Optimal Value Research Articles

The Relationship Between the Endoscopic Healing Index, Fecal Calprotectin, and Magnetic Resonance Enterography in Crohn's Disease.

The serum-based endoscopic healing index (EHI) test identifies endoscopic Crohn's disease (CD) activity. Data are lacking on the relationship between EHI with other endpoints. We assessed the relationship between EHI and the simplified Magnetic Resonance Index of Activity. Data were prospectively collected on patients with CD with either an EHI or fecal calprotectin (FCAL) within 90 days of magnetic resonance enterography (MRE). Diagnostic accuracy was assessed using area under the receiver operator characteristics. Proportions with any, severe, and terminal ileum MR inflammation were compared above/below identified thresholds for both EHI and FCAL. A total of 241 MREs paired to either EHI or FCAL from 155 patients were included. Both EHI and FCAL had similar accuracy to diagnose inflammation (area under the receiver operator characteristics: EHI: 0.635 to 0.651, FCAL: 0.680 to 0.708). Optimal EHI values were 42 and 26 for inflammation on MRE and endoscopy, respectively. Patients with EHI ≥42 (100% vs. 63%, P =0.002), FCAL >50µg/g (87% vs. 64%, P <0.001) and FCAL >250µg/g (90% vs. 75%, P =0.02) had higher rates of simplified Magnetic Resonance Index of Activity ≥1 compared with lower values. EHI differentiated ileitis numerically more than FCAL (delta: 24% to 25% vs. 11% to 21%). Patients with FCAL ≥50µg/g had higher rates of severe inflammation compared with FCAL <50µg/g (75% vs. 47%, P <0.001), whereas smaller differentiation existed for EHI threshold of 42 (63% vs. 49%, P =0.35). Both EHI and FCAL were specific in their confirmation of inflammation and disease activity on MRE in patients with CD. However, MRE-detected inflammation was frequently present in the presence of low EHI and FCAL in similar proportions.

HT-WSO: A hybrid meta-heuristic approach-aided multi-objective constraints for energy efficient routing in WBANs

Generally, Wireless Body Area Networks (WBANs) are regarded as the collection of small sensor devices that are effectively implanted or embedded into the human body. Moreover, the nodes included in the WBAN have large resource constraints. Hence, reliable and energy-efficient data transmission plays a significant role in the implementation and in constructing of most of the merging applications. Regarded to complicated channel environment, limited power supply, as well as varying link connectivity has made the construction of WBANs routing protocol become difficult. In order to provide the routing protocol in a high energy-efficient manner, a new approach is suggested using hybrid meta-heuristic development. Initially, all the sensor nodes in WBAN are considered for experimentation. In general, the WBAN is comprised of mobile nodes as well as fixed sensor nodes. Since the existing models are ineffective to achieve high energy efficiency, the new routing protocol is developed by proposing the Hybrid Tunicate-Whale Swarm Optimization (HT-WSO) algorithm. Subsequently, the proposed work considers the multiple constraints for deriving the objective function. The network efficiency is analyzed using the objective function that is formulated by distance, hop count, energy, path loss, and load and packet loss ratio. To attain the optimum value, the HT-WSO derived from Tunicate Swarm Algorithm (TSA) and Whale Optimization Algorithm (WOA) is employed. In the end, the ability of the working model is estimated by diverse parameters and compared with existing traditional approaches. The simulation outcome of the designed method achieves 13.3%, 23.5%, 25.7%, and 27.7% improved performance than DHOA, Jaya, TSA, and WOA. Thus, the results illustrate that the recommended protocol attains better energy efficiency over WBANs.

