Facilitated Prediction of Micropollutant Degradation via UV-AOPs in Various Waters by Combining Model Simulation and Portable Measurement

The degradation of micropollutants in water via ultraviolet(UV)-based advanced oxidation processes(AOPs)is strongly dependent on the water matrix.Various reactive radicals(RRs)formed in UV-AOPs have different reaction selectivities toward water matrices and degradation efficiencies for target micropollutants.Hence,process selection and optimization are crucial.This study developed a facilitated prediction method for the photon fluence-based rate constant for micropollutant degradation(K′_(p,MP))in various UV-AOPs by combining model simulation with portable measurement.Portable methods for measuring the scavenging capacities of the principal RRs(RRSCs)involved in UV-AOPs(i.e.,HO^(·),SO_(4)^(·-),and Cl^(·))using a mini-fluidic photoreaction system were proposed.The simulation models consisted of photochemical,quantitative structure–activity relationship,and radical concentration steady-state approximation models.The RRSCs were determined in eight test waters,and a higher RRSC was found to be associated with a more complex water matrix.Then,by taking sulfamethazine,caffeine,and carbamazepine as model micropollutants,the k′_(p,MP) values in various UV-AOPs were predicted and further verified experimentally.A lower k′_(p,MP) was found to be associated with a higher RRSC for a stronger RR competition;for example,k′_(p,MP) values of 130.9 and 332.5 m^(2) einstein^(–1),respectively,were obtained for carbamazepine degradation by UV/H_(2)O_(2) in the raw water(RRSC=9.47×10^(4) s^(-1))and sand-filtered effluent(RRSC=2.87×10^(4) s^(-1))of a drinking water treatment plant.The developed method facilitates process selection and optimization for UV-AOPs,which is essential for increasing the efficiency and cost-effectiveness of water treatment.

Product reliability assessment based on proportional hazard degradation model

In order to evaluate the reliability of long-lifetime products with degradation data, a new proportional hazard degradation model is proposed. By the similarity between time-degradation data and stress-accelerated lifetime, and the failure rate function of degradation data which is assumed to be proportional to the time covariate, the reliability assessment based on a proportional hazard degradation model is realized. The least squares method is used to estimate the model＇s parameters. Based on the failure rