Probability Function Research Articles

Genome-wide investigation of caffeic acid O-methyltransferases and expression analysis under different abiotic stresses and anthracnose infection in pepper (Capsicum annuum L.)

ABSTRACT The caffeic acid O-methyl transferases (COMTs) facilitate the biosynthesis of lignin and play important roles in many processes, including growth, development and response to stresses. Pepper (Capsicum annuum L.) is a major horticultural crop with significant nutritional and medicinal values. Nevertheless, a comprehensive assessment of the COMT genes in pepper and their involvement in drought, salt tolerance and anthracnose resistance remains unestablished. In the present study, 80 pepper COMT genes were identified and classified into four major groups based on domain structure and phylogenetics. Additionally, the sequence analysis, gene structures, and duplication events were analysed. The genes were unevenly distributed across 10 chromosomes, and 10 pairs of tandem and 13 pairs of segmental duplicated genes were detected in the COMT gene family. Examination of cis-acting regulatory elements detected the presence of phytohormone-specific, stress-receptive and light responsive elements in the pepper COMT promoters. Moreover, qRT-PCR analysis revealed significant upregulated expression of CanCOMT48 to drought stress, CanCOMT14 to salinity stress and CanCOMT18 to anthracnose infection by Colletotrichum truncatum, suggesting their important functions in different stresses. Our results provide significant insights into the expression and probable function of selective COMTs which could be used as potential candidate genes for pepper tolerance breeding.

Novel Bayesian updating based interpolation method for estimating failure probability function in the presence of random-interval uncertainty

Under random-interval uncertainty, the failure probability function (FPF) represents the failure probability variation as a function of the random input distribution parameter. To quickly capture the effect of the distribution parameters on failure probability and decouple the reliability-based design optimization, a novel Bayesian updating method is proposed to efficiently estimate the FPF. In the proposed method, the prior augmented failure probability (AFP) is first estimated in the space spanned by random input and distribution parameter vectors. Subsequently, by treating the distribution parameter realization as an observation, the FPF can be estimated using posterior AFP based on Bayesian updating. The main novelty of this study is the elaborate treatment of the distribution parameter realization as an observation, whereby the FPF is transformed into the posterior AFP based on Bayesian updating, and can be obtained by sharing the prior AFP simulation samples. The computational cost of the proposed method is the same as that of estimating the prior AFP. To improve the efficiency of recognizing the sample state, and improve AFP and in turn FPF estimation, the adaptive Kriging model for random-interval uncertainty was inserted into the proposed method. The feasibility and novelty of the proposed method were verified on several examples.

Giant Fusiform Cells of the Brain: Discovery, Identification, and Probable Functions

Giant Fusiform Cells of the Brain: Discovery, Identification, and Probable Functions

Small field chaos in spin glasses: Universal predictions from the ultrametric tree and comparison with numerical simulations

Replica symmetry breaking (RSB) for spin glasses predicts that the equilibrium configuration at two different magnetic fields are maximally decorrelated. We show that this theory presents quantitative predictions for this chaotic behavior under the application of a vanishing external magnetic field, in the crossover region where the field intensity scales proportionally to [Formula: see text], being N the system size. We show that RSB theory provides universal predictions for chaotic behavior: They depend only on the zero-field overlap probability function [Formula: see text] and are independent of other system features. In the infinite volume limit, each spin-glass sample is characterized by an infinite number of states that have a tree-like structure. We generate the corresponding probability distribution through efficient sampling using a representation based on the Bolthausen-Sznitman coalescent. Using solely [Formula: see text] as input we can analytically compute the statistics of the states in the region of vanishing magnetic field. In this way, we can compute the overlap probability distribution in the presence of a small vanishing field and the increase of chaoticity when increasing the field. To test our computations, we have simulated the Bethe lattice spin glass and the 4D Edwards-Anderson model, finding in both cases excellent agreement with the universal predictions.

