Reliable Model Research Articles

The Cavernous Nerve Injury Rat Model: A Pictorial Essay on Post-Radical Prostatectomy Erectile Dysfunction Research.

Understanding and addressing post-radical prostatectomy (RP) erectile dysfunction (ED) is of paramount importance for clinicians. Cavernous nerve (CN) injury rat model studies have provided consistently promising experimental data regarding regaining erectile function (EF) after nerve damage-induced ED. However, these findings have failed to translate efficiently into clinical practice, with post-RP ED therapeutic management remaining cumbersome and enigmatic. This disparity highlights the need for further standardization and optimization of the elaborate surgical preparation protocols and multifaceted reporting parameters involved in reliable CN injury rat model experimentation. Even so, despite its technical complexity, this animal model remains instrumental in exploring the functional implications of RP, i.e., surgical lesions of the neurovascular bundles (NVBs). Herein, besides cavernous nerve (CN) dissection, injury, and electrostimulation, multiple pressure measurements, i.e., mean arterial pressure (MAP) and intra-cavernosal pressure (ICP), must also be achieved. A transverse cervical incision allows for carotid artery cannulation and MAP measurements. Conversely, ICP measurements entail circumcising the penis, exposing the ischiocavernous muscle, and inserting a needle into the corporal body. Finally, using an abdominal incision, the prostate is revealed, and the major pelvic ganglia (MPG) and CNs are dissected bilaterally. Specific surgical techniques are used to induce CN injuries. Herein, we provide a narrative and illustrative overview regarding these complex experimental procedures and their particular requirements, reflecting on current evidence and future research perspectives.

Cytokine induced inflammatory bowel disease model using organ-on-a-chip technology

Over 2 million people in North America suffer from inflammatory bowel disease (IBD), a chronic and idiopathic inflammatory condition. While previous research has primarily focused on studying immune cells as a cause and therapeutic target for IBD, recent findings suggest that non-immune cells may also play a crucial role in mediating cytokine and chemokine signaling, and therefore IBD symptoms. In this study, we developed an organ-on-a-chip co-culture model of Caco2 epithelial and HUVEC endothelial cells and induced inflammation using pro-inflammatory cytokines TNF-α and IFN-γ. We tested different concentration ranges and delivery orientations (apical vs. basal) to develop a consistently inducible inflammatory response model. We then measured pro-inflammatory cytokines and chemokines IL-6, IL-8, and CXCL-10, as well as epithelial barrier integrity. Our results indicate that this model 1. induces IBD-like cytokine secretion in non-immune cells and 2. decreases barrier integrity, making it a feasible and reliable model to test the direct actions of potential anti-inflammatory therapeutics on epithelial and endothelial cells.

PyBEST: Improved functionality and enhanced performance

PyBEST: Improved functionality and enhanced performance

Economic Variables as an Early Warning Sign for Monitoring Construction Cost Indices – “An Empirical Study on Egypt”

The construction industry plays a crucial role in any nation's economic growth and prosperity through the implementation of the SDGs. Researchers reveal that the 17 SDGs are directly dependent by 17% and indirectly reliant by 27% on the construction industry's activities. In addition, the construction projects, whatever their type or components, continue for several years, during which many price changes and sharp economic fluctuations occur, whether at the local or international level, reflected in overall economic performance and the construction industry's health, leading to the project's cost changes and a shortage in the allocated budgets. Due to the difficulties this industry faces in reaching these goals, including the Egypt Vision 2030, we chose to look into these effects by using the ENR methodology to develop an explanatory construction cost indices model in Egypt similar to models used in other countries while making appropriate changes for the Egyptian markets. To illustrate the relationship between these selected economic variables and construction costs in Egypt and to act as an early warning sign to quantify, track, and predict the construction cost movements and trends in Egypt's construction sectors through a reliable model. A mixed research methodology consisted of semi-structured interviews with 15 industry experts and an empirical study covering 1990 through 2020 for selected local and international economic variables. We concluded that national and international economic variables significantly impacted as explanatory and predictive variables in Egypt's construction industry's cost indices; these variables are as follows: gross domestic product (GDP), consumer price index (CPI), USD to EGP exchange rate ($), inflation rate, lending rate, money supply, and unemployment rate, as well as international crude oil prices, gold prices, and copper prices. Nevertheless, further investigation is required with more economic variables across various construction industry sectors over various time intervals and different time series statistical and econometric modeling techniques.

A Comprehensive Review of the Effects of Glycemic Carbohydrates on the Neurocognitive Functions Based on Gut Microenvironment Regulation and Glycemic Fluctuation Control.

