Optimal Metrics Research Articles

Qualitatively Analyzing Optimization Objectives in the Design of HPC Resource Manager

A correct evaluation of scheduling algorithms and a good understanding of their optimization criteria are key components of resource management in HPC. In this work, we discuss bias and limitations of the most frequent optimization metrics from the literature. We provide elements on how to evaluate performance when studying HPC batch scheduling. We experimentally demonstrate these limitations by focusing on two use-cases: a study on the impact of runtime estimates on scheduling performance, and the reproduction of a recent high-impact work that designed an HPC batch scheduler based on a network trained with reinforcement learning. We demonstrate that focusing on quantitative optimization criterion (“our work improves the literature by X%”) may hide extremely important caveat, to the point that the results obtained are opposed to the actual goals of the authors. Key findings show that mean bounded slowdown and mean response time are hazardous for a purely quantitative analysis in the context of HPC. Despite some limitations, utilization appears to be a good objective. We propose to complement it with the standard deviation of the throughput in some pathological cases. Finally, we argue for a larger use of area-weighted response time, that we find to be a very relevant objective.

Effect of Chlorine Inclusion in Wide Band Gap FAPbBr3 Perovskites

AbstractWide bandgap perovskites have recently gained attention owing to their physical properties, versatility, and potential in various optoelectronic devices including LEDs, detectors, and building‐integrated photovoltaics (BIPV). However, BIPV materials must meet conflicting requirements, necessitating high performance, high transparency in the visible spectrum and color neutrality. This study investigates the controlled addition of chlorine in FAPb(Br1‐xClx)3 perovskites to achieve band gaps exceeding 2.4 eV. Increasing chlorine content from xCl = 0.00 to xCl = 0.25 widens the band gap from 2.37 to 2.52 eV, effectively improving the visible light transparency. Advanced characterization techniques including X‐ray diffraction, synchrotron radiation photoelectron spectroscopy, photoluminescence imaging, and fast transient absorption spectroscopy, complemented by density functional theory, reveal insights into absorption properties, electronic structure, and ultrafast recombination dynamics as a function of the thin film chemical composition. Furthermore, this study evaluates energetic disorder, carrier recombination rates, and non‐radiative losses for different compositions by extracting quantitative parameters such as Urbach energy and quasi‐Fermi level splitting, offering novel insights and guidelines for the design and optimization of emerging photovoltaic (PV) materials. Optimal PV performance metrics are achieved with a wide bandgap bromine perovskite containing 14% chlorine, striking a balance between morphology, transparency, and voltage losses.

The Parameter Optimization of Support Vector Machine with Genetic Algorithm in Risk Early Warning Models

Machine Learning algorithms are widely used by lenders in risk early warning models. With Machine Learning, the risk levels of individual and corporate customers are determined at the account and customer level. Lenders want to manage risk by evaluating the payment performance of customer or account with the help of Machine Learning algorithms. Banks, which have an important place among lenders, develop risk early warning models with the help of learning algorithms using customer information. In the development process of risk early warning models, while banks generally use customer information and credit bureau information for the individual segment, they use financial, non-financial and behaviour-based information for the corporate segment. In this study, it is planned to develop a risk early model for customers in corporate service segment. For the customers of corporate service segment, Balance Sheet and Income Statement items were used and the financial ratios were calculated for risk early warning models. In the development of risk early warning models, Mutual Information method was used as a novel feature selection approach and Support Vector Machine method (linear function, radial basis function and sigmoid function) was used as a supervised learning approach. By changing the neighbourhood metric (k), important patterns were discovered with the Mutual Information method in feature selection process. The optimal C and gamma parameters for Support Vector Machine models have been tried to be determined with the Genetic Algorithm, which is among the Meta-Heuristic algorithms. In order to find the optimal metrics in this study, the metric values for all parameters of the SVM model (function specific) have been kept quite wide. In this dataset of corporate service customers, the small neighbourhood metric has been found to have a significant impact on model learning and performance.

