Predicting the presence of adherent perinephric fat using MRI radiomics combined with machine learning.

OBJECTIVES: Adherent perinephric fat (APF) poses significant challenges to surgical procedures. This study aimed to evaluate the usefulness of machine learning algorithms combined with MRI-based radiomics features for predicting the presence of APF. MATERIALS AND METHODS: Patients with renal cell carcinoma who underwent surgery between April 2019 and February 2022 at Chonnam National University Hwasun Hospital were retrospectively screened, and 119 patients included. Twenty-one and seventeen patients were set aside for the internal and external test sets, respectively. Pre-operative T1-weighted MRI acquired at 60 s following a contrast injection (T1w-60) were collected. For each T1w-60 data, two regions of interest (ROIs) were manually drawn: the perinephric fat tissue and an aorta segment on the same level as the targeted kidney. Preprocessing steps included resizing voxels, N4 Bias Correction filtering, and aorta-based normalization. For each patient, 851 radiomics features were extracted from the ROI of perinephric fat tissue. Gender and BMI were added as clinical factors. Least Absolute Shrinkage and Selection Operator was adopted for feature selection. We trained and evaluated five models using a 4-fold cross validation. The final model was chosen based on the highest mean AUC across four folds. The performance of the final model was evaluated on the internal and external test sets. RESULTS: A total of 15 features were selected in the final set. The final model achieved the accuracy, sensitivity, specificity, and AUC of 81% (95% confidence interval, 61.9-95.2%), 72.7% (42.9-100%), 90% (66.7-100%), and 0.855 (0.615-1.0), respectively on the internal test set, and 88.2% (70.6-100%), 100% (100-100%), 80% (50%-100%), 0.971 (0.871-1.0), respectively on the external test set. CONCLUSIONS: Our study demonstrated the feasibility of machine learning algorithms trained with MRI-based radiomics features for APF prediction. Further studies with a multi-center approach are necessary to validate our findings.

Accurate prediction of pure uric acid urinary stones in clinical context via a combination of radiomics and machine learning.

PURPOSE: Oral chemolysis is an effective and non-invasive treatment for uric acid urinary stones. This study aimed to classify urinary stones into either pure uric acid (pUA) or other composition (Others) using non-contrast-enhanced computed tomography scans (NCCTs). METHODS: Instances managed at our institution from 2019 to 2021 were screened. They were labeled as either pUA or Others based upon composition analyses, and randomly split into training or testing data set. Several instances contained multiple NCCTs which were all collected. In each of NCCTs, individual urinary stone was treated as individual sample. From manually drawn volumes of interest, we extracted original and wavelet radiomics features for each sample. The most important features were then selected via the Least Absolute Shrinkage and Selection Operator for building the final model on a Support Vector Machine. Performance on the testing set was evaluated via accuracy, sensitivity, specificity, and area under the precision-recall curve (AUPRC). RESULTS: There were 302 instances, of which 118 had pUA urinary stones, generating 576 samples in total. From 851 original and wavelet radiomics features extracted for each sample, 10 most important features were ultimately selected. On the testing data set, accuracy, sensitivity, specificity, and AUPRC were 93.9%, 97.9%, 92.2%, and 0.958, respectively, for per-sample prediction, and 90.8%, 100%, 87.5%, and 0.902, respectively, for per-instance prediction. CONCLUSION: The machine learning algorithm trained with radiomics features from NCCTs can accurately predict pUA urinary stones. Our work suggests a potential assisting tool for stone disease treatment selection.

A new compact and low phase imbalance microstrip coupler for 5G wireless communication systems.

Microstrip couplers play a crucial role in signal processing and transmission in various applications, including RF and wireless communication, radar systems, and satellites. In this work, a novel microstrip 180° coupler is designed, fabricated and measured. The layout configuration of this coupler is completely new and different from the previously reported Rat-race, branch-line and directional couplers. To obtain the proposed coupler, the meandrous coupled lines are used and analyzed mathematically. To improve the performance of our coupler, an optimization method is used. The designed coupler is very compact with an overall size of 0.014λg2. The obtained values of S21 and S31 are -3.45 dB and -3.75 dB, respectively at the operating frequency, while the fractional bandwidth (FBW) is 56.2%. It operates at fo = 1.61 GHz (suitable for 5G applications) and can suppress harmonics up to 2.17fo. Another advantage of this coupler is its low phase imbalance, while the phase difference between S21 and S31 is 180°± 0.023°. Therefore, our device is a balanced coupler with ±0.3 dB magnitude unbalance at its operating frequency. It is important to note that it is very difficult to find a coupler that has all these advantages at the same time. The proposed 180° coupler is fabricated and measured. The comparison shows that the measurement and simulation results are in good agreement. Therefore, the proposed coupler can be easily used in designing high-performance 5G communication systems.