Optimization and Modeling of a Dual-Chamber Microbial Fuel Cell (DCMFC) for Industrial Wastewater Treatment: A Box–Behnken Design Approach

Microbial fuel cells (MFCs) have garnered significant attention due to their capacity to generate electricity using renewable and carbon-neutral energy sources such as wastewater. Extensive experimental work and modeling techniques have been employed to dissect these processes and understand their respective impacts on electricity generation. The driving force is to enhance MFC performance for practical applications commercially. Among the various statistical modeling approaches, one particularly robust tool is the Design of Experiments (DoE). It serves to establish the relationships between different variables that influence MFC performance and allows for the optimization of the MFC configuration and operation for scaled-up performances in terms of bioelectricity generation. This study focused on optimizing microbial fuel cells (MFCs) for bioelectricity production using industrial wastewater treatment, employing the Box–Behnken design (BBD) methodology. Through an analysis of response surface models and ANOVA tests, it was found that a combined approach of reduced quadratic, reduced two-factor interaction, and linear models yielded sound results, particularly in voltage yield, COD removal, and current density. Second-order regression models predicted optimal conditions for various parameters, with surface area, temperature, and catholyte dosage identified as critical input variables for optimization. Under these conditions, conducted by the four-factor and three-level Box–Behnken design methodology in a double-chamber MFC unit considering eight output variables—CCV yield, % COD removal, current density, power density, % TSS removal, % CE, and % PO43−—the optimum values were 700 mV, 54.4%, 54.4 mA/m2, 73.7 mW/m2, 99%, 21.2%, and 100%, respectively. At optimum operating conditions, the results revealed a desirability of 76.6% out of a total of 92 iterations. The paper highlights the effectiveness of statistical ANOVA fit-statistics modeling and optimization in enhancing DCMFC performance, recommending its use as a sustainable bioenergy source. Furthermore, validation results supported the above optimization output response findings and confirmed the viability of biorefinery wastewater as an anolyte for scaling up DCMFC bioelectricity generation.

Microorganism-mediated denitrogenation of aquaculture systems provoked by poly-β-hydroxybutyrate (PHB)

The biodegradable polymer poly-β-hydroxybutyrate (PHB) is a promising carbon source for biological mitigation of nitrogen pollution, a significant problem in aquaculture that physical and chemical methods have not provided a comprehensive solution. Here we investigated the impact of PHB on the zero-water-change largemouth bass culture by 30- and 40-day experiments. PHB loaded into the filter circulation pump at 4 g L−1, optimum value determined by the first experiment, significantly reduced the levels of nitrate by 99.65%, nitrite by 95.96%, and total nitrogen by 85.22% compared to the control without PHB. PHB also significantly increased denitrifying bacteria (e.g., Proteobacteria and Fusobacteria) and expression of denitrification genes (e.g., nirK and nirS) in the microbial community, improving growth and health parameters of largemouth bass. While the impact may vary in other culture systems, PHB thus demonstrated its remarkable utility in aquaculture, highlighting ecological assessment and application to larger aquaculture operations as future considerations.

Optimization of baffle and tapering integration in the PEM fuel cell flow field employing artificial intelligence

In this study, two surrogate models are developed to investigate and enhance the performance of parallel flow field Proton Exchange Membrane Fuel Cells (PEMFCs) with two modifications. The main channels are tapered, and baffles are inserted to enhance the mass transfer. A data set is generated by the multi-phase, three-dimensional CFD model. Two different data-driven models based on Artificial Intelligence (AI) and Modified Response Surface Methodology (MRSM) provide surrogate models. Subsequently, two multi-objective optimization methods are employed to reveal the optimum case. The results show that the AI model achieves superior accuracy, whereas the MRSM model has greater simplicity. An increased number of baffles and an optimal tapering ratio contribute to higher mass flux and improved uniformity in reactant distribution. The insertion of baffles and the tapering of main channels result in a remarkable 67 % increase in output power density. Optimum tapering also reduces pressure drop, whereas baffles, while improving performance, contribute to increased pressure drop and potential PEMFC degradation. The identified optimum configuration of baffles and tapering, along with the optimum values for voltage, pressure, and anode and cathode stoichiometries, results in an impressive output power density of 0.93 W cm−2 and a parasitic power ratio of 0.17, respectively.