Performance evaluation of image co-registration methods in photoacoustic mesoscopy of the vasculature

Objective:The formation of functional vasculature in solid tumours enables delivery of oxygen and nutrients, and is vital for effective treatment with chemotherapeutic agents. Longitudinal characterisation of vascular networks can be enabled using mesoscopic photoacoustic imaging, but requires accurate image co-registration to precisely assess local changes across disease development or in response to therapy. Co-registration in photoacoustic imaging is challenging due to the complex nature of the generated signal, including the sparsity of data, artefacts related to the illumination/detection geometry, scan-to-scan technical variability, and biological variability, such as transient changes in perfusion. To better inform the choice of co-registration algorithms, we compared five open-source methods, in physiological and pathological tissues, with the aim of aligning evolving vascular networks in tumours imaged over growth at different time-points.Approach:Co-registration techniques were applied to 3D vascular images acquired with photoacoustic mesoscopy from murine ears and breast cancer patient-derived xenografts, at a fixed time-point and longitudinally. Images were pre-processed and segmented using an unsupervised generative adversarial network. To compare co-registration quality in different settings, pairs of fixed and moving intensity images and/or segmentations were fed into five methods split into the following categories: affine intensity-based using 1)mutual information (MI) or 2)normalised cross-correlation (NCC) as optimisation metrics, affine shape-based using 3)NCC applied to distance-transformed segmentations or 4)iterative closest point algorithm, and deformable weakly supervised deep learning-based using 5)LocalNet co-registration. Percent-changes in Dice coefficients, surface distances, MI, structural similarity index measure and target registration errors were evaluated.Main results:Co-registration using MI or NCC provided similar alignment performance, better than shape-based methods. LocalNet provided accurate co-registration of substructures by optimising subfield deformation throughout the volumes, outperforming other methods, especially in the longitudinal breast cancer xenograft dataset by minimising target registration errors.Significance:We showed the feasibility of co-registering repeatedly or longitudinally imaged vascular networks in photoacoustic mesoscopy, taking a step towards longitudinal quantitative characterisation of these complex structures. These tools open new outlooks for monitoring tumour angiogenesis at the meso-scale and for quantifying treatment-induced co-localised alterations in the vasculature.

Optimal Illumination Distance Metrics for Person Re-identification in Complex Lighting Conditions

Person Re-identification is extensively applied in public security and surveillance. However, environmental factors like time and location often lead to varying lighting conditions in captured pedestrian images, significantly impacting identification accuracy. Current approaches mitigate this issue through lighting transformation techniques, aiming to normalize images to a standard lighting condition for consistent person re-identification results. Yet, these methods overlook the fact that different content may hold distinct identification values under diverse lighting conditions. To address this, we conducted an analysis on the identification distance between images of the same or different pedestrians under predefined lighting conditions. From this analysis, we introduce the concept of optimal lighting: a condition where the distance between image pairs is minimized compared to other lighting scenarios. We propose utilizing this optimal lighting distance in the image retrieval process for final ranking. Our study, validated on synthetic datasets Market-IA and Duke-IA, demonstrates that optimal lighting is independent of image texture information. Each image pair exhibits a unique optimal lighting, yet consistently shows a minimum distance value.

Prediction of wall geometry forcold-metal-transfer-based wire-arc additive manufacturing

Wire-arc additive manufacturing (WAAM) is an advanced technique for fabricating large metal components through layer-by-layer material deposition using arc welding methods. This study focused on optimizing the WAAM process by employing machine learning models to predict and control bead geometries, specifically bead height (BH) and bead width (BW), while ensuring consistent height increments in multibead walls. Based on CMT technology in cold metal transfer experiments, linear regression models achieved high accuracy in predicting BH and BW. Analysis of variance results highlighted the considerable influence of voltage (V) and travel speed (TS) on bead geometries. For multibead wall characteristics, polynomial regression models incorporating non-linear terms, such as travel speed (TS²) and dwell time (Dt²), were developed to predict height (H) and waviness (W). Various optimization metrics were employed to balance the trade-offs between H and W for identifying optimal welding conditions that achieved the target H while minimizing W. A notable innovation of this research is the optimization of dwell time (Dt) for each layer to achieve a linear incremental H profile, minimizing W and ensuring consistent layer quality.

