Significant Parameters Research Articles

Evaluation of plastic injection molding for PA6/ABS/CaCO3 nanocomposites using Taguchi method and Moldflow simulation approach

Nowadays, nanocomposites are widely used in the industry. Polymer nanocomposites are widely used in the automotive industry because they have very favorable properties. These properties, including mechanical, electrical, and thermal characteristics, change depending on the combination of materials used in composite synthesis. In this paper, the injection molding of an automobile part called a control arm protector is investigated. Since warpage and shrinkage are general and important challenges in injection molded parts, Taguchi test design and Autodesk Moldflow® simulation approach are used to find the best injection condition for the mentioned part. To perform this, PA6/ABS polymer composite combined with nano CaCO3 is used. Three main injection molding process parameters, including melt temperature, mold temperature, and injection pressure, as well as nano CaCO3 amount, are evaluated. Moreover, analysis of the gate location is investigated. In the following, an analysis of variance is conducted to identify the significant parameters. Regression correlations were also established. Finally, optimization of the process is carried out by the desirability method.

404 Mobile Pupillometry for Detection of Vasospasm, Delayed Cerebral Ischemia, and Neuro Exam Change in Subjects With Aneurysmal Subarachnoid Hemorrhage

INTRODUCTION: The pupillary light reflex (PLR) parameters are quantitative biomarkers that can be used to detect neurological disease. Vasospasm complicates aneurysmal subarachnoid hemorrhage (aSAH) trajectory, causing morbidity and mortality. METHODS: We prospectively collected PLR parameters from patients with aSAH admitted to a neurological intensive care unit twice daily using quantitative smartphone pupillometry recordings. PLR parameters included: Maximum pupil diameter (MAX), minimum pupil diameter (MIN), percent change in pupil diameter (CHANGE), latency in beginning of pupil constriction to light (LAT), mean constriction velocity (MCV), maximum constriction velocity (MAXCV), mean dilation velocity (MDV). One-tailed t-tests were performed to determine changes in PLR parameters between the following comparisons: (1) patients with and without radiographic vasospasm (defined by computed tomography angiography (CTA) or digital subtraction angiography (DSA)); (2) patients with and without delayed cerebral ischemia (DCI); and (3) patients with and without neurological exam change due to vasospasm. RESULTS: 42 total subjects were enrolled. For patients with (n = 13) and without (n = 29) radiographic vasospasm, significant PLR parameter differences were observed in: MAX (p = .024) and MIN (p = .0018). For patients with (n = 14) and without (n = 28) DCI, significant PLR parameter differences were observed in: CHANGE (p = .008). For patients with (n = 13) and without (n = 29) neurological exam change due to vasospasm, significant PLR parameter differences were observed in: MIN (34.7 vs 31.7, p = .014), CHANGE (20.6 vs 24.7, p = .025), and LAT (.36 vs .2, p = .0002). All other PLR parameters were not significantly different between groups for the three comparisons. CONCLUSIONS: Quantitative smartphone pupillometry may be of clinical use in the detection of radiographic vasospasm, DCI, and neuro exam changes via separate and distinct parameters from the PLR.

Debiased Graph Neural Networks With Agnostic Label Selection Bias.

Most existing graph neural networks (GNNs) are proposed without considering the selection bias in data, i.e., the inconsistent distribution between the training set with the test set. In reality, the test data are not even available during the training process, making selection bias agnostic. Training GNNs with biased selected nodes leads to significant parameter estimation bias and greatly impacts the generalization ability on test nodes. In this article, we first present an experimental investigation, which clearly shows that the selection bias drastically hinders the generalization ability of GNNs, and theoretically proves that the selection bias will cause the biased estimation on GNN parameters. Then to remove the bias in GNN estimation, we propose a novel debiased GNNs (DGNN) with a differentiated decorrelation regularizer. The differentiated decorrelation regularizer estimates a sample weight for each labeled node such that the spurious correlation of learned embeddings could be eliminated. We analyze the regularizer in causal view and it motivates us to differentiate the weights of the variables based on their contribution to the confounding bias. Then, these sample weights are used for reweighting GNNs to eliminate the estimation bias, and thus, help to improve the stability of prediction on unknown test nodes. Comprehensive experiments are conducted on several challenging graph datasets with two kinds of label selection biases. The results well verify that our proposed model outperforms the state-of-the-art methods and DGNN is a flexible framework to enhance existing GNNs.

