Quantitative Predictions Research Articles

Study and application of joint assessment model for construction risk of Xiangxi Yangtze River Bridge

Closure of main arch rib is the key process of steel box girder arch bridge construction. The way to achieve quantitative and accurate prediction of closure error risk remains to be studied in depth. Taking the construction risk model of closure of main arch rib of Xiangxi Yangtze River Bridge as an example, firstly, the causal relationship between prior data and closure error of main arch rib is considered to construct Bayesian network topology; Further, the central limit theorem (CLT) is used to update the prior distribution of the parent nodes in the network using dynamic multi-source monitoring data; Then the finite element analysis and BP neural network are combined to reproduce the corresponding relationship between the arch rib closure error and the influencing factors. Finally, the probability distribution is determined by the Monte Carlo method, and the quantitative prediction of the joint construction risk error is realized. The analysis data shows that the risk probability of arch rib vertical error of Xiangxi Yangtze River Bridge is transmissible, and the risk level of arch rib closure error reaches Grade IV when it is 75∼125mm. Therefore, the prestress of anchor cable, tower buckle offset and local temperature should be closely monitored during construction. The calculated value of the joint prediction model of construction risk is consistent with the actual closure control error trend of the project, which can provide reference for the prediction and regulation of related bridge construction risks.

Quantitative Analysis and Prediction of EMI From Spread Spectrum of Differential Random Signaling Based on IC Source Modeling

Quantitative Analysis and Prediction of EMI From Spread Spectrum of Differential Random Signaling Based on IC Source Modeling

Structure Seer – a machine learning model for chemical structure elucidation from node labelling of a molecular graph

We introduce a graph convolution based machine-learning model which provides a quantitative probabilistic prediction on the atom connectivity based on the composition of the molecule along with a list of atom-attributed isotropic shielding constants.

Insights into the in-vitro Susceptibility and Drug-Drug Interaction Profiles Against Drug-Resistant and Susceptible Mycobacterium tuberculosis Clinical Isolates in Amhara, Ethiopia.

In Ethiopia, tuberculosis (TB) is a major public health problem. The aim of the study was to determine the in vitro susceptibility level of drugs and drug interaction profiles against drug-resistant and susceptible M. tuberculosis clinical isolates. A laboratory-based cross-sectional study was conducted between January 2023 and August 2023. GenoType MTBDRplus v.2.0 was facilitated in genetic mutation detection. Minimum inhibitory concentration (MIC) was determined using resazurin microtitre assay (REMA), while fractional inhibitory concentration index (FICI) using resazurin drug combination microtitre assay (REDCA) for in vitro quantitative susceptibility and drug interaction prediction. Among 32 clinical isolates, a total of 14 (43.8%) RIF, 20 (62.5%) INH, 2 (6.3%) EMB-related resistant and 14 (43.8%) MDR isolates were identified. Five of RIF-resistant isolates (55.6%) carrying rpoB common mutations at codon S450L were associated with high levels of RIF-resistance with MICs of ≥ 2μg/mL, whereas 100% of isolates harboring rpoB substitutions at codons D435V and H445Y were linked with moderate or low-level RIF-resistance in the MIC ranges from 0.5 to 1μg/mL. A proportion of 81.8% of isolates harboring katG S315T mutations were associated with high-level INH resistance (MIC ≥ 1μg/mL), while the 18.2% of isolates with S315T katG mutations and 100% of isolates with inhA C-15T mutations were linked to the low-level of INH resistance with MIC variability from 0.25 to 0.5μg/mL. Our results indicated that most FICIs of the dual drugs INH+RIF and INH+LEV combination for 9 (28.1%) and 4 (12.5%) INH-resistant isolates, respectively, were ≤0.5, whereas triple drugs INH+RIF+EMB, INH+RIF+LEV and INH+EMB+LEV combination for 6 (18.8%), 11 (34.4%) and 8 (25%) INH-resistant isolates were from 0.62 to 0.75, all showed synergistic effect. The study highlights that isolates with rpoB S450L and katG S315T substitutions were associated with high level of RIF and INH resistance. It is concluded that REDCA can quantitatively determine anti-mycobacterial synergy and that LEV being of potential use against INH-resistant isolates including MDR-TB when combined with RIF+INH and INH+EMB.

