Prediction System Research Articles

Assessing NOAA Rip-Current Hazard Likelihood Predictions: Comparison with Lifeguard Observations and Parameterizations of Bathymetric and Transient Rip-Current Types

Abstract Rip currents, fast offshore-directed flows, are the leading cause of death and rescues on surf beaches worldwide. The National Oceanographic and Atmospheric Administration (NOAA) seeks to minimize this threat by providing rip current hazard likelihood forecasts based on environmental conditions from the Nearshore Wave Prediction System. Rip currents come in several types, including bathymetric rip currents that form when waves break on sandbars interspersed with channels, and transient rip currents that form when there are breaking waves coming from multiple directions. The NOAA model was developed and tested in an area where bathymetric rip currents may be the most prevalent type of rip current. Therefore, model performance in regions where other types of rip currents (e.g., transient rip currents) may be more ubiquitous remains unknown. To investigate the efficacy of the NOAA model guidance in the context of different rip-current types, we compared modeled rip-current probabilities with physical-based parameterizations of bathymetric and transient rip-current speeds. We also compared these probabilities to lifeguard observations of bathymetric and transient rip currents from Salt Creek Beach, CA in Summer-Fall 2021. We found that the NOAA model skillfully predicts a wide range of hazardous parameterized bathymetric speeds but generally underpredicts hazardous transient rip-current speeds and the hazardous rip currents observed at Salt Creek Beach. Our results demonstrate how wave parameters, including directional spread, may serve as environmental indicators of rip-current hazard. By evaluating factors that influence the skill of modeled rip-current predictions, we strive towards improved rip-current hazard forecasting.

Diversity of Stratospheric Error Growth Across Subseasonal Prediction Systems

AbstractThe stratosphere has previously been shown to be a significant source of subseasonal tropospheric predictability. The ability of ensemble prediction systems to appropriately exploit this depends on their ability to reproduce the statistical properties of the real atmosphere. In this study, we investigate predictability properties of the coupled stratosphere‐troposphere system in the sub‐seasonal to seasonal prediction project hindcasts by fitting a simple, minimal model. We diagnose the signal and noise components of each system in the stratosphere and troposphere and their coupling. We find that while the correlation skill scores are similar in most systems, the signal to noise properties can be substantially different. In the stratosphere, some systems are significantly overconfident, with a quantifiable impact on the tropospheric confidence. We link the method and details of the design of a prediction system to these predictive properties.

Front-end speech processing system with SVM algorithm in rail passenger flow management

In order to achieve the goal of dynamically adjusting daily passenger flow to effectively control the overall efficiency of the transportation system, this study constructs a real-time monitoring and prediction system for subway passenger flow based on front-end voice processing technology and support vector machine models. The study first conducted a railway passenger flow analysis, and then used a support vector machine model to construct a preliminary prediction system. In order to achieve global optimization, the study also introduced particle swarm optimization algorithm to construct an optimization prediction model based on PSO-SVM. The results show that the proposed PSO-SVM method has undergone 48 iterations of training, and the predicted values closely match the actual passenger flow curve. The maximum RE error is 2%, and the overall prediction error is 98%. The decision coefficient of PSO-SVM is 0.998932. Therefore, this indicates that it has high performance and feasibility in predicting and controlling passenger flow during peak hours of urban rail transit.

Development of an Artificial Intelligence-Based Predictive Anomaly Detection System to Nuclear Power Plant

Abstract In a large-scale plant such as a nuclear power plant, thousands of process values are measured for the purpose of monitoring the plant performance and the system health. It is difficult for plant operators to constantly monitor all of the process values. We present a data-driven method to comprehensively monitor a large number of process values and detect early signs of anomalies, including unknown events, with few false positives. In order to learn the complex changing internal state of a nuclear power plant and accurately predict the normal process values, we have developed a two-stage auto-encoder (TSAE), a type of neural network, composed of a time window auto-encoder and a deviation auto-encoder. TSAE realizes to detect anomalous signals during the plant transient conditions by collecting time-series data and learning the nonlinear temporal correlation among them. In the actual plant, some process values which are physically uncorrelated with each other happen to behave similarly (pseudo-correlation). Learning the pseudo-correlation by the algorithm causes false positives because the predicted values of unrelated process values are incorrectly correlated. Therefore, Toshiba has proposed the model classification concept of separating the process values into two groups based on physical correlation and applied a model structure of TSAE. As a result, it becomes possible to learn only with the process values that are physically correlated and enhance the performance of prediction/detection. We assessed the improved TSAE with simulated process values of a nuclear power plant and showed excellent performances with early detection and few false positives.

