Inadequacy Of Model Research Articles

Evaluation of Drivers’ Mental Model, Trust, and Reliance Toward Level 2 Automated Vehicles

Level 2 automated vehicles (AVs) have been commercially available for years, yet the extent to which drivers are cognizant of their capabilities and limitations remains largely unknown. This study aimed to assess the public’s mental model of Level 2 AVs and investigate the relationships between mental model, trust, and reliance. Moreover, it sought to explore driver heterogeneity through cluster analysis to identify typical driver classes. Employing Tesla Autopilot as a representative Level 2 automation system, we designed a questionnaire measuring mental model from three dimensions: functional condition, sensorimotor preparation, and automation monitoring. This questionnaire was administered to both Tesla Autopilot owners (N = 357) and non-owners (N = 357). The results showed that, on average, drivers possessed an inadequate mental model regarding Level 2 AVs, and those with a relatively better mental model exhibited lower levels of trust and reliance. Owning a Level 2 AV did not result in a better understanding of the system, but it led to a higher trust level. Moreover, the results suggested that drivers’ trust and reliance in AVs tended to increase within the initial 6 months of usage and then kept stabilized after that. Through cluster analysis, we identified three distinct driver classes that exhibited significant differences in their mental model levels and attitudes towards AVs: Class 1 (blurred positive), Class 2 (sober neutral), and Class 3 (cautiously positive). This further validates the presence of heterogeneity among drivers. The insights gained from this study can serve as a valuable resource for supporting and guiding the development and implementation of training programs aimed at enhancing drivers’ comprehension of Level 2 AVs.

Semantic Segmentation of Satellite Images for Landslide Detection Using Foreground-Aware and Multi-Scale Convolutional Attention Mechanism.

Advancements in satellite and aerial imagery technology have made it easier to obtain high-resolution remote sensing images, leading to widespread research and applications in various fields. Remote sensing image semantic segmentation is a crucial task that provides semantic and localization information for target objects. In addition to the large-scale variation issues common in most semantic segmentation datasets, aerial images present unique challenges, including high background complexity and imbalanced foreground-background ratios. However, general semantic segmentation methods primarily address scale variations in natural scenes and often neglect the specific challenges in remote sensing images, such as inadequate foreground modeling. In this paper, we present a foreground-aware remote sensing semantic segmentation model. The model introduces a multi-scale convolutional attention mechanism and utilizes a feature pyramid network architecture to extract multi-scale features, addressing the multi-scale problem. Additionally, we introduce a Foreground-Scene Relation Module to mitigate false alarms. The model enhances the foreground features by modeling the relationship between the foreground and the scene. In the loss function, a Soft Focal Loss is employed to focus on foreground samples during training, alleviating the foreground-background imbalance issue. Experimental results indicate that our proposed method outperforms current state-of-the-art general semantic segmentation methods and transformer-based methods on the LS dataset benchmark.

Non-Parametric Fuzzy Regression Discontinuity Design in Modelling Viral Load Suppression Among People Living With HIV/AIDS (PLWHA)

Adherence to antiretroviral therapy (ART) is a significant determinant of viral load suppression in HIV patients. There are inadequate statistical models that bring out the direct effects of ART on the suppression of HIV/AIDS. Traditional regression models address the general determinants of viral load suppression. Regression discontinuity designs, on the other hand, bring out the causal effects of ART on viral load suppression based on various thresholds. This study used the non-parametric fuzzy regression discontinuity design (FRDD) to model viral load suppression in PLWHA. The study began with developing a non-parametric FRDD, simulating the model to assess its performance, and applying the model to the Quality-of-Care dataset from Kaggle. The study focused on viral load suppression as the outcome variable, CD4 count and age as the running variables, gender, and whether a patient received counseling as additional covariates. The optimal thresholds were 40.5 years and 320 cubic millimeters for the CD4. There was an increasing negative treatment effect of ART on viral load suppression as the cutoff points for CD4 count increase. At the same time, there was an increasing negative treatment effect of ART on viral load suppression with increasing age. The compliance ratios for respondents increased with the negative increase in the treatment effect. Other analyses, such as the McCrary density test, bunching test, and manipulation test, indicated that the non-parametric fuzzy regression discontinuity design is effective in modeling viral load suppression.