Optimal resource allocation for rapid convergence to stable healthy state in epidemic spreading models

The networked Susceptible-Infected-Susceptible (SIS) model has been widely investigated as a model for the spread of epidemics within networked systems. In networks with SIS dynamics and unstable healthy states, a critical question is how to distribute compensatory curing resources (with constrained total cost) among individuals, ensuring that the network converges to a healthy state as fast as possible. This paper introduces a novel approach to this problem by developing an algorithm for the optimal allocation of compensatory curing resources required by agents in a given network. This solution approach has been made possible by reformulating the original convergence rate optimization problem and an additional constraint guaranteeing a minimum convergence rate as a standard semidefinite programming problem. The applicability of the proposed algorithm to arbitrary undirected topologies and other variations of the SIS model, including the one with a weighted cost function, has been demonstrated. In the case of symmetry preserving curing and infection rates, it has been shown that the problem over a given network with a symmetric topology can be reduced to a smaller network with each orbit acting as a node. Additionally, for networks with one or two orbits, the problem has been addressed analytically, and several examples have been included. Based on two different scenarios inspired by the SARS outbreak in Hong Kong and the COVID-19 outbreak in the USA, it is shown that the algorithm's optimal results outperform the uniform distribution of additional curing resources. The paper also explores how optimal performance metrics change with the given upper limit on the total amount of additional curing resources.

Assessment on resilience of urban agglomeration transportation system considering passenger choice and load-capacity factor

Intercity transportation system (ICTS), characterized by large-scale, high spatial-temporal concentration, and sparser departure frequencies, is more vulnerable in unexpected events. Understanding the resilience characteristics of ICTS is crucial for maintaining the network service capabilities. Aiming to conduct effective resilience assessment on ICTS, we develop the resilience simulation model by introduce dual-regulated parameters for network load and capacity into cascading propagation model under interruption events, and quantify the impact of travel distance, time costs, and route redundancy on travel choice of passengers. Meanwhile, propose service resilience indicators from both the passenger's and the system's perspectives. Finally, we conduct a case study on the resilience of ICTS in Beijing-Tianjin-Hebei Urban Agglomerations (BTH-UA). The results show that: 1) Multimodal transportation systems usually exhibit better resilience than unimodal systems. 2) For various resilience optimization metrics, it is essential to choose targeted recovery strategies to maximize network resilience. 3) Traveler sensitivity to travel time significantly influences the resilience of passenger-based network services. 4) Changes in transportation supply capacity and travel demand will impact the system's resilience. The research findings can provide valuable references for the resilience development and management of urban transportation systems.

Machine learning based multi-objective optimization on shear behavior of the inter-module connection

Prefabricated prefinished volumetric construction (PPVC) has become a research hotspot in recent years. Inter-module connections have a crucial influence on the mechanical behavior of PPVC. However, current studies on shear behavior and optimization design method of the inter-module connection are insufficient. This paper investigated shear behavior and machine learning based optimization method of an innovative fully bolted liftable connection (FBLC) for PPVC. The failure mode, force transferring mechanism, and ultimate load bearing capacity of the FBLC under shear force were revealed by the shear behavior tests. Four specimens were tested and the design parameters included the strength and number of the long stay bolts. Subsequently, a refined finite element model (FEM) of the FBLC was established and validated with the ratios of the shear bearing capacity between the FEA and test results ranging from 0.99 to 1.10. Then, six mainstream machine learning algorithms were utilized to predict shear behavior of the FBLC. The Genetic Algorithm Optimized Neural Network (GANN) provided better prediction accuracy on the shear bearing capacity, with an improvement on R2 by 0.1% to 3% compared with other algorithms. Similarly, the Support Vector Regression (SVR) showed higher prediction accuracy on the ultimate displacement, improving R2 by 0.4% to 12.9% compared with other algorithms. A stacking algorithm combing the GANN and SVR was developed as the proxy model between the input variables and optimization metrics. In addition, the NSGA-II algorithm was linked to establish a multi-objective optimization method on shear behavior of the FBLC. The yield load, ultimate load and steel consumption were selected as the optimization objectives and the stacking algorithm was used as the proxy model. The Pareto optimal solution sets on the optimization objectives were explored by the NSGA-II algorithm and the optimization design method of the FBLC was established. Compared with the unoptimized specimen, the yield and ultimate shear bearing capacity of the optimized specimen were increased by 113.5% and 123.6%, respectively, with the steel consumption reduced by 26.3%. Finally, a four-story PPVC was established, and the static analysis was carried out under vertical load and wind load. The shear behavior of the FBLC and inter-story drift ratio of the PPVC before and after optimization were compared to verify the reliability of the optimization method.