Development and validation of a predictive model for choledocholithiasis.

Gallstone-related conditions affect a significant portion of the population, with varying prevalence among different ethnic groups. Complications such as pancreatitis and cholangitis are associated with the presence of common bile duct (CBD) stones. Existing guidelines for diagnosing choledocholithiasis lack precision, leading to excessive use of invasive procedures like endoscopic retrograde cholangiopancreatography (ERCP). A prospective study was conducted at Hospital Central "Dr. Ignacio Morones Prieto," involving 374 patients in the development cohort and 154 patients in the validation cohort. Patients meeting inclusion criteria underwent biochemical testing and ultrasonography. A predictive scoring system was developed using logistic regression and validated in an independent cohort. Clinical and laboratory variables were collected, and model performance was assessed using receiver-operator characteristic (ROC) curves. The predictive model incorporated variables such as age, pancreatitis, cholangitis, bilirubin levels, and CBD stone presence on ultrasound. The model demonstrated an area under the ROC curve (AUC) of 93.81% in the validation dataset. By adjusting the threshold defining high-risk probability to 40%, the model improved specificity and sensitivity compared to existing guidelines. Notably, the model reclassified patients, leading to a more accurate risk assessment. The developed algorithm accurately predicts choledocholithiasis non-invasively in patients with symptomatic gallstones. This tool has the potential to reduce reliance on costly or invasive procedures like magnetic resonance cholangiopancreatography and ERCP, offering a more efficient and cost-effective approach to patient management. The user-friendly calculator developed in this study could streamline diagnostic procedures, particularly in resource-limited healthcare settings, ultimately improving patient care.

On‐line machine‐learning forecast uncertainty estimation for sequential data assimilation

AbstractQuantifying forecast uncertainty is a key aspect of state‐of‐the‐art numerical weather prediction and data assimilation systems. Ensemble‐based data assimilation systems incorporate state‐dependent uncertainty quantification based on multiple model integrations. However, this approach is demanding in terms of computations and development. In this work, a machine‐learning method is presented based on convolutional neural networks that estimates the state‐dependent forecast uncertainty represented by the forecast error covariance matrix using a single dynamical model integration. This is achieved by the use of a loss function that takes into account the fact that the forecast errors are heteroscedastic. The performance of this approach is examined within a hybrid data assimilation method that combines a Kalman‐like analysis update and the machine‐learning‐based estimation of a state‐dependent forecast error covariance matrix. Observing system simulation experiments are conducted using the Lorenz'96 model as a proof‐of‐concept. The promising results show that the machine‐learning method is able to predict precise values of the forecast covariance matrix in relatively high‐dimensional states. Moreover, the hybrid data assimilation method shows similar performance to the ensemble Kalman filter, outperforming it when the ensembles are relatively small.

Agent-Based System for Continuous Control and its Application to Activated Sludge Process

This paper presents a concept of architecture and ontology layouts for development of multiagent model-based predictive control systems. The presented architecture provides guidelines to simplify development of agent-based systems and improve its maintainability. The proposed multiagent system (MAS) layout is split into multiple subsystems that include agents dedicated to performing assigned tasks. MAS implementation was prepared which can use provided algorithms, actuators and can react to changes in its environment to reach the best available control quality. An example of MAS based on the proposed architecture is shown in application of dissolved oxygen (DO) concentration control in a laboratory activated sludge setup with biological reactor. For that application, MAS incorporates agent-based controllers from Boundary-Based Predictive Controllers (BBPC) family. Presented experiments prove flexibility, resilience and online reconfiguration ability of the proposed multiagent system.

