Practical Design Research Articles

Machine learning-assisted characterization of the thermal conductivity of cement-based grouts for borehole heat exchangers

This research utilized machine learning (ML) techniques to forecast the thermal conductivity (TC) of cement-based grouts for borehole heat exchangers. Nine commonly used ML models were established and tested. Additionally, the accuracy of the ML models was contrasted with three conventional models. The results demonstrate that the back propagation neural network (BPNN) model emerges as the optimum prediction model with its highest accuracy (e.g., an R2 of 0.991 on the test dataset). In addition, the BPNN model outperformed the three conventional models, while showing a notable increase of 29.3 % in R2 compared with the optimum conventional model (i.e., Hashin-Shtrikam model). Finally, the SHapley Additive exPlanations analysis was conducted to comprehensively evaluate the importance of each input variable, and to analyse the individual relationships of the TC with input features. In conclusion, the proposed ML model proves an effective tool for forecasting the TC of grouts for borehole heat exchangers. This advancement facilitates the practical design and selection of grouts, ultimately improving the performance of ground source heat pumps.

Multicultural Perspective Design and Implementation Strategies for University Music Curricula

This study explores how to integrate multicultural elements into the design and implementation of university music curricula, aiming to achieve several key objectives. First, it seeks to broaden students’ musical horizons by exposing them to a wider variety of musical genres. Second, it aims to strengthen students’ cultural identity, helping them gain a deeper understanding and respect for their own culture through learning about music from diverse cultural backgrounds. Lastly, the study aims to enhance students’ intercultural communication skills, using music as a medium to foster exchange and collaboration among students from different cultural contexts. By conducting an in-depth analysis of multicultural theories and considering the current state of university music curricula both domestically and internationally, the paper proposes a series of practical design principles and implementation strategies. These insights are intended to offer valuable guidance for the reform and development of music curricula in Chinese universities.

Artificial intelligence large language model scores highly on focused practice designation in metabolic and bariatric surgery board practice questions.

Artificial intelligence models such as ChatGPT (Open AI) have performed well on the exams of various medical and surgical fields. It is not yet known how ChatGPT performs on similar metabolic and bariatric surgery (MBS) questions. Assess the performance of ChatGPT on Focused Practice Designation in Metabolic and Bariatric Surgery board-style questions. United States. Questions obtained from the largest commercially available bank of FPD-MBS practice questions were entered into ChatGPT-4, as is, without prior training. We assessed the overall percentage correct as well as the percentage correct within each of the five American Board of Surgery (ABS) question categories. One-way ANOVA was used to determine if the frequency of correct answers differed between categories. Out of 255 questions, ChatGPT-4 correctly answered 189 (74.1%). Between the five question categories there was no difference between the frequency of correct answers (p = 0.22). It did not matter if questions were entered individually or in groups of up to 10. Without prior training, ChatGPT-4 scored highly when evaluated on the largest practice question bank for the FPD-MBS exam.

Exploring the far-reaching consequences of malnutrition during pregnancy and beyond: A comprehensive review

In India, maternal and infant malnutrition continues to be a significant contributor to health problems. Fetal malnutrition can result from an inadequate nutrient supply, necessary nutrition services, and ideal practices before, during, and following pregnancy. This comprehensive review delves into the complex relationships among hormone changes, microbial ecosystems, and nutrition, and how these relationships affect the health of mothers and their offspring. The review highlights how important it is to maintain good nutrition during pregnancy and to comprehend the dietary modifications that pregnant people make on their own to create interventions that work. Important minerals like calcium, iron, folic acid, iodine, and vitamin D are covered. Acknowledging the mutualistic association between the eating habits of mothers and the diverse microbiomes found in their gut, vagina, and oral channels, the review explores the influence on hormone levels and their consequences during gestation. Diverse fetal developmental needs are depicted by the table that outlines food requirements for each of the three trimesters. The detrimental effects of poor prenatal nutrition on a mother's health and its repercussions for neonates are examined. The effects of malnutrition after delivery, including difficulties throughout pregnancy, healing periods, and general effects on mental and physical health, are well studied. The study, which adopts a worldwide viewpoint, finds that societal norms, information gaps, and poverty are the main causes of malnutrition. Prevention techniques are discussed, emphasizing the significance of taking a comprehensive approach to addressing this vital issue in both the Indian and global contexts.