Development of a Microfluidic Vascularized Osteochondral Model as a Drug Testing Platform for Osteoarthritis.

Osteoarthritis (OA) is a degenerative joint disease characterized by changes in cartilage and subchondral bone. To date, there are no available drugs that can counteract the progression of OA, partly due to the inadequacy of current models to recapitulate the relevant cellular complexity. In this study, an osteochondral microfluidic model is developed using human primary cells to mimic an OA-like microenvironment and this study validates it as a drug testing platform. In the model, the cartilage compartment is created by embedding articular chondrocytes in fibrin hydrogel while the bone compartment is obtained by embedding osteoblasts, osteoclasts, endothelial cells, and mesenchymal stem cells in a fibrin hydrogel enriched with calcium phosphate nanoparticles. After developing and characterizing the model, Interleukin-1β is applied to induce OA-like conditions. Subsequently, the model potential is evaluated as a drug testing platform by assessing the effect of two anti-inflammatory drugs (Interleukin-1 Receptor antagonist and Celecoxib) on the regulation of inflammation- and matrix degradation-related markers. The model responded to inflammation and demonstrated differences in drug efficacy. Finally, it compares the behavior of the "Cartilage" and "Cartilage+Bone" models, emphasizing the necessity of incorporating both cartilage and bone compartments to capture the complex pathophysiology of OA.

MixUNETR: A U-shaped network based on W-MSA and depth-wise convolution with channel and spatial interactions for zonal prostate segmentation in MRI

Magnetic resonance imaging (MRI) plays a pivotal role in diagnosing and staging prostate cancer. Precise delineation of the peripheral zone (PZ) and transition zone (TZ) within prostate MRI is essential for accurate diagnosis and subsequent artificial intelligence-driven analysis. However, existing segmentation methods are limited by ambiguous boundaries, shape variations and texture complexities between PZ and TZ. Moreover, they suffer from inadequate modeling capabilities and limited receptive fields. To address these challenges, we propose a Enhanced MixFormer, which integrates window-based multi-head self-attention (W-MSA) and depth-wise convolution with parallel design and cross-branch bidirectional interaction. We further introduce MixUNETR, which use multiple Enhanced MixFormers as encoder to extract features from both PZ and TZ in prostate MRI. This augmentation effectively enlarges the receptive field and enhances the modeling capability of W-MSA, ultimately improving the extraction of both global and local feature information from PZ and TZ, thereby addressing mis-segmentation and challenges in delineating boundaries between them. Extensive experiments were conducted, comparing MixUNETR with several state-of-the-art methods on the Prostate158, ProstateX public datasets and private dataset. The results consistently demonstrate the accuracy and robustness of MixUNETR in MRI prostate segmentation. Our code of methods is available at https://github.com/skyous779/MixUNETR.git.

Figurative Language and Sensory Perception: Corpus-Based Computer-Assisted Study of the Nature and Motivation of Synesthetic Metaphors in Olive Oil Tasting Notes

ABSTRACT Meaning in sensory language is often built through figurative mechanisms, such as synesthetic metaphors, where a sensorial domain is used to talk about perceptions from a different sense, as in sweet [ taste ] texture [ touch ]. The motivation of synesthetic transfers of meaning has been studied in general and literary language, resulting in attempts to reveal universal patterns regarding the directionality of meaning transfer and sensorial conceptual preference. However, those universals have not been proven in any sensory Language for Specific Purposes (LSP). We a sensory LSP corpus of olive oil tasing notes in English to explore the nature of these metaphors, test existent models and explanatory accounts and identify tendencies present in synesthetic meaning transfers. The computer-assisted semi-automatic scalable methodology followed consists of the innovative quasi-simultaneous identification of semantic incongruences and the classification of synesthetic expressions in the discourse according to the source and target sensorial domains. Results show the inadequacy of existent models to explain synesthetic behavior in olive oil tasting language. The patterns found are discussed in the light of cognitive constraints and LSP genre analysis to conclude that a multi-causal approach is needed to explain the motivation of synesthetic transfers of meaning.

