Fuel types misrepresent forest structure and composition in interior British Columbia: a way forward.

Background: A clear understanding of the connectivity, structure, and composition of wildland fuels is essential for effective wildfire management. However, fuel typing and mapping are challenging owing to a broad diversity of fuel conditions and their spatial and temporal heterogeneity. In Canada, fuel types and potential fire behavior are characterized using the Fire Behavior Prediction (FBP) System, which uses an association approach to categorize vegetation into 16 fuel types based on stand structure and composition. In British Columbia (BC), provincial and national FBP System fuel type maps are derived from remotely sensed forest inventory data and are widely used for wildfire operations, fuel management, and scientific research. Despite their widespread usage, the accuracy and applicability of these fuel type maps have not been formally assessed. To address this knowledge gap, we quantified the agreement between on-site assessments and provincial and national fuel type maps in interior BC. Results: We consistently found poor correspondence between field assessment data and both provincial and national fuel types. Mismatches were particularly frequent for (i) dry interior ecosystems, (ii) mixedwood and deciduous fuel types, and (iii) post-harvesting conditions. For 58% of field plots, there was no suitable match to the extant fuel structure and composition. Mismatches were driven by the accuracy and availability of forest inventory data and low applicability of the Canadian FBP System to interior BC fuels. Conclusions: The fuel typing mismatches we identified can limit scientific research, but also challenge wildfire operations and fuel management decisions. Improving fuel typing accuracy will require a significant effort in fuel inventory data and system upgrades to adequately represent the diversity of extant fuels. To more effectively link conditions to expected fire behavior outcomes, we recommend a fuel classification approach and emphasis on observed fuels and measured fire behavior data for the systems we seek to represent. Supplementary Information: The online version contains supplementary material available at 10.1186/s42408-024-00249-z.

Antecedentes: Un entendimiento claro sobre la conectividad, estructura, y composición de los combustibles vegetales es esencial para un manejo efectivo de los incendios de vegetación. Sin embargo, la tipificación y mapeo de los combustibles son aspectos desafiantes debido a la amplia diversidad de las condiciones de los combustibles y su variabilidad espacial y temporal. En Canadá, los tipos de combustibles y el comportamiento potencial del fuego están caracterizados por el uso del Sistema de Predicción del Comportamiento del Fuego (Fire Behavior Prediction System, FBP), el cual usa una "aproximación asociada" para categorizar la vegetación en 16 tipos de combustibles basados en la estructura y composición de los rodales. En la Columbia Británica (BC) los mapas del sistema provincial y nacional de FBP son derivados de datos de inventarios tomados mediante sensores remotos, que son ampliamente usados para operaciones de incendios de vegetación, manejo de combustibles, e investigación científica. A pesar de su amplio uso, la exactitud y aplicabilidad de esos mapas de tipos de combustibles no han sido adecuadamente comprobadas. Para determinar este vacío en el conocimiento, cuantificamos la concordancia entre las determinaciones in situ y los mapas de combustibles provinciales y nacionales en el interior de BC. Resultados: Encontramos consistentemente una pobre correspondencia entre las determinaciones de los datos de campo y los tipos de combustibles provinciales y nacionales. Los desfasajes fueron particularmente frecuentes para: i) los ecosistemas secos del interior, ii) bosques mixtos y tipos de combustibles en bosques deciduos, y iii) condiciones de postcosecha. Para el 58% de las parcelas a campo, no hubo una concordancia adecuada entre la estructura y composición existentes. Estos desajustes fueron derivados de la exactitud y disponibilidad de datos del inventario forestal, y la baja aplicabilidad del Sistema FBP a los combustibles del interior de la Columba Británica. Conclusiones: Los desajustes en la determinación de los tipos de combustibles que nosotros identificamos pueden limitar la investigación científica, pero también es un desafío para las decisiones en las operaciones de incendios y en el manejo de los combustibles. El mejoramiento de la exactitud en la determinación de tipos de combustibles requerirá de un esfuerzo significativo en el inventario de datos y sistemas mejorados para representar adecuadamente la diversidad de los combustibles existentes. Para ligar más efectivamente las condiciones a los resultados del comportamiento, recomendamos una aproximación a la clasificación de los combustibles y énfasis en datos de los combustibles observados y del comportamiento medido para los sistemas que pretendemos representar.

