Time-efficient Model Research Articles

Detection of β - Thalassemia Carriers using Data Mining Techniques

Thalassemia, a genetic blood disorder, presents a significant challenge in Sri Lanka due to its high prevalence. Traditional methods of identifying tha-lassemia carriers, such as genetic and blood testing, are both costly and time-consuming, and potentially not available for certain demographic groups. However, there haven’t been many studies done on the efficacy of data mining models for thalassemia carrier detection, therefore the field is still in its in fancy. As such, evaluating their accuracy and utility in clinical practice is crucial. This study aims to develop a time-efficient model to detect the β-thalassemia carriers, which can reduce the time to take a decision and develop the built model as a decision support tool. Eight blood parameters - includ-ing RBC, HGB, HCT, MCV, MCH, MCHC, RDW, and HbA2 were selected based on literature. Two model-fitting approaches were introduced, each un-der different data selection methods: Method 1: Model fitting before handling the class imbalance problem and Method 02: Model fitting with random over-sampling technique. Support Vector Machine (SVM) and Probabilistic Neural Network (PNN) models were utilized for β-thalassemia carrier detection. Method 2 exhibited superior performance, especially with the PNN Model 2, achieving an impressive 98.75% overall classification accuracy. Moreover, the implemented PNN Model 2 could be utilized as an efficient decision-support tool, offering both time and cost savings in identifying β-thalassemia carriers. Nonetheless, for further investigation, consulting a medical expert is recommended.

Numerical simulation method for masonry partition walls affected by the deflections of concrete structures

Non structural masonry walls can be damaged when submitted to in plane loads, imposed by the vertical deflections of their support structures (e.g. concrete slabs).Given the high complexity of this problem associated to the masonry anisotropy and behaviour of interface joints wall/structure, including the difficulty of reproducing real scale prototypes for laboratory testing, the use of more generalised and time efficient models are important aspects to be considered. Therefore, a numerical simulation method and its application is presented to simulate this type of problem. This method is based on a FEM macro-model, witch includes a constitutive damage model for concrete calibrated for masonry walls and a shear-cohesion model to simulate the interface joints. This method was simulated in a case study of a masonry partition wall loaded by the vertical deflections of adjacent concrete slabs. Results obtained highlighted a high risk of damage in the masonry partitions when these are affected by the structural deflections limits referred in literature, specially if no detachment wall/structure joints and reinforcement techniques are used.

A Human Brain-Chip for Modeling Brain Pathologies and Screening Blood–Brain Barrier Crossing Therapeutic Strategies

Background/Objectives: The limited translatability of preclinical experimental findings to patients remains an obstacle for successful treatment of brain diseases. Relevant models to elucidate mechanisms behind brain pathogenesis, including cell-specific contributions and cell-cell interactions, and support successful targeting and prediction of drug responses in humans are urgently needed, given the species differences in brain and blood-brain barrier (BBB) functions. Human microphysiological systems (MPS), such as Organ-Chips, are emerging as a promising approach to address these challenges. Here, we examined and advanced a Brain-Chip that recapitulates aspects of the human cortical parenchyma and the BBB in one model. Methods: We utilized human primary astrocytes and pericytes, human induced pluripotent stem cell (hiPSC)-derived cortical neurons, and hiPSC-derived brain microvascular endothelial-like cells and included for the first time on-chip hiPSC-derived microglia. Results: Using Tumor necrosis factor alpha (TNFα) to emulate neuroinflammation, we demonstrate that our model recapitulates in vivo-relevant responses. Importantly, we show microglia-derived responses, highlighting the Brain-Chip’s sensitivity to capture cell-specific contributions in human disease-associated pathology. We then tested BBB crossing of human transferrin receptor antibodies and conjugated adeno-associated viruses. We demonstrate successful in vitro/in vivo correlation in identifying crossing differences, underscoring the model’s capacity as a screening platform for BBB crossing therapeutic strategies and ability to predict in vivo responses. Conclusions: These findings highlight the potential of the Brain-Chip as a reliable and time-efficient model to support therapeutic development and provide mechanistic insights into brain diseases, adding to the growing evidence supporting the value of MPS in translational research and drug discovery.

