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In flexible manufacturing lines with delayed differentiation, the production process may fluctuate sharply when a control action is performed. As a result, the steady-state analysis algorithm is inaccurate for these production lines, and transient behavior studies have become crucial. However, dynamic analysis remains unexplored compared with the well-established theoretical system of steady-state analysis. Therefore, in this study, we propose a fast algorithm for predicting the production process performance in the delayed differentiation-based flexible production line under operation control. We first formulate practical problems existing in the auto, food, and furniture industries into a mathematical formation. Then, we offer closed-form formulae for predicting the production process performance using the built stochastic model in the production line with three machines. We also propose an algorithm to predict the performance of a production line having more than three machines. The proposed methods were verified to be highly accurate through comparison experiments. In terms of theoretical contributions, this study offers a research foundation for other transient-based studies. From a practical perspective, the proposed algorithms can be employed to predict the production process performance of processing lines under production control in advance. [ABSTRACT FROM AUTHOR]

Simple Summary: The prediction of tumor response after neoadjuvant FLOT therapy is highly necessary. The use of deep learning on gastroesophageal biopsies enabled us to extract predictive information. This prediction model could be easily applied in clinical decision making. Patients could avoid unnecessary treatment or receive an intensified FLOT therapy. Background: The aim of this study was to establish a deep learning prediction model for neoadjuvant FLOT chemotherapy response. The neural network utilized clinical data and visual information from whole-slide images (WSIs) of therapy-naïve gastroesophageal cancer biopsies. Methods: This study included 78 patients from the University Hospital of Cologne and 59 patients from the University Hospital of Heidelberg used as external validation. Results: After surgical resection, 33 patients from Cologne (42.3%) were ypN0 and 45 patients (57.7%) were ypN+, while 23 patients from Heidelberg (39.0%) were ypN0 and 36 patients (61.0%) were ypN+ (p = 0.695). The neural network had an accuracy of 92.1% to predict lymph node metastasis and the area under the curve (AUC) was 0.726. A total of 43 patients from Cologne (55.1%) had less than 50% residual vital tumor (RVT) compared to 34 patients from Heidelberg (57.6%, p = 0.955). The model was able to predict tumor regression with an error of ±14.1% and an AUC of 0.648. Conclusions: This study demonstrates that visual features extracted by deep learning from therapy-naïve biopsies of gastroesophageal adenocarcinomas correlate with positive lymph nodes and tumor regression. The results will be confirmed in prospective studies to achieve early allocation of patients to the most promising treatment. [ABSTRACT FROM AUTHOR]

Advanced carbon emission factors of a power grid can provide users with effective carbon reduction advice, which is of immense importance in mobilizing the entire society to reduce carbon emissions. The method of calculating node carbon emission factors based on the carbon emissions flow theory requires real-time parameters of a power grid. Therefore, it cannot provide carbon factor information beforehand. To address this issue, a prediction model based on the graph attention network is proposed. The model uses a graph structure that is suitable for the topology of the power grid and designs a supervised network using the loads of the grid nodes and the corresponding carbon factor data. The network extracts features and transmits information more suitable for the power system and can flexibly adjust the equivalent topology, thereby increasing the diversity of the structure. Its input and output data are simple, without the power grid parameters. We demonstrated its effect by testing IEEE-39 bus and IEEE-118 bus systems with average error rates of 2.46% and 2.51%. [ABSTRACT FROM AUTHOR]

Advantageous variants of the SARS-CoV-2 virus have arisen through mutations, particularly on a single amino acid basis. These point mutations can cause changes in the structure of SARS-CoV-2 and affect the efficiency of interaction with the ACE2 protein. N501Y and E484K mutations affecting binding by ACE2 have been widely observed. This study aimed to predict SARS-CoV-2 mutations that could be as effective as N501Y and E484K and pose a danger due to their high contagiousness. Experimental data on SARS-CoV-2 and ACE2 binding and stability were associated with different amino acid properties and integrated into machine learning and computational biology techniques. As a result of the analyses made in algorithms, N501M, Q414A, N354K, Q498H and N460K have been predicted to be likely to have a dangerous effect. The N501W mutations are most likely to have dangerous effects on the spread of the coronavirus. We suggest that attention should be paid to the position 501 mutation since this position is repeated in the lists of mutations that the algorithm detected as dangerous. G446, G447, Y505, T500, Q493, Y473, and G476 were determined as the positions where dangerous variants could be seen as a result of the analyses of the multiple interaction data created with the ACE2 and RBD interaction data. The 13 dangerous positions and mutations have been detected to accurately describe the position of the mutations caused by the Omicron variant and were among the known dangerous mutations similar to those occurring at Q498, G446, Y505 and Q493 positions.

