Minimum Value Research Articles

Interpreting Change in Disorders of Consciousness Using the Coma Recovery Scale-Revised.

The purpose of this study was to differentiate clinically meaningful improvement or deterioration from normal fluctuations in patients with disorders of consciousness (DoC) following severe brain injury. We computed indices of responsiveness for the Coma Recovery Scale-Revised (CRS-R) using data from a clinical trial of 180 participants with DoC. We used CRS-R scores from baseline (enrollment in a clinical trial) and a 4-week follow-up assessment period for these calculations. To improve precision, we transformed ordinal CRS-R total scores (0-23 points) to equal-interval measures on a 0-100 unit scale using Rasch Measurement theory. Using the 0-100 unit total Rasch measures, we calculated distribution-based 0.5 standard deviation (SD) minimal clinically important difference, minimal detectable change using 95% confidence intervals, and conditional minimal detectable change using 95% confidence intervals. The distribution-based minimal clinically important difference evaluates group-level changes, whereas the minimal detectable change values evaluate individual-level changes. The minimal clinically important difference and minimal detectable change are derived using the overall variability across total measures at baseline and 4 weeks. The conditional minimal detectable change is generated for each possible pair of CRS-R Rasch person measures and accounts for variation in standard error across the scale. We applied these indices to determine the proportions of participants who made a change beyond measurement error within each of the two subgroups, based on treatment arm (amantadine hydrochloride or placebo) or categorization of baseline Rasch person measure to states of consciousness (i.e., unresponsive wakefulness syndrome and minimally conscious state). We compared the proportion of participants in each treatment arm who made a change according to the minimal detectable change and determined whether they also changed to another state of consciousness. CRS-R indices of responsiveness (using the 0-100 transformed scale) were as follows: 0.5SD minimal clinically important difference = 9 units, minimal detectable change = 11 units, and the conditional minimal detectable change ranged from 11 to 42 units. For the amantadine and placebo groups, 70% and 58% of participants showed change beyond measurement error using the minimal detectable change, respectively. For the unresponsive wakefulness syndrome and minimally conscious state groups, 54% and 69% of participants changed beyond measurement error using the minimal detectable change, respectively. Among 115 participants (64% of the total sample) who made a change beyond measurement error, 29 participants (25%) did not change state of consciousness. CRS-R indices of responsiveness can support clinicians and researchers in discerning when behavioral changes in patients with DoC exceed measurement error. Notably, the minimal detectable change can support the detection of patients who make a "true" change within or across states of consciousness. Our findings highlight that the continued use of ordinal scores may result in incorrect inferences about the degree and relevance of a change score.

Enhancing Ethiopian power distribution with novel hybrid renewable energy systems for sustainable reliability and cost efficiency

Economic development relies on access to electrical energy, which is crucial for society’s growth. However, power shortages are challenging due to non-renewable energy depletion, unregulated use, and a lack of new energy sources. Ethiopia’s Debre Markos distribution network experiences over 800 h of power outages annually, causing financial losses and resource waste on diesel generators (DGs) for backup use. To tackle these concerns, the present study suggests a hybrid power generation system, which combines solar and biogas resources, and integrates Superconducting Magnetic Energy Storage (SMES) and Pumped Hydro Energy Storage (PHES) technologies into the system. The study also thoroughly analyzes the current and anticipated demand connected to the distribution network using a backward/forward sweep load flow analysis method. The results indicate that the total power loss has reached its absolute maximum, and the voltage profiles of the networks have dropped below the minimal numerical values recommended by the Institute of Electrical and Electronics Engineers (IEEE) standards (i.e., 0.95–1.025 p.u.). After reviewing the current distribution network’s operation, additional steps were taken to improve its effectiveness, using metaheuristic optimization techniques to account for various objective functions and constraints. In the results section, it is demonstrated that the whale optimization algorithm (WOA) outperforms other metaheuristic optimization techniques across three important objective functions: financial, reliability, and greenhouse gas (GHG) emissions. This comparison is based on the capability of the natural selection whale optimization algorithm (NSWOA) to achieve the best possible values for four significant metrics: Cost of Energy (COE), Net Present Cost (NPC), Loss of Power Supply Probability (LPSP), and GHG Emissions. The NSWOA achieved optimal values for these metrics, namely 0.0812 €/kWh, 3.0017 × 106 €, 0.00875, and 7.3679 × 106 kg reduced, respectively. This is attributable to their thorough economic, reliability, and environmental evaluation. Finally, the forward/backward sweep load flow analysis employed during the proposed system’s integration significantly reduced the impact of new energy resources on the distribution network. This was evident in the reduction of total power losses from 470.78 to 18.54 kW and voltage deviation from 6.95 to 0.35 p.u., as well as the voltage profile of the distribution system being swung between 1 and 1.0234 p.u., which now comply with the standards set by the IEEE. Besides, a comparison of the cost and GHG emission efficiency of the proposed hybrid system with existing (grid + DGs) and alternative (only DGs) scenarios was done. The findings showed that, among the scenarios examined, the proposed system is the most economical and produces the least amount of GHG emissions.

