Model Building Research Articles

A boundary-aware point clustering approach in Euclidean and embedding spaces for roof plane segmentation

Roof plane segmentation from airborne light detection and ranging (LiDAR) point clouds is an important technology for three-dimensional (3D) building model reconstruction. One of the key issues of plane segmentation is how to design powerful features that can exactly distinguish adjacent planar patches. The quality of point feature directly determines the accuracy of roof plane segmentation. Most of existing approaches use handcrafted features, such as point-to-plane distance, normal vector, etc., to extract roof planes. However, the abilities of these features are relatively low, especially in boundary areas. To solve this problem, we propose a boundary-aware point clustering approach in Euclidean and embedding spaces constructed by a multi-task deep network for roof plane segmentation. We design a three-branch multi-task network to predict semantic labels, point offsets and extract deep embedding features. In the first branch, we classify the input data as non-roof, boundary and plane points. In the second branch, we predict point offsets for shifting each point towards its respective instance center. In the third branch, we constrain that points of the same plane instance should have the similar embeddings. We aim to ensure that points of the same plane instance are close as much as possible in both Euclidean and embedding spaces. However, although deep network has strong feature representative ability, it is still hard to accurately distinguish points near the plane instance boundary. Therefore, we first robustly group plane points into many clusters in Euclidean and embedding spaces to find candidate planes. Then, we assign the rest boundary points to their closest clusters to generate the final complete roof planes. In this way, we can effectively reduce the influence of unreliable boundary points. In addition, to train the network and evaluate the performance of our approach, we prepare a synthetic dataset and two real datasets. The experiments conducted on synthetic and real datasets show that the proposed approach significantly outperforms the existing state-of-the-art approaches in both qualitative evaluation and quantitative metrics. To facilitate future research, we will make datasets and source code of our approach publicly available at https://github.com/Li-Li-Whu/DeepRoofPlane.

Model Building for Prediction of Valuation of Growth Stocks

It has been a great challenge for investors to evaluate growth stocks because these types of stocks are especially hard to forecast because of their inconsistencies and uncertainty. This paper establishes not only regression models, which include a regular Multivariable Linear Regression (MVLR) and a Fixed Effects (FE) Regression Model but also the application of a Panel Vector Autoregression (PVAR) Model on a dataset aiming to predict the Price-to-Sales Ratio (P/S) with several variables. Excel, Python, and Rstudio environments are used in the process of coding and model building. More specifically, Excel is used to conduct the MVLR model, Python is used for the FE model, and Rstudio is used for the PVAR model. By comparing different models of each type, the most optimized model was determined using a training set. After applying predictions with all types of models using another testing set, the best model is identified to be the best approach to the problem. Ultimately, all three models are compared. PVAR model and FE model are proved to be the more reliable ones out of the three models, because the R-squared for MVLR is 0.1859, lower than that of the FE model, 0.524, and the Mean Absolute Error (MAE) of for PVAR Model and FE model are respectfully 53.2139 and 55.1529. This project serves as a good starting point for future analysis and prediction of financial metrics like P/S ratio and is able to have great significance as it discovers some key points for the prediction of P/S value like its connection with time and entity despite the fact that the models themselves aren’t the best fit mainly caused by the limitations of data and variable access.

Effect of prevalent irregular configuration of infills on the seismic collapse of Indian RC buildings

Rapid urbanization and land scarcity obligated the urban population for multi-use of Reinforced Concrete (RC) buildings to meet both residential and commercial purposes together. Un-Reinforced Masonry (URM) infills are often placed irregularly in plan and or elevation of the buildings in some floor(s), particularly in the ground floor to suit the need of occupational requirements accompanied by the inherent irregularities due to openings for doors and windows to meet functional requirements for residence. Seismic performance of infilled frame highly depends upon degree of infill-frame interaction, and it is of utmost importance to classify infilled frame buildings in various Model Building Types (MBTs) based on the type of irregular placement of infills in addition to the parameters affecting seismic performance. Based on field pilot surveys carried out in Indian cities, URM infilled buildings have been classified into 14 different MBTs. Seismic performance of the prevalent categories of MBTs have been evaluated through Incremental Dynamic Analysis (IDA). It has been observed that irregular placement of infills increases the seismic collapse risk significantly, and reduces collapse capacity to approximately 50% as compared to ideal uniformly infilled building. Global collapse occurred due to failure of structural members in the vicinity of irregularity created due to infills.