New insights on impact wear of polycrystalline diamond compact: Effect of interface state on kinetic response and damage behavior

The impact wear of polycrystalline diamond compact (PDC) cutters caused by the discontinuous cutting during the drilling process significantly affects the lifespan and efficiency of the drilling tools. Different PDC impact contact interface states are realized through the selection of impact mating materials. Understanding the effects of different interface states on the kinetic response, damage behavior and impact wear mechanism of PDC is significant in guiding the practical design of material structures with excellent impact wear resistance. Therefore, the kinetic response and damage behavior of PDC impact with different mating materials are investigated. The results show that the kinetic response and damage behavior of PDC are correlated with the mechanical properties of the mating material and physicochemical changes at the impact surface interface. Adhesion and filling resulting from strong covalent bonding and filling effects mitigate the interfacial stress and reduce the average impact force of SiC and Al2O3. Moreover, brittle fracture and filling effects increase the consumption of kinetic energy of SiO2, resulting in an energy absorption of up to 86 %. In addition, the strong covalent bonding effects exacerbated the damage to the PDC interfaces, leading to the internal fracture of diamond particles and detachment at grain boundaries. Furthermore, the filling effect formed plays a specific protective role for diamonds. However, it increases the energy absorption rate and reduces the mating material damage efficiency. In this work, the relationship between impact interface state and impact wear mechanism has been established by understanding the kinetic response, damage morphology, and structural evolution.

Comparative evaluation of different endodontic access cavity designs on canal transportation and centering ability using bondent platinum file system on mandibular molar – An in vitro cone-beam computed tomography study

ABSTRACT Aim: This study aimed to compare the effects of different endodontic access cavity designs on canal transportation (CT) and centering ability (CA) using the bondent platinum file system on mandibular molars, utilizing cone-beam computed tomography (CBCT). Methodology: Thirty extracted human permanent mandibular molars which fulfilled the inclusion criteria were divided into three groups, Group 1 (TEC): Traditional endodontic access cavity (control group), Group 2 (CEC): Conservative endodontic access cavity, and Group 3 (TrEC): Truss endodontic access cavity. The teeth were scanned using CBCT initially followed by random allocation into three groups. Root canals were prepared using Bondent platinum file system in mesiobuccal and mesiolingual canals using #25.06 file and in distal canal #30.06 file. Postpreparation, CBCT scans to assess CT and CA at 3 mm, 5 mm, and 7 mm from the apex. Data analysis was performed using a one-way analysis of variance and Tukey’s post hoc test. Results: Significant differences (P < 0.01) in mean CT and CA were observed between Group 1 (TEC) and Groups 2 (CEC) and 3 (TrEC). Dentin removal was highest at 3 mm, followed by 5 mm and 7 mm. The distal canal showed the greatest dentin loss compared to other canals. Conclusion: Minimal access cavity designs (CEC and TrEC) demonstrated less CT and improved CA compared to the traditional access cavity design (TEC). Study limitations acknowledged that conservative access cavity designs result in less deviation (CT) and better centering of the prepared canals compared to the traditional approach (TEC). These results imply the potential benefits of adopting less invasive access cavity designs in endodontic practice to enhance treatment outcomes.

Deep Learning Aided Intelligent Reflective Surfaces for 6G: A Survey

The envisioned sixth-generation (6G) networks anticipate robust support for diverse applications, including massive machine-type communications, ultra-reliable low-latency communications, and enhanced mobile broadband. Intelligent Reflecting Surfaces (IRS) have emerged as a key technology capable of intelligently reconfiguring wireless propagation environments, thereby enhancing overall network performance. Traditional optimization techniques face limitations in meeting the stringent performance requirements of 6G networks due to the intricate and dynamic nature of the wireless environment. Consequently, Deep Learning (DL) techniques are employed within the IRS framework to optimize wireless system performance. This paper provides a comprehensive survey of the latest research in DL-aided IRS models, covering optimal beamforming, resource allocation control, channel estimation and prediction, signal detection, and system deployment. The focus is on presenting promising solutions within the constraints of different hardware configurations. The survey explores challenges, opportunities, and open research issues in DL-aided IRS, considering emerging technologies such as digital twins (DTs), computer vision (CV), blockchain, network function virtualization (NFC), integrated sensing and communication (ISAC), software-defined networking (SDN), mobile edge computing (MEC), unmanned aerial vehicles (UAVs), and non-orthogonal multiple access (NOMA). Practical design issues associated with these enabling technologies are also discussed, providing valuable insights into the current state and future directions of this evolving field.