Thermal Analysis of a Casson Boundary Layer Flow over a Penetrable Stretching Porous Wedge

This work aims to analyze the Casson thermal boundary layer flow over an expanding wedge in a porous medium with convective boundary conditions and ohmic heating. Moreover, the effects of porosity and viscous dissipation are studied in detail and included in the analysis. The importance of this study is due to its applications in biomedical engineering where the analysis of behavior of non-Newtonian blood flow in arteries and veins is desired. Within the context of blood flow, it is also applicable to many other fields, for instance, radiative therapy, MHD generators, soil machines, melt-spinning, and insulation processes. The modeled problem is a set of PDEs, which is nondimensionalized to derive a nonlinear boundary value problem (BVP). The obtained BVP is solved using the shooting technique, endowed with the order four Runge-Kutta and Newton methods. The impact of different parameters on the momentum and temperature fields is investigated along with two important parameters of physical significance, i.e., the Nusselt number and the surface drag force. Results are validated, and an excellent agreement is seen for the parameters of interest using MATLAB built-in function bvp4c. A significant finding is that by increasing the Casson liquid parameter, the velocity decreases as the wedge expands quicker than the free stream velocity at R=2. However, the velocity increases for the case when R=0.1. A decrease in the Darcy number increases the temperature profile. Furthermore, the convective parameter accelerated the heat transmission rate, and a rise in the Prandtl number thickens the thermal boundary layer. The findings of this investigation contribute to problems in fluid dynamics and heat transfer that involve studying the behavior of a non-Newtonian fluid with Casson rheological properties near a solid porous wedge surface.

Elongation of the proximal descending thoracic aorta and associated hemodynamics increase the risk of acute type B aortic dissection.

Acute type B aortic dissection (ATBAD) is a life-threatening aortic disease. However, little information is available on predicting and understanding of ATBAD. The study sought to explore the underlying mechanism of ATBAD by analyzing the morphological and hemodynamic characteristics related to aortic length. The length and tortuosity of the segment and the whole aorta in the ATBAD group (n= 163) and control group (n= 120) were measured. A fixed anatomic landmark from the distal of left subclavian artery (LSA) to the superior border of sixth thoracic vertebra was proposed as the proximal descending thoracic aorta (PDTA), and the dimensionless parameter, length ratio, was introduced to eliminate the individual differences. The significant morphological parameters were filtrated and the associations between parameters were investigated using statistical approaches. Furthermore, how aortic morphology influenced ATBAD was explored based on idealized aortic models and hemodynamic-related metrics. The PDTA length was significantly increased in the ATBAD group compared with the control group and had a strong positive correlation with the whole aortic length (r= 0.89). The length ratio (LR2) and tortuosity (T2) of PDTA in the ATBAD group were significantly increased (0.15 ± 0.02 vs 0.12 ± 0.02 and 1.73 ± 0.48 vs 1.50 ± 0.36; P< 0.001), and LR2 was positive correlation with T2 (r= 0.73). In receiver-operating curve analysis, the area under the curve was 0.835 for LR2 and 0.641 for T2. Low and oscillatory shear (LOS) was positive correlation with LR2, and the elevated LOS occurred in the distal of LSA. Elongation of PDTA is associated with ATBAD, and the length ratio is a novel predictor. Elongated PDTA induced more aggressive hemodynamic forces, and high LOS regions may correspond to the entry tear location. The synergy of the morphological variation and aggressive hemodynamics creates contributory conditions for ATBAD.