Predictive Model Building for Aggregation Kinetics Based on Molecular Dynamics Simulations of an Antibody Fragment.

Computational methods including machine learning and molecular dynamics simulations have strong potential to characterize, understand, and ultimately predict the properties of proteins relevant to their stability and function as therapeutics. Such methods would streamline the development pathway by minimizing the current experimental testing required for many protein variants and formulations. The molecular understanding of thermostability and aggregation propensity has advanced significantly along with predictive algorithms based on the sequence-level or structural-level information on a protein. However, these approaches focus largely on a comparison of protein sequence variations to correlate the properties of proteins to their stability, solubility, and aggregation propensity. For therapeutic protein development, it is of equal importance to take into account the impact of the formulation conditions to elucidate and predict the stability of the antibody drugs. At the macroscopic level, changing temperature, pH, ionic strength, and the addition of excipients can significantly alter the kinetics of protein aggregation. The mechanisms controlling aggregation kinetics have been traced back to a combination of molecular features, including conformational stability, partial unfolding to aggregation-prone states, and the colloidal stability governed by surface charges and hydrophobicity. However, very little has been done to evaluate these features in the context of protein dynamics in different formulations. In this work, we have combined a range of molecular features calculated from the Fab A33 protein sequence and molecular dynamics simulations. Using the power of advanced, yet interpretable, statistical tools, it has been possible to uncover greater insights into the mechanisms behind protein stability, validating previous findings, and also develop models that can predict the aggregation kinetics within a range of 49 different solution conditions.

Predicting the risk of colorectal cancer among diabetes patients using a random survival forest-guided approach.

Colorectal cancer (CRC) is the third most frequently diagnosed cancer worldwide. Diabetes and CRC share many overlapping lifestyle risk factors such as obesity, heavy alcohol use, and diet. This study aims to develop a risk scoring system for CRC prediction among diabetes patients using routine medical records. A retrospective cohort study was conducted using electronic health records of Hong Kong. Patients who received diabetes care in public general outpatient clinics between 2010 and 2019 and had no cancer history were identified, and followed up until December 2019. The outcome was diagnosis of CRC during follow-up. For model building, predictors were first selected using random survival forest, and weights were subsequently assigned to selected predictors using Cox regression. Of the 386,325 patients identified, 4,199 patients developed CRC during a median follow-up of 6.2 years. The overall incidence rate of CRC was 1.93 per 1000 person-years. In the final scoring system, age, waist-to-hip ratio, and serum creatinine were included as predictors. The C-index on test set was 0.651 (95%CI: 0.631-0.669). Elevated serum creatinine (≥127 µmol/L) could be a potential important predictor of increased CRC risk. While obesity is a well-known risk factor for CRC, renal dysfunction could be potentially linked to an elevated risk of CRC among diabetes patients. Further studies are warranted to explore whether renal function could be a potential parameter to guide screening recommendation for diabetes patients.

Analysis of Public Interest in Smartfren SIM Cards using the K-Nearest Neighbors Method

The use of Smartfren SIM cards is increasing along with the public's need for fast and stable internet services. However, a deep understanding of public interest in the SIM card is necessary to optimize marketing strategies and increase sales. Proper analysis can help companies identify potential target markets and develop effective marketing strategies. We chose the K-Nearest Neighbors method to analyze public interest in using Smartfren SIM cards. This study aims to develop and evaluate the K-Nearest Neighbors model in predicting public interest in using Smartfren SIM cards. This study uses a dataset containing information about Smartfren SIM card users. We divide the data into two sets: a training set for model building and a test set for evaluating model performance. We apply the K-Nearest Neighbors method to classify the data into two categories: interested and not interested. We evaluate the model performance using accuracy, precision, recall, and F1-score metrics. We present the evaluation results as a confusion matrix. The developed K-Nearest Neighbors model showed excellent performance with an accuracy of 94.29%, a precision of 94.20%, a recall of 100%, and an F1-score of 97.01%. These results indicate that the K-Nearest Neighbors model is effective in predicting people's interest in Smartfren SIM cards. The high recall value indicates that the model is able to identify all interested individuals without missing any, while the high precision value indicates that the model rarely makes false positive prediction errors. This study concludes that the K-Nearest Neighbors method is very effective for use in analyzing people's interest in using Smartfren SIM cards. We can rely on the developed model's strong performance for real-world applications in marketing strategies.