Diagnostic performance of ultrasound-based artificial intelligence for predicting key molecular markers in breast cancer: A systematic review and meta-analysis.

Breast cancer (BC) diagnosis and treatment rely heavily on molecular markers such as HER2, Ki67, PR, and ER. Currently, these markers are identified by invasive methods. This meta-analysis investigates the diagnostic accuracy of ultrasound-based radiomics as a novel approach to predicting these markers. A comprehensive search of PubMed, EMBASE, and Web of Science databases was conducted to identify studies evaluating ultrasound-based radiomics in BC. Inclusion criteria encompassed research on HER2, Ki67, PR, and ER as key molecular markers. Quality assessment using Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and Radiomics Quality Score (RQS) was performed. The data extraction step was performed systematically. Our meta-analysis quantifies the diagnostic accuracy of ultrasound-based radiomics with a sensitivity and specificity of 0.76 and 0.78 for predicting HER2, 0.80, and 0.76 for Ki67 biomarkers. Studies did not provide sufficient data for quantitative PR and ER prediction analysis. The overall quality of studies based on the RQS tool was moderate. The QUADAS-2 evaluation showed that the studies had an unclear risk of bias regarding the flow and timing domain. Our analysis indicated that AI models have a promising accuracy for predicting key molecular biomarkers' status in BC patients. We performed the quantitative analysis for HER2 and Ki67 biomarkers which yielded a moderate to high accuracy. However, studies did not provide adequate data for meta-analysis of ER and PR prediction accuracy of developed models. The overall quality of the studies was acceptable. In future research, studies need to report the results thoroughly. Also, we suggest more prospective studies from different centers.

A nomogram based on quantitative EEG to predict the prognosis of nontraumatic coma patients in the neuro-intensive care unit

ObjectiveWe aimed to establish a quantitative electroencephalography-based prognostic prediction model specifically tailored for nontraumatic coma patients to guide clinical work. MethodsThis retrospective study included 126 patients with nontraumatic coma admitted to the First Affiliated Hospital of Chongqing Medical University from December 2020 to December 2022. Six in-hospital deaths were excluded. The Glasgow Outcome Scale assessed the prognosis at 3 months after discharge. The least absolute shrinkage and selection operator regression analysis and stepwise regression method were applied to select the most relevant predictors. We developed a predictive model using binary logistic regression and then presented it as a nomogram. We assessed the predictive effectiveness and clinical utility of the model. ResultsAfter excluding six deaths that occurred within the hospital, a total of 120 patients were included in this study. Three predictor variables were identified, including APACHE II score [39.129 (1.4244–1074.9000)], sleep cycle [OR: 0.006 (0.0002–0.1808)], and RAV [0.068 (0.0049–0.9500)]. The prognostic prediction model showed exceptional discriminative ability, with an AUC of 0.939 (95 % CI: 0.899–0.979). ConclusionA lack of sleep cycles, smaller relative alpha variants, and higher APACHE II scores were associated with a poor prognosis of nontraumatic coma patients in the neurointensive care unit at 3 months after discharge. Clinical implicationThis study presents a novel methodology for the prognostic assessment of nontraumatic coma patients and is anticipated to play a significant role in clinical practice.

Elucidating gas-surface interactions relevant to atmospheric particle growth using combined temperature programmed desorption and temperature-dependent uptake.

Understanding growth mechanisms for particles in air is fundamental to developing a predictive capability for their impacts on human health, visibility, and climate. In the case of highly viscous semi-solid or solid particles, the likelihood of impinging gases being taken up to grow the particle will be influenced by the initial uptake coefficient and by the residence time of the adsorbed gas on the surface. Here, a new approach that combines Knudsen cell capabilities for gas uptake measurements with temperature programmed desorption (TPD) for binding energy measurements of gases is described. The application of this unique capability to the uptake of organic gases on silica demonstrates its utility and the combination of thermodynamic and kinetic data that can be obtained. Lower limits to the initial net uptake coefficients at 170 K are (3.0 ± 0.6) × 10-3, (4.9 ± 0.6) × 10-3 and (4.3 ± 0.8) × 10-3 for benzene, 1-chloropentane, and methanol, respectively, and are reported here for the first time. The uptake data demonstrated that the ideal gas lattice model was appropriate, which informed the analysis of the TPD data. From the thermal desorption measurements, desorption energies of 34.6 ± 2.5, 45.8 ± 5.5, and 40.0 ± 5.6 kJ mol-1 (errors are 1σ) are obtained for benzene, 1-chloropentane, and methanol, respectively, and show good agreement with previously reported measurements. A multiphase kinetics model was applied to quantify uptake, desorption, and diffusion through the particle multilayers and hence extract desorption kinetics. Implications for uptake of organics on silica surfaces in the atmosphere and the utility of this system for determining relationships between residence times of organic gases and particle surfaces of varying composition are discussed in the context of developing quantitative predictions for growth of aerosol particles in air.