The diagnostic performance of AFP and PIVKA-II models for non-B non-C hepatocellular carcinoma.

OBJECTIVE: This study aims to describe the diagnostic performance of alpha-fetoprotein (AFP), alpha-fetoprotein L3 isoform (AFP-L3), protein induced by vitamin K absence II (PIVKA-II), and combined biomarkers for non-B non-C hepatocellular carcinoma (NBNC-HCC). RESULTS: A total of 681 newly-diagnosed primary liver disease subjects (385 non-HCC, 296 HCC) who tested negativity for the hepatitis B surface antigen (HBsAg) and hepatitis C antibody (anti-HCV) enrolled in this study. At the cut-off point of 3.8 ng/mL, AFP helps to discriminate HCC from non-HCC with an area under the curve (AUC) value of 0.817 (95% confidence interval [CI]: 0.785-0.849). These values of AFP-L3 (cut-off 0.9%) and PIVKA-II (cut-off 57.7 mAU/mL) were 0.758 (95%CI: 0.725-0.791) and 0.866 (95%CI: 0.836-0.896), respectively. The Bayesian Model Averaging (BMA) statistic identified the optimal model, including patients' age, aspartate aminotransferase, AFP, and PIVKA-II combination, which helps to classify HCC with better performance (AUC = 0.896, 95%CI: 0.872-0.920, P < 0.001). The sensitivity and specificity of the optimal model reached 81.1% (95%CI: 76.1-85.4) and 83.2% (95%CI: 78.9-86.9), respectively. Further analyses indicated that AFP and PIVKA-II markers and combined models have good-to-excellent performance detecting curative resected HCC, separating HCC from chronic hepatitis, dysplastic, and hyperplasia nodules.

The impact of thermal insulating materials in heat loss control in smart green buildings using experimental and swarm intelligent analysis.

The efficacy of saving energy standards depends on the ability to anticipate the heat loss of buildings. Environmentally friendly materials, also known as eco-friendly or sustainable materials, have a minimal negative impact on the environment throughout their life cycle. These materials are designed to conserve resources, reduce pollution, and promote sustainability. The characteristics of non-stationary and non-linear heat loss through environmentally friendly materials make it challenging to anticipate accurately. At the same time, many of the industry's presently accessible computational models have been created with this in mind; the majority call for powerful computers and time-consuming computations. The artificial neural network (ANN) has been utilized for prediction, and ground-breaking research has shown the viability of this strategy. This research proposes an artificial neural network (ANN) prototype to estimate construction cooling load usage. ANN is integrated with the vortex search algorithm (VS), stochastic fractal search (SFS), and multi-verse optimizer (MVO) models to compare the three models' outcomes and suggest a more accurate strategy. These techniques make a linear mapping among the output and input parameters, often utilized for modeling and regression. The value of the multiple determination coefficient is also determined. The values of the training R2 (coefficient of multiple determination) are 0.9464, 0.99827, and 0.99522 for VS-MLP, SFS-MLP, and MVO-MLP, respectively, with an unknown dataset which is acceptable. The training RMSE amounts for VS-MLP, SFS-MLP, and MVO-MLP are 0.06433, 0.00619, and 0.01028 for the unknown dataset, which is acceptable. According to the MAE values of 0.0082902, 0.0047834, and 0.0076534 in the training phase for VS-MLP, SFS-MLP, and MVO-MLP approaches and the values of testing MAE error of 0.029107, 0.018167, and 0.029212 for VS-MLP, SFS-MLP, and MVO-MLP approaches, respectively, it is obtained that the SFS-MLP has a lower MAE value. The lowest RMSE value and the higher R2 value indicate the favorable accuracy of the SFS-MLP technique.

Parametric study and process modeling for metronidazole removal by rhombic dodecahedron ZIF-67 crystals.

