Specific Requirements Research Articles

Adiabatic Compressed Air Energy Storage system performance with application-oriented designed axial-flow compressor

Medium and long-duration energy storage systems are expected to play a critical role in the transition towards electrical grids powered by renewable energy sources. ACAES is a promising solution, capable of handling power and energy ratings over hundreds of MW and MWh, respectively. One challenge with ACAES is achieving the required highly efficient operation in the compressor over the range of conditions encountered in the system as the pressure in the air store changes. In this paper, an application-oriented axial-flow compressor is designed, aiming towards efficient operation throughout the operation range, whilst also associating the performance prediction to a practical compressor geometry. A two-step design methodology based on inviscid, axisymmetric flow conditions has been implemented, leading to the flowtrack, blade-row geometries and the compressor performance map. The compressor model is integrated into an ACAES model, including two compression spools, two expansion stages with preheat, a constant volume high pressure storage operating between 5.5 and 7.7 MPa and two separate Thermal Energy Storage units. While the existing ACAES literature either ignores the transient off-design operation or uses generic numerical correlations (which are not associated to a particular geometry), the key novelty of this paper is the application of a detailed design method for turbomachinery to ACAES. The results indicate that the designed compressor requires 33 stages over the two spools, and is able to operate efficiently over the storage pressure range, showing that if the application-oriented design procedure is applied to the compressor, it does not stop ACAES reaching 70% round-trip efficiency, outputting 35MW for approximately 15 h. Importantly, the specific ACAES requirement of conserving heat at higher temperatures has been fulfilled by decreasing the number of intercoolers. Finally, it is recommended that a similar level of scrutiny is applied to the other components (i.e. expanders, heat exchangers and TES units), keeping in mind the unique set of operational requirements of ACAES. This work is an important step towards removing the common misconception that off-the-shelf components can be easily be used in typical ACAES designs.

Investigations for the development of smart trousers for paraplegic wheelchair users. Part 1 – Design recommendations for smart trousers to improve the thermal comfort of the legs of paraplegics

In this article, a study was conducted on the design of smart trousers to improve the thermal comfort of the legs of paraplegics. It was based on the survey of paraplegics about the thermoregulation of their legs in cold environments, the warming of the legs during and after outdoor activities, the type of clothing for outdoor activities and the need for smart heating clothing for the lower extremities. The skin surface temperatures on the legs of fully mobile people and paraplegics were measured in a neutral state to find out to which temperature the microclimate inside the trousers can be warmed and the smart trousers can be used safely. The survey of paraplegics was conducted nationwide and included sixty-one adult male and female subjects. Skin surface temperatures were measured at eight measurement points on the right and left leg and performed on eighteen participants. After evaluating all the results of this study, recommendations for the design of smart heating trousers were proposed. The results of this part of the research can provide designers with important information about the specific requirements for smart heating trousers and enable them to design and develop products that meet real needs and are safe for paraplegic wheelchair users. Furthermore, this work aims to raise awareness of the needs of wheelchair users that enable them to integrate into the social environment on an equal footing.

Requirement Engineering Methods for Virtual Reality Software Product Development - A Mapping Study

Software practitioners use various methods in Requirements Engineering (RE) to elicit, analyze and specify the requirements of a enterprise products. The methods impact the final product characteristics and influence product delivery. Ad-hoc usage of the methods by software practitioners can lead to inconsistency and ambiguity in the product. With the notable rise in enterprise products, games, etc. across various domains, Virtual Reality (VR) has become an essential technology for the future. The methods adopted for requirement engineering for developing VR products requires a detailed study. This paper presents a mapping study on requirement engineering methods prescribed and used for developing VR applications including requirements elicitation, requirements analysis, and requirements specification. Our study provides insights into the use of such methods in the VR community and suggests using specific requirement engineering methods in various fields of interest. We also discuss future directions in requirement engineering for VR products.