Orbital CT scan parameters in dysthyroid optic neuropathy: a systematic review and meta-analysis.

Addressing Dysthyroid Optic Neuropathy (DON) is crucial due to its debilitating impact in thyroid eye disease (TED). Prompt treatment can preserve vision. Despite lacking definitive diagnostic criteria, computed tomography's (CT) parameters are commonly used for diagnosis. However, these parameters exist without consensus on their diagnostic performance. Systematic review and meta-analysis. We conducted a meta-analysis of studies assessing orbital CT diagnostic performance for DON in adults with TED. We searched various databases including Medline, PubMed, Scopus, and EMBASE, and others electronic databases, until July 2023. Evaluated CT parameters includes Barrett index (BI), fat prolapse via superior-orbital-fissure (SOF), superior-ophthalmic-vein-dilatation (SOVD), and the Nugent score. Diagnostic Test Accuracy analysis (DTA) was performed using R. A total of 9 articles with documented target parameters, collectively analysed 212 orbits with DON. Nugent score exhibited highest diagnostic ability with a log diagnostic odd ratio (logDOR) of 2.64 (95% CI, 2.02, 3.25). Another significant DON indicator was a BI ≥ 50%, with a logDOR of 1.97 (95% CI, 1.17; 2.77). Conversely, fat prolapse via SOF and SOVD proved less sensitive, with a logDOR of 1.42 and 1.09 respectively. Regarding the SROC curve, Nugent score and the BI have the greatest AUC. Variations in study locale, participant demographics, and measurement methods accounted for heterogeneity in meta-analysis. Nugent score and a BI ≥ 50% prove to be significant diagnostic parameters for DON, distinguishing them from fat prolapse via SOF and SOVD. Prioritizing these parameters can lead to prompt treatment and thus enhanced visual outcomes. CRD42023446376.

The Effectiveness of Adjuvant PD-1 Inhibitors in Patients With Surgically Resected Stage III/IV Acral Melanoma.

Our aim was to assess the efficacy of adjuvant programmed cell death protein-1 (PD-1) inhibitors and compare the other adjuvant treatments in patients with surgically resected stage III or IV acral melanoma. This study is a multicenter, retrospective analysis. We included 114 patients with stage III or IV acral malignant melanoma who underwent surgery within the past 10 years. We analyzed the effect of adjuvant programmed cell death protein-1 inhibitors on disease-free survival (DFS). The mean follow-up was 40 months, during which 69 (59.5%) patients experienced recurrence. Among the participants, 64 (56.1%) received systemic adjuvant therapy. Specifically, 48.4% received anti-PD-1 therapy, 29.7% received interferon, 14.1% received tezozolomide, and 7.8% received B-Raf proto-oncogene/mitogen-activated protein kinase inhibitors. Patients who received adjuvant therapy had a median DFS of 24 (10.9-37.2) months, whereas those who did not receive adjuvant therapy had a median DFS of 15 (9.8-20.2) months. Multivariate analysis for DFS revealed that the receipt of adjuvant therapy and lymph node metastasis stage were independent significant parameters ( P = 0.021, P = 0.018, respectively). No statistically significant difference was observed for DFS between programmed cell death protein-1 inhibitor treatment and other adjuvant treatments. Regarding overall survival (OS), patients who received adjuvant treatment had a median OS of 71 (30.4-111.7) months, whereas those who did not receive adjuvant treatment had a median OS of 38 (16.7-59.3; P = 0.023) months. In addition, there were no significant differences in OS observed between various adjuvant treatment agents ( P = 0.122). In our study, we have shown that adjuvant therapy had a positive effect on both DFS and OS in patients with stages III-IV acral melanoma who underwent curative intent surgery. Notably, we found no significant differences between anti-PD-1 therapy and other adjuvant therapies.