Metronidazole (MNZ) is an extensively used antibiotic against bacterial infections for humans and farm animals. Prevention of antibiotics discharge is essential to prevent adverse environmental and health impacts. A member of metal-organic frameworks, zeolite imidazole framework-67 with cobalt sulfate precursor (ZIF-67-SO4) and exceptional physio-chemical properties was prepared via room temperature precipitation to adsorb MNZ. The study framework was designed by Box-Behnken Design to evaluate the effect of pH, ZIF-67-SO4 dose, and contact time on adsorption efficiency. The polynomial model fitted the adsorption system indicated the optimal condition for 97% MNZ removal occurs at pH = 7, adsorbent dosage = 1 g/L, and mixing time = 60 min. The model also revealed that the removal increased with contact time and decreased at strong pH. Equilibrium and kinetic study also indicated the adsorption of MNZ followed the intra-particle diffusion model and the Langmuir isotherm model with a qmax = 63.03 mg/g. The insignificant loss in removal efficacy in use-reuse adsorption cycles reflected the practical viability of ZIF-67-SO4.

Boosting the adsorptive and photocatalytic performance of MIL-101(Fe) against methylene blue dye through a thermal post-synthesis modification.

Photocatalytic degradation under ultra-low powered light is a viable advanced oxidation process technique against extensive emerging contaminants. As a new and remarkable class of nanoporous materials, metal-organic frameworks (MOFs), attract interest for the supreme adsorptive and photocatalytic functionalities. An outstanding MOF, MIL-101(Fe) chosen as a photocatalyst template for the synthesis of α-Fe2O3 by a simple thermal modification to improve the structural properties toward methylene blue (MB) eradication. Octahedron-like α-Fe2O3 photocatalyst (Modified MIL-101(Fe), M-MIL-101(Fe)) was superior in dispersion and separation properties in aqueous medium. Moreover, the adsorptive and catalytic performance was increased for modified form by ~ 7.3% and ~ 17.1% compared to pristine MIL-101(Fe), respectively. Synergistic improvement of MB removal achieved by simultaneous adsorption/degradation under 5-W LED irradiation. Parametric study indicated an 18.1% and 44.5% improvement in MB removal was observed by increasing pH from 4 to 10, and M-MIL-101(Fe) dose from 0.2 to 1 g L-1, respectively. MB removal followed the pseudo-second-order kinetics model and the process efficiency dropped by 38% as MB concentration increased from 5 to 20 mg L-1. Radical trapping tests revealed the significant role of [Formula: see text] and electron radicals as the major participants in dye degradation. A significant loss in the efficiency of M-MIL-101(Fe) was observed in the reusability tests that is good to study further. In conclusion, a simple thermal post-synthesis modification on MIL-101(Fe) improved its structural, catalytic, and adsorptive properties against MB.

Fabrication of new composite NCuTiO2/CQD for photocatalytic degradation of ciprofloxacin and pharmaceutical wastewater treatment: degradation pathway, toxicity assessment.

In this research, the photocatalytic degradation of CIP from aqueous solutions using CQD decorated on N-Cu co-doped titania (NCuTCQD) was made during two synthesis steps by sol-gel and hydrothermal methods. The fabricated catalysts were analyzed using various techniques, including XRD, FT-IR, BET, FESEM, EDX, and DRS. The results showed that N and Cu atoms were doped on TiO2 and CQD was well deposited on NCuT. The investigation of effective operational parameters demonstrated that the complete removal of ciprofloxacin (CIP: 20 mg/L) could be achieved at pH 7.0, NCuTCQD4wt%: 0.8 g/L, and light intensity: 100 mW/cm2 over 60 min reaction time. The O2•- and OH˙ radicals were identified as the primary reactive species during the decontamination process. The synthesized photocatalyst could be recycled after six consecutive cycles of CIP decomposition with an insignificant decrease in performance. Pharmaceutical wastewater was treated through the optimum degradation conditions which showed the photocatalytic degradation eliminated 89% of COD and 75% of TOC within 180 min. In the effluent toxicity evaluation, the EC50 values for treated and untreated pharmaceutical wastewater increased from 62.50% to 140%, indicating that the NCuTCQD4wt%/Vis system can effectively reduce the toxic effects of pharmaceutical wastewater on aquatic environments.

microRNA-382 as a tumor suppressor? Roles in tumorigenesis and clinical significance.

