Model Building Research Articles

Gravitational waves from patterns of electroweak symmetry breaking: an effective perspective

Abstract The future space-borne gravitational wave (GW) detectors would provide a promising probe for the new physics beyond the standard model that admits the first-order phase transitions. The predictions for the GW background vary sensitively among different concrete particle physics models but also share a large degeneracy in the model buildings, which motivates an effective model description on the phase transition based on different patterns of the electroweak symmetry breaking (EWSB). 
In this paper, using the scalar $N$-plet model as a demonstration, we propose an effective classification for three different patterns of EWSB: (1) radiative symmetry breaking with classical scale invariance, (2) Higgs mechanism in generic scalar extension, and (3) higher dimensional operators.
We conclude that a strong first-order phase transition could be realized for (1) and (2) with a small quartic coupling and a small isospin of an additional $N$-plet field for the light scalar field model with and without the classical scale invariance, and (3) with a large mixing coupling between scalar fields and a large isospin of the $N$-plet field for the heavy scalar field model.

Centennial city: the City of Auckland model

Auckland Council is lucky to care for the surviving portion of a city model made for the New Zealand Centennial Exhibition in 1940. The model represents a section of Auckland central, covering the area from Albert Park to Nelson Street, and Waitematā Harbour to what is now Aotea Square. The model was displayed in Dominion Court, one of a dozen models which represented New Zealand's main cities and towns. Fletchersʹ architects Ronald Muston and Lewis Walker were responsible for the design of Dominion Court and the interpretive models and dioramas within. Muston had a special interest in the models and even travelled to Hollywood to learn about current trends in movie model design and advertising techniques. The ingenuity of the model makers included ships that ʺsailedʺ on the harbours, faux glow worms in the papier mâché Waitomo Caves, and a model of Aoraki/Mount Cook which was over 7.5m high. Thousands of wooden model buildings were made and hand‐painted by artists and technical college students and were based on aerial and street‐level photographs and specific colour palettes for each city. The model came to Auckland City Council after the exhibition closed and has been displayed inside and outside of council. It was recently conserved and is once again on public display.

E.163 1945 "Emergency Standard Specification for Dwellinghouse Construction" – a forgotten ancestor

The outbreak of World War II led to a focus on the most efficient use of materials within New Zealand. In 1944 the New Zealand Standards Council was requested to develop an "Emergency Standard Specification for Dwellinghouse Construction." Unlike NZSS 95 "New Zealand Standard Model Building By‐Law" which was limited to structural stability, public health and other council‐controlled issues, this new specification (NZSS E.163) could deal with materials and construction methods. Published in January 1945, only two copies appear extant – a partial copy in Wellington City Archives and a full copy in Auckland City Archives, although the Committee minutes are held in Archives New Zealand. As well as dealing with house construction (foundations, concrete work, carpentry, etc.) it deals with internal finishing (solid‐ and fibrous‐plaster, terrazzo, painting and paperhanging, etc.) and services (plumbing, drainage and electrical). E.163 was promoted as being of real benefit to occupants (owners or tenants) as they could be assured as to "essential considerations relating to materials, workmanship, and design" – placing quality, not speed at the heart of the then Government's actions. The paper compares the requirements of E.163 with the relevant parts of NZSS95, as well as exploring its relationship to NZS 3604 "Code of Practice for Light Timber Frame Buildings Not Requiring Specific Design" and modern model specifications.

Passive energy saving through thermal retrofitting of existing government office building envelopes - Case study for office-research building in Cairo, Egypt

