course layers was carried out with life cycle cost analysis and life cycle assessment for 1 km of a four-lane national highway.

Subgrade soil stabilized with GDP and CRP is characterized as per Indian Standards (IS)-2720 to determine the optimum dosage. Further, the mechanistic-empirical pavement design was carried out using Indian Road Congress-37 (2018), analyzed using IITPAVE software and validated with ANSYS software. The life cycle cost analysis is carried out using the net present value method, and the life cycle assessment is performed according to the cradle-to-grave approach.

A soil mix comprising 10% GDP and 2.5% CRP yielded a soaked California bearing ratio value of 6.58%. In addition, the design of bituminous concrete pavement with cement-treated granular layers showed a 26.9% reduction in life cycle cost and 59.4% reduction in total carbon footprint per kilometer compared to the pavement with traditional aggregate layers.

The research on subgrade stabilization with sustainable materials like GDP and CRP incorporating mechanistic empirical pavement design, life cycle cost analysis and life cycle assessment is limited. Overall, the study recommends the use of GDP and CRP to stabilize soil for subgrade application and incorporate cement-treated granular layers, which offer economic and environmental benefits compared to traditional pavement construction.

Multiple encapsulated phase change materials (PCMs) are used in a wide range of applications, including convective drying, electronic cooling, waste heat recovery and air conditioning. Therefore, it is important to understand the performance of multiple PCMs in channels with flow separation and develop methods to increase their effectiveness. The aim of the study is to analyze the phase transition dynamics of multiple encapsulated PCMs mounted in a U-shaped tube under inclined magnetic field by using ternary nanofluid.

The PCMs used in the upper horizontal channel, vertical channel and lower horizontal channel are denoted by M1, M2 and M3. Magnetic field is uniform and inclined while finite element method is used as the solution technique. Triple encapsulated-PCM system study is carried out taking into account different values of Reynolds number (Re, ranges from 300 to 1,000), Hartmann number (Ha ranges from 0 and 60), magnetic field inclination (between 0 and 90) and solid volume fraction of ternary nanofluid (between 0 and 0.03). The dynamic response of the liquid fraction is estimated for each PCM with varying Re, Ha and t using an artificial neural network.

It is observed that for PCMs M2 and M3, the influence of Re on the phase transition is more effective. For M2 and M3, entire transition time (t-F) lowers by approximately 47% and 47.5% when Re is increased to its maximum value, whereas it only falls by 10% for M1. The dynamic characteristics of the phase transition are impacted by imposing MGF and varying its strength and inclination. When Ha is raised from Ha = 0 to Ha = 50, the t-F for PCM-M2 (PCM-M3) falls (increases) by around 30% (29%). For PCMs M1, M2 and M3, the phase transition process accelerates by around 20%, 30% and 28% when the solid volume fraction is increased to its maximum value.

Outcomes of this research is useful for understanding the phase change behavior of multiple PCMs in separated flow and using various methods such as nano-enhanced magnetic field to improve their effectiveness. Research outputs are beneficial for initial design and optimization of using multiple PCMs in diverse energy system technologies, including solar power, waste heat recovery, air conditioning, thermal management and drying.

Based on the strong feature representation ability of the convolutional neural network (CNN), generous object detection methods in remote sensing (RS) have been proposed one after another. However, due to the large variation in scale and the omission of relevant relationships between objects, there are still great challenges for object detection in RS. Most object detection methods fail to take the difficulties of detecting small and medium-sized objects and global context into account. Moreover, inference time and lightness are also major pain points in the field of RS.

To alleviate the aforementioned problems, this study proposes a novel method for object detection in RS, which is called lightweight object detection with a multi-receptive field and long-range dependency in RS images (MFLD). The multi-receptive field extraction (MRFE) and long-range dependency information extraction (LDIE) modules are put forward.

To concentrate on the variability of objects in RS, MRFE effectively expands the receptive field by a combination of atrous separable convolutions with different dilated rates. Considering the shortcomings of CNN in extracting global information, LDIE is designed to capture the relationships between objects. Extensive experiments over public datasets in RS images demonstrate that our MFLD method surpasses the state-of-the-art methods. Most of all, on the NWPU VHR-10 dataset, our MFLD method achieves 94.6% mean average precision with 4.08 M model volume.

This paper proposed a method called lightweight object detection with multi-receptive field and long-range dependency in RS images.

This paper aims to present a study on stability analysis of Jeffrey fluids in the presence of emergent chemical gradients within microbial systems of anisotropic porous media.