Improper glycemic carbohydrates (GCs) consumption can be a potential risk factor for metabolic diseases such as obesity and diabetes, which may lead to cognitive impairment. Although several potential mechanisms have been studied, the biological relationship between carbohydrate consumption and neurocognitive impairment is still uncertain. In this review, the main effects and mechanisms of GCs' digestive characteristics on cognitive functions are comprehensively elucidated. Additionally, healthier carbohydrate selection, a reliable research model, and future directions are discussed. Individuals in their early and late lives and patients with metabolic diseases are highly susceptible to dietary-induced cognitive impairment. It is well known that gut function is closely related to dietary patterns. Unhealthy carbohydrate diet-induced gut microenvironment disorders negatively impact cognitive functions through the gut-brain axis. Moreover, severe glycemic fluctuations, due to rapidly digestible carbohydrate consumption or metabolic diseases, can impair neurocognitive functions by disrupting glucose metabolism, dysregulating calcium homeostasis, oxidative stress, inflammatory responses, and accumulating advanced glycation end products. Unstable glycemic status can lead to more severe neurological impairment than persistent hyperglycemia. Slow-digested or resistant carbohydrates might contribute to better neurocognitive functions due to stable glycemic response and healthier gut functions than fully gelatinized starch and nutritive sugars.

Modeling binary collision of evaporating drops

We investigate the interactions between two drops in a heated environment and analyze the effect of evaporation on bouncing, coalescence and reflexive separation phenomena. A reliable mass transfer model is incorporated in a coupled level-set and volume-of-fluid framework to accurately model the evaporation process and the evolution of drop interfaces during the interactions. The numerical technique is extensively validated against the benchmark problems involving the evaporation of a single drop. We analyze the contour plots of temperature and vapor mass fraction fields for each collision outcome. Our numerical simulations reveal that vapor entrapment during the separation process, with high-velocity vapor manages to escape. Increasing evaporation rates result in slower post-collision drop separation. Furthermore, the differences in kinetic energy and surface energy are analyzed for different Stefan numbers. The coalescence of drops exhibits energy oscillations until dissipation, while the bouncing and reflexive separations lack such oscillations. In the reflexive separation regime, the kinetic energy of the drops becomes zero after detachment.

UXO and environmental risk factors impacting EOD operations in German waters**

AbstractThis article presents risk factors that are associated with the handling of unexploded ordnance (UXO) during explosive ordnance disposal (EOD) operations in German waters. The construction of offshore wind parks and the German immediate action program are expected to increase the number of EOD operations. Existing literature and guidelines do not offer a structured and reproducible framework for assessing EOD risk. To fill this gap, a network of EOD risk factors was developed by means of a literature review and validation via expert consultation. The study was scoped to “personnel and equipment at the EOD location” as the risk receptor and “undesired detonation” as the undesired event under investigation. Factors are subdivided into UXO factors that depend on the object that should be handled and factors that describe the object's surrounding environment. While the former can be researched by an EOD expert, the latter must be measured on site or acquired from a model. Each of these factors contributes to risk, some directly and others indirectly via other factors. The complexity of the resulting network, with its 33 factors, demonstrates the need for a reliable and reproducible model to quantify EOD risk. Its purpose is not to replace EOD experts but to aid them in their decision‐making process. Such a tool can provide valuable support for the high‐cost and high‐risk EOD operations.

Early Metabolic Measures Predict Long-term Insulin Independence in Recipients of Total Pancreatectomy and Islet Autotransplantation.

Although diabetes after total pancreatectomy and islet autotransplantation (TP-IAT) is one of the biggest concerns for TP-IAT recipients and physicians, reliable prediction of post-TP-IAT glycemic control remains unestablished. This study was conducted to identify early predictors of insulin independence and goal glycemic control by hemoglobin A1c (HbA1c) ≤ 6.5% after TP-IAT. In this single-center, retrospective study, patients who underwent TP-IAT (n = 227) were reviewed for simple metabolic markers or surrogate indices of β-cell function obtained 3 mo after TP-IAT as part of standard clinical testing. Long-term metabolic success was defined as (1) insulin independence and (2) HbA1c ≤ 6.5% 1, 3, and 5 y after TP-IAT. Single- and multivariate modeling used 3-mo markers to predict successful outcomes. Of the 227 recipients, median age 31 y, 30% male, 1 y after TP-IAT insulin independence, and HbA1c ≤ 6.5% were present in 39.6% and 72.5%, respectively. In single-predictor analyses, most of the metabolic markers successfully discriminated between those attaining and not attaining metabolic goals. Using the best model selected by random forests analysis, we accurately predicted 1-y insulin independence and goal HbA1c control in 77.3% and 86.4% of the patients, respectively. A simpler "clinically feasible" model using only transplanted islet dose and BETA-2 score allowed easier prediction at a small accuracy loss (74.1% and 82.9%, respectively). Metabolic testing measures performed 3 mo after TP-IAT were highly associated with later diabetes outcomes and provided a reliable prediction model, giving valuable prognostic insight early after TP-IAT and help to identify recipients who require early intervention.