Control of microstructure, defects and mechanical properties in direct energy deposited SS316L/Inconel 718 functionally graded material via mechanical vibration

In this study, a mechanical vibration assisted laser direct energy deposition was used to fabricate a functionally graded material composed of stainless steel 316L and Inconel 718. The effect of mechanical vibration on the microstructure, defects and mechanical properties of the prepared functionally graded material was studied. The results indicate that in the sample without the assistance of mechanical vibration, plenty of brittle Laves phase and NbC carbides form and induce the cracking in the specific compositional gradient range from 60 % stainless steel 316L/40 % Inconel 718 to 40 % stainless steel 316L/60 % Inconel 718. The Vickers hardness obtains a minimal value near the compositional region of 50 % stainless steel 316L/50 % Inconel 718. The tensile properties are terrible with an ultimate tensile strength of 309.7 MPa and an elongation of 7.8 %. In the sample with the assistance of mechanical vibration, the synchronous mechanical vibration could effectively restrain the cracks formation in the compositional gradient region by suppressing the brittle phases precipitation, weakening the dislocation aggregation and refining the grain size, and resultantly achieve decent tensile properties with an ultimate tensile strength of 642.6 MPa and an elongation of 22.4 % and significantly improve the deterioration of hardness in the compositional gradient region.

A waste hemp‐based biomass carbon aerogel with high fire‐resistance

AbstractTo enhance the efficient utilization of waste hemp rods, a kind of three‐dimensional (3D), mesh‐like lamellar structured hemp‐based biomass carbon aerogel (CA) was developed by freeze‐drying and subsequent high‐temperature carbonization method. The microstructure, flame resistance, thermal stability, and thermal conductivity of CA were investigated using several analytical techniques, including scanning electron microscopy (SEM), Fourier‐transform infrared spectroscopy (FTIR), X‐ray diffraction (XRD), simulated combustion tests, and thermogravimetric analysis (TG). Optimum formulation yielded at an alkali treatment concentration of 20% and a carbonization temperature of 700°C, CA with a 3D network of reticulated lamellae was obtained. CA exhibited minimal values for thermal conductivity (0.06 W/m K) and thermal diffusion coefficient (0.41 mm2/s), which are indicative of superior insulative properties. Moreover, the material demonstrated remarkable thermal endurance and flame resistance, achieving a char yield of 70.03% when subjected to a temperature of 700°C in an oxidative environment.

Feasibility Verification and Upper Bound Computation in Global Minimization Using Approximate Active Index Sets