Why do things expand on heating? A discussion on the need for asymmetric potentials to understand thermal expansion

Abstract Understanding thermal expansion is pivotal in various contexts such as comprehending sea level rise due to climate change. Considering the importance of these topics for physics education, this article aims to clarify the characteristics of the models used to represent and explain thermal expansion. Limitations of the linear expansion equation commonly found in textbooks are shown, emphasising the importance of conceptual discussion about thermal expansion. Microscopic models are also explored, demonstrating the inadequacy of the harmonic oscillator model in explaining thermal expansion and introducing the necessity of employing asymmetric potentials. Through the graphic of the Lennard-Jones potential and an algebraic deduction akin to Enrico Fermi’s approach, the thermal expansion of solids and its relation with asymmetric interactions is explored.

Discrepancies in precipitation changes over the Southwest River Basin of China based on ISIMIP3b

Selecting appropriate global climate models (GCMs) is crucial for minimizing uncertainty in regional climate projections under future scenarios. Previous studies have predominantly assessed the modeling capability of GCMs for regional precipitation climatology and its long-term patterns based on annual and seasonal precipitation data. Building upon these, we primally evaluated the performance of five GCMs from phase 3b of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3b) in simulating precipitation concentration and its variations in the Southwest River Basin (SWRB) of China using the precipitation concentration index (PCI). The results indicate that: (1) The 5 GCMs generally capture the spatial distribution of annual average precipitation in the SWRB but significantly overestimate its magnitude, with a maximum regional average deviation of 207.80 mm. Furthermore, all models tend to overestimate the overall drying trend in the SWRB and show limited capability in simulating interdecadal variations of annual precipitation. (2) While the 5 GCMs reasonably simulate the spatial distribution of annual average PCI in the SWRB, they tend to overestimate its values, with a maximum regional average deviation of 1.54. Additionally, their simulation performance in capturing PCI trends and interdecadal variations is also limited. (3) The 5 GCMs tend to overestimate seasonal precipitation in the SWRB, with the best simulation performance for the distribution of autumn precipitation, followed by spring and summer, and the poorest for winter. Significant differences exist in the simulation performance of the models for seasonal precipitation proportions, which result in discrepancies in the models’ representation of PCI. Moreover, the models’ poor simulation performance of PCI trends is partly due to their inadequate modeling of trends in seasonal precipitation proportions. The findings will contribute to laying the foundation for meteorological hydrological research and water resource management in the SWRB.

How to mathematically model a drill-string: lumped or continuous models?

This work compares the efficacy of lumped and continuous models in simulating drill-string dynamics. We aim to critically explore the inadequacy of lumped models and challenge their perpetuated use in the literature. Lumped models represent the drill-string with a pre-determined number of discrete masses, sometimes as few as one or two. This inherently restricts their ability to capture the distributed nature of drill-strings, compromising their accuracy. In this paper, these limitations are made evident by comparing simulations of lumped models with simulations of continuous models that increase in complexity. First, predictions of the axial behaviour obtained with two models, one lumped and another continuous, are analysed. Second, simulations involving a 3-D fully coupled continuous model are evaluated. The simulations demonstrate the inaccuracy of lumped models. Also, they refute the frequent assumption of representing the bottom hole assembly (BHA) as a single lumped mass. Beyond these results, theoretical arguments based on fundamental wave-reflection phenomena are employed to further support the inadequacy of lumped formulations for this purpose. Third, this paper also discusses the prevalent strategy of validating lumped models through comparisons with laboratory experiments. This approach suffers from being a makeshift validation, as similitude principles are often violated in laboratory setups, resulting in a practice that may have helped perpetuate lumped models in the literature. Consequently, using lumped models for general drill-string dynamics prediction is not appropriate. Recognising their limitations and exploring alternative approaches, such as continuous models, is crucial for accurate drill-string dynamics prediction.