Advantageous variants of the SARS-CoV-2 virus have arisen through mutations, particularly on a single amino acid basis. These point mutations can cause changes in the structure of SARS-CoV-2 and affect the efficiency of interaction with the ACE2 protein. N501Y and E484K mutations affecting binding by ACE2 have been widely observed. This study aimed to predict SARS-CoV-2 mutations that could be as effective as N501Y and E484K and pose a danger due to their high contagiousness. Experimental data on SARS-CoV-2 and ACE2 binding and stability were associated with different amino acid properties and integrated into machine learning and computational biology techniques. As a result of the analyses made in algorithms, N501M, Q414A, N354K, Q498H and N460K have been predicted to be likely to have a dangerous effect. The N501W mutations are most likely to have dangerous effects on the spread of the coronavirus. We suggest that attention should be paid to the position 501 mutation since this position is repeated in the lists of mutations that the algorithm detected as dangerous. G446, G447, Y505, T500, Q493, Y473, and G476 were determined as the positions where dangerous variants could be seen as a result of the analyses of the multiple interaction data created with the ACE2 and RBD interaction data. The 13 dangerous positions and mutations have been detected to accurately describe the position of the mutations caused by the Omicron variant and were among the known dangerous mutations similar to those occurring at Q498, G446, Y505 and Q493 positions. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to externally validate a reported model for identifying patients requiring extended stay following lower limb arthroplasty in a new setting. Design: External validation of a previously reported prognostic model, using retrospective data. Setting: Medium-sized hospital orthopaedic department, Australia. Participants: Electronic medical records were accessed for data collection between Sep-2019 and Feb-2020 and retrospective data extracted from 200 randomly selected total hip or knee arthroplasty patients. Intervention: Participants received total hip or knee replacement between 2-Feb-16 and 4-Apr-19. This study was a non-interventional retrospective study. Main measures: Model validation was assessed with discrimination, calibration on both original and adjusted forms of the candidate model. Decision curve analysis was conducted on the outputs of the adjusted model to determine net benefit at a predetermined decision threshold (0.5). Results: The original model performed poorly, grossly overestimating length of stay with mean calibration of −3.6 (95% confidence interval −3.9 to −3.2) and calibration slope of 0.52. Performance improved following adjustment of the model intercept and model coefficients (mean calibration 0.48, 95% confidence interval 0.16 to 0.80 and slope of 1.0), but remained poorly calibrated at low and medium risk threshold and net benefit was modest (three additional patients per hundred identified as at-risk) at the a-priori risk threshold. Conclusions: External validation demonstrated poor performance when applied to a new patient population and would provide limited benefit for our institution. Implementation of predictive models for arthroplasty should include practical assessment of discrimination, calibration and net benefit at a clinically acceptable threshold. [ABSTRACT FROM AUTHOR]

This research aims to identify and classify the types of microalgae and investigate their growth and control conditions and their optimal use for construction purposes and urban landscapes. How to use types of microalgae in climates, types of buildings, and geographical directions and fronts of buildings requires comprehensive knowledge of microalgae. Different microalgae can be more beneficial for different purposes, including, shading on buildings and translucent surfaces, insulation used, and biomass production. Also, estimating the intensity of light radiation to different levels and climates is necessary to provide better growth conditions and higher efficiency. With a comprehensive review and analysis of these conditions, this article has presented two innovative methods to match the required characteristics of each microalga with the existing conditions of the urban landscape and buildings so that it can be determined in different parts of the building. Moreover, which microalgae are most effective for urban landscapes in different climates, on surfaces with different slopes and angles, and for different purposes? Finally, a new perspective and vision for research and further applications of microalgae at the scale of the building and urban landscapes have been presented.

Fixed-wing unmanned aerial vehicles (UAVs) possess high speed and non-hovering capabilities, rendering them uniquely advantages for reconnaissance and detection. The focus of this paper is to addressing the problem of formation control for fixed-wing UAVs in the presence of communication delay. To tackle this problem, for the non-holonomic kinematic model, we propose an intuitive and practical control law based on the leader-follower method to ensure that UAVs maintain a predetermined geometric formation. The stability analysis of the system with communication delay is conducted by constructing a strict Lyapunov–Krasovskii function. Furthermore, we consider the impact of communication delay on formation accuracy and present a prediction algorithm capable of forecasting the actual position of each UAV. To validate our theoretical findings, both digital simulation and hardware-in-loop experiment are conducted. [ABSTRACT FROM AUTHOR]

In the era of Industry 4.0 and 5.0, a transformative wave of softwarisation has surged. This shift towards software-centric frameworks has been a cornerstone and has highlighted the need to comprehend software applications. This research introduces a novel agent-based architecture designed to sense and predict software application metrics in industrial scenarios using AI techniques. It comprises interconnected agents that aim to enhance operational insights and decision-making processes. The forecaster component uses a random forest regressor to predict known and aggregated metrics. Further analysis demonstrates overall robust predictive capabilities. Visual representations and an error analysis underscore the forecasting accuracy and limitations. This work establishes a foundational understanding and predictive architecture for software behaviours, charting a course for future advancements in decision-making components within evolving industrial landscapes. [ABSTRACT FROM AUTHOR]