Lactate Albumin Ratio as Predictor of 28 Days Mortality in Septic Shock Patients and its Correlation to SOFA Score for Assessment of Organ Dysfunction: A Prospective Observational Study

Background: Sepsis and septic shock are leading cause of mortality in ICU patients. Prognostication and early intervention forms most integral part of management of septic shock. There many validated score to assess the critical state of septic shock patients. APACHE2 and SOFA scores are widely used world over. Multiple parameters used in the score and complexity involved have lead researchers to search for more convenient and simple markers. Lactate albumin Ratio (L/A ratio) turns out to be one such marker, combining two easily available parameters and abbreviating fallacies associated with both parameters when assessed individually. We aimed to assess L/A ratio as predictor of all cause 28 days’ mortality in septic shock patients and correlated L/A ratio to the SOFA score to assess organ dysfunction.&#x0D; Methods: It was a prospective observational study. 130 patients admitted in the state of septic shock were studied. L/A ratio was obtained at 24 hours after admission in the ICU. SOFA score was also calculated at 24 hours. ROC curve was plotted for L/A ratio to evaluate diagnostic accuracy, as predictor of 28 days’ mortality. Multivariable analysis of L/A ratio with other significant parameters was performed. Spearman rank correlation coefficient was used for correlation of L/A ratio with SOFA score.&#x0D; Results: L/A ratio at 24 hours (AUC 0.99; 95% CI: 0.95 to 0.99), p value =0.0001, was significant predictor of mortality at 28 days, at cut off point of &gt;1.15. Significant positive correlation was found between L/A ratio and SOFA score at 24 hours, with correlation coefficient of 0.828, p value=0.0001.&#x0D; Conclusion: L/A ratio was found to be independent predictor of 28 days’ Mortality in Septic Shock Patients. Also strong correlation was obtained of L/A ratio to SOFA score to assess organ dysfunction in septic shock patients.

Size effect on compressive behavior of GFRP bars

In the last three decades, studies investigating the use of Glass Fiber Reinforced Polymer (GFRP) bars as an alternative to conventional steel rebars have increased due to their corrosive resistance. In addition to corrosion resistance, GFRP bars utilize high specific tensile strength, which makes them highly desirable in civil engineering applications. However, major design guidelines for GFRP-reinforced concrete structures currently do not consider their compressive contribution. Nevertheless, there is a growing trend in utilizing GFRP bars as compressive elements, driven by various studies demonstrating their ability to bear compressive loads effectively. This increasing demand underscores the need to comprehend the mechanical properties of GFRP bars, particularly in terms of their compressive behavior. Furthermore, a standardized test method to evaluate their compressive properties has not yet been developed. Addressing these gaps, this research paper focuses on investigating the influence of specimen size on the compressive strength of GFRP bars, specifically emphasizing on the compressive properties of GFRP bars. Compressive tests were conducted on GFRP specimens with varying diameters while maintaining a constant slenderness ratio. The findings from these compression tests shed light on the critical role of size in the compressive behavior of GFRP. This research emphasizes the importance of considering size as a significant parameter in designing mechanical properties for GFRP reinforcements.

3D VISIBILITY ANALYSES USING BIM &amp; GIS INTEGRATION

Abstract. Visibility is a significant parameter for defining, developing, and evaluating space in the AEC (Architecture, Engineering, and Construction) Industry. Therefore, visibility is considered when designing visually comfortable buildings. Visibility is also a significant parameter in building appraisal. Therefore, in this study, visibility was examined through 3-dimensional (3D) isovists, which are thought to represent the human perception of vision. Visibility was examined regarding spatial openness and mathematically calculated “visual openness indexes” were developed for building valuation. The analysis area was selected in Istanbul, and the urban model was created by using Geographic Information System (GIS) data in Building Information Modelling (BIM) software. Due to, economic reasons affecting different stakeholders in building valuation, it is necessary to create an accurate urban model. For this reason, in the study, the process of obtaining current and accurate GIS data covering the world from open sources is preferred. Then, the process of correctly transferring GIS data to BIM is explained. A sufficient urban model for analysis is created. To calculate the openness indexes, the dormitory in the Golden Horn was modelled in BIM, and different rooms' openness indexes were calculated. Some solutions have been presented to ensure the modelling process with large file sizes and in the calculation of visibility factors. To allow different AEC stakeholders to calculate these indexes, modelling, and calculation is done within BIM. Eventually, it was calculated how much the building users cognitively perceive the openness of the view, and a mathematical comparison of the different rooms' openness indexes was provided.

