Static Variables Research Articles

Temporal Patterns in Migration Flows Evidence from South Sudan

ABSTRACTWhat explains the variation in migration flows over time and space? Existing work has contributed to a rich understanding of the factors that affect why and when people leave. What is less understood are the dynamics of migration flows over time. Existing work typically focuses on static variables at the country‐year level and ignores the temporal dynamics. Are there recurring temporal patterns in migration flows? And can we use these patterns to improve our forecasts of the number of migrants? Here, we introduce new methods to uncover temporal sequences—motifs—in the number of migrants over time and use these motifs for forecasting. By developing a multivariable shape similarity‐based model, we show that temporal patterns do exist. Moreover, using these patterns results in better out‐of‐sample forecasts than a benchmark of statistical and neural networks models. We apply the new method to the case of South Sudan.

Effects of neural mobilization of sciatic nerve and its branches in plantar foot pressures and stabilometry

Benefits of neural mobilization (NM) have been described in musculoskeletal patients. The effects of NM on balance appear to be unclear in research, and no studies have tested the possible effects of NM on plantar pressures. Eighteen subjects were evaluated pre and post bilateral gliding of the sciatic nerve and its branches posterior tibial nerve, lateral dorsocutaneous, medial and intermediate dorsocutaneous nerves. Static variables of the plantar footprint and stabilometric variables were measured in a pre-post study. We found no differences in plantar pressure variables, Rearfoot maximum pressure (p = 0.376), Rearfoot medium pressure (p = 0.106), Rearfoot surface (p = 0.896), Midfoot maximum pressure (p = 0.975), Midfoot medium pressure (p = 0.950), Midfoot surface (p = 0.470) Forefoot maximum pressure (p = 0.559), Forefoot medium pressure(p = 0.481), Forefoot surface (p = 0.234), and stabilometric variables either, X-Displacement eyes-open (p = 0.086), Y-Displacement eyes-open (p = 0.544), Surface eyes-open (p = 0.411), Medium speed latero-lateral displacement eyes-open (p = 0.613), Medium speed anteroposterior displacement eyes-open (p = 0.442), X Displacement eyes-closed (p = 0.126), Y-Displacement eyes-closed (p = 0.077), Surface eyes-closed (p = 0.502), Medium speed latero-lateral displacement eyes-closed (p = 0.956), Medium speed anteroposterior displacement eyes-closed (p = 0.349). All variables don´t have significant differences however the measurements had a high reliability with at least an ICC of 0.769. NM doesn´t change plantar pressures or improve balance in healthy non-athletes subjects. NCT05190900.

Prediction of Landslide Susceptibility in the Karakorum under the Context of Climate Change

Climate change has recently increased the frequency of landslides in alpine areas. Susceptibility mapping is crucial for anticipating and assessing landslide risk. However, traditional methods focus on static environmental variables to emphasize the spatial distribution of landslides, ignoring temporal dynamics in landslide development in the context of climate change. In this work, we focused on static and dynamic environment factors and utilized the certainty factor-logistic regression (CF-LR) model to assess and predict landslide susceptibility in Taxkorgan County, located in the Karakorum. The assessment and prediction were based on a catalog of climate change-related landslides over the past 20 years, the causative factors, and predicted climatic variables for the Shared Socioeconomic Pathways (SSP1-2.6) scenario. The results indicated that elevation, slope, groundwater, slope length gradient (LS) factor, Topographic Wetness Index (TWI), valley depth, and maximum precipitation were the key causes of slides below the snow line. The key factors causing debris flow above the snow line were elevation, slope, topographic relief, aspect, LS factor, distance to the river, and maximum temperature. The accuracy of slide and debris flow susceptibility was 0.92 and 0.89, respectively. The area of slides with medium, high, and very high susceptibility is 25.5% of the Taxkorgan. In addition, 82.6% of the slides happened in this region, and 49.5% of the entire area is covered by debris flows with medium, high, and very high susceptibility. Moreover, this area accounts for 91.8% of all debris flows. Until 2060, the region’s climate is anticipated to become warmer and wetter. Slides below the snow line will gradually decrease and shift eastward, and debris flows above the snow line will expand. Our findings will contribute to the management of landslide risks at the regional scale.