This study uses an effective method that combines non-dimensionalization, normal mode analysis and linear stability analysis to examine the stability of Jeffrey fluids in the presence of emergent chemical gradients inside microbial systems in anisotropic porous media. The study focuses on determining critical values and understanding how temperature gradients, concentration gradients and chemical reactions influence the onset of bioconvection patterns. Mathematical transformations and analytical approaches are used to investigate the system’s complicated dynamics and the interaction of numerous characteristics that influence stability.

The analysis is performed using the Jeffrey-Darcy type and Boussinesq estimation. The process involves using non-dimensionalization, using the normal mode approach and conducting linear stability analysis to convert the field equations into ordinary differential equations. The conventional thermal Rayleigh Darcy number RDa,c is derived as a comprehensive function of various parameters, and it remains unaffected by the bio convection Lewis number Łe. Indeed, elevating the values of ζ and γ′ in the interval of 0 to 1 has been noted to expedite the formation of bioconvection patterns while concurrently expanding the dimensions of convective cells. The purpose of this investigation is to learn how the temperature gradient affects the concentration gradient and, in turn, the stability and initiation of bioconvection by taking the Soret effect into the equation. The results provide insightful understandings of the intricate dynamics of fluid systems affected by chemical and biological elements, providing possibilities for possible industrial and biological process applications. The findings illustrate that augmenting both microbe concentration and the bioconvection Péclet number results in an unstable system. In this study, the experimental Rayleigh number RDa,c was determined to be 4π2at the critical wave number ( δcˇ) of π.

The study’s novelty originated from its investigation of a novel and complicated system incorporating Jeffrey fluids, emergent chemical gradients and anisotropic porous media, as well as the use of mathematical and analytical approaches to explore the system’s stability and dynamics.

Small and medium-sized contractors are critical to micro and macroeconomic performance. These contractors in South Africa have long been confronted with the problem of business failure because of a plethora of factors, including poor productivity. The purpose of this study is to investigate salient issues undermining the productivity of small and medium-sized contractors in South Africa. This study proposes alternative possibilities to engender productivity improvement.

Qualitative data were collected using semi-structured interviews with 15 contractors in Gauteng Province, South Africa. The research data were analysed using content and causal layered analyses.

Challenges to contractors’ productivity were associated with inadequately skilled workers, management competence and political factors. Skills development, construction business and political factors were dominant stakeholders’ perceptions. Metaphors for construction labour productivity are presented and reconstructed as alternative directions for productivity improvement.

Contractors lose a substantial amount of South African Rand to poor productivity. Alternative directions provided in this study can be leveraged to increase profitability in construction organizations, enhance the social well-being of South Africans and ultimately improve the contribution of contractors to the South African economy.

The causal layered analysis (CLA) applied in this study is novel to construction labour productivity research. The four connected layers of CLA, which make a greater depth of inquiry possible, were explored to investigate labour productivity in construction organizations.

Based on the conceptual design of seismic resistance in buildings, this study aims to put forward a new construction structure energy-saving block structure with invisible multiribbed frame.

The structure is composed of energy-saving block wall panels with invisible multiribbed frames, lightweight partition wall plates and cast-in-place reinforced concrete floor slabs. The structure design is simple and the construction is convenient and fast. The comprehensive economic index of the structure is better than that of brick-and-concrete composite construction. The self-weight of the energy-saving blocks that make up the wall is only about 25% of that of solid clay bricks. The thermal insulation and energy-saving effects of the structure can meet the national energy-saving requirements of buildings.

This new structure meets the requirements of national technology and economy, wall deformation, thermal insulation and energy-saving, and can be used mainly for multistory and mid- to high-rise residential buildings. For the core components of the new structure energy-saving block and invisible multiribbed frame composite wall, as the axial compression ratio increases in the test parameters range, the peak bearing capacity and ductility of the wall increase and the initial stiffness of the wall decreases. The axial compression ratio has a significant effect on the energy dissipation capacity of the wall. The displacement ductility coefficients ν are all greater than 2, indicating the optimal seismic performance of the wall.

This structure is a new, economical, lightweight, energy-saving, seismic resistant, multistory and mid- to high-rise structure that fully conforms to national conditions.

This study investigates the impact of the fire decay phase on structural damage using the sectional analysis method. The primary objective of this work is to forecast the non-dimensional capacity parameters for the axial and flexural load-carrying capacity of reinforced concrete (RC) sections for heating and the subsequent post-heating phase (decay phase) of the fire.

The sectional analysis method is used to determine the moment and axial capacities. The findings of sectional analysis and heat transfer for the heating stage are initially validated, and the analysis subsequently proceeds to determine the load capacity during the fire’s heating and decay phases by appropriately incorporating non-dimensional sectional and material parameters. The numerical analysis includes four fire curves with different cooling rates and steel percentages.

