Curve Technique Research Articles

Explore the factors related to the death of offspring under age five and appraise the hazard of child mortality using machine learning techniques in Bangladesh

BackgroundChild mortality is a reliable and significant indicator of a nation’s health. Although the child mortality rate in Bangladesh is declining over time, it still needs to drop even more in order to meet the Sustainable Development Goals (SDGs). Machine Learning models are one of the best tools for making more accurate and efficient forecasts and gaining in-depth knowledge. A deeper understanding is crucial for significantly reducing child mortality rates. Accurate predictions using machine learning models can empower authorities to implement timely interventions and raise awareness. So, the study aimed to explore the factors related to child mortality and assess the efficacy of various machine-learning models in predicting child mortality in Bangladesh.Methods and materialsAbout Forty-two thousand observations, except the missing observations, were extracted for this study from the Bangladesh Demographic and Health Survey (BDHS) data conducted in 2017-18. The survey utilized a two-stage stratified sampling method, selecting 675 enumeration areas-250 in urban settings and 425 in rural areas-resulting in effective data collection from 672 clusters and 20160 households. The Chi-square test and recursive feature elimination (RFE) are used to find the relevant risk factors of child mortality among the number of factors. Six ML-based algorithms were implemented for predicting child mortality, such as Naïve Bayes, Classification and Regression Trees, Random Forest, C5.0 Classification, Gradient Boosting Machine, and Logistic Regression. Model evaluation metrics like accuracy, specificity, sensitivity, negative predictive value, F1 score, positive predictive value, k-fold cross-validation, and area under the curve (AUC) techniques were used to evaluate the performance of the models.Results and discussionThe child mortality rate is 8.2%, according to the data. The bivariate analysis showed that the child mortality rate was higher among the children whose mothers were uneducated, impoverished, underweight, aged 35-49, and gave birth before age 20. Families’ water sources and religious connections had no statistically significant impact on child mortality. The prediction of child mortality using machine learning models is the main objective of this study. None of the machine learning models correctly classified dead occurrences. Therefore, this study conducted over-sampling and under-sampling analysis. Approximately 76727 and 6910 observations were sampled for over-sampling and under-sampling techniques, respectively. According to the findings of the over-sampling data, the Random Forest outperformed all the other models in terms of total performance based on training and testing sets, with an accuracy of seventy percent. The k-fold cross-validation approach demonstrated the Random Forest model’s superior performance, and achieved the highest AUC (0.701). On the other hand, the Gradient Boosting Machine has the highest assessment for predicting child mortality in under-sampling analysis. The k-fold cross-validation also illustrated the better performance of the Gradient Boosting Machine.ConclusionThe Gradient Boosting Machine and Random Forest produce the best predictive power for classifying child mortality and may help to ameliorate policy decision-making in this regard.

Intercomparison of sediment transport curve and novel deep learning techniques in simulating sediment transport in the Wadi Mina Basin, Algeria

Intercomparison of sediment transport curve and novel deep learning techniques in simulating sediment transport in the Wadi Mina Basin, Algeria

An emerging frontier of battery innovation: tackling lattice rotation in single-crystalline cathodes.

Due to a lack of spatially resolved characterization studies on statistical and individual particle microstructure at multiple scales, a knowledge gap exists in understanding the mechanistic link between rapid performance failure and atomic-scale structure degradation in single-crystalline Ni-rich battery cathodes. In a recent publication in Science, Huang et al. developed a multi-crystal rocking curve technique (combining X-ray and electron microscopy to capture both statistical and individual lattice distortions), which enables multiscale observations and further proves that the accumulation of the unrecoverable lattice rotation in cathodes upon repeated cycling exacerbates mechanical failure and electrochemical decay. The elucidation of failure mechanisms in single-crystalline cathodes offers valuable insights into the development of long-lasting and high-energy-density cathodes in next-generation batteries, encompassing strategies to mitigate lattice rotation and enhance lattice structure tolerance against lattice distortion within individual particles.

Association between body roundness index and osteoarthritis: a cross-sectional analysis of NHANES 2011-2018.

