Parametric Model Research Articles

A comprehensive analytical and computational assessment of soil water characteristics curves in Atlantic Canada: Application of a novel SelectKbestbased GEP model

For sustainable agriculture, irrigation is one of the most important management practices in a semi-arid climate. Determining the site-specific soil type-based irrigation water requirements requires the development of a soil water characteristics curve (SWCC). This study focuses on exploring the suitability of analytical SWCC models based on the observed data of SWCC developed using the pressure membrane apparatus. A novel SelectKbest gene expression programming (GEP) model is used to create the generalized machine learning SWCC model for Atlantic Canadian soils. The composite soil samples with representative soil textures of loamy sand, loam, and sandy loam are collected from the selected locations in Prince Edward Island and New Brunswick provinces of Canada. The suitability of models at three regions shows that at all sites, including PEI-1 (loam soil) 1, PEI-2 (loamy sand), and NB-3 (sandy loam) if SWCC data has four measured suction potential levels, then McBee & Bomb and Tani exponential models may be selected. Van Genuchten, double exponential, Ruso, Kosuki, and Gardner's four parameters models should be a good choice for five-section potential. Similarly, for six measured suction potential values, the Fredlund & Xing, Omuto, and Van Genuchten models having parameters should be selected for determining the optimum soil hydraulic parameters. The HydroMe program with Brooks and Corey, four, and five parameters Van Genuchten models performed well. The HydroMe program is calibrated and validated with standard RETC for selected regions of Atlantic Canada, which suggests that the HydroMe program is flexible in developing efficient agricultural water management algorithms. Various performance metrics like R2 and RMSE are employed to assess the robustness of the model. A generalized SWCC model (R2=0.976 and RMSE=0.04) is developed using a novel SelectKbest-based GEP modeling approach for Atlantic Canadian soils. These novel approaches can significantly improve site-specific agricultural water management and decision support systems.

Deep learning-based harmonization and super-resolution of Landsat-8 and Sentinel-2 images

Multi-spectral satellite images of the Earth’s surface are used in various applications, from water quality assessment and urban planning to climate monitoring, disaster response, infrastructure oversight, and agricultural surveillance. Many of these applications would benefit from higher spatial and temporal resolution of observations, which could be achieved by combining observations from several sources. This study introduces a deep learning-based pipeline to harmonize the spectral and spatial discrepancies between the Landsat-8 and Sentinel-2 Earth Observation satellites. Through established image quality metrics, we demonstrate a significant enhancement in the spatial resolution of Landsat-8 images. Field observation experiments show that leveraging unified images from both satellites increases the availability of cloud-free images by 21% annually on average in our study area. Additionally, our pipeline enhances the Normalized Difference Vegetation Index (NDVI) correlation between Landsat-8 and Sentinel-2 observations by about 4.9%, offering significant performance gains in a downstream crop segmentation task. Our 100M parameter model, trained on European data, generalizes to most regions with only minor limitations. Furthermore, we show that the pipeline can provide uncertainty estimates for its outputs, which are valuable for decision-making in downstream applications. Our code base is available online: https://github.com/venkatesh-thiru/Deep-Harmonization.

Statistical analysis on the incidence and predictors of death among second-line ART patients in public hospitals of North Wollo and Waghemira Zones, Ethiopia, 2021