Systemic Inflammation Response Index and Systemic Immune-inflammation Index for Prediction of Pregnancy Loss: a Case-control Study.

The systemic inflammation response index (SIRI) and systemic immune inflammation index (SII) have recently been investigated as new prognostic markers for obstetric morbidities. However, there are few studies on their predictive role in patients with pregnancy loss. Predicting miscarriages may be useful to support and prevent selected cases.The aim of this study was to investigate the role of SIRI and SII in the prediction of pregnancy loss. A total of 800 patients were included in the retrospective case-control study at a tertiary hospital.Group 1 consisted of 200 patients who had a pregnancy loss for the first time; group 2 consisted of 200 patients with recurrent pregnancy loss; the control group consisted of 400 patients who had a healthy pregnancy. The groups were compared in terms of maternal characteristics, SIRI and SII. Receiver operating characteristic analysis was performed to determine optimal cut-off values for SIRI and SII in predicting pregnancy loss. SIRI and SII were higher in the group with recurrent pregnancy loss than in the control group (p < 0.001).SIRI was higher in the first pregnancy loss group than in the control group (p < 0.001).To predict recurrent pregnancy loss, optimal cut-off values were 1.57 (80% sensitivity, 70% specificity) and 924.12 (74% sensitivity, 57% specificity) for SIRI and SII, respectively. For first pregnancy loss prediction, the optimal cut-off value was 1.38 for SIRI, with 75% sensitivity and 60% specificity. SIRI and SII may be used as inflammatory markers to predict recurrent pregnancy loss. High SIRI values can also help to predict first pregnancy loss.

C-reactive protein/lymphocyte ratio as a prognostic biomarker in acute pancreatitis: A cross-sectional study assessing disease severity.

The C-reactive protein/lymphocyte ratio (CLR) is a prognostic biomarker of various diseases. However, its significance in acute pancreatitis (AP) remains unknown. The main aim of this study was to investigate the association between the CLR and disease severity in patients with AP. This cross-sectional study included 476 AP patients (mild acute pancreatitis (MAP), n =176; moderately severe acute pancreatitis (MSAP) and severe acute pancreatitis (SAP), n=300). The primary exposure of interest was the baseline CLR. The primary outcome was the incidence of moderate to severe AP. Multivariate logistic regression and restricted cubic spline analyses were performed to evaluate the association between the CLR and the incidence of moderate to severe AP. Receiver operating characteristic (ROC) analysis was conducted to assess the predictive efficacy, sensitivity, and specificity of CLR in predicting the incidence of moderate to severe AP. The mean age of the patients was 44±13.2 years, and 76.5% were male. The distribution of CLR was 31.6 (interquartile range, 4.5, 101.7). Moderate to severe AP occurred in 300 cases (63.0%). After multiple adjustments, CLR was independently associated with the incidence of moderate to severe AP (odds ratio [OR], 1.04; 95% confidence interval [CI], 1.03, 1.05; P< 0.001). A nonlinear relationship was found between CLR and the incidence of moderate to severe AP, with a threshold of approximately 45. The effect size and CI below and above the threshold value were 1.061 (1.033-1.089) and 1.014 (0.997-1.031), respectively. The area under the curve (AUC) for CLR was 87.577% (95% CI: 84.443% ~ 90.710%) with an optimal cut-off value of 30.835, resulting in a sensitivity of 73.7% and a specificity of 88.6%. There was a nonlinear relationship with a saturation effect between the CLR and the incidence of moderate to severe AP. The CLR measured within 24 h of admission may serve as a promising biomarker for predicting the emergence of moderate to severe AP, thereby providing a more scientifically grounded basis for preventing such cases. Nonetheless, further research is warranted to validate and strengthen these findings.