MicroRNAs (miRNAs) are small single-stranded RNAs belonging to a class of non-coding RNAs with an average length of 18-22 nucleotides. Although not able to encode any protein, miRNAs are vastly studied and found to play role in various human physiologic as well as pathological conditions. A huge number of miRNAs have been identified in human cells whose expression is straightly regulated with crucial biological functions, while this number is constantly increasing. miRNAs are particularly studied in cancers, where they either can act with oncogenic function (oncomiRs) or tumor-suppressors role (referred as tumor-suppressor/oncorepressor miRNAs). miR-382 is a well-studied miRNA, which is revealed to play regulatory roles in physiological processes like osteogenic differentiation, hematopoietic stem cell differentiation and normal hematopoiesis, and liver progenitor cell differentiation. Notably, miR-382 deregulation is reported in pathologic conditions, such as renal fibrosis, muscular dystrophies, Rett syndrome, epidural fibrosis, atrial fibrillation, amelogenesis imperfecta, oxidative stress, human immunodeficiency virus (HIV) replication, and various types of cancers. The majority of oncogenesis studies have claimed miR-382 downregulation in cancers and suppressor impact on malignant phenotype of cancer cells in vitro and in vivo, while a few studies suggest opposite findings. Given the putative role of this miRNA in regulation of oncogenesis, assessment of miR-382 expression is suggested in a several clinical investigations as a prognostic/diagnostic biomarker for cancer patients. In this review, we have an overview to recent studies evaluated the role of miR-382 in oncogenesis as well as its clinical potential.

Nanocatalysts as fast and powerful medical intervention: Bridging cochlear implant therapies and advanced modelling using Hidden Markov Models (HMMs) for effective treatment of inner ear infections.

As human population growth and waste from technologically advanced industries threaten to destabilise our delicate ecological equilibrium, the global spotlight intensifies on environmental contamination and climate-related changes. These challenges extend beyond our external environment and have significant effects on our internal ecosystems. The inner ear, which is responsible for balance and auditory perception, is a prime example. When these sensory mechanisms are impaired, disorders such as deafness can develop. Traditional treatment methods, including systemic antibiotics, are frequently ineffective due to inadequate inner ear penetration. Conventional techniques for administering substances to the inner ear fail to obtain adequate concentrations as well. In this context, cochlear implants laden with nanocatalysts emerge as a promising strategy for the targeted treatment of inner ear infections. Coated with biocompatible nanoparticles containing specific nanocatalysts, these implants can degrade or neutralise contaminants linked to inner ear infections. This method enables the controlled release of nanocatalysts directly at the infection site, thereby maximising therapeutic efficacy and minimising adverse effects. In vivo and in vitro studies have demonstrated that these implants are effective at eliminating infections, reducing inflammation, and fostering tissue regeneration in the ear. This study investigates the application of hidden Markov models (HMMs) to nanocatalyst-loaded cochlear implants. The HMM is trained on surgical phases in order to accurately identify the various phases associated with implant utilisation. This facilitates the precision placement of surgical instruments within the ear, with a location accuracy between 91% and 95% and a standard deviation between 1% and 5% for both sites. In conclusion, nanocatalysts serve as potent medicinal instruments, bridging cochlear implant therapies and advanced modelling utilising hidden Markov models for the effective treatment of inner ear infections. Cochlear implants loaded with nanocatalysts offer a promising method to combat inner ear infections and enhance patient outcomes by addressing the limitations of conventional treatments.

Understanding the lncRNA/miRNA-NFκB regulatory network in diabetes mellitus: From function to clinical translation.

Diabetes mellitus (DM) and its significant ramifications make out one of the primary reasons behind morbidity worldwide. Noncoding RNAs (ncRNAs), such as microRNAs and long noncoding RNAs, are involved in regulating manifold biological processes, including diabetes initiation and progression. One of the established pathways attributed to DM development is NF-κB signaling. Neurons, ß cells, adipocytes, and hepatocytes are among the metabolic tissues where NF-κB is known to produce a range of inflammatory chemokines and cytokines. The direct or indirect role of ncRNAs such as lncRNAs and miRNAs on the NF-κB signaling pathway and DM development has been supported by many studies. As a result, effective diabetes treatment and preventive methods will benefit from a comprehensive examination of the interplay between NF-κB and ncRNAs. Herein, we provide a concise overview of the role of NF-κB-mediated signaling pathways in diabetes mellitus and its consequences. The reciprocal regulation of ncRNAs and the NF-κB signaling pathway in diabetes is then discussed, shedding light on the pathogenesis of the illness and its possible therapeutic interventions.

The Diagnostic Power of Circulating miR-1246 in Screening Cancer: An Updated Meta-analysis.