SPECT/CT Quantification of 99m-TC PYP Uptake to Assess Tafamidis Treatment Response in ATTR Cardiac Amyloidosis

BackgroundCardiac imaging with bone-avid tracers for the diagnosis of ATTR cardiac amyloidosis employs only limited quantification, but SPECT/CT acquisition can provide volumetric assessment with quantification of tracer uptake. Tafamidis is routinely used in the treatment of cardiac amyloidosis but there is scant data on changes in imaging results during therapy. The purpose of this study was to perform longitudinal assessment of Tc-99m-PYP imaging to determine if tafamidis therapy results in any change in quantitative measures of tracer uptake. MethodsA prospective, single-center study of ATTR patients being treated with tafamidis using Tc-99m-PYP SPECT/CT to quantify cardiac tracer uptake in the whole heart and left ventricle. Standardized uptake values (SUVs) were adjusted for blood pool activity. Comparison of baseline activity was made to values obtained approximately every 6 months during treatment. ResultsTwenty-two patients (77.0 ± 7.5 years old, 86.4% male) were on tafamidis for 15.3 ± 4.0 months with an average time between baseline and final follow-up study of 16.8 ± 4.7 months. Thirteen (59.1%) had multiple follow-up amyloid studies. Statistically significant reductions in total SUVs, SUV volume, and percentage of injected dose were seen. Adjusted for the maximal aortic SUV, the total SUV’s in the left ventricle decreased by 36.9%, the SUV volume by 38.7%, and the percentage of injected dose decreased by 34.9% (all p-values ≤0.0001). Over the study duration there was a decrease of 7.7%/month in the measured metrics. ConclusionThe quantitative SUV measurements from Tc-99m-PYP SPECT/CT revealed an overall decrease in scintographic amyloid burden during the course of tafamidis therapy, but additional work is needed to determine the optimal metrics and improve the reproducibility of the quantification.

Enhanced Estimation of Crown-Level Leaf Dry Biomass of Ginkgo Saplings Based on Multi-Height UAV Imagery and Digital Aerial Photogrammetry Point Cloud Data

Ginkgo is a multi-purpose economic tree species that plays a significant role in human production and daily life. The dry biomass of leaves serves as an accurate key indicator of the growth status of Ginkgo saplings and represents a direct source of economic yield. Given the characteristics of flexibility and high operational efficiency, affordable unmanned aerial vehicles (UAVs) have been utilized for estimating aboveground biomass in plantations, but not specifically for estimating leaf biomass at the individual sapling level. Furthermore, previous studies have primarily focused on image metrics while neglecting the potential of digital aerial photogrammetry (DAP) point cloud metrics. This study aims to investigate the estimation of crown-level leaf biomass in 3-year-old Ginkgo saplings subjected to different nitrogen treatments, using a synergistic approach that combines both image metrics and DAP metrics derived from UAV RGB images captured at varying flight heights (30 m, 60 m, and 90 m). In this study, image metrics (including the color and texture feature parameters) and DAP point cloud metrics (encompassing crown-level structural parameters, height-related and density-related metrics) were extracted and evaluated for modeling leaf biomass. The results indicated that models that utilized both image metrics and point cloud metrics generally outperformed those relying solely on image metrics. Notably, the combination of image metrics obtained from the 60 m flight height with DAP metrics derived from the 30 m height significantly enhanced the overall modeling performance, especially when optimal metrics were selected through a backward elimination approach. Among the regression methods employed, Gaussian process regression (GPR) models exhibited superior performance (CV-R2 = 0.79, rRMSE = 25.22% for the best model), compared to Partial Least Squares Regression (PLSR) models. The common critical image metrics for both GPR and PLSR models were found to be related to chlorophyll (including G, B, and their normalized indices such as NGI and NBI), while key common structural parameters from the DAP metrics included height-related and crown-related features (specifically, tree height and crown width). This approach of integrating optimal image metrics with DAP metrics derived from multi-height UAV imagery shows great promise for estimating crown-level leaf biomass in Ginkgo saplings and potentially other tree crops.