Multi-objective economic load dispatch using hybrid NSGA-II and PVDE techniques

Over decades, numerous methods have been used to optimize objective functions. Where cost and emissions clash. The improved non-dominated sorting genetic algorithm (NSGA-II) employs elitism to discover the optimum value and speed convergence in multi-objective optimization problems. Population variant differential evolution algorithm alters differential evolution (DE). The main distinction between DE and population variant differential evolution algorithm (PVDE) is population replenishment. NSGA-II and PVDE are combined in the suggested hybrid approach. The hybrid technique solves multi-objective optimization problems efficiently by combining two or more methods. The hybrid technique solves multi-objective optimization problems well. This optimization problem pits cost vs pollution. The hybrid approach exposes half the population to the NSGA-II algorithm and half to the PVDE algorithm. In optimization problems with opposing aims, such as minimizing costs and emissions, a hybrid technique is utilized to find the optimal solution. Elitist diversity-preserving strategies avoid optimization issues becoming converging too soon. A 10-generator IEEE 39 bus test system was validated using this method. The hybrid NSGA-II and PVDE methodology achieves global optimal solutions with more durability, simplicity, and optimization performance than existing methods.

Fundamental LCZ Mapping with ArcGIS-Based Spatial and Correlational Analyses to Study the Impacts of Infrastructural Area- Maximum Volume (A-MV) Attributes on OTC – An Urban Aw Summer Case

The aim of this study is to map the study location stretches into 100mx100m local climate zones (LCZs) on the basis of primary observations and secondary photographic data for spatial analysis of various urban infrastructural elements (grey(G)-built structures, blue(B)-water bodies and green (Gr)-natural vegetation) and impact assessment of their estimated area (EA) and maximum volume (EMV)) on the air temperature (Ta) that characterizes the outdoor thermal comfort (OTC). Climate Modification by Urban Infrastructure, Fundamental Concepts of Urban Infrastructure Mensuration Parameters, Fundamental Concepts of Urban Grey Blue Green Infrastructure (UGBGI) and Concepts Of OTC, Koppen Classification and LCZs are the linked areas. Study locations are based on socio-economic and geophysical attributes. Initial 200 pilot survey responses regarding thermal sensation votes and other subjective details were used. Preferred values of Physiological Equivalent Temperature (PET) and optimum Ta have been used. After the mapping in ArcGIS and polygonal delineation of the areas, EA values were compiled in tabular format. Based on primary measurements and secondary studies, a maximum height (hmax) value was assigned to each sub-category for EMV evaluation. Statistically, the EA and estimated perimeter (EP) values were analyzed using QQ, scatter plots, histogram and generate near table tools in ArcGIS. EA and EMV values were correlated with mean Ta values by linear regression. The R-square values generated gave a clear idea about the extent of their impacts on Ta. From the established relationships and preferred PET and optimum Ta values, optimum EA, EMV and hmax values of the broader categories(G,B,Gr) required for preferred OTC levels were finally evaluated.

Synthesis of Optimal Control of Spacecraft Angular Momentum for Spatial Turn Taking into Account Energy Costs Using Quaternions

Background: In this paper, we propose solving the specific original problem of control synthesis of spacecraft attitude. Optimization of the control program is made with the use of a new criterion of quality that combines energy costs and duration of reorientation under restrictions on control (the presence of a time factor limits the duration of slew maneuver). Methods: The construction of optimal control for angular momentum change is based on the quaternion method and L.S. Pontryagin maximum principle. An analytical solution to the problem was obtained on the base of a differential equation relating the orientation quaternion and angular momentum of a spacecraft. Results: Key properties of the optimal solution are formulated in analytical form; the features of optimal motion are studied in detail. The control law is formulated in the form of explicit dependence between control and phase variables. In a case when the controlling torque is limited by the given restriction (at the beginning and end of a turn), analytical formulas have been written for the duration of braking and acceleration. Main relations which determine optimal values of parameters of the algorithm for control of angular momentum are given. Examples and results of mathematical modeling of spacecraft motion formed by optimal control were given. This data in addition to the theoretical descriptions illustrates the process of reorientation in evident form and demonstrates the practical feasibility of a designed method for control of angular momentum during spatial turn. Conclusion: The designed optimal algorithm of control of spacecraft motion improves the efficiency of spacecraft attitude system, and originates more economical performance of spacecraft during flight on orbit.