Egypt used several million kilowatts per hour of electrical power in 2023. According to 2022 data, public utilities and agencies used 15% of Egypt's power. Most of Cairo’s government buildings built in the 50th and the 60th of the last century are composed of reinforced concrete structure skeletons, flat concrete roofs, and burnt brick exterior and interior walls. At that time, building energy efficiency was not a priority. Thus, uninsulated building envelopes were built. Nowadays, renovating buildings requires increasing energy efficiency. Previous research showed that lack of thermal insulation in roofs, walls, window solar heat gain, and window frames thermal bridging increases heating and cooling costs. The current research aims to enhance existing buildings energy saving by improving thermal insulation of its envelope components. The existing research building in Cairo was assessed for environmental factors to accomplish the research goal. The research used a three-dimensional (3D) terrestrial laser scanner to assess building facades for creating an environmental study model. The research used the building office top floor construction as a comparative environmental simulation using (Design builder® version6 software) as a base case for yearly energy consumption due to heating and cooling loads, followed by improving the thermal insulation of each building element individually in the base environmental model and comparing its effect on annual energy saving percentage, then applying all of the best annual energy saving building envelope element thermal insulation retrofitting techniques to the improved building model. The research results showed that improving roof thermal insulation saves 33.5% of the yearly top floor total electrical energy, 12% for exterior walls, 0.75% for window glazing, and 1.56 % by retrofitting window frames. Applying all of the best energy-saving thermal insulation retrofit techniques to the whole structure saved 31% of annual energy consumption.

Quantitative Structure Metabolism Relationship (QSMR): Comparative Molecular Field Analysis of CYP2D6 Substrates

Comparative molecular field analysis (CoMFA) was performed for CYP2D6 substrates. The CoMFA model was built from the data set of substrates undergoing different CYP2D6 mediated metabolic reactions. The developed model has proven to be predictive, with cross validated correlation coefficient (q2) of 0.531 and non-cross validated correlation coefficient (r2) of 0.969. CoMFA model was also validated with independent test set of substrates which are not involved in model building. The obtained model helps to understand the stereoelectronic structural features that are responsible for the CYP2D6 mediated metabolism. The information obtained from contour maps of CoMFA could be useful to design a therapeutic analogues with improved metabolic profile toward CYP2D6.

Malay Entry into Business through Scaling the Heights of Corporate Malaysia from mid-1980s – An Assessment

The aim of this paper is in identifying and analysing, from an economic perspective, the policies, programs and instruments the government used in promoting Malay entry into business, the form of this entry, including of rent-seeking, the extent to which Malays have scaled the heights of Corporate Malaysia as well as of the factors accounting for the success or failure of this agenda in the post-1985 period after the launch of the corporatisation and privatisation era. The discussion on the performance as well as of the key problems and issues encountered from the country’s use of the corporatisation or privatisation route to promote entry, is also combined with a similar discussion on its use of: (a) government linked investment companies (GLICs) in promoting Malay entry into business from an almost non-existent level in 1957, (b) the private finance initiative (PFI) or the build, lease, maintain and transfer (BLMT) model in promoting entry as an owner-manager in the era of corporatisation and institutional fund management, as well as (c) multiple instruments in aggressively promoting entry within an industry or sector with multiple goals.

Automation of Local Regression Model Building for Spectroscopic Data

ABSTRACTThe concept of using local calibration for spectroscopic analysis has been discussed since the late 1980s. Since that time, many papers have described modifications to different aspects of the local modeling methodology. In this paper, we briefly discuss some of the modifications and describe an approach for the unattended automation of local model development. Ways to reduce calculation time are discussed. Four example spectroscopic datasets using Raman, FT‐NIR, and dispersive NIR are analyzed, and the local model prediction performance is compared to standard PLS prediction performance. Using independent prediction sets, local modeling is shown to improve prediction performance by 17% to 55%.

Resource Optimization for Grid-Connected Smart Green Townhouses Using Deep Hybrid Machine Learning

This paper examines Connected Smart Green Townhouses (CSGTs) as a modern residential building model in Burnaby, British Columbia (BC). This model incorporates a wide range of sustainable materials and smart components such as recycled insulation, Photovoltaic (PV) solar panels, smart meters, and high-efficiency systems. The CSGTs operate in grid-connected mode to balance on-site renewables with grid resources to improve efficiency, cost-effectiveness, and sustainability. Real datasets are used to optimize resource consumption, including electricity, gas, and water. Renewable Energy Sources (RESs), such as PV systems, are integrated with smart grid technology. This creates an effective framework for managing energy consumption. The accuracy, efficiency, emissions, and cost are metrics used to evaluate CSGT performance. CSGTs with one to four bedrooms are investigated considering water systems and party walls. A deep Machine Learning (ML) model combining Long Short-Term Memory (LSTM) and a Convolutional Neural Network (CNN) is proposed to improve the performance. In particular, the Mean Absolute Percentage Error (MAPE) is below 5%, the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are within acceptable levels, and R2 is consistently above 0.85. The proposed model outperforms other models such as Linear Regression (LR), CNN, LSTM, Random Forest (RF), and Gradient Boosting (GB) for all bedroom configurations.