Experimental and numerical study on flow characteristics of dual‐cavity die based on multi‐objective optimization

AbstractSlot die coating is a common method of manufacturing electrodes for lithium‐ion batteries. In this paper, a multi‐objective optimization research on the geometry of dual‐cavity die is investigated based on the numerical simulation of lithium battery anode slurry flow combined with the surface response optimization method. First, we use the Plackett–Burman test method to screen the geometric parameters significantly affecting the optimization objective. Then, based on the Box–Behnken response surface analysis method, we determine the optimal combination of parameters. The results show that the inner cavity radius and slot gap are the main structural variables affecting exit‐velocity uniformity and die deformation. The slot gap and slot length mainly affect the inlet pressure. The coefficient of deviation of exit velocity uniformity and average inlet pressure of the optimized die structure decreased by 51.3% and 7.6%, respectively. This research provides theoretical guidance for the design of slot coating dies.Highlights Screening significant geometric parameters using Plackett–Burman test methodology. Box–Behnken response surface analysis to determine optimal parameters. Velocity uniformity and inlet pressure increased by 51.3% and 7.6%, respectively.

Navigating the Dynamics of Squeeze Film Dampers: Unraveling Stiffness and Damping Using a Dual Lens of Reynolds Equation and Neural Network Models for Sensitivity Analysis and Predictive Insights

The squeeze film damper (SFD) is proven to be highly effective in mitigating rotor vibration as it traverses the critical speed, thus making it extensively utilized in the aeroengine domain. In this paper, we investigate the stiffness and damping of SFD using the Reynolds equation and neural network models. Our specific focus includes examining the structural and operating parameters of SFDs, such as clearance, feed pressure of oil, rotor whirl, and rotational speed. Firstly, the pressure distribution analytical model of the oil film inside the SFD based on the hydrodynamic lubrication theory is established, as described by the Reynolds equation. It obtained oil film forces, pressure, stiffness, and damping values under various sets of structural, lubrication, and operating parameters, including length, clearance, boundary pressure at both sides, rotational speed, and whirling motion, by applying difference computations to the Reynolds equation. Secondly, according to the significant analyses of the obtained oil film stiffness and damping, the following three parameters of the most significance are found: clearance, rotational speed, and rotor whirl. Furthermore, neural network models, including GA-BP and decision tree models, are established based on the obtained results of difference computation. The numerical simulation and calculation of these models are then applied to show their validity with all given parameters and the three significant parameters separately as two sets of model input. Regardless of either set of model inputs, these established neural network models are capable of predicting the nonlinear stiffness and damping of the oil film inside an SFD. These sensitive parameters merely require measurement, followed by the utilization of a neural network to predict stiffness and damping instead of the Reynolds equation. This process serves structural enhancement, facilitates parameter optimization in SFDs, and provides crucial support for refining the design parameters of SFDs.

A comprehensive 4E (energy, exergy, economic, environmental) analysis of novel pyramid solar still coupled with pulsating heat pipe: An experimental study

