Performance Metrics Research Articles

A STUDY ON PORTFOLIO MANAGEMENT SERVICE – WITH SPECIAL REFERENCE TO MOTILAL OSWAL

The study investigates Portfolio Management Services (PMS) with a specific focus on Motilal Oswal, a prominent entity in the financial services domain. PMS entails the provision of tailored investment portfolios designed to align with the unique objectives and risk profiles of individual investors. The study meticulously examines various dimensions of PMS offered by Motilal Oswal, encompassing features, performance metrics, risk mitigation strategies, and client satisfaction levels. Employing a comprehensive research methodology comprising both qualitative and quantitative analyses, the research endeavors to yield insights into the efficacy and efficiency of Motilal Oswal's PMS offerings. Through the collection and analysis of data from primary and secondary sources, including investor surveys, financial reports, and industry publications, this dissertation aims to contribute significantly to the existing body of knowledge surrounding PMS. Furthermore, it aspires to furnish valuable recommendations for investors seeking optimal portfolio management solutions and for financial institutions aiming to refine their PMS frameworks. By delving into the nuances of PMS, with a special emphasis on Motilal Oswal, this study not only seeks to enhance understanding but also aims to offer practical insights that can inform decision-making processes in the realm of investment management.

Development of big data assisted effective enterprise resource planning framework for smart human resource management.

The planning of human resources and the management of enterprises consider the organization's size, the amount of effort put into operations, and the level of productivity. Inefficient allocation of resources in organizations due to skill-task misalignment lowers production and operational efficiency. This study addresses organizations' poor resource allocation and use, which reduces productivity and the efficiency of operations, and inefficiency may adversely impact company production and finances. This research aims to develop and assess a Placement-Assisted Resource Management Scheme (PRMS) to improve resource allocation and usage and businesses' operational efficiency and productivity. PRMS uses expertise, business requirements, and processes that are driven by data to match resources with activities that align with their capabilities and require them to perform promptly. The proposed system PRMS outperforms existing approaches on various performance metrics at two distinct levels of operations and operating levels, with a success rate of 0.9328% and 0.9302%, minimal swapping ratios of 12.052% and 11.658%, smaller resource mitigation ratios of 4.098% and 4.815%, mean decision times of 5.414s and 4.976s, and data analysis counts of 6387 and 6335 Success and data analysis increase by 9.98% and 8.2%, respectively, with the proposed strategy. This technique cuts the switching ratio, resource mitigation, and decision time by 6.52%, 13.84%, and 8.49%. The study concluded that PRMS is a solid, productivity-focused corporate improvement method that optimizes the allocation of resources and meets business needs.

Analysis of potential microRNA biomarkers for multiple sclerosis

Multiple sclerosis (MS) is a chronic demyelinating autoimmune neurodegenerative disorder for which no specific blood biomarker is available. MicroRNAs (miRNAs) have been investigated for their diagnostic potential in MS. However, MS-associated miRNAs are rarely replicated in different MS populations, thus impeding their use in clinical testing. Here, we evaluated the fold expression of seven reported MS miRNAs associated with MS incidence and clinical characteristics in 76 MS patients and 75 healthy control plasma samples. We found miR-23a-3p to be upregulated in relapsing-remitting MS (RRMS), while miR-326 was downregulated. MiR-150-5p and -320a-3p were significantly downregulated in secondary progressive MS (SPMS) patients compared to RRMS. High disability was associated with low miR-320a-3p, whereas low BDNF levels were associated with upregulation of miR-150-5p and downregulation of miR-326 expression in the total cohort. MiR-23a-3p and miR-326 showed significant diagnostic sensitivity, specificity, and accuracy for RRMS diagnosis. In addition, miR-150-5p and miR-320a-3p had comparable significant diagnostic test performance metrics distinguishing SPMS from RRMS. Therefore, there is potential for including miR-23a-3p and miR-326 in an RRMS diagnostic miRNA panel. Moreover, we have shown that miR-150-5p and miR-320a-3p could be novel RRMS conversion to SPMS biomarkers. The use of these miRNAs in MS diagnosis and prognosis warrants further investigation.