Deep fit_predic: a novel integrated pyramid dilation EfficientNet-B3 scheme for fitness prediction system.

This study introduces novel deep learning (DL) techniques for effective fitness prediction using a person's health data. Initially, pre-processing is performed in which data cleaning, one-hot encoding and data normalization are performed. The pre-processed data are then fed into the feature selection stage, where the useful features are extracted using the enhanced chameleon swarm (ECham-Sw) optimization technique. Then, a clustering process is performed using Minkowski integrated gravity center clustering (Min-GCC) to cluster the health profiles of each individual. Finally, the Pyramid Dilated EfficientNet-B3 (PyDi-EfficientNet-B3) technique is proposed to predict the fitness of each individual efficiently with enhanced accuracy of 99.8%.

Predicting infarction growth rate II using ANFIS-based binary particle swarm optimization technique in ischemic stroke.

Ischemic stroke, a severe medical condition triggered by a blockage of blood flow to the brain, leads to cell death and serious health complications. One key challenge in this field is accurately predicting infarction growth - the progressive expansion of damaged brain tissue post-stroke. Recent advancements in artificial intelligence (AI) have improved this prediction, offering crucial insights into the progression dynamics of ischemic stroke. One such promising technique, the Adaptive Neuro-Fuzzy Inference System (ANFIS), has shown potential, but it faces the 'curse of dimensionality' and long training times as the number of features increased. This paper introduces an innovative, automatic method that combines Binary Particle Swarm Optimization (BPSO) with ANFIS architecture, achieves reduction in dimensionality by reducing the number of rules and training time. By analyzing the Pearson correlation coefficients and P-values, we selected clinically relevant features strongly correlated with the Infarction Growth Rate (IGR II), extracted after one CT scan. We compared our model's performance with conventional ANFIS and other machine learning techniques, including Support Vector Regressor (SVR), shallow Neural Networks, and Linear Regression. •Inputs: Real data about ischemic stroke represented by clinically relevant features.•Output: An innovative model for more accurate and efficient prediction of the second infarction growth after the first CT scan.•Results: The model achieved commendable statistical metrics, which include a Root Mean Square Error of 0.091, a Mean Squared Error of 0.0086, a Mean Absolute Error of 0.064, and a Cosine distance of 0.074.

Informer-Based Temperature Prediction Using Observed and Numerical Weather Prediction Data.

This paper proposes an Informer-based temperature prediction model to leverage data from an automatic weather station (AWS) and a local data assimilation and prediction system (LDAPS), where the Informer as a variant of a Transformer was developed to better deal with time series data. Recently, deep-learning-based temperature prediction models have been proposed, demonstrating successful performances, such as conventional neural network (CNN)-based models, bi-directional long short-term memory (BLSTM)-based models, and a combination of both neural networks, CNN-BLSTM. However, these models have encountered issues due to the lack of time data integration during the training phase, which also lead to the persistence of a long-term dependency problem in the LSTM models. These limitations have culminated in a performance deterioration when the prediction time length was extended. To overcome these issues, the proposed model first incorporates time-periodic information into the learning process by generating time-periodic information and inputting it into the model. Second, the proposed model replaces the LSTM with an Informer as an alternative to mitigating the long-term dependency problem. Third, a series of fusion operations between AWS and LDAPS data are executed to examine the effect of each dataset on the temperature prediction performance. The performance of the proposed temperature prediction model is evaluated via objective measures, including the root-mean-square error (RMSE) and mean absolute error (MAE) over different timeframes, ranging from 6 to 336 h. The experiments showed that the proposed model relatively reduced the average RMSE and MAE by 0.25 °C and 0.203 °C, respectively, compared with the results of the CNN-BLSTM-based model.