Acquired immune deficiency virus, caused by the human immunodeficiency virus, is a significant global health concern. Sub-Saharan Africa particularly Ethiopia faces a high prevalence of human immunodeficiency virus. In low-income settings like Ethiopia, early mortality rates are elevated due to severe opportunistic infections and advanced disease at Anti-retroviral treatment initiation. Despite available treatments, delayed treatment initiation among Human Immunodeficiency Virus -infected individuals in Africa, including Ethiopia, leads to disease progression and increased mortality risk. This study aimed to identify the factors contributing to the death of HIV patients under treatment at second line regimen in public hospitals of North Wollo and Waghemira Zones. A retrospective cohort study with 474 patients was conducted in selected hospitals of North Wollo and Waghemira Zones. A parametric Weibull regression model was employed, and the adjusted hazard ratio served as the measure of association. Variables significantly affected the outcome of the study was determined at a p-value < 0.05, along with a 95% confidence interval for the variables. The patients were within the average age of 38.6(standard deviation ± 12.5) years and majority (45.57%) had no formal education. The overall death incidence rate among second-line anti-retroviral treatment patients was 1.98 per 100-person years [95% CI 1.4—2.9%]. Poor adherence to antiretroviral treatment, male gender, and being underweight significantly increased the hazard of death. Conversely, increased anti-retroviral treatment duration had a significant and negative impact, reducing the hazard of death among patients. The study reveals a high incidence of death among second line anti-retroviral treatment users. Independent predictors include poor adherence, male gender, and underweight status, all significantly increasing the risk of death. On the positive side, the hazard of death decreases with longer anti-retroviral treatment duration. A critical concern and counseling should be given for better ART adherence, to change their nutritional status and for males.

MRI-based intratumoral and peritumoral radiomics for preoperative prediction of glioma grade: a multicenter study

BackgroundAccurate preoperative prediction of glioma is crucial for developing individualized treatment decisions and assessing prognosis. In this study, we aimed to establish and evaluate the value of integrated models by incorporating the intratumoral and peritumoral features from conventional MRI and clinical characteristics in the prediction of glioma grade.MethodsA total of 213 glioma patients from two centers were included in the retrospective analysis, among which, 132 patients were classified as the training cohort and internal validation set, and the remaining 81 patients were zoned as the independent external testing cohort. A total of 7728 features were extracted from MRI sequences and various volumes of interest (VOIs). After feature selection, 30 radiomic models depended on five sets of machine learning classifiers, different MRI sequences, and four different combinations of predictive feature sources, including features from the intratumoral region only, features from the peritumoral edema region only, features from the fusion area including intratumoral and peritumoral edema region (VOI-fusion), and features from the intratumoral region with the addition of features from peritumoral edema region (feature-fusion), were established to select the optimal model. A nomogram based on the clinical parameter and optimal radiomic model was constructed for predicting glioma grade in clinical practice.ResultsThe intratumoral radiomic models based on contrast-enhanced T1-weighted and T2-flair sequences outperformed those based on a single MRI sequence. Moreover, the internal validation and independent external test underscored that the XGBoost machine learning classifier, incorporating features extracted from VOI-fusion, showed superior predictive efficiency in differentiating between low-grade gliomas (LGG) and high-grade gliomas (HGG), with an AUC of 0.805 in the external test. The radiomic models of VOI-fusion yielded higher prediction efficiency than those of feature-fusion. Additionally, the developed nomogram presented an optimal predictive efficacy with an AUC of 0.825 in the testing cohort.ConclusionThis study systematically investigated the effect of intratumoral and peritumoral radiomics to predict glioma grading with conventional MRI. The optimal model was the XGBoost classifier coupled radiomic model based on VOI-fusion. The radiomic models that depended on VOI-fusion outperformed those that depended on feature-fusion, suggesting that peritumoral features should be rationally utilized in radiomic studies.

Auxetic fixation devices can achieve superior pullout performances compared to standard fixation concepts

Despite bone screws being the most commonly inserted implant in orthopaedic surgery, 10% of fracture fixation failure is a result of screw migration or pullout. In this study, the effect of four auxetic structures on the pullout performance of a novel unthreaded bone fastener was investigated through experiments and numerical simulations. The auxetic fasteners included the re-entrant, rotating squares, missing rib, and tetrachiral structures. Parametric CAD models were developed for each, and polymer samples manufactured using a stereolithography process. Pullout testing using bone analogue material found the rotating squares fastener to achieve superior pullout resistance 2.5 times that of the non-auxetic control sample. With a pullout to push-in force ratio of 33.7, this fastener achieved high pullout resistance with a low insertion force improving ease of installation. The Poisson’s ratio of the structure was determined using image analysis to be −1.31, similar to the missing rib and re-entrant types. The low axial stiffness of 12.1 N mm−1 for the rotating squares fastener was the reason for superior performance, allowing axial and resulting transverse strain to be initiated at relatively low load. The effect of increased diametral interference was investigated, and the re-entrant structure found to be superior with pullout resistance improved by 342%. This work provides a foundation for further development of unthreaded auxetic bone fasteners, which have the potential to replace screws for some orthopaedic applications and significantly reduce the prevalence of pullout as a failure mode.