Analyses of genetic diversity and population structure of endemic and endangered species Sideritis gulendamii (Lamiaceae) and implications for its conservation

AbstractSideritis gulendamii is a perennial, endangered plant endemic to Türkiye with narrow and fragmented populations encountered in gypsum and marl soil. This species is known as mountain tea and also has a significant potential for use for medicinal and aromatic purposes. Assessing the genetic diversity of this species holds significant importance in its conservation efforts due to its specific habitat requirements, small and isolated populations, and recent threats imposed on these populations. This study determined genetic diversity at the species and population levels using the inter simple sequence repeat (ISSR) fingerprinting method on 157 individuals from seven natural populations. We observed that the species-level genetic diversity is relatively high (H = 0.189, I = 0.298), but the population-level is low (H = 0.150, I = 0.231). The greater total genetic variation within populations was determined as 76% using the analysis of molecular variance (AMOVA). The obtained value of genetic differentiation (Gst = 0.208, Φpt = 0.24) among populations is considered to be at a moderate level, whereas gene flow appears to be high (Nm = 1.899). Supportive results were reached using the STRUCTURE cluster analysis, Unweighted Pair Group Method with Arithmetic Averages (UPGMA) dendrogram, and Principal Coordinate Analysis (PCoA). The positive correlation between the matrices of genetic distance and geographic distance is determined to be statistically significant, with values of r = 0.566 and p < 0.001 in the Mantel test. Some strategies were developed using the genetic variability pattern of this endangered species for its conservation.

ANTICORROSION PROTECTION OF PIPELINES AND KEY TRENDS IN THIS FIELD

This study examines the significance of protective coatings for pipelines in hydropower and thermal power plants. It covers the role of primers in establishing strong bonds and explores the use of various coatings, including bituminous, epoxy, vinyl, and chlorinated rubber coatings. Key findings include the impact of environmental factors on coating durability, the adaptability of epoxy resins, and the efficacy of vinyl coatings in marine atmospheres. The study provides valuable insights for selecting appropriate coatings based on specific conditions and requirements. Keywords: Protective coatings, pipelines, hydropower, thermal power plants, epoxy coatings, vinyl coatings

Comparative analysis of digital holographic microscopy and digital lensless holographic microscopy for quantitative phase imaging

This study provides a detailed comparison of two widely used quantitative phase imaging (QPI) techniques: single-shot off-axis digital holographic microscopy (DHM) and digital lensless holographic microscopy (DLHM). The primary aim is to evaluate and contrast critical aspects of their imaging performance, including spatial phase sensitivity, phase measurement accuracy, and spatial lateral resolution. Employing typical configurations for both DHM and DLHM, the study utilizes a customized phase test target featuring linear phase changes introduced by a specially designed linear density attenuation filter. Ground truth data from an atomic force microscope is incorporated to validate the experimental findings. The comparative analysis reveals that DHM and DLHM exhibit nearly identical spatial phase sensitivity, with DHM demonstrating a minimal 3.2% measurement error compared to DLHM's 4% in height measurement accuracy. Notably, DHM achieves a finer spatial lateral resolution down to 3.1 µm, surpassing DLHM's 5.52 µm. While DHM outperforms DLHM in precision and resolution, the latter offers advantages in terms of portability and cost-effectiveness. These findings provide valuable insights for researchers and practitioners, aiding in the informed selection of QPI methods based on specific application requirements.

Advancements and Challenges in IoT Simulators: A Comprehensive Review.

The Internet of Things (IoT) has emerged as an important concept, bridging the physical and digital worlds through interconnected devices. Although the idea of interconnected devices predates the term "Internet of Things", which was coined in 1999 by Kevin Ashton, the vision of a seamlessly integrated world of devices has been accelerated by advancements in wireless technologies, cost-effective computing, and the ubiquity of mobile devices. This study aims to provide an in-depth review of existing and emerging IoT simulators focusing on their capabilities and real-world applications, and discuss the current challenges and future trends in the IoT simulation area. Despite substantial research in the IoT simulation domain, many studies have a narrow focus, leaving a gap in comprehensive reviews that consider broader IoT development metrics, such as device mobility, energy models, Software-Defined Networking (SDN), and scalability. Notably, there is a lack of literature examining IoT simulators' capabilities in supporting renewable energy sources and their integration with Vehicular Ad-hoc Network (VANET) simulations. Our review seeks to address this gap, evaluating the ability of IoT simulators to simulate complex, large-scale IoT scenarios and meet specific developmental requirements, as well as examining the current challenges and future trends in the field of IoT simulation. Our systematic analysis has identified several significant gaps in the current literature. A primary concern is the lack of a generic simulator capable of effectively simulating various scenarios across different domains within the IoT environment. As a result, a comprehensive and versatile simulator is required to simulate the diverse scenarios occurring in IoT applications. Additionally, there is a notable gap in simulators that address specific security concerns, particularly battery depletion attacks, which are increasingly relevant in IoT systems. Furthermore, there is a need for further investigation and study regarding the integration of IoT simulators with traffic simulation for VANET environments. In addition, it is noteworthy that renewable energy sources are underrepresented in IoT simulations, despite an increasing global emphasis on environmental sustainability. As a result of these identified gaps, it is imperative to develop more advanced and adaptable IoT simulation tools that are designed to meet the multifaceted challenges and opportunities of the IoT domain.