Phase-type stress-strength reliability models under progressive type-II right censoring

. The study of stress-strength reliability estimation based on phase-type distribution helps to gather results on estimation of stress-strength reliability with any probability distribution that is defined on the non negative real numbers as any discrete or continuous probability distributions on the positive real line can be represented as phase-type. The matrix representation of the parameters of phase-type distributions helps in their flexible evaluation and easy manipulation. Also in many of the experimental studies, it is very convenient and useful to apply progressive type-II right censoring mechanism in the process of data collection. In this article, we consider the estimation of stress-strength reliability (R) based on phase-type distribution under progressive type-II right censoring mechanism. Both stress and strength random variables are assumed to follow either continuous phase-type or discrete phase-type distribution. We have developed the algorithm for computing Maximum likelihood estimate (MLE) of R based on the expectation maximization (EM) method and the Bayes estimate of R using Markov Chain Monte Carlo technique. A detailed numerical illustration using simulated data/ real data sets are carried out.

Estimating amino acid substitution models from genome datasets: a simulation study on the performance of estimated models.

Estimating parameters of amino acid substitution models is a crucial task in bioinformatics. The maximum likelihood (ML) approach has been proposed to estimate amino acid substitution models from large datasets. The quality of newly estimated models is normally assessed by comparing with the existing models in building ML trees. Two important questions remained are the correlation of the estimated models with the true models and the required size of the training datasets to estimate reliable models. In this article, we performed a simulation study to answer these two questions based on simulated data. We simulated genome datasets with different numbers of genes/alignments based on predefined models (called true models) and predefined trees (called true trees). The simulated datasets were used to estimate amino acid substitution model using the ML estimation methods. Our experiments showed that models estimated by the ML methods from simulated datasets with more than 100 genes have high correlations with the true models. The estimated models performed well in building ML trees in comparison with the true models. The results suggest that amino acid substitution models estimated by the ML methods from large genome datasets are a reliable tool for analyzing amino acid sequences.

Time-Varying Reliability Analysis of Multi-Cracked Beams Considering Maintenance Dependence

Time-varying reliability models of multi-cracked beam structures are established in this paper, which provide a theoretical method for the safety evaluation of multi-cracked beam structures. The reliability models proposed in this paper consider the interaction between the complex statistical correlations between system parameters during system operation and maintenance correlations, which is a difficult problem in the time-varying reliability modeling that takes into account work mechanisms and maintenance behavior. In the proposed models, multiple cracked elements are regarded as a dependent series system. The stresses, crack extensions, and multiple failure modes between each element constitute the complex failure dependence of the system. The time-varying reliability models of a multi-cracked beam structure are established via the neural network method and failure dependence analysis. Moreover, the failure dependence coefficient is proposed to quantify the time-varying failure dependence. Based on the working principle of the beam structures and the maintenance mechanism for the cracked state of the beams, a time-varying system reliability mode considering the maintenance dependence is proposed. Furthermore, the maintenance dependence coefficient index is proposed to quantitatively measure the interaction between the maintenance dependence and the failure dependence. Finally, the validity of the model is verified through the Monte Carlo simulation method. In the numerical examples, the relationship between maintenance dependence and failure dependence is illustrated and the influences of the statistical characteristics of the maintenance characteristic parameters on the maintainability and failure dependence are analyzed.

Systematic Review on Prediction and Monitoring of Climate Change

Climate change poses a severe and very bad threat to human society and global ecosystems, necessitating careful planning, monitoring, and forecasting. This paper reviews spread a range of monitoring techniques and predictive models to better understand, anticipate, and mitigate the effects of climate change. Predictive models combine a wide and large range of methods, from statistical and machine learning-based models to complex Earth system models that integrate atmospheric, oceanic, and terrestrial components. These models look at historical data of history, greenhouse gas emissions, and various climate drivers to anticipate future climatic scenarios. Tracking changes in temperature, precipitation patterns, sea level rise, and other climate change indicators over time also requires the use of monitoring tools that include satellite observations, ground-based sensors, and remote sensing technology.. Creating sustainable behaviors to counteract the negative consequences of climate change, educating politicians, and integrating reliable monitoring systems and predictive models all help in the development of adaptive systems. To address the complexities, difficulties, and breakthroughs in climate change prediction and monitoring, scientists, policymakers, and stakeholders should work together.