Rise of life‐threatening superbugs, pandemics and epidemics warrants the need for cost‐effective and novel pharmacological interventions. Availability of publicly available proteomes of pathogens supports development of high‐throughput discovery platforms to prioritize potential drug‐targets and develop testable hypothesis for pharmacological screening. The pipeline builder for identification of target (PBIT) was developed in 2016 and updated in 2021, with the purpose of accelerating the search for drug‐targets by integration of methods like comparative and subtractive genomics, essentiality/virulence and druggability analysis. Since then, it has been used for identification of drugs and vaccine targets, safety profiling of multiepitope vaccines and mRNA vaccine construction against a broad‐spectrum of pathogens. This tool has now been updated with functionalities related to systems biology and immuno‐informatics and validated by analyzing 48 putative antigens of Mycobacterium tuberculosis documented in literature. PBITv3 available as both online and offline tools will enhance drug discovery against emerging drug‐resistant infectious agents. PBITv3 can be freely accessed at http://pbit.bicnirrh.res.in/. [ABSTRACT FROM AUTHOR]

Hydropower stations that are part of the grid system frequently encounter challenges related to the uneven distribution of power generation and associated benefits, primarily stemming from delays in obtaining timely load data. This research addresses this issue by developing a scheduling model that combines power load prediction and dual-objective optimization. The practical application of this model is demonstrated in a real-case scenario, focusing on the Shatuo Hydropower Station in China. In contrast to current models, the suggested model can achieve optimal dispatch for grid-connected hydropower stations even when power load data is unavailable. Initially, the model assesses various prediction models for estimating power load and subsequently incorporates the predictions into the GA-NSGA-II algorithm, specifically an enhanced elite non-dominated sorting genetic algorithm. This integration is performed while considering the proposed objective functions to optimize the discharge flow of the hydropower station. The outcomes reveal that the CNN-GRU model, denoting Convolutional Neural Network-Gated Recursive Unit, exhibits the highest prediction accuracy, achieving R-squared and RMSE (i.e., Root Mean Square Error) values of 0.991 and 0.026, respectively. The variance between scheduling based on predicted load values and actual load values is minimal, staying within 5 (m3/s), showcasing practical effectiveness. The optimized scheduling outcomes in the real case study yield dual advantages, meeting both the demands of ship navigation and hydropower generation, thus achieving a harmonious balance between the two requirements. This approach addresses the real-world challenges associated with delayed load data collection and insufficient scheduling, offering an efficient solution for managing hydropower station scheduling to meet both power generation and navigation needs.

Abstract Background Osteoporosis poses a growing healthcare challenge owing to its rising prevalence and a significant treatment gap, as patients are widely underdiagnosed and consequently undertreated, leaving them at high risk of osteoporotic fracture. Several tools aim to improve case-finding in osteoporosis. One such tool is the Fracture Risk Evaluation Model (FREM), which in contrast to other tools focuses on imminent fracture risk and holds potential for automation as it relies solely on data that is routinely collected via the Danish healthcare registers. The present article is an analysis protocol for a prediction model that is to be used as a modified version of FREM, with the intention of improving the identification of subjects at high imminent risk of fracture by including pharmacological exposures and using more advanced statistical methods compared to the original FREM. Its main purposes are to document and motivate various aspects and choices of data management and statistical analyses. Methods The model will be developed by employing logistic regression with grouped LASSO regularization as the primary statistical approach and gradient-boosted classification trees as a secondary statistical modality. Hyperparameter choices as well as computational considerations on these two approaches are investigated by an unsupervised data review (i.e., blinded to the outcome), which also investigates and handles multicollinarity among the included exposures. Further, we present an unsupervised review of the data and testing of analysis code with respect to speed and robustness on a remote analysis environment. The data review and code tests are used to adjust the analysis plans in a blinded manner, so as not to increase the risk of overfitting in the proposed methods. Discussion This protocol specifies the planned tool development to ensure transparency in the modeling approach, hence improving the validity of the enhanced tool to be developed. Through an unsupervised data review, it is further documented that the planned statistical approaches are feasible and compatible with the data employed.

Background: The aim of this study was to establish a deep learning prediction model for neoadjuvant FLOT chemotherapy response. The neural network utilized clinical data and visual information from whole-slide images (WSIs) of therapy-naïve gastroesophageal cancer biopsies. Methods: This study included 78 patients from the University Hospital of Cologne and 59 patients from the University Hospital of Heidelberg used as external validation. Results: After surgical resection, 33 patients from Cologne (42.3%) were ypN0 and 45 patients (57.7%) were ypN+, while 23 patients from Heidelberg (39.0%) were ypN0 and 36 patients (61.0%) were ypN+ (p = 0.695). The neural network had an accuracy of 92.1% to predict lymph node metastasis and the area under the curve (AUC) was 0.726. A total of 43 patients from Cologne (55.1%) had less than 50% residual vital tumor (RVT) compared to 34 patients from Heidelberg (57.6%, p = 0.955). The model was able to predict tumor regression with an error of ±14.1% and an AUC of 0.648. Conclusions: This study demonstrates that visual features extracted by deep learning from therapy-naïve biopsies of gastroesophageal adenocarcinomas correlate with positive lymph nodes and tumor regression. The results will be confirmed in prospective studies to achieve early allocation of patients to the most promising treatment.