Stronger Patent Regime, Innovation and Scientist Mobility

This paper analyzes the effects of a stronger patent regime on innovation incentives, patenting propensity and scientist mobility when an innovating firm can partially recover its damage due to scientist movement from the infringing rival. The strength of the patent system, which is a function of litigation success probability and damage recovery proportion, stipulates expected indemnification. We show that stronger patents fail to reduce the likelihood of infringement and further, decrease the innovation’s expected profitability. Higher potential reparation also reduces the scientist’s expected return on R&D knowledge, entailing greater R&D investment. Our results suggest important considerations for patent reforms.

Using harmonized FITBIR datasets to examine associations between TBI history and cognitive functioning

Objective Demonstrate how patient-level traumatic brain injury (TBI) data from studies in the Federal Interagency Traumatic Brain Injury Research (FITBIR) Informatics System can be harmonized and pooled to examine relationships between TBI and cognitive functioning. Method We harmonized and pooled data across studies and analyzed rates of probable cognitive functioning deficits by TBI history and severity. Results Four publicly available FITBIR studies with 3,445 participants included data on cognitive dysfunction, though only one included comparison groups (mild TBI vs. no history of TBI) and could be used in the final comparative analyses. Of the 1,539 participants, 82% had a history of mild TBI and 67% had data suggesting the presence of cognitive dysfunction. Participants with a history of mild TBI were mostly male (87%), 25–39 years old (53%), and Non-Hispanic White (60%). Conclusions: One publicly available FITBIR study reported cognitive dysfunction data as of January 2021, though findings were similar to prior research and supported an association between mild TBI and cognitive dysfunction. This proof-of-concept study shared newly developed methods including harmonization, analysis syntax, and meta-data via the FITBIR website to encourage dissemination of these TBI data resources in line with FAIR data goals.

Source apportionment of PM2.5 episodes in the Taichung metropolitan area, Taiwan

To analyze the physicochemical mechanisms affecting the variation in fine suspended particulate matter (PM2.5) concentrations in Taichung City, the largest city in central Taiwan (the second largest city in Taiwan), during a high-pollution event from November 3 to 6, 2021, we applied the sulfur tracking method (STM) and integrated source apportionment method (ISAM) of the WRF/CMAQ model to simulate the impacts of various emission sources. The sources of pollution in Taichung City are very similar, which shows that the impacts of point, line, and area sources should not be neglected in addition to the boundary conditions. SO42− is mainly generated from point emissions and the production of H2O2, Fe, Mn, and O3. NO3− is also mainly generated from point sources in Taichung City, with HNO3 being the main source at noon and ANO3 at other times of the day. NH4+ is mainly generated from area sources in Taichung City. OM is more complex, mainly originating from line sources in Taichung City and other sources, such as point/area emissions in Taichung City and other emissions from Changhua County. The most important mechanism is low-volatility/semivolatile oxidized combustion of OC at noon, followed by low-volatility/semivolatile POA, which is produced in the morning or evening. EC mainly originates from line sources in Taichung City and Changhua County. In other nearby counties, EC is dominated by local emission sources. In addition, when the concentration of PM2.5 is high, the Neutralization Ratio (NR) is high and PM2.5 is relatively neutral or slightly alkaline. On the contrary, when the concentration of PM2.5 is low, the NR is lower than 1 and the aerosol is acidic. Besides, this study used positive matrix factorization (PMF), which indicates that the PM2.5 at the UAPRS originated from eight kinds of pollution, namely, windblown dust, oil cracking, iron and steel industry, sea salt, transport, Cl− containing exhaust, biomass burning, fossil combustion containing abundant SO42− and the heavy oil refining and coal combustion industry. The direction of the source of pollution can be traced by a conditional bivariate probability function (CBPF). Overall, fossil fuel combustion, mainly involving sulfate, is the largest source of pollution, with the heavy oil refining and coal combustion industry contributing less, and the remaining factors contribute relatively evenly.