Process Integration of Hydrogen Production Using Steam Gasification and Water-Gas Shift Reactions: A Case of Response Surface Method and Machine Learning Techniques

An equilibrium-based steady-state simulator model that predicts and optimizes hydrogen production from steam gasification of biomass is developed using ASPEN Plus software and artificial intelligence techniques. Corn cob’s chemical composition was characterized to ensure the biomass used as a gasifier and with potential for production of hydrogen. Artificial intelligence is used to examine the effects of the significant input variables on response variables, such as hydrogen mole fraction and hydrogen energy content. Optimizing the steam-gasification process using response surface methodology (RSM) considering a variety of biomass-steam ratios was carried out to achieve the best results. Hydrogen yield and the impact of main operating parameters were considered. A maximum hydrogen concentration is found in the gasifier and water-gas shift (WGS) reactor at the highest steam-to-biomass (S/B) ratio and the lowest WGS reaction temperature, while the gasification temperature has an optimum value. ANFIS was used to predict hydrogen of mole fraction, 0.5045 with the input parameters of S/B ratio of 2.449 and reactor pressure and temperature of 1 bar and 848°C, respectively. With the steam-gasification model operating at temperature (850°C), pressure (1 bar), and S/B ratio of 2.0, an ASPEN simulator achieved a maximum of 0.5862 mole fraction of hydrogen, while RSM gave an increase of 19.0% optimum hydrogen produced over the ANFIS prediction with the input parameters of S/B ratio of 1.053 and reactor pressure and temperature of 1 bar and 850°C, respectively. Varying the gasifier temperature and S/B ratio have, on the other hand, a crucial effect on the gasification process with artificial intelligence as a unique tool for process evaluation, prediction, and optimization to increase a significant impact on the products especially hydrogen.

Using sustainability pillar indicators to evaluate the management effectiveness of three protected areas in the state of Selangor, Peninsular Malaysia

The existing methodologies to measure management effectiveness of protected area have often been focus on identifying weaknesses and strengths to set priorities in terms of policy intervention, resource distribution and adaptive management. It is not adequately captured the extent of protected area sustainability as a conservation site even though management effectiveness is influenced by the interaction of environment, socioeconomics and governance, which are pillars of sustainability. Thus, the objective of this study was to propose a methodology based on sustainability pillar indicators to evaluate management effectiveness, using three protected areas in Peninsular Malaysia as case studies: Sungai Dusun, Bukit Kutu and Klang Gate wildlife reserves. In this study, a questionnaire survey was used to evaluate the management effectiveness of the study sites. The evaluation framework of the questionnaire was based on six components of management effectiveness and indicators from three sustainability pillars or aspects: environment, socioeconomics and governance. Respondents of the survey consisted of local residents in each study site and management staff involved in the management of the protected areas. Each indicator from different aspects of evaluation was assigned a selected parameter to be rated by the respondents from ‘1’ (strongly disagree) to ‘5’ (strongly agree). Based on the rating values the percentage of optimum value was calculated to determine the overall management rating for each study site which interpreted according to five management levels from ‘unsatisfactory’ to ‘very satisfactory’. The percentage of optimum values revealed that the environment and governance aspects of all study sites had moderately satisfactory while for socioeconomic only Klang Gate had moderately satisfactory but satisfactory for Bukit Kutu and Sungai Dusun. The overall percentage of optimum revealed that the management effectiveness level of all reserves was moderately satisfactory. However, Sungai Dusun had the highest percentage of optimum (71.3%), followed by Bukit Kutu (67.7%) and Klang Gate (66.5%). In conclusion, the sustainability level of the reserves is still functioning as intended despite deficiencies especially in governance aspect. Thus, the proposed methodology is simple and has adoptable way to measure both management effectiveness and capture the sustainability of the protected areas.