Quality Evaluation of Road Surface Markings with Uncertainty Aware Regression and Progressive Pretraining

Maintaining high-quality road markings is essential for both safety and traffic flow. However, there has been limited research on automating the process of evaluating the quality of these markings and identifying degraded ones that need to be fixed. Our paper introduces a new approach that uses uncertainty aware (UA) regression to evaluate the quality of road surface markings. The approach is based on deep learning models and a unique training method called “progressive pretraining (PPT).” We used a dataset of RGB images which we converted to binary masks. These masks were then augmented and used to train convolutional neural networks models with a PPT strategy. The results showed that both the hybrid and UA models managed to outperform the baseline model in some metrics such as mean average error which was at 24.38% and accuracy with 81.27%. Additionally, each model showed unique strengths across various performance metrics, highlighting the efficacy of integrating uncertainty and progressive learning in quality assessment tasks. This study presents a solid proof of concept for the application of UA methods in quality evaluation tasks in general, and surface marking quality evaluation in particular.

Metal bond strength regulation enables large-scale synthesis of intermetallic nanocrystals for practical fuel cells.

Structurally ordered L10-PtM (M = Fe, Co, Ni and so on) intermetallic nanocrystals, benefiting from the chemically ordered structure and higher stability, are one of the best electrocatalysts used for fuel cells. However, their practical development is greatly plagued by the challenge that the high-temperature (>600 °C) annealing treatment necessary for realizing the ordered structure usually leads to severe particle sintering, morphology change and low ordering degree, which makes it very difficult for the gram-scale preparation of desirable PtM intermetallic nanocrystals with high Pt content for practical fuel cell applications. Here we report a new concept involving the low-melting-point-metal (M' = Sn, Ga, In)-induced bond strength weakening strategy to reduce Ea and promote the ordering process of PtM (M = Ni, Co, Fe, Cu and Zn) alloy catalysts for a higher ordering degree. We demonstrate that the introduction of M' can reduce the ordering temperature to extremely low temperatures (≤450 °C) and thus enable the preparation of high-Pt-content (≥40 wt%) L10-Pt-M-M' intermetallic nanocrystals as well as ten-gram-scale production. X-ray spectroscopy studies, in situ electron microscopy and theoretical calculations reveal the fundamental mechanism of the Sn-facilitated ordering process at low temperatures, which involves weakened bond strength and consequently reduced Ea via Sn doping, the formation and fast diffusion of low-coordinated surface free atoms, and subsequent L10 nucleation. The developed L10-Ga-PtNi/C catalysts display outstanding performance in H2-air fuel cells under both light- and heavy-duty vehicle conditions. Under the latter condition, the 40% L10-Pt50Ni35Ga15/C catalyst delivers a high current density of 1.67 A cm-2 at 0.7 V and retains 80% of the current density after extended 90,000 cycles, which exceeds the United States Department of Energy performance metrics and represents among the best cathodic electrocatalysts for practical proton-exchange membrane fuel cells.

Sparse Approximate Pseudoinverse Preconditioning for Sparse Supervised Learning Problems with More Features than Samples

In this paper, we propose a new technique for handling a class of sparse supervised learning problems, specifically focusing on regression, binary, and multi-class classification problems. Sparse regression problems are associated with datasets including both arithmetic and categorical features and by encoding the dataset leads to an underdetermined sparse linear system. Furthermore, logistic regression can be used for both sparse binary and multi-class classification problems, solving an underdetermined sparse linear system. A new technique called Sparse Approximate Pseudoinverse Preconditioning (SAPP), namely the Explicit Preconditioned Conjugate Gradient for Normal Equations (EPCGNE) method based on Generic Approximate Sparse Pseudoinverse matrices is introduced for solving underdetermined sparse least square problems. Numerical experiments were carried out demonstrating a significant improvement of the performance metrics for the proposed SAPP scheme compared to other learners.