Time-efficient model predictive control for autonomous tugs with adaptive input constraints

Time-efficient model predictive control for autonomous tugs with adaptive input constraints

Cognitive Computing Advancements: Improving Precision Crop Protection through UAV Imagery for Targeted Weed Monitoring

Early detection of weeds is crucial to manage weeds effectively, support decision-making and prevent potential crop losses. This research presents an innovative approach to develop a specialized cognitive system for classifying and detecting early-stage weeds at the species level. The primary objective was to create an automated multiclass discrimination system using cognitive computing, regardless of the weed growth stage. Initially, the model was trained and tested on a dataset of 31,002 UAV images, including ten weed species manually identified by experts at the early phenological stages of maize (BBCH14) and tomato (BBCH501). The images were captured at 11 m above ground level. This resulted in a classification accuracy exceeding 99.1% using the vision transformer Swin-T model. Subsequently, generative modeling was employed for data augmentation, resulting in new classification models based on the Swin-T architecture. These models were evaluated on an unbalanced dataset of 36,556 UAV images captured at later phenological stages (maize BBCH17 and tomato BBCH509), achieving a weighted average F1-score ranging from 94.8% to 95.3%. This performance highlights the system’s adaptability to morphological variations and its robustness in diverse crop scenarios, suggesting that the system can be effectively implemented in real agricultural scenarios, significantly reducing the time and resources required for weed identification. The proposed data augmentation technique also proved to be effective in implementing the detection transformer architecture, significantly improving the generalization capability and enabling accurate detection of weeds at different growth stages. The research represents a significant advancement in weed monitoring across phenological stages, with potential applications in precision agriculture and sustainable crop management. Furthermore, the methodology showcases the versatility of the latest generation models for application in other knowledge domains, facilitating time-efficient model development. Future research could investigate the applicability of the model in different geographical regions and with different types of crops, as well as real-time implementation for continuous field monitoring.

Secure Aviation Control through a Streamlined ADS-B Perception System

Automatic dependent surveillance-broadcast (ADS-B) is the future of aviation surveillance and traffic control, allowing different aircraft types to exchange information periodically. Despite this protocol’s advantages, it is vulnerable to flooding, denial of service, and injection attacks. In this paper, we decided to join the initiative of securing this protocol and propose an efficient detection method to help detect any exploitation attempts by injecting these messages with the wrong information. This paper focused mainly on three attacks: path modification, ghost aircraft injection, and velocity drift attacks. This paper aims to provide a revolutionary methodology that, even in the face of new attacks (zero-day attacks), can successfully detect injected messages. The main advantage was utilizing a recent dataset to create more reliable and adaptive training and testing materials, which were then preprocessed before using different machine learning algorithms to feasibly create the most accurate and time-efficient model. The best outcomes of the binary classification were obtained with 99.14% accuracy, an F1-score of 99.14%, and a Matthews correlation coefficient (MCC) of 0.982. At the same time, the best outcomes of the multiclass classification were obtained with 99.41% accuracy, an F1-score of 99.37%, and a Matthews correlation coefficient (MCC) of 0.988. Eventually, our best outcomes outdo existing models, but we believe the model would benefit from more testing of other types of attacks and a bigger dataset.

Denoising AutoEncoder-based Representation Learning for Multi-Task Whole Slide Image Analysis

Along with the advancement of artificial intelligence, there have been significant improvements in the field of whole slide images (WSI). WSI in machine learning is mainly utilized for pathological analysis, consisting of diverse tasks such as classification of normal versus tumor patches, segmentation of precise areas of potential tumor, or object detection indicating tumor sites. However, training these distinct models for each individual task is both time intensive and inefficient. Therefore, there is a high demand for developing a unified learning algorithm capable of concurrently handling multiple WSI tasks. To address the aforementioned problem, a representation learning based transfer learning method is proposed to process multiple downstream tasks including classification, segmentation, and object detection. Synthesizing two stages, the proposed method utilizes the reconstruction of images from representation learning and pretrained parameters from transfer learning method to create a more accurate and time-efficient model for analyzing WSI. Overall, the proposed method offers a better representation of WSI, which leads to enhanced accuracy in analysis and interpretation. Through extensive experiments, I have found that the proposed method outperforms previous state-of-the-art networks in various downstream tasks including classification, segmentation, and object detection. I expect the proposed method to be applied in real world scenarios with increased practicality and accuracy.