Application of output constraints to a physics-informed hybrid model for the prediction of the threshold of deep-penetration laser welding

Physics-informed hybrid models, the combination of physics and machine learning, have already shown considerable benefits for quantitative predictions of process constraints, such as the threshold of deep-penetration laser welding. However, despite the improved prediction accuracy and extrapolation capability of such models, there can still be cases where the predictions of the model, including the confidence region, result in values that are not consistent with physical boundary conditions. Therefore, this paper presents the application of additional output constraints to a physics-informed hybrid model to further improve the compliance of the model with physics. Gaussian processes are used for the machine learning model and output warping is used to incorporate the output constraints directly into the model. The approach is demonstrated at the example of a hybrid model for the prediction of the threshold of deep-penetration laser welding.

Quantitative Prediction of Biological Activity of Anti-Breast Cancer Drug Candidates Based on Light-GBM Algorithm

Quantitative Prediction of Biological Activity of Anti-Breast Cancer Drug Candidates Based on Light-GBM Algorithm

Deciphering Sequence-Specific DNA Binding by H-NS Using Molecular Simulation.

H-NS is a DNA organizing protein that occurs in Gram-negative bacteria. It can form long filaments between two DNA duplexes by first binding to a high-affinity AT-rich nucleotide sequence and extending from there. Using molecular dynamics simulations and steered molecular dynamics, we are able to determine the free energy of formation and dissociation of a protein-DNA complex comprising an H-NS DNA-binding domain and a specific nucleotide sequence. The molecular dynamics simulations allow detailed characterization of the interactions between the protein and a specific nucleotide sequence. To quantify the strength of the interaction, we employ an additional potential based on protein-DNA contacts to speed up dissociation of the protein-DNA complex. The work required for the dissociation results in an estimate of the free energy of dissociation/complex formation. Our protocol can provide quantitative prediction of protein-DNA complex stability, while also providing high-resolution insights into recognition mechanisms. In this chapter, we have used this approach to quantify the sequence specificity of H-NS DNA-binding domains to various nucleotide sequences, thus elucidating the mechanism with which H-NS can specifically bind to AT-rich DNA.

Convolutional neural network for predicting the TOC content of source rocks in the Shahejie Formation, Liaohe Western Depression

The total organic carbon (TOC) content is an important index to evaluate the quality of hydrocarbon source rocks. It is proposed that accurate and effective prediction methods of TOC content are beneficial to the progress of conventional and unconventional oil and gas exploration research. The Liaohe western depression is rich in oil and gas resources. Among them, the Shahejie Formation Section 4 is the main oil-generating layer, and a comprehensive and accurate evaluation of its organic matter content can provide ideas for shale oil exploration in this area. Based on this, this article proposes three methods for quantitative prediction of TOC content in the Shahejie Formation Section 4 in the Liaohe western depression: simple linear regression, ΔLogR, and convolutional neural network (CNN) simulation, which has different correlation coefficients of 0.3, 0.55, and 0.88. Compared with the laboratory sample analysis and testing results, the prediction results of the three methods show that the CNN simulation method is more reliable and accurate for predicting TOC content. Therefore, this method is more suitable for evaluating hydrocarbon source rocks in the Liaohe western depression and has great potential for application in the evaluation of hydrocarbon resources in the future.