Understanding the Impact of Seismic Hazard and Climate Conditions on Multi Criteria–Based Retrofitting of Existing Buildings

A large share of the reinforced concrete (RC) building stock in Mediterranean countries faces a dual challenge of seismic vulnerability and energy inefficiency, calling for urgent renovation efforts. While energy upgrades have been the focus of previous renovation policies, recent research highlights the critical need for integrated retrofitting solutions that address both structural integrity and energy performance. Multi-criteria decision-making (MCDM) approaches are a promising tool for optimizing the combined choice of these integrated interventions, considering various decision variables (DVs) of economic, social, environmental, and technical nature. To understand the impact of climate and seismic hazard conditions on multi-criteria-based retrofitting assessment, a case-study RC school building is selected and assumed to be located in three distinct climate conditions, cold, mild, and warm, and three seismic hazard levels, low, medium and high. Moreover, given the complexity and challenges of quantifying seismic performance metrics for practitioners, an available simplified (practice-oriented) approach is compared herein with a more thorough research-based one for quantifying the seismic performance of RC buildings within the MCDM framework. Both approaches are applied to the case-study building, considering twelve possible combinations of energy and seismic interventions. The accuracy of the practice-oriented approach and its impact on the retrofitting rankings is evaluated, emphasizing the importance of accessible and efficient evaluation methods in facilitating informed decision-making for building renovation.

Novel AI-based tool for primary tooth segmentation on CBCT using convolutional neural networks: A validation study.

Primary teeth segmentation on cone beam computed tomography (CBCT) scans is essential for paediatric treatment planning. Conventional methods, however, are time-consuming and necessitate advanced expertise. The aim of this study was to validate an artificial intelligence (AI) cloud-based platform for automated segmentation (AS) of primary teeth on CBCT. Its accuracy, time efficiency, and consistency were compared with manual segmentation (MS). A dataset comprising 402 primary teeth (37 CBCT scans) was retrospectively retrieved from two CBCT devices. Primary teeth were manually segmented using a cloud-based platform representing the ground truth, whereas AS was performed on the same platform. To assess the AI tool's performance, voxel- and surface-based metrics were employed to compare MS and AS methods. Additionally, segmentation time was recorded for each method, and intra-class correlation coefficient (ICC) assessed consistency between them. AS revealed high performance in segmenting primary teeth with high accuracy (98 ± 1%) and dice similarity coefficient (DSC; 95 ± 2%). Moreover, it was 35 times faster than the manual approach with an average time of 24 s. Both MS and AS demonstrated excellent consistency (ICC = 0.99 and 1, respectively). The platform demonstrated expert-level accuracy, and time-efficient and consistent segmentation of primary teeth on CBCT scans, serving treatment planning in children.

Performance of artificial intelligence versus clinicians on the detection of contact between mandibular third molar and inferior alveolar nerve

AbstractPurposeThis study aimed to assess the performance of the ResNet‐50 deep learning algorithm in classifying panoramic images to determine contact between mandibular third molars and inferior alveolar nerve (IAN), comparing its performance with newly graduated dentists and oral and maxillofacial surgery specialists.MethodsPanoramic radiographs of mandibular third molars were retrieved from the Radiology Department, School of Dentistry, Kashan University of Medical Sciences. The images were independently classified as “contact” or “none‐contact” by the ResNet‐50 model, three newly graduated dentists, and three oral and maxillofacial surgery specialists. A maxillofacial radiologist sets the gold standard using cone beam‐CT. The accuracy, precision, recall, specificity, and dice coefficient were calculated for each group, with inter‐rater reliability assessed using Cohen's kappa value.ResultsOf the 548 images retrieved, 15% were allocated as the testing dataset, amounting to 82 images. The deep learning model showed the highest performance metrics, with an accuracy of 87.80%, precision of 78.57%, recall of 84.61%, specificity of 89.28%, and dice coefficient of 81.48%. Conversely, novice dentists had the lowest performance metrics (accuracy: 74.39% ± 2.99%, precision: 57.75% ± 4.33%, recall: 74.36% ± 1.81%, specificity: 74.4% ± 4.46%, and dice coefficient: 64.87% ± 2.69%). Specialists demonstrated accuracy of 84.96% ± 1.52%, precision of 72.65% ± 2.78%, recall of 84.61% ± 3.14%, specificity of 85.12% ± 2.23%, and dice coefficient of 78.11% ± 1.99%.ConclusionDeep learning algorithms can achieve comparable outcomes with oral and maxillofacial specialists and may outperform novice clinicians in diagnosing contact between third molars and IAN.

