Model Building Research Articles

Predicting UK Domestic Electricity and Gas Consumption between Differing Demographic Household Compositions

This paper examines the influence of building characteristics, occupant demographics and behaviour on gas and electricity consumption, differentiating between family groups; homes with children; homes with elderly; and homes without either. Both regression and Lasso regression analyses are used to analyse data from a 2019 UK-based survey of 4358homes (n = 1576 with children, n = 436 with elderly, n = 2330 without either). Three models (building, occupants, behaviour) were tested against electricity and gas consumption for each group. Results indicated that homes without children or elderly consumed the least energy. Property Type emerged as the strongest predictor in the Building Model (except for homes with elderly), while Current Energy Efficiency was less significant, particularly for homes with elderly occupants. Homeownership and number of occupants were the most influential factors in the Occupants Model, though this pattern did not hold for homes with elderly. Many occupant and behaviour variables are often considered ‘unregulated energy’ in calculations such as SAP and are thus typically disregarded. However, this study found these variables to be significant, especially as national standards improve. The findings suggest that incorporating occupant behaviour into energy modelling could help reduce the energy performance gap.

Seismic performance assessment and fragility analysis of concrete structures equipped with self-centered hybrid wall systems

ABSTRACT In this study, comprehensive analytical and parametric studies were conducted on seismic performance, collapse and fragility curves of concrete structures equipped with self-centered unbonded post-tensioned hybrid wall system in comparison with conventional structural wall system. For this purpose, analytical models of prototype buildings were developed and to further study the behaviour of hybrid wall, parametric analysis was performed considering four parameters: wall aspect ratio, initial stress of post-tensioned tendons, the total area of PT tendons, the total area of mild bars. The performance of buildings was evaluated under non-linear quasi cyclic and time history method considering two seismic hazard levels, the design-based earthquake level and the risk-targeted maximum credible earthquake level. Hysteresis response, energy dissipation ability and damping ratio, relative self-centering efficiency, residual drift, peak roof drift, column curvature ductility, incremental dynamic analysis data, seismic collapse assessment and fragility curves for collapse and repairable limit states were studied.

High-throughput prediction of stalk cellulose and hemicellulose content in maize using machine learning and Fourier transform infrared spectroscopy

Cellulose and hemicellulose are key cross-linked carbohydrates affecting bioethanol production in maize stalks. Traditional wet chemical methods for their detection are labor-intensive, highlighting the need for high-throughput techniques. This study used Fourier transform infrared (FTIR) spectroscopy combined with machine learning (ML) algorithms on 200 large-scale maize germplasms to develop robust predictive models for stalk cellulose, hemicellulose and holocellulose content. We identified several peak height features correlated with three contents, used them as input data for model building. Four ML algorithms demonstrated higher predictive accuracy, achieving coefficient of determination (R2) ranging from 0.83 to 0.97. Notably, the Categorical Boosting algorithm yielded optimal models with coefficient of determination (R2) exceeding 0.91 for the training set and over 0.81 for the test set. The approach combined FTIR spectroscopy with ML algorithms offers a precise and high-throughput tool for predicting stalk cellulose, hemicellulose and holocellulose contents, benefiting maize genetic breeding for bioenergy and biofuels.

Role of policyholders’ beliefs in life insurance lapses: a model building approach

Purpose This study aims to understand the lapse behavior of life insurance policyholders. Despite being accessible for nearly two centuries, only a small fraction of individuals purchase such policies and many of those who do let them lapse. The belief hypothesis model (BHM) is introduced to elucidate the correlation between policyholders' beliefs and their decisions regarding life insurance lapses. Design/methodology/approach BHM establishes a comprehensive linkage between core beliefs, external data and the lapse behavior exhibited by policyholders. To derive policyholders’ core beliefs about life insurance lapses, the authors conducted a semistructured, in-depth interview with 42 policyholders and 11 insurance advisors, using a grounded theory approach with zero-order, first-order and second-order coding. Findings The study's findings reveal that policy lapsation is influenced by various factors such as policyholders' beliefs about life insurance, process-related attitudes, trust in insurers and advisors and personal financial viewpoints. Policyholders who consider life insurance unnecessary or misunderstand its purpose are likelier to lapse their policies. Cumbersome documentation processes and technical issues also contribute to policy lapsation, emphasizing the significance of simplified procedures. Trust in insurers and advisors, personal financial literacy and payment preferences influence policy lapsation. Practical implications The findings of this research can be practically applied by companies to improve customer retention and by regulatory bodies to encourage policyholders to honor their insurance commitments. Originality/value Distinguishing itself from conventional hypotheses-driven and factor-centric models, BHM integrates consumer beliefs, thus enriching comprehension and decision-making insights.