Analysis Of Public Interest In Smartfren SIM Cards Using The K-Nearest Neighbors Method

The use of Smartfren SIM cards is increasing along with the public's need for fast and stable internet services. However, a deep understanding of public interest in the SIM card is necessary to optimize marketing strategies and increase sales. Proper analysis can help companies identify potential target markets and develop effective marketing strategies. We chose the K-Nearest Neighbors method to analyze public interest in using Smartfren SIM cards. This study aims to develop and evaluate the K-Nearest Neighbors model in predicting public interest in using Smartfren SIM cards. This study uses a dataset containing information about Smartfren SIM card users. We divide the data into two sets: a training set for model building and a test set for evaluating model performance. We apply the K-Nearest Neighbors method to classify the data into two categories: interested and not interested. We evaluate the model performance using accuracy, precision, recall, and F1-score metrics. We present the evaluation results as a confusion matrix. The developed K-Nearest Neighbors model showed excellent performance with an accuracy of 94.29%, a precision of 94.20%, a recall of 100%, and an F1-score of 97.01%. These results indicate that the K-Nearest Neighbors model is effective in predicting people's interest in Smartfren SIM cards. The high recall value indicates that the model is able to identify all interested individuals without missing any, while the high precision value indicates that the model rarely makes false positive prediction errors. This study concludes that the K-Nearest Neighbors method is very effective for use in analyzing people's interest in using Smartfren SIM cards. We can rely on the developed model's strong performance for real-world applications in marketing strategies.

Perencanaan Ulang Bangunan Gedung Dormitory Kawasan Sains dan Teknologi (KST) Nuklir Yogyakarta Berbasis Building Information Modelling (BIM) 5D

Building Information Modeling (BIM) is a method of processing data during the life cycle of a construction project that uses three-dimensional, real-time, and dynamic building modeling software, thereby creating a complete model related to project data from the planning to the construction phase of the project. In the re-planning of the Yogyakarta Nuclear Science and Technology (KST) Dormitory Building based on Building Information Modeling (BIM) 5D, a special moment resisting frame system (SRPMK) structure was used. The planned building consists of 7 floors, including the lower structure consisting of a 50x50 square pile foundation and five types of pile cap, the upper structure consisting of columns, beams, and floor plates. The quality of concrete in the structure used is fc' 40 Mpa and 35 Mpa with reinforcing steel quality fy 420B. In this planning, 2D and 3D modeling was carried out. Structural analysis was carried out using the SAP2000 application, which was then continued with 2D 3D modeling and Quantity take-off results using Revit and Scheduling using Ms. Projects. The Draft Budget (RAB) results for this planning are Rp. 37,691,368,000.00. The planned duration of work is 26 weeks. The results of the scheduling and RAB are then integrated using the Naviswork software so that the simulation results of the work to be planned can be seen.

A study on source domain selection for transfer learning-based cross-building cooling load prediction

Transfer learning (TL) can utilize data from information-rich source domain buildings to help information-poor target domain buildings establish cooling load prediction models, thereby enabling cross-building cooling load prediction. However, the prediction performance varies based on the type of source domain building used, making the reasonable selection of source domain buildings crucial. Traditionally, data similarity of source-target building pairs is measured to identify the appropriate source domain building. Yet, this method will result in erroneous judgments if the similarity index used is inappropriate, the target domain building’s available data are scarce, or all candidate source domain buildings exhibit weak similarities. To address this issue, this study explores the selection of source domain buildings based on building feature consistency in a data-centric manner. Specifically, 81 buildings with different features and 240 source-target building pairs are designed. The TL method is then used to develop prediction models for each pair. The decision tree method is finally employed to evaluate the correlation between the feature consistency of source-target building pairs and model prediction performance. From this correlation, a selection strategy is derived that specifies the consistency requirements for three key features (scale, function, and climate zone) of source-target building pairs at different accuracy levels. This strategy offers a methodical guide for the selection of the source domain building.