The objective of this study was to investigate the potential association between body roundness index (BRI) and the risk of osteoarthritis (OA) in US adults. A cross-sectional analysis consisting of 20,232 participants was conducted using data from the National Health and Nutrition Examination Survey (NHANES) from 2011 to 2018. Participants (≥20 years of age) were included and divided into OA and non-OA groups. Then, the demographics and characteristics of the participants were compared between the two groups. The relationship between BRI and OA was assessed using a multivariate logistic regression model with fitted smoothed curve techniques. Additionally, subgroup analyses on the correlation between BRI and OA were performed. The BRI scores in OA group were significantly higher than in the non-OA group (6.60 ± 2.62 vs. 5.46 ± 2.34, p < 0.001). Multivariate logistic analysis revealed that a significantly positive association between BRI and OA (OR = 1.12, 95% CI: 1.09-1.14, p < 0.001). In the subgroup analysis, only the race subgroup showed a significant difference between BRI and OA (p < 0.001). Our findings highlight a significantly positive association between BRI and OA prevalence in the general US population.

Patient satisfaction and operator proficiency in gasless transaxillary endoscopic thyroidectomy under IONM: a retrospective cohort study.

This study aims to evaluate the surgical safety and effectiveness of gasless transaxillary endoscopic thyroidectomy (GTET), assess patients' short-term perceptions and long-term outcomes, and delineate the learning curve and key surgical techniques of the operators. Clinicopathological and postoperative follow-up data from patients with unilateral thyroid cancer in the same period were collected. These patients were divided into the GTET group and the traditional open surgery group to compare and analyze the differences and explore the factors affecting the learning curve of GTET. Patients who chose GTET had better general health and thyroid conditions than those in the open group, and the quality of postoperative life was better in the GTET group than in the open group, with the main differences between the two groups being appearance and neck and shoulder function. The GTET learning curve in this study peaked at 19 cases, with slight differences between left and right, and a larger sample size is still needed to explore the factors affecting the learning curve. GTET has a reliable safety and efficacy profile for patients with unilateral thyroid cancer. Intraoperative nerve monitoring (IONM) techniques require some adaptation in GTET. In some respects, patients' postoperative experience and quality of life are superior to those of conventional open surgery. There is a learning curve for GTET, but large samples are still needed to explore its true significance.

Landslide susceptibility assessment along highways (SH-12 and NH-717A) in Darjeeling Himalayas

Landslides are the utmost damaging natural along with anthropogenic hazards which cause huge damage to assets and human lives in hilly environments all over the world. The key aim of this study is to explore landslide susceptibility (LS) maps along highways (SH-12 and NH-717A) in the Darjeeling Himalayas, India. Four data-driven bivariate statistical models (BSMs) were used to develop LS maps based on geospatial techniques. A total of 90 (100 %) polygons with different size of landslide locations were identified to develop landslide inventory/distribution (LI/LD) map by incorporating Google Earth and satellite imageries, and filed investigation through the global positioning system (GPS). The LI/LD map is distributed into testing 30 % (27 landslide locations), and training 70 % (63 landslide locations) of the models. Eighteen landslide causative factors (LCFs) were selected to develop LS maps. Multicollinearity analysis showed that there is no collinearity problem in the LCFs. The receiver operating characteristics (ROC) curve and frequency ratio (FR) value techniques were applied for validation. The relative importance of each LCF for each model has been measured using Jack-knife test. The LS maps were divided into six landslide susceptibility zones (LSZs). The ROC curve showed the accuracy of the LSZ maps is 93.50 % for FR, 85.90 % for evidential belief functions (EBFs), 81.80 % for fuzzy membership (FM) using FR, and 77.20 % for FM using cosine amplitude (CA) approaches respectively. Among all models, the FR model showed the highest percentage of accuracy for LS assessment and prediction. The FR values of six LSZs rise from very low to very high LSZs which indicate a positive correlation between LSZs and FR values. The results of Jack-knife test showed that terrain ruggedness index (TRI) factor has the highest relative importance for all models in LSZ mappings. The study findings will help to the planner, and policymakers for spatial planning, implement slope management strategies, land use management in mountainous environments.

