Numerical Representation Research Articles

Bridging the gap: a framework for monetizing BIM by integrating industry insights for informed decision-making

PurposeThis study addresses the critical imperative of quantifying building information modeling (bimalliance) benefits by augmenting existing methodologies, with a focus on monetization. Engaging industry practitioners, the research develops a comprehensive framework through an exhaustive literature review and a survey in the Swedish construction industry, incorporating insights from 128 respondents.Design/methodology/approachThe framework, validated by industry experts, systematically assesses tangible BIM benefits against associated costs. It introduces a novel method in construction, addressing the lack of a unified approach. The resulting framework facilitates nuanced feasibility determinations by systematically evaluating BIM benefits against costs.FindingsDespite its acknowledged limitations, the framework effectively captures a comprehensive range of costs and benefits, providing a more accurate and detailed estimation of BIM’s impact on project outcomes.Practical implicationsWith practical implications, the framework enhances BIM understanding and application, contributing to effective project management throughout the construction supply chain lifecycle. Moreover, it aims to improve efficacy within the architecture, engineering, construction and operations industry.Originality/valueThe study empowers organizations and decision-makers with a bespoke tool for evaluating BIM feasibility, contributing to decision-making through a clarified numerical representation.

Investigating the Triple Code Model in numerical cognition using stereotactic electroencephalography.

The ability to conceptualize numerical quantities is an essential human trait. According to the "Triple Code Model" in numerical cognition, distinct neural substrates encode the processing of visual, auditory, and non-symbolic numerical representations. While our contemporary understanding of human number cognition has benefited greatly from advances in clinical imaging, limited studies have investigated the intracranial electrophysiological correlates of number processing. In this study, 13 subjects undergoing stereotactic electroencephalography for epilepsy participated in a number recognition task. Drawing upon postulates of the Triple Code Model, we presented subjects with numerical stimuli varying in representation type (symbolic vs. non-symbolic) and mode of stimuli delivery (visual vs. auditory). Time-frequency spectrograms were dimensionally reduced with principal component analysis and passed into a linear support vector machine classification algorithm to identify regions associated with number perception compared to inter-trial periods. Across representation formats, the highest classification accuracy was observed in the bilateral parietal lobes. Auditory (spoken and beeps) and visual (Arabic) number formats preferentially engaged the superior temporal cortices and the frontoparietal regions, respectively. The left parietal cortex was found to have the highest classification for number dots. Notably, the putamen exhibited robust classification accuracies in response to numerical stimuli. Analyses of spectral feature maps revealed that non-gamma frequency, below 30 Hz, had greater-than-chance classification value and could be potentially used to characterize format specific number representations. Taken together, our findings obtained from intracranial recordings provide further support and expand on the Triple Code Model for numerical cognition.

Multi-headed Ensemble Residual CNN: A Powerful Tool for Fibroblast Growth Factor Prediction

Fibroblast Growth Factor (FGF) performs a significant role in the repair, nervous system, development, and maintenance, making it a promising target for treating neurological diseases such as Parkinson's, stroke, Alzheimer's, and Huntington's disorders. However, the detection of FGF remains challenging due to the high cost and time required by traditional methods. To address this issue, we propose the first sequence-based computational method, FGF-MERCNN, for identifying FGFs using deep learning. Novel datasets were constructed by converting primary sequences into numerical representations through Enhanced Group Amino Acid Composition (EGAAC), Dipeptide Deviation from Expected Mean (DDE), and Group Tripeptide Composition (GTPC). These features were combined and analyzed using several deep learning models, including Convolutional Neural Networks (CNN), Multilayer Perceptrons (MLP), Gated Recurrent Units (GRU), and Multiheaded Ensemble Residual CNN (MERCNN). Among these, the MERCNN model achieved superior performance with accuracies of 88.60% on the training set and 83.68% on the test set, as validated by 5-fold cross-validation. The results of this study represent a significant advancement in FGF detection, offering a faster, cost-effective solution with the potential to improve diagnostic and therapeutic strategies for neurological diseases.