Seismic Behavior of Precast Prestressed Concrete Frame with Hinge-Relocated Mortise–Tenon Connections

To satisfy the easy-construction demands of precast concrete (PCa) frames after an earthquake, a PCa frame with mortise–tenon (MT) connections is proposed in this paper. MT connections are secured solely through the binding force of unbonded prestressed tendons without grouting for easy construction. The design and construction of the joint are detailed. During an earthquake, the hinge system of the connection allows for slight rotational movements. Finite element analysis was employed to assess the joint’s hysteresis behavior, revealing a three-stage earthquake response mechanism: closing, hinge relocation, and self-centering. Based on the hysteresis performance of the beam and column in the precast prestressed concrete (PCaPC) frame, a seismic response model for PCaPC buildings was established.

Performance characterization of a wireless sensors network system (WSNS) for measurements of hurricane wind effects on structures

This paper presents a new wireless sensors network system (WSNS) designed for high-resolution absolute pressure measurements, wind speed, and direction. The system is tailored to assess the impact of hurricane winds on residential structures, both in laboratory settings and field environments. Importantly WSNS stands out for its unique ability to provide waterproof, surface-mounted external pressure measurements. The system's performance is evaluated during deployment on a full-scale house model at the Wall of Wind facility. The WSNS sensors were installed on different surfaces of a single-story residential building model. The sensor locations mirrored the locations of surface pressure taps connected to a Scanivalve differential pressure measurement system. Due to the size and shape of the WSNS pressure module, a casing effect was observed, which may result in pressure offsets under certain wind speeds and directions, depending on the sensor's location and the conditions, including dry and light rain (50 mm per hour). The comparison between WSNS and Scanivalve indicates that the sensor's casing geometry does not cause significant differences in the time-averaged measurements for low-turbulence regions. Conversely, this is not true for high-turbulence regions, which should be marked for future deployments using smaller surface-mounted pressure taps.

Multivariate SPC via sequential multiblock-PLS

The sequential multi-block partial least squares (SMB-PLS) is proposed for implementing a multivariate statistical process control scheme. This is of interest when the system is composed of several blocks following a sequential order and presenting correlated information, for instance, a raw material properties block followed by a process variables block that is manipulated according to raw material properties. The SMB-PLS uses orthogonalization to separate correlated information between blocks from orthogonal variations. This allows monitoring the system in different stages considering only the remaining orthogonal part in each block. Thus, the SMB-PLS increases the interpretability and process understanding in the model building (Phase I), since it provides a deep insight about the nature of the system variations. Besides, it prevents any special cause from propagating to subsequent blocks enabling their use in the model exploitation (Phase II). The methodology is applied to a real case study from a food manufacturing process.

Aerodynamic Noise Simulation of a Super-High-Rise Building Facade with Shark-Like Grooved Skin.

The wind-driven aerodynamic noise of super-high-rise building facades not only affects the experience of use inside the building but also reduces the life cycle of building facade materials to some extent. In this paper, we are inspired by the micro-groove structure of shark skin with damping and noise reduction properties and apply bionic skin to reduce the aerodynamic noise impact of super-high-rise buildings. The aerodynamic noise performance of smooth and super-high-rise building models with bionic grooves is simulated via CFD to investigate the noise reduction performance of different bionic groove patterns, such as I-shape, ∪-shape, V-shape, and ∩-shape patterns, and their corresponding acoustic noise reduction mechanisms. This study showed that the bionic shark groove skin has a certain noise reduction effect, and the I-shaped groove has the best noise reduction effect. By applying bionic skin, the aerodynamic noise of super-high-rise buildings can be effectively reduced to improve the use experience and environmental quality of the buildings and provide a new research idea and application direction for the aerodynamic noise reduction design of building facades.