Application of dynamic material balance to evaluate oil wells in Buzurgan oil field

The Material Balance Equation is a crucial tool utilized in reservoir studies to evaluate fluids and rock properties at static pressures. The Flowing and Dynamic Material Balance methods offer a significant advantage by avoiding the requirement to shut down wells, as they use flowing pressure instead of static pressure under constant or variable flow rates. The concept of "Dynamic Material Balance" involves converting the bottom hole flowing pressure at any point at any given time to the average reservoir pressure at that point. This allows for the use of classical material balance calculations and the development of classical material balance plots. In this study, the Dynamic Material Balance and Agrawal Type Curve techniques were used to estimate average reservoir pressures, initial hydrocarbon in place, and ultimate oil recovery for a well in the Mishrif reservoir, the main reservoir in the Buzurgan oil field. Many wells in this field experience problems such as high-pressure decline or continuous water production, necessitating ongoing evaluation. While the Dynamic Material Balance method focuses on boundary-dominated flow data, the Agrawal-type curve technique analyzes data from both transient and boundary flow periods. Agarwal decline curves were constructed using relationships of pseudo pressure normalized production, material balance pseudo time, and dimensionless variables in well-test analysis. The results from both methods showed comparable results with an absolute percentage error of (0.738) %, (3.07) %, and 5.7% for oil-in-place, drainage area, and average reservoir pressure, respectively. This strong correlation between the Dynamic Material Balance and Type Curve results indicates their accuracy and reliability.

A Real Case: How to Combine Polarization Curve and EIS Techniques to Identify Problematic Cells in a Commercial PEM Stack

Nowadays, the mobility sector is assessing different technologies to substitute the internal combustion engines in order to reduce its CO2 emissions; one of these possible alternatives is the Polymer Electrolyte Membrane (PEM) fuel cell. So, the development of non-destructive diagnostic tools that could identify defective cells and/or any malfunctioning behavior and can be easily embarked on in any vehicle will expand the durability of PEM fuel cells, improve their performance, and enable them to carry out predictive maintenance. In this research, we use an in-house developed methodology that combines the polarization curve and electrochemical impedance spectroscopy (EIS) techniques to characterize different cells of a commercial PEM stack, identifying malfunctioning ones.

A Hybrid Path Planning Technique for the Time-Efficient Navigation of Unmanned Vehicles in an Unconstrained Environment

Optimal path planning refers to choosing a collision-free, shortest, and smooth path, which is achieved by proposing a new technique, Optimized A* (OA*). The proposed OA* is the hybrid version of three techniques that are ABA* (Adaptive Bidirectional A*), VTM (Vector triangulation method), and PSF (Path smoothing filter which is the modification of Bezier curve technique). The conventional A* only deal with the heuristic values of concerned nodes, but the proposed ABA* algorithm also considers the occupied cells/ obstacles present near the starting and goal nodes in the grid cells to select the shortest path from start to destination. Due to the involvement of the occupied cells, the execution time, path curves, and the number of operations is primarily reduced because occupied nodes are considered as closed nodes, and its heuristic calculation is not considered in the conventional method. The trajectory generated by the proposed ABA* technique is also further made smoother by implementing a path smoothing filter that is the combination of VTM and modified Bezier curve technique based on selecting optimal control point for the collision free trajectory. While implementing the proposed OA* makes the path trajectory become smoother by reducing the number of sharp turns by 85.36% w.r.t traditional ACO, 73.91% w.r.t improved ACO, and 64.70% w.r.t hybrid solution of ACO+ A*. Due to the reduction in the number of sharp turns in the path trajectory, the acceleration of the mobile robot is increased by 52.96% w.r.t traditional ACO, 28.63% w.r.t improved ACO, and 19.96% w.r.t hybrid solution of ACO+ A*.