High-fidelity synthesis with causal disentangled representation

There exists a general problem in numerous disentangled representation learning algorithms that improves disentanglement performance by sacrificing generation performance. In order to solve the contradiction between disentanglement and generative performance, we propose a high-fidelity synthetic causal disentangled representation learning framework (HCAPE) combining Variational Auto-Encoder (VAE) and Generative Adversarial Network (GAN). First, in the inference stage, the assumption that the latent factors of the variation are mutually independent is abandoned. And a Structural Causal Model (SCM) in the encoder is resorted to learn causal representations. Second, a supervision term and balance parameter η were introduced to reduce the interference of disentanglement performance on generation performance. Finally, in the generation stage, a discriminator estimates the model’s gradient information, restoring high-frequency details in the image, thus synthesizing high-fidelity images. Experimental results show that the causal representations learned by the model are semantically interpretable and can generate counterfactual data. Most importantly, the model is able to generate high-fidelity images, achieving an optimal balance between disentangling and generative performance.

Groundwater flow system of the Abijata-Langano-Ziway lakes basin, Ethiopia

The Abijata-Langano-Ziway Lakes Basin (ALZLB) is situated in the Central part of the Main Ethiopian Rift. The availability and dynamics of groundwater in the Abijata-Langano-Ziway Lakes Basin (ALZLB) are primarily controlled by its geological and hydrogeological characteristics, shaped by volcanic-tectonic and sedimentary processes. The basin faces significant challenges, including drastic change in land use pattern, rapid population growth sustained by subsistence farming, over-extraction of water resources, and vulnerability to climate change and fragile ecosystems. These issues emphasize the urgent need for effective water resource management.To understand this complex system, a numerical groundwater flow model was employed to characterize the groundwater flow system within the ALZLB and examine its interaction with surface water bodies. The MODFLOW model translates the conceptual understanding of the basin's hydrogeology into a mathematical representation, allowing for numerical analysis. The model incorporates input parameters such as hydraulic conductivity and boundary conditions representing groundwater inflow and outflow. Steady-state numerical calculation was used to characterize the qualitative hydrogeological conceptual model into numerical representation and thereby describe the groundwater system.The calibrated model exhibited excellent agreement between simulated and observed groundwater levels. Statistical measures indicated a strong correlation (R2 = 0.98) and high efficiency (NSE = 0.97) in replicating the observed data. Additionally, the Mean Error (ME) of −8.3 m suggests minimal bias in the simulations. Further analysis of the histogram residuals revealed that a significant portion of the simulated values (65 % and 82 %) fell within ±20 m and ±30 m of the observed groundwater levels, respectively. This revealed the model's accuracy in capturing the groundwater system's behavior.The model identified groundwater recharge and constant head boundaries as the primary sources of groundwater inflow, contributing 445 million cubic meters per year (MCM/year) and 90 MCM/year, respectively. Conversely, constant head boundaries represented the most significant outflow pathway, with a simulated discharge of 519 MCM/year. The calibrated balance between inflow and outflow (discrepancy of −0.75 %) confirms that the model effectively simulates steady-state groundwater flow conditions. The calibrated model demonstrates the model's capability to accurately represent the basin's groundwater system.

Impact of Alignments on the Accuracy of Protein Subcellular Localization Predictions.

Alignments in bioinformatics refer to the arrangement of sequences to identify regions of similarity that can indicate functional, structural, or evolutionary relationships. They are crucial for bioinformaticians as they enable accurate predictions and analyses in various applications, including protein subcellular localization. The predictive model used in this article is based on a deep - convolutional architecture. We tested configurations of Deep N-to-1 convolutional neural networks of various depths and widths during experimentation for the evaluation of better-performing values across a diverse set of eight classes. For without alignment assessment, sequences are encoded using one-hot encoding, converting each character into a numerical representation, which is straightforward for non-numerical data and useful for machine learning models. For with alignments assessment, multiple sequence alignments (MSAs) are created using PSI-BLAST, capturing evolutionary information by calculating frequencies of residues and gaps. The average difference in peak performance between models with alignments and without alignments is approximately 15.82%. The average difference in the highest accuracy achieved with alignments compared with without alignments is approximately 15.16%. Thus, extensive experimentation indicates that higher alignment accuracy implies a more reliable model and improved prediction accuracy, which can be trusted to deliver consistent performance across different layers and classes of subcellular localization predictions. This research provides valuable insights into prediction accuracies with and without alignments, offering bioinformaticians an effective tool for better understanding while potentially reducing the need for extensive experimental validations. The source code and datasets are available at http://distilldeep.ucd.ie/SCL8/.