Background: MicroRNA-1246 (miR-1246), an oncomiR that regulates the expression of multiple cancer-related genes, has been attracted and studied as a promising indicator of various tumors. However, diverse conclusions on diagnostic accuracy have been shown due to the small sample size and limited studies included. This meta-analysis is aimed at systematically assessing the performance of extracellular circulating miR-1246 in screening common cancers. Methods: We searched the PubMed/MEDLINE, Web of Science, Cochrane Library, and Google Scholar databases for relevant studies until November 28, 2022. Then, the summary receiver operating characteristic (SROC) curves were drawn and calculated area under the curve (AUC), diagnostic odds ratio (DOR), sensitivity, and specificity values of circulating miR-1246 in the cancer surveillance. Results: After selection and quality assessment, 29 eligible studies with 5914 samples (3232 cases and 2682 controls) enrolled in the final analysis. The pooled AUC, DOR, sensitivity, and specificity of circulating miR-1246 in screening cancers were 0.885 (95% confidence interval (CI): 0.827-0.892), 27.7 (95% CI: 17.1-45.0), 84.2% (95% CI: 79.4-88.1), and 85.3% (95% CI: 80.5-89.2), respectively. Among cancer types, superior performance was noted for breast cancer (AUC = 0.950, DOR = 98.5) compared to colorectal cancer (AUC = 0.905, DOR = 47.6), esophageal squamous cell carcinoma (AUC = 0.757, DOR = 8.0), hepatocellular carcinoma (AUC = 0.872, DOR = 18.6), pancreatic cancer (AUC = 0.767, DOR = 12.3), and others (AUC = 0.887, DOR = 27.5, P = 0.007). No significant publication bias in DOR was observed in the meta-analysis (funnel plot asymmetry test with P = 0.652; skewness value = 0.672, P = 0.071). Conclusion: Extracellular circulating miR-1246 may serve as a reliable biomarker with good sensitivity and specificity in screening cancers, especially breast cancer.

Examining financial distress of the Vietnamese listed firms using accounting-based models.

Financial distress is generally considered the most severe consequence for firms with poor financial performance. The emergence of the Covid-19 pandemic has adversely impacted the global business system and exacerbated the number of financially distressed firms in many countries. Only firms with strong financial fundamentals can survive extreme events such as the Covid-19 pandemic and the ongoing Russia-Ukraine conflict. Vietnam is no exception. However, studies examining financial distress using accounting-based indicators, particularly at the industry level, have largely been ignored in the Vietnamese context, particularly with the emergence of the Covid-19 pandemic. This study, therefore, comprehensively examines financial distress for 500 Vietnamese listed firms during the 2012-2021 period. Our study uses interest coverage and times-interest-earned ratios to proxy a firm's financial distress. First, our findings confirm the validity of Altman's Z"- score model in Vietnam only when the interest coverage ratio is used as a proxy for financial distress. Second, our empirical findings indicate that only four financial ratios, including EBIT/Total Assets, Net Income/Total Assets, Total Liabilities/Total Assets, and Total Equity/Total Liabilities, can be used in predicting financial distress in Vietnam. Third, our analysis at the industry level indicates that the "Construction & Real Estates" industry, a significant contributor to the national economy, exhibits the most significant risk exposure, particularly during the Covid-19 pandemic. Policy implications have emerged based on the findings from this study.

Advanced Tree-Based Techniques for Predicting Unconfined Compressive Strength of Rock Material Employing Non-Destructive and Petrographic Tests.

The accurate estimation of rock strength is an essential task in almost all rock-based projects, such as tunnelling and excavation. Numerous efforts to create indirect techniques for calculating unconfined compressive strength (UCS) have been attempted. This is often due to the complexity of collecting and completing the abovementioned lab tests. This study applied two advanced machine learning techniques, including the extreme gradient boosting trees and random forest, for predicting the UCS based on non-destructive tests and petrographic studies. Before applying these models, a feature selection was conducted using a Pearson's Chi-Square test. This technique selected the following inputs for the development of the gradient boosting tree (XGBT) and random forest (RF) models: dry density and ultrasonic velocity as non-destructive tests, and mica, quartz, and plagioclase as petrographic results. In addition to XGBT and RF models, some empirical equations and two single decision trees (DTs) were developed to predict UCS values. The results of this study showed that the XGBT model outperforms the RF for UCS prediction in terms of both system accuracy and error. The linear correlation of XGBT was 0.994, and its mean absolute error was 0.113. In addition, the XGBT model outperformed single DTs and empirical equations. The XGBT and RF models also outperformed KNN (R = 0.708), ANN (R = 0.625), and SVM (R = 0.816) models. The findings of this study imply that the XGBT and RF can be employed efficiently for predicting the UCS values.