Physicochemical Properties and Fine Structure of Starch in Jinong Xiangruan 1 and DGR1 Soft Rice Varieties Cultivated in Different Regions of China

Rice, a staple food for billions around the globe, is cultivated in numerous forms. Among them, soft rice is well known, which is characterized by its tender, creamy consistency and desirable texture. In this study, we examined the physicochemical properties and fine structure of starch from two soft rice varieties, Jinong Xiangruan 1 and DGR1, cultivated in different regions in China (Baodi District, Tianjin City; Liaoning Province; and Fengyang City, Anhui Province). The aim was to understand how amylopectin content (AC) influences rice quality. This research aims to bridge the knowledge gap regarding the role of amylopectin in determining rice’s adhesive consistency and viscosity. Significant regional differences were observed in yield components such as the number of grains per panicle, seed setting rates and 1000-grain weight, with Liaoning generally showing higher performance metrics compared to other regions. Physicochemical analysis highlighted that though glue consistency and taste values showed little regional variation, AC significantly influenced rice hardness and viscosity. Rapid Visco Analyzer (RVA) profile analysis further demonstrated distinct differences in viscosity characteristics, underscoring the regional impacts on starch behavior. Additionally, molecular weight distribution and amylopectin chain length analysis, conducted via SEC-MALLS-RI and ICS ion chromatography, revealed notable differences in starch composition across varieties and locations. The findings suggest that environmental conditions play a crucial role in defining starch characteristics and, consequently, the eating quality of rice. This provides valuable insights for breeding high-quality japonica rice with broad adaptability.

Assessment of sustainable and machinable performance metrics of monocrystalline silicon carbide wafer with electrophoretic-assisted multi-diamond wire sawing

Assessment of sustainable and machinable performance metrics of monocrystalline silicon carbide wafer with electrophoretic-assisted multi-diamond wire sawing

Self‐attention convolutional neural network optimized with Remora Optimization Algorithm for energy aware resource management in Non‐orthogonal Multiple Access networks

SummaryNon‐orthogonal multiple access (NOMA) is an efficient technology for future wireless communication systems. The resource management on NOMA networks is flattering for improving energy efficiency (EE) of systems. Therefore, self‐attention convolutional neural network (SACNN) optimized with Remora Optimization Algorithm (ROA) for energy aware resource management in NOMA network (SACNN‐ROA‐EE) is proposed in this paper for solving user allocation, channel allocation, and power allocation problems. The Matching‐Coalition approach is considered to face the user allocation issue, and the SACNN is considered to face the channel allocation and power allocation problems. SACNN does not adopt any optimization methods to determine the optimal parameters in channel allocation and power allocation. Therefore, the ROA is applied to optimize the SACNN weight parameters. The proposed technique is activated in MATLAB, and its efficacy is analyzed with under performances metrics, such as minimum user rate, sum rate, energy, and spectral performance. The proposed SACNN‐ROA‐EE attains 24.35%, 27.84%, and 38.58% better sum rate and 27.45%, 43.28%, 30.56% better EE compared with existing methods, such as EE utilizing communication deep neural network optimized with power allocation optimization (PAO‐CDNN‐EE), EE under deep transfer deterministic policy gradient along deep deterministic policy gradient approach (DPPGA‐DTDPPG‐EE), and EE utilizing deep reinforcement learning with baseline algorithms (BA‐DRL‐EE), respectively.