Does the Merger of the Indonesian Islamic Bank Matter for Its Social Mission of Economic Empowerment?

Research Originality: Economic empowerment through financing Micro Small Medium Enterprises (MSMEs) is one dimension of the social mission of sharia banking in Indonesia. The merger action of Bank of Sharia Indonesia (BSI) raises the question of whetherthe performance of economic empowerment has changed.Research Objectives: This research aims to analyse the differences in the BSI’s performance of economic empowerment during preand post-merger periods and analyse the issues and strategies for improving BSI's post-merger economic empowerment performance.Research Methods: It is a research using mixed method combining a quantitative explorative analysis approach using Wilcoxon Test, and a qualitative one using Analytic Network Process.Empirical Results: This research finds that BSI's economic empowerment performance has increased in terms of the quantity but decreased in terms of proportion in the post-merger period. ANP research demonstrates that risk management is a priority issue for the BSI post-merger period. The social mission of economic empowerment indicates several priority issues, namely, the relatively higher rate of profit margin than interest, the inadequate supervision model, the low rate of risk management literacy, and the relatively higher risk of profit loss sharing (PLS)-based financing. The strategy analysis indicates that policy intervention for BSI’s MSMEs financing is a top priority in improving the performance of its economic empowerment.Implications: This research recommends some recommendations for sharia banks’ MSMEs financing equivalent to the one of conventional banks. Economic digitalization as technological means to widen accessibility of economic empowerment and distribution segmentation focusing more on the halal sector.JEL Classification: A13, G21, G34How to Cite:Isman, A. F., & Hidayah, N. (2024). Does the Merger of the Indonesian Islamic Bank Matter for Its Social Mission of Economic Empowerment? Etikonomi, 23(2), 333 – 352. https://doi.org/10.15408/etk.v23i2.39810.

Computational fluid dynamics simulation of ammonia leakage scenarios during ship-to-ship bunkering

Ammonia is emerging as a potential marine fuel, yet its toxicity and risk of accidental leaks pose significant safety challenges compared to other alternatives. This study focuses on the critical ship-to-ship bunkering process using Computational Fluid Dynamics (CFD) simulations to analyse diverse ammonia leakage scenarios considering various leak orientations relative to the wind and the ships. The numerical setup was initially validated against controlled experiments, demonstrating satisfactory agreement. Results indicate that atmospheric ammonia dispersion is significantly influenced by obstacles like the superstructure and hull in the near field, with diminishing effect in the far field. Conventional Gaussian plume models are inadequate within the first few hundred metres from the release source due to the complex near-field flow patterns and increased turbulence generated by the wakes of the hull and superstructure. These findings underscore the necessity for improved ship-scale dispersion models to accurately evaluate the impact of obstacles on ammonia dispersion. The consequence analysis reveals that upward leaks create larger risk zones with potential for irreversible health effects, while horizontal leaks may expose personnel to life-threatening concentrations. Vertical leaks in crosswind configurations generate lethal zones covering tens of metres on deck. These results highlight the importance of considering the exact location of the transfer hose relative to the ship and all potential wind orientations in risk assessments. Given the extensive range of potential dispersion patterns and the inadequacy of conventional Gaussian dispersion models in this context, we strongly recommend vessel-specific CFD simulations before certifying ammonia bunkering operations as safe. This approach is crucial for accurate risk assessment at the ship scale, where complex geometries and multi-directional flows invalidate the assumptions of traditional point-source, uni-directional dispersion models.