Performance Evaluation of a Simple Strategy to Optimize Formula Constants for Zero Mean or Minimal Standard Deviation or Root-Mean-Squared Prediction Error in Intraocular Lens Power Calculation

PurposeTo investigate the performance of a simple prediction scheme for the formula constants optimized for a mean (MPE), standard deviation (SDPE) or root-mean-squared refractive prediction error (RMSPE). DesignRetrospective cross-sectional study. MethodsUsing IOLMaster 700 biometric data from 888 eyes treated with the Hoya Vivinex lens and 821 eyes treated with the Alcon SA60AT lens, plus the power of the implanted lens and postoperative spherical equivalent refraction, optimized constants for SRKT, Hoffer Q, Holladay 1, Haigis, and K6 formulae were calculated using an iterative nonlinear optimization for zero MPE and minimal SDPE and RMSPE. Start values were detuned by ±1.5 from the MPE optimized constants and formula constants generated using the simple prediction scheme were compared to the corresponding directly optimized constants. ResultsFor all 5 formulae under test and with both datasets, constants optimized using the simple scheme showed excellent agreement with those from the iterative method with either MPE or RMSPE used as the optimization metric, and good agreement with SDPE as the metric. Constants optimized for zero MPE or minimal RMSPE agreed within 0.05, whereas constants for minimal SDPE could be systematically off by up to 0.6 from the MPE values, making SDPE unsuitable as an optimization metric. ConclusionsThis simple formula constant optimization scheme performs excellently for 4 disclosed formulae and one nondisclosed formula in our 2 monocentric datasets with zero MPE or minimal RMSPE as metrics. Multicentric studies with other study populations and biometers are required to further investigate the clinical applicability.

Secure IRS-assisted energy scavenging NOMA impaired by realistic factors

Intelligent reflective surface (IRS)-assisted energy scavenging (ES) non-orthogonal multiple access (NOMA) with jamming (sIEnoma) is promising for high spectral efficiency, energy efficiency, information security, and communication reliability. Nonetheless, practical imperfections, such as channel state information imperfection (CSIi), hardware imperfection (HWi), fading severity, and nonlinear energy scavenging (nlES) impact scavenged energy, information security, and communication reliability of sIEnoma. Accordingly, this paper proposes performance analysis for sIEnoma under these imperfections to quickly assess its potentials without time-consuming simulations. Results show that imperfections (HWi, CSIi, fading severity, nlES) and specifications (IRS, NOMA) significantly impact performance of sIEnoma. Nevertheless, proper adoption of power splitting factor, desired data rate, HWi level, and time switching coefficient can hinder complete outage in sIEnoma. Additionally, optimal metrics are achieved by configuring system parameters properly. Moreover, the proposed sIEnoma outperforms its baseline (IRS-assisted ES orthogonal multiple access with jamming).

Impact of early-stage cooperative vehicle infrastructure systems on traffic and energy consumption under various traffic conditions: From application to policy

Cooperative Vehicle Infrastructure Systems (CVIS) play a crucial role in promoting sustainable transportation. Existing studies have primarily focused on the theoretical algorithms of CVIS, with insufficient research on energy consumption evaluation using practical data, particularly under diverse traffic conditions. This study addresses this gap by collecting long-term sequential application data (2.19 million) of CVIS across a wide region, analyzing the performance trends of CVIS under various traffic conditions, and offering practical policy recommendations for optimizing and deploying CVIS effectively. The data from drivers using CVIS are categorized into passive CVIS (P-CVIS) or active CVIS (A-CVIS) based on the utilization of the speed guidance function. The study reveals that drivers with insufficient proficiency in A-CVIS tend to engage in more aggressive driving behaviors, characterized by higher acceleration rates and greater variability in acceleration distribution in response to recommended speeds. These behaviors lead to increased acceleration energy consumption for both vehicles (2.7 % and 9.0 %) and road networks (65.2 % and 24.7 %) during peak and off-peak hours, respectively. While A-CVIS can still play an optimizing role during peak hours, the majority of optimization metrics are slightly lower compared to off-peak periods. The study concludes by offering recommendations and regulatory measures to enhance A-CVIS.

Hybrid Optimization Path Planning Method for AGV Based on KGWO.

To address the path planning problem for automated guided vehicles (AGVs) in challenging and complex industrial environments, a hybrid optimization approach is proposed, integrating a Kalman filter with grey wolf optimization (GWO), as well as incorporating partially matched crossover (PMX) mutation operations and roulette wheel selection. Paths are first optimized using GWO, then refined with Kalman filter corrections every ten iterations. Moreover, roulette wheel selection guides robust parent path selection, while an elite strategy and partially matched crossover (PMX) with mutation generate diverse offspring. Extensive simulations and experiments were carried out under a densely packed goods scenario and complex indoor layout scenario, within a fully automated warehouse environment. The results showed that this hybrid method not only enhanced the various optimization metrics but also ensured more predictable and collision-free navigation paths, particularly in environments with complex obstacles. These improvements lead to increased operational efficiency and safety, highlighting the method's potential in real-world applications.