Stochastic differential equations for non-analog Monte Carlo model of point kinetics equations

The stochastic form of one-point and two-point reactor kinetics models have been extensively investigated in the literature. We have noticed that, in some studies, the probabilistic nature of neutron interaction mechanisms was not incorporated truly. For instance, in the case of negative reactivity insertions, one of the presented probability functions becomes negative, making the Monte Carlo (MC) simulation impossible. In this manuscript, the non-analog stochastic point reactor kinetics models are developed to simulate the stochastic behavior of nuclear systems. To reduce the imposed variance from the leakage and capture of neurons, both capture and leakage events are sampled implicitly. Precursors are also forced to decay in each simulation step. The true variances of the neutron’s and delayed neutron precursor’s populations are determined from the solution of the developed non-analog stochastic models. Also, they are compared with those of both analog and non-analog Monte Carlo methods. The results show that the analog Monte Carlo (MC) has a larger variance than the non-analog MC.

Comparative study of multiple statistical damage mechanics models for rock behaviors under high temperature

Under the influence of external factors such as high temperature, chemical corrosion, and loading-unloading cycles, micro-cracks and pores may develop within rock structures. Continuum damage mechanics (CDM) characterizes these micro-defects arising within rocks, investigating the physico-mechanical behaviors of rocks in damaged states. This study, grounded in the theoretical framework of CDM and utilizing the Mohr-Coulomb failure criterion, integrates different probability distributions and damage coupling methods to develop three statistical damage models. The investigation primarily explores the effects of strength criteria, probability functions, and damage coupling mechanisms on the statistical damage models. In addressing the complex interplay between rock micro-defects and external stressors such as high temperatures and chemical corrosion, this study leverages the principles of CDM within the Mohr-Coulomb criterion framework. It advances the field by developing three innovative statistical damage models, enriched by diverse probability distributions and damage coupling methods. The research delineates the substantial impact of strength criteria, probability functions, and damage coupling mechanisms on the behavior of these models. Notably, it achieves a synthesis of theoretical constructs and experimental validation, demonstrating the models' capacity to reflect the nuanced behaviors of damaged rock accurately. Furthermore, this investigation contributes a methodological blueprint for statistical damage model construction, underscoring the critical selection of strength criteria and probability distributions. These efforts fortify the theoretical underpinnings necessary for the enhanced engineering application of statistics-driven damage analysis.

Experimental studies of H2/Ar plasma in a cylindrical inductive discharge with an expansion region

Abstract The electrical parameters of H2/Ar plasma in a cylindrical inductive discharge with an expansion region are investigated by a Langmuir probe, where Ar fractions range from 0% to 100%. The influence of gas composition and pressure on electron density, the effective electron temperature and the electron energy probability functions (EEPFs) at different spatial positions are present. In driver region, with the introduction of a small amount of Ar at 0.3 Pa, there is a rapid increase in electron density accompanied by a decrease in the effective electron temperature. Additionally, the shape of the EEPF transitions from a three-temperature distribution to a bi-Maxwellian distribution due to an increase in electron–electron collision. However, this phenomenon resulting from the changes in gas composition vanishes at 5 Pa due to the prior depletion of energetic electrons caused by the increase in pressure during hydrogen discharge. The EEPFs for the total energy in expansion region is coincident to these in the driver region at 0.3 Pa, as do the patterns of electron density variation between these two regions for differing Ar fractions. At 5 Pa, as the discharge transitions from H2 to Ar, the EEPFs evolved from a bi-Maxwellian distribution with pronounced low energy electrons to a Maxwellian distribution in expansion region. This evolve may be attributed to a reduction in molecular vibrational excitation reactions of electrons during transport and the transition from localized electron dynamics in hydrogen discharge to non-localized electron dynamics in argon discharge. In order to validate the experimental results, we use the COMSOL simulation software to calculate electrical parameters under the same conditions. The evolution and spatial distribution of the electrical parameters of the simulation results agree well with the trend of the experimental results.