Self-Assembly of Silicon Nanotubes Driven by a Biphasic Transition from the Natural Mineral Montmorillonite in Molten Salt Electrolysis.

Silicon nanotubes (SNTs) have been considered as promising anode materials for lithium-ion batteries (LIBs). However, the reported strategies for preparing SNTs generally have special requirements for either expensive templates or complex catalysts. It is necessary to explore a cost-effective and efficient approach for the preparation of high-performance SNTs. In this work, a biphasic transformation strategy involving "solid-state reduction" and "dissolution-deposition" in molten salts is developed to prepare SNTs using montmorillonite as a precursor. The rod-like intermediate of silicon-aluminum-calcium is initially reduced in solid state, which then triggers the continuous dissolution and deposition of calcium silicate in the inner space of the intermediate to form a hollow structure during the subsequent reduction process. The transition from solid to liquid is crucial for improving the kinetics of deoxygenation and induces the self-assembly of SNTs during electrolysis. When the obtained SNTs is used as anode materials for LIBs, they exhibit a high capacity of 2791mAhg-1 at 0.2Ag-1, excellent rate capability of 1427mAhg-1 at 2Ag-1, and stable cycling performance with a capacity of 2045mAhg-1 after 200 cycles at 0.5Ag-1. This work provides a self-assembling, controllable, and cost-effective approach for fabricating SNTs.

Enzyme Kinetics Analysis: An online tool for analyzing enzyme initial rate data and teaching enzyme kinetics.

Enzymes are nature's catalysts, mediating chemical processes in living systems. The study of enzyme function and mechanism includes defining the maximum catalytic rate and affinity for substrate/s (among other factors), referred to as enzyme kinetics. Enzyme kinetics is a staple of biochemistry curricula and other disciplines, from molecular and cellular biology to pharmacology. However, because enzyme kinetics involves concepts rarely employed in other areas of biology, it can be challenging for students and researchers. Traditional graphical analysis was replaced by computational analysis, requiring another skill not core to many life sciences curricula. Computational analysis can be time-consuming and difficult in free software (e.g., R) or require costly software (e.g., GraphPad Prism). We present Enzyme Kinetics Analysis (EKA), a web-tool to augment teaching and learning and streamline EKA. EKA is an interactive and free tool for analyzing enzyme kinetic data and improving student learning through simulation, built using R and RStudio's ShinyApps. EKA provides kinetic models (Michaelis-Menten, Hill, simple reversible inhibition models, ternary-complex, and ping-pong) for users to fit experimental data, providing graphical results and statistics. Additionally, EKA enables users to input parameters and create data and graphs, to visualize changes to parameters (e.g., or number of measurements). This function is designed for students learning kinetics but also for researchers to design experiments. EKA (enzyme-kinetics.shinyapps.io/enzkinet_webpage/) provides a simple, interactive interface for teachers, students, and researchers to explore enzyme kinetics. It gives researchers the ability to design experiments and analyze data without specific software requirements.

Polyurethane‐polyisocyanurate (PUR‐PIR) foams based on castor oil modified with tire rubber waste: Statistical‐based analysis

AbstractThis study explores the utilization of bio‐polyol, derived from castor oil modified with waste tire rubber (WTR), in the synthesis of rigid polyurethane–polyisocyanurate foams. Two WTR concentrations (10% and 20%) were incorporated, investigating their impact on foaming, cellular structure, and various foam properties. Response surface methodology assessed the influence of polyol/isocyanate ratios and WTR content on thermal, physical, and mechanical responses. Results indicated significant effects of polyol/isocyanate ratios on thermal and density properties, while WTR content exhibited an opposite trend. Mechanical behavior showed a distinct pattern compared to thermal and physical aspects, suggesting no conclusive correlation between compressive stress variability and polyol/isocyanate ratio or WTR content. Consequently, the application of these foams depends on specific property requirements, highlighting the potential of sustainable bio‐polyols and recycled WTR in replacing conventional petroleum‐based counterparts in polyurethane foam production.Highlights Development of rigid polyurethane–polyisocyanurate foams based on castor oil. Polyurethane–polyisocyanurate foams modified with waste tire rubber. Increased isocyanate index impacts thermal stability and density. Response surface methodology showed variability in compressive stress behavior and the polyol/isocyanate ratio. For application of polyurethane–polyisocyanurate foams will depend on the desired properties.