VaxOptiML: leveraging machine learning for accurate prediction of MHC-I and II epitopes for optimized cancer immunotherapy.

Cancer immunotherapy hinges on accurate epitope prediction for advancing vaccine development. VaxOptiML (available at https://vaxoptiml.streamlit.app/ ) is an integrated pipeline designed to enhance epitope prediction and prioritization. This study aims to develop and deploy a robust tool for accurate prediction and prioritization of highly immunogenic and optimized MHC-I and MHC-II T-cell epitopes for cancer vaccine development and immunotherapy. Utilizing a curated dataset of experimentally validated epitopes and employing sophisticated machine learning techniques, VaxOptiML features three models: epitope prediction from target sequences, personalized HLA typing, and prioritizationthe predicted epitopesbased on immunogenicity scores. Our rigorous data extraction, cleaning, and feature extraction processes, coupled with model building, yield exceptional accuracy, sensitivity, specificity, and F1 score, surpassing existing prediction methods. Comprehensive visual representations underscore VaxOptiML's robustness and efficacy in accelerating epitope discovery and vaccine design for cancer immunotherapy. Deployed via Streamlit for public use, VaxOptiML enhances accessibility and usability for researchers and clinicians, demonstrating significant potential in cancer immunotherapy.

Practical Repair Cost Assessment of Steel Office Buildings with Diverse Beam-to-Column Connection Types in Japan

This study investigates the seismic performance of beam-to-column connections in Japanese steel office buildings and evaluates their impact on repair costs as part of practical seismic loss assessment. Repair cost analysis is conducted following FEMA P-58 guidelines, utilizing a probabilistic seismic performance approach. Additionally, this study incorporates the damage and loss of non-structural components, accounting for uncertainties arising from various factors. By applying story loss functions and the Capacity Spectrum Method within the assessment framework of this study, the applicability of these methods in Japanese design practices is validated. A numerical model of a typical steel building is established based on prior experimental data, and a series of numerical analyses are conducted to quantitatively assess variations in building response distribution and fragility resulting from different beam-to-column connection types. Analysis results indicate that, within the range up to maximum strength, differences in connection performance contribute more to variations in fragility than to building response. Component fragility is particularly influenced by connection type and dimensions that affect inter-story drift ratios. This approach highlights the potential cost differences driven by structural specifications and emphasizes the importance of connection-specific fragility in accurately estimating seismic repair costs in practical design. Furthermore, improvements in structural performance have limited effects in reducing non-structural damage, underscoring the necessity of directly enhancing non-structural component performance.

Retrofit Analysis of Exterior Windows for Large Office Buildings in Different Climate Zones of China

In the energy-saving retrofit of existing buildings, investors are particularly concerned about the energy-saving performance of exterior windows and the payback period of additional costs. This study evaluates representative cities in four different climate zones in China to simulate the energy consumption of large office buildings after replacing different glass windows and conducting energy-saving and economic feasibility assessments. The research method includes the following steps: First, a baseline model of large office buildings in four cities was established using AutoBPS and OpenStudio. Then, the baseline and retrofit models of replacing glass windows were simulated using the EnergyPlus V9.3.0 to obtain multiple hourly energy consumption results. The commercial electricity and gas prices in the four cities were adjusted to calculate the total cost within 20 years after replacing different types of windows. Using the discounted payback period (DPP), net present value (NPV), and profitability index (PI) as evaluation indicators, a feasibility analysis was conducted in the four regions to evaluate the economic feasibility of replacing building windows. The simulation results show that considering economic feasibility and meeting energy-saving standards, it is more economical to choose windows with moderate U-value and SHGC value in the four regions than to choose windows with the smallest U-value and SHGC value, and that both energy savings and economic benefits are closely related to building age, with older buildings (especially those in Changsha and Shenzhen) showing greater benefits. Furthermore, the optimal window types in the four cities determined in this study can recover the investment cost within the window life, with Harbin (SC), Beijing (C), Changsha (HC), and Shenzhen (HW) with the payback period of 6.60, 15.66, 10.16, and 11.42 years, respectively. The research model established in this study provides a useful evaluation path for selecting windows for the energy-saving retrofit of large office buildings in cities in different climate zones and provides data support for the decision making of energy-saving retrofit investors.