Hand Vibration Reduction Using Nonlinear Vibration Absorber for the Vibro-Impact Hammer Model

Abstract This work investigates the effectiveness of a passive vibration control device to minimize the amplitude of vibrations from a hand-held impact machine (HHIM) transmitted to a hand-arm system (HAS). For this purpose, we employ a simplified lumped parameter model of an HHIM and the hand-arm system consisting of lumped masses, springs, and viscous dampers. For a better understanding of the system dynamics, we use a vibro-impact model for a more accurate representation of HHIM–ground interactions. We then examine the effectiveness of a cubic nonlinear vibration absorber in minimizing these undesirable vibrations via a nonlinear bifurcation analysis of the system. A parametric study on the numerical bifurcation analysis of the system reveals the criticality of different design parameters of the proposed nonlinear vibration absorber on the system dynamics. Our findings demonstrate that an appropriate selection of absorber parameters could significantly change the amplitude and the periodicity of vibrations transmitted to the hand. Furthermore, we observe the existence of complex motions such as period-2, period-4, and chaotic attractors in the system for the given values of the absorber and operating parameters.

High-Voltage Cable Buffer Layer Ablation Fault Identification Based on Artificial Intelligence and Frequency Domain Impedance Spectroscopy

In recent years, the occurrence of high-voltage cable buffer layer ablation faults has become frequent, posing a serious threat to the safe and stable operation of cables. Failure to promptly detect and address such faults may lead to cable breakdowns, impacting the normal operation of the power system. To overcome the limitations of existing methods for identifying buffer layer ablation faults in high-voltage cables, a method for identifying buffer layer ablation faults based on frequency domain impedance spectroscopy and artificial intelligence is proposed. Firstly, based on the cable distributed parameter model and frequency domain impedance spectroscopy, a mathematical model of the input impedance of a cable containing buffer layer ablation faults is derived. Through a simulation, the input impedance spectroscopy at the first end of the cables under normal conditions, buffer layer ablation, local aging, and inductive faults is performed, enabling the identification of inductive and capacitive faults through a comparative analysis. Secondly, the frequency domain amplitude spectroscopy of the buffer layer ablation and local aging faults are used as datasets and are input into a neural network model for training and validation to identify buffer layer ablation and local aging faults. Finally, using multiple evaluation metrics to assess the neural network model validates the superiority of the MLP neural network in cable fault identification models and experimentally confirms the effectiveness of the proposed method.

Undrained cavity expansion-contraction analysis in CASM and its application for pressuremeter tests

Many geotechnical scenarios involve cavity unloading from a loaded state, particularly in pressuremeter tests, and the unloading data of pressuremeter tests has exceptional attraction as it is less disturbed by the insertion process. However, the analyses for continuous cavity loading and unloading (i.e., cavity initially experiences expansion and then contracts) in critical state soils are rarely studied. To this end, a novel semi-analytical solution based on the unified state parameter model for clay and sand (CASM) is proposed for the whole expansion-contraction of spherical and cylindrical cavities under undrained condition. The problem assumes that the cavity is unloaded after a monotonic loading stage, leading to plastic regions during both loading and unloading periods. The cavity response for the whole expansion-contraction process is investigated, with the total pressure and stress paths at the cavity wall presented and validated against numerical simulation. The developed solution is successfully implemented to interpret both loading and unloading data of pressuremeter tests. The undrained shear strength, in-situ effective horizontal stress and initial overconsolidation ratio are back analyzed by using a curve fitting method based on the proposed solution.