Predicting System Degradation with a Guided Neural Network Approach.

Evaluating the physical degradation behavior and estimating the lifetime of engineering systems and structures is crucial to ensure their safe and reliable operation. However, measuring lifetime through actual operating conditions can be a difficult and slow process. While valuable and quick in measuring lifetimes, accelerated life testing is often oversimplified and does not provide accurate simulations of the exact operating environment. This paper proposes a data-driven framework for time-efficient modeling of field degradation using sensor measurements from short-term actual operating conditions degradation tests. The framework consists of two neural networks: a physics discovery neural network and a predictive neural network. The former models the underlying physics of degradation, while the latter makes probabilistic predictions for degradation intensity. The physics discovery neural network guides the predictive neural network for better life estimations. The proposed framework addresses two main challenges associated with applying neural networks for lifetime estimation: incorporating the underlying physics of degradation and requirements for extensive training data. This paper demonstrates the effectiveness of the proposed approach through a case study of atmospheric corrosion of steel test samples in a marine environment. The results show the proposed framework's effectiveness, where the mean absolute error of the predictions is lower by up to 76% compared to a standard neural network. By employing the proposed data-driven framework for lifetime prediction, systems safety and reliability can be evaluated efficiently, and maintenance activities can be optimized.

Brivaracetam or levetiracetam in status epilepticus?: Lessons from the photosensitivity model

First, a short history is given of the use of the EEG as a biomarker of efficacy in anti-seizure medication (ASM) development. The generalized epileptiform EEG response to Intermittent Photic Stimulation (IPS), the photoparoxysmal EEG response or PPR, in particular, is a reliable reproducible measure since the 1950s. Over time, a "Photosensitivity Model", testing within the same patients the impact of potential new oral ASMs, along with dose-ranging data, on PPRs, has been developed successfully. The classical Photosensitivity Model consists of IPS and blood sampling for ASM measurement performed hourly between 8AM and 5PM over three consecutive days. This single-blind, placebo-controlled, pharmacokinetic-pharmacodynamic (PK/PD) Model is now commonly utilized as a Proof-of-Concept Phase 2a trial. For Generalized Tonic-Clonic Status Epilepticus (GTCSE), it is especially relevant to know the time for CNS entry and effect minutes after i.v. ASM treatment, since "time is brain". We, therefore, adapted successfully the Model to a time-efficient Model with the determination of photosensitivity ranges in minutes after equivalent doses of iv brivaracetam (BRV) and levetiracetam (LEV). This modified design allows one to monitor the time to CNS effect (i.e., PPR elimination) of a quickly-acting FDA-approved ASM given i.v., a crucial element in status epilepticus treatment. This paper was presented at the 8th London-Innsbruck Colloqium on Status Epilepticus and Acute Seizures held in September 2022.

The Chick Embryo Xenograft Model for Malignant Pleural Mesothelioma: A Cost and Time Efficient 3Rs Model for Drug Target Evaluation.

Malignant pleural mesothelioma (MPM) has limited treatment options and poor prognosis. Frequent inactivation of the tumour suppressors BAP1, NF2 and P16 may differentially sensitise tumours to treatments. We have established chick chorioallantoic membrane (CAM) xenograft models of low-passage MPM cell lines and protocols for evaluating drug responses. Ten cell lines, representing the spectrum of histological subtypes and tumour suppressor status, were dual labelled for fluorescence/bioluminescence imaging and implanted on the CAM at E7. Bioluminescence was used to assess viability of primary tumours, which were excised at E14 for immunohistological staining or real-time PCR. All MPM cell lines engrafted efficiently forming vascularised nodules, however their size, morphology and interaction with chick cells varied. MPM phenotypes including local invasion, fibroblast recruitment, tumour angiogenesis and vascular remodelling were evident. Bioluminescence imaging could be used to reliably estimate tumour burden pre- and post-treatment, correlating with tumour weight and Ki-67 staining. In conclusion, MPM-CAM models recapitulate important features of the disease and are suitable to assess drug targets using a broad range of MPM cell lines that allow histological or genetic stratification. They are amenable to multi-modal imaging, potentially offering a time and cost-efficient, 3Rs-compliant alternative to rodent xenograft models to prioritise candidate compounds from in vitro studies.