Method of reference models for synthesis of intellectual systems of nonlinear dynamic objects identification

The paper is devoted to resolving the contradiction between the accuracy of modeling nonlinear dynamic objects and the speed of models building under conditions of limited computing resources. The purpose of the work is to reduce the time for building models of nonlinear dynamic objects with continuous characteristics while ensuring a given modeling accuracy. This goal is achieved by further developing the method of synthesing intelligent systems based on the superposition of pre-trained reference models in the form of neural networks reflecting the basic properties of the object. The scientific novelty of the work novelty consists in the development of a method for identifying nonlinear dynamic objects in the form of neural networks with time delays based on a set of pre-trained neural network models that reflect the basic properties of the subject area. In contrast to the traditional approach based on pre-trained neural networks the developed method allows building models of lower complexity and with shorter training time while ensuring the required accuracy. To determine the initial parameters of the model, expressions based on the superposition of reference models in the form of neural networks are proposed. The practical usefullness of the work consists in the development of an algorithm for the method of reference models for training neural networks with time delays in the tasks of identifying nonlinear dynamic objects with continuous characteristics, which can significantly reduce the training time of neural networks without losing the accuracy of the model. The value of the study lies in determining the area of effective use of the proposed method, namely, in the availability of a sufficient amount of qualitative data for the building of reference models. Insufficient data or poor data quality can significantly reduce the accuracy of reference models and, as a result, significantly reduce the training time of the target model.

Energy consumption forecasting in PCM-integration buildings considering building and environmental parameters for future climate scenarios

Despite numerous machine learning methods being employed to predict the energy consumption of PCM-integrated buildings, key research gaps remain. Most studies focus solely on building parameters while omitting important environmental parameters, including precipitation and air pressure. No study evaluated and proposed prediction models for PCM-integrated buildings considering future climate scenarios. Also, as per the authors′ knowledge, no researcher has assessed the impact of variations in the hyperparameter, especially for decision tree-based prediction models to develop a reliable prediction model with less complexity and a high degree of interpretability between independent and dependent variables. This research addresses these gaps by evaluating fine, medium, and coarse decision trees for predicting energy consumption in PCM-integrated buildings under future climate scenarios by considering extensive building and environmental parameters simultaneously. A database for energy consumption was created through energy simulations for 11 cities in hot semi-arid climates. The Fine Decision Tree (FDT3) emerged as the most accurate prediction model, with R2 values over 94 % in training and testing phases based on model evaluation and validation processes. Parametric analysis further revealed that both environmental and building parameters are crucial in accurately predicting the energy consumption of PCM-integrated buildings using FDT3.

Life-Cycle Performance Modeling for Sustainable and Resilient Structures under Structural Degradation: A Systematic Review

The performance of structures degrades during their service life due to deterioration and extreme events, compromising the social development and economic growth of structure and infrastructure systems. Buildings and bridges play a vital role in the socioeconomic development of the built environment. Hence, it is essential to understand existing tools and methodologies to efficiently model the performance of these structures during their life cycle. In this context, this paper aims to explore the existing literature on the life-cycle performance modeling, assessment, enhancement, and decision making of buildings and bridge infrastructure systems under deterioration and extreme events for a sustainable and resilient built environment. The main objectives are to (1) systematically review the existing literature on life-cycle performance modeling of buildings and bridges based on the PRISMA methodology, (2) provide a bibliometric analysis of the systematically assessed journal articles, (3) perform an analysis of the included articles based on the identified components of life-cycle performance modeling, and (4) provide a discussion on the utilized tools, techniques, methodologies, and frameworks for buildings and bridge infrastructure systems in the life-cycle context. The provided systematic literature review and subsequent discussions could provide an overview to the reader regarding the individual components and existing methodologies of life-cycle performance management under deterioration and extreme events.

Comprehensive Review of Building Energy Management Models: Grid-Interactive Efficient Building Perspective

Energy management models for buildings have been designed primarily to reduce energy costs and improve efficiency. However, the focus has recently shifted to GEBs with a view toward balancing energy supply and demand while enhancing system flexibility and responsiveness. This paper provides a comprehensive comparative analysis of GEBs and other building energy management models, categorizing their features into internal and external dimensions. This review highlights the evolution of building models, including intelligent buildings, smart buildings, green buildings, and zero-energy buildings, and introduces eight distinct features of GEBs related to their efficient, connected, smart, and flexible aspects. The analysis is based on an extensive literature review and a detailed comparison of building models across the aforementioned features. GEBs prioritize interaction with the power grid, which distinguishes them from traditional models focusing on internal efficiency and occupant comfort. This paper also discusses the technological components and research trends associated with GEBs, providing insights into their development and potential evolution in the context of sustainable and efficient building design.