Optimization of Antifungal Properties of Hop Cone Carbon Dioxide Extracts Based on Response Surface Methodology.

Response surface methodology (RSM) was employed to optimize the process parameters of the supercritical carbon dioxide extraction of hop cones in terms of their antifungal properties against Fusarium culmorum and Aspergillus niger. The effects of temperature (40-50 °C), pressure (200-300 bar), and CO2 consumption (25-75 kgCO2/kg) on the extraction yield, content of α- and β-acids, as well as pathogens' growth inhibition were investigated. Both pressure and CO2 consumption had a significant effect on antifungal properties. It was observed that the best results for antifungal properties were obtained when hop cones were extracted with pure carbon dioxide at the temperature of 50 °C, under the pressure of 300 bar with CO2 consumption at the level of 75 kgCO2/kg of feed for extraction. The highest antifungal properties of hop cone supercritical carbon dioxide extracts were analyzed as 100% for Fusarium culmorum and 68% for Aspergillus niger, calculated as the growth inhibition of tested pathogens. The aim of the study was to determine the optimum values of extraction parameters to achieve the maximum response and enable us to investigate the interaction of these parameters on the antifungal properties of hop cone extracts.

Optimization of batch process parameters for chromium (VI) removal from synthetic wastewater using eggshell–clay composite

In this study, a biodegradable spherical adsorbent bead was developed from eggshell (ES) and Bijoypur clay (BC) for the removal of hexavalent chromium from synthetic tannery wastewater. This Eggshell–Clay polymer composite (ES–BC) was made by incorporating them with PVA and alginate. To analyze the morphology and chemical composition of the adsorbent before and after the chromium adsorption, SEM, FTIR, XRD, and EDX were used. The impact of adsorption parameters such as adsorbent dosage (0.67–2.23 g/L), pH of the solution (1.5–9), contact time (10–240 min), initial Cr (VI) concentration (25–100 ppm), and solution temperature (25–65 °C), percentage clay in ES-BC adsorbent (0–40%) for Cr (VI) adsorption were investigated. The optimum value for adsorbent dosage, pH, contact time, initial Cr concentration, temperature, and clay to eggshell ratio was found 1.17 g/L, 1.5, 150 min, 58.2 ppm, 25 °C, and 80/20% respectively. The isotherm model was well-fitted to the Freundlich isotherm suggesting heterogeneous adsorption in multilayer on surface. The adsorption kinetics followed pseudo-second-order kinetics supporting the chemisorption process. Moreover, a decrease in enthalpy (H°) and negative Gibbs free energy (G°) with increased temperature indicated exothermic and spontaneous reaction. Therefore, this research investigated that the composite may serve as a viable option for treating Cr (VI) containing tannery effluent.

Elimination of nickel and chromium(VI) ions from industerial wastewater by electrodialysis/characteristics/impact of parameters