Machine learning application to disaster damage repair cost modelling of residential buildings

Restoring residential buildings following earthquake damage requires a significant level of resources. Being able to predict these resource requirements in advance and accurately improves the effectiveness of disaster preparedness and subsequent recovery activities. This research explored how the latest ML algorithms could be used for antecedent earthquake loss modelling. A cost database for repairing residential buildings damaged by the Emilia Romagna earthquake in Italy was analysed using six state-of-the-art ML models to explore their ability to predict repair cost rates(cost per floor area) for a domestic building damaged by earthquakes. A Gradient Boost Regression model outperformed five other models in predicting earthquake damage repair cost rate. The performance of this model was significantly accurate and covers about 76% of the cases. A further SHAP analysis revealed that operational level, damage level and non-housing area of the buildings as top 3 important features when predicting the resultant damage repair cost rate. Overall this research advanced antecedent earthquake loss modelling approaches to increase the accuracy of estimates by incorporating more variables than the widely used damage level based simple methodology.

Student Learning Satisfaction and Academic Performance in Philippine Science High School Chemistry: A Prediction Model Building Study for Online Learning

Learning satisfaction is a key indicator of enriching student learning whether it is face-to-face or online mode of delivery. Learning has shifted more to remote online learning due to the Covid-19 pandemic. This study aimed to identify the determinants of Student Learning Satisfaction (SLS) in online learning and develop a model that represented the relationship of the determinants to SLS. The study explored both internal and external determinants hypothesised to influence SLS among 169 students in grades 9-12 taking Chemistry at the Philippine Science High School Cordillera Administrative Region Campus (PSHS CARC). The internal factors included gender, academic self-concept (ASC), academic motivation (AM), interest to learn (IL), and mental well-being (MW), while the external factors comprised teacher-related indicators (Teacher), assessment-related indicators (Assessment), learning guide-related indicators (LG) and Knowledge Hub-related indicators (KHub). The study utilised Structural Equation Modelling (SEM) with SmartPLS software to develop the SLS model and Academic Performance (AP) model. Both online learning platforms used by PSHS CARC, the LG, and KHub, were found to influence SLS. The external determinants (LG, KHub, Teacher, Assessment) dominated the SLS models while internal determinants (ASC, AM, IL, MW) dominated the AP model for the PSHS CARC Chemistry students. The external determinants influenced the student's learning satisfaction while the internal determinants influenced the AP of the Chemistry students. Assessment indirectly affected SLS. Another finding was that gender, LG, and ASC directly influenced AP. The results validate that online learning tools (LG and KHub) were crucial determinants in ensuring learning satisfaction and academic success in the online learning of PSHS CARC Chemistry students. The models (SLS and AP) can be utilised to evaluate and enhance the quality of online learning and the development of effective online learning strategies and policies especially regarding the use of online tools like LG and KHub.

Aeroelastic stability of a sandwich shell supported by annular ribs and a cylinder

In connection with the promising use of sandwich shells in the designs of modern technology and primarily in aircraft, the methodology of building models for studying the aeroelastic stability of sandwich shells reinforced with annular ribs and an internal hollow cylinder is considered. Equations are obtained and the main stages of solving the problem using the proposed set of methods are considered. Dependencies were constructed with the help of which the influence of the thickness of the reinforcing cylinder, the number of annular ribs and the length of the shell on the critical speed of flow was studied. The considered methodology makes it possible to construct an initial approximation model for studying the aeroelastic stability of reinforced sandwich shells.