Tailoring multi-type nanoprecipitates in high-entropy alloys towards superior tensile properties at cryogenic temperatures

In this work, the quasi-static tensile properties in the face-centered cubic-based Al0.5Cr0.9FeNi2.5V0.2 HEAs containing two types of heterogeneous nanoprecipitates, i.e., dual-lamellar and spherical nanoprecipitates, at ambient (293 K) and liquid nitrogen (77 K) temperatures are thoroughly investigated. The microstructure formed by aging at 873 K comprises L12 and body-centered cubic dual-lamellar (DL) nanoprecipitates. In contrast, aging at 773 K results in solely spherical L12 nanoparticles. Both nanoprecipitates enhance mechanical strength as temperatures drop to 77 K; however, the DL nanoprecipitates additionally boost the work hardening rate, whereas the spherical nanoparticles notably improve ductility. To investigate the underlying deformation mechanisms, we perform interrupted mechanical tests and microstructure characterizations at various strains. The DL nanoprecipitates are observed to go through a multistage work hardening rate response by gradually introducing new boundaries to block dislocation motion, activating the stacking fault (SF) networks, and forming Lomer–Cottrell locks. A combination of interface hardening, dislocation hardening, SF-induced hardening, and precipitation hardening in DL samples leads to stronger hetero-deformation-induced hardening at cryogenic temperatures. In comparison, while samples with only spherical nanoparticles exhibit a monotonous decrease in the work-hardening rate, the spherical nanoparticles can be sheared by dislocations, effectively alleviating strain concentration and thereby enhancing ductility at cryogenic temperatures. Overall, this work provides practical design principles of nanoprecipitates for fine-tuning the balance of strength and ductility in FCC-based HEAs at cryogenic temperatures.

Macro-element modelling for lateral response of monopiles with local scour hole via hyperbolic hardening relation

Monopile is a popular choice in the foundation supporting offshore wind turbines (OWTs), with local scour significantly impacting their lateral responses. Macro-element model, which encapsulates the response between the monopile and the surrounding seabed soils into a force-displacement relation, has been extensively developed to describe offshore foundations. However, such kind of models specifically targeting monopiles subjected to lateral loading in local scour remain underdeveloped. This work proposes a macro-element model with a succinct hyperbolic hardening relation for laterally loaded monopiles in local scour conditions, using the evolutionary polynomial regression (EPR) machine learning technique for easy and optimal design. First, the finite element model is verified and extended to generate force-displacement responses considering the monopile geometries, soil characteristics, and local scour geometries. These results are then utilised to determine the optimal hyperbolic hardening relation of the macro-element model. Next, the EPR technique is employed to determine the relationship between the hyperbolic hardening relation parameters and the influencing factors. Finally, the macro-element model is successfully evaluated by comparing with measurements from centrifuge tests and numerical solutions by finite element analysis, demonstrating its applicability in practical design and the ability to reproduce FEA results with a significant reduction in computational cost.

Thermodynamic Comparative Analysis of Cascade Refrigeration System Pairing R744 with R404A, R448A and R449A with Internal Heat Exchanger: Part 2—Exergy Characteristics

The cascade refrigeration systems (CRS) used in hypermarkets and supermarkets, which are used by many people, have been employing R744 for the low-temperature cycle (LTC) and R404A for the high-temperature cycle (HTC) due to environmental and public safety issues. However, the use of R404A is limited due to its high GWP, and therefore research on alternative refrigerants is necessary. Nevertheless, there is no detailed study in the literature that compares and analyzes the three refrigerants for practical design by applying R744 for LTC and R404A, R448A, and R449A for HTC in CRS. Therefore, this study aims to provide data for the practical detailed design of an alternative system to R744/R404A CRS. Under standard conditions, we analyzed how the exergy destruction rate (EDR) and exergy efficiency (EE) of the system and the EDR of each component change when the important factors affecting CRS (degree of superheating (DSH), degree of subcooling (DSC), and internal heat exchanger (IHX) efficiency of HTC, DSH of LTC, condensation temperature (CT), evaporation temperature (ET), cascade evaporation temperature (CET), and temperature difference of CHX) are varied over a wide range. The main conclusions are as follows. (1) Under the given constant conditions, the smallest change in system EDR based on R448A is DSH of HTC (decreased by 0.07–0.1 kW), followed by IHX of HTC (decreased by 0.12–0.3 kW), DSH of LTC (increased by 0.19–0.25 kW), DSC of HTC (decreased by 0.59–0.69 kW), temperature difference of CHX (increased by 1.57–1.83 kW), CET (decreased and then increased by 0.67–4.43 kW), CT (increased by 1.49–3.9 kW), ET (decreased by 2.39–4.61 kW). (2) The highest change rate of system EE based on R448A is CET (increased and then decreased by 1.38–8.28%), followed by temperature difference of CHX (decreased by 2.96–3.16%), ET (increased and then decreased by 0.63–2.75%), DSC of HTC (increased by 1.26–1.34%), CT (increased and then decreased by 0.24–1.12%), IHX of HTC (increased by 0.11–1.02%), DSH of LTC (decreased by 0.35–0.49%), and DSH of HTC (increased by 0.14–0.19%).