Ocean internal tides suppress tropical cyclones in the South China Sea

Tropical Cyclones (TCs) are devastating natural disasters. Analyzing four decades of global TC data, here we find that among all global TC-active basins, the South China Sea (SCS) stands out as particularly difficult ocean for TCs to intensify, despite favorable atmosphere and ocean conditions. Over the SCS, TC intensification rate and its probability for a rapid intensification (intensification by ≥ 15.4 m s−1 day−1) are only 1/2 and 1/3, respectively, of those for the rest of the world ocean. Originating from complex interplays between astronomic tides and the SCS topography, gigantic ocean internal tides interact with TC-generated oceanic near-inertial waves and induce a strong ocean cooling effect, suppressing the TC intensification. Inclusion of this interaction between internal tides and TC in operational weather prediction systems is expected to improve forecast of TC intensity in the SCS and in other regions where strong internal tides are present.

Factors influencing subseasonal predictability of northern Eurasian cold spells

AbstractCold‐air outbreaks have significant impacts on human health, energy consumption, agriculture, and overall well‐being. This study aims to evaluate the effectiveness of Subseasonal‐to‐Seasonal (S2S) models in predicting cold conditions over northern Eurasia, defined here as the lower tercile of weekly mean 2‐metre temperature anomalies. To assess the predictability of these events we use ensemble hindcasts from five prediction systems from the S2S database. Our analysis focuses on identifying the conditions under which the models confidently predict cold temperatures with a high (>0.5) probability 3–4 weeks ahead, which potentially can represent windows of forecast opportunity. We compare the group of forecasts that correctly predicted the events to the group that forecasted events that did not occur in practice (false alarms). Most of the confident forecasts of cold spells, both correct and false alarms, have cold anomalies already in the initial conditions, often in conjunction with either a negative phase of the North Atlantic Oscillation, or Scandinavian Blocking. We find that S2S models tend to overpredict cold temperatures, with false alarms occurring more likely when the forecasts are initialized during a weak polar vortex. Furthermore, most of the confident false alarms receive the signal from the stratosphere rather than following internal tropospheric dynamics. False alarms initialized during the weak polar vortex conditions are more common when the vortex is in a recovery stage and, subsequently, the downward ‐propagating signal is short‐lived in the troposphere. The analysis of forecasts during different Madden–Julian Oscillation (MJO) phases shows that nearly half of all confident correct cold‐temperature forecasts are initialized during an active MJO in phases 6–8. On the other hand, most false alarms occur during phase 3, which we suggest is due to the presence of the Scandinavian Blocking regime in the initial conditions for this phase.

Assessment for operational assimilation of horizontal line of sight winds from the European Space Agency's Aeolus at the Met Office

AbstractThe European Space Agency's Aeolus satellite was launched in August 2018 and began delivering horizontal line‐of‐sight (HLOS) wind observations in early September 2018. In early 2019, the Met Office began assessing the suitability of the HLOS winds for operational assimilation into its global numerical weather prediction system. We performed a number of assimilation experiments to assess the impact of HLOS wind observations on our global forecasts. We have found that assimilating HLOS winds changes the zonal winds in the analysis fields predominantly in the Tropics and Southern Hemisphere, with the largest changes being in the upper troposphere and lower stratosphere. This has a positive impact on the accuracy of the global weather forecasts, with improvements in the root‐mean‐square error seen throughout the troposphere. Assimilation of Aeolus HLOS winds improves the standard deviation of the observation minus background (a 6 hr forecast) of almost all other observation types, suggesting that the numerical weather prediction model analysis is improved, which consequently improves the 6 hr forecast. In a set of short‐period observation denial experiments, we found that assimilating Aeolus has an impact similar in magnitude to assimilating surface winds from scatterometers. Assimilating winds from the Rayleigh channel has approximately three times the impact that assimilating HLOS winds from the Mie channel does. Both channels contribute a measureable improvement to the global forecast, and we therefore started operational assimilation of winds from the Mie channel in December 2020 and the Rayleigh channel operationally in May 2022.