Abstract 4119992: 12-lead electrocardiograms predict adverse cardiovascular outcomes of emergency department patients

Introduction: Chest pain is a common cause for presentation to the emergency department (ED). Identifying patients at increased risk of adverse cardiovascular outcomes may help guide testing. Aims: We sought to determine if deep learning enabled 12-lead electrocardiogram (ECG) analysis may improve prediction of adverse cardiovascular outcomes of ED patients presenting with chest pain compared to conventional approaches. Methods: A pretrained convolutional neural network was applied to derive numerical representations of the index ECG waveforms from patients presenting to the Brigham and Women’s Hospital (BWH) ED with chest pain. We trained a neural network model (‘CP-AI’) to predict 30-day major adverse cardiovascular events (MACE) defined as myocardial infarction (MI), pulmonary embolism (PE), aortic dissection (AD), or mortality using age, sex, cardiac biomarkers (troponin and d-dimer), and the numerical ECG representations as inputs ( Figure 1 ). In a holdout sample of Massachusetts General Hospital (MGH) ED patients, we compared the performance of the CP-AI model to alternative models fit on (1) demographics (‘Age and Sex’) (2) demographics + cardiac biomarkers (‘Biomarker Model’), and (3) demographics + numerical ECG representations (‘ECG Model’). Results: The BWH derivation sample included 18,811 individuals (51% female, mean age 65 ± 16 years). The MGH validation sample included 14,476 individuals (45% female, mean age 65 ± 15 years). The CP-AI model significantly outperformed the comparison models for prediction of 30-day MACE with an area under the receiver operating characteristic curve (AUROC) of 0.82 [95% CI 0.81-0.83] ( Figure 1 ). CP-AI also significantly outperformed the comparison models for classification of the 30-day MACE components including MI (AUROC 0.91 [95% CI 0.90-0.92]), PE (AUROC 0.74 [95% CI 0.72-0.76]), and mortality (AUROC 0.86 [95% CI 0.85-0.87]), but not aortic dissection (0.71 [95% CI 0.68-0.75]). Conclusions: The integration of deep learning ECG analysis with conventional assessment improved prediction of adverse cardiovascular of ED patients presenting with chest pain.

Optimal Feature Selection and Classification for Parkinson’s Disease Using Deep Learning and Dynamic Bag of Features Optimization

Parkinson’s Disease (PD) is a neurological condition that worsens with time and is characterized bysymptoms such as cognitive impairment andbradykinesia, stiffness, and tremors. Parkinson’s is attributed to the interference of brain cells responsible for dopamine production, a substance regulating communication between brain cells. The brain cells involved in dopamine generation handle adaptation and control, and smooth movement. Convolutional Neural Networks are used to extract distinctive visual characteristics from numerous graphomotor sample representations generated by both PD and control participants. The proposed method presents an optimal feature selection technique based on Deep Learning (DL) and the Dynamic Bag of Features Optimization Technique (DBOFOT). Our method combines neural network-based feature extraction with a strong optimization technique to dynamically choose the most relevant characteristics from biological data. Advanced DL architectures are then used to classify the chosen features, guaranteeing excellent computational efficiency and accuracy. The framework’s adaptability to different datasets further highlights its versatility and potential for further medical applications. With a high accuracy of 0.93, the model accurately identifies 93% of the cases that are categorized as Parkinson’s. Additionally, it has a recall of 0.89, which means that 89% of real Parkinson’s patients are accurately identified. While the recall for Class 0 (Healthy) is 0.75, meaning that 75% of the real healthy cases are properly categorized, the precision decreases to 0.64 for this class, indicating a larger false positive rate.