Investigating the common pharmacophoric points of PDK1 inhibitors as anti-cancer agents using an alignment independent 3D-QSAR, molecular docking and molecular dynamic simulation

The glycolytic enzyme 3-Phosphoinositide-dependent protein kinase-1 (PDK1), a member of the serine/threonine kinase family, is a promising target for developing novel anticancer drugs due to its vital role in growth and survival of cancer cells and tumor angiogenesis. A MIF base alignment independent, Three-Dimensional Quantitative Structure-Activity Relationship (3D-QSAR) study was conducted to highlight the correlation between PDK1 inhibitors and their structural characteristics, thus indicating the most effective molecular properties of the inhibitors as anticancer agents. Using the Kennard-Stone algorithm to separate calibration and validation subsets, Partial Least-Square (PLS) method application shows excellent agreement between experimental and predicted values (R2=0.85, Q2LOO=0.77 and R2Perd=0.93), making it a reliable model for future drug design approaches. In addition, the active site of the PDK1 receptor protein was examined via Molecular Docking with both passive and active compounds. In order to study the effect of inhibitors on PDK1 structure, 10 ns Molecular Dynamics (MD) simulation of free protein and protein-ligand complex was performed and compared. Comparing the 3D-QSAR, docking and molecular simulation outcomes, the prevailing pharmacophoric use of PDK1 inhibitors as anti-cancer agents have been highlighted. The comprehensive information of this approach reveals important structural variables and their relationship with PDK1 inhibitory activity and PDK1 cavity residues which could be valuable for designing anticancer candidates.

Surface response regression and machine learning techniques to predict the characteristics of pervious concrete using non-destructive measurement: Ultrasonic pulse velocity and electrical resistivity

It is crucial to assess the characteristics of pervious concrete even post-construction. The quality monitoring of such a procedure is tricky in pervious concrete that it is typically avoided. As a potential means of enhancing the aforementioned quality control, the current study investigates the possibility of predicting characteristics of pervious concrete through response surface methodology and machine learning techniques using non-destructive test measurement (ultrasonic velocity and electrical resistivity). A total of 225 datasets from the experimental study were taken for this study. To recognize the best reliable model for predicting characteristics of pervious concrete, response surface methodology up to sixth order polynomial and five different machine learning techniques were used as statistical assessment tools. Using both ultrasonic pulse velocity and electrical resistivity as predictors for estimating porosity and compressive strength via response surface methodology, using a quadratic model for porosity prediction and a cubic model for compressive strength prediction are recommended. The machine learning models used in the research exhibited superior performance compared to the response surface methodology. Among the many machine learning models evaluated in this study, boosted decision tree regression model better predicted porosity (R2 = 0.92) and compressive strength (R2 = 0.92) of pervious concrete. Therefore, prediction models for the characteristics of pervious concrete are created using non-destructive measurement and machine learning techniques, which may ensure that the construction sector can utilize the offered models without any theoretical expertise.

Time-Dependent Unavailability Exploration of Interconnected Urban Power Grid and Communication Network

This paper presents a time-dependent reliability analysis created for a critical energy infrastructure use case, which consists of an interconnected urban power grid and a communication network. By utilizing expert knowledge from the energy and communication sectors and integrating the renewal theory of multi-component systems, a representative reliability model of this interconnected energy infrastructure, based on real network located in the Czech Republic, is established. This model assumes reparable and non-reparable components and captures the topology of the interconnected infrastructure and reliability characteristics of both the power grid and the communication network. Moreover, a time-dependent reliability assessment of the interconnected system is provided. One of the significant outputs of this research is the identification of the critical components of the interconnected network and their interdependencies by the directed acyclic graph. Numerical results indicate that the original design has an unacceptable large unavailability. Thus, to improve the reliability of the interconnected system, a slightly modified design, in which only a limited number of components in the system are modified to keep the additional costs of the improved design limited, is proposed. Consequently, numerical results indicate reducing the unavailability of the improved interconnected system in comparison with the initial reliability design. The proposed unavailability exploration strategy is general and can bring a valuable reliability improvement in the power and communication sectors.