Clinician Perspectives on Palliative Care for Older Adults With Serious Mental Illnesses: A Multisite Qualitative Study

ObjectivesApproximately 5.5% of the population live with serious mental illnesses (SMI). Older adults with SMI experience a high burden of serious medical illnesses and disparities in advance care planning, symptom management, and caregiver support. The objectives of this study are to explore interdisciplinary clinician perspectives on the palliative care needs of older adults with SMI and serious medical illnesses. Design, setting, and participantsThis qualitative study utilized thematic analysis of semi-structured interviews of interdisciplinary clinicians practicing palliative care, geriatrics, or geriatric/consultation-liaison psychiatry at four hospitals within an urban health system. MeasurementsThemes related to care of older adults with serious mental illness and serious medical illness with respect to clinician experiences, challenges in care, and opportunities to improve care. ResultsThe authors interviewed 45 clinicians. Major themes identified were: (1) Current paradigms of palliative care do not meet the needs of patients with SMI; (2) Clinicians are motivated to care for this population but require more training and interdisciplinary practice; (3) There is a need for structural integration of psychiatric and palliative care services. ConclusionsThe study underscores the inadequacy of current palliative care models in meeting the unique needs of older adults with SMI. Models of integrated psychiatric and serious illness care and enhanced training are needed to improve the delivery of palliative care. Integrated care models and workforce development at the interface of serious illness care and psychiatric have the potential to improve outcomes for this vulnerable population.

An AHP-based evaluation framework for automotive stamping suppliers

PurposeThis study aims to establish a robust evaluation framework for suppliers within the automotive supply chain, specifically in the stamping sector. The primary objectives are to elucidate the performance criteria of suppliers, identify indicators and scales for measuring these criteria and find the importance of the criteria.Design/methodology/approachThe evaluation framework comprises a criteria hierarchy and indicators developed based on the evaluation criteria of major automotive manufacturers. Specific indicators and measurement scales are recommended for assessing suppliers. Importance weights for the criteria are assigned based on the input of nine experts using the Analytic Hierarchy Process (AHP). Finally, four sheet metal stamping tooling (SMST) suppliers are evaluated by four specialists using the proposed evaluation framework.FindingsThe study introduces a novel classification of criteria, encompassing financial and commercial perspectives, delivery capability, supplier facility and cultural approaches and business process necessities. The findings underscore the significance of financial and commercial stability in the selection of SMST suppliers, emphasizing their role in mitigating risks associated with disruptions, bankruptcies and unforeseen events. Additionally, several SMST evaluation factors identified in this study contribute to the development of resilience capabilities, highlighting the crucial importance of their inclusion and assessment in the proposed evaluation framework.Originality/valueThis research presents a comprehensive model for evaluating SMST suppliers, which tackles the multidisciplinary challenges within the automotive supply chain. Given the inadequacy or nonexistence of current SMTS selection models, this study bridges the gap by exploring potential and necessary criteria, alongside 116 specific indicators and measurement scales.

The INSIGHT platform: Enhancing NAD(P)-dependent specificity prediction for co-factor specificity engineering

Enzyme specificity towards cofactors like NAD(P)H is crucial for applications in bioremediation and eco-friendly chemical synthesis. Despite their role in converting pollutants and creating sustainable products, predicting enzyme specificity faces challenges due to sparse data and inadequate models. To bridge this gap, we developed the cutting-edge INSIGHT platform to enhance the prediction of coenzyme specificity in NAD(P)-dependent enzymes. INSIGHT integrates extensive data from principal bioinformatics resources, concentrating on both NADH and NADPH specificities, and utilizes advanced protein language models to refine the predictions. This integration not only strengthens computational predictions but also meets the practical demands of high-throughput screening and optimization. Experimental validation confirms INSIGHT's effectiveness, boosting our ability to engineer enzymes for efficient, sustainable industrial and environmental processes. This work advances the practical use of computational tools in enzyme research, addressing industrial needs and offering scalable solutions for environmental challenges.