An efficient analytical approach to assess root cause of nonlinear electric vehicle gear whine

Noise, vibration, and harshness (NVH) issues pose considerable challenges for electric vehicle powertrain engineers. Gear vibrations generate an intrusive gear whine noise, with significant impact on the sound quality of electric powertrains. Dynamic transmission error (DTE) is the most quantitative indicator for gear NVH. Backlash, time variable meshing stiffness and damping contribute to DTE. Hence, a better understanding of these excitation sources is essential. A gear tribodynamics model is developed using potential energy method to estimate time variable meshing stiffness (TVMS). A fully analytical time-efficient model is proposed for lubricated contact stiffness based on transitions in the regimes of lubrication. The model accounts for the combined effects of surface elasticity and lubricant stiffness. Film thickness and damping coefficients are transiently updated at each instant during meshing cycle. The predictions from this model are compared with measured results from the literature and predicted results from Hertz contact model. The lubricated contact model successfully shows the contribution of the lubricant stiffness to TVMS and its variations with elasticity and viscosity parameters during meshing cycle. Gear harmonic and super-harmonic resonances are accurately estimated in terms of amplitude, frequencies and stiffness softening nonlinearities. Time history responses and phase-displacement diagrams show good agreement with the gear dynamics response at the main harmonic and second super-harmonic frequencies. The proposed model has a reasonable accuracy, significantly better than those from Hertzian contact models, and is considerably time efficient in comparison to numerical EHL solvers.

Structural Finite Element Analysis of REBCO Tape Delamination with Solid-Shell Element under Various Loads

Structural Finite Element Analysis was performed to study the delamination behaviour of layer laminated REBCO tapes using a Solid-Shell element under various loading conditions, including (1) transverse tensile delamination, (2) shear delamination and (3) peeling delamination. A Solid-Shell element is well suited to simulate REBCO tape because the material and thickness of all the layers can be specified within a single element. The results obtained from the models utilizing Solid-Shell elements are compared to a more computationally intensive method known as Cohesive Zone Method (CZM), which has successfully modelled the initiation and propagation of REBCO’s delamination [1]. The results show that the stress state of REBCO tape in transverse tensile and shear directions can be accurately captured by the proposed method employing Solid-Shell elements if the transverse tensile and shear delamination stress is measured experimentally for a specific REBCO and used as failure criteria. In this work, the failure criteria adopted from previous experimental studies are 50 MPa in the transverse tensile direction and 10 MPa in the shear direction. This work provides a time-efficient modelling tool to predict and mitigate delamination in REBCO tapes used in complex systems.

Fast Experimental Magnetic Model Identification for Synchronous Reluctance Motor Drives

The accurate magnetic model is mandatory for high-performance control of high anisotropy synchronous machines. This paper presents a time-efficient and accurate magnetic model identification based on triangle current injection while the machine under the test is driven at a constant speed by a prime mover. The current injection pattern allows scanning the whole range of current, reducing the identification time compared to the standard constant-speed method (CSM) with the same level of accuracy. The ohmic voltage drop and inverter nonlinearities are compensated by using the average voltage of motor and generator modes. The synchronous reluctance machine is used as a case study for validation through the comparison between the experimental results obtained by the proposed method and the CSM against finite element simulation. Moreover, the temperature variation of the machine winding is measured showing no considerable changes during the identification test.

Chorioallantoic membrane (CAM) assay to study treatment effects in diffuse intrinsic pontine glioma.

Diffuse intrinsic pontine glioma (DIPG) is a lethal pediatric brain tumor. While there are a number of in vivo rodent models for evaluating tumor biology and response to therapy, these models require significant time and resources. Here, we established the chick-embryo chorioallantoic (CAM) assay as an affordable and time efficient xenograft model for testing a variety of treatment approaches for DIPG. We found that patient-derived DIPG tumors develop in the CAM and maintain the same genetic and epigenetic characteristics of native DIPG tumors. We monitored tumor response to pharmaco- and radiation therapy by 3-D ultrasound volumetric and vasculature analysis. In this study, we established and validated the CAM model as a potential intermediate xenograft model for DIPG and its use for testing novel treatment approaches that include pharmacotherapy or radiation.