Detonation development in H2, CH4, and PRF-air mixtures under engine-relevant conditions: From a chemical perspective

The progression of highly boosted internal combustion engines (ICEs) faces impediments due to the super-knock phenomenon primarily associated with detonation development during end gas autoignition. Although it is well known that hot spot potentially triggers super-knock, quantitative prediction of super-knock and detonation development across diverse states and fuels remains a challenge. This study numerically investigates the transient autoignition process induced by a hot spot, considering different initial states and fuels (including alternative fuels H2, CH4, and conventional PRFs), with an emphasis on the chemical effects. It is found that the intense exothermic stage (IES) in a stoichiometric H2/air mixture greatly differs from those in hydrocarbon/air (CH4/air and PRF/air) and lean or diluted H2/air mixtures. Through sensitivity analysis, key elementary reactions dominating the autoignition are identified, attributing their impact on the IES primarily to their influence on the radical pool rather than on the heat release rate. It is found that only reactions H + O2⇌O+OH and H2O2(+M)⇌OH+OH(+M) significantly affect both excitation time and ignition delay time, suggesting that the kinetic pathway controlling the IES differs somewhat from that governing the overall ignition. For the autoignition induced by a hot spot, two types of detonation feature are identified: one corresponds to stoichiometric H2/air with broad detonation regime and short excitation time, while the other is associated with other fuel/air mixtures exhibiting relatively narrow detonation regime and prolonged excitation time. These findings highlight that the excitation time predominantly governs the detonation development. All hydrogen/air mixtures (stoichiometric, lean, and diluted) manifest a suppressed detonation intensity compared to hydrocarbon/air mixtures. Moreover, due to its significantly higher reactivity in the IES, stoichiometric H2/air has an additional normal detonation sub-mode for hot spot with larger temperature gradient. This sub-mode greatly expands the detonation regime and thereby results in a notably higher detonation propensity for the stoichiometric H2/air mixture.

Experimentally-based Fe-catalyzed ethene oligomerization machine learning model provides highly accurate prediction of propagation/termination selectivity.

Linear α-olefins (1-alkenes) are critical comonomers for ethene copolymerization. A major impediment in the development of new homogeneous Fe catalysts for ethene oligomerization to produce comonomers and other important commercial products is the prediction of propagation versus termination rates that control the α-olefin distribution (e.g., 1-butene through 1-decene), which is often referred to as a K-value. Because the transition states for propagation versus termination are generally separated by less than a one kcal mol-1 difference in energy, this selectivity cannot be accurately predicted by either DFT or wavefunction methods (even DLPNO-CCSD(T)). Therefore, we developed a sub-kcal mol-1 accuracy machine learning model based on several hundred experimental selectivity values and straightforward 2D chemical and physical features that enables the prediction of α-olefin distribution K-values. As part of our model, we developed a new ad hoc feature that boosted the model performance. This machine learning model captures the effects of a broad range of ligand architectures and chemically nonintuitive trends in oligomerization selectivity. Our machine learning model was experimentally validated by prediction of a K-value for a new Fe phosphaneyl-pyridinyl-quinoline catalyst followed by experimental measurement that showed precise agreement. In addition to quantitative predictions, we demonstrate how this machine learning model can provide qualitative catalyst design using proximity of pairs type analysis.

Quantitative prediction of the flammability limits of filter paper in microgravity conditions

Under JAXA's FLARE project, ongoing since May 2022, long-duration microgravity combustion experiments have been conducted using filter paper. These experiments utilize the Solid Combustion Experiment Module (SCEM) in the ISS/Kibo module. The dimensions of the filter paper used were 130 mm in length and either 40 mm or 20 mm in width, with the experiments conducted in an environment of 298 K and 100 kPa. This paper highlights the results of 19 flame spread experiments performed in opposed-flow environments and compares the predetermined flammability limiting curve with the flammability map obtained in orbital experiments. One of the objectives of the FLARE project is to predict the flammability limiting curve in microgravity environments based on the results of tests that can be conducted on the ground. These experiments produced intriguing findings, demonstrating the effects of both radiation loss and blow-off during flame spread, as the opposed flow velocity was incrementally increased from 10 cm/s to 18 cm/s in 2 cm/s steps, within an environment containing 15.5 % oxygen concentration. When comparing with the combustible map obtained from orbital experiments, it was discovered that the original limit curve accurately replicated the blow-off branch but overestimated the flammable region in areas with opposed-flow velocity below 10 cm/s. This discrepancy was attributed to the inadequate consideration of the effect of opposed-flow velocity on the radiation quenching branch. Therefore, by considering the velocity distribution measurement results obtained in orbit and modeling the impact of the effective flow velocity opposing the flame front on both the radiation quenching branch and the blow-off branch, we successfully quantitatively replicated the results of the orbital experiments. These findings indicate that the flammability limits of thin flat plate samples can be accurately predicted by properly assessing the effect of the boundary layer on the sample surface.