Transport properties of interface-type analog memristors

Interface-type, analog memristors have quite a reputation for real-time applications in edge sensorics, edge computing, and neuromorphic computing. The -type conducting BiFeO3 (BFO) is such an interface-type, analog memristor, which is also nonlinear and can therefore not only store, but also process data in the same memristor cell without data transfer between the data-storage unit and the data-processing unit. Here we present a physical memristor model, which describes the hysteretic current-voltage curves of the BFO memristor in the small and large current-voltage range. Extracted internal state variables are reconfigured by the ion drift in the two write branches and are determining the electron transport in the two read branches. Simulation of electronic circuits with the BFO interface-type, analog memristors was not possible so far because previous physical memristor models have not captured the full range of internal state variables. We show quantitative agreement between modeled and experimental current-voltage curves exemplarily of three different BFO memristors in the small and large current-voltage ranges. Extracted dynamic and static internal state variables in the two full write branches and in the two full read branches, respectively, can be used for simulating electronic circuits with BFO memristors, e.g., in edge sensorics, edge computing, and neuromorphic computing. Published by the American Physical Society 2024

CardioRiskNet: A Hybrid AI-Based Model for Explainable Risk Prediction and Prognosis in Cardiovascular Disease.

The global prevalence of cardiovascular diseases (CVDs) as a leading cause of death highlights the imperative need for refined risk assessment and prognostication methods. The traditional approaches, including the Framingham Risk Score, blood tests, imaging techniques, and clinical assessments, although widely utilized, are hindered by limitations such as a lack of precision, the reliance on static risk variables, and the inability to adapt to new patient data, thereby necessitating the exploration of alternative strategies. In response, this study introduces CardioRiskNet, a hybrid AI-based model designed to transcend these limitations. The proposed CardioRiskNet consists of seven parts: data preprocessing, feature selection and encoding, eXplainable AI (XAI) integration, active learning, attention mechanisms, risk prediction and prognosis, evaluation and validation, and deployment and integration. At first, the patient data are preprocessed by cleaning the data, handling the missing values, applying a normalization process, and extracting the features. Next, the most informative features are selected and the categorical variables are converted into a numerical form. Distinctively, CardioRiskNet employs active learning to iteratively select informative samples, enhancing its learning efficacy, while its attention mechanism dynamically focuses on the relevant features for precise risk prediction. Additionally, the integration of XAI facilitates interpretability and transparency in the decision-making processes. According to the experimental results, CardioRiskNet demonstrates superior performance in terms of accuracy, sensitivity, specificity, and F1-Score, with values of 98.7%, 98.7%, 99%, and 98.7%, respectively. These findings show that CardioRiskNet can accurately assess and prognosticate the CVD risk, demonstrating the power of active learning and AI to surpass the conventional methods. Thus, CardioRiskNet's novel approach and high performance advance the management of CVDs and provide healthcare professionals a powerful tool for patient care.

Effects of dynamic versus static parameter-guided fluid resuscitation in patients with sepsis: A randomized controlled trial.

Fluid resuscitation is an essential component for sepsis treatment. Although several studies demonstrated that dynamic variables were more accurate than static variables for prediction of fluid responsiveness, fluid resuscitation guidance by dynamic variables is not standard for treatment. The objectives were to determine the effects of dynamic inferior vena cava (IVC)-guided versus (vs.) static central venous pressure (CVP)-guided fluid resuscitation in septic patients on mortality; and others, i.e., resuscitation targets, shock duration, fluid and vasopressor amount, invasive respiratory support, length of stay and adverse events. A single-blind randomized controlled trial was conducted at Thammasat University Hospital between August 2016 and April 2020. Septic patients were stratified by acute physiologic and chronic health evaluation II (APACHEII) <25 or ≥25 and randomized by blocks of 2 and 4 to fluid resuscitation guidance by dynamic IVC or static CVP. Of 124 patients enrolled, 62 were randomized to each group, and one of each was excluded from mortality analysis. Baseline characteristics were comparable. The 30-day mortality rates between dynamic IVC vs. static CVP groups were not different (34.4% vs. 45.9%, p=0.196). Relative risk for 30-day mortality of dynamic IVC group was 0.8 (95%CI=0.5-1.2, p=0.201). Different outcomes were median (interquartile range) of shock duration (0.8 (0.4-1.6) vs. 1.5 (1.1-3.1) days, p=0.001) and norepinephrine (NE) dose (6.8 (3.9-17.8) vs. 16.1 (7.6-53.6) milligrams, p=0.008 and 0.1 (0.1-0.3) vs. 0.3 (0.1-0.8) milligram⋅kilogram -1, p=0.017). Others were not different. Dynamic IVC-guided fluid resuscitation does not affect mortality of septic patients. However, this may reduce shock duration and NE dose, compared with static CVP guidance.