A calibration friendly approach to identify drugs of abuse mixtures with a portable near-infrared analyzer.

Both the increasing number and diversity of illicit‐drug seizures complicate forensic drug identification. Traditionally, colorimetric tests are performed on‐site, followed by transport to a laboratory for confirmatory analysis. Higher caseloads increase laboratory workload and associated transport and chain‐of‐evidence assurance performed by police officers. Colorimetric tests are specific only for a small set of drugs. The rise of new psychoactive substances therefore introduces risks for erroneous results. Near‐infrared (NIR)‐based analyzers may overcome these encumbrances by their compound‐specific spectral selectivity and broad applicability. This work introduces a portable NIR analyzer that combines a broad wavelength range (1300–2600 nm) with a chemometric model developed specifically for forensic samples. The application requires only a limited set of reference spectra for time‐efficient model training. This calibration‐light approach thus eliminates the need of extensive training sets including mixtures. Performance was demonstrated with 520 casework samples resulting in a 99.6% true negative and 97.6% true positive rate for cocaine. Similar results were obtained for MDMA, methamphetamine, ketamine, and heroin. Additionally, 236 samples were analyzed by scanning directly through their plastic packaging. Also here, a >97% true positive rate was obtained. This allows for non‐invasive, operator‐safe chemical identification of potentially potent drugs of abuse. Our results demonstrate the applicability for multiple drug‐related substances. Ideally, the combination of this NIR approach with other portable techniques, such as Raman and IR spectroscopy and electrochemical tests, may eventually eliminate the need for subsequent laboratory analysis; therefore, saving tremendous resources in the overall forensic process of confirmatory illicit drug identification.

Effect of Coating and Low Viscosity Oils on Piston Ring Friction under Mixed Regime of Lubrication through Analytical Modelling

This paper presents the impact of coating topography in piston ring-liner conjunction under mixed regime of lubrication using low viscosity oils. The study provides a time efficient analytical model including mixed-hydrodynamics regime of lubrication under different contact conditions. The method modified the expressions of the contact load and area of Greenwood-Tripp model in order to capture the real asperities interaction into contact. The model represents the tribological behavior of a thin top ring at Top Dead Centre, where boundary and mixed conditions are predominant. Electroplated CrN and PVD TiN coated rings were studied to predict the ring friction. The results are compared with an uncoated steel ring. The CrN coating shows slighter coefficient of friction, due to the coating morphology and roughness parameters. The TiN coating presents thicker lubricant films and higher coefficient of friction because the surface topography is quite rough with high peaks. This can be explained because of the major contribution of the roughness parameter and asperity slope in the boundary friction prediction.

Can India Go in for Pre- School / Pre- Verbal Eye Screening Now with Red Reflex? - A Feasibility Study

Aims: To study the feasibility and challenges for early vision screening of the 0-6 yr preverbal/ preschool children in both urban and rural settings of India using the simple Red Reflex test.
 Study Design:Operational research.
 Place and Duration of Study: Four settings (two urban and two rural) were designed to target the 0-6year old children. Model I .Pre School (Urban slum Population), Model II. Health Centre (Urban Hospital), Model III. Immunization OPD of Municipal Hospital (Rural hospital) Model IV. Anganwadi Centre (Rural Population) was designed and conducted July 2017 –Dec 2017 (6 months).
 Methodology: All models were conducted as single day vision screening camps except Model III which was an opportunistic screening for a period of 3 months (July – Sept 2017). Vision screening was done using Red Reflex test and follow up was done for three months. Data was analyzed on MS Excel and expressed in proportions / percentages. 
 Results: Maximum children (155) were screened in Model I. Out of the 301 total children screened 33 (10.96%) had abnormal Red Reflex test. The yield was maximum 6 (17.14%) in Model III. Urban health centre Model I was the most time efficient model(average 0.74 minutes/child). 
 Conclusions: Single day screening of population based cohort is time efficient but opportunistic screening using universal immunization programme model had maximum yield.