A novel immunohistochemical score predicts the postoperative prognosis of gastric cancer patients.

BACKGROUND AND AIM: Immunohistochemistry indicators are increasingly being used to predict the survival prognosis of cancer patients after surgery. This study aimed to combine some markers to establish an immunohistochemical score (MSI-P53-Ki-67[MPK]) and stratify postoperative patients with gastric cancer according to the score. METHODS: We used 245 patients who underwent surgery at one center as the training cohort and 111 patients from another center as the validation cohort. All patients were treated between January 2012 and June 2018. The training cohort was screened for prognostic factors, and MPK scores were established using univariate and multifactorial COX risk proportional models. Patients were prognostically stratified according to the MPK score after gastrectomy for gastric cancer. Overall survival (OS) and recurrence-free survival (RFS) rates were compared among low-, intermediate-, and high-risk groups using the Kaplan-Meier method, and survival curves were plotted. Finally, the MPK score was validated using the validation cohort. RESULTS: In the training group, there were statistically significant differences in OS and RFS in the low, medium, and high-risk groups (P < 0.001). Thirty patients were in the high-risk group (12.2%). The median survival times of the three groups were 64.0, 44.0, and 23.0, respectively, and median times to recurrence were 54.0, 35.0, and 16.0 months, respectively. In the validation group, the prognosis in the three risk groups remained significantly different (P < 0.001). CONCLUSIONS: The novel MPK score could effectively predict the postoperative OS and RFS of gastric cancer patients, risk-stratify postoperative patients, and identify postoperative high-risk patients for refined management.

A prediction system: Regulating effect of small-molecule additives on properties of amorphous solid dispersions prepared by hot-melt extrusion technology.

Amorphous solid dispersions (ASDs) with solubility advantage are suffering from the recrystallization risk and subsequent reduced dissolution triggered by high hygroscopicity of hydrophilic polymers and the supersaturation of ASD solutions. To address these issues, in this study, small-molecule additives (SMAs) in the Generally Recognized as Safe (GRAS) list were introduced into drug-polymer ASD. For the first time, we systematically revealed the intrinsic correlation between SMAs and properties of ASDs at the molecular level and constructed a prediction system for the regulation of properties of ASDs. The types and dosages of SMAs were screened by Hansen solubility and Flory-Huggins interaction parameters, as well as differential scanning calorimetry. X-ray photoelectron spectroscopy and adsorption energy (Eabs) calculation showed that the surface group distribution of ASDs and Eabs between ASD system and solvent were vital factors affecting the hygroscopicity and then stability. The radial distribution function revealed that interactions between components were proposed to be the critical factor for the dissolution performance. Based on this, a prediction system for regulating the properties of ASDs was successfully constructed mainly via molecular dynamics simulations and simple solid-state characterizations, and then validated by cases, which efficiently reduces the time and economic cost of pre-screening ASDs.

A Nomogram Model for Predicting the Polyphenol Content of Pu-Erh Tea.

To investigate different contents of pu-erh tea polyphenol affected by abiotic stress, this research determined the contents of tea polyphenol in teas produced by Yuecheng, a Xishuangbanna-based tea producer in Yunnan Province. The study drew a preliminary conclusion that eight factors, namely, altitude, nickel, available cadmium, organic matter, N, P, K, and alkaline hydrolysis nitrogen, had a considerable influence on tea polyphenol content with a combined analysis of specific altitudes and soil composition. The nomogram model constructed with three variables, altitude, organic matter, and P, screened by LASSO regression showed that the AUC of the training group and the validation group were respectively 0.839 and 0.750, and calibration curves were consistent. A visualized prediction system for the content of pu-erh tea polyphenol based on the nomogram model was developed and its accuracy rate, supported by measured data, reached 80.95%. This research explored the change of tea polyphenol content under abiotic stress, laying a solid foundation for further predictions for and studies on the quality of pu-erh tea and providing some theoretical scientific basis.