We propose a new upper bounding procedure for global minimization problems with continuous variables and possibly nonconvex inequality and equality constraints. Upper bounds are crucial for standard termination criteria of spatial branch-and-bound (SBB) algorithms to ensure that they can enclose globally minimal values sufficiently well. However, whereas for most lower bounding procedures from the literature, convergence on smaller boxes is established, this does not hold for several methods to compute upper bounds even though they often perform well in practice. In contrast, our emphasis is on the convergence. We present a new approach to verify the existence of feasible points on boxes, on which upper bounds can then be determined. To this end, we resort to existing convergent feasibility verification approaches for purely equality and box constrained problems. By considering carefully designed modifications of subproblems based on the approximation of active index sets, we enhance such methods to problems with additional inequality constraints. We prove that our new upper bounding procedure finds sufficiently good upper bounds so that termination of SBB algorithms is guaranteed after a finite number of iterations. Our theoretical findings are illustrated by computational results on a large number of standard test problems. These results show that compared with interval Newton methods from the literature, our proposed method is more successful in feasibility verification for both, a full SBB implementation (42 instead of 26 test problems) and exhaustive sequences of boxes around known feasible points (120 instead of 29 test problems). History: Accepted by Antonio Frangioni, Area Editor for Design & Analysis of Algorithms–Continuous. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0162 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0162 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Behavior of froth in absence and presence of particles

This work aims to study the effect of entrained and captured particles on the properties of the froth. It was established that the entrained particles act as froth suppressors, while captured particles act as froth boosters. The average liquid hold up of froths containing entrained particles increases with the increase of the gas flow rate. On the contrary, the froths with mostly captured particles exhibit the opposite dependence. Our study on the liquid hold up on the top of the froth versus the gas flow rate show that the froth with entrained particles increases its liquid hold up on the top until reaching a maximum, beyond which a decrease is observed. Surprisingly, the froth with mostly captured particles shows different behavior – its liquid hold up on the top practically does not depend on the gas flow rate, yet it can be seen at a certain minimal value. Overall, this work reports than entrained and captured particles cause substantiall different and even opposite effects on the froth stability and the liquid hold of the flotation froth.

Nondestructive assessment of ZnO surge arrester operating condition based on time-domain dielectric characteristics

Given the expansion of the economy and the rise in electricity demand, it is imperative to ensure the safe operation of the power system. Zinc oxide (ZnO) surge arresters play a crucial role in preventing overvoltage that can potentially harm the grid. The study presents an innovative non-destructive assessment approach for evaluating the working condition of ZnO surge arresters based on time-domain dielectric characteristics, aiming to guarantee their safe functioning within the power grid. The proposed technique provides a platform for conducting aging experiments updates the procedure for preparing piezoresistors in ZnO surge arresters and examines their performance. Furthermore, a test bed was utilized to investigate polarization/depolarization currents. The findings revealed that the pressure-sensitive voltage exhibited a minimum value of 693.09 U1mA ·cm2 using the traditional solid-phase approach while employing the chemical precipitation method with a 2:1 alcohol-to-water volume ratio resulted in a maximum value of 1028.41 U1mA ·cm2. Under the traditional solid-phase method, the voltage gradient varied from 411.04 V·mm−1 to 585.12 V·mm−1 when using the chemical precipitation method with an alcohol-to-water volume ratio of 2:1 V ·mm−1. The condition of ZnO surge arresters can be evaluated non-destructively by the polarization/depolarisation charge differential, which increases with the average service life. In conclusion, this study provides methodological criteria for assessing ZnO surge arrester conditions to ensure safe operation of power grids.

Multi-variants vision transformer-based malware image classification model using multi-criteria decision-making

Visualization-based malware detection gets more and more attention for detecting sophisticated malware that traditional antivirus software may miss. The approach involves creating a visual representation of the memory or portable executable files (PEs). However, most current visualization-based malware classification models focus on convolution neural networks instead of Vision transformers (ViT) even though ViT has a higher performance and captures the spatial representation of malware. Therefore, more research should be performed on malware classification using vision transformers. This paper proposes a multi-variants vision transformer-based malware image classification model using multi-criteria decision-making. The proposed method employs Multi-variants transformer encoders to show different visual representation embeddings sets of one malware image. The proposed architecture contains five steps: (1) patch extraction and embeddings, (2) positional encoding, (3) multi-variants transformer encoders, (4) classification, and (5) decision-making. The variants of transformer encoders are transfer learning-based models i.e., it was originally trained on ImageNet dataset. Moreover, the proposed malware classifier employs MEREC-VIKOR, a hybrid standard evaluation approach, which combines multi-inconsistent performance metrics. The performance of the transformer encoder variants is assessed both on individual malware families and across the entire set of malware families within two datasets i.e., MalImg and Microsoft BIG datasets achieving overall accuracy 97.64 and 98.92 respectively. Although the proposed method achieves high performance, the metrics exhibit inconsistency across some malware families. The results of standard evaluation metrics i.e., Q, R, and U show that TE3 outperform the TE1, TE2, and TE4 variants achieving minimal values equal to 0. Finally, the proposed architecture demonstrates a comparable performance to the state-of-the-art that use CNNs.