Thermal buckling of organic nanobeams resting on viscous elastic foundation

This study presents an analytical solution for organic solar nanobeams by including the new high-order shear deformation theory and considering the impact of size effects using non-local elasticity theory. The beam is supported by a viscoelastic base and experiences thermal loads that are distributed in accordance with uniform and nonlinear principles. The calculation formulae are derived from the consideration of significant deformations in the beam, resulting in increased complexity of the calculations. The equilibrium equation is derived from the fundamental principle of maximum work potential. The explicit equation for the critical thermal load is derived, providing a convenient means for the calculation and analysis of the impacts of relevant factors. The critical thermal buckling load encompasses both real and imaginary components, with the real component corresponding to the thermal load responsible for inducing beam instability, and the imaginary component associated with the dissipation of this load. This consideration is particularly pertinent when accounting for the foundation resistance. This finding enhances the level of interest in the research outcomes. This study also examined the impact of certain characteristic factors of the foundation and organic beams on the thermal buckling behavior of the beam, establishing a scientific basis for practical design applications.

Mass and Heat Exchange in Rotating Compressor Cavities With Variable Cob Separation

Abstract Next generation aeroengines will operate at ever-increasing pressure ratios with smaller cores, where the control of blade-tip clearances across the flight cycle is an emerging design challenge. Such clearances are affected by the thermal expansion of the compressor disks that hold the blades, where acute thermal stresses govern operating life. The cavities formed by corotating disks feature a heated shroud at high radius and cooler cobs at low radius. A three-dimensional, unsteady and unstable flow structure is induced by destabilizing buoyancy forces. The radial distribution of disk temperature is driven by a conjugate heat transfer at Grashof numbers of order 1013. Such flows are further influenced by the heat and mass exchange with an axial throughflow of cooling air at low radius, where the interaction depends on the Rossby number and separation of the disk cobs. This paper is the first to study the effect of cob separation ratio on mass and heat exchange for compressor cavities. A model is developed to predict the cavity-throughflow interaction, and disk and fluid-core temperatures. The judicious use of a physics-based methodology provides reliable, reduced-order solutions to the complex conjugate problem, thereby making it appropriate for practical engine thermo-mechanical design. The model is validated by detailed experimental measurements using the Bath Compressor Cavity Rig, where variable disk cob spacings were investigated over a range of engine-representative conditions. The unsteady pressure measurements collected in the frame of reference of the rotating disks reveal new insight into the fundamentally aperiodic nature of the flow structure. This new understanding of heat transfer informs an expedient reduced-order model and enables more efficient design of future high pressure-ratio aeroengines.

Development of Soft Computing-based Predictive Tools for Estimating the Young Modulus of Weak Rocks

The deformation characteristics of rocks are of vital importance in addressing most geomechanical issues as they are one of the most critical input parameters in rock engineering analyses. For this reason, robust forecasting models are required when analysing the stability of tunnels, slopes, mine galleries, and other underground excavations. In this research, novel predictive models are proposed to estimate the tangential Young modulus (Eti) of weak rocks. To achieve this, an extensive literature review is performed to obtain a comprehensive database including critical physico-mechanical properties of various weak rocks. Thanks to the advantages of soft computing methods such as genetic algorithm (GA), adaptive neuro-fuzzy inference system (ANFIS), artificial neural networks (ANN) and multivariate adaptive regression splines (MARS), novel predictive models are established. The effectiveness of the developed predictive models is investigated using various statistical measures and it is concluded that empirical models utilizing ANN and ANFIS methodologies are the most effective tools for estimating the Eti of weak rocks. In addition, a practical design chart is also developed for assessing the Eti of weak rocks.