Cohort Profile: A European Multidisciplinary Network for the Fight against HIV Drug Resistance (EuResist Network).

The EuResist cohort was established in 2006 with the purpose of developing a clinical decision-support tool predicting the most effective antiretroviral therapy (ART) for persons living with HIV (PLWH), based on their clinical and virological data. Further to continuous extensive data collection from several European countries, the EuResist cohort later widened its activity to the more general area of antiretroviral treatment resistance with a focus on virus evolution. The EuResist cohort has retrospectively enrolled PLWH, both treatment-naïve and treatment-experienced, under clinical follow-up from 1998, in nine national cohorts across Europe and beyond, and this article is an overview of its achievement. A clinically oriented treatment-response prediction system was released and made available online in 2008. Clinical and virological data have been collected from more than one hundred thousand PLWH, allowing for a number of studies on the response to treatment, selection and spread of resistance-associated mutations and the circulation of viral subtypes. Drawing from its interdisciplinary vocation, EuResist will continue to investigate clinical response to antiretroviral treatment against HIV and monitor the development and circulation of HIV drug resistance in clinical settings, along with the development of novel drugs and the introduction of new treatment strategies. The support of artificial intelligence in these activities is essential.

iMIGS: An innovative AI based prediction system for selecting the best patient-specific glaucoma treatment.

The use of AI-based techniques in healthcare are becoming more and more common and more disease-specific. Glaucoma is a disorder in eye that causes damage to the optic nerve which can lead to permanent blindness. It is caused by the elevated pressure inside the eye due to the obstruction to the flow of the drainage fluid (aqueous humor). Most recent treatment options involve minimally invasive glaucoma surgery (MIGS) in which a stent is placed to improve drainage of aqueous humor from the eye. Each MIGS surgery has a different mechanism of action, and the relative efficacy and chance of success is dependent on multiple patient-specific factors. Hence the ophthalmologists are faced with the critical question; which method would be better for a specific patient, both in terms of glaucoma control but also taking into consideration patient quality of life? In this paper, an Adaptive Neuro-Fuzzy Inference System (ANFIS) has been developed in the form of a Treatment Advice prediction system that will offer the clinician a suggested MIGS treatment from the baseline clinical parameters. ANFIS was used with a real-world MIGS data set which was a retrospective case series of 372 patients who underwent either of the four MIGS procedures from July 2016 till May 2020 at a single center in the UK.•Inputs used: Clinical measurements of Age, Visual Acuity, Intraocular Pressure (IOP), and Visual Field, etc.•Output Classes: iStent, iStent and Endoscopic Cyclophotocoagulation (ICE2), PreserFlo MicroShunt (PMS) and XEN-45).•Results: The proposed ANFIS system was found to be 91% accurate with high Sensitivity (80%) and Specificity (90%).

A novel air pollution prediction system based on data processing, fuzzy theory, and multi-strategy improved optimizer.

PM2.5 is an important air pollution index, which has been widely concerned. An excellent PM2.5 prediction system can effectively help people protect their respiratory tract from injury. However, due to the strong uncertainty of PM2.5 data, the accuracy of traditional point prediction and interval prediction method is not satisfactory, especially for interval prediction, which is usually difficult to achieve the expected interval coverage (PINC). In order to solve the above problems, a new hybrid PM2.5 prediction system is proposed, which can quantify the certainty and uncertainty of future PM2.5 at the same time. For point prediction, a multi-strategy improved multi-objective crystal algorithm (IMOCRY) is proposed; the chaotic mapping and screening operator are added to make the algorithm more suitable for practical application. At the same time, the combined neural network based on unconstrained weighting method further improves the point prediction accuracy. For interval prediction, a new strategy is proposed, which uses the combination of fuzzy information granulation and variational mode decomposition to process the data. The high-frequency components are extracted by the VMD method, and then quantified by FIG method. By this way, the fuzzy interval prediction results with high coverage and low interval width are obtained. Through 4 groups of experiments and 2 groups of discussions, the advanced nature, accuracy, generalization, and fuzzy prediction ability of the prediction system are all satisfactory, which verified the effect of the system in practical application.