Remote regions and poor communities often suffer the most from the shortage of fresh water. Providing a sustainable supply of fresh water and improving the quality of life for millions of people worldwide is crucial.In the present study, two experimental setups, namely (i) a modified pyramid solar still (MPSS) coupled with pulsating heat pipe and (ii) the conventional pyramid solar still (CPSS), were designed, constructed, and investigated under the same ambient conditions of Surat City, India. The MPSS and CPSS were compared for freshwater yield, energy, exergy, and economic analysis at four different depths. The experimental results demonstrate that the accumulated distillate yield for the MPSS was higher than that of the CPSS. The maximum freshwater yield obtained for MPSS and CPSS was approximately 4.10 L/m2-day and 3.05 L/m2-day at 2 cm depth. During the experiment, improvement in the yield of MPSS due to the attachment of PHP was 34.42%, 37.13%, 31.87%, and 29.62 % as compared to CPSS for 2 cm, 3 cm, 4 cm, and 5 cm, respectively. Moreover, the energy and exergy efficiency of MPSS was higher than CPSS. The maximum enhancement for average energy and average exergy efficiency obtained in MPSS was 25.51% and 29.31% compared to CPSS at 2 cm water depth. The economic analysis exhibits that the cost per liter of fresh water was decreased by up to 13.33% compared with CPSS at 2 cm depth. Based on the environmental assessment, the MPSS's most significant environmental parameter (CO2 mitigation) at 2 cm of water depth was 18.79 tons. The present study's advantage is using a pulsating heat pipe and passively transferring the additional heat energy into the basin.

Performance evaluation of coated tool in end milling of XH67MBTHO alloy

ABSTRACT XH67MBTHO is a promising nickel-based alloys which finds applications in aerospace engineering due to its exceptional hot hardness, corrosion resistance and strength at superior temperatures. However, due to its weak thermal conductivity(k=13.4W/mK) and high tendency to work harden, XH67MBTHO exhibits poor machinability. Therefore, the major objective of this study is to investigate the machinability characteristics of XH67MBTHO, using TiAlN coated milling inserts at three levels of speed, feed, and axial depth of cut. Significant parameters affecting responses were identified using ANOVA. SEM and EDS analysis of worn-out tool shows delamination, adhesion, abrasion and diffusion were major tool wear mechanism. Also, as machining conditions shift from higher to lower, tool life increases to 1200 seconds from 49.18 seconds and surface roughness decreases from 0.52 µm to 0.18 µm respectively. Also, a regression and Artificial Neural Network model were developed to predict the process response in terms of input parameters.

Ultrasound-assisted sustainable recycling of valuable metals from spent Li-ion batteries via optimisation using response surface methodology

In this study, the use of response surface methodology was proposed to optimise a novel green leaching process for recovering Li and Co from spent lithium-ion batteries (LIBs), assisted by ultrasonic irradiation and gluconic acid. This work investigated various parameters such as temperature, time, gluconic acid concentration, and ultrasound power (US power). To enhance the interpretability and optimisation of experimental data on metal leaching, the central composed design (CCD) was utilised to establish a predictive model. A statistical analysis using the analysis of variance (ANOVA) test was conducted to validate the quadratic model and determine the significant operating parameters, indicated by p-values (< 0.05). The model exhibited Adj R2 values of 0.98 for Li leaching and 0.96 for Co leaching, respectively. To confirm the results, additional characterisations of the solid residues were performed using X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), and Fourier transformation infrared spectrometer (FT-IR/NIR). The leach liquor was subjected to precipitation and crystallisation methods to recover Li and Co. The purity of Li2CO3 and Co3O4 was confirmed to be ≥ 99% and ≥ 98.8%, respectively, through ICP-OES analysis. Finally, the recovered Li2CO3 and Co3O4 were utilised in a 1:1 molar ratio for regenerating new LiCoO2 cathode materials using a solid-state method. The resulting cathode materials exhibited high purity (≥ 99.43%) as confirmed by ICP-OES analysis. The obtained results demonstrate that ultrasound irradiation significantly enhanced the dissolution of metal oxides within the mixture. Furthermore, gluconic acid exhibited a dual function, acting as both an oxidising and reducing agent. Consequently, this process presents a promising and sustainable alternative for recycling valuable metals from spent LIBs.