A novel spatial prediction method for soil heavy metal based on unbiased conditional kernel density estimation

Soil contamination by heavy metals has emerged as a significant global problem. Accurately mapping the spatial distribution of soil heavy metal pollutant concentrations is indispensable for effective agriculture and environmental management. Nevertheless, challenges arise in obtaining comprehensive data at all desired locations, due to limitations in measuring equipment capacity and the associated capital costs. To obtain soil heavy metal maps efficiently and accurately, this paper proposes a nonparametric spatial prediction method, based on unbiased conditional kernel density estimation (UCKDE). The proposed method incorporates the advantages of both geostatistics and machine learning, including stability, adaptability, and the ability to account for various types of auxiliary information. Additionally, it can directly predict the probabilistic density function (PDF) of soil heavy metal content at the target location based on sampling data without complex parameter settings, providing both a deterministic single value and a probabilistic prediction interval. The proposed method and ordinary kriging (OK) were implemented for the spatial prediction of the six heavy metals (As, Cd, Cu, Hg, Mn, and Sb) with the greatest coefficients of variation (CV = 0.53, 1.14, 0.66, 1.05, 0.81 and 0.74, respectively) in Qingxi Town, Chongqing, China. The results showed that the predictive capability of the proposed method (with RMSE values of 5.82, 0.61, 14.76, 0.15, 383.84, and 0.85, respectively) is superior to that of OK (with RMSE values of 5.29, 0.87, 16.37, 0.22, 493.22, and 1.58, respectively) in most cases, particularly when the CV value is high. Besides, the prediction accuracy of the proposed method can be further enhanced by incorporating parent material, resulting in RMSE values of 3.02, 0.51, 8.98, 0.08, 194.16, and 0.56, respectively. The results affirm the reliability of the proposed method and suggest its effectiveness as a tool for soil heavy metal pollution prediction in practical applications.

Effective parameter identification of the GMS friction model for feed systems in CNC machines

One of the main factors influencing machine tool feed system tracking performance is friction. By creating an accurate friction model and implementing feed-forward compensation based on the model, the negative impacts of friction can be efficiently reduced. The generalized Maxwell-slip (GMS) model is commonly used to model feed system friction; however, simple and effective parameter identification methods are lacking. In this paper, a parameter identification method based on a metaheuristic Gaussian swarm optimization (GSO) algorithm is proposed. The method divides the parameters into two parts via a theoretical derivation, and employs GSO to identify each part successively. The proposed GSO is a novel metaheuristic algorithm inspired by the Gaussian probability function. The excellent performance of the GSO ensures that the friction parameters can be accurately and quickly identified. The results of the simulation and physical identification experiments show that the proposed GSO-based identification method can accurately identify the parameters of the GMS model with average and maximum relative errors of 3.96% and 14.05%, respectively. The identified model can accurately predict the friction of the feed system. Additionally, after friction compensation, the tracking error was decreased by an average of 78.9%.

Efficient generation of barrier crossing trajectories using approximate Brownian bridges.

We examine continuous random walks that are conditioned to reach one region before another. These conditioned processes are used to generate stochastic trajectories for barrier crossing events, which are generally rare and difficult to sample. The processes are generated using a Brownian bridge technique, resulting in near perfect sampling efficiency without accruing error in the conditional statistics of the process. The construction requires the hitting probability or committer function, which is a solution to the backward Fokker-Planck equation, a partial-differential equationthat can be difficult to solve through general means. Therefore, we derive a one-dimensional approximation through asymptotic methods for barrier crossing trajectories. We show that this approximation has a simple analytical representation and approaches the true solution as the barrier height increases. Brownian bridge trajectories generated with this approximate solution are then shown to result in accurate conditional statistics when used in conjunction with importance sampling, even in the case when potential energy barriers are not large. We show this idea's effectiveness by simulating rare events in a stochastic chemical reaction network (Schögl reaction) with multiple steady states. This methodology shows great promise for future implementation to simulate rare barrier crossing events for a wide variety of physical processes.