Humidity-aware model predictive control for residential air conditioning: A field study

Model predictive control of residential air conditioning could reduce energy costs and greenhouse gas emissions while maintaining or improving occupants’ thermal comfort. However, most approaches to predictive air conditioning control either do not model indoor humidity or treat it as constant. This simplification stems from challenges with modeling indoor humidity dynamics, particularly the high-order, nonlinear equations that govern heat and mass transfer between the air conditioner’s evaporator coil and the indoor air. This paper develops a machine-learning approach to modeling indoor humidity dynamics that is suitable for real-world deployment at scale. This study then investigates the value of humidity modeling in four field tests of predictive control in an occupied house. The four field tests evaluate two different building models: One with constant humidity and one with time-varying humidity. Each modeling approach is tested in two different predictive controllers: One that focuses on reducing energy costs and one that focuses on constraining electric power below a utility-specified threshold. The two models lead to similar performance for reducing energy costs. Combining the results of this study and a prior heating study of the same house, the estimated year-round energy cost savings were $340–497 or 22%–31% (95% confidence intervals); these savings were consistent across both humidity models. However, in the demand response tests, the simplifying assumption of constant humidity led to far more frequent and severe violations of the power constraint.

A simplified model for seismic risk assessment of three-dimensional irregular steel moment frame buildings

Seismic risk assessment of irregular buildings requires nonlinear dynamic analysis and three-dimensional (3D) models. However, creating and analyzing such models can be intricate and time-consuming, making the assessment process challenging. Furthermore, this complexity escalates when evaluating building stocks or generating a dataset of buildings with diverse attributes for use in learning algorithms. Therefore, this paper proposes an energy-based model for irregular steel moment-resisting frame buildings to simplify 3D models and improve the time efficiency of structural assessment compared to conventional 3D models. The proposed model is derived from the fish-bone model but has enhanced configuration, elastic, and inelastic properties. These improvements aim to broaden its applicability to encompass 3D irregular mid-rise buildings. Four-, six-, and nine-story irregular buildings in plan and height are utilized to validate the proposed model. The responses investigated in these buildings include eigenvalues, pushover curves, time history responses, incremental dynamic analysis results, fragility curves, and risk analysis outcomes. The findings demonstrate that the simplified model responses are in close agreement with those of 3D models. The comparison of results between the proposed simplified model and existing models indicates that the accuracy of the simplified model is at least 2.2 times that of the existing models. Moreover, it provides an average error reduction of 60 % in contrast to the existing models. These advancements have been achieved while the proposed simplified model maintains a level of time efficiency comparable to the current models. The results from the analysis of the created buildings using the proposed simplified model indicate that the model is suitable for application in the seismic risk assessment of building inventories and generating datasets for future research involving machine learning-based methodologies.

Innovative silica aerogel fiberglass walls and roofs: A path to lower carbon emissions and higher energy savings