From eradication to conservation: identifying areas for restoration and management of a Pliocene relic in a changing climate

By 1985, approximately 400,000 ha of the keystone species Shinnery oak's (Quercus havardii) historic distribution had been eliminated for agricultural purposes across the southwestern United States. These trends indicate a need for targeted conservation and restoration efforts, especially considering the increased attention received for its role in providing habitat for endangered fauna. Setting spatially explicit conservation targets can be challenging for species with limited distributions, as the change in climate conditions over time may disconnect the relationship between environmental suitability and static, topo‐edaphic factors. Our objective was to identify areas for Shinnery oak restoration and conservation and explore the role climate plays in determining these areas while considering topo‐edaphic relationships. We constructed ecological niche models to estimate Shinnery oak's distribution under current climate conditions and temporally transferred our model using an ensemble‐mean of general circulation models to identify areas predicted to retain environmental suitability for Shinnery oak through 2100. The current distribution model was best informed by one climate and two topo‐edaphic variables. We created a second distribution model excluding topo‐edaphic variables to estimate future plant–climate relationships. Incorporating insights from models informed by both static (e.g. soil) and dynamic (e.g. climate) variables, we identified areas for conservation characterized by persistent climate suitability and high soil type suitability. Lastly, we incorporated data on land use and ownership to explore socio‐ecological influences on management decisions. By identifying areas of agreement between our modeled distributions and considering social context, we prioritized conservation areas where environmental suitability persists under changing conditions to facilitate restoration success.

Accelerated MPC: A real-time model predictive control acceleration method based on TSMixer and 2D block stochastic configuration network imitative controller

Modern industrial processes demand real-time model predictive control (MPC) method within the constraints of limited computing resources. This paper introduces an accelerated MPC method to address the issue. Specifically, we employ the time series mixing model (TSMixer) to construct a predictive model capable of accurately forecasting the behavior of the system under control. Furthermore, we extend the capabilities of TSMixer by integrating a feature classification model (TSMixer with FCM). This enhanced version takes into account both future information and static variables, resulting in superior accuracy compared to the transformer model while maintaining a significantly smaller parameter count. Finally, to further enhance MPC’s responsiveness, we develop a two-dimensional block stochastic configuration network (2D-BSCN) imitative controller. This innovative network clones the behavior of rolling optimization, effectively replacing online optimization to reduce computational time. The experimental results show that the accelerated MPC has an excellent performance in numerical simulation and actual industrial processes. Notably, the algorithm achieves a smaller FLOPs and tracking time than other state-of-the-art models, well within the requirement for industrial process control.

Variability in airborne noise emissions of container ships approaching ports

Container ships emit airborne noise that can vary significantly due to diverse vessel characteristics and operating conditions. This study aims to investigate the factors influencing noise emission from container ships moving in ports to enhance our understanding and improve noise prediction models. Using a dataset comprising long-term sound pressure level measurements, video recordings, and weather station data, a multiple regression analysis was conducted to assess the impact of static (ship-specific) and dynamic (pass-by specific) variables on noise emission. Static variables included ship dimensions and age, while dynamic variables encompassed distance from the microphone, speed, and draught. A k-means unsupervised clustering analysis was performed using 1/3rd octave band spectra to identify subcategories of container carriers based on sound emission characteristics. Significant correlations between emissions and both static and dynamic variables were found. The k-means clustering analysis yielded distinct subcategories of container carriers based on their sound emission profiles. This study highlights the importance of considering various factors, including ship characteristics and operating conditions, when assessing noise emission from container ships. By better understanding the factors contributing to noise emission, we can effectively mitigate noise pollution and minimize the impact on surrounding communities.