Novel single nucleotide polymorphisms of insulin-like growth factor-binding protein 7 (IGFBP7) gene significantly associated with growth traits in striped catfish (Pangasianodon hypophthalmus Sauvage, 1878).

Breeding program to improve economically important growth traits in striped catfish (Pangasianodon hypophthalmus) requires effective molecular markers. This study was conducted to identify single nucleotide polymorphisms (SNPs) of Insulin-like Growth Factor-Binding Protein 7 (IGFBP7) gene which plays multiple roles in regulating growth, energy metabolism and development. The association between SNPs in IGFBP7 gene and growth traits in striped catfish was analyzed in order to uncover the SNPs that have potential to be valuable markers for improving growth traits. Firstly, fragments of IGFBP7 gene from ten fast-growing fish and ten slow-growing fish were sequenced in order to discover SNPs. After filtering the detected SNPs, an intronic SNP (2060A > G) and two non-synonymous SNPs (344 T > C and 4559C > A) causing Leu78Pro and Leu189Met in protein, respectively, were subjected to further validated by individual genotyping in 70 fast-growing fish and 70 slow-growing fish using single base extension method. Our results showed that two SNPs (2060A > G and 4559 C > A (p. Leu189Met)) were significantly associated with the growth in P. hypophthalmus (p < 0.001), thus being candidate SNP markers for the growth traits of this fish. Moreover, linkage disequilibrium and association analysis with growth traits of haplotypes generated from the 3 filtered SNPs (344 T > C, 2060 A > G and 4559 C > A) were examined. These revealed that the non-coding SNP locus (2060A > G) had higher genetic diversity at which the G allele was predominant over the A allele in the fast-growing fish. Furthermore, the results of qPCR showed that expression of IGFBP7 gene with genotype GG (at locus 2060) in fast-growing group was significantly higher than that with genotype AA in slow-growing group (p < 0.05). Our study provides insights into the genetic variants of IGFBP7 gene and useful data source for development molecular marker for growth traits in breeding of the striped catfish.

Evaluation of waste management and energy saving for sustainable green building through analytic hierarchy process and artificial neural network model.

A significant portion of the solid waste filling landfills worldwide is debris from construction and demolition projects. Across the world, a significant portion of the solid waste filling landfills is made up of construction and demolition waste. Recycling construction waste may help cut down on the quantity of waste sent to landfills and the requirement for energy and other natural resources. To help with construction waste reduction, a management hierarchy that begins with rethink, reduce, redesign, refurbish, reuse, incineration, composting, recycle, and eventually disposal is likely to be effective. The objective of this research is to investigate the viability of the Analytic Hierarchy Process (AHP) as a data gathering instrument for the development of a solid waste management assessment tool, followed by an examination of an artificial neural network (ANN). Using a standardized questionnaire, all data was gathered from waste management practitioners in three industry sectors. The survey data was subsequently analyzed using ANN and later AHP. The suggested framework consisted of four components: (1) the development of different level structures for fluffy AHP, (2) the calculation of weights, (3) the collection of data, and (4) the making of decisions. An ANN feedforward with error back propagation (EBP) learning computation is coupled to identify the association between the items and the store execution. It was found that the combination of AHP and ANN has emerged as a key decision support tool for landfilling, incineration, and composting waste management strategies, taking into account the environmental profile and economic and social characteristics of each choice. Composting has the highest sustainable performance when a balanced weight distribution of criteria is assumed, especially if the environmental component is considered in comparison to the other criteria. However, if social and economic features are addressed, incineration or landfilling have more favorable characteristics, respectively.

Exploring the Complementarity of Measures of Instructional Practices.

The assessment of instructional quality has been and continues to be a desirable, yet difficult endeavor in higher education. The development of new teaching evaluation frameworks along with instruments to measure various aspects of teaching practices holds promise. The challenge rests in the implementation of these frameworks and measures in authentic settings. Part of this challenge is for instructors, researchers, and administrators to parse through and select a meaningful set of tools from the plethora of existing instruments. In this study, we aim to start clarifying the landscape of measures of instructional practice by exploring the complementarity of two existing instruments: the Classroom Observation Protocol for Undergraduate STEM (COPUS) and the Learner-Centered Teaching Rubrics (LCTR). We collected classroom observations and course artifacts from 28 science instructors from research-intensive institutions across the United States. Results show the need to use both instruments to capture nuanced and comprehensive description of a faculty member's instructional practice. This study highlights the messiness of measuring instructional quality and the need to explore the implementation of teaching evaluation frameworks and measures of instructional practices in authentic settings.

