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.

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.

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.

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.

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.

On computation of stabilizing loop gain and delay ranges for bi-proper delay systems.

A graphical method for exactly computing the stabilizing loop gain and delay ranges was proposed [Le BN, Wang Q-G, Lee T-H. Development of D-decomposition method for computing stabilizing gain ranges for general delay systems. J Process Control 2012] for a strictly proper process by determining the boundary functions which may change system׳s stability. A bi-proper process is rare but causes great complications for the method, due to the new phenomena that do not exist for a strictly proper process, such as a non-zero gain at infinity frequency, which may cause infinite intersections of boundary functions within a finite delay range. This paper addresses such a kind of processes and develops a general method that can produce the exact and complete set of the loop gain and delay for closed-loop stabilization, which is hard to find with analytical methods.

Two-stage hybrid optimization of fiber Bragg gratings for design of linear phase filters.

We present a new hybrid optimization method for the synthesis of fiber Bragg gratings (FBGs) with complex characteristics. The hybrid optimization method is a two-tier search that employs a global optimization algorithm [i.e., the tabu search (TS) algorithm] and a local optimization method (i.e., the quasi-Netwon method). First the TS global optimization algorithm is used to find a "promising" FBG structure that has a spectral response as close as possible to the targeted spectral response. Then the quasi-Newton local optimization method is applied to further optimize the FBG structure obtained from the TS algorithm to arrive at a targeted spectral response. A dynamic mechanism for weighting of different requirements of the spectral response is employed to enhance the optimization efficiency. To demonstrate the effectiveness of the method, the synthesis of three linear-phase optical filters based on FBGs with different grating lengths is described.