Improved forecasting of the compressive strength of ultra‐high‐performance concrete (UHPC) via the CatBoost model optimized with different algorithms

AbstractThis paper focuses on the applicability of CatBoost models constructed using various optimization techniques for improved forecasting the compressive strength of ultra‐high‐performance concrete (UHPC). Phasor particle swarm optimization (PPSO), dwarf mongoose optimization (DMO), and atom search optimization (ASO), which have been very popular recently, are preferred as optimization algorithms. A comprehensive and reliable data set is used to develop the CatBoost models, which include 785 test results with 15 input features. The performance of the CatBoost models (PPSO‐CatBoost, DMO‐CatBoost, and ASO‐CatBoost) optimized with different algorithms is thoroughly assessed by means of various statistical metrics and error analysis to determine the model with the best forecasting capability, and this model is compared with the models obtained from previous studies. In addition, Shapley additive exPlanations (SHAP) analysis is used to ensure the interpretability of the forecasting models and to overcome the “black box” problem of machine learning (ML) models. The obtained results demonstrate that all CatBoost models outstandingly forecast the compressive strength of UHPC. Among these models, the DMO‐CatBoost model stands out compared to the other models in various performance metrics, such as high coefficient of determination (R2) values, low root mean squared error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) values, along with a smaller error ratio. In other words, the RMSE, R2, MAPE, and MAE values of the DMO‐CatBoost model for the training set are 3.67, 0.993, 0.019, and 2.35, respectively, whereas those for the test set are 6.15, 0.978, 0.038, and 4.51. Additionally, the performance ranking of the algorithms used to optimize the hyperparameters of the CatBoost model is as follows: DMO > PPSO > ASO. On the other hand, SHAP analysis showed that age, fiber dosage, and cement dosage significantly influence the compressive strength of UHPC. These findings can guide structural engineers in the design and optimization of UHPC, thus assisting them in developing strategies to improve the strength properties of the material. Finally, based on the best forecasting model developed in this work, a graphical user interface has been developed to easily forecast the compressive strength of UHPC in practical applications without additional tools or software.

IMNE: Maximizing influence through deep learning-based node embedding in social network

Influence Maximization (IM) is a critical problem in social network analysis and marketing. It involves identifying a subset of nodes in a social network whose activation or influence can lead to the maximal spread of information, ideas, or behaviors within the network. Although many approaches have been developed in the literature to deal with this problem, most of these approaches are ineffective in dealing with large-scale social networks due to free parameters and computational complexity. Embeddings are used to learn low-dimensional representations of nodes in a social network. These embeddings capture the structural and semantic information of nodes and their relationships within the network. By training deep learning models on graph-structured data, node embeddings can capture complex patterns and dependencies in social networks, enabling more effective downstream tasks such as IM. Accordingly, this paper proposes an efficient algorithm to address the IM problem in social networks using deep learning-based Node Embedding (IMNE), which includes shell decomposition, graph/node embedding, and search space reduction as well as the use of local structural features. Our approach combines the power of deep learning for representation learning with the rich structural information present in social networks to address the challenge of IM in complex and dynamic social networks. IMNE uses the Independent Cascade (IC) information diffusion model to determine the labels needed to train the model by calculating the influence of nodes. Experimental results on several real-world networks considering different performance metrics show that IMNE performs better compared to existing baseline and state-of-the-art methods.