The Class Ceiling in Politics

Prior studies have documented that working-class individuals rarely become parliamentarians. We know less about when in the career pipeline to parliament workers disappear, and why. We study these questions using detailed data on the universe of Swedish politicians’ careers over a 50-year period. We find roughly equal-sized declines in the proportion of workers on various rungs of the political career ladder ranging from local to national office. We reject the potential explanations that workers lack political ambition, public service motivation, honesty, or voter support. And while workers’ average high school grades and cognitive test scores are lower, this cannot explain their large promotion disadvantage, a situation that we label a class ceiling. Organizational ties to blue-collar unions help workers advance, but only to lower-level positions in left-leaning parties. We conclude that efforts to improve workers’ numerical representation should apply throughout the career ladder and focus on intra-party processes.

Android Based Academic Performance Monitoring System For Parents/Guardians

Academic feedback is essential in secondary schools to keep a rap-Port between students, teachers, and parents and guardians. There Are three main factors that contribute towards a student’s progress: Attitude, attendance and aptitude. Monitoring their progress is key To a student’s development in school and allows both teachers and Parents or guardians to support them to a greater extent. Annual Reports are sent to a student’s home to summarise their performance Over the academic year, following set criterion from the government. One aspect of a student’s report is the teacher’s written comment, Providing more details on a student’s attitude towards their learning. However, families whose primary language is not English may Struggle to interpret this information. Working in schools has demonstrated the diversity of students and their wide range of backgrounds, Including– but not limited to– language barriers. This work pro-Poses a system called SENSE (Student performance quantifier using Sentiment analysis) for improving the information conveyed in Secondary school reports through means of natural language processing. By combining the three key features which contribute to-Wards a student’s progress, a numerical representation is produced for an easier interpretation. This reduces the likelihood of a tarnished relationship between home and schools through better means of conveying information and maintains communication between students, teachers and parents or guardians..

Numerical Modeling, Trim, and Linearization of a Side-by-Side Helicopter in Hovering Conditions

In the present paper, a flight dynamics model is adopted to represent the trim and stability characteristics of a side-by-side helicopter in hovering conditions. This paper develops a numerical representation of the rotorcraft behavior and proposes a set of guidelines for trimming and linearizing the highly coupled rotor dynamics derived by the modeling approach. The trim algorithm presents two nested loops to compute a solution of the steady-state conditions averaged around one blade’s revolution. On the other hand, a 38-state-space linear representation of the helicopter and rotor dynamics is obtained to study the effects of flap, lead–lag, and inflow on the overall stability. The results are compared with an analytical framework developed to validate the rotorcraft stability and compare different modeling approaches. The analysis showed that non-uniform inflow modeling led to a coupled longitudinal inflow–phugoid mode which made the vehicle prone to dangerous instabilities. The flap and lead–lag dynamics introduced damping in the system and can be considered beneficial for rotor dynamics.

Analysis of 2019 Ohio Disease Intervention Specialist (DIS) Records for Syphilis Cases Using Clustering Algorithms.

Developments in natural language processing (NLP) and unsupervised machine learning methodologies (e.g., clustering) have given researchers new tools to analyze both structured and unstructured health data. We applied these methods to 2019 Ohio disease intervention specialist (DIS) syphilis records, to determine whether these methods can uncover novel patterns of co-occurrence of individual characteristics, risk factors, and clinical characteristics of syphilis that are not yet reported in the literature. The 2019 DIS syphilis records (n=1,996) contain both structured data (categorical and numerical variables) and unstructured notes. In the structured data, we examined case demographics, syphilis risk factors, and clinical characteristics of syphilis. For the unstructured text, we applied TF-IDF (term frequency multiplied by inverse document frequency) weights, a common way to convert text into numerical representations. We performed agglomerative clustering with cosine similarity using the CLUTO software. The cluster analysis yielded six clusters of syphilis cases based on patterns in the structured and unstructured data. The average internal similarities were much higher than the average external similarities, indicating that the clusters were well-formed. The factors underlying three of the clusters related to patterns of missing data. The factors underlying the other three clusters were sexual behaviors and partnerships. Notably, one of the three consisted of individuals who reported oral sex with male or anonymous partners while intoxicated, and one was comprised mainly of males who have sex with females. Our analysis resulted in clusters that were well-formed mathematically, but did not reveal novel epidemiological information about syphilis risk factors or transmission that were not already known.