Wind power prediction through acoustic data-driven online modeling and active wake control

Accurate online prediction of wind power is essential for the reliable operation of power systems. However, the precision of wind turbine power forecasts is often hindered by wake effects and inadequate online modeling capabilities. To tackle this issue, this paper proposes a data-driven online prediction method for wind turbine power, leveraging acoustic data for training. The approach involves a neural network that learns acoustic feature changes related to wind output power, by enhancing the kernel extreme learning machine (KELM) technique and applying data augmentation. Additionally, to address the wake effects in real wind fields, active wake control is integrated, considering that most wind turbines operate in the wake of upstream turbines. Experiments using actual wind tunnel measurement data were conducted to assess the performance of the proposed method. The findings demonstrate that this method surpasses other techniques without increasing computational costs. The mean absolute error (MAE) of the proposed method was only 0.4111 and 0.4302, and the root mean square error (RMSE) was just 0.4868 and 0.5195, respectively, in both experiments. Moreover, the proposed acoustic data-driven online power prediction method proves stable in high background noise environments and achieves accurate wind power prediction with just a 10% data sample following the implementation of the active wake control strategy. This work significantly contributes to the efficient utilization of wind energy resources.

Naturally occurring horse model of miscarriage reveals temporal relationship between chromosomal aberration type and point of lethality

Chromosomal abnormalities are a common cause of human miscarriage but rarely reported in any other species. As a result, there are currently inadequate animal models available to study this condition. Horses present one potential model since mares receive intense gynecological care. This allowed us to investigate the prevalence of chromosomal copy number aberrations in 256 products of conception (POC) in a naturally occurring model of pregnancy loss (PL). Triploidy (three haploid sets of chromosomes) was the most common aberration, found in 42% of POCs following PL over the embryonic period. Over the same period, trisomies and monosomies were identified in 11.6% of POCs and subchromosomal aberrations in 4.2%. Whole and subchromosomal aberrations involved 17 autosomes, with chromosomes 3, 4, and 20 having the highest number of aberrations. Triploid fetuses had clear gross developmental anomalies of the brain. Collectively, data demonstrate that alterations in chromosome number contribute to PL similarly in women and mares, with triploidy the dominant ploidy type over the key period of organogenesis. These findings, along with highly conserved synteny between human and horse chromosomes, similar gestation lengths, and the shared single greatest risk for PL being advancing maternal age, provide strong evidence for the first animal model to truly recapitulate many key features of human miscarriage arising due to chromosomal aberrations, with shared benefits for humans and equids.

Breast Cancer Detection Cum Classification using Feature Optimization and Transfer Learning

Early detection of breast cancer can significantly reduce its high mortality rate. However, manual diagnosis from mammography images always requires a skilled professional. Various researchers have developed artificial intelligence-based methods to address this challenge, yet they encounter several issues such as overlapping cancerous and noncancerous regions, extraction of irrelevant features, and inadequate training models. In this paper, we propose a novel computationally automated biological mechanism for categorizing breast cancer. This approach leverages a new optimization technique based on the Moth Flame Optimization (MFO) algorithm, which enhances the classification of breast cancer cases. The proposed framework consists of two main stages: Utilizing Deep Neural Networks (DNN) based on transfer learning. Implementing a Convolutional Neural Network (DCNN) optimized with MFO. By integrating transfer learning with an optimized DCNN for classification, our method significantly improves accuracy compared to recent approaches. The proposed framework was evaluated using publicly available datasets, achieving an average classification accuracy of 97.95%. To ensure the statistical significance and robustness of our results, we conducted tests that demonstrated the effectiveness and superior performance of our methodology compared to current methods.

SwinDefNet: A Novel Surface Water Mapping Model in Mountain and Cloudy Regions Based on Sentinel-2 Imagery

Surface water plays a pivotal role in the context of climate change, human activities, and ecosystems, underscoring the significance of precise monitoring and observation of surface water bodies. However, the intricate and diverse nature of surface water distribution poses substantial challenges to accurate mapping. The extraction of water bodies from medium-resolution satellite remote sensing images using CNN methods is constrained by limitations in receptive fields and inadequate context modeling capabilities, resulting in the loss of boundary details of water bodies and suboptimal fusion of multi-scale features. The existing research on this issue is limited, necessitating the exploration of novel deep-learning network combinations to overcome these challenges. This study introduces a novel deep learning network combination, SwinDefNet, which integrates deformable convolution and Swin Transformer for the first time. By enhancing the effective receptive field and integrating global semantic information, the model can effectively capture the diverse features of water bodies at various scales, thereby enhancing the accuracy and completeness of water extraction. The model was evaluated on Sentinel-2 satellite images, achieving an overall accuracy of 97.89%, an F1 score of 92.33%, and, notably, an accuracy of 98.03% in mountainous regions. These findings highlight the promising potential of this combined approach for precise water extraction tasks.