Management Skills of Educational Managers and the Advancement of Technological Pedagogical Knowledge of Teachers in Private Junior High Schools in Davao City

Technology plays a great role in innovating and renovating contemporary teaching practices. Hence, this study aimed at determining the influence of the education manager’s management skills on the advancement in technological, pedagogical content knowledge (TPACK) of teachers in private junior high schools in Davao City. This study utilized a quantitative prediction research design using a survey method. The 205 teacher-respondents chosen using universal sampling answered a two-part validated online survey questionnaire which asks about the educational manager’s management skills and the teacher’s TPACK. Data were described, analyzed and interpreted using mean, standard deviation, Pearson r, and linear regression. The findings showed a very high level of management skills among educational managers and a very high level of TPACK among teachers. Standard Multiple Linear Regression Analysis using the Stepwise Method and the equation model showed that the educational managers’ management skills such as managing acculturation, managing the future, managing teaching principles, and managing teaching methods significantly predict the TPACK of private Junior High School teachers and these five management skills have relatively contributed to the variation of TPACK. Thus, there is a need to strengthen education manager’s formation programs focusing on the areas of energizing employees and teacher’s training in technology knowledge.

Quantitative prediction model and prewarning system of water yield capacity (WYC) from coal seam roof based on deep learning and joint advanced detection

As one of the most abundant fossil fuels in the world, the safe and efficient mining of coal resources has greatly affects all fields of human production and life. Accurate prediction of the water yield capacity (WYC) of coal seam roofs and advanced detection of water abundance is of great significance to preventing roof water disasters in coal mines. In the process of coal mining, complex roof water inrush mechanism, and changeable factors are faced. Moreover, the uncertainty of the interconnection between various factors increases the difficulty of roof water disaster prevention. To improve the accuracy and reliability of WYC prediction, this study analyzed the main control factors of coal seam roof water inrush and built a WYC quantitative prediction model based on the SAA-CNN algorithm. 47 samples (40 training sets and 7 test sets) were selected to train and learn the model, and the WYC of the 3rd coal mining roof in Jiangzhuang Coal Mine (JZCM) was predicted. To verify the reliability of the model, BP, RF, SVM, CNN, and SAA-CNN models were used for training and error comparison. Finally, the 1001 working face of JZCM was selected to use a variety of geophysical methods and drilling combined for advanced detection and construct the dynamic monitoring and prewarning system of coal seam roof water inrush risk. The results show that the accuracy of the WYC prediction model is SAA-CNN＞RF＞CNN＞SVM＞BP. The average absolute error of the SAA-CNN model is 0.87 m3/h, and the average relative error is 3.56 %. In this study, the quantitative and accurate prediction of WYC of coal seam roofs is realized. Combined with the advanced detection method of “electric-magnetic-drilling”, the prewarning system of water inrush risk of mining is constructed, which provides a new method for the prevention of water disasters in coal seam roofs.

Quantitative gait analysis and prediction using artificial intelligence for patients with gait disorders

Quantitative Gait Analysis (QGA) is considered as an objective measure of gait performance. In this study, we aim at designing an artificial intelligence that can efficiently predict the progression of gait quality using kinematic data obtained from QGA. For this purpose, a gait database collected from 734 patients with gait disorders is used. As the patient walks, kinematic data is collected during the gait session. This data is processed to generate the Gait Profile Score (GPS) for each gait cycle. Tracking potential GPS variations enables detecting changes in gait quality. In this regard, our work is driven by predicting such future variations. Two approaches were considered: signal-based and image-based. The signal-based one uses raw gait cycles, while the image-based one employs a two-dimensional Fast Fourier Transform (2D FFT) representation of gait cycles. Several architectures were developed, and the obtained Area Under the Curve (AUC) was above 0.72 for both approaches. To the best of our knowledge, our study is the first to apply neural networks for gait prediction tasks.