A Study on the Competency Model Building (CMB) for the Curriculum-based Moral Education (ME) of Foreign Language Teachers: An Empirical Study Based on Multi-factor Analysis

50 EFL teachers, 100 students, and 12 administrative leaders from 10 universities in Southern China are selected as subjects to explore the structural features of EFL teachers’ competence in curriculum-based moral education. Questionnaires, interviews, and classroom observations are conducted to gather their insights and feedback on how to integrate moral education into English language teaching (ELT). The data is analyzed using both qualitative and quantitative multi-factor analysis. A novel competency model is proposed with four layers: teaching design, situational construction, classroom interaction, and multiple assessment. Recommendations are provided to enhance foreign language teachers’ awareness of moral education and the quality of curriculum-based moral education.

Optimization-Based Vibration Control Analysis of Dampers in High-Rise Building

Tuned mass dampers (TMDs) are a useful control of vibrations for tall buildings and can be considered equal to burdens such as breezes and earthquakes. The better limits for TMD are to reduce the earthquake vibrations of tall buildings including soil–structure interaction (SSI) influences. This study proposes the multi-objective Black Widow Optimization (BWO) with Elman neural network (ENN) computation that is brought on to find the optimal limits of TMD. Considering that this proposed model analyzed the mass, stiffness, and damping ratio of TMD to get the maximum accelerations and displacement of 40 story building model, from the execution results, starting from this proposed BWO–Elman model to the expected methodology, the most outrageous TMD limits are better, likewise, the reduction of acceleration and displacement are 25% and 19.86% in the proposed study. This study assists the researchers with a better understanding of earthquake vibration and leads the fashioners to achieve superior TMD for tall designs. The impact of vibration control on the displacements and accelerations of both controlled and uncontrolled buildings is investigated in this work. It uses a MATLAB software technique to analyze their modal replies, moreover, this proposed optimal TMD model is compared with conventional techniques by acceleration and stiffness parameters.

Remote Sensing of Chlorophyll-a in Clear vs. Turbid Waters in Lakes

Chlorophyll-a (Chl-a), a proxy for phytoplankton biomass, is one of the few biological water quality indices detectable using satellite observations. However, models for estimating Chl-a from satellite signals are currently unavailable for many lakes. The application of Chl-a prediction algorithms may be affected by the variance in optical complexity within lakes. Using Lake Winnipeg in Canada as a case study, we demonstrated that separating models by the lake’s basins [north basin (NB) and south basin (SB)] can improve Chl-a predictions. By calibrating more than 40 commonly used Chl-a estimation models using Landsat data for Lake Winnipeg, we achieved higher correlations between in situ and predicted Chl-a when building models with separate Landsat-to-in situ matchups from NB and SB (R2 = 0.85 and 0.76, respectively; p < 0.05), compared to using matchups from the entire lake (R2 = 0.38, p < 0.05). In the deeper, more transparent waters of the NB, a green-to-blue band ratio provided better Chl-a predictions, while in the shallower, highly turbid SB, a red-to-green band ratio was more effective. Our approach can be used for rapid Chl-a modeling in large lakes using cloud-based platforms like Google Earth Engine with any available satellite or time series length.

Resilience of code compliant reinforced concrete buildings to progressive collapse: A numerical analysis investigation