Self-correlated spatial random variables: From an auto- to a sui- model respecification

This paper marks the 50-year publication anniversary of Besag's seminal spatial auto- models paper. His classic article synthesizes generic autoregressive specifications (i.e., a response variable appears on both sides of a regression equation and/or probability function equal sign) for the following six popular random variables: normal, logistic (i.e., Bernoulli), binomial, Poisson, exponential, and gamma. Besag dismisses these last two while recognizing failures of both as well as the more scientifically critical counts-oriented auto-Poisson. His initially unsuccessful subsequent work first attempted to repair them (e.g., pseudo-likelihood estimation), and then successfully revise them within the context of mixed models, formulating a spatially structured random effects term that effectively and efficiently absorbs and accounts for spatial autocorrelation in geospatial data. One remaining weakness of all but the auto-normal is a need to resort to Markov chain Monte Carlo (MCMC) techniques for legitimate estimation purposes. Recently, Griffith succeeded in devising an innovative uniform distribution genre—sui-uniform random variables—that accommodates spatial autocorrelation, too. Its most appealing feature is that, by applying two powerful mathematical statistical theorems (i.e., the probability integral transform, and the quantile function), it redeems Besag's auto- model failures. This paper details conversion of Besag's initial six modified variates, exemplifying them with both simulation experiments and publicly accessible real-world georeferenced data. The principal outcome is valuable spatial statistical advancements, with special reference to Moran eigenvector spatial filtering.

(017) COST-EFFECTIVENESS MODELING OF IMPLANTABLE PROSTHESIS AS A FIRST-LINE OPTION FOR ERECTILE DYSFUNCTION MANAGEMENT

Abstract Introduction Erectile dysfunction (ED) rates in the United States have increased over the past several decades due to multiple factors, including an aging population and rising rates of diabetes and peripheral vascular disease. Implantable penile prostheses (IPP) are highly effective for refractory ED, however, insurance coverage is variable and often requires demonstration of both failed oral and injectable therapy. Delayed IPP placement in high-risk patients is likely to reduce quality of life metrics as well as increase costs associated with failed pharmacological trials. Objective We aimed to evaluate the quality associated life years (QALY) and the cost per QALY of direct IPP placement for PDE5 inhibitor-resistant ED. Methods A literature review was conducted to gather probability functions of success and complications associated with PDE5 inhibitors, intracavernosal injection (ICI), and IPP, as well as costs and QALYs associated with each therapy. TreeAge was used to generate a Markov model, which evaluated two separate strategies: 1) a standard pathway, in which all patients would be trialed on PDE5 inhibitors and ICI for 1 to 6 months each, and 2) a direct-to-IPP pathway, in which patients would immediately be offered an IPP. Each model was run for 10 000 iterations for 10 years per iteration. The standard pathway was run for 9 scenarios, in which each population would have a varying response rate to PDE5 inhibitors and ICI (10% to 90% response). Cumulative QALY, costs, and the incremental cost-effectiveness ratio (ICER) of each strategy were calculated. Results The standard pathway model considered 10% to 90% response rates to traditional medical management, resulting in average 10-year costs ranging between $22 914 and $44 187. In comparison, the direct-to-IPP group had average 10-year costs of $17 750 (Fig. 1). The ICERs for the standard pathway compared to the direct-to-IPP approach ranged linearly from -$34 221 to -$45 595 (from 10% to 90% medical management success). Average total QALY for a 10-year period ranged from 6.99 (90% medical management success) to 7.61 (direct-to-IPP). Conclusions In this analysis, we sought to quantify the cost-effectiveness of the prevailing management strategy for erectile dysfunction compared to an IPP-first strategy. We found that the ICER of medical management compared to a direct-to-IPP strategy ranged from -$34 K to -$46 K, indicating that medical management was both more costly and less effective in providing patients with ED quality of life. While it is unreasonable to suggest that IPP should be a first-line treatment for all patients with erectile dysfunction, these data are informative for counseling in populations particularly susceptible to high rates of erectile dysfunction, such as those with diabetic neuropathy, a history of radical prostatectomy, or peripheral vascular disease. In these populations, counseling directly on penile prosthesis before PDE5 inhibitors or ICI trials could be both cost-effective and fit within the current American Urological Association guideline statements. Disclosure Any of the authors act as a consultant, employee or shareholder of an industry for: Gregory Joice MD is a consultant for Boston Scientific.