ABSTRACT Buildings account for a significant portion of global energy use and consumption. Buildings have substantial energy consumption due to the use of heating, ventilation, and cooling systems. It is evident that the insulation materials used in the design of building envelopes can efficiently reduce the cost of air conditioning by reducing external heat gains and losses. Additionally, the use of environmentally friendly and cost-effective can be beneficial from an environmental standpoint. This paper aims to evaluate the potential for cost savings in the air conditioning of buildings incorporated with silica aerogel fiberglass insulation in hollow concrete bricks (wall) and hollow roof tile (roof). Silica aerogel fiberglass insulation is extensively used for heat insulation purposes, including building insulations, due to its superior thermal insulation and flame retardant properties. The thermo-physical properties of silica fiberglass insulation and building materials were determined experimentally as per ASTM D 5334 standards. Twenty distinct building design concepts are investigated in this study, each featuring insulation-integrated concrete blocks composed of silica aerogel fiberglass: (WEM1) insulation layer in the outer side, (WEM2) insulation layer in the center, (WEM3) insulation layer in the inner side, (WEM4) insulation layers in the outer-middle, (WEM5) insulation layers in the middle-inner, and (WEM6) insulation layers in the outer-inner sides. Additionally, two arrangements of insulation-stuffed roof tiles, REM1 above the RCC and REM2 below the RCC. In addition, novel building designs integrated with insulation were analyzed numerically, related to environmental conditions that refer to two different scenarios in India (hot dry and composite climates). Thermo-economic analysis of novel wall and roof designs with silica fiberglass insulation materials on air-conditioning costs, carbon dioxide emissions, and payback time are compared with conventional roof and wall patterns. The thermo-economic analysis was carried out based on the number of degree-hours of heating and cooling to determine the building’s annual energy usage. The building model arrangement (WEM6-REM1), which consists of an insulation composite wall (WEM6) and insulation roof tile (REM1), demonstrated the greatest annual air conditioning cost savings of 3.4 $/m2/year and 1.74 $/m2/year when compared to conventional building design for climatic conditions of Kurnool and Gauhati. The building model arrangement (WEM6-REM1) composed of silica fiberglass aerogel insulation exhibits the highest reduction in carbon emissions of 65 kg/kWh and 33 kg/kWh, when compared to conventional building design analyzed in both hot-arid; (Kurnool) and composite (Gauhati) climatic conditions. The placement of insulation in the roof alone (REM1) has the shortest payback duration of 2.15 years. The results of this study point to a viable approach to enhancing building design decision-making and narrowing the performance gap in sustainable and energy-efficient strategies.

Utilizing anomalous signals for element identification in macromolecular crystallography.

AlphaFold2 has revolutionized structural biology by offering unparalleled accuracy in predicting protein structures. Traditional methods for determining protein structures, such as X-ray crystallography and cryo-electron microscopy, are often time-consuming and resource-intensive. AlphaFold2 provides models that are valuable for molecular replacement, aiding in model building and docking into electron density or potential maps. However, despite its capabilities, models from AlphaFold2 do not consistently match the accuracy of experimentally determined structures, need to be validated experimentally and currently miss some crucial information, such as post-translational modifications, ligands and bound ions. In this paper, the advantages are explored of collecting X-ray anomalous data to identify chemical elements, such as metal ions, which are key to understanding certain structures and functions of proteins. This is achieved through methods such as calculating anomalous difference Fourier maps or refining the imaginary component of the anomalous scattering factor f''. Anomalous data can serve as a valuable complement to the information provided by AlphaFold2 models and this is particularly significant in elucidating the roles of metal ions.

ИННОВАЦИИ В ЖИЗНЕННОМ ЦИКЛЕ СТРОИТЕЛЬСТВА ПОСРЕДСТВОМ ИНТЕГРАЦИИ BIM/ГИС

Innovation in the life cycle of construction is an important topic for the modern construction industry. The introduction of BIM (Building Information Modeling) and GIS (Geoinformation System) technologies into the construction process changes the traditional methodology of designing buildings and structures. The integration of BIM and GIS allows you to improve the accuracy and quality of information in design and construction. This is achieved through digital control and management of data stored in a single database accessible to all project participants. The use of system integration in the design of buildings makes it possible to display much more accurately the achievements of ergonomics and safety, work with energy, optimization of materials, as well as forecasting operating costs. Innovations in the construction life cycle can reduce time and financial costs, as well as improve the quality of work in the entire project delivery chain. This is important for various sectors, including residential construction, commercial real estate and infrastructure facilities. BIM and GIS also help to simplify the interaction between all project participants - from architects and engineers to builders and operational teams. This provides more effective problem solving and improves interaction throughout the project lifecycle. This study is dedicated to the topic of integration of BIM/GIS platforms. This article will describe the main terms and definitions, compare the functionality of the platforms under consideration. The advantages of technology integration will also be presented, the experience of using information modeling technologies used for urban planning activities on the example of a BIM model of a cafe building will be considered. The construction model will be exported from the BIM space to the GIS environment, during which some pros and cons of the transfers are identified, on the basis of which conclusions are formulated about the further application and integration of Graphisoft and Autodesk products.