Numerical simulation of proton backscattering spectra in GEANT4 toolkit

Rutherford backscattering spectroscopy (RBS) is a widely used technique for the atomic-scale analysis of sample composition, lattice displacement and impurity profiling. RBS is based on the elastic scattering of incident charged particles by target nuclei and the subsequent detection of scattered particles. The interpretation of RBS spectra, however, poses challenges due to overlapping peaks, corresponding to scattering from different atomic species, and uncertainties from energy loss, scattering geometry and detector response. To address this, an open source simulation model based on the versatile GEANT4 simulation toolkit has been developed. The flexibility of the open source enables users to tailor the model to its specific requirements, such as the use of specific particle stopping powers, cross-sections, and physics processes. This work presents the results of the comparison between the experimental and simulated backscattering spectra in crystalline silicon, silicon carbide and silicon dioxide samples by 1–2.5 MeV energy protons, obtained in random orientation conditions. The results demonstrate the capability of the model to accurately simulate backscattering spectra in both amorphous materials and single crystals. The overall agreement between the simulated and experimental results is highly promising for future development and use in the interpretation and simulation of RBS spectra.

Detecting Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder Using Multimodal Time-Frequency Analysis with Machine Learning Using the Electroretinogram from Two Flash Strengths.

Autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) are conditions that similarly alter cognitive functioning ability and challenge the social interaction, attention, and communication skills of affected individuals. Yet these are distinct neurological conditions that can exhibit diverse characteristics which require different management strategies. It is desirable to develop tools to assist with early distinction so that appropriate early interventions and support may be tailored to an individual's specific requirements. The current diagnostic procedures for ASD and ADHD require a multidisciplinary approach and can be lengthy. This study investigated the potential of electroretinogram (ERG), an eye test measuring retinal responses to light, for rapid screening of ASD and ADHD. Previous studies identified differences in ERG amplitude between ASD and ADHD, but this study explored time-frequency analysis (TFS) to capture dynamic changes in the signal. ERG data from 286 subjects (146 control, 94 ASD, 46 ADHD) was analyzed using two TFS techniques. Key features were selected, and machine learning models were trained to classify individuals based on their ERG response. The best model achieved 70% overall accuracy in distinguishing control, ASD, and ADHD groups. The ERG to the stronger flash strength provided better separation and the high frequency dynamics (80-300Hz) were more informative features than lower frequency components. To further improve classification a greater number of different flash strengths may be required along with a discrimination comparison to participants who meet both ASD and ADHD classifications and carry both diagnoses.

Multi-objective Feature Attribution Explanation For Explainable Machine Learning

The feature attribution-based explanation (FAE) methods, which indicate how much each input feature contributes to the model’s output for a given data point, are one of the most popular categories of explainable machine learning techniques. Although various metrics have been proposed to evaluate the explanation quality, no single metric could capture different aspects of the explanations. Different conclusions might be drawn using different metrics. Moreover, during the processes of generating explanations, existing FAE methods either do not consider any evaluation metric or only consider the faithfulness of the explanation, failing to consider multiple metrics simultaneously. To address this issue, we formulate the problem of creating FAE explainable models as a multi-objective learning problem that considers multiple explanation quality metrics simultaneously. We first reveal conflicts between various explanation quality metrics, including faithfulness, sensitivity, and complexity. Then, we define the considered multi-objective explanation problem and propose a multi-objective feature attribution explanation (MOFAE) framework to address this newly defined problem. Subsequently, we instantiate the framework by simultaneously considering the explanation’s faithfulness, sensitivity, and complexity. Experimental results comparing with six state-of-the-art FAE methods on eight datasets demonstrate that our method can optimize multiple conflicting metrics simultaneously and can provide explanations with higher faithfulness, lower sensitivity, and lower complexity than the compared methods. Moreover, the results have shown that our method has better diversity, i.e., it provides various explanations that achieve different tradeoffs between multiple conflicting explanation quality metrics. Therefore, it can provide tailored explanations to different stakeholders based on their specific requirements.