Unveiling Key Factors Shaping Energy Storage Strategies for Sustainable Energy Communities

This research delves into a case study of a photovoltaic (PV) energy community, leveraging empirical data to explore the integration of renewable energy sources and storage solutions. By evaluating energy generation and consumption patterns within real-world energy communities (a nominal generation capacity of 33 kWn) in Gipuzkoa, Spain, from May 2022 to May 2023, this study comprehensively examines operational dynamics and performance metrics. This study highlights the critical role of energy consumption patterns in facilitating the integration of renewable energy sources and underscores the importance of proactive strategies to manage demand fluctuations effectively. Against the backdrop of rising energy costs and environmental concerns, renewable energies and storage solutions emerge as compelling alternatives, offering financial feasibility and environmental benefits within energy communities. This study emphasizes the necessity of research and development efforts to develop efficient energy storage technologies and the importance of economic incentives and collaborative initiatives to drive investments in renewable energy infrastructure. The analyzed results provide valuable insights into operational dynamics and performance metrics, further advancing our understanding of their transformative potential in achieving a sustainable energy future. Specifically, our study suggests that storage capacity should ideally support an average annual capacity of 23%, with fluctuations observed where this capacity may double or reduce to a minimum in certain months. Given the current market conditions, our findings indicate the necessity of significant public subsidies, amounting to no less than 67%, to facilitate the installation of storage infrastructure, especially in cases where initial investments are not covered by the energy community.

Merits of Multi-Indicator Precautionary Approach Management in a Male-Only Crab Fishery

Snow crab replaced groundfish fisheries as the primary focus of the Newfoundland and Labrador (NL) fishing industry in the mid-1990s. Over the past three decades, management philosophies in this male-only fishery have shifted from promoting industry expansion to promoting industry rationalization to implementing Precautionary Approach (PA) management. Until the mid-2010s, there were regionalized management strategies characterized by higher exploitation rates in northern than southern sub-stock units along the NL marine shelves. However, in the late 2010s, exploitation rates were permitted to elevate to high levels across the entire stock range in association with a large resource decline, and evidence of biological harm through fishing emerged in chronically depleted areas. In 2019, when stock and fishery productivity were near historical lows, a multi-indicator PA system was informally introduced into the management of the resource. This event coincided with anticipated improvements in stock and fishery performance. This paper examines the extent to which the multi-indicator PA management system, focused on the promotion of both biological protection and maximization of fishing efficiency, may have contributed to recent improvements in stock and fishery status. A suite of indicators from areas implementing PA guidance into management in 2019 prior to the formal adoption of the PA system in 2023 suggest the system has been highly beneficial in promoting rapid recovery and improving stock and fishery performance metrics to levels matching or exceeding historical levels under similar conditions. We discuss the capacity of the system to better safeguard biological aspects of resource and fisheries productivity moving forward under a scenario of an expectant resource decline.

Emotions unveiled: detecting COVID-19 fake news on social media

The COVID-19 pandemic has highlighted the pernicious effects of fake news, underscoring the critical need for researchers and practitioners to detect and mitigate its spread. In this paper, we examined the importance of detecting fake news and incorporated sentiment and emotional features to detect this type of news. Specifically, we compared the sentiments and emotions associated with fake and real news using a COVID-19 Twitter dataset with labeled categories. By utilizing different sentiment and emotion lexicons, we extracted sentiments categorized as positive, negative, and neutral and eight basic emotions, anticipation, anger, joy, sadness, surprise, fear, trust, and disgust. Our analysis revealed that fake news tends to elicit more negative emotions than real news. Therefore, we propose that negative emotions could serve as vital features in developing fake news detection models. To test this hypothesis, we compared the performance metrics of three machine learning models: random forest, support vector machine (SVM), and Naïve Bayes. We evaluated the models’ effectiveness with and without emotional features. Our results demonstrated that integrating emotional features into these models substantially improved the detection performance, resulting in a more robust and reliable ability to detect fake news on social media. In this paper, we propose the use of novel features and methods that enhance the field of fake news detection. Our findings underscore the crucial role of emotions in detecting fake news and provide valuable insights into how machine-learning models can be trained to recognize these features.