The study’s findings indicate that the rate at which the cooling process occurs after undergoing heating substantially impacts the axial and flexural capacity. The maximum degradation in axial and flexural capacity occurred in the range of 15–20% for cooling rates of 3 °C/min and 5 °C/min as compared to the capacity obtained at 120 min of heating for all steel percentages. As the fire cooling rate reduced to 1 °C/min, the highest deterioration in axial and flexural capacity reached 48–50% and 42–46%, respectively, in the post-heating stage.

The established non-dimensional parameters for axial and flexural capacity are limited to the analysed section in the study owing to the thermal profile, however, this can be modified depending on the section geometry and fire scenario.

The study primarily focusses on the degradation of axial and flexural capacity at various time intervals during the entire fire exposure, including heating and cooling. The findings obtained showed that following the completion of the fire’s heating phase, the structural capacity continued to decrease over the subsequent post-heating period. It is recommended that structural members' fire resistance designs encompass both the heating and cooling phases of a fire. Since the capacity degradation varies with fire duration, the conventional method is inadequate to design the load capacity for appropriate fire safety. Therefore, it is essential to adopt a performance-based approach while designing structural elements' capacity for the desired fire resistance rating. The proposed technique of using non-dimensional parameters will effectively support predicting the load capacity for required fire resistance.

The fire-resistant requirements for reinforced concrete structures are generally established based on standard fire exposure conditions, which account for the fire growth phase. However, it is important to note that concrete structures can experience internal damage over time during the decay phase of fires, which can be quantitatively determined using the proposed non-dimensional parameter approach.

This research explores the role of concrete 3D printing (C3DP) in the development of culturally appropriate housing in Indigenous Reserves in Canada through the design, building and evaluation of the Star Lodge project located in the Siksika Nation of Alberta, Canada. The project aims to assess the potential of C3DP in addressing the escalating housing demands in Indigenous communities in Canada.

The research involved a collaborative and multidisciplinary approach, engaging Blackfoot Elders, Knowledge Keepers from the Siksika Nation, Siksika Housing and Nidus3D. Central to this was the design, build and documentation of the Star Lodge project to analyse the advantages and challenges, guided by weekly meetings and site visits.

The project harnessed C3DP to streamline construction, enhance durability, reduce maintenance costs and enhance the energy performance of the homes. Notable time savings were achieved compared to conventional construction methods. Challenges included developing strategies to overcome extreme cold weather conditions, achieving a consistent concrete mix and integrating conventional construction elements such as drywall construction in interiors. The project served as a platform for collaboration and community participation, shaping the design and construction process while raising awareness of innovative construction techniques in the community.

This study provides an evidence-based framework for the evaluation of C3DP technology by analysing the Star Lodge Project, the first C3DP project in Alberta and the largest of its kind in Canada. By addressing housing challenges in Indigenous communities, the research holds broader implications for sustainable development and Indigenous empowerment across Canada.

The rapid growth of 3D printing has transformed the cost-effective production of prototypes and functional items, primarily using extrusion technology with thermoplastics. This study aims to focus on optimizing mechanical properties, precisely highlighting the crucial role of mechanical compressive strength in ensuring the functionality and durability of 3D-printed components, especially in industrial and engineering applications.

Using the Box−Behnken experimental design, the research investigated the influence of layer thickness, wall perimeter and infill level on mechanical resistance through compression. Parameters such as maximum force, printing time and mass utilization are considered for assessing and enhancing mechanical properties.

The layer thickness was identified as the most influential parameter over the compression time, followed by the degree of infill. The number of surface layers significantly influences both maximum strength and total mass. Optimization strategies suggest reducing infill percentage while maintaining moderate to high values for surface layers and layer thickness, enabling the production of lightweight components with adequate mechanical strength and reduced printing time. Experimental validation confirms the effectiveness of these strategies, with generated regression equations serving as a valuable predictive tool for similar parameters.

This research offers valuable insights for industries using 3D printing in creating prototypes and functional parts. By identifying optimal parameters such as layer thickness, surface layers and infill levels, the study helps manufacturers achieve stronger, lighter and more cost-efficient components. For industrial and engineering applications, adopting the outlined optimization strategies can result in components with enhanced mechanical strength and durability, while also reducing material costs and printing times. Practitioners can use the developed regression equations as predictive tools to fine-tune their production processes and achieve desired mechanical properties more effectively.

This research contributes to the ongoing evolution of additive manufacturing, providing insights into optimizing structural rigidity through polylactic acid (PLA) selection, Box−Behnken design and overall process optimization. These findings advance the understanding of fused deposition modeling (FDM) technology and offer practical implications for more efficient and economical 3D printing processes in industrial and engineering applications.