Experimental and theoretical investigation on magnetorheological elastomers containing carbonyl iron particles coated with silane coupling agent

Abstract Magnetorheological (MR) elastomer composites, comprising soft silicone rubber, various additives, and different weight fractions of carbonyl iron particles (CIPs) coated with silane coupling agent, are produced via a novel manufacturing process in an anisotropic state. This study encompasses both experimental and modeling investigations into the dynamic viscoelastic properties of MREs in shear mode under varying magnetic fields, displacement amplitudes, and frequencies. Two MRE vibration mitigation devices are fabricated to experimentally assess the shear storage modulus and the loss factor of MREs. The experimental findings reveal a pronounced MR effect in the MRE devices, where both the shear storage modulus and the loss factor increase with rising magnetic fields, frequencies, and particle weight fractions, yet decrease with higher displacement amplitudes. A modified fractional-derivative equivalent parametric model, grounded in a magnetic field- and frequency- dependent shear modulus model along with internal variable theory, is proposed to describe the effects of these key influencing factors on the MREs' dynamic viscoelastic properties. Comparative analysis of experimental and numerical data demonstrates that this refined mathematical model can accurately represent the dynamic viscoelastic properties of MREs.

Sandwich boosting for accurate estimation in partially linear models for grouped data

Abstract We study partially linear models in settings where observations are arranged in independent groups but may exhibit within-group dependence. Existing approaches estimate linear model parameters through weighted least squares, with optimal weights (given by the inverse covariance of the response, conditional on the covariates) typically estimated by maximizing a (restricted) likelihood from random effects modelling or by using generalized estimating equations. We introduce a new ‘sandwich loss’ whose population minimizer coincides with the weights of these approaches when the parametric forms for the conditional covariance are well-specified, but can yield arbitrarily large improvements in linear parameter estimation accuracy when they are not. Under relatively mild conditions, our estimated coefficients are asymptotically Gaussian and enjoy minimal variance among estimators with weights restricted to a given class of functions, when user-chosen regression methods are used to estimate nuisance functions. We further expand the class of functional forms for the weights that may be fitted beyond parametric models by leveraging the flexibility of modern machine learning methods within a new gradient boosting scheme for minimizing the sandwich loss. We demonstrate the effectiveness of both the sandwich loss and what we call ‘sandwich boosting’ in a variety of settings with simulated and real-world data.

Chest CT‐based automated vertebral fracture assessment using artificial intelligence and morphologic features

AbstractBackgroundSpinal degeneration and vertebral compression fractures are common among the elderly that adversely affect their mobility, quality of life, lung function, and mortality. Assessment of vertebral fractures in chronic obstructive pulmonary disease (COPD) is important due to the high prevalence of osteoporosis and associated vertebral fractures in COPD.PurposeWe present new automated methods for (1) segmentation and labelling of individual vertebrae in chest computed tomography (CT) images using deep learning (DL), multi‐parametric freeze‐and‐grow (FG) algorithm, and separation of apparently fused vertebrae using intensity autocorrelation and (2) vertebral deformity fracture detection using computed vertebral height features and parametric computational modelling of an established protocol outlined for trained human experts.MethodsA chest CT‐based automated method was developed for quantitative deformity fracture assessment following the protocol by Genant et al. The computational method was accomplished in the following steps: (1) computation of a voxel‐level vertebral body likelihood map from chest CT using a trained DL network; (2) delineation and labelling of individual vertebrae on the likelihood map using an iterative multi‐parametric FG algorithm; (3) separation of apparently fused vertebrae in CT using intensity autocorrelation; (4) computation of vertebral heights using contour analysis on the central anterior‐posterior (AP) plane of a vertebral body; (5) assessment of vertebral fracture status using ratio functions of vertebral heights and optimized thresholds. The method was applied to inspiratory or total lung capacity (TLC) chest scans from the multi‐site Genetic Epidemiology of COPD (COPDGene) (ClinicalTrials.gov: NCT00608764) study, and the performance was examined (n = 3231). One hundred and twenty scans randomly selected from this dataset were partitioned into training (n = 80) and validation (n = 40) datasets for the DL‐based vertebral body classifier. Also, generalizability of the method to low dose CT imaging (n = 236) was evaluated.ResultsThe vertebral segmentation module achieved a Dice score of .984 as compared to manual outlining results as reference (n = 100); the segmentation performance was consistent across images with the minimum and maximum of Dice scores among images being .980 and .989, respectively. The vertebral labelling module achieved 100% accuracy (n = 100). For low dose CT, the segmentation module produced image‐level minimum and maximum Dice scores of .995 and .999, respectively, as compared to standard dose CT as the reference; vertebral labelling at low dose CT was fully consistent with standard dose CT (n = 236). The fracture assessment method achieved overall accuracy, sensitivity, and specificity of 98.3%, 94.8%, and 98.5%, respectively, for 40,050 vertebrae from 3231 COPDGene participants. For generalizability experiments, fracture assessment from low dose CT was consistent with the reference standard dose CT results across all participants.ConclusionsOur CT‐based automated method for vertebral fracture assessment is accurate, and it offers a feasible alternative to manual expert reading, especially for large population‐based studies, where automation is important for high efficiency. Generalizability of the method to low dose CT imaging further extends the scope of application of the method, particularly since the usage of low dose CT imaging in large population‐based studies has increased to reduce cumulative radiation exposure.