Decades-old carbon reserves are widespread among tree species, constrained only by sapwood longevity.

Carbon reserves are distributed throughout plant cells allowing past photosynthesis to fuel current metabolism. In trees, comparing the radiocarbon (Δ14C) of reserves to the atmospheric bomb spike can trace reserve ages. We synthesized Δ14C observations of stem reserves in nine tree species, fitting a new process model of reserve building. We asked how the distribution, mixing, and turnover of reserves vary across trees and species. We also explored how stress (drought and aridity) and disturbance (fire and bark beetles) perturb reserves. Given sufficient sapwood, young (< 1 yr) and old (20-60+ yr) reserves were simultaneously present in single trees, including 'prebomb' reserves in two conifers. The process model suggested that most reserves are deeply mixed (30.2 ± 21.7 rings) and then respired (2.7 ± 3.5-yr turnover time). Disturbance strongly increased Δ14C mean ages of reserves (+15-35 yr), while drought and aridity effects on mixing and turnover were species-dependent. Fire recovery in Sequoia sempervirens also appears to involve previously unobserved outward mixing of old reserves. Deep mixing and rapid turnover indicate most photosynthate is rapidly metabolized. Yet ecological variation in reserve ages is enormous, perhaps driven by stress and disturbance. Across species, maximum reserve ages appear primarily constrained by sapwood longevity, and thus old reserves are probably widespread.

Comparison of Seismic and Structural Parameters of Settlements in the East Anatolian Fault Zone in Light of the 6 February Kahramanmaraş Earthquakes

On 6 February 2023, two very large destructive earthquakes occurred in the East Anatolian Fault Zone (EAFZ), one of Türkiye’s primary tectonic members. The fact that these earthquakes occurred on the same day and in the same region increased the extent of the destruction. Within the scope of this study, twenty different settlements affected by earthquakes and located directly on the EAFZ were taken into consideration. Significant destruction and structural failure at different levels were induced in reinforced concrete (RC) structures, the dominant urban building stock in these regions. To determine whether the earthquake hazard is adequately represented, the PGA values predicted in the last two earthquake hazard maps used in Türkiye for these settlements were compared with the measured PGAs from actual earthquakes. Subsequently, the damage to reinforced concrete structures in these settlements was evaluated within the scope of construction and earthquake engineering. In the final part of the study, static pushover analyses were performed on a selected example of a reinforced concrete building model, and target displacement values for different performance levels were determined separately for each earthquake. For the 20 different settlements considered, the displacements were also derived based on the values predicted in the last two earthquake hazard maps, and comparisons were made. While the target displacements were exceeded in some settlements, there was no exceedance in the other settlements. The realistic presentation of earthquake hazards will enable the mentioned displacements predicted for different performance levels of structures to be determined in a much more realistic manner. As a result, the performance grades predicted for the structures will be estimated more accurately.

Comparison of Multiple Regression and Model Averaging Model-Building Approach for Missing Data with Multiple Imputation

Model construction is of significant importance for the extraction of information from datasets and the prediction of responses based on predictor variables. The objective of this study is to compare the Multiple Regression (MR) and model averaging approaches in the context of missing data and to validate the effectiveness of the Multiple Imputation (MI) method used to address missing data issues. A comparison was performed between the results obtained from the multiple-imputed data and those derived from the Complete Case (CC) data, using a diabetes dataset from Hospital Besar Alor Setar. Prior to the application of MI and model building, k-fold cross-validation was employed to partition the dataset, resulting in 90% of the data lacking complete covariates for training and 10% of the data comprising complete covariates for testing. Subsequently, MI was applied to the 90% training dataset. Model M115, derived from the multiple-imputed data, was identified as the optimal model for MR. In the model averaging approach, two models were identified as optimal: Model 1 (without interaction variables) and Model 2 (with interaction variables). The first one, exhibited the lowest values of Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). These results indicate that model averaging, specifically Model 1, is the superior model-building approach for this study, demonstrating improved performance compared to MR and validating the effectiveness of the MI method.