Exposure Assessment of Ambient PM2.5 Levels during a Sequence of Dust Episodes: A Case Study Coupling the WRF-Chem Model with GIS-Based Postprocessing.

A sequence of dust intrusions occurred from the Sahara Desert to the central Mediterranean in the second half of June 2021. This event was simulated by means of the Weather Research and Forecasting coupled with chemistry (WRF-Chem) regional chemical transport model (CTM). The population exposure to the dust surface PM2.5 was evaluated with the open-source quantum geographical information system (QGIS) by combining the output of the CTM with the resident population map of Italy. WRF-Chem analyses were compared with spaceborne aerosol observations derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) and, for the PM2.5 surface dust concentration, with the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis. Considering the full-period (17-24 June) and area-averaged statistics, the WRF-Chem simulations showed a general underestimation for both the aerosol optical depth (AOD) and the PM2.5 surface dust concentration. The comparison of exposure classes calculated for Italy and its macro-regions showed that the dust sequence exposure varies with the location and entity of the resident population amount. The lowest exposure class (up to 5 µg m-3) had the highest percentage (38%) of the population of Italy and most of the population of north Italy, whereas more than a half of the population of central, south and insular Italy had been exposed to dust PM2.5 in the range of 15-25 µg m-3. The coupling of the WRF-Chem model with QGIS is a promising tool for the management of risks posed by extreme pollution and/or severe meteorological events. Specifically, the present methodology can also be applied for operational dust forecasting purposes, to deliver safety alarm messages to areas with the most exposed population.

A clinical decision support system for predicting coronary artery stenosis in patients with suspected coronary heart disease.

Invasive coronary angiography imposes risks and high medical costs. Therefore, accurate, reliable, non-invasive, and cost-effective methods for diagnosing coronary stenosis are required. We designed a machine learning-based risk-prediction system as an accurate, noninvasive, and cost-effective alternative method for evaluating suspected coronary heart disease (CHD) patients. Electronic medical record data were collected from suspected CHD patients undergoing coronary angiography between May 1, 2017, and December 31, 2019. Multi-Class XGBoost, LightGBM, Random Forest, NGBoost, logistic models and MLP were constructed to identify patients with normal coronary arteries (class 0: no coronary artery stenosis), minimum coronary artery stenosis (class 1: 0 < stenosis <50%), and CHD (class 2: stenosis ≥50%). Model stability was verified externally. A risk-assessment and management system was established for patient-specific intervention guidance. Of 1577 suspected CHD patients, 81 (5.14%) had normal coronary arteries. The XGBoost model demonstrated the best overall classification performance (micro-average receiver operating characteristic [ROC] curve: 0.92, macro-average ROC curve: 0.89, class 0 ROC curve: 0.88, class 1 ROC curve: 0.90, class 2 ROC curve: 0.89), with good external verification. In class-specific classification, the XGBoost model yielded F1 values of 0.636, 0.850, and 0.858, for Classes 0, 1, and 2, respectively. The visualization system allowed disease diagnosis and probability estimation, and identified the intervention focus for individual patients. Thus, the system distinguished coronary artery stenosis well in suspected CHD patients. Personalized probability curves provide individualized intervention guidance. This may reduce the number of invasive inspections in negative patients, while facilitating decision-making regarding appropriate medical intervention, improving patient prognosis.

A Fully Automatic Postoperative Appearance Prediction System for Blepharoptosis Surgery with Image-based Deep Learning.