Spatial and temporal forecasting of groundwater anomalies in complex aquifer undergoing climate and land use change

Monitoring groundwater (GW) level variations, or anomalies in multiple wells, over long periods of time is essential to understanding changes in regional groundwater resource availability. However, it is challenging to predict these GW anomalies over the long term in agricultural areas due to complicated boundary conditions, heterogeneous hydrogeological characteristics, and groundwater extraction, as well as nonlinear interactions among these factors. To overcome this challenge, we developed an advanced modeling framework based on a recurrent neural network of long short-term memory (LSTM) as an alternative to complex and computationally expensive physical models. GW anomalies were forecast two months in advance (t + 2) based on the evaluation of drivers that influence GW dynamics in densely irrigated agricultural regions. An application and evaluation of this new approach was conducted in the Wisconsin Central Sands (WCS) region in the U.S., one of the most productive agricultural regions. The modeling framework was developed for the period 1958–2020 by utilizing easily accessible dynamic and static variables to represent hydrometeorological and geological characteristics. GW anomaly observations were acquired from 26 piezometers (wells) installed in the sandy aquifer in WCS over 10–60 years. The subset of GW observations from ∼ 10–60 years, not used in model training, can forecast GW anomalies two months out with a coefficient of determination R2 ∼ 0.8. Additionally, MAE was less than 0.34 m/month across the study region for both temporal and spatial modeling frameworks. Groundwater anomalies showed high spatiotemporal variability, and their responses are influenced differently by boundary conditions, catchment geology, climate, and topography across locations. Sites with higher autocorrelation with previous two-months GW anomalies reduced bias by increasing R2. Land use change and irrigation pumping have interactive effects on GW anomalies forecasting. The novelty of this study is identifying the regional drivers of GW fluxes. This case-specific information and location-related simplification, modification, and assumption of LSTM is a unique contribution to the existing literature. Our framework can be used as an alternative method of simulating water availability and groundwater level changes in areas where subsurface properties are unknown or difficult to determine.

Enhancing the diagnosis of functionally relevant coronary artery disease with machine learning

Functionally relevant coronary artery disease (fCAD) can result in premature death or nonfatal acute myocardial infarction. Its early detection is a fundamentally important task in medicine. Classical detection approaches suffer from limited diagnostic accuracy or expose patients to possibly harmful radiation. Here we show how machine learning (ML) can outperform cardiologists in predicting the presence of stress-induced fCAD in terms of area under the receiver operating characteristic (AUROC: 0.71 vs. 0.64, p = 4.0E-13). We present two ML approaches, the first using eight static clinical variables, whereas the second leverages electrocardiogram signals from exercise stress testing. At a target post-test probability for fCAD of <15%, ML facilitates a potential reduction of imaging procedures by 15–17% compared to the cardiologist’s judgement. Predictive performance is validated on an internal temporal data split as well as externally. We also show that combining clinical judgement with conventional ML and deep learning using logistic regression results in a mean AUROC of 0.74.

Modelling Diameter at Breast Height Distribution for Eight Commercial Species in Natural-Origin Mixed Forests of Ontario, Canada

Diameter at breast height (DBH) is a unique attribute used to characterize forest growth and development for forest management planning and to understand forest ecology. Forest managers require an array of DBHs of forest stands, which can be reconstructed using selected probability distribution functions (PDFs). However, there is a lack of practices that fit PDFs of sub-dominating species grown in natural mixed forests. This study aimed to fit PDFs and develop predictive models for PDF parameters, so that the predicted distribution would represent dynamic forest structures and compositions in mixed forest stands. We fitted three of the simplest forms of PDFs, log-normal, gamma, and Weibull, for the DBH of eight tree species, namely balsam fir (Abies balsamea [L.] Mill.), eastern white pine (Pinus strobus L.), paper birch (Betula papyrifera Marshall), red maple (Acer rubrum L.), red pine (Pinus resinosa Aiton), trembling aspen (Populus tremuloides Michx), and white spruce (Picea glauca [Moench] Voss), all grown in natural-origin mixed forests in Ontario province, Canada. We estimated the parameters of the PDFs as a function of DBH mean and standard deviation for these species. Our results showed that log-normal fit the best among the three PDFs. We demonstrated that the predictive model could estimate the recovered parameters unbiasedly for all species, which can be used to reconstruct the DBH distributions of these tree species. In addition to prediction, the cross-validated R2 for the DBH mean ranged between 0.76 for red maple and 0.92 for red pine. However, the R2 for the regression of the standard deviation ranged between 0.00 for red pine and 0.69 for sugar maple, although it produced unbiased predictions and a small mean absolute bias. As these mean and standard deviations are regressed with dynamic covariates (such as stem density and stand basal area), in addition to climate and static geographic variables, the predicted DBH distribution can reflect change over time in response to management or any type of disturbance in the regime of the given geography. The predictive model-based DBH distributions can be applied to the design of appropriate silviculture systems for forest management planning.