Oil and gas pipeline robot localization techniques: A review

Globally, pipelines are the primary means for transporting resources. To ensure the safety and reliability of pipeline transportation, it is essential to regularly use pipeline robots for inspections. Localization techniques for these robots are critical as they determine the real-time position and status of the robot inside the pipeline, playing an extremely important role in pipeline operations. Understanding the latest research trends in pipeline robot localization techniques can help advance the overall development of the pipeline field and reduce the potential for research duplication. This paper reviews the global developments in pipeline robot localization techniques over the past decade, categorizing them into three major methods: in-pipe localization, out-of-pipe localization and multi-source information fusion localization. It analyzes the fundamentals, strengths and weaknesses of eight major localization techniques for pipeline robots, identifies the current limitations and solutions in these technologies, discusses the field test analysis and practical design factors, and anticipates the open research challenges and future prospects of this field.

Developing a data-enabled nudge intervention for childhood antibiotics in primary care: a qualitative study.

Preschool children (aged≤5 years old) have the highest antibiotic prescribing rate in general practice, mostly for self-limiting acute respiratory tract infections (RTIs). Research from over 250 000 UK children suggests that a child's antibiotic history for RTI may be a good predictor for re-consulting a health professional for the same illness episode and increase clinical workload. To develop a data-enabled nudge intervention to optimise antibiotic prescribing for acute RTI based on a child's antibiotic history in general practice DESIGN & SETTING: Two phase qualitative study with parents/carers of preschool children and primary care clinicians METHOD: In phase 1, through an initial focus group with eight parents/carers and 'think aloud' interviews with 11 clinicians, we co-designed the intervention (computer screen prompt and personalised consultation leaflet). In phase 2, 13 clinicians used the intervention, integrated into the GP computer software, and share their feedback through 'think aloud' interviews. Interviews were audio-recorded, transcribed, and analysed thematically. We co-created a data-driven intervention that automatically integrates a child's antibiotic history for acute RTI and personalised leaflet into the electronic medical records. We found that parents and clinicians found this intervention, in principle, acceptable and feasible to use in primary care consultations. Delivering such interventions, integrated into practice workflow, could be efficiently scaled up to promote effective antimicrobial stewardship and reduce unnecessary antibiotic use in primary care. Further research will test this intervention in a future trial.

ERGONOMICS IN AYURVEDA- A CRITICAL REVIEW

Ayurveda is referred to as a "complete health science" because it strongly emphasises maintaining good health and preventing illness in addition to treating current conditions. The preservation of health, or Swastasya swastya Rakshana, is the primary goal of Ayurveda. Ergonomics is the safe and practical design of objects for human use in a way that minimizes hazards. Although ergonomics is a relatively new field in the modern world, Ayurveda has discussed it in Vishamasana and emphasises the characteristics of furniture used, and various health conditions brought on by incorrect posture and seated patterns. Thus, this study is an initial attempt to clarify the risks associated with postural ergonomics, their impact on health, and how Ayurveda might help control them.

Numerical investigation of punch-through mitigation in stiff-over-soft clays using skirted spudcan

Punch-through failure, a critical issue in offshore engineering, occurs when a foundation penetrates through strong-over-weak soil layers. This paper presents a study on the effectiveness of skirt addition to spudcans in addressing punch-through failure through numerical simulations using the axisymmetric Particle Finite Element Method (PFEM). The validity of the PFEM is verified by simulating a spudcan penetration centrifuge test. Subsequently, PFEM simulations are conducted to analyse the penetration behaviour of both generic and skirted spudcans in stiff-over-soft clays. Results from the simulations reveal the bearing responses of spudcans as they penetrate into clays, elucidate associated failure mechanisms, and underscore the significant of considering the spudcan-driving process in estimating punch-through failure mitigation. Findings indicate that, the skirt effectively mitigates punch-through failure by increasing plug height in the lower soft layer, thus reducing the rate of bearing capacity reduction. Furthermore, critical factors such as skirt length, upper layer thickness, lower layer strength gradient, and the strength ratio between lower and upper layers are assessed through comprehensive parametric studies to evaluate the efficacy of skirted spudcans in mitigating punch-through failure, with eventual goal to provide essential guidance for practical design considerations in offshore foundation systems.