An improved adaptive Kriging model for importance sampling reliability and reliability global sensitivity analysis

An improved adaptive Kriging model for importance sampling (IS) reliability and reliability global sensitivity analysis is proposed by introducing the IS density function into learning function. Considering the variance information of Kriging prediction, the formula of traditional IS method is extended to the form considering the uncertainty of symbol function. The estimated variance of failure probability caused by the prediction uncertainty of Kriging model is obtained, and the corresponding coefficient of variation (COV) is defined. Based on the standard deviation information of failure probability, a novel learning function considering the characteristic of IS density function is proposed, which are used to minimize the prediction uncertainty of Kriging. The corresponding stopping criterion is defined based on the COV information. In order to increase the likelihood that the selected sample points fall around the limit state boundary, the penalty function method is introduced to improve the learning function. Once the failure probability is obtained, the variable global sensitivity index is calculated through the failed sample set and Bayes theorem. The results show that: By introducing IS density function and penalty function into learning function, the sample points which contribute more to the failure probability can be obtained more effectively in IS method. The proposed method has high accuracy and efficiency compared with traditional Kriging-based IS method.

Method for Assessing Reliability of the Power Supply System for Electronic Security Systems of Intelligent Buildings Taking Into Account External Natural Interference

The article presents issues related to the reliability of power supply for electronic security devices in intelligent buildings. These systems function under varied environmental conditions and are exposed to external or internal natural and artificial interference; lightning, in particular. The authors conducted actual experimental tests of two surge arresters that are used to protect against a lightning impulse. As a result of conducted tests, followed by reliability and operational modelling, it was concluded that their use in internal connection structures increased the probability of a system staying in a state of full fitness. All of the deliberations included in the article enabled developing a new method for assessing the power supply continuity in electronic security systems of intelligent buildings, taking into account external natural interference. This method can also be applied to assess the power supply continuity in other electronic systems and devices.

Modeling the joint rotational stiffness of a radial-type flight intersection joint: An analytical approach, numerical simulation, and experimental validation

Joint rotational stiffness (JRS) of a flight intersection joint (FIJ) and its accurate quantification play an essential role in studying the dynamic modal characteristics of a flight vehicle. This JRS of a FIJ is mostly determined through elaborate experiments due to the non-availability of a reliable predictive model. Therefore, in this paper, an analytical model is proposed to evaluate JRS of a radial type FIJ when subjected to external bending moment. In this model, at first, the tensile and compressive part of the FIJ section is identified by determining the neutral axis position. The respective part's contributing stiffness is then estimated by employing a spring-mass model. Later, the JRS expression is derived from the moment equilibrium condition and expressed as a function of the stiffness of the tensile and compressive part and neutral axis position. Subsequently, finite element analysis (FEA) is conducted with different numbers of screws, followed by detailed experimental investigations. The proposed analytical model is validated with the results from FEA and experiments for different screw configurations of FIJ. Further, the proposed model is extended to account for the effect of joint clearances on the JRS and the corresponding moment-rotation characteristics of the FIJ.

In Vitro Three-Dimensional (3D) Models for Melanoma Immunotherapy.

Melanoma is responsible for the majority of skin cancer-related fatalities. Immune checkpoint inhibitor (ICI) treatments have revolutionized the management of the disease by significantly increasing patient survival rates. However, a considerable number of tumors treated with these drugs fail to respond or may develop resistance over time. Tumor growth and its response to therapies are critically influenced by the tumor microenvironment (TME); it directly supports cancer cell growth and influences the behavior of surrounding immune cells, which can become tumor-permissive, thereby rendering immunotherapies ineffective. Ex vivo modeling of melanomas and their response to treatment could significantly advance our understanding and predictions of therapy outcomes. Efforts have been directed toward developing reliable models that accurately mimic melanoma in its appropriate tissue environment, including tumor organoids, bioprinted tissue constructs, and microfluidic devices. However, incorporating and modeling the melanoma TME and immune component remains a significant challenge. Here, we review recent literature regarding the generation of in vitro 3D models of normal skin and melanoma and the approaches used to incorporate the immune compartment in such models. We discuss how these constructs could be combined and used to test immunotherapies and elucidate treatment resistance mechanisms. The development of 3D in vitro melanoma models that faithfully replicate the complexity of the TME and its interaction with the immune system will provide us with the technical tools to better understand ICI resistance and increase its efficacy, thereby improving personalized melanoma therapy.

A Bayesian nonparametric system reliability model which integrates multiple sources of lifetime information

We present a Bayesian nonparametric system reliability model which scales well and provides a great deal of flexibility in modeling. The Bayesian approach naturally handles the disparate amounts of component and subsystem data that may exist. However, traditional Bayesian reliability models are quite computationally complex, relying on MCMC techniques. Our approach utilizes the conjugate properties of the beta-Stacy process, which is the fundamental building block of our model. These individual models are linked together using a method of moments estimation approach. This model is computationally fast, allows for right-censored data, and is used for estimating and predicting system reliability.