Leveraging group model building to operationalize implementation strategies across implementation phases: an exemplar related to child maltreatment intervention selection

BackgroundImplementation strategies can help support the adoption and implementation of health interventions that are appropriate for a local context and acceptable to decision makers and community members. Implementation strategies should be designed to handle the complexity of the multi-level, dynamic contexts in which interventions are implemented. Systems science theories and methods explicitly attend to complexity and can be valuable for specifying implementation strategies. Group Model Building (GMB) combines research partner engagement strategies with systems science to support researchers’ and partners’ learning about complex problems and to identify solutions through consensus. This paper specifies how GMB can operationalize implementation strategies — methods for supporting evidence implementation in real-world practice — and describes how GMB can aid in selecting and tailoring both health interventions and implementation strategies. A case study in child maltreatment prevention planning is provided to illustrate how GMB was used to specify the “actions” — strategy activities — for three implementation strategies (conduct local consensus discussions; build a coalition; model and simulate change) during the earliest implementation phases, with the goal of supporting intervention selection decisions. Examples are provided of generalizable research products that can be produced concurrently through GMB, in addition to contextually-driven implementation support.MethodsParticipants (n = 8) were engaged over four sessions using tailored GMB activities. Participants generated a qualitative system dynamics model that described their theory of change for how to prevent child maltreatment in their communities. This theory of change reflected a dynamic understanding of the interconnected determinants of child maltreatment.ResultsGMB was acceptable to participants and resulted in products that could be used for implementation planning (e.g., to model and simulate change) and future research. GMB fostered trust and idea sharing between participants.ConclusionGMB can facilitate learning about which outcomes are (or are not) impacted by interventions, which resources and approaches are required for quality implementation (e.g., implementation strategies), and tradeoffs in outcomes and resources between interventions. GMB also provides a structured, effective process to generate a shared implementation vision amongst participants. Lessons learned include methods for developing trust with and between participants, and the need for researchers to tailor GMB actions for participant and project needs.

BIMReason: Validating BIM model correctness

AbstractCompliance inspections and building analysis are critical to the success of any construction project. At present, such assessments are primarily conducted manually by experts in the architecture, engineering and construction (AEC) sector resulting in a tedious, labor‐intensive, and generally inefficient undertaking. Yet, with the gradual adoption of Building Information Modeling (BIM), automated building analysis and compliance checking become feasible. The Industry Foundation Classes (IFC) has received a lot of traction in the AEC industry as a vendor‐neutral data model. Its well‐defined semantics can be exploited by reasoning engines that allow for semantic reasoning on building models, the core mechanism required for automated compliance checking and building analysis. In this paper, a general‐purpose model checking framework for IFC building models, as well as an appropriate specification layout are introduced. Model checking via semantic reasoning is realized using various technologies from the Semantic Web. To present and evaluate our implementation, a sample specification is developed and tested on two IFC building models.

A recommendation model for optimizing transfer learning hyper-parameter settings in building heat load prediction with limited data samples

The transfer learning method has gained increasing attention in the domain of building load prediction, particularly in scenarios with limited data samples. Its core principle involves leveraging knowledge obtained from abundant data in source buildings to aid the learning process of models for the target buildings. Existing research has predominantly concentrated on optimizing the selection of source building data to improve transfer learning effectiveness, while the optimization of transfer learning hyper-parameter settings is often neglected. This study proposes a recommendation model tailored for transfer learning hyper-parameter settings in the context of small sample prediction for building heat loads. The objective is to automatically suggest suitable transfer learning hyper-parameter combination based on the specific features of the building heat load data samples. In this study, 200 real building profiles were utilized to generate the input–output dataset required for the recommendation model. By employing data mining techniques such as clustering and classification, the correlation between the features of source building data and the most effective transfer learning hyper-parameter combination is investigated. The developed recommendation model for optimal transfer learning hyper-parameter settings achieves a classification accuracy of 90.5%，and the performance evaluation was conducted using an additional dataset of 30 source buildings. The results show that by employing this recommendation model, the prediction error of the target buildings can be reduced by 0.12% to 6.64% compared to the conventional method of empirically determining transfer learning hyper-parameter settings.