A Neural Network Approach for Software Reliability Prediction

The increasing reliance on computer software has raised significant concerns regarding software reliability evaluation. Over the past four decades, various software reliability models have been developed, encompassing both parametric and nonparametric approaches. However, no single model has demonstrated effectiveness in handling all types of datasets. In response to this challenge, the deep neural network (DNN), a powerful deep learning model, has emerged as a promising solution. By leveraging the flexibility and adaptability of artificial neural networks (ANN), the DNN model exhibits remarkable prediction performance by capturing training variables and exploring deeper layers. This study presents an approach that utilizes a DNN model based on an ANN architecture with multiple activation functions. This approach aims to estimate software reliability and predict the frequency of software flaws. Through extensive experimental analysis and validation, the proposed DNN model surpasses the predictive accuracy achieved by existing parametric and nonparametric models. These results highlight the potential of the DNN model in effectively addressing the complexities involved in software reliability evaluation. By combining the power of deep learning with the adaptability of ANN, the proposed model offers a promising and accurate solution for software reliability prediction.

Rate-dependent energy dissipation of graded viscoelastic structures fabricated by grayscale vat photopolymerization

A major benefit of additive manufacturing technologies is precise control over structural topologies and material properties, which allows to tailor, for instance, energy absorption and dissipation. While vat photopolymerization is generally restricted to a single material, grayscale masked stereolithography (gMSLA) allows to customize material behavior by grading the light intensity within a structure. This study investigates the impact and opportunities of grayscale grading strategies on the rate-dependent mechanical behavior of structures fabricated by gMSLA. Considering the viscoelastic nature of polymers, rate-dependent energy dissipation is explored, introducing a parametric linear viscoelastic constitutive model for varying grayscales. The investigation includes the comprehensive characterization of mechanical properties, numerical finite element simulation, validation through experimental procedures, and exploration of dissipation energy under different strain rates. In this way, a rational function successfully determines the critical strain rate at which the maximum dissipation occurs. Overall, the research offers a comprehensive investigation of the mechanical dissipation behavior of graded 3D printed structures, laying the foundation for further studies and advancements aimed at optimizing these structures for enhanced energy absorption capabilities.