Spotlighting of microbial electrodeionization cells for sustainable wastewater treatment: Application of machine learning.

Microbial electrodeionization cells (MECs) have been investigated for various potential applications, including the elimination of persistent pollutants, chemical synthesis, the recovery of resources, and the development of biosensors. Nevertheless, MEC technology is still developing, and practical large-scale applications face significant obstacles. This review aims to investigate MEC implementations in sustainable wastewater treatment. Ideas and concepts of MEC technology, the setup of the electrodeionization component, the membranes of MECs, the working mechanism of MECs, and the various microorganisms used in MECs are discussed. Additionally, difficulties and prospective outcomes were discussed. The goal of this review is to support scientists and engineers in fully grasping the most recent developments in MEC technologies and applications.

Application of logistic regression, support vector machine and random forest on the effects of titanium dioxide nanoparticles using macroalgae in treatment of certain risk factors associated with kidney injuries.

The use of titanium dioxide (TiO2) nanoparticles in many biological and technical domains is on the rise. There hasn't been much research on the toxicity of titanium dioxide nanoparticles in biological systems, despite their ubiquitous usage. In the current investigation, samples were exposed to various dosages of TiO2 nanoparticles for 4 days, 1 month, and 2 months following treatment. ICP-AES was used to dose TiO2 into the tissues, and the results showed that the kidney had a significant TiO2 buildup. On the other hand, apoptosis of renal tubular cells is one of the most frequent cellular processes contributing to kidney disease (KD). Nevertheless, the impact of macroalgal seaweed extract on KD remains undetermined. In this work, machine learning (ML) approaches have been applied to develop prediction algorithms for acute kidney injury (AKI) by use of titanium dioxide and macroalgae in hospitalized patients. Fifty patients with (AKI) and 50 patients (non-AKI group) have been admitted and considered. Regarding demographic data, and laboratory test data as input parameters, support vector machine (SVM), and random forest (RF) are utilized to build models of AKI prediction and compared to the predictive performance of logistic regression (LR). Due to its strong antioxidant and anti-inflammatory powers, the current research ruled out the potential of using G. oblongata red macro algae as a source for a variety of products for medicinal uses. Despite a high and fast processing of algorithms, logistic regression showed lower overfitting in comparison to SVM, and Random Forest. The dataset is subjected to algorithms, and the categorization of potential risk variables yields the best results. AKI samples showed significant organ defects than non-AKI ones. Multivariate LR indicated that lymphocyte, and myoglobin (MB) ≥ 1000 ng/ml were independent risk parameters for AKI samples. Also, GCS score (95% CI 1.4-8.3 P = 0.014) were the risk parameters for 60-day mortality in samples with AKI. Also, 90-day mortality in AKI patients was significantly high (P < 0.0001). In compared to the control group, there were no appreciable changes in the kidney/body weight ratio or body weight increases. Total thiol levels in kidney homogenate significantly decreased, and histopathological analysis confirmed these biochemical alterations. According to the results, oral TiO2 NP treatment may cause kidney damage in experimental samples.

Assessment of graphene-based polymers for sustainable wastewater treatment: Development of a soft computing approach.

Since graphene possesses distinct electrical and material properties that could improve material performance, there is currently a growing demand for graphene-based electronics and applications. Numerous potential applications for graphene include lightweight and high-strength polymeric composite materials. Due to its structural qualities, which include low thickness and compact 2D dimensions, it has also been recognized as a promising nanomaterial for water-barrier applications. For barrier polymer applications, it is usually applied using two main strategies. The first is the application of graphene, graphene oxide (GO), and reduced graphene oxide (rGO) to polymeric substrates through transfer or coating. In the second method, fully exfoliated GO or rGO is integrated into the material. This study provides an overview of the most recent findings from research on the use of graphene in the context of water-barrier applications. The advantages and current limits of graphene-based composites are compared with those of other nanomaterials utilized for barrier purposes in order to emphasize difficult challenges for future study and prospective applications.