Parametric study of WEDM of titanium grade 12 using RSM and desirability approach

The present experimental study aims to scrutinize the effect of different process parameters on the material removal rate (MRR) and surface roughness (SR) of titanium grade 12 alloy during wire electro-discharge machining (WEDM) using response surface methodology. Four parameters (pulse-on time, pulse-off time, wire feed and gap voltage) and three levels of each selected variable are considered to conduct the experimental work. Depending on experimental results, a mathematical model is generated for both MRR and SR. An analysis of variance (Anova) study is performed to find significant process parameters. The Anova results show that the developed models for both MRR and SR are significant, and pulse-on time is found to be the most significant parameter. Additionally, the desirability approach is used to scrutinize the single-objective and multi-objective criteria of response variables. The desirability functions for all of the cases are found to be 1. The maximum MRR is observed to be 12.4845 mm3/s, while the minimum value of SR is found to be 1.4911 μm. For multi-objective optimization, the maximum value of MRR and the minimum value of SR are obtained as 12.0942 mm3/min and 1.7167 μm, respectively. Finally, the surface morphology of the machined surfaces is examined using scanning electron microscopy micrographs.

Development and Validation of a Diagnostic Histopathological Scoring System for Capsular Contracture Based on 720 Breast Implant Capsules.

Capsular contracture is traditionally evaluated with the Baker classification, but this has notable limitations regarding reproducibility and objectivity. The aim of this study was to develop and validate procedure-specific histopathological scoring systems to assess capsular contracture severity. Biopsies of breast implant capsules were used to develop histopathological scoring systems for patients following breast augmentation and breast reconstruction. Ten histological parameters were evaluated by multivariable logistic regression to identify those most associated with capsular contracture. Significant parameters (P < .05) were selected for the scoring systems and assigned weighted scores (1-10). Validation was assessed from the area under the curve (AUC) and the mean absolute error (MAE). A total of 720 biopsies from 542 patients were included. Four parameters were selected for the augmentation scoring system, namely, collagen layer thickness, fiber organization, inflammatory infiltration, and calcification, providing a combined maximum score of 26. The AUC and MAE for the augmentation scoring system were 81% and 0.8%, which is considered strong. Three parameters were selected for the reconstruction scoring system, namely, fiber organization, collagen layer cellularity, and inflammatory infiltration, providing a combined maximum score of 19. The AUC and MAE of the reconstruction scoring system were 72% and 7.1%, which is considered good. The new histopathological scoring systems provide an objective, reproducible, and accurate assessment of capsular contracture severity. We propose these novel scoring systems as a valuable tool for confirming capsular contracture diagnosis in the clinical setting, for research, and for implant manufacturers and insurance providers in need of a confirmed capsular contracture diagnosis.

Artificial Intelligence in Early Diagnosis of Preeclampsia.

Every day, 810 women die of preventable causes related to pregnancy and childbirth worldwide, and preeclampsia is among the top three causes of maternal deaths. To develop a diagnostic system with artificial intelligence for the early diagnosis of preeclampsia. This retrospective study included pregnant women who were screened for the inclusion criteria on the hospital's database, and the sample consisted of the data of 1158 pregnant women diagnosed with preeclampsia and 9194 pregnant women who were not diagnosed with preeclampsia at Kahramanmaras Necip Fazıl City Hospital Gynecology and Pediatrics Additional Service Building, Kahramanmaras/Turkey. The statistical analysis was performed using the Statistical Package for social sciences (SPSS) version 22 for windows. Artificial intelligence models were created using Python, scikit-learn, and TensorFlow. The model achieved 73.7% sensitivity (95% confidence interval (CI): 70.2%-77.1%) and 92.7% specificity (95% CI: 91.7%-93.6%) on the test set. Furthermore, the model had 90.6% accuracy (95% CI: 90.1% - 91.1%) and an area under the curve (AUC) value of 0.832 (95% CI: 0.818-0.846). The significant parameters in predicting preeclampsia in the model were hemoglobin (HGB), age, aspartate transaminase level (AST), alanine transferase level (ALT), and the blood group. Artificial intelligence is effective in the prediction and diagnosis of preeclampsia.