Based on the development of the 3D vascular printer, the forming process of ink from the nozzle to the rotating rod was studied. In this study, to online detect the ink flow from the nozzle during 3D bioprinting of tubular tissue, we established a geometric model according to the region of interest (ROI) of the ink flow picture of 3D printing of tubular tissue, selected description features of the ink contour, and studied how to select mathematical expressions of the features. Principal component analysis (PCA) was used to simplify the image features into 15 features. We used a back propagation (BP) neural network to predict the printing ink flow. The results show that the error between the actual ink flow rate and the flow rate based on the BP neural network is within 5%. The BP neural network can be used to monitor the quality status of the printing target in real time, evaluate the 3D bioprinting quality online, and predict the printing ink flow for the subsequent improvement of the 3D bioprinting accuracy of tubular tissue. [ABSTRACT FROM AUTHOR]

Objectives: Patients with single-ventricle physiology have a significant risk of cardiorespiratory deterioration between their first- and second-stage palliation surgeries. Detection of deterioration episodes may allow for early intervention and improved outcomes. Methods: A prospective study was executed at Nationwide Children's Hospital, Children's Hospital of Philadelphia, and Children's Hospital Colorado to collect physiologic data of subjects with single ventricle physiology during all hospitalizations between neonatal palliation and II surgeries using the Sickbay software platform (Medical Informatics Corp). Timing of cardiorespiratory deterioration events was captured via chart review. The predictive algorithm previously developed and validated at Texas Children's Hospital was applied to these data without retraining. Standard metrics such as receiver operating curve area, positive and negative likelihood ratio, and alert rates were calculated to establish clinical performance of the predictive algorithm. Results: Our cohort consisted of 58 subjects admitted to the cardiac intensive care unit and stepdown units of participating centers over 14 months. Approximately 28,991 hours of high-resolution physiologic waveform and vital sign data were collected using the Sickbay. A total of 30 cardiorespiratory deterioration events were observed. the risk index metric generated by our algorithm was found to be both sensitive and specific for detecting impending events one to two hours in advance of overt extremis (receiver operating curve = 0.927). Conclusions: Our algorithm can provide a 1- to 2-hour advanced warning for 53.6% of all cardiorespiratory deterioration events in children with single ventricle physiology during their initial postop course as well as interstage hospitalizations after stage I palliation with only 2.5 alarms being generated per patient per day.

The object of research in the paper is extrapolation problems based on interpolation polynomials. Polynomial-based prediction methods are well known. However, the problem is that such methods often give very large errors in practice. The permissible error of extrapolation even by one grid step is not ensured by the high accuracy of interpolation using polynomials. The paper proposes an algorithm that allows to significantly improve polynomial forecasts by optimizing the procedure for choosing the power of the polynomial, on the basis of which the forecast is built. The algorithm is based on the procedure for building all polynomial forecasts according to known data and analysis of these forecasts. In particular, the presence of monotonicity and a tendency to convergence allows determining the optimal degree of the polynomial. In the absence of monotonicity, provided that certain ratios are met, the forecast can be constructed as the arithmetic average of all polynomial forecasts. An important result is the estimation of the error of the forecasting method by averaging polynomial forecasts. The development of the algorithm became possible due to the use of a special method of constructing a one-step polynomial forecast. The method differs in that it allows to build a forecast without using the cumbersome procedure of calculating the unknown coefficients of the polynomial. The numerical results presented in the work demonstrate the effectiveness of the forecasting technique based on the average of polynomial forecasts. In particular, for the test functions, the relative error was about 2–5 %, while polynomials of different degrees in the worst case yielded more than 50 %. The obtained results can be useful for building shortterm forecasts of series of economic dynamics, forecasting the behavior of arbitrary processes with a dominant deterministic component. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has gained significance in agriculture, using remote sensing and machine learning to help farmers make high-precision management decisions. This technology can be applied in viticulture, making it possible to monitor disease occurrence and prevent them automatically. The study aims to achieve an intelligent grapevine disease detection method, using an IoT sensor network that collects environmental and plant-related data. The focus of this study is the identification of the main parameters which provide early information regarding the grapevine's health. An overview of the sensor network, architecture, and components is provided in this paper. The IoT sensors system is deployed in the experimental plots located within the plantations of the Research Station for Viticulture and Enology (SDV) in Murfatlar, Romania. Classical methods for disease identification are applied in the field as well, in order to compare them with the sensor data, thus improving the algorithm for grapevine disease identification. The data from the sensors are analyzed using Machine Learning (ML) algorithms and correlated with the results obtained using classical methods in order to identify and predict grapevine diseases. The results of the disease occurrence are presented along with the corresponding environmental parameters. The error of the classification system, which uses a feedforward neural network, is 0.05. This study will be continued with the results obtained from the IoT sensors tested in vineyards located in other regions. [ABSTRACT FROM AUTHOR]

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In this study, the power function model was modified to explain the creep strain of coarse-grain and fine-grain soils in a uniform manner. And then, two creep subsidence prediction algorithms considering stress history and without considering stress history were proposed based on the modified power function model and Bjerrum’s reclassification of consolidation. The two proposed algorithms were in comparison with two widely used subsidence prediction algorithms in practical engineering based on field subsidence observation data from the subgrade of Haolebaoji-Ji’an Railway. According to the comparison results, the prediction algorithm considering stress history provides a more precise and reliable prediction over two conventional algorithms with a limited amount of available observation data. However, the prediction algorithm without considering stress history have poor prediction results.