&lt;p&gt;Healthy water is a concern for the world due to rapid population growth and technological advancement. The entry of heavy metals into water as a result of various activities causes water pollution that has persistent effects. In this study, a specially designed electrodialysis cell was used to remove chromium (VI) and nickel ions from wastewater. The chambers were partitioned by Ionac MC 3470 cation exchange and Ionac MA 3475 anion exchange membranes. The cathode and anode were made of carbon fiber and stainless steel, respectively. The effects of voltage, initial pH, time, Na2SO4 concentration, feed flow rate and metal ion concentration on metal removal efficiency, energy consumption, current efficiency, current density and flux were investigated. Optimum values for 98.5% removal of 80 mg/L Cr(VI) ion in 90 min voltage 30 V, pH=3, addition of Na2SO4 0.5 g and feed flow rate 52. 8 ml/min, as observed. At the end of this period, concentration 1 mg/L, energy consumption 40 W/L, flow efficiency 30% and flux 12 x 10.-5 mol/m2s were calculated. Optimum values of 25 V, pH=3, addition of Na2SO4 0.2 g and Qf = 42.6 mL/min were observed for 94.3% removal of 50 mg/L Ni2+ ions in 90 min. At the end of this period, the nickel ion concentration was 4 mg/L, the energy consumption was 34 Wh/L, the flow efficiency was 96.51%, and the flux was calculated as 40×10-5 mol/m2s. This study shows that the electrodialysis method can be effectively used to elimonation chromium (VI) and nickel (II) ions from dilute wastewaters.&lt;/p&gt;

Removal of carcinogenic chromium(VI) from effluent of electroplating industry using adsorptive separation

AbstractThe wastewater from an electroplating industry contains chromium VI which is highly carcinogenic at a typical level of 21.5 to 47 mg L−1 which should be reduced to acceptable value of 0.05 mg L−1 for the water to be suitable for drinking purpose. In this study, Tulsion A23 resin was applied as an adsorbent for successful removal of Cr(VI), also dealing with understanding the effect of various parameters on extent of removal of the contaminant. It was elucidated that contact time of 120 min is required for the equilibrium and the optimum pH value was 5 and the optimum resin loading was 4 g L−1 at which 97% Cr(VI) removal was observed. Different adsorption isotherm models were applied and it was elucidated that Langmuir model was the best fit with R2 as 0.99. In the case of kinetic study, pseudo‐second order model was best fitted with higher R2 value. The regeneration of the resin was performed using 3 M NaOH and it was shown that after the three cycles of regeneration, the resin showed 82% Cr(VI) removal. Overall, successful application of Tulsion A23 resin was demonstrated for the removal of Cr(VI) from real effluent for the first time.

Impact of Relative Improvement in Quantitative Flow Ratio on Clinical Outcomes After Percutaneous Coronary Intervention - A Subanalysis of the PANDA III Trial.

The clinical impact of relative improvements in coronary physiology in patients receiving percutaneous coronary intervention (PCI) for coronary artery disease (CAD) remains undetermined.Methods and Results: The quantitative flow ratio (QFR) recovery ratio (QRR) was calculated in 1,424 vessels in the PANDA III trial as (post-PCI QFR-pre-PCI QFR)/(1-pre-PCI QFR). The primary endpoint was the 2-year vessel-oriented composite endpoint (VOCE; a composite of vessel-related cardiac death, vessel-related non-procedural myocardial infarction, and ischemia-driven target vessel revascularization). Study vessels were dichotomously stratified according to the optimal QRR cut-off value. During the 2-year follow-up, 41 (2.9%) VOCEs occurred. Low (<0.86) QRR was associated with significantly higher rates of 2-year VOCEs than high (≥0.86) QRR (6.6% vs. 1.4%; adjusted hazard ratio [aHR] 5.05; 95% confidence interval [CI] 2.53-10.08; P<0.001). Notably, among vessels with satisfactory post-procedural physiological results (post-PCI QFR >0.89), low QRR also conferred an increased risk of 2-year VOCEs (3.7% vs. 1.4%; aHR 3.01; 95% CI 1.30-6.94; P=0.010). Significantly better discriminant and reclassification performance was observed after integrating risk stratification by QRR and post-PCI QFR to clinical risk factors (area under the curve 0.80 vs. 0.71 [P=0.010]; integrated discrimination improvement 0.05 [P<0.001]; net reclassification index 0.64 [P<0.001]). Relative improvement of coronary physiology assessed by QRR showed applicability in prognostication. Categorical classification of coronary physiology could provide information for risk stratification of CAD patients.

Prognostic Implications of Fractional Flow Reserve and Coronary Flow Reserve After Drug-Eluting Stent Implantation.