Purpose: To automatically predict the postoperative appearance of blepharoptosis surgeries and evaluate the generated images both objectively and subjectively in a clinical setting. Design: Cross-sectional study. Participants: This study involved 970 pairs of images of 450 eyes from 362 patients undergoing blepharoptosis surgeries at our oculoplastic clinic between June 2016 and April 2021. Methods: Preoperative and postoperative facial images were used to train and test the deep learning-based postoperative appearance prediction system (POAP) consisting of 4 modules, including the data processing module (P), ocular detection module (O), analyzing module (A), and prediction module (P). Main Outcome Measures: The overall and local performance of the system were automatically quantified by the overlap ratio of eyes and by lid contour analysis using midpupil lid distances (MPLDs). Four ophthalmologists and 6 patients were invited to complete a satisfaction scale and a similarity survey with the test set of 75 pairs of images on each scale. Results: The overall performance (mean overlap ratio) was 0.858 ± 0.082. The corresponding multiple radial MPLDs showed no significant differences between the predictive results and the real samples at any angle (P > 0.05). The absolute error between the predicted marginal reflex distance-1 (MRD1) and the actual postoperative MRD1 ranged from 0.013 mm to 1.900 mm (95% within 1 mm, 80% within 0.75 mm). The participating experts and patients were "satisfied" with 268 pairs (35.7%) and "highly satisfied" with most of the outcomes (420 pairs, 56.0%). The similarity score was 9.43 ± 0.79. Conclusions: The fully automatic deep learning-based method can predict postoperative appearance for blepharoptosis surgery with high accuracy and satisfaction, thus offering the patients with blepharoptosis an opportunity to understand the expected change more clearly and to relieve anxiety. In addition, this system could be used to assist patients in selecting surgeons and the recovery phase of daily living, which may offer guidance for inexperienced surgeons as well.

An Efficient and Privacy-Preserving Scheme for Disease Prediction in Modern Healthcare Systems.

With the Internet of Things (IoT), mobile healthcare applications can now offer a variety of dimensionalities and online services. Disease Prediction Systems (DPS) increase the speed and accuracy of diagnosis, improving the quality of healthcare services. However, privacy is garnering an increasing amount of attention these days, especially concerning personal healthcare data, which are sensitive. There are a variety of prevailing privacy preservation techniques for disease prediction that are rendered. Nonetheless, there is a chance of medical users being affected by numerous disparate diseases. Therefore, it is vital to consider multi-label instances, which might decrease the accuracy. Thus, this paper proposes an efficient privacy-preserving (PP) scheme for patient healthcare data collected from IoT devices aimed at disease prediction in the modern Health Care System (HCS). The proposed system utilizes the Log of Round value-based Elliptic Curve Cryptography (LR-ECC) to enhance the security level during data transfer after the initial authentication phase. The authorized healthcare staff can securely download the patient data on the hospital side. Utilizing the Herding Genetic Algorithm-based Deep Learning Neural Network (EHGA-DLNN) can test these data with the trained system to predict the diseases. The experimental results demonstrate that the proposed approach improves prediction accuracy, privacy, and security compared to the existing methods.

Automatic illness prediction system through speech.

Due to the COVID-19 epidemic and the curfew caused by it, many people have sought to find an ADPS on the internet in the last few years. This hints to a new age of medical treatment, all the more so if the number of internet users continues to expand. As a result, automatic illness prediction online applications have attracted the interest of a large number of researchers worldwide. This work aims to develop and implement an automated illness prediction system based on speech. The system will be designed to forecast the sort of ailment a patient is suffering from based on his voice, but this was not feasible during the trial, therefore the diseases were divided into three categories (painful, light pain and psychological pain), and then the diagnose process were implemented accordingly. The medical dataset named "speech, transcription, and intent" served as the baseline for this study. The smoothness, MFCC, and SCV properties were used in this work, which demonstrated their high representation to human being medical situations. The noise reduction forward-backward filter was used to eliminate noise from wave files captured online in order to account for the high level of noise seen in the deployed dataset. For this study, a hybrid feature selection method was created and built that combined the output of a genetic algorithm (GA) with the inputs of a NN algorithm. Classification was performed using SVM, neural network, and GMM. The greatest results obtained were 94.55% illness classification accuracy in terms of SVM. The results showed that diagnosing illness through speech is a difficult process, especially when diagnosing each type of illness separately, but when grouping the different illness types into groups, depending on the amount of pain and the psychological situation of the patient, the results were much higher.