Weibull parametric model for survival analysis in women with endometrial cancer using clinical and T2-weighted MRI radiomic features

BackgroundSemiparametric survival analysis such as the Cox proportional hazards (CPH) regression model is commonly employed in endometrial cancer (EC) study. Although this method does not need to know the baseline hazard function, it cannot estimate event time ratio (ETR) which measures relative increase or decrease in survival time. To estimate ETR, the Weibull parametric model needs to be applied. The objective of this study is to develop and evaluate the Weibull parametric model for EC patients’ survival analysis.MethodsTraining (n = 411) and testing (n = 80) datasets from EC patients were retrospectively collected to investigate this problem. To determine the optimal CPH model from the training dataset, a bi-level model selection with minimax concave penalty was applied to select clinical and radiomic features which were obtained from T2-weighted MRI images. After the CPH model was built, model diagnostic was carried out to evaluate the proportional hazard assumption with Schoenfeld test. Survival data were fitted into a Weibull model and hazard ratio (HR) and ETR were calculated from the model. Brier score and time-dependent area under the receiver operating characteristic curve (AUC) were compared between CPH and Weibull models. Goodness of the fit was measured with Kolmogorov-Smirnov (KS) statistic.ResultsAlthough the proportional hazard assumption holds for fitting EC survival data, the linearity of the model assumption is suspicious as there are trends in the age and cancer grade predictors. The result also showed that there was a significant relation between the EC survival data and the Weibull distribution. Finally, it showed that Weibull model has a larger AUC value than CPH model in general, and it also has smaller Brier score value for EC survival prediction using both training and testing datasets, suggesting that it is more accurate to use the Weibull model for EC survival analysis.ConclusionsThe Weibull parametric model for EC survival analysis allows simultaneous characterization of the treatment effect in terms of the hazard ratio and the event time ratio (ETR), which is likely to be better understood. This method can be extended to study progression free survival and disease specific survival.Trial registrationClinicalTrials.gov NCT03543215, https://clinicaltrials.gov/, date of registration: 30th June 2017.

Governing accelerating Universe via newly reconstructed Hubble parameter by employing empirical data simulations

A new parametrization of the Hubble parameter is proposed to explore the issue of the cosmological landscape. The constraints on model parameters are derived through the Markov Chain Monte Carlo (MCMC) method by employing a comprehensive union of datasets such as 34 data points from cosmic chronometers (CC), 42 points from baryonic acoustic oscillations (BAO), a recently updated set of 1701 Pantheon+ (P22) data points derived from Type Ia supernovae (SNeIa), and 162 data points from gamma-ray bursts (GRBs). Furthermore, the models are compared by using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC), so that a comparative assessment of model performance can be available. Additionally, we compare the Dainotti relation via Gaussian likelihood analysis versus new likelihoods and Calibration of the Dainotti relation through a model-independent method. The kinematic behavior of the models is also investigated by encompassing the transition from deceleration to acceleration and the evolution of the jerk parameter. From the analysis of the parametric models, it is strongly indicated that the Universe is currently undergoing an accelerated phase with diagnostics of the model validating the quintessence phase.

Transmissibility-based operational modal analysis: A unified scheme and uncertainty quantification

The transmissibility-based Operational Modal Analysis (OMA) technique has attracted substantial research interest over the past two decades due to its capability to extract structural modal parameters in the presence of non-white excitation. The transmissibility function used in OMA can be categorized as Response Transmissibility (RT) and Power Spectral Density Transmissibility (PSDT). Modal parameters can be identified by combining RTs from multiple load cases or PSDTs from multiple transfer outputs. Nevertheless, the mathematical formulations and implementation procedures of the various RT-based and PSDT-based methods exhibit significant differences. Furthermore, the uncertainty associated with modal parameter estimates is rarely assessed in these methods. This work aims to propose a scheme that unifies various transmissibility-based OMA algorithms through a consistent parametric formulation. Within this scheme, a universal parametric model and a standardized implementation procedure are developed to accommodate two types of transmissibility in both time and frequency domains and six OMA algorithms; additionally, a generalized framework for quantifying the uncertainties associated with modal parameter estimates is provided. The proposed scheme is finally validated using a planar truss structure.

Computation of the Mann–Whitney Effect under Parametric Survival Copula Models

The Mann–Whitney effect is a measure for comparing survival distributions between two groups. The Mann–Whitney effect is interpreted as the probability that a randomly selected subject in a group survives longer than a randomly selected subject in the other group. Under the independence assumption of two groups, the Mann–Whitney effect can be expressed as the traditional integral formula of survival functions. However, when the survival times in two groups are not independent of each other, the traditional formula of the Mann–Whitney effect has to be modified. In this article, we propose a copula-based approach to compute the Mann–Whitney effect with parametric survival models under dependence of two groups, which may arise in the potential outcome framework. In addition, we develop a Shiny web app that can implement the proposed method via simple commands. Through a simulation study, we show the correctness of the proposed calculator. We apply the proposed methods to two real datasets.