Simplifying Computer Aided Ergonomics: A User-Product Interaction-Modeling Framework in CAD Based on a Taxonomy of Elementary Affordances

OCCUPATIONAL APPLICATIONS This contribution provides a framework for modeling user-product interactions (in CAD) for in-depth ergonomic analysis of product design, using digital human models. The framework aims to be applicable to a wide range of different products while being suitable for designers – especially those who do not have specialized ergonomic expertise or training in human behavior – by providing an intuitive, standardized, and time-efficient modeling procedure. The framework contains 31 elementary affordances, which describe mechanical dependencies between product geometries and human end effectors. These elementary affordances serve as a tool for interaction modeling. Additionally, the paper provides a taxonomy of elementary affordances, which can be used to formalize / abstract the nature of user-product interactions and to describe them as elementary affordances. Furthermore, an implementation of the interaction-modeling framework is presented in a CAD environment and provides an example of how the framework could be used in terms of a computer aided ergonomics tool.

Depth integrated modelling of submarine landslide evolution

Submarine landslides are a major geohazard among worldwide continental slopes, posing significant threats to offshore infrastructure, marine animal habitats and coastal urban centres. This study establishes an original numerical package for time-efficient modelling of the entire submarine landslide evolution covering the pre-failure shear band propagation, slab failure and post-failure dynamics. The numerical scheme is based on the conservation of mass and the conservation of momentum and combines the shear band propagation theory and the depth-integrated method, with the consideration of the drag force from the ambient water. Shear band propagation in the weak layer and slab failure in the sliding layer are controlled by the strain softening and rate dependency of the corresponding undrained strength parameters. The post-failure behaviour in the sliding layer, such as retrogression upslope and frontally confined and frontally emergent mechanisms downslope, is also simulated. The numerical results from the proposed method are comparable to the analytical solutions and the large deformation finite element analysis. Application of this method to a back analysis of the St. Niklausen slide in Lake Lucerne reproduced the observed shape of the mass transport deposits, the position of the main scar and the travel distance. Because of its easy implementation and efficiency, the proposed numerical method for modelling of submarine landslides seems promising for practical applications.

Time-efficient three-dimensional transmural scar assessment provides relevant substrate characterization for ventricular tachycardia features and long-term recurrences in ischemic cardiomyopathy

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): - Ministerio de Ciencia e Innovación and the ProCNIC Foundation - Grant TEC2017-82408-R Background We aimed to validate a 3D methodology for transmural scar assessment using time-efficient upsampled models from 2D delayed gadolinium-enhanced cardiac magnetic resonance (LGE-CMR) sequences and determine the clinical implications in ischemic cardiomyopathy-related ventricular tachycardia (VT) episodes. Methods Translational study including 10 pigs with myocardial infarction and 15 patients with spontaneous VT episodes and underlying infarct-related scar. Pigs underwent 3D LGE-CMR sequences to test 3D transmural-based scar assessment using several downsampled resolutions from the original images. Validation in patients included 3D and 2D LGE-CMR studies performed within the same protocol. Transmural-based scar areas were used for correlation analyses with the cycle length (CL) of VT episodes. Scar volumes were used as a benchmark comparison. Results In pigs, 3D transmural-based scar areas showed interclass correlation coefficients >0.94 between original 3D high-resolution- and downsampled-derived models. In patients, scar area quantification in myocardial regions with 3D transmurality <0.2 (for upsampled 2D-derived models) and <0.1 (for 3D-derived models) showed direct correlation with the CL of spontaneous VT episodes (r = 0.69; p < 0.01 and r = 0.79; p < 0.01, respectively) (See Abstract Figure). After ablation, patients with VT recurrences showed lower scar areas on 3D and upsampled 2D models using <0.1 and <0.2 transmurality criteria, respectively, than patients without VT recurrences (48.5 [37.7, 67.5] vs. 91.5 [84.3, 117.3] cm2, respectively, for 3D-derived models; p = 0.004, and 64.0 [43.5, 69.5] vs. 83.0 [70.2, 99.7] cm2, respectively, for upsampled 2D models; p = 0.04). Conversely, time-consuming scar volume reconstructions from 2D or 3D LGE-CMR sequences did not show significant differences between patients with and without VT recurrences. Conclusions Three-dimensional transmural scar assessment in upsampled 2D-LGE-CMR-derived models provides time-efficient characterization of infarct-related myocardial substrates associated with the CL of spontaneous VT episodes and VT recurrences after ablation. Abstract Figure