Fuzzy Inference and Sequence Model-Based Collision Risk Prediction System for Stand-On Vessel.

Although the International Regulations for Preventing Collision at Sea (COLREGs) provide guidelines for determining the encounter relations between vessels and assessing collision risk, most collision accidents occur in crossing situations. Accordingly, prior studies have investigated methods to identify the relation between the give-way and stand-on vessels in crossing situations to allow the stand-on vessel to make the optimal collision-avoidance decision. However, these studies were hindered by several limitations. For example, the collision risk at the current time (t) was evaluated as an input variable obtained at the current time (t), and collision-avoidance decisions were made based on the evaluated collision risk. To address these limitations, a collision risk prediction system was developed for stand-on vessels using a fuzzy inference system based on near-collision (FIS-NC) and a sequence model to facilitate quicker collision avoidance decision making. This was achieved by predicting the future time point (t + i) collision risk index (CRI) of the stand-on vessel at the current time point (t) when the own-ship is determined to be the stand-on vessel in different encounter relations. According to the performance verification results, navigators who use the developed system to predict the CRI are expected to avoid collisions with greater clearance distance and time.

Conceptual design of the dual X-ray absorptiometry health informatics prediction system for osteoporosis care.

Osteoporotic fractures are a major and growing public health problem, which is strongly associated with other illnesses and multi-morbidity. Big data analytics has the potential to improve care for osteoporotic fractures and other non-communicable diseases (NCDs), reduces healthcare costs and improves healthcare decision-making for patients with multi-disorders. However, robust and comprehensive utilization of healthcare big data in osteoporosis care practice remains unsatisfactory. In this paper, we present a conceptual design of an intelligent analytics system, namely, the dual X-ray absorptiometry (DXA) health informatics prediction (HIP) system, for healthcare big data research and development. Comprising data source, extraction, transformation, loading, modelling and application, the DXA HIP system was applied in an osteoporosis healthcare context for fracture risk prediction and the investigation of multi-morbidity risk. Data was sourced from four DXA machines located in three healthcare centres in Ireland. The DXA HIP system is novel within the Irish context as it enables the study of fracture-related issues in a larger and more representative Irish population than previous studies. We propose this system is applicable to investigate other NCDs which have the potential to improve the overall quality of patient care and substantially reduce the burden and cost of all NCDs.

Patient-Specific Characteristics Associated with Favorable Response to Vagus Nerve Stimulation.

OBJECTIVE: The expansion in treatments for medically refractory epilepsy heightens the importance of identifying patients who are likely to benefit from vagus nerve stimulation (VNS). Here, we identify predictors with a positive VNS response. METHODS: We present a retrospective analysis of 158 patients with medically refractory epilepsy. Patients were categorized as VNS responders or nonresponders. Baseline characteristics and time to VNS response were recorded. Univariate and multivariate Cox regression were used to identify predictors of response. Recursive partitioning analysis was used to identify likely VNS responders. RESULTS: Eighty-nine (56.3%) patients achieved ≥50% seizure frequency reduction. Left-hand dominance (hazard ratio [HR] 1.703, P = 0.038), age at epilepsy onset ≥15 years (HR 2.029, P = 0.005), duration of epilepsy ≥8 years (HR 1.968, P = 0.007) and age at implantation ≥35 years (HR 1.809, P = 0.020), and baseline seizure frequency <5/month (HR 1.569, P = 0.044) were significant univariate predictors of VNS response. Following multivariate Cox regression, left-hand dominance, age at epilepsy onset ≥15 years, and duration of epilepsy ≥8 years remained significant. With recursive partitioning analysis, patients with either age at epilepsy onset ≥15 years, left-hand dominance, or baseline seizure frequency <5/month were stratified into Group A and had a 73.9% responder rate; the remaining patients stratified into Group B had a 43.8% responder rate. CONCLUSIONS: Patients with age at epilepsy onset ≥15 years, left-hand dominance, or baseline seizure frequency <5/month are ideal candidates for VNS.