Improved seizure liability detection by combining rat hippocampal brain slice electrophysiology with in vivo behavior observation following intracerebroventricular drug administration

An adverse effect of drug candidates, seizure is a serious issue in drug development. Improving evaluation systems for seizure liability is crucial for selecting good candidates. Firstly, in vitro electrophysiological measurement by a multielectrode array system in rat hippocampal brain slices was employed to confirm an increase in electrically evoked population spike (PS) area, the occurrence of multiple population spikes (MPSs), and thereby the seizure liability of five positive control chemicals: picrotoxin, 4-aminopyridine, pentylenetetrazole, penicillin G, and chlorpromazine. Aspirin, a negative control, did not affect PS area or generate MPSs. Furthermore, baclofen, an anticonvulsant drug, decreased PS area and inhibited the increase in PS area or occurrence of MPSs induced by picrotoxin. A comparative study of seizure liability among carbapenem antibiotics revealed that tienam > carbenin > omegacin and finibax. Despite leading to a strong decrease in PS area, physostigmine, cisplatin, and paroxetine still produced MPSs. Therefore, the increase in PS area or the occurrence of the MPS are considered significant evaluation parameters for seizure liability.In contrast, the in vitro electrophysiological measurement could not detect the seizure liability of diphenhydramine or fluvoxamine. A follow-up study of in vivo mouse behavioral change induced by intracerebroventricular administration of these drugs clearly detected convulsions. The in vitro electrophysiological study using hippocampal brain slices combined with in vivo behavior observation study of drug candidates administered by intracerebroventricular injection can implement to assess the seizure liability of even small amounts, especially in the early stages of drug development.

Current State of Advances in Quantification and Modeling of Hydrological Droughts

Hydrological droughts may be referred to as sustained and regionally extensive water shortages as reflected in streamflows that are noticeable and gauged worldwide. Hydrological droughts are largely analyzed using the truncation level approach to represent the desired flow condition such as the median, mean, or any other flow quantile of an annual, monthly, or weekly flow sequence. The quantification of hydrologic droughts is accomplished through indices, such as the standardized streamflow index (SSI) in tandem with the standardized precipitation index (SPI) commonly used in meteorological droughts. The runs of deficits in the SSI sequence below the truncation level are treated as drought episodes, and thus, the theory of runs forms an essential tool for analysis. The parameters of significance from the modeling perspective of hydrological droughts (or tantamount to streamflow droughts in this paper) are the longest duration and the largest magnitude over a desired return period of T-year (or month or week) of the streamflow sequences. It is to be stressed that the magnitude component of the hydrological drought is of paramount importance for the design and operation of water resource storage systems such as reservoirs. The time scales chosen for the hydrologic drought analysis range from daily to annual, but for most applications, a monthly scale is deemed appropriate. For modeling the aforesaid parameters, several methodologies are in vogue, i.e., the empirical fitting of the historical drought sequences through a known probability density function (pdf), extreme number theorem, Markov chain analysis, log-linear, copulas, entropy-based analyses, and machine learning (ML)-based methods such as artificial neural networks (ANN), wavelet transform (WT), support vector machines (SVM), adaptive neuro-fuzzy inference systems (ANFIS), and hybrid methods involving entropy, copulas, and machine learning-based methods. The forecasting of the hydrologic drought is rigorously conducted through machine learning-based methodologies. However, the traditional stochastic methods such as autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA), copulas, and entropy-based methods are still popular. New techniques for flow simulation are based on copula and entropy-based concepts and machine learning methodologies such as ANN, WT, SVM, etc. The simulated flows could be used for deriving drought parameters in consonance with traditional Monte Carlo methods of data generation. Efforts are underway to use hydrologic drought models for reservoir sizing across rivers. The ML methods whilst combined in the hybrid form hold promise in drought forecasting for better management of existing water resources during the drought periods. Data mining and pre-processing techniques are expected to play a significant role in hydrologic drought modeling and forecasting in future.