Reference-then-supervision framework for infrared and visible image fusion

Infrared and visible image fusion has drawn increasing attention of researchers in recent years, wherein the complementary information between two source images is extracted to synthesize a new fusion image with richer information. Deep neural network is the latest technique to tackle various image fusion problems. However, the natural absence of ground truth makes it very challenging to optimize deep learning models. While the current methods mainly consider to uses the two source images themselves or their visual features, to provide supervision for learning, which is easy to result in the imbalance between detail preservation and brightness distribution. To boost the performance of the fusion models, it is meaningful and necessary to establish a flexible framework that can combine the advantages of existing models to produce reliable supervision for model training. In this work, we propose a novel reference-then-supervision framework, which aims to fully leverage and exploit the available favorable reference information based on the performance of existing methods and then construct high-quality reliable supervision to assist in model building. For this purpose, we design an automatic filter to produce favorable reference and devise an adaptive enhancement method to construct reliable supervision, which helps to aggregate the advantages of various existing fusion methods for yielding visually pleasing results adapting to different complex scenarios. Extensive experiments on two commonly used datasets and our built challenging test set demonstrate that our framework can greatly improve the performance of existing fusion methods. Ablation study and empirical analysis also present the efficacy of our framework design. Furthermore, the applications on downstream pedestrian detection and object tracking tasks indicate the great potential of our framework. Our code and data are publicly available at https://github.com/zhenglab/ReferenceSupervisionIVIF.

Simplification algorithm of 3D building model based on triangle folding

Simplification algorithm of 3D building model based on triangle folding

Enhanced computed tomography radiomics predicts solute carrier family 7, member 11 in head and neck squamous cell carcinoma.

Traditional prognostic indicators for head and neck squamous cell carcinoma (HNSCC), such as clinicopathological features, human papillomavirus status, and imaging examinations, often lack precision in guiding medical therapy. Therefore, discovering novel tumor biomarkers that can accurately assess prognosis and aid in personalized medical treatment for HNSCC is critical. Solute carrier family 7, member 11 (SLC7A11), is implicated in ferroptosis, and various malignant tumor therapies regulate its expression. However, the mechanisms regulating SLC7A11 expression, the transporter activity, and its specific role in controlling ferroptosis in cancer cells remain unknown. Thus, in this study, we aimed to develop an improved computed tomography (CT) radiomics model that could predict SLC7A11 expression in patients with HNSCC. We used patient genomic data and corresponding augmented CT images for prognostic analysis and building models. Further, we investigated the potential molecular mechanisms underlying SLC7A11 expression in the immune microenvironment. Our radiomics model successfully predicted SLC7A11 mRNA expression in HNSCC tissues and elucidated its association with relevant genes and prognostic outcomes. SLC7A11 expression level was high within tumor tissues and was connected to the infiltration of eosinophil, CD8+ T-cell, and macrophages, which was associated with poor overall survival. Our models demonstrated robust predictive power. The distribution of radiomics scores (RAD scores) within the training and validation sets was markedly different between the high- and low-expression groups of SLC7A11. SLC7A11 is likely an important factor in the prognosis of HNSCC. SLC7A11 expression can be predicted effectively and reliably by radiomics models based on enhanced CT.

Pre-service mathematics teachers’ perceptions based on two differing mathematically underpinned debates

This study explores how pre-service mathematics teachers build alternative model simulations of real-world scenarios. Inclusion in the formal structures for wealth generation and accumulation is a fervently debated issue in South Africa. Share owning in companies listed on the Johannesburg Stock Exchange (JSE) in South Africa is one of many wealth-building tools. Related discussions evolved on the spread of share ownership among black and white citizens. Two mathematically motivated positions were reported in a newspaper. In one of their mathematics courses, prospective mathematics teachers were presented with the article and asked to reflect on it with the prompt 'Which of these two methods for determining the "number of Black South Africans holding shares on the JSE" would your group support?' Audio- and video-recorded data were collected and subjected to thematic analysis. The themes that emerged from the analysis were economic empowerment, authority of research and trustworthiness of information. The discussions reflected on the complexities and rationalities involved in decision-making of mathematically driven opposing positions on issues of social importance. The mathematically derived results of such issues will eventually be resolved in the political field. The findings revealed that building of alternative mathematical models by pre-service mathematics teachers begins with an explicit problem setting, followed by the development of mathematical models that included real-world problems.Contribution: This article contributes to pre-service mathematics teacher education by exploring how such teachers deal with the issue of critical engagement of mathematically based arguments, highlighted in the Curriculum Assessment and Policy Standards (CAPS) document for school mathematics.