Towards AI-Generated Essay Classification Using Numerical Text Representation

The detection of essays written by AI compared to those authored by students is increasingly becoming a significant issue in educational settings. This research examines various numerical text representation techniques to improve the classification of these essays. Utilizing a diverse dataset, we undertook several preprocessing steps, including data cleaning, tokenization, and lemmatization. Our system analyzes different text representation methods such as Bag of Words, TF-IDF, and fastText embeddings in conjunction with multiple classifiers. Our experiments showed that TF-IDF weights paired with logistic regression reached the highest accuracy of 99.82%. Methods like Bag of Words, TF-IDF, and fastText embeddings achieved accuracies exceeding 96.50% across all tested classifiers. Sentence embeddings, including MiniLM and distilBERT, yielded accuracies from 93.78% to 96.63%, indicating room for further refinement. Conversely, pre-trained fastText embeddings showed reduced performance, with a lowest accuracy of 89.88% in logistic regression. Remarkably, the XGBoost classifier delivered the highest minimum accuracy of 96.24%. Specificity and precision were above 99% for most methods, showcasing high capability in differentiating between student-created and AI-generated texts. This study underscores the vital role of choosing dataset-specific text representations to boost classification accuracy.

Molli: A General Purpose Python Toolkit for Combinatorial Small Molecule Library Generation, Manipulation, and Feature Extraction.

The construction, management, and analysis of large in silico molecular libraries is critical in many areas of modern chemistry. Herein, we introduce the MOLecular LIibrary toolkit, "molli", which is a Python 3 cheminformatics module that provides a streamlined interface for manipulating large in silico libraries. Three-dimensional, combinatorial molecule libraries can be expanded directly from two-dimensional chemical structure fragments stored in CDXML files with high stereochemical fidelity. Geometry optimization, property calculation, and conformer generation are executed by interfacing with widely used computational chemistry programs such as OpenBabel, RDKit, ORCA, NWChem, and xTB/CREST. Conformer-dependent grid-based feature calculators provide numerical representation and interface to robust three-dimensional visualization tools that provide comprehensive images to enhance human understanding of libraries with thousands of members. The package includes a command-line interface in addition to Python classes to streamline frequently used workflows. Parallel performance is benchmarked on various hardware platforms, and common workflows are demonstrated for different tasks ranging from optimized grid-based descriptor calculation on catalyst libraries to an NMR chemical shift prediction workflow from CDXML files.

Deep Learning-Based Reconstruction of 3D Morphology of Geomaterial Particles from Single-View 2D Images.

The morphology of particles formed in different environments contains critical information. Thus, the rapid and effective reconstruction of their three-dimensional (3D) morphology is crucial. This study reconstructs the 3D morphology from two-dimensional (2D) images of particles using artificial intelligence (AI). More than 100,000 particles were sampled from three sources: naturally formed particles (desert sand), manufactured particles (lunar soil simulant), and numerically generated digital particles. A deep learning approach based on a voxel representation of the morphology and multi-dimensional convolutional neural networks was proposed to rapidly upscale and reconstruct particle morphology. The trained model was tested using the three particle types and evaluated using different multi-scale morphological descriptors. The results demonstrated that the statistical properties of the morphological descriptors were consistent for the real 3D particles and those derived from the 2D images and the model. This finding confirms the model's validity and generalizability in upscaling and reconstructing diverse particle samples. This study provides a method for generating 3D numerical representations of geological particles, facilitating in-depth analysis of properties, such as mechanical behavior and transport characteristics, from 2D images.

Emotion topology: extracting fundamental components of emotions from text using word embeddings.

This exploratory study examined the potential of word embeddings, an automated numerical representation of written text, as a novel method for emotion decomposition analysis. Drawing from a substantial dataset scraped from a Social Media site, we constructed emotion vectors to extract the dimensions of emotions, as annotated by the readers of the texts, directly from human language. Our findings demonstrated that word embeddings yield emotional components akin to those found in previous literature, offering an alternative perspective not bounded by theoretical presuppositions, as well as showing that the dimensional structure of emotions is reflected in the semantic structure of their text-based expressions. Our study highlights word embeddings as a promising tool for uncovering the nuances of human emotions and comments on the potential of this approach for other psychological domains, providing a basis for future studies. The exploratory nature of this research paves the way for further development and refinement of this method, promising to enrich our understanding of emotional constructs and psychological phenomena in a more ecologically valid and data-driven manner.