In the era of Industry 4.0 and 5.0, a transformative wave of softwarisation has surged. This shift towards software-centric frameworks has been a cornerstone and has highlighted the need to comprehend software applications. This research introduces a novel agent-based architecture designed to sense and predict software application metrics in industrial scenarios using AI techniques. It comprises interconnected agents that aim to enhance operational insights and decision-making processes. The forecaster component uses a random forest regressor to predict known and aggregated metrics. Further analysis demonstrates overall robust predictive capabilities. Visual representations and an error analysis underscore the forecasting accuracy and limitations. This work establishes a foundational understanding and predictive architecture for software behaviours, charting a course for future advancements in decision-making components within evolving industrial landscapes.

In order to accurately predict apple freshness degree, effectively reduce the storage risk of apples, and avoid economic losses, in this study, an electronic nose system based on a gas sensor array and wireless transmission module was proposed to acquire smell information from apples. On the basis of our lab’s existing apple freshness recognition systems, Fuji apples stored in indoor conditions were taken as the research object, a prediction model based on back propagation(BP) neural network optimized by sparrow search algorithm(SSA) improved by chaotic sequence(Tent) was established to predict apple freshness by predicting apple smell information. The results showed that the determination coefficient R2 of the Tent-SSA-BP prediction model using gas sensor array was 0.942 03 with a mean square error of 0.000 4, which was higher than that of the BP neural network prediction model of 0.800 57 and had higher prediction stability. The Tent-SSA-BP prediction made up for the current problems of low prediction accuracy, low detection efficiency, and damage to apples of traditional dynamic models. Simultaneously, compared with other prediction methods such as infrared spectroscopy, the model had the advantages of lower economic cost, easier operation, and portability. In conclusion, prediction of the apple freshness achieved by prediction of smell information volatilized from apples had the characteristics of simple operation, low cost, and reliable results, avoided damage to apples in the process of prediction of freshness, and achieved non-destructive testing. [ABSTRACT FROM AUTHOR]

Objective: To assess if early clinical and electroencephalography (EEG) features predict later seizure development in infants with hypoxic‐ischemic encephalopathy (HIE). Methods: Clinical and EEG parameters <12 h of birth from infants with HIE across eight European Neonatal Units were used to develop seizure‐prediction models. Clinical parameters included intrapartum complications, fetal distress, gestational age, delivery mode, gender, birth weight, Apgar scores, assisted ventilation, cord pH, and blood gases. The earliest EEG hour provided a qualitative analysis (discontinuity, amplitude, asymmetry/asynchrony, sleep–wake cycle [SWC]) and a quantitative analysis (power, discontinuity, spectral distribution, inter‐hemispheric connectivity) from full montage and two‐channel amplitude‐integrated EEG (aEEG). Subgroup analysis, only including infants without anti‐seizure medication (ASM) prior to EEG was also performed. Machine‐learning (ML) models (random forest and gradient boosting algorithms) were developed to predict infants who would later develop seizures and assessed using Matthews correlation coefficient (MCC) and area under the receiver‐operating characteristic curve (AUC). Results: The study included 162 infants with HIE (53 had seizures). Low Apgar, need for ventilation, high lactate, low base excess, absent SWC, low EEG power, and increased EEG discontinuity were associated with seizures. The following predictive models were developed: clinical (MCC 0.368, AUC 0.681), qualitative EEG (MCC 0.467, AUC 0.729), quantitative EEG (MCC 0.473, AUC 0.730), clinical and qualitative EEG (MCC 0.470, AUC 0.721), and clinical and quantitative EEG (MCC 0.513, AUC 0.746). The clinical and qualitative‐EEG model significantly outperformed the clinical model alone (MCC 0.470 vs 0.368, p‐value.037). The clinical and quantitative‐EEG model significantly outperformed the clinical model (MCC 0.513 vs 0.368, p‐value.012). The clinical and quantitative‐EEG model for infants without ASM (n = 131) had MCC 0.588, AUC 0.832. Performance for quantitative aEEG (n = 159) was MCC 0.381, AUC 0.696 and clinical and quantitative aEEG was MCC 0.384, AUC 0.720. Significance: Early EEG background analysis combined with readily available clinical data helped predict infants who were at highest risk of seizures, hours before they occur. Automated quantitative‐EEG analysis was as good as expert analysis for predicting seizures, supporting the use of automated assessment tools for early evaluation of HIE. [ABSTRACT FROM AUTHOR]

CRISSPAC is an open-source web-based platform for the prediction of the Syntax Score and the severity of coronary artery disease (CAD) providing a variety of data analytics and machine learning solutions presented via an-easy-to-use graphical interface environment. The aim of the software is two-fold: (1) to facilitate both retrospective and prospective objectives dedicated to the diagnosis of the complexity of CAD; (2) to support open science principles through the public availability of the source code. Our envision is to promote software extensibility and utilization towards accurate diagnosis, decision-making processes and personalized patient management and counseling activities.