Modeling of groundwater productivity in the Alfred Nzo District, South Africa, using relative frequency ratio and Shannon entropy models

Study regionAlfred Nzo District, Eastern Cape Province, South Africa. Study focusThe study aimed to identify groundwater potential zones in the Alfred Nzo District using advanced geographic information systems and remote sensing applications by integrating relative frequency ratio (RF) with predictor rate and Shannon entropy (SE) models. Seven influential factors that are thought to control groundwater availability in the Alfred Nzo District were considered to identify groundwater potential zones. These factors are drainage density, lineament density, rainfall, slope, land use and land cover, lithology, and soil class. The factors were weighted in the RF and SE approaches. About 70 % of boreholes were used as training datasets, and 30 % were used for validation. Groundwater potential zones were delineated by integrating all these factors and their corresponding weights in ArcGIS software. The accuracy of each model was evaluated using receiver operating characteristic curve and area under curve techniques. New hydrological insights for the regionFive groundwater potential zones were found in the research area, which includes very poor, poor, moderate, good, and very good, by RF and SE models. The results of the models revealed that the SE model, with a success rate of 79.80 %, performed better than the RF, with a success rate of 75.10 %. The results of this research could be helpful in adequately managing groundwater resources in the research area.

Validated Area Under Curve Quantitative UV Spectrophotometric Analysis of Rebamipide in Drug Formulations

A very simple, accurately perfect, precisely repeatable procedure has been proposed for the estimate of rebamipide in medicines. Evaluation of previous works indicates the fact that, so far no UV spectrophotometric scheme for quantifiable estimate by area under curve technique for the drug rebamipide is not described. Therefore, there was a need to plan a different methodology to analyze the medicine employing dimethyl formamide in the form of solvent. This medication monitors Beer’s law in the range of concentration 20 to 200 ìg/mL in the ultraviolet range, particularly areas between 320 and 340 nm for are under curve because the absorption maximum was found to be 330 nm in the selected solvent. The recovery readings proved the offered technique’s accuracy and outcomes were authenticated par accordance with the International Council for Harmonisation (ICH) references. The discoveries were reasoned to be consistent as well as agreeable. As a consequence, this optional technique was successfully used to estimate rebamipide quantitatively in conventional analytical applications.

Hagedorn temperature from the thermal scalar in AdS and pp-wave backgrounds

We propose a thermal scalar equation of motion (EOM) that takes into account curvature corrections for backgrounds supported by Ramond-Ramond fluxes. This can be used to obtain the Hagedorn temperature for type II string theory on AdS and pp-wave backgrounds. For Ramond-Ramond flux supported pp-waves we show that the proposed thermal scalar EOM reproduces the leading curvature correction in the Hagedorn temperature equation obtained from the type II string theory spectrum. Furthermore, we use the thermal scalar EOM to obtain curvature corrections to the Hagedorn temperature for the AdS5 × S5 and AdS4 × ℂP3 backgrounds. These corrections match with strong coupling results of the integrable dual field theories, recently obtained by the Quantum Spectral Curve technique.

Characterization, inhibition efficiency and DFT simulation of 1,2-bis(2-bromobenzylidene)hydrazine towards aluminum 1050 in H2SO4 1M

In this study, 1,2-bis(2-bromobenzylidene)hydrazine (BBBH) has been synthesized and for the first time its corrosion inhibition efficiency was studied for pure aluminum corrosion in 1M sulfuric acid using polarization curve technique. After treatment of experimental data at 298 K, the results revealed that the compound is a good inhibitor and the efficiency increase with its concentration, to attain 83.23 % for 600 ppm. The adsorption process of this Schiff base on aluminum surface obeys Langmuir isotherm. To complete the inhibition study, thermodynamic parameters including activation energy, Gibbs free energy, were discussed. In order to characterize the Aluminum surface, scanning electron microscopy (SEM), DRX and X-ray photoelectron XPS were used to demonstrate the effect of the inhibitor and the formation of a protective layer. The molecular electronic structure of the title compound was established using the density functional theory (DFT) approach to understand molecular geometry, stability, and chemical reactivity. The FTIR, UV, 1H-NMR and Raman spectroscopy were further applied to examine its purity.