Enhancing Literature Review Efficiency: A Case Study on Using Fine-Tuned BERT for Classifying Focused Ultrasound-Related Articles

Over the past decade, focused ultrasound (FUS) has emerged as a promising therapeutic modality for various medical conditions. However, the exponential growth in the published literature on FUS therapies has made the literature review process increasingly time-consuming, inefficient, and error-prone. Machine learning approaches offer a promising solution to address these challenges. Therefore, the purpose of our study is to (1) explore and compare machine learning techniques for the text classification of scientific abstracts, and (2) integrate these machine learning techniques into the conventional literature review process. A classified dataset of 3588 scientific abstracts related and unrelated to FUS therapies sourced from the PubMed database was used to train various traditional machine learning and deep learning models. The fine-tuned Bio-ClinicalBERT (Bidirectional Encoder Representations from Transformers) model, which we named FusBERT, had comparatively optimal performance metrics with an accuracy of 0.91, a precision of 0.85, a recall of 0.99, and an F1 of 0.91. FusBERT was then successfully integrated into the literature review process. Ultimately, the integration of this model into the literature review pipeline will reduce the number of irrelevant manuscripts that the clinical team must screen, facilitating efficient access to emerging findings in the field.

Deep learning-based GoogLeNet-embedded no-pooling dimension fully-connected network for short-term wind power prediction

The dependence of wind power on the natural environment leads to volatility, which can cause hidden dangers to the safe and stable operation of the power grid. In this work, a deep learning-based GoogLeNet-embedded no-pooling dimension fully-connected prediction network is proposed for the short-term prediction issue of wind power generation, and the deep learning-based GoogLeNet-embedded no-pooling dimension fully-connected network is compared with five algorithms including long short-term memory network and NasNet. The dataset was collected in Natal. The six algorithms employed predicted the value of wind power for the coming day. Among all, the deep learning-based GoogLeNet embedded no-pooling dimension fully-connected network achieved the optimal prediction results and evaluation metrics. The percentage reduction of each metric value from the second smallest long short-term memory network for the deep learning-based GoogLeNet-embedded no-pooling dimension fully-connected network is 27.0% for mean absolute error, 27.2% for mean absolute percentage error, 34.8% for mean squared error, 19.9% for root mean square error and 21.6% for symmetric mean absolute percentage error.

Quantitative CT analysis of idiopathic pulmonary fibrosis and correlation with lung function study

BackgroundIdiopathic Pulmonary Fibrosis (IPF) is a progressive fibrotic lung disease. However, the field of quantitative CT scan analysis in conjunction with pulmonary function test for IPF patients remains relatively understudied. In this study, we evaluated the diagnostic value of features derived high-resolution computed tomography (HRCT) for patients with IPF and correlated them with pulmonary function tests.MethodsWe retrospectively analyzed the chest HRCT images and pulmonary function test results of 52 patients with IPF during the same period (1 week) and selected 52 healthy individuals, matched for sex, age, and body mass index (BMI) and with normal chest HRCT as controls. HRCT scans were performed using a Philips 256-row Brilliance iCT scanner with standardized parameters. Lung function tests were performed using a Jaeger volumetric tracer for forced vital capacity (FVC), total lung capacity (TLC), forced expiratory volume in first second (FEV1), FEV1/FVC, carbon monoxide diffusing capacity (DLCO), and maximum ventilation volume (MVV) metrics. CT quantitative analysis, including tissue segmentation and threshold-based quantification of lung abnormalities, was performed using 3D-Slicer software to calculate the percentage of normal lung areas (NL%), percentage of ground-glass opacity areas (GGO%), percentage of fibrotic area (F%) and abnormal lesion area percentage (AA%). Semi-quantitative analyses were performed by two experienced radiologists to assess disease progression. The aortic-to-sternal distance (ASD) was measured on axial images as a standardized parameter. Spearman or Pearson correlation analysis and multivariate stepwise linear regression were used to analyze the relationship between the data in each group, and the ROC curve was used to determine the optimal quantitative CT metrics for identifying IPF and controls.ResultsROC curve analysis showed that F% distinguished the IPF patient group from the control group with the largest area under the curve (AUC) of 0.962 (95% confidence interval: 0.85–0.96). Additionally, with F% = 4.05% as the threshold, the Youden’s J statistic was 0.827, with a sensitivity of 92.3% and a specificity of 90.4%. The ASD was significantly lower in the late stage of progression than in the early stage (t = 5.691, P < 0.001), with a mean reduction of 2.45% per month. Quantitative CT indices correlated with all pulmonary function parameters except FEV1/FVC, with the highest correlation coefficients observed for F% and TLC%, FEV1%, FVC%, MVV% (r = − 0.571, − 0.520, − 0.521, − 0.555, respectively, all P-values < 0.001), and GGO% was significantly correlated with DLCO% (r = − 0.600, P < 0.001). Multiple stepwise linear regression analysis showed that F% was the best predictor of TLC%, FEV1%, FVC%, and MVV% (R2 = 0.301, 0.301, 0.300, and 0.302, respectively, all P-values < 0.001), and GGO% was the best predictor of DLCO% (R2 = 0.360, P < 0.001).ConclusionsQuantitative CT analysis can be used to diagnose IPF and assess lung function impairment. A decrease in the ASD may indicate disease progression.