The unconfined compressive strength estimation of rocks using a novel hybridization technique based on the regulated Gaussian processor

The unconfined compressive strength (UCS) of rocks is a crucial factor in geotechnical engineering, assuming a central role in various civil engineering undertakings, including tunnel construction, mining operations, and the design of foundations. The precision in forecasting UCS holds paramount importance in upholding the security and steadfastness of these endeavors. This article introduces a fresh methodology for UCS prognostication by amalgamating Gaussian process regression (GPR) with two pioneering optimization techniques: sand cat swarm optimization (SCSO) and the equilibrium slime mould algorithm (ESMA). Conventional techniques for UCS prediction frequently encounter obstacles like gradual convergence and the potential for becoming ensnared in local minima. In this investigation, GPR is the foundational predictive model due to its adeptness in managing nonlinear associations within the dataset. The fusion of GPR with cutting-edge optimizers is envisioned to elevate the precision and expeditiousness of UCS prognostications.An extensive collection of rock samples, each accompanied by UCS measurements, is harnessed to assess the suggested methodology. The efficacy of the GPSC and GPES models is juxtaposed with the conventional GPR technique. The findings reveal that incorporating SCSO and ESMA optimizers into GPR brings about a noteworthy enhancement in UCS prediction accuracy and expedites convergence. Notably, the GPSC models exhibit exceptional performance, evidenced by an exceptional R2 value of 0.995 and an impressively minimal RMSE value of 1.913. These findings emphasize the GPSC model’s potential as an exceedingly auspicious tool for experts in the realms of engineering and geology. It presents a sturdy and dependable method for UCS prediction, a resource of immense value in augmenting the security and efficiency of civil engineering endeavors.

Chemical composition, antioxidant, antimicrobial and antibiofilm activities of essential oil from the Moroccan endemic Plant, Calendula maroccana (Ball) B. D. Jacks.

Chemical composition, antioxidant, antimicrobial and antibiofilm activities of essential oil from the Moroccan endemic Plant, <i>Calendula maroccana</i> (Ball) B. D. Jacks.

Toxicity and Phytochemical Analysis of Five Medicinal Plants

Recent studies have shown that long-term uses of herbs have been associated with a rise in morbidity and mortality rates. While most researches are focused on bioactivity investigations, the toxicity of many plants has not been reported. There is a paucity of data on the potential toxicity of the following plants: Harungana madagascariensis (HM), Pterocarpus osun (PO), Phoenix dactylifera (PD), Annona muricata (AM), and Rutidea parviflora (RP). To evaluate the toxicity of the above-mentioned plants; two tests were employed namely: The Brine shrimp lethality test (BSLT) and the Allium cepa test. A correlation between the oral acute toxicity assay in mice and the LC50 obtained from BSLT has been established. Allium cepa test measures the potential genotoxic effects of plant extracts exerted on the root meristem of A. cepa (onions). Plant extracts were administered in concentrations ranging from 100 to 2500 µg/ml to the A. cepa for 72 h to obtain their Mitotic Indices (MI) and EC50. Results of the MI at 2500 µg/ml for HM, PO, PD, AM, and RP were 3.75, 4.96, 5.96, 6.10, and 6.71 while 281.81, 398.11, 501.19, 630.96, and 707.9 µg/ml were obtained as the respective EC50 values. Furthermore, 10-1000 mcg/ml concentrations were administered in the BSLT and the obtained LC50 values were 116.3, 250, 581.5, 581.5, and 750 µg/ml. The toxicity result demonstrated that the five plants were moderately toxic, with RP exhibiting minimal toxicity values and thus potentially having a good safety profile. The phytochemical screening of these plants revealed the presence of some pharmacologically important classes of compounds that are abundant. Several bioactive and toxic compounds were identified in the GC-MS analysis for some of the plants.