Landslide susceptibility mapping of a hilly region through a semi-quantitative technique

ABSTRACT Characterization of landslide susceptibility carries great importance in hilly regions as it is one of the frequently occurring natural hazards which comes with huge destruction of life as well as property. Identification of such landslide susceptible zones is usually decided based on the necessity and available resources. It is considered a valuable input for planning developmental activities by policymakers. The current study focuses on Pabbar Catchment, located in the state of Himachal Pradesh in India and prepares a Landslide Susceptibility Map (LSM) at a catchment scale using Analytical Hierarchy Approach (AHP). Eleven geo-morphological attributes of the catchment called causative factors were used in thematic form while creating the LSM. Being a semi-quantitative method, AHP performed satisfactorily to produce a realistic distribution of landslide susceptibility with a prediction accuracy of 0.768 under the Area Under Curve (AUC) technique. Approximately 72% of the geographical area from the catchment falls under the ‘high’ landslide susceptible zone. Unavailability of LSM at this scale is addressed through this study that may be useful to regional planners for future land use planning.

Behaviour Analysis of Modeling and Model Evaluating Methods in System Identification for a Multiprocess Station

Systems are designed to perform specific task by giving certain input which produces the required output in an orderly manner known as process. The input, output, and the state variables should be known that will help in interacting with the system. The relation between these variables can be brought out by building a model that resembles or expresses the original performance of the system. The parameters of the model are estimated using the least squares approximation, maximum likelihood, maximum log-likelihood, and Bayesian parameter estimation methods by utilizing the experimental data from the multiprocess station. The selected parameters are converted to nine different transfer function models that represent the given dynamic system. The models framed are analyzed by the criterion curve technique using seven criterion functions evaluating the fitness of the model. Order of the model is found from Hankel matrix representation methods such as singular value decomposition and determinant method. Response of the models is compared with the original response to choose the best fit model by calculating ISE standard. All the above methods are used to model the system without physical and theoretical laws which is known as system identification.

Landslide susceptibility modeling in the Kulfo river catchment, rift valley, Ethiopia: An integrated geospatial and statistical analysis

Landslides occur when debris, rocks, or soil particles move downward. Examining the susceptibility of landslides is essential for safeguarding human well-being and assessing the consequences of landslides on the natural surroundings and ecosystems. This study utilized the frequency ratio technique to evaluate the probability of landslides happening in the Kulfo River watershed, situated in the Rift Valley area of Ethiopia. In order to ensure a comprehensive analysis, many data sources were employed, including satellite images, geological data, and historical records of landslides. This study developed a systematic approach to assess the probability of landslides by considering ten (10) influential factors: land use/land cover, slope, aspect, elevation, curvature, lithology, proximity to lineament, and normalized difference vegetation index (NDVI). The ten influential elements were prioritised based on literature review, expert knowledge, and preliminary study area analysis. The aforementioned causal elements are integrated with a comprehensive landslide inventory map. The sensitivity of the study's area to landslides was mapped using a frequency ratio (FR) model. Subsequently, it was classified as representing various degrees of vulnerability, spanning from extremely minimal to quite significant. The effectiveness of the suggested model was measured by evaluating the accuracy of the generated map of landslide susceptibility by the area under the curve (AUC) technique. Based on the most recent study results, the success rate curve has an area under the curve (AUC) of 79.6%, which indicates a highly satisfactory level of performance. Policymakers may utilize the findings of this study to make educated decisions on how to mitigate the risks of landslides in relation to land use and preparedness for disasters.

Assessing classification system for landslide susceptibility using frequency ratio, analytical hierarchical process and geospatial technology mapping in Aizawl district, NE India

Landslides are a severe geological hazard in northeastern India, particularly in Mizoram’s Aizawl district. To assess this hazard, we conducted a landslide susceptibility mapping study using the frequency ratio (FR) and analytical hierarchy process (AHP) methods. Twelve factors, including slope, elevation, aspect, curvature, topographic wetness index, topographic ruggedness index, rainfall erosivity factor, geology, land use land cover, normalized difference vegetation index (NDVI), and distance to road, were considered. The final susceptibility maps were created by combining these factors, each weighted according to its importance. Since no standard classification guidelines existed, we compared different systems like GIS-based geometric interval (GIC), natural breaks (NBC), and quantile interval (QIC) with mean-standard deviation (MSC). The area was divided into five susceptibility zones: very high, high, moderate, low, and very low. We evaluated the accuracy of these systems using success rate curve (SRC) and quality sum ratio (Qs) techniques. SRC results showed similar performance for GIC, NBC, QIC, and MSC classifications. The AHP method also yielded comparable results. After analyzing the AUC under SRC and Qs, we selected the MSC classification system as the optimal approach for landslide susceptibility mapping. Notably, the distance to roads factor had the highest predictive value, highlighting its importance in identifying slope instability for effective management strategies. As landslides pose significant risks in northeastern India, and our study using FR and AHP methods, along with MSC classification, provides valuable insights for mitigating this geological hazard.