Lung nodule detection using Eyrie Flock-based Deep Convolutional Neural Network

PROBLEM: Lung cancer is a dangerous and deadly disease with high mortality and reduced survival rates. However, the lung nodule diagnosis performance is limited by its heterogeneity in terms of texture, shape, and intensity. Furthermore, the high degree of resemblance between the lung nodules and the tissues that surround the lung nodules makes the building of a reliable detection model more difficult. Moreover, there are several methods for diagnosing and grading lung nodules; still, the accuracy of detection with the variations in intensity is a challenging task. AIM & METHODS: For the detection of lung nodules and grading, this research proposes an Eyrie Flock Optimization-based Deep Convolutional Neural Network (Eyrie Flock-DeepCNN). The proposed Eyrie Flock Optimization integrates the fishing characteristics of Eyrie’s and the flocking characteristics of Tusker to accelerate the convergence speed which inturns enhance the training process and improve the generalization performance of the DeepCNN model. In the Eyrie Flock optimization, two optimal issues are considered: (i) segmenting the lung nodule and (ii) fine-tuning hyperparameters of Deep CNN. RESULTS: The capability of the newly developed method is evaluated by the terms of Specificity, Sensitivity, and Accuracy, attaining 98.96%, 95.21%, and 94.12%, respectively. CONCLUSION: Efficiently utilized the Deep CNN along with the help of the Eyrie Flock optimization algorithm which enhances the efficiency of the classifier and convergence of the model.

UCFTNet: multi-class dtection of city critical disaster information based on U-shaped cross fusion transformer

ABSTRACT With the continuous enhancement of remote sensing data acquisition capabilities, the demand for efficient extraction of multi-class critical urban disaster information has become increasingly urgent. However, current deep learning-based models primarily focus on single disaster objects and struggle with accuracy in complex post-disaster scenarios. To address this, we propose UCFTNet, a semantic segmentation model for urban post-disaster damaged roads and buildings. UCFTNet employs a U-shaped encoder-decoder structure and ConvNext modules, integrating spatial and multi-scale information of damaged buildings and roads from different scales and channels. Additionally, the decoder uses the CCT module and CFF module to merge multi-scale features and eliminate irrelevant background interference. Finally, a weighted loss function is designed to enhance the model's focus on disaster category pixels. Experimental results demonstrate that UCFTNet improves the accuracy of extracting damaged buildings and roads by at least 5% across three urban disaster datasets, confirming its effectiveness in post-disaster damage information extraction.

AI-based tree modeling for multi-point dioxin concentrations in municipal solid waste incineration

Numerous investigations have shown that the municipal solid waste incineration (MSWI) has become one of the major sources of dioxin (DXN) emissions. Currently, the primary issue that needs to be addressed for DXN emission reduction control is the online measurement of DXN. Data-driven AI algorithms enable real-time DXN concentration measurement, facilitating its control. However, researchers mainly focus on building models for DXN emissions at the stack. This approach does not allow for the construction of models that online measurement of DXN generation and absorption throughout the whole process. To achieve optimal pollution control, models that encompass the whole process are necessary, not just models focused on the stack. Therefore, this article focuses on modeling the whole process of DXN concentrations, including generation, adsorption, and emission. It uses machine learning techniques based on advanced tree-based data-driven deep and broad learning algorithms. The determination of data characteristics at different phases is grounded in the understanding of the DXN mechanism, offering a novel framework for DXN modeling. State-of-the-art tree-based models, including adaptive deep forest regression algorithm based on cross layer full connection, tree broad learning system, fuzzy forest regression, and aid modeling technologies, are applied to handle diverse data characteristics. These characteristics encompass high-dimensional small samples, low-dimensional ultra-small size samples, and medium-dimensional small samples across different phases related to DXN. The most interesting is the robust validation where the proposed a whole process tree-based model for DXN is validated using nearly one year of authentic data on DXN generation, adsorption, and emission phases in an MSWI plant of Beijing. The proposed modeling framework can be used to explore the mechanism characterization and support the pollution reduction optimal control.