On stability behaviors of 5D M-theory black objects

Using N = 2 supergravity formalism, we investigate certain behaviors of five-dimensional black objects from the compactification of M-theory on a Calabi–Yau three-fold. The manifold has been constructed as the intersection of two homogeneous polynomials of degrees (ω + 2, 1) and (2, 1) in a product of two weighted projective spaces given by WP4(ω,1,1,1,1)×P1 . First, we determine the allowed electric charge regions of the BPS and non BPS black holes obtained by wrapping M2-branes on appropriate two cycles in such a proposed Calabi–Yau three-fold. After that, we calculate the entropy of these solutions which takes a maximal value corresponding to ω = 1 defining the ordinary projective space P4 . For generic values of ω, we show that the non BPS states are unstable. Then, we conduct a similar study of five-dimensional black strings. Concerning the allowed magnetic charge regions of the BPS and non BPS black stringy solutions derived from M5-branes on dual divisors, we calculate the tension taking a minimal value for P4 . By determining the recombination factor, we show that the non-BPS black string states are stable in the allowed regions in the magnetic charge space.

Linear Transformations Preserving the Minimal Values of the Cyclicity Index of Tropical Matrices

Linear Transformations Preserving the Minimal Values of the Cyclicity Index of Tropical Matrices

Design, optimization and pharmaceutical characterization of wound healing film dressings with chloramphenicol and ibuprofen

Objective The aim of the present study was to develop and optimize a wound dressing film loaded with chloramphenicol (CAM) and ibuprofen (IBU) using a Quality by Design (QbD) approach. Significance The two drugs have been combined in the same dressing as they address two critical aspects of the wound healing process, namely prevention of bacterial infection and reduction of inflammation and pain related to injury. Methods Three critical formulation variables were identified, namely the ratios of Kollicoat SR 30D, polyethylene glycol 400 and polyvinyl alcohol. These variables were further considered as factors of an experimental design, and 17 formulations loaded with CAM and IBU were prepared via solvent casting. The films were characterized in terms of dimensions, mechanical properties and bioadhesion. Additionally, the optimal formulation was characterized regarding tensile properties, swelling behavior, water vapor transmission rate, surface morphology, thermal behavior, goniometry, in vitro drug release, cell viability, and antibacterial activity. Results The film was optimized by setting minimal values for the folding endurance, adhesive force and hardness. The optimally formulated film showed good fluid handling properties in terms of swelling behavior and water vapor transmission rate. IBU and CAM were released from the film up to 80.9% and 82.5% for 8 h. The film was nontoxic, and the antibacterial activity was prominent against Micrococcus spp. and Streptococcus pyogenes. Conclusions The QbD approach was successfully implemented to develop and optimize a novel film dressing promising for the treatment of low-exuding acute wounds prone to infection and inflammation.

Longitudinal association of grip strength with cardiovascular and all-cause mortality in older urban Lithuanian population.

Ageing populations experience greater risks associated with health and survival. It increases the relevance of identifying variables associated with mortality. Grip strength (GS) has been identified as an important biomarker for all cause and cardiovascular mortality, however, its prognostic value has not been studied in Lithuania. The aim of the present study is to evaluate the relationship of GS to vital status in a representative sample of the Lithuanian 45-72-year-old urban population during the period of 12 years of follow-up and to explore associations of GS with all-cause mortality and mortality from cardiovascular diseases (CVD). Within the framework of the international study Health, Alcohol and Psychosocial Factors in Eastern Europe (HAPIEE) 7,115 men and women 45-72 years of age were examined in the baseline survey (2006 to 2008). Data from the Official Lithuanian Mortality Register were used to evaluate CVD and all-cause mortality from follow-up till 2020. Cox proportional hazards regression was used, and four models for all-cause and CVD mortality were assessed. The mean GS was significantly higher among survivors' men and women as compared to individuals deceased from CVD and other causes of death. In survivor men and women groups, minimal values of GS in all terciles were higher as compared to all three deceased groups. In both men and women groups, the lowest GS (1st tercile) was associated with a significantly higher risk of all-cause and CVD mortality as compared to the highest levels of GS (3rd tercile) in three Cox regression models. In both men and women were found to have a 1.34- and 1.35-fold higher risk of all-cause mortality, respectively, at lower GS, but no significant difference in the risk of CVD mortality. When GS was treated in all models as decrement per 1kg and decrement per 1 SD, in both men and women, the risk of all-cause mortality significantly increased with decreasing of GS. The mean GS was significantly higher among survivors' men and women as compared to deceased from CVD and other causes of death. Risk of all-cause mortality significantly increased with decreasing of GS.