Predicting marine and aeolian contributions to the Sand Engine’s evolution using coupled modelling

Quantitative predictions of marine and aeolian sediment transport in the nearshore–beach–dune system are important for designing Nature-Based Solutions (NBS) in coastal environments. To quantify the impact of the marine-aeolian interactions on shaping NBS, we present a framework coupling three existing process-based models: Delft3D Flexible Mesh, SWAN and AeoLiS. This framework facilitates the continuous exchange of bed levels, water levels and wave properties between numerical models focussing on the aeolian and marine domain. The coupled model is used to simulate the morphodynamic evolution of the Sand Engine mega-nourishment. Results display good agreement with the observed aeolian and marine volumetric developments, showing similar marine-driven erosion from the main peninsula and aeolian-driven infilling of the dune lake. To estimate the magnitude of the interactions between aeolian and marine processes, a comparison between the simulated morphological development by the coupled and stand-alone models was made. This comparison shows that aeolian sediment transport to the foredune, i.e. 214,000 m3 over 5 years, extracts sediment from the marine domain. As a result, the alongshore redistribution of sediment from the main peninsula by marine-driven processes decreased by 70,000 m3, representing 1.7% of the total marine-driven dispersion. From the aeolian perspective, marine-driven deposition and erosion reshape the cross-shore profile, controlling the supply-limited aeolian sediment transport and the magnitude of sediment deposition in the foredunes. In the region with persistent accretion along the Sand Engine’s southern flank, a higher than average foredune deposition was predicted due to morphological development of the region where sediment is picked up by aeolian transport. Including these marine processes in the coupled model resulted in an increase of 1.3% in foredune growth in year 1 and up to 6.7% in year 5 along this accretive section. At the northern flank, where the developing lagoon and tidal channel provided increased shelter to the supratidal beach, predicted foredune deposition reduced up to −11.5% over the evaluation period. Our findings show that both aeolian and marine transports impact reshaping the nourished sand, where developments in one domain affect the other. The study findings echo that the interplay between aeolian- and marine-driven morphodynamics could play a relevant role when predicting sandy NBS.

Preoperative Imaging Evaluation of Endometrial Cancer in FIGO 2023.

The staging of endometrial cancer is based on the International Federation of Gynecology and Obstetrics (FIGO) staging system according to the examination of surgical specimens, and has revised in 2023, 14 years after its last revision in 2009. Molecular and histological classification has incorporated to new FIGO system reflecting the biological behavior and prognosis of endometrial cancer. Nonetheless, the basic role of imaging modalities including ultrasound, computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography, as a preoperative assessment of the tumor extension and also the evaluation points in CT and MRI imaging are not changed, other than several point of local tumor extension. In the field of radiology, it has also undergone remarkable advancement through the rapid progress of computational technology. The application of deep learning reconstruction techniques contributes the benefits of shorter acquisition time or higher quality. Radiomics, which extract various quantitative features from the images, is also expected to have the potential for the quantitative prediction of risk factors such as histological types and lymphovascular space invasion, which is newly included in the new FIGO system. This article reviews the preoperative imaging diagnosis in new FIGO system and recent advances in imaging analysis and their clinical contributions in endometrial cancer. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 3.

A new pore-strength model for the quantitative strength prediction of ceramics under mode-I loading

Strength prediction under the action of pores is crucial for the safety of ceramics. Here, a quantitative prediction model of pore-strength response under mode-I loading was developed based on the new mapping relationship between stress intensity factor and tip radius. The new model was proved to have great application after comparison and analysis of the pore-strength test data of three typical ceramics as well as the pertinent data as-reported in the literature. Results indicated that when the tip radius of pore (ρ) exceeds the critical value (ρc = KIc2/(πσ02)), the stress intensity factor increases as a power function with increasing ρ, at which point the strength decay is significantly weakened, and when the ρ is lower than the ρc, the pore can be equated to a crack problem. This work can not only forecast and assess the failure strength of ceramics containing pores, but also provide guidance for the design of extremely dependable ceramics.