Probabilistic human health risk assessment for arsenic, nickel and lead exposures based on two-dimensional Monte Carlo simulation

Human health risk assessment (HHRA) in probabilistic space is an ongoing research activity that plays a crucial role in managing water quality risks. This study formulates a probabilistic HHRA based on two-dimensional Monte Carlo simulation (MCS) for a set of groundwater samples exposed to trace elements of arsenic (As), nickel (Ni), and lead (Pb) for dermal and oral pathways. The developed two-dimensional MCS captures the parameter variability in Dimension I and the functional uncertainty of the probability functions in Dimension II. The probabilistic HHRA was implemented in the Tabriz plain, a strategic aquifer in northwest Iran. The results of probabilistic HHRA indicate that the minimum and maximum values for total risk are 10 and 44 times greater than the tolerable contamination range (TCR = 1 × 10−4), respectively. The HHRA results also delineate the hotspots in the aquifer for individual and total designated elements. The results also indicate that remedial strategies are necessary for As and Ni as their exposure values at the 95th percentile exceed the TCL. We also used the correlation coefficient matrix and the factor analysis to detect the probable sources of the designated trace elements. The results show that As and Pb are likely to have geogenic sources. Our findings also suggest that geogenic and anthropogenic sources contribute to Ni concentration in the aquifer. These findings support the decision to protect the public health of the over 1.7 Million people who use groundwater resources for drinking.

Characteristics and Source Analysis of VOCs Pollution During Emergency Response in Tianjin

To elucidate the characteristics of VOCs chemical components during heavy pollution episodes, hourly online VOCs data derived from 11 heavy pollution events in Tianjin from 2019 to 2020 were employed. The positive matrix factorization （PMF） and conditional bivariate probability function （CBPF） were employed to analyze the sources of VOCs during heavy pollution episodes. The results indicated that the average VOCs volume fraction during these episodes was recorded at 35.7×10-9. Furthermore, it was observed that during the winter emergency response period, there was a discernible increase in the volume fraction of VOCs when compared to that during the autumn season. Specifically, there was a notable upswing of 48% in the olefins category, whereas alkanes registered a 4% increase. Additionally, the VOCs component structure changed significantly during the heavy pollution episodes. During the orange warning period, the proportion of alkanes increased by 36%, and the proportion of acetylene decreased by 32%. During the yellow warning period, the proportion of alkanes increased by 14%, and the proportion of acetylene decreased by 5%. During the emergency response period, motor vehicle emission sources, natural gas evaporative sources, and solvent use sources were the main contributors of VOCs in environmental receptors, contributing 17.5%, 15.4%, and 15.2%, respectively. Compared with that during the period antecedent to the emergency response, the contribution of vehicle emission sources and diesel volatile sources to VOCs in environmental receptors decreased by 2.0% to 5.5% and 2.1% to 6.6%, respectively, and the contribution of solvent use sources decreased by 0.2% to 2.4% during the yellow warning period. During the orange warning period, the contribution of motor vehicle emission sources was reduced by 0.1% to 8.3%, and the contribution of solvent use sources was reduced by 0.5% to 6.2%.

Random telegraph processes with nonlocal memory.