Advanced confidence methods in deep learning

The typical aim of classification tasks is to maximize the accuracy of the predicted label for a given input. This accuracy increases with the confidence, which is the maximal value of the output units, and when the accuracy equals confidence, calibration is achieved. Herein, several methods are proposed to enhance the accuracy of inputs with similar confidence, extending significantly beyond calibration. Using the first gap between the maximal and second maximal output values, the accuracy of the inputs with similar confidence is enhanced. The extension of the confidence or confidence gap to their minimal value among a set of augmented inputs further enhances the accuracy of inputs with similar confidence. Enhanced accuracies are demonstrated on EfficientNet-B0 trained on ImageNet and CIFAR-100, and VGG-16 trained on CIFAR-100. The results suggest improved applications for high-accuracy classification tasks that require manual operation for a given fraction of low-accuracy inputs.

Calorimetric Studies and Thermodynamic Modeling of Ag-Mg-Ti Liquid Alloys.

Due to the absence of thermodynamic data concerning the Ag-Mg-Ti system in the existing literature, this study aims to fill this gap by offering the outcomes of calorimetric investigations conducted on ternary liquid solutions of these alloys. The measurements were performed using the drop calorimetry method at temperatures of 1294 K and 1297 K for the liquid solutions with the following constant mole fraction ratio: xAg/xMg = 9/1, 7/3, 1/1, 3/7 [(Ag0.9Mg0.1)1-xTix, (Ag0.7Mg0.3)1-xTix, (Ag0.5Mg0.5)1-xTix, (Ag0.3Mg0.7)1-xTix)], and xAg/xTi = 19/1 [(Ag0.95Ti0.05)1-xMgx]. The results show that the mixing enthalpy change is characterized by negative deviations from the ideal solutions and the observed minimal value equals -13.444 kJ/mol for the Ag0.95Ti0.05 alloy and xMg = 0.4182. Next, based on the thermodynamic properties of binary systems described by the Redlich-Kister model and the determined experimental data from the calorimetric measurements, the ternary optimized parameters for the Ag-Mg-Ti liquid phase were calculated by the Muggianu model. Homemade software (TerGexHm 1.0) was used to optimize the calorimetric data using the least squares method. Next, the partial and molar thermodynamic functions were calculated and are presented in the tables and figures. Moreover, this work presents, for comparative purposes, the values of the enthalpy of mixing of liquid Ag-Mg-Ti alloys, which were calculated using Toop's model. It was found that the best agreement between the modeled and experimental data was observed for the cross-sections xAg/xTi = 19/1 [(Ag0.95Ti0.05)1-xMgx] and xAg/xMg = 9/1 [(Ag0.9Mg0.1)1-xTix]. The results of the experiments presented in this paper are the first step in the investigation and future evaluation of the thermodynamics of phases and the calculation of the phase diagram of the silver-magnesium-titanium system.

Minimal Important Clinical Difference Values Are Not Uniformly Valid in the Active Duty Military Population Recovering from Shoulder Surgery