Why Students Cannot Easily Integrate Component Skills: An Investigation of the Composition Effect in Programming

Programming skills are increasingly important to the current digital economy, yet these skills have long been regarded as challenging to acquire. A central challenge in learning programming skills involves the simultaneous use of multiple component skills. This article investigates why students struggle with integrating component skills—a challenge called the composition effect. This effect was first studied in learning mathematics, and findings in that area have led to more effective instruction in mathematics. In the current work, we systematically investigate the nature of the composition effect in novice code tracing through a classroom study in a university-level introductory programming course. We designed a set of problems that require integrating component skills, along with matched problems that use the same component skills separately. The composition effect was defined as the performance difference between these two types of problems. We further singled out errors that were committed in integration problems but not in the matched problems and conducted qualitative coding to categorize these errors. Statistical analyses produced three main findings. First, we found a robust composition effect across problems and students, mostly with small or medium effect sizes. Second, we identified two potential sources of errors for this composition effect: conceptual misunderstandings of how to integrate component skills and process errors resulting from slips related to cognitive load challenges. In particular, additional conceptual knowledge often appears to be needed when component skills are integrated in specific ways. Moreover, the primary source appears to be misunderstandings of how skills integrate. Composition effects may not generalize across topics, which suggests that domain topics may serve as the conceptual basis for the composition effect. Third, we found stable individual differences in susceptibility to the composition effect in both overall and finer-grained levels. Our findings provide implications for building theories that may generally explain challenges in learning complex skills and may result in practical designs for skill integration enhanced curricula and instruction to foster student mastery in computing education.

Limit hypersurface state-of-the-art damage assessment approach for a galloping energy harvester, accounting for memory effects

Energy harvesters (i.e., EH) constituting nowadays an essential part of renewable energy engineering; hence, apart from numerical modeling, experimental studies are necessary, constituting reliable input for durable design and structural safety assessments. In the current study, galloping EH’s performance was analyzed, utilizing extensive laboratory wind-tunnel tests, conducted under realistic windspeed conditions. The novel structural multivariate risks assessment methodology, presented in the current study is particularly feasible for multi-dimensional nonlinear EH dynamic systems, that have been either directly Monte Carlo (i.e., MC) numerically simulated or physically measured over a representative temporal lapse, providing piecewise ergodic time series. As shown in this analysis, the suggested multivariate methodology accurately enables accurate predictions of the EH dynamic system’s failure/hazard or damage risks, based on laboratory-measured EH system’s dynamics. Furthermore, nonlinear inter-correlations between various systems’ critical components are not always easily handled by classic risk assessment techniques, when dealing with a high-dimensional system’s raw time series. The primary goal of this study was validation and benchmarking the novel risk assessment methodology, which can extract pertinent information, contained within the EH system’s dynamics, based on lab-recorded time histories. In conclusion, the novel hypersurface methodology presented in this study is generic, providing additional capability to accurately, yet efficiently predict damage/failure risks for a variety of nonlinear EH multidimensional systems. Relatively narrow confidence bands have been reported for the forecasted damage and failure levels, indicating both the robustness of the experimental setup, as well as practical design virtues of the advocated Gaidai hypersurface risks assessment methodology. Note that the presented methodology being mathematically exact does not rely on pre-assumptions and yet it is of general purpose.

Effect of thermal resistance and infrared transparency on radiative cooling efficiency

Radiative cooling materials that reflect sunlight and emit infrared light to the cold sky can lower the surface temperatures of building envelopes, reducing cooling energy usage by buildings. Over the last decade, various structures, such as thin films/coatings and thick infrared-transparent/opaque foams, have been developed for these applications. Understanding the influence of thermal resistance and infrared transparency of radiative cooling materials, along with the thermal resistance of the substrate to which they are applied, is vital for optimizing their energy savings potential. Herein, we employed a heat transfer model that integrates conduction, convection, and radiation to investigate the energy savings of radiative cooling materials with varying roof and material thermal resistances. Our results suggest that radiative cooling materials are most effective on roofs with low thermal resistance, like metal roofs, and increasing roof thermal resistance reduces their energy-saving effect. Thermally insulating infrared-transparent radiative cooling foams can significantly boost energy savings by minimizing convection and radiation losses. Conversely, compared to thin film, thermally insulating infrared-opaque foams can enhance or reduce energy savings depending on whether their surface temperatures are above or below room temperature. Throughout summer, infrared-transparent foam consistently achieves the highest energy savings, whereas infrared-opaque foam shows the least. This work provides guidance for the practical application and structural design of radiative cooling materials.