Quantum Computing in The Cloud - A Systematic Literature Review

Quantum computing was proposed to simulate processes that surpass the capabilities of its counterpart, classical computing. Utilizing the principles of quantum mechanics, it improves the computing power of quantum computing. Top developers namely IBM, Rigetti, D-Wave, Qutech and Google have invested greatly in the technology. Nowadays, users can access the quantum computing system publicly over the network in a cloud environment, this system architecture is known as cloud-based quantum computing. However, different developers deliver different architecture and functionality of the system on their platforms. This has indirectly spawned a question of which cloud-based quantum computing platform is a better option based on certain specific requirements by an individual or group. The main objective of this study is to provide a proposed framework using the existing cloud-based service of quantum computing based on previous studies for users with their specific demands.

Research on the technology of intelligent vehicle sensor positioning system in autonomous driving

In this comprehensive exploration of positioning and navigation technologies, we have delved into the intricacies of GPS (Global Positioning System), IMU (Inertial Measurement Unit), and VPS (Visual Positioning Systems) systems, shedding light on their unique attributes and applications. GPS, a global satellite-based system, offers precise positioning on a worldwide scale, although it encounters challenges in complex urban and environmental conditions. Its role in navigation and tracking is undeniable. IMU, characterized by accelerometers and gyroscopes, excels in delivering high-precision positioning over short durations, making it invaluable for applications in aviation, robotics, and virtual reality. However, it is susceptible to drift over time. Visual Positioning Systems (VPS), harnessing computer vision and visual sensors, provide remarkable sub-meter to centimeter-level accuracy when conditions are optimal. Their significance is particularly pronounced in indoor navigation, augmented reality, and robotics, although they may face challenges in less favorable environments. These technologies are not isolated but can synergize to enhance accuracy and reliability. GPS and IMU collaborate to compensate for signal disruptions, while GPS and VPS join forces to tackle urban complexities. IMU and VPS integration offer precise indoor navigation and augmented reality experiences, delivering impeccable positioning and orientation data. Ultimately, the choice of technology hinges on specific application requirements, encompassing accuracy, environmental considerations, cost factors, and the need for complementary systems. As these technologies advance, they hold the promise of revolutionizing navigation across various domains, from autonomous vehicles to immersive augmented reality environments.

Mechanistic prediction of folding angles in 4D printed shape memory polymers under varied loading conditions

Four-dimensional printing technology empowers 3D-printed structures to change shapes upon external stimulation. However, most studies did not consider recovery under loaded conditions. This paper introduces a mechanistic prediction model for forecasting recovery angles in 4D printing utilizing shape memory polymer under various loads. The model integrates Neo–Hookean model to describe the non-linear stress–strain relationship with experimentally determined force density data to characterize polymer restoration properties under various loads. Validation was demonstrated by the recovery experiment of a 3D-printed polylactic acid-thermoplastic polyurethane composite structure loaded by means of a cord and pulley mechanism. The predictive outcomes exhibited reasonable agreement with experimental results, demonstrating a trend of more accurate forecasts as the applied load increased. The model can accommodate various active materials provided that the pertaining force density data is accessible. The predictive model supports the design, optimization and material selection for 4D-printed structures to meet specific performance requirements.

Views of German mental health professionals on the use of digital mental health interventions for eating disorders: a qualitative interview study

IntroductionDigital mental health interventions (DMHIs) are getting increasingly important for mental health care. In the case of eating disorders (EDs), DMHIs are still in early stages. Few studies so far investigated the views of mental health professionals for EDs on the integration of DMHIs in routine care.ObjectiveTo gain insights into the experiences, perspectives, and expectations of mental health professionals for EDs regarding DMHIs and to identify requirements for the future integration of DMHIs into routine care.MethodsSemi-structured qualitative telephone interviews with 24 German mental health professionals treating patients with EDs were conducted. A content analysis following a deductive-inductive approach asked for experiences, advantages and chances, disadvantages and boundaries, desired functions and properties, target groups, and general conditions and requirements for DMHIs for patients with EDs.ResultsOnly few professionals reported experiences with DMHIs besides video-based psychotherapy during the pandemic. From the therapists’ point of view, DMHIs have the potential to deliver low-threshold access for patients with EDs. Useful functionalities were seen in digital meal records, skills training, and psychoeducation. However, a stable therapeutic alliance was reported as an important prerequisite for the successful integration into care. Therapists expressed concerns in case of severe anorexia nervosa or suicidality. The participants felt to be informed inadequately on recent developments and on the evidence base of DMHIs.ConclusionsMental health professionals for EDs show positive attitudes towards DMHIs, however many barriers to the integration in routine care were observed. The highest potential was seen for the use of DMHIs in addition to outpatient care and in aftercare. Specific requirements for DMHIs are related to different areas of the healthcare spectrum and for the different symptom profiles in anorexia nervosa, bulimia nervosa and binge eating disorder. Targeted DMHIs are needed and appropriate especially for concepts of blended care.