The prediction of short-term traffic flow is the theoretical basis of intelligent transportation as well as the key technology in traffic flow induction systems. The research on short-term traffic flow prediction has showed the considerable social value. At present, the support vector regression (SVR) intelligent prediction model that is suitable for small samples has been applied in this domain. Aiming at parameter selection difficulty and prediction accuracy improvement, the artificial bee colony (ABC) is adopted in optimizing SVR parameters, which is referred to as the ABC-SVR algorithm in the paper. The simulation experiments are carried out by comparing the ABC-SVR algorithm with SVR algorithm, and the feasibility of the proposed ABC-SVR algorithm is verified by result analysis. Continuously, the simulation experiments are carried out by comparing the ABC-SVR algorithm with particle swarm optimization SVR (PSO-SVR) algorithm and genetic optimization SVR (GA-SVR) algorithm, and a better optimization effect has been attained by simulation experiments and verified by statistical test. Simultaneously, the simulation experiments are carried out by comparing the ABC-SVR algorithm and wavelet neural network time series (WNN-TS) algorithm, and the prediction accuracy of the proposed ABC-SVR algorithm is improved and satisfactory prediction effects have been obtained. [ABSTRACT FROM AUTHOR]

With the emergence of various types of wireless communication products and the explosive growth of wireless network services, spectrum resources have become increasingly scarce. As the basis of cognitive radio technology, cognitive devices need to have real-time spectrum sensing capabilities, and classify and predict the perceived radio signals. Introducing Bayesian network for model construction, and using genetic algorithm to optimize and improve the model structure can more accurately establish the optimal network directed graph in structural learning, so as to better explore the high-risk predictive factors of machine learning. This paper makes a comparative analysis of the basic methods of the Bayesian network model in structural learning and parameter learning, and discusses the algorithm models that may be used to improve the structural learning. The adaptive search scoring function genetic algorithm is introduced into the network topology map construction part of the Bayesian network model structure learning, and a Bayesian network machine learning prediction model based on genetic optimization is proposed. The prediction algorithm of the maximum posterior probability of the perceptual signal considers which system the signal is most likely to come from at the next moment, and uses the estimation of the signal characteristics of the system as the signal prediction result. This paper further derives the upper bound of signal feature estimation error. Based on the prediction algorithm, the cognitive device can avoid the communication channel that will be occupied by the main user system in advance, thereby saving channel switching overhead. Simulation analysis verifies that the prediction algorithm designed in this paper has better prediction accuracy, and shows that as the number of signal samples sensed increases, the accuracy of the prediction algorithm is also improved accordingly. The classification accuracy rate based on Bayesian network is above 83% and the fluctuation rate is reduced to 2.5%. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has gained significance in agriculture, using remote sensing and machine learning to help farmers make high-precision management decisions. This technology can be applied in viticulture, making it possible to monitor disease occurrence and prevent them automatically. The study aims to achieve an intelligent grapevine disease detection method, using an IoT sensor network that collects environmental and plant-related data. The focus of this study is the identification of the main parameters which provide early information regarding the grapevine’s health. An overview of the sensor network, architecture, and components is provided in this paper. The IoT sensors system is deployed in the experimental plots located within the plantations of the Research Station for Viticulture and Enology (SDV) in Murfatlar, Romania. Classical methods for disease identification are applied in the field as well, in order to compare them with the sensor data, thus improving the algorithm for grapevine disease identification. The data from the sensors are analyzed using Machine Learning (ML) algorithms and correlated with the results obtained using classical methods in order to identify and predict grapevine diseases. The results of the disease occurrence are presented along with the corresponding environmental parameters. The error of the classification system, which uses a feedforward neural network, is 0.05. This study will be continued with the results obtained from the IoT sensors tested in vineyards located in other regions.