On a theorem by Schlenk

In this paper we prove a generalisation of Schlenk’s theorem about the existence of contractible periodic Reeb orbits on stable, displaceable hypersurfaces in symplectically aspherical, geometrically bounded, symplectic manifolds, to a forcing result for contractible twisted periodic Reeb orbits. We make use of holomorphic curve techniques for a suitable generalisation of the Rabinowitz action functional in the stable case in order to prove the forcing result. As in Schlenk’s theorem, we derive a lower bound for the displacement energy of the displaceable hypersurface in terms of the action value of such periodic orbits. The main application is a forcing result for noncontractible periodic Reeb orbits on quotients of certain symmetric star-shaped hypersurfaces. In this case, the lower bound for the displacement energy is explicitly given by the difference of the two periods. This theorem can be applied to many physical systems including the Hénon–Heiles Hamiltonian and Stark–Zeeman systems. Further applications include a new proof of the well-known fact that the displacement energy is a relative symplectic capacity on R2n\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${\\mathbb {R}}^{2n}$$\\end{document} and that the Hofer metric is indeed a metric.

Integrating Traditional Machine Learning and Deep Learning for Precision Screening of Anticancer Peptides: A Novel Approach for Efficient Drug Discovery.

The rapid and effective identification of anticancer peptides (ACPs) by computer technology provides a new perspective for cancer treatment. In the identification process of ACPs, accurate sequence encoding and effective classification models are crucial for predicting their biological activity. Traditional machine learning methods have been widely applied in sequence analysis, but deep learning provides a new approach to capture sequence complexity. In this study, a two-stage ACPs classification model was innovatively proposed. Three novel coding strategies were explored; two mainstream Natural Language Processing (NLP) models and 11 machine learning models were fused to identify ACPs, which significantly improved the prediction accuracy of ACPs. We analyzed the correlation between peptide chain amino acids and evaluated the relevant performance of the model by the ROC curve and t-SNE dimensionality reduction technique. The results indicated that the deep learning and machine learning fusion models of M3E-base and KNeighborsDist models, especially when considering the semantic information on amino acid sequences, achieved the highest average accuracy (AvgAcc) of 0.939, with an AUC value as high as 0.97. Then, in vitro cell experiments were used to verify that the two ACPs predicted by the model had antitumor efficacy. This study provides a convenient and effective method for screening ACPs. With further optimization and testing, these strategies have the potential to play an important role in drug discovery and design.

Influence of the modal damping ratio calculation method in the analysis of dynamic events obtained in structural health monitoring of bridges

This paper includes a review of the several calculation methods available to calculate the modal damping ratio from dynamic events obtained in structural health monitoring of bridges. A comparative analysis among methods involving the logarithmic decrement, bandwidth applied over the spectrum, adjustment to the theoretical curve and random decrement technique is included. Additionally, an alternative calculation method based on signal spectra reduction has been formulated, whose main advantage lies in the fact that it directly allows the study of multiple-degrees-of-freedom systems, which describe typical structures, without the need for applying filters; this method gives extremely precise values despite simplification of the calculations. First, a comparative analysis is carried out on a set of theoretically simulated waves. In these cases, although there are differences in detail, the obtained results are reasonably close to the theoretical values. Second, an analysis of the registered accelerograms during a test campaign of a real structure, the viaduct “The Arches of Alconétar”, is performed. It has been observed that in this case, unlike what happens with the theoretical simulations, the obtained results significantly vary depending on the selected calculation method. For this reason, it is important to know which method has been used to calculate the modal damping ratio in structural health monitoring systems, as well as to be cautious in setting thresholds.