Weighted metrics are required when evaluating the performance of prediction models in nested case–control studies

BackgroundNested case–control (NCC) designs are efficient for developing and validating prediction models that use expensive or difficult-to-obtain predictors, especially when the outcome is rare. Previous research has focused on how to develop prediction models in this sampling design, but little attention has been given to model validation in this context. We therefore aimed to systematically characterize the key elements for the correct evaluation of the performance of prediction models in NCC data.MethodsWe proposed how to correctly evaluate prediction models in NCC data, by adjusting performance metrics with sampling weights to account for the NCC sampling. We included in this study the C-index, threshold-based metrics, Observed-to-expected events ratio (O/E ratio), calibration slope, and decision curve analysis. We illustrated the proposed metrics with a validation of the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA version 5) in data from the population-based Rotterdam study. We compared the metrics obtained in the full cohort with those obtained in NCC datasets sampled from the Rotterdam study, with and without a matched design.ResultsPerformance metrics without weight adjustment were biased: the unweighted C-index in NCC datasets was 0.61 (0.58–0.63) for the unmatched design, while the C-index in the full cohort and the weighted C-index in the NCC datasets were similar: 0.65 (0.62–0.69) and 0.65 (0.61–0.69), respectively. The unweighted O/E ratio was 18.38 (17.67–19.06) in the NCC datasets, while it was 1.69 (1.42–1.93) in the full cohort and its weighted version in the NCC datasets was 1.68 (1.53–1.84). Similarly, weighted adjustments of threshold-based metrics and net benefit for decision curves were unbiased estimates of the corresponding metrics in the full cohort, while the corresponding unweighted metrics were biased. In the matched design, the bias of the unweighted metrics was larger, but it could also be compensated by the weight adjustment.ConclusionsNested case–control studies are an efficient solution for evaluating the performance of prediction models that use expensive or difficult-to-obtain biomarkers, especially when the outcome is rare, but the performance metrics need to be adjusted to the sampling procedure.

Intelligent design of shear wall layout based on diffusion models

AbstractThis study explores artificial intelligence (AI) for shear wall layout design, aiming to overcome challenges in data feature sparsity and the complexity of drawing representations in existing AI‐based methods. We pioneer an innovative method leveraging the potential of diffusion models, establishing a suitable drawing representation, and examining the impact of various conditions. The proposed image‐prompt diffusion model incorporating a mask tensor featuring tailored training methods demonstrates superior feature extraction and design effectiveness. A comparative study reveals the advanced capabilities of the Struct‐Diffusion model in capturing engineering designs and optimizing performance metrics such as inter‐story drift ratio (in elastic analysis), offering significant improvements over previous methods and paving the way for future innovations in intelligent designs.

IAQ Prediction in Apartments Using Machine Learning Techniques and Sensor Data

This study explores the capability of machine learning techniques (MLTs) in predicting IAQ in apartments. Sensor data from kitchen air monitoring were used to determine the conditions in the living room. The analysis was based on several air parameters—temperature, relative humidity, CO2 concentration, and TVOC—recorded in five apartments. Multiple input–multiple output prediction models were built. Linear (multiple linear regression and multilayer perceptron (MLP)) and nonlinear (decision trees, random forest, k-nearest neighbors, and MLP) methods were investigated. Five-fold cross-validation was applied, where four apartments provided data for model training and the remaining one was the source of the test data. The models were compared using performance metrics (R2, MAPE, and RMSE). The naive approach was used as the benchmark. This study showed that linear MLTs performed best. In this case, the coefficients of determination were highest: R2 = 0.94 (T), R2 = 0.94 (RH), R2 = 0.63 (CO2), R2 = 0.84 (TVOC, based on the SGP30 sensor), and R2 = 0.92 (TVOC, based on the SGP30 sensor). The prediction of distinct indoor air parameters was not equally effective. Based on the lowest percentage error, best predictions were attained for indoor air temperature (MAPE = 1.57%), relative humidity (MAPE = 2.97%RH), and TVOC content (MAPE = 0.41%). Unfortunately, CO2 prediction was loaded with high error (MAPE = 20.83%). The approach was particularly effective in open-kitchen apartments, and they could be the target for its application. This research offers a method that could contribute to attaining effective IAQ control in apartments.