Prediction of Engineering Students Learning Outcomes using Machine Learning

Data science and machine learning, over the years have proven very well-organized and significant in many sectors including education. Machine learning is an aspect of artificial intelligence in which a computing system can able to learn from data and make conclusions. The recent development in education sector provides assessment tools to predict the student performance by exploring education data using machine learning and data mining techniques. Student performance assessment is an important measurement metrics in education which affects the university accreditation. Student performance improvement plan must be implemented in those universities, by counselling the low performer students. It helps both students and teachers to overcome the problems experienced by the student during studies and teaching techniques of teachers. In this review paper, different student performance prediction literature related to find out low performer student. The survey results indicated that different machine learning techniques are used to overcome the problems related to predicting student at risk and assessment of student performance. Machine learning techniques plays an important role in progress and prediction of student performance, thus improving student performance prediction system

Validation and Evaluation of 5 Scoring Systems for Predicting Metastatic Risk in Pheochromocytoma and Paraganglioma.

Currently, 5 scoring systems have been proposed in the literature for predicting metastatic risk in pheochromocytoma and paraganglioma (PPGL): Pheochromocytoma of the Adrenal Gland Scaled Score (PASS), Grading System for Adrenal Pheochromocytoma and Paraganglioma (GAPP), Composite Pheochromocytoma/paraganglioma Prognostic Score (COPPS), Age, Size, Extra-adrenal location, Secretion type (ASES) score, and Size, Genetic, Age, and PASS (SGAP) model. To validate and evaluate these 5 scoring systems, we conducted a retrospective review of cases diagnosed as PPGL at the Department of Pathology, West China Hospital of Sichuan University, between January 2012 and December 2019. A total of 185 PPGL cases were included, comprising 35 cases with metastasis and 150 cases remained metastasis-free for over 8 years after surgery. The criteria of the 5 scoring systems were used for scoring and risk classification. The predictive performance of the 5 scoring systems was validated, compared, and evaluated using concordance index (C-index) and decision curve analysis (DCA). The C-indices for PASS, GAPP, and SGAP were 0.600, 0.547, and 0.547, respectively, indicating low discriminative ability. In contrast, COPPS and ASES had C-indices of 0.740 and 0.706, respectively, indicating better discriminative performance. DCA also showed that the predictive capability of COPPS was superior to that of ASES, with both outperformed PASS, while PASS had better predictive ability than GAPP and SGAP. Our analysis indicated that pathology-based scoring systems cannot accurately predict metastatic risk of PPGL. Establishing a precise prediction system requires integrating clinical, pathologic, and molecular information, using a scientific methodology for predictive factor selection and weight assessment.

A system of predictive scores to evaluate the risk of multilevel noncontiguous spinal fractures in patients with traumatic cervical spine injury.

To develop a predictive scoring system to identify traumatic cervical spine injury patients at a high risk of having multilevel noncontiguous spinal fractures. This 12-year retrospective observational cohort study included 588 traumatic cervical spine-injured patients. Patients were categorized into two groups: patients with multilevel noncontiguous spinal fractures and patients without this remote injury. Potential risk factors were examined using multivariable analysis to derive a predictive risk score from independent predictors. Results are presented as odds ratio with a 95% confidence interval (95% CI). The accuracy of the calculated predicted score was demonstrated by the area under the receiver operating characteristic curve (AuROC). The incidence of noncontiguous fracture among the patients was 17% (100 of 588). The independent risk factors associated with multilevel noncontiguous spinal fractures were motor weakness, intracranial injury, intrathoracic injury, and intraabdominal injury. The AuROC of the prediction score was 0.74 (95% CI 0.69, 0.80). The patients were classified into three groups, low-risk group (score< 1), moderate-risk group (score 1-2.5), and high-risk group (score≥ 3), based on the predicted risk of multilevel noncontiguous spinal fractures. This tool can potentially help preventing the missed diagnosis of cervical spine injuries with multilevel noncontiguous spinal fractures. CT scans or MRI of the entire spine to investigate remote multilevel noncontiguous spinal fractures may have a role in cervical spine-injured patients who have at least one of the independent risk factors and are strongly suggested for patients with scores in the high-risk group.

Latest Innovations in Internet of Things (IoT): Digital Transformation Across Industries

This research article explores recent advances in IoT technology and its huge impact on various sectors. This research method uses a qualitative research approach involving in-depth interviews and thematic analysis. This research explores key IoT innovations such as wearable devices, smart medical devices, predictive maintenance systems, and IoT-based transportation solutions. The research findings highlight how these innovations are driving digital transformation across industries, leading to improved healthcare delivery, increased manufacturing efficiency, optimized transportation operations, sustainable agricultural practices, and personalized retail experiences. This study also aligns these empirical findings with theoretical frameworks regarding IoT, digital transformation, and industry-specific applications, emphasizing the strategic importance of leveraging IoT as a catalyst for innovation, competitiveness, and value creation. This research provides valuable insights for businesses, policymakers and researchers looking to leverage IoT technology to drive digital transformation and achieve sustainable growth across industries.