Fractional flow reserve (FFR) after percutaneous coronary intervention (PCI) provides prognostic information, but limited data are available regarding prognostication using post-PCI coronary flow reserve (CFR). In this study we aimed to assess the prognostic value of post-procedural FFR and CFR for target vessel failure (TVF) after PCI.Methods and Results: This lesion-based post-hoc pooled analysis of previously published registry data involved 466 patients with chronic coronary syndrome with single-vessel disease who underwent pre- and post-PCI FFR and CFR measurements, and were followed-up to determine the predictors of TVF. The prognostic value of post-PCI CFR and FFR was compared with that of FFR or CFR alone. Post-PCI FFR/CFR discordant results were observed in 42.5%, and 10.3% of patients had documented TVF. Receiver-operating characteristic curve analysis revealed that the optimal cutoff values of post-PCI FFR and CFR to predict the occurrence of TVF were 0.85 and 2.26, respectively. Significant differences in TVF were detected according to post-PCI FFR (≤0.85 vs. >0.85, P=0.007) and post-PCI CFR (<2.26 vs. ≥2.26, P<0.001). Post-PCI FFR ≤0.85 and post-PCI CFR <2.26 were independent prognostic predictors. After PCI completion, discordant results between FFR and CFR were not uncommon. Post-PCI CFR categorization showed incremental prognostic value for predicting TVF independent of post-PCI FFR risk stratification.

Modelling and optimisation of biodiesel production using waste cooking oil using the response surface methodology

ABSTRACT Modelling the effect of reaction time, catalyst concentration, and oil-to-methanol ratio on the waste cooking oil biodiesel was studied using response surface methodology. Biodiesel was made using three different heterogeneous catalysts. Analysis showed catalysts 2 and 3 to be significant in transesterification. However, all catalysts had second-degree significance for esterification yield. For catalysts 1 and 2 there was a clear interaction between catalyst concentration and oil-to-methanol ratio. Clear interaction was also noticed between reaction time and oil-to-methanol ratio for all catalysts. There was moderate to no interaction between the effects of time and catalyst concentrations for all catalysts. The best yield was found at lower oil-to-methanol ratios (nearly 0.02) and higher catalyst concentrations (at 10%). Reaction times close to 7 hours for catalyst 1 and close to 5 for the other catalysts were the optimum values for best yield and lowest acid value.

Self-powered PEDOT: PSS/CSWCNT/Mxenes thermoelectric films and wearable electronics devices

In this work, Ti3C2–Cl is introduced into poly(3,4-ethylenedioxythiophene): poly(4-styrenesulfonic acid) / single-walled carbon nanotubes (PEDOT: PSS/SWCNT, noted as PPSC) films to prepare ternary thermoelectric films of PEDOT: PSS/SWCNT/Ti3C2–Cl (PPSCT). The microscopic morphology, structures, and thermoelectric properties of PPSC and PPSCT films show that the introduction of Ti3C2–Cl results in a tendency of decreasing electrical as well as thermal conductivity, and the Seebeck coefficient is increased. When the content of Ti3C2–Cl reaches 8 wt%, the conductivity, Seebeck coefficient, and thermal conductivity of the PPSCT films reach the optimum values of 1683 S/cm, 21.69 μV/K, and 14.97 W/mK, respectively, and the power factor (PF) and thermoelectric figure of merit (ZT) are 79.23 μW/mK2 and 1.58 × 10–3. The UPS and Hall effect results reveal that the improvement of the Seebeck effect of PPSCT mainly results from the ternary interfacial energy barrier as well as the change of carrier concentration. In addition, the wearable electronics devices demonstrate an output voltage of 4.5 mV and an output power of 54.36 nW under a 50 K temperature difference. The device displays a good monitoring sensitivity by reaching an output voltage of 0.6 mV at a 7 K temperature difference between the skin and the environment.