BackgroundThe Criticality Index-Mortality uses physiology, therapy, and intensity of care to compute mortality risk for pediatric ICU patients. If the frequency of mortality risk computations were increased to every 3 h with model performance that could improve the assessment of severity of illness, it could be utilized to monitor patients for significant mortality risk change.ObjectivesTo assess the performance of a dynamic method of updating mortality risk every 3 h using the Criticality Index-Mortality methodology and identify variables that are significant contributors to mortality risk predictions.PopulationThere were 8,399 pediatric ICU admissions with 312 (3.7%) deaths from January 1, 2018 to February 29, 2020. We randomly selected 75% of patients for training, 13% for validation, and 12% for testing.ModelA neural network was trained to predict hospital survival or death during or following an ICU admission. Variables included age, gender, laboratory tests, vital signs, medications categories, and mechanical ventilation variables. The neural network was calibrated to mortality risk using nonparametric logistic regression.ResultsDiscrimination assessed across all time periods found an AUROC of 0.851 (0.841–0.862) and an AUPRC was 0.443 (0.417–0.467). When assessed for performance every 3 h, the AUROCs had a minimum value of 0.778 (0.689–0.867) and a maximum value of 0.885 (0.841,0.862); the AUPRCs had a minimum value 0.148 (0.058–0.328) and a maximum value of 0.499 (0.229–0.769). The calibration plot had an intercept of 0.011, a slope of 0.956, and the R2 was 0.814. Comparison of observed vs. expected proportion of deaths revealed that 95.8% of the 543 risk intervals were not statistically significantly different. Construct validity assessed by death and survivor risk trajectories analyzed by mortality risk quartiles and 7 high and low risk diseases confirmed a priori clinical expectations about the trajectories of death and survivors.ConclusionsThe Criticality Index-Mortality computing mortality risk every 3 h for pediatric ICU patients has model performance that could enhance the clinical assessment of severity of illness. The overall Criticality Index-Mortality framework was effectively applied to develop an institutionally specific, and clinically relevant model for dynamic risk assessment of pediatric ICU patients.

Purpose: Amnestic mild cognitive impairment (aMCI) is a transitional state between normal aging and Alzheimer's disease (AD). However, not all aMCI patients are observed to convert to AD dementia. Therefore, developing a predictive algorithm for the conversion of aMCI to AD dementia is important. Parametric methods, such as logistic regression, have been developed; however, it is difficult to reflect complex patterns, such as non-linear relationships and interactions between variables. Therefore, this study aimed to improve the predictive power of aMCI patients' conversion to dementia by using an interpretable machine learning (IML) algorithm and to identify the factors that increase the risk of individual conversion to dementia in each patient. Methods: We prospectively recruited 705 patients with aMCI who had been followed-up for at least 3 years after undergoing baseline neuropsychological tests at the Samsung Medical Center between 2007 and 2019. We used neuropsychological tests and apolipoprotein E (APOE) genotype data to develop a predictive algorithm. The model-building and validation datasets were composed of data of 565 and 140 patients, respectively. For global interpretation, four algorithms (logistic regression, random forest, support vector machine, and extreme gradient boosting) were compared. For local interpretation, individual conditional expectations (ICE) and SHapley Additive exPlanations (SHAP) were used to analyze individual patients. Results: Among the four algorithms, the extreme gradient boost model showed the best performance, with an area under the receiver operating characteristic curve of 0.852 and an accuracy of 0.807. Variables, such as age, education, the scores of visuospatial and memory domains, the sum of boxes of the Clinical Dementia Rating scale, Mini-Mental State Examination, and APOE genotype were important features for creating the algorithm. Through ICE and SHAP analyses, it was also possible to interpret which variables acted as strong factors for each patient. Conclusion: We were able to propose a predictive algorithm for each aMCI individual's conversion to dementia using the IML technique. This algorithm is expected to be useful in clinical practice and the research field, as it can suggest conversion with high accuracy and identify the degree of influence of risk factors for each patient. [ABSTRACT FROM AUTHOR]

This study proposes an algorithm for predicting the running data of information systems based on discrete second-order difference clustering. The wide stationary time series model of information system operation data is established, and the association rules mining and feature distributed transmission sequence fitting of information system operation data are conducted by binary semantic information representation method. The principal component feature detection and matching of information system operation data are carried out. High-order spectral feature analysis and extraction of information system operation data is realized based on big data analysis, and the prediction algorithm is improved. The proposed method has high accuracy, good convergence and high real-time performance, which can improve the scheduling ability of information system operation data. [ABSTRACT FROM AUTHOR]