A New Wave Breaking Benchmark On Rubble Mound Breakwaters

The potential of predictive maintenance for critical port infrastructure, reducing risks and extending its life cycle, remains to be unlocked by suitable quantitative information that fosters predictive maintenance and sustained investments towards progressive climate-proofing of the infrastructure. The PI-BREAK (Predictive Intelligent system for BREAKwater maintenance) project will apply state-of-the-art monitoring and modelling techniques to the maintenance of the Punta Lucero rubble mound breakwater in the port of Bilbao, Spain. This critical infrastructure is crucial to the welfare of coastal communities and the project aims to demonstrate how predictive maintenance can enhance port safety and efficiency, while also supporting economic recovery through low carbon footprint adaptation to future scenarios. By combining short-term risk reduction (for service limit states) with long-term structural reliability (for ultimate limit states) PI-BREAK will develop a sequenced maintenance model to progressively extend the breakwater life span with controlled risk levels and impacts.

Bioequivalence prediction with small-scale biphasic dissolution and simultaneous dissolution-permeation apparatus—An aripiprazole case study

Both biphasic dissolution and simultaneous dissolution-permeation (D-P) systems have great potential to improve the in vitro-in vivo correlation compared to simple dissolution assays, but the assay conditions, and the evaluation methods still need to be refined in order to effectively use these apparatuses in drug development.Therefore, this comprehensive study aimed to compare the predictive accuracy of small-volume (16–20 mL) D-P system and small-volume (40–80 mL) biphasic dissolution apparatus in bioequivalence prediction of five aripiprazole (ARP) containing marketed drug products.Assay conditions, specifically dose dependence were studied to overcome the limitations of both small-scale systems. In case of biphasic dissolution the in vivo maximum plasma concentration (Cmax) prediction greatly improved with the dose reduction of ARP, while in case of the D-P setup the use of whole tablet gave just as accurate prediction as the scaled dose. With the dose reduction strategy both equipment was able to reach 100 % accuracy in bioequivalence prediction for Cmax ratio. In case of the in vivo area under the curve (AUC) prediction the predictive accuracy for the AUC ratio was not dependent on the dose, and both apparatus had a 100 % accuracy predicting bioequivalence based on AUC results.This paper presents for the first time that not only selected parameters of flux assays (like permeability, initial flux, AUC value) were used as an input parameter of a mechanistic model (gastrointestinal unified theory) to predict absorption rate but the whole in vitro flux profile was used. All fraction absorbed values estimated by Predictor Software fell within the ±15 % acceptance range during the comparison with the in vivo data.

Tropical cyclone wave data assimilation impact on air-ocean-wave coupled Hurricane Harvey (2017) forecast

The impact of surface wave assimilation on hurricane track and intensity forecasts has been investigated using a fully coupled air-ocean-wave tropical cyclone data assimilation and forecast modeling system. A new 3DVAR wave assimilation method in the Navy Coupled Ocean Data Assimilation system (NCODA) maps the 1D wave energy spectra from buoys to 2D directional wave energy spectra using the maximum likelihood method (MLM) and corrects the wave model forecast component directional wave energy spectra. The Coupled Ocean/Atmosphere Mesoscale Prediction System for Tropical Cyclone Prediction (COAMPS-TC) is used to conduct three Hurricane Harvey (2017) air-ocean-wave coupled data assimilation and forecasting experiments with and without the wave data assimilation. Hurricane Harvey traversed through the Western Gulf of Mexico from 24 August to 1 September, 2017 and made landfall in the Texas and Louisiana coast. Validation of track, maximum wind speed, significant wave height, and mean absolute wave periods show wave assimilation of the 1D wave energy spectra from 13 National Data Buoy Center (NDBC) buoys reduced the forecast errors of these parameters compared to experiments without the wave assimilation. In spite of this positive outcome, the wave assimilation is unable to reduce Harvey’s 0-120 h forecast mean wave direction errors and correlation compared to the NDBC buoy time series

Risk prediction of advanced colorectal neoplasia among diabetic patients: A derivation and validation study.