Potential of 14C-based vs. ΔCO-based ΔffCO2 observations to estimate urban fossil fuel CO2 (ffCO2) emissions

Abstract. Atmospheric transport inversions are a powerful tool for independently estimating surface CO2 fluxes from atmospheric CO2 concentration measurements. However, additional tracers are needed to separate the fossil fuel CO2 (ffCO2) emissions from non-fossil CO2 fluxes. In this study, we focus on radiocarbon (14C), the most direct tracer of ffCO2, and the continuously measured surrogate tracer carbon monoxide (CO), which is co-emitted with ffCO2 during incomplete combustion. In the companion paper by Maier et al. (2024), we determined discrete 14C-based and continuous ΔCO-based estimates of the ffCO2 excess concentration (ΔffCO2) compared with a clean-air reference for the urban Heidelberg observation site in southwestern Germany. The ΔCO-based ΔffCO2 concentration was calculated by dividing the continuously measured ΔCO excess concentration by an average 14C-basedΔCO/ΔffCO2 ratio. Here, we use the CarboScope inversion framework adapted for the urban domain around Heidelberg to assess the potential of both types of ΔffCO2 observations to investigate ffCO2 emissions and their seasonal cycle. We find that, although they are more precise, 14C-based ΔffCO2 observations from almost 100 afternoon flask samples collected in the 2 years of 2019 and 2020 are not well suited for estimating robust ffCO2 emissions in the main footprint of this urban area, which has a very heterogeneous distribution of sources including several point sources. The benefit of the continuous ΔCO-based ΔffCO2 estimates is that they can be averaged to reduce the impact of individual hours with an inadequate model performance. We show that the weekly averaged ΔCO-based ΔffCO2 observations allow for a robust reconstruction of the seasonal cycle of the area source ffCO2 emissions from temporally flat a priori emissions. In particular, the distinct COVID-19 signal – with a steep drop in emissions in spring 2020 – is clearly present in these data-driven a posteriori results. Moreover, our top-down results show a shift in the seasonality of the area source ffCO2 emissions around Heidelberg in 2019 compared with the bottom-up estimates from the Netherlands Organization for Applied Scientific Research (TNO). This highlights the huge potential of ΔCO-based ΔffCO2 to validate bottom-up ffCO2 emissions at urban stations if the ΔCO/ΔffCO2 ratios can be determined without biases.

Enriching Building Information Modeling Models through Information Delivery Specification

The efficient acquisition and dissemination of information are crucial in building information modeling (BIM). Current BIM models face significant challenges, including inadequate modeling techniques, poorly defined information requirements, and low interoperability. These issues result in poor information quality and complicate the transition from information acquisition to model processing. Public authorities often provide documentation in various formats, requiring manual transfer to software, which is error-prone and burdensome. This process is particularly difficult for small and medium enterprises lacking resources and knowledge. To address these issues, the IDS (Information Delivery Specification) Collab Tool is under development. This tool aims to automate the import of requirements into authoring software, perform automated compliance checks, and enhance interoperability among stakeholders. It will assist designers in providing accurate information according to requirements through the IDS standard, improving model quality and efficiency from early design stages. Adapting BIM models to specific project requirements and aligning new IDS capabilities with traditional industry practices remain significant challenges. Preliminary evaluations indicate the tool’s potential to significantly improve workflow efficiency and compliance in BIM modeling. However, broader awareness and adoption of the IDS standard are needed. Further research and refinement are essential to fully realize the benefits of digital tools in revolutionizing design and construction practices.