There are multiple methods for calculating the minimal clinically important difference (MCID) threshold, and previous reports highlight heterogeneity and limitations of anchor-based and distribution-based analyses. The Warfighter Readiness Survey assesses the perception of a military population's fitness to deploy and may be used as a functional index in anchor-based MCID calculations. The purpose of the current study in a physically demanding population undergoing shoulder surgery was to compare the yields of two different anchor-based methods of calculating MCID for a battery of PROMs, a standard receiver operator curve (ROC) -based MCIDs and baseline-adjusted ROC MCIDs. All service members enrolled prospectively in a multicenter database with prior shoulder surgery that completed pre- and postoperative PROMs at a minimum of 12 months were included. The PROMs battery included Single Assessment Numeric Evaluation (SANE), American Shoulder and Elbow Surgeons score (ASES), Patient Reported Outcome Management Information System (PROMIS) Physical Function (PF), PROMIS Pain Interference (PI), and the Warfighter Readiness survey. Standard anchor-based and baseline-adjusted ROC MCIDs were employed to determine if the calculated MCIDs were both statistically and theoretically valid (95% confidence interval either completely negative or positive). There were 117 patients (136 operations) identified, comprised of 83% males with a mean age of 35.7 ± 10.4 years and 47% arthroscopic labral repair/capsulorrhaphy. Using the standard, anchor-based ROC MCID calculation, the area under the curve (AUC) for SANE, ASES, PROMIS PF, and PROMIS PI were greater than 0.5 (statistically valid). For ASES, PROMIS PF, and PROMIS PI, the calculated MCID 95% CI all crossed 0 (theoretically invalid). Using the baseline-adjusted ROC MCID calculation, the MCID estimates for SANE, ASES, and PROMIS PI were both statistically and theoretically valid if the baseline score was less than 70.5, 69, and 65.7. When MCIDs were calculated and anchored to the results of standard, anchor-based MCID, a standard ROC analysis did not yield statistically or theoretically valid results across a battery of PROMs commonly used to assess outcomes after shoulder surgery in the active duty military population. Conversely, a baseline-adjusted ROC method was more effective at discerning changes across a battery of PROMs among the same cohort.

Biogeochemical modelling to assess the effect of bioclogging on multiple electron acceptor-mediated petroleum hydrocarbon bioremediation in vadose zone.

Bioremediation is an economically viable and sustainable clean-up strategy. Hydrodynamic, as well as transport characteristics of the porous medium, can evolve over the period as a result of biological clean-up activities. The present study proposes a 2-D numerical framework to simulate the effect of bioclogging on multiple electron acceptor-mediated petroleum hydrocarbon bioremediation in the vadose zone. For modelling, a spill of BTEX (benzene, toluene, ethylbenzene and xylene) is assumed near source zone. The developed model results are validated using three previously published datasets on flow, transport and biodegradation in the vadose zone. Simulations are performed for three types of soil, including clay, sand and loam. The analysis shows that sand has a maximum infiltration rate and clay has a minimum. Hydraulic conductivity and saturation profile peaks reach their minimal value at a shallower depth (around four times) when bioclogging is present compared to when it is absent. The migration depth and concentration of BTEX are observed to be restricted to a shallower depth in aquifers with the presence of microbial clogging. The outcome shows that electron acceptor consumption is more (around sevenfold for oxygen, fourfold for nitrate and threefold for sulphate) in the presence of bioclogging at the shallower zone. Zeroth order spatial moment and sensitivity analyses show that biological clogging, number of electron acceptors and inhibition constant substantially affect BTEX bioremediation in the vadose zone.

Merging Counter-Propagation and Back-Propagation Algorithms: Overcoming the Limitations of Counter-Propagation Neural Network Models.

Artificial neural networks (ANNs) are nowadays applied as the most efficient methods in the majority of machine learning approaches, including data-driven modeling for assessment of the toxicity of chemicals. We developed a combined neural network methodology that can be used in the scope of new approach methodologies (NAMs) assessing chemical or drug toxicity. Here, we present QSAR models for predicting the physical and biochemical properties of molecules of three different datasets: aqueous solubility, acute fish toxicity toward fat head minnow, and bio-concentration factors. A novel neural network modeling method is developed by combining two neural network algorithms, namely, the counter-propagation modeling strategy (CP-ANN) with the back-propagation-of-errors algorithm (BPE-ANN). The advantage is a short training time, robustness, and good interpretability through the initial CP-ANN part, while the extension with BPE-ANN improves the precision of predictions in the range between minimal and maximal property values of the training data, regardless of the number of neurons in both neural networks, either CP-ANN or BPE-ANN.