Colorectal cancer (CRC) is the third most common cancer in the world. This study devises and validates a clinical scoring system for risk prediction of advanced colorectal neoplasia (ACN) to guide colonoscopy evaluation among diabetic patients. We identified 55 964 diabetic patients who received colonoscopies from a large database in a Chinese population (2008-2018). We recruited a derivation cohort based on random sampling. The risk factors of CRC evaluated by univariate analysis were examined for ACN, defined as advanced adenoma, CRC, or any combination thereof using binary logistic regression analysis. We used the adjusted odds ratios (aORs) for independent risk factors to devise a risk score, ranging from 0 to 6: 0-4 "average risk" (AR) and 5-6 "high risk" (HR). The other subjects acted as an independent validation cohort. The prevalence of ACN in both the derivation and validation cohorts was 2.0%. Using the scoring system constructed, 78.5% and 21.5% of patients in the validation cohort were classified as AR and HR, respectively. The prevalence of ACN in the AR and HR groups was 1.5% and 4.1%, respectively. Individuals in the HR group had a 2.78-fold increased prevalence of ACN than the AR group. The concordance (c-) statistics was 0.70, implying a good discriminatory capability of the risk score to stratify high-risk individuals who should consider colonoscopy. The clinical risk scoring system based on age, gender, smoking, presence of hypertension, and use of aspirin is useful for ACN risk prediction among diabetic patients.

Development of a Medical Condition Prediction Model Using Natural Language Processing with K-Nearest Neighbour

Capturing the effect of drugs being used by patients and using this review to predict the medical ailment they are facing is a good approach to easily predict medical conditions. A lot of researchers use clinical and demographic data (risk factors) to predict diseases, the limitation of this approach is that not all the instances would have the right clinical results and there is usually missing values, low prediction accuracy, inadequately pre-processed dataset, failure to consider feature selection and un-experimentation of alternative values of K when using K-nearest neighbour. Using drug review would go a long way as their effect and symptoms as reported by the user through their review would capture relevant information needed. This study employed an open access drug review dataset to predict the medical condition, this dataset consist of training and testing split which was integrated and later split using 80-20 splitting with stratification. The dataset went through some natural language processing techniques such as lemmatization, stemming, removal of stop words, tokenization, and vectorization among others. Forward –backward feature selection technique was employed with the comments having significant effect to the prediction of the condition. K-nearest neighbour was then employed to predict the medical condition using the drug review as the feature with the condition as the target variable. Different values of nearest neighbours were used to train the model with k=1 given the best predictive average accuracy of 89% with weighted average precision of 90%. The model gave the same average accuracy of 84% when k was initialised to 3, 4, 5 and 6 respectively. Moreover, the model obtained a better result when compared with exciting systems. Therefore, with the use of artificial intelligence, medical doctors and patients can easily use drug review to predict certain medical condition using clinical predictive decision support system.

Enhancing Road Safety in Internet of Vehicles Using Deep Learning Approach For Real-Time Accident Prediction And Prevention

The paper proposes an Internet of Vehicles (IoV)-based Accident Prediction and Prevention System that leverages the Internet of Things (IoT) to tackle the road safety challenges arising from the increased rate and volume of traffic due to population growth. In order to enhance road safety and efficiency, the IoV devices enable real-time data transmission and analysis. The proposed multi-tier framework tracks vehicle and roadside unit (RSU) data, encompassing road traffic conditions and vehicle data. The framework integrates vehicles, road traffic, weather conditions, and external factors. On a cloud-based control server, the proposed Spatio-Temporal Conv-Long Short-Term Memory Autoencoder (STCLA) framework deals with and analyzes the resulting data. This research addresses road safety on the Internet of Vehicles via DL. It proposes a novel framework for real-time accident prevention and prediction, demonstrating its effectiveness and potential impact. In a year-long research in Hubei Province, China, data from two road segments demonstrated a substantial boost in predictive accuracy, achieving an Area Under the Receiver Operating Characteristic Curve (AUROC) score of 0.94.