We study two-state (dichotomous, telegraph) random ergodic continuous-time processes with dynamics depending on their past. We take into account the history of the process in an explicit form by introducing integral nonlocal memory term into conditional probability function. We start from an expression for the conditional transition probability function describing additive multistep binary random chain and show that the telegraph processes can be considered as continuous-time interpolations of discrete-time dichotomous random sequences. An equationinvolving the memory function and the two-point correlation function of the telegraph process is analytically obtained. This integral equationdefines the correlation properties of the processes with given memory functions. It also serves as a tool for solving the inverse problem, namely for generation of a telegraph process with a prescribed pair correlation function. We obtain analytically the correlation functions of the telegraph processes with two exactly solvable examples of memory functions and support these results by numerical simulations of the corresponding telegraph processes.

Estimation and Management of Wind Energy in Ikorodu Lagos Lagoon, Using Weibull Parametric Measurement to Electricity Production in Nigeria

This paper is an approach on the estimation and approximation management of wind energy production around the lagoon axis of Ikorodu, Lagos state, Nigeria. The article concentrates on the availability of renewable resource such as wind to generate electrical energy in the greater Ikorodu metropolis of Lagos state Nigeria. Here, probability distribution function is used to generate the wind data. In this paper, three distinct methods are presented; Data time series analysis, Weibull probability function, and theoretical comparison with analytical concept. This research uses two important parameters for analyzing wind data: shape factor “k” and scale factor “c” from Weibull distribution function. The theoretical uses mathematical equations of popular methods such as: (a) Moment method, (b) Empirical Formula, or statistical standard deviation, (c) Peak likelihood, (d) modified peak likelihood, (e) double modified peak likelihood (f) graphical method or smallest mean square, and (g) energy sequence factor. The results obtained are tested to optimize the value from the Weibull parameters by adopting five techniques: (i) root average square error methodology, X2, power of agreement , MAPE, and RRMSE. The results expatiated on the practical and theoretical techniques design to confront the outcome of wind energy harnessed per 1.5 km2. Here, a differential optimization technique is used to determine the precision report. This serve as the basis of error litmus check existing between the wind energy determined by theory of statistical and mathematical Weibull Parametric function and the practical time-series data analysis in LSTM. Again, the wind data (speed and energy/power) were measured and recorded between January 2020 to December 2023 in the Ijede-Ikorodu Lagoon area of Lagos State. The optimized value for the shape factor k and scale factor c parametric measurement and management for maximizing the output electrical energy are obtained by using a well robust Weibull distribution function techniques and by absolutely determining and selecting the best position and location for installing the wind/wave alternators/generator. These generators come with turbines as a single unit. The measurement of the yearly average wind speed and average wind power are 10.09 ms-1 and 10.1 KWm-2, concurrently.

NOx Abatement by a TiO2-Based Coating under Real-Life Conditions and Laboratory-Scale Durability Assessment

In urban environments, various pollutants generated by road traffic, human, and industrial activities degrade outdoor and indoor air quality. Among these pollutants, nitrogen oxides (NOx) are subject to air quality regulations designed to protect human health and the environment. It is therefore crucial to keep their concentration as low as possible. Advanced oxidation processes are a practical choice for the degradation of NOx; among them, heterogeneous photocatalysis has proven to be a viable route. However, while the efficiency of this process has been widely demonstrated on a laboratory scale, it is still the subject of debate for real-life applications. The purpose of this study was to present a new field experiment on the application of a photocatalytic coating to outdoor walls. Air quality monitoring stations were used to evaluate the NOx concentration reduction instead of the chemiluminescent analyzer, in order to increase the number of sampling points. Statistical analysis was carried out to interpret the results. Density probability functions were plotted and showed a positive impact of the coating, leading to lower NOx concentrations. This work was completed by a laboratory-scale assessment of the coating’s durability using abrasion, QUV, and immersion/drying tests. The air depollution capacity of the chosen coating was significantly reduced after QUV testing.