Effects of dynamic versus static parameter-guided fluid resuscitation in patients with sepsis: A randomized controlled trial

Background Fluid resuscitation is an essential component for sepsis treatment. Although several studies demonstrated that dynamic variables were more accurate than static variables for prediction of fluid responsiveness, fluid resuscitation guidance by dynamic variables is not standard for treatment. The objectives were to determine the effects of dynamic inferior vena cava (IVC)-guided versus (vs.) static central venous pressure (CVP)-guided fluid resuscitation in septic patients on mortality; and others, i.e., resuscitation targets, shock duration, fluid and vasopressor amount, invasive respiratory support, length of stay and adverse events. Methods A single-blind randomized controlled trial was conducted at Thammasat University Hospital between August 2016 and April 2020. Septic patients were stratified by acute physiologic and chronic health evaluation II (APACHE II) &lt;25 or ≥25 and randomized by blocks of 2 and 4 to fluid resuscitation guidance by dynamic IVC or static CVP. Results Of 124 patients enrolled, 62 were randomized to each group, and one of each was excluded from mortality analysis. Baseline characteristics were comparable. The 30-day mortality rates between dynamic IVC vs. static CVP groups were not different (34.4% vs. 45.9%, p=0.196). Relative risk for 30-day mortality of dynamic IVC group was 0.8 (95%CI=0.5-1.2, p=0.201). Different outcomes were median (interquartile range) of shock duration (0.8 (0.4-1.6) vs. 1.5 (1.1-3.1) days, p=0.001) and norepinephrine (NE) dose (6.8 (3.9–17.8) vs. 16.1 (7.6–53.6) milligrams, p=0.008 and 0.1 (0.1-0.3) vs. 0.3 (0.1-0.8) milligram⋅kilogram−1, p=0.017). Others were not different. Conclusions Dynamic IVC-guided fluid resuscitation does not affect mortality of septic patients. However, this may reduce shock duration and NE dose, compared with static CVP guidance.

Enhancing Soil Moisture Forecasting Accuracy with REDF-LSTM: Integrating Residual En-Decoding and Feature Attention Mechanisms

This study introduces an innovative deep learning model, Residual-EnDecode-Feedforward Attention Mechanism-Long Short-Term Memory (REDF-LSTM), designed to overcome the high uncertainty challenges faced by traditional soil moisture prediction methods. The REDF-LSTM model, by integrating a residual learning encoder–decoder LSTM layer, enhanced LSTM layers, and feedforward attention, not only captures the deep features of time series data but also optimizes the model’s ability to identify key influencing factors, including land surface features, atmospheric conditions, and other static environmental variables. Unlike existing methods, the innovation of this model lies in its first-time combination of the residual learning encoder–decoder and feedforward attention mechanisms in the soil moisture prediction field. It delves into the complex patterns of time series through the encoder–decoder structure and accurately locates key influencing factors through the feedforward attention mechanism, significantly improving predictive performance. The choice to combine the feedforward attention mechanism and encoder–decoder with the LSTM model is to fully leverage their advantages in processing complex data sequences and enhancing the model’s focus on important features, aiming for more accurate soil moisture prediction. After comparison with current advanced models such as EDLSTM, FAMLSTM, and GANBiLSTM, our REDF-LSTM demonstrated the best performance. Compared to traditional LSTM models, it achieved an average improvement of 13.07% in R2, 20.98% in RMSE, 24.86% in BIAS, and 11.1% in KGE key performance indicators, fully proving its superior predictive capability and potential application value in precision agriculture and ecosystem management.