Objectives: To develop and demonstrate the feasibility of a Global Open Source Severity of Illness Score (GOSSIS)-1 for critical care patients, which generalizes across healthcare systems and countries.Design: A merger of several critical care multicenter cohorts derived from registry and electronic health record data. Data were split into training (70%) and test (30%) sets, using each set exclusively for development and evaluation, respectively. Missing data were imputed when not available.Setting/patients: Two large multicenter datasets from Australia and New Zealand (Australian and New Zealand Intensive Care Society Adult Patient Database [ANZICS-APD]) and the United States (eICU Collaborative Research Database [eICU-CRD]) representing 249,229 and 131,051 patients, respectively. ANZICS-APD and eICU-CRD contributed data from 162 and 204 hospitals, respectively. The cohort included all ICU admissions discharged in 2014-2015, excluding patients less than 16 years old, admissions less than 6 hours, and those with a previous ICU stay.Interventions: Not applicable.Measurements and Main Results: GOSSIS-1 uses data collected during the ICU stay's first 24 hours, including extrema values for vital signs and laboratory results, admission diagnosis, the Glasgow Coma Scale, chronic comorbidities, and admission/demographic variables. The datasets showed significant variation in admission-related variables, case-mix, and average physiologic state. Despite this heterogeneity, test set discrimination of GOSSIS-1 was high (area under the receiver operator characteristic curve [AUROC], 0.918; 95% CI, 0.915-0.921) and calibration was excellent (standardized mortality ratio [SMR], 0.986; 95% CI, 0.966-1.005; Brier score, 0.050). Performance was held within ANZICS-APD (AUROC, 0.925; SMR, 0.982; Brier score, 0.047) and eICU-CRD (AUROC, 0.904; SMR, 0.992; Brier score, 0.055). Compared with GOSSIS-1, Acute Physiology and Chronic Health Evaluation (APACHE)-IIIj (ANZICS-APD) and APACHE-IVa (eICU-CRD), had worse discrimination with AUROCs of 0.904 and 0.869, and poorer calibration with SMRs of 0.594 and 0.770, and Brier scores of 0.059 and 0.063, respectively.Conclusions: GOSSIS-1 is a modern, free, open-source inhospital mortality prediction algorithm for critical care patients, achieving excellent discrimination and calibration across three countries. [ABSTRACT FROM AUTHOR]

To identify the road adhesion coefficient ahead, the grayscale co-occurrence matrix and HSV color space were used to extract the seven texture feature parameters of typical road images. Based on the similarity characteristics, the Burckhardt μ-s model was improved, and a real-time estimation algorithm of the current road peak adhesion coefficient was proposed. The effectiveness and real-time performance were verified by Carsim and Simulink co-simulation. A road-tire adhesion characteristic model was established based on hidden Markov model (HMM), and the correctness of the model was verified. The HMM model algorithm was verified through real vehicle experiments. The results show that the recognition rate of HMM model is more than 90% for the road-tire adhesion coefficient ahead, and the proposed model can be used in the automatic emergency braking module of smart cars. The vehicle can brake in advance under the front harsh road environment, which effectively shortens the braking distance. [ABSTRACT FROM AUTHOR]

Antimicrobial peptides (AMPs) play a pivotal role in the innate immune system as a frontline defense against microbial threats. AMPs can serve as biomarkers and alternative antibiotics, overcoming mortality related to multidrug-resistant pathogens in urinary tract infections (UTIs). While the relevance of AMPs in UTIs has been validated and AMP drugs approved by the US Food and Drug Administration are in clinical use, information about their modification status, regulation, and mechanism of action remains sparse. Only a small fraction of sequences with potential AMP activity, predicted on the basis of known AMP characteristics, have been validated. Elucidation of the global profile of AMPs in the bladder, kidney, and urine under UTI conditions would facilitate an in-depth, disease-specific understanding of the innate immune system and the development of tailored AMP biomarkers and antibiotics. This mini-review focuses on a comprehensive strategy for global profiling and validation of AMPs in UTIs that incorporates AMP data repositories, prediction algorithms, and proteomics for healthy individuals and UTI patients. PATIENT SUMMARY: Short protein molecules called peptides that have antimicrobial activity show promise for the treatment of urinary tract infections. More research and testing of naturally occurring and synthetic peptides with this activity are needed to fully understand how they can help in patient care., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Summary: Background: Donor-characteristics and donor characteristics-based decision algorithms are being progressively used in the decision process whether or not to accept an available donor kidney graft for transplantation. While this may improve outcomes, the performance characteristics of the algorithms remains moderate. To estimate the impact of donor factors of grafts accepted for transplantation on transplant outcomes, and to test whether implementation of donor-characteristics-based algorithms in clinical decision-making is justified, we applied an instrumental variable analysis to outcomes for kidney donor pairs transplanted in different individuals. Methods: This analysis used (dis)congruent outcomes of kidney donor pairs as an instrument and was based on national transplantation registry data for all donor kidney pairs transplanted in separate individuals in the Netherlands (1990-2018, 2,845 donor pairs), and the United Kingdom (UK, 2000-2018, 11,450 pairs). Incident early graft loss (EGL) was used as the primary discriminatory factor. It was reasoned that a scenario with a dominant impact of donor variables on transplantation outcomes would result in high concordance of EGL in both recipients, whilst dominance of asymmetrical outcomes could indicate a more complex scenario, involving an interaction of donor, procedural and recipient factors. Findings: Incidences of congruent EGL (Netherlands: 1·2%, UK: 0·7%) were slightly lower than the arithmetical (stochastic) incidences, suggesting that once a graft has been accepted for transplantation, donor factors minimally contribute to incident EGL. A long-term impact of donor factors was explored by comparing outcomes for functional grafts from donor pairs with asymmetrical vs. symmetrical outcomes. Recipient survival was similar for both groups, but a slightly compromised graft survival was observed for grafts with asymmetrical outcomes in the UK cohort: (10-years Hazard Ratio for graft loss: 1·18 [1·03-1·35] p