This study investigates the resilience of dual-system reinforced concrete (RC) structures impacted by progressive collapse where the local failure of a primary structural component leads to the sequential collapse of adjoining elements. The study is conducted on buildings designed in the United Arab Emirates in the context of extreme events. Nonlinear dynamic analysis of twenty-seven different building models is performed to evaluate the impact of critical variables, building height, and column spacing on progressive collapse potential under the Unified Facilities Criteria (UFC) regulations. The research methodology aims to study impacts associated with sudden removal of critical columns (corner, edge, and internal) that replicate severe accident scenarios in buildings. The structural responses were assessed based on the ASCE 41 performance design criteria. The formation of plastic hinges was analyzed to determine the ductility and resilience of the structures. The study finds that buildings with removed corner columns exhibit more significant vertical displacements than those with edge or internal column removals. The results also indicate that columns adjacent to the removed ones generally remained elastic and within their structural capacities, showing structural resilience to localized damage. However, the flexural strength of RC flat slabs near removed or adjacent columns demonstrates insufficient flexural capacities. This study underscores the imperative for enhanced design strategies and emphasizes the critical importance of bolstering structural resilience against extreme events. Moreover, it intends to draw the attention of policymakers locally to the need to consider progressive collapse when designing reinforced concrete structures.

Biogas production and techno‐economic feasibility studies of setting up household biogas technology in Africa: A critical review

AbstractThis critical review examines the potential of household biogas technology in Africa, focusing on biogas production and techno‐economic feasibility studies. The review highlights the benefits of biogas technology, including renewable energy generation, waste management, and improved public health. A significant portion of Africa's rural population, approximately 60%, grapples with limited access to reliable power sources. The study analyzes various biogas production systems, including anaerobic digesters and biomass gasifiers, and evaluates their economic viability in different African contexts. The review identifies key factors influencing the adoption of household biogas technology, including policy and regulatory frameworks, financing mechanisms, and public awareness. Biogas production stands out as one of the promising renewable energy sources. However, it also discusses the challenges and limitations of implementing this technology on a continental scale. The study analyzes various biogas production systems, including anaerobic digesters and biomass gasifiers, and evaluates their economic viability in different African contexts. The review identifies key factors influencing the adoption of household biogas technology, including policy and regulatory frameworks, financing mechanisms, and public awareness. The study concludes by recommending strategies for scaling up biogas technology in Africa, including capacity building, technology transfer, and innovative financing models. The review aims to provide a comprehensive resource for researchers, policymakers, and practitioners working towards sustainable energy solutions in Africa.

Analytic bounds on late-time axion-scalar cosmologies

The cosmological dynamics of multiple scalar/pseudoscalar fields are difficult to solve, especially when the field-space metric is curved. This presents a challenge in determining whether a given model can support cosmic acceleration, without solving for the on-shell solution. In this work, we present bounds on late-time FLRW-cosmologies in classes of theories that involve arbitrary numbers of scalar and pseudoscalar fields coupled both kinetically (leading to a curved field space metric) and through scalar potentials. Such bounds are proven analytically, independently of initial conditions, with no approximation in the field equations and without referring to explicit solutions. Besides their broad applications to cosmological model building, our bounds can be applied to studying asymptotic cosmologies of certain classes of string compactifications.

Computed tomography radiomics models of tumor differentiation in canine small intestinal tumors.

Radiomics models have been widely exploited in oncology for the investigation of tumor classification, as well as for predicting tumor response to treatment and genomic sequence; however, their performance in veterinary gastrointestinal tumors remains unexplored. Here, we sought to investigate and compare the performance of radiomics models in various settings for differentiating among canine small intestinal adenocarcinoma, lymphoma, and spindle cell sarcoma. Forty-two small intestinal tumors were contoured using four different segmentation methods: pre- or post-contrast, each with or without the inclusion of intraluminal gas. The mesenteric lymph nodes of pre- and post-contrast images were also contoured. The bin settings included bin count and bin width of 16, 32, 64, 128, and 256. Multinomial logistic regression, random forest, and support vector machine models were used to construct radiomics models. Using features from both primary tumors and lymph nodes showed significantly better performance than modeling using only the radiomics features of primary tumors, which indicated that the inclusion of mesenteric lymph nodes aids model performance. The support vector machine model exhibited significantly superior performance compared with the multinomial logistic regression and random forest models. Combining radiologic findings with radiomics features improved performance compared to using only radiomics features, highlighting the importance of radiologic findings in model building. A support vector machine model consisting of radiologic findings, primary tumors, and lymph node radiomics features with bin count 16 in post-contrast images with the exclusion of intraluminal gas showed the best performance among the various models tested. In conclusion, this study suggests that mesenteric lymph node segmentation and radiological findings should be integrated to build a potent radiomics model capable of differentiating among small intestinal tumors.