Integrating Deep Learning and Synthetic Biology: A Co-Design Approach for Enhancing Gene Expression via N-Terminal Coding Sequences.

N-terminal coding sequence (NCS) influences gene expression by impacting the translation initiation rate. The NCS optimization problem is to find an NCS that maximizes gene expression. The problem is important in genetic engineering. However, current methods for NCS optimization such as rational design and statistics-guided approaches are labor-intensive yield only relatively small improvements. This paper introduces a deep learning/synthetic biology codesigned few-shot training workflow for NCS optimization. Our method utilizes k-nearest encoding followed by word2vec to encode the NCS, then performs feature extraction using attention mechanisms, before constructing a time-series network for predicting gene expression intensity, and finally a direct search algorithm identifies the optimal NCS with limited training data. We took green fluorescent protein (GFP) expressed by Bacillus subtilis as a reporting protein of NCSs, and employed the fluorescence enhancement factor as the metric of NCS optimization. Within just six iterative experiments, our model generated an NCS (MLD62) that increased average GFP expression by 5.41-fold, outperforming the state-of-the-art NCS designs. Extending our findings beyond GFP, we showed that our engineered NCS (MLD62) can effectively boost the production of N-acetylneuraminic acid by enhancing the expression of the crucial rate-limiting GNA1 gene, demonstrating its practical utility. We have open-sourced our NCS expression database and experimental procedures for public use.

The Advancement of Artificial Intelligence's Application in Hybrid Solar and Wind Power Plant Optimization: A Study of the Literature

The harnessing of solar, wind, and hydroelectric energy sources has rendered them easily accessible renewable resources, owing to their abundant availability. There is a growing body of research evincing interest in the deployment of hybrid renewable energy systems. Over recent decades, adopting hybrid technologies has engendered a positive trend, marked by broader considerations of configurations and applications within these systems. This study analytically examines the potential of hybrid solar and wind energy harvesting devices. As such, the project aims to extensively evaluate relevant literature and statistical analysis of data extracted from journal papers published between 2004 and 2023. A specific objective is to develop a complete database matrix surrounding multiple categories, including component configurations, methodological approaches, and supporting software infrastructures. Moreover, an assessment of the socio-economic, environmental, and ecological impacts of these systems is undertaken to ascertain their salience. Furthermore, this inquiry delves into the optimization strategies of these systems leveraging artificial intelligence methodologies. Critical lacunae identified during this review pertain to more emphasis on optimization metrics for PV-wind hybrid energy systems, impeding a holistic understanding of their implications on energy, economics, environment, and society. Our findings underscore prevalent methodologies such as computational modelling utilizing software suites like MATLAB/Simulink, HOMER, and others to derive empirical data. Additionally, parametric analyses emerge as the predominant approach, characterized by the application of algorithms such as Particle Swarm Optimization (PSO), Fuzzy Logic Control (FLC), and Genetic Algorithms (GA), among others. PV-wind hybrid energy systems are classified into autonomous and grid-interconnected configurations, with primary components comprising PV-wind generators. The anticipated trajectory suggests a burgeoning development of these hybrid energy harvesting systems, underpinned by their potential as clean, sustainable, and eco-friendly energy sources.