The finance and growth nexus revisited: a truly Schumpeterian perspective

Abstract The nexus between the financial system and economic development is an issue that is as important for economic theory as it is for economic policy. This article provides a theoretical and empirical analysis of the standard literature that uses Joseph A. Schumpeter as an academic patron. The starting point of our study is the theoretical finding that Schumpeter is misinterpreted in the finance and growth literature. We show that this leads to puzzling empirical results and difficulties in explaining even fundamental relationships. After a critical analysis of the literature, we provide our own empirical analysis using a panel of 43 countries to explore the relationships between finance and growth variables based on a truly Schumpeterian and thus monetary growth model. Our empirical analysis shows that (i) dynamic credit variables are better at describing the finance and growth nexus than static credit variables, (ii) as saving is not a prerequisite for credit, we do not find a statistically significant relationship between household saving and gross domestic product (GDP) growth, and (iii) in line with the empirical literature, we find a bi-directional relationship between credit and GDP growth.

The Impact of Bosu Training on The Development of Static and Dynamic Balance in Teenage Basketball Players

The study aimed to investigate the impacts of Bosu training on the development of static and dynamic balance in male basketball players aged 14-16 years who play actively in basketball clubs in Bingöl and train more than 7 hours weekly according to a training schedule. The study included 30 basketball players, 15 in the control group and 15 in the experimental group. The experimental group attended 30 minutes of Bosu training in addition to basketball training for 8 weeks. Anthropometric measurements, such as height, weight, BMI, and static and dynamic balance measurements, were taken before and after the training period. The data were analysed using the SPSS package. The distribution of the pre-test and post-test data was examined for each group. Normality of the data distribution and homogeneity of variances were checked using the Mauchly sphericity test and the Levene test. Then, the dependent variables within each group were compared using the paired-sample t-test. The study used a p-value of less than 0.05 to assess the significance of the results. The mean values of height, weight, and BMI of the experimental group after the 8-week Bosu training program showed no significant difference (p&gt;0.05). The analysis results regarding the Bosu training program's static and dynamic balance measurements yielded a statistically significant difference between the pre- and post-test comparisons of the experimental group for most of the static and dynamic balance variables (p&lt;0.05). However, there was no statistical difference in the control group or between groups (p&gt;0.05). It was concluded that Bosu training can positively contribute to the improvement of static and dynamic balance in young basketball players.

The Impact of Hemsball Training on Balance and Punto (Approaching) Scores in Boccia Athletes

This study aimed to investigate the impact of an 8-week hemsball training on the development of balance and punto (approaching the target) performance in athletes aged 12-14 who play boccia in Bingöl province teams and train for more than 7 hours per week. The sample consisted of athletes who met the aforementioned criteria. The study involved 30 athletes, with 15 in the control group and 15 in the experimental group, who voluntarily played boccia. The experimental group received 60 minutes of hemsball training followed by boccia training, while the control group only received boccia training. Anthropometric measurements, including height, body weight, and BMI, as well as measurements of static and dynamic balance and punto (approach to target) scores, were taken before and after the 8-week exercise period. The data was evaluated using the SPSS 25 package program. The pre-test and post-test distributions of the study data were examined for each group, and normality and homogeneity of variances were determined using the Mauchly Sphericity Test and Levene test. Based on the test results, the dependent variables within each group were compared using the Paired-Sample T-test. The significance of the results was evaluated at a p-value of less than 0.05. The study found no significant difference in the mean values of height, body weight, and BMI of the experimental group after the 8-week hemsball training program (p&gt;0.05). According to the data analysis results regarding the static and dynamic balance measurements of the hemsball training application, a significant difference at the p&lt;0.05 level was found in the pre-test and post-test comparisons of the experimental group in the majority of the static and dynamic balance variables. No statistical difference was found in the control group and the between-group comparisons. According to the data analysis results regarding the Punto score measurements, a significant difference at the p&lt;0.05 level was found in the pre and post-test comparisons of the experimental group in the punto score variable. No difference was found for the control group. Similarly, no statistical difference was observed between the groups. As a result, it can be concluded that training with hemsball has a positive effect on the performance of young boccia athletes in static and dynamic balance and the punto score.