A Segmented Hybrid Algorithm for Beam Shaping Combining Iterative and Simulated Annealing Approaches

In recent years, laser technology has made significant advancements, yet there are specific requirements for the energy concentration and uniformity of lasers in various fields, such as optical communication, laser processing, 3D printing, etc. Beam shaping technology enables the transformation of ordinary Gaussian-distributed laser beams into square or circular flat-top uniform beams. Currently, LCOS-based beam shaping algorithms do not adequately meet these requirements, and most of these algorithms do not simultaneously consider the impact of phase quantization and zero-padding, leading to a decrease in the practicality of phase holograms. To address these issues, this paper proposes a novel segmented beam shaping algorithm that combines iterative and simulated annealing approaches. This paper validated the reliability of the proposed algorithm through numerical simulations. Compared to other algorithms, the proposed algorithm can effectively reduce the root mean square error by an average of nearly 37% and decrease the uniformity error by almost 39% without a significant decrease in diffraction efficiency.

Effect of phthalate-free plasticizer addition on thermal, mechanical, and aging properties of nitrile rubber (NBR)

Nitrile rubber (NBR) is widely used in the automotive, aerospace, and chemical sectors due to its resistance to fuels, oil, thermal aging, and fatigue. A typical NBR compound is prepared with several components that are added to obtain a product to meet specific industry requirements. Plasticizer is one of the main components used in NBR compounds. Typically, phthalates like DBP (dibutyl phthalate) or DOP (dioctyl phthalate) are used for NBR plasticization. However, these phthalates-based plasticizers are associated with toxicity and often prohibited by international regulatory standards. In this way, in the pursuit of a non-toxic NBR compound, the use of phthalate-free plasticizer, namely dioctyl terephthalate (DOTP), has been proposed. In this work, NBR compounds with DOTP, DBP, or DOP were processed in an open mill and vulcanized in hydraulic press. The unvulcanized samples were tested in a moving die rheometer (MDR). Subsequently, thermal, mechanical, and aging analyses were conducted for the vulcanized samples. The lowest torque observed in the rheometric curve, the displacement value of glass transition temperature (Tg), the smaller Shore A hardness, and the high elongation at break indicate that the addition of DOTP results in higher plasticization. The better-aging properties indicate lower migration of the DOTP plasticizer. Thus, the use of DOTP as a plasticizer holds promise as a viable alternative to replace the phthalates-based plasticizer in NBR compounds.

Theoretical study of the synergic relationships between the design parameters in energy-saving building design

With the rapid development of the economy, people have increasingly higher requirements for the comfort of living spaces, and the result is the sharp increase in building energy consumption. Several design parameters influence living space comfort and building energy efficiency. Since the same design standard can include different design parameter combinations, synergic relationships may exist between these criteria for one case. Identifying these synergic relationships requires an inverse problem approach. This paper established a model by combining an improved genetic algorithm (IGA) and numerical calculation to determine the synergic design parameter relationships (e.g. the thermophysical building material properties and energy-saving factors). For Isum=0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{\ ext{I}}}_{{\ ext{sum}}}= {0} $$\\end{document}, the shading coefficient significantly influenced the linear function between the thermal conductivity and insulation thickness. In this case, the insulation thickness was exponentially related to the shading coefficient, while the thermal conductivity of the insulation material significantly impacted the synergic relationship. For ESR=65%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\ ext{ESR}}{=65}\\%$$\\end{document}, the insulation thickness was a segmented function of the shading coefficient. The results verified that the proposed model was efficient and reliable for identifying the synergic relationships between energy-saving parameters. In engineering applications, designers can select the optimal design parameter combination based on the relationship curve between the parameters in this paper according to the local market conditions and specific design requirements.