Modelling Time to Recovery from Multidrug Resistant Tuberculosis in Southern Ethiopia

Introduction: Multidrug resistant tuberculosis (MDR-TB) is a global heath threat, resistant to key anti-TB drugs. It is ranked among the top 10 causes of death worldwide. Therefore, the current study investigated time to recovery from MDR-TB in southern Ethiopia. Data, Materials and Methods: Restrospective data from selected hospitals in SNNPR (January 2016 to December 2021) were analyzed. A cluster sample of 301 MDR-TB patients (131 NEMMCH, 121 BH, 49 AGH) was considered. Results: Among the 301 cases, 116 (38.5%) were censored. While 185 (61.5%) were recovered. Parametric shared frailty models were employed to account unobserved heterogeneity among the Hospitals and patients and AFT models were employed. the median recovery time of MDR-TB is 22 months. The clustering effect of frailty model was hospitals. Weibull-gamma shared frailty model was appropriate for this data. Conclusion: The final model showed that males have higher recovery rates than females. Extra pulmonary MDR-TB and Urban residency correleted with longer recovery times. The recovery rate increases with increasing baseline weight, education level, and occupation. But, the recovery rate decreases with smoking, co-morbidities, previous drug history, history of TB, and alcohol use Recommendation: All concerned bodies should be cognizant on the risk factors of MDR-TB in SNNP region By providing on early case detection and appropriate treatment of drug-susceptible MDR-TB, since it is essential to shorten the recovery time of MDR-TB patients in line with WHO guidelines

Labor differentiation and cotton productivity in Burkina Faso

AbstractAgriculture is a very important sector in Africa's economic development, particularly in Burkina Faso, as it employs a large proportion of the population. Given the importance of labor in this sector, a good allocation of the different types of labor could help increase agricultural productivity in Burkina Faso. This research contributes to the literature by determining the specific contributions of each type of labor in enhancing cotton productivity. The sample of this research is 477 cotton farms, and a semiparametric stochastic frontier model has been used in the analysis. The results show that the proportion of wage labor has a nonlinear effect and contributes to improving cotton productivity when the number of educated people in the household increases. But family labor decreases cotton productivity when the number of educated people in the household increase. The comparison between the findings of the semiparametric and parametric frontier shows that technical efficiency is 72.44% when education is used as the channel through which production factors affect cotton productivity. However, this technical efficiency is 54.96% when production factors directly affect cotton productivity in the parametric frontier model. Promoting education in rural areas will help to increase the number of people educated and consequently improve cotton productivity.

Evaluating head impact intensities and accelerations using flexible wearable sensors for traumatic brain injury assessment

In response to the significant health threat posed by Traumatic Brain Injury (TBI) resulting from sudden collisions on the head, there is an urgent need for timely assessment and evaluation. With the wide applications of wearable devices in health monitoring, here we report a flexible and wearable smart headband based on a polyvinylidene fluoride (PVDF) sensor to evaluate the impact force and acceleration for TBI assessment. Based on the electromechanical coupling characteristics of PVDF, a lumped parameters model of an equivalent circuit was obtained by a system identification method, which accurately transforms the output signal of the PVDF sensor to the impact force for TBI assessment of focal brain damage. Besides, a drop tower was used to impose different impact intensity levels on a dummy head wearing the smart headband; the results (MSE< 0.018) of transformed impact force show the universal applicability of the model. Moreover, the correlation analysis results show that the impact force has a positive correlation (R>85 %) with impact acceleration at the head center of gravity, which enables the TBI assessment of diffuse brain damage. This smart headband can be used for real-time monitoring of human head impact and has excellent potential for application in medical and sports devices.