Implementation of surface mechanomyography as a novel approach for objective evaluation of phasic muscle stretch reflexes in people with type 2 diabetes

IntroductionDiabetic peripheral neuropathy is the most common complication of diabetes producing metabolic disruptions in the peripheral nervous system. Alteration in the predictable nature of tendon reflexes is the most common indicator suggesting the possibility of diabetic neuropathy. Evaluation of tendon reflexes is a part of various clinical scoring systems that assess neuropathy. The conventional reflex grading scales are subjective, lack temporal data, and have high inter-rater variability. Hence, an indigenous quantification tool was developed to evaluate the tendon reflexes in order to assess diabetic peripheral neuropathy. Materials and methodsA cross-sectional study was carried out in 140 healthy volunteers and 140 patients with type 2 diabetes. The mean age of controls and diabetics (49.1 ± 8.9, 50.7 ± 7.5) years, weight (66.9 ± 9.4, 69.8 ± 11.5) kilograms and BMI (24.5 ± 3.8, 26.1 ± 4.7), respectively. All of them are subjected to evaluation of tendon reflexes using the reflex quantification tool comprised of surface mechanomyography and electrogoniometry that can provide various static and dynamic variables of tendon reflex. ResultsThe dynamic variables such as reflex amplitude, muscle velocity and angular velocity were significantly low in diabetic patients (p: <0.001) whereas latency and duration (p: <0.001) were prolonged. Furthermore, no significant difference was observed in the application of tendon striking force (p: 0.934) among the participants. ConclusionThe current study demonstrates that the proposed reflex quantification tool provides several dynamic variables of patellar tendon reflex, which are significantly affected and altered in diabetic patients suggesting the involvement of peripheral neurons.

Regression Model for the Prediction of Total Motor Power Used by an Industrial Robot Manipulator during Operation

Motor power models are a key tool in robotics for modeling and simulations related to control and optimization. The authors collect the dataset of motor power using the ABB IRB 120 industrial robot. This paper applies a multilayer perceptron (MLP) model to the collected dataset. Before the training of MLP models, each of the variables in the dataset is evaluated using the random forest (RF) model, observing two metrics-mean decrease in impurity (MDI) and feature permutation score difference (FP). Pearson’s correlation coefficient was also applied Based on the scores of these values, a total of 15 variables, mainly static variables connected with the position and orientation of the robot, are eliminated from the dataset. The scores demonstrate that while both MLPs achieve good scores, the model trained on the pruned dataset performs better. With the model trained on the pruned dataset achieving R¯2=0.99924,σ=0.00007 and MA¯PE=0.33589,σ=0.00955, the model trained on the original, non-pruned, data achieves R¯2=0.98796,σ=0.00081 and MA¯PE=0.46895,σ=0.05636. These scores show that by eliminating the variables with a low influence from the dataset, a higher scoring model is achieved, and the created model achieves a better generalization performance across five folds used for evaluation.

Temporal pattern mining for knowledge discovery in the early prediction of septic shock

Temporal pattern mining can be employed to detect patterns and trends in a patient's health status as it evolves over time. However, these methods often produce an overwhelming number of patterns, impeding knowledge discovery and practical implementation in acute care settings. To address this, we propose a framework that focuses on identifying a concise set of relevant temporal patterns and static variables from electronic health records for the early prediction of septic shock. Sepsis is caused by an adverse immune response to infection that triggers widespread inflammation throughout the body, which can progress to septic shock and ultimately result in death if not treated promptly. The analysis of health state patterns in sepsis patients over time offers the potential to predict septic shock prior to its onset, enabling proactive healthcare interventions. Our framework incorporates a temporal pattern mining method and four feature selection techniques. We discover that selecting features based on a model-based wrapper approach yields the highest prediction performance among these techniques. On the other hand, the use of information value identifies more multi-state patterns with abnormal health states, providing healthcare providers with valuable indicators of patient deterioration.