Productivity Enhancement by Prediction of Liquid Steel Breakout during Continuous Casting Process in Manufacturing of Steel Slabs in Steel Plant Using Artificial Neural Network with Backpropagation Algorithms.

Breakout is one of the major accidents that often arise in the continuous casting shops of steel slabs in Bokaro Steel Plant, Jharkhand, India. Breakouts cause huge capital loss, reduced productivity, and create safety hazards. The existing system is not capable of predicting breakout accurately, as it considers only one process parameter, i.e., thermocouple temperature. The system also generates false alarms. Several other process parameters must also be considered to predict breakout accurately. This work has considered multiple process parameters (casting speed, mold level, thermocouple temperature, and taper/mold) and developed a breakout prediction system (BOPS) for continuous casting of steel slabs. The BOPS is modeled using an artificial neural network with a backpropagation algorithm, which further has been validated by using the Keras format and TensorFlow-based machine learning platforms. This work used the Adam optimizer and binary cross-entropy loss function to predict the liquid breakout in the caster and avoid operator intervention. The experimental results show that the developed model has 100% accuracy for generating an alarm during the actual breakout and thus, completely reduces the false alarm. Apart from the simulation-based validation findings, the investigators have also carried out the field application-based validation test results. This validation further unveiled that this breakout prediction method has a detection ratio of 100%, the frequency of false alarms is 0.113%, and a prediction accuracy ratio of 100%, which was found to be more effective than the existing system used in continuous casting of steel slab. Hence, this methodology enhanced the productivity and quality of the steel slabs and reduced substantial capital loss during the continuous casting of steel slabs. As a result, the presented hybrid algorithm of artificial neural network with backpropagation in breakout prediction does seem to be a more viable, efficient, and cost-effective method, which could also be utilized in the more advanced automated steel-manufacturing plants.

Ensemble forecast experiments of summertime sea ice in the Arctic Ocean using the TOPAZ4 ice-ocean data assimilation system.

Precise information on sea ice thickness (SIT) and its prediction at medium-range (2-week) timescale is crucial for the safe maritime navigation in the Arctic Ocean. In this study, we investigate the sensitivity of medium-range prediction skill of summertime SIT distribution in the Arctic marginal seas to atmospheric forecast data, using the 51-member ECMWF operational ensemble prediction system (EPS). For a synoptic-scale cyclone event occurred in July 5-6, 2015, two-week probabilistic forecast experiments were conducted with the TOPAZ4 ice-ocean forecast system, starting on 1st July. The ensemble correlation analysis between the forecast SIT and the meteorological parameters shows that the forecast error of SIT distribution is sensitive to the sea ice drift speed until 1-week, indicating that realistic sea ice drift improves the sea ice thickness prediction. On the other hand, beyond 1 week lead, the forecast error of SIT distribution is more sensitive to surface heat flux rather than sea ice drift. The surface heat flux signal is confined to the sea ice edge region, where the shortwave radiation flux is related to the SIT change through the sea ice melting process. The shortwave radiation flux in the sea ice edge is mostly determined by the sea ice distribution, suggesting that the skillful prediction of sea ice distribution, which is largely affected by synoptic-scale disturbance, at shorter lead times indirectly affects the medium-range forecast skill. A comparison of different ensemble perturbation techniques shows that the prediction skill is better at shorter lead times (up to 1 week), when using an atmospheric EPS rather than the random perturbations used in the operational forecast system, but the random perturbations are advantageous beyond 1 week. Thus, the application of the EPS to an ice-ocean coupled forecast system leads to a more precise sea ice prediction on medium-range timescale, which we expect to become of practical use for the optimum shipping route in the Arctic Ocean.