Evacuation from Cramped Interiors with Aisle Seats: Uncertainty Induced by the Random Choice of Initial Positions

Considering evacuation from cramped interiors with aisle seats, the initial seating positions of pedestrians represent a non-negligible source of variance in total evacuation time. By means of Pathfinder simulations, this uncertainty is quantitatively investigated for a train and lecture hall geometry with homogeneous and heterogeneous groups of agents. Furthermore, an energy-inspired description of the occupation configuration is introduced that enables a proper numerical representation of the configuration space of the initial seating positions.

A French Adaptation and Validation of Retirement Semantic Differential (RSD).

Retirement is associated with numerous representations, some of them being negative and the other positive. Yet, these representations affect the health of individuals in their transition to retirement. However, although the socio-political context in France favors the emergence of numerous representations of retired people, to our knowledge there is no scale validated in French that would allow us to evaluate them. Thus, the objective of this study was to adapt and validate a scale assessing representations of retired people, called the Retirement Semantic Differential (RSD), for a French population. The scale consists of a series of bipolar adjectives related to retirement, such as "active/passive" and "happy/sad", with participants' responses indicating the connotative meaning, positive or negative, about representations of retirement. A total of 279 participants aged 18 to 55 years, recruited online, completed the adapted version of the RSD. The results show that the scale has good psychometric properties. The analysis found a three-factor model, and some items were removed, resulting in a reduced version of the scale (11 items). The results will be discussed in terms of cultural and socio-political differences. This scale could contribute to a better understanding of the deleterious effects on health of the transition to retirement and serve to improve the effectiveness of interventions aimed at reducing the negative effects of these representations upon young retirees or those preparing for retirement.

Quantized State Estimation for Linear Dynamical Systems.

This paper investigates state estimation methods for dynamical systems when model evaluations are performed on resource-constrained embedded systems with finite precision compute elements. Minimum mean square estimation algorithms are reformulated to incorporate finite-precision numerical errors in states, inputs, and measurements. Quantized versions of least squares batch estimation, sequential Kalman, and square-root filtering algorithms are proposed for fixed-point implementations. Numerical simulations are used to demonstrate performance improvements over standard filter formulations. Steady-state covariance analysis is employed to capture the performance trade-offs with numerical precision, providing insights into the best possible filter accuracy achievable for a given numerical representation. A low-latency fixed-point acceleration state estimation architecture for optomechanical sensing applications is realized on Field Programmable Gate Array System on Chip (FPGA-SoC) hardware. The hardware implementation results of the estimator are compared with double-precision MATLAB implementation, and the performance metrics are reported. Simulations and the experimental results underscore the significance of modeling quantization errors into state estimation pipelines for fixed-point embedded implementations.

The effect of Southern Ocean topography on the global MOC and abyssal water mass distribution

Abstract We investigate the role of Southern Ocean topography and wind stress in the deep and abyssal ocean overturning and water mass composition using a suite of idealized global ocean circulation models. Specifically, we address how the presence of a meridional ridge in the vicinity of Drake Passage and the formation of an associated Southern Ocean gyre influences the water mass composition of the abyssal cell. Our experiments are carried out using a numerical representation of the global ocean circulation in an idealized two-basin geometry under varying wind-stress and Drake Passage ridge height. In the presence of a low Drake Passage ridge the overall strength of the meridional overturning circulation is primarily influenced by wind stress, with a topographically-induced weakening of the mid-depth cell and concurrent strengthening of the abyssal cell occurring only after ridge height passes 2500m. Passive tracer experiments show that a strengthening mid-depth cell leads to increased abyssal ventilation by North Atlantic water masses, as more North Atlantic Deep Water (NADW) enters the Southern Ocean and then spreads into the Indo-Pacific. We repeat our tracer experiments without restoring in the high-latitude Southern Ocean in order to identify the origin of water masses that circulate through the Southern Ocean before sinking into the abyss as Antarctic Bottom Water. Our results from these “exchange” tracer experiments show that an increasing ridge height in Drake Passage and the concurrent gyre spin-up lead to substantially decreased NADW-origin waters in the abyssal ocean, as more surface waters from north of the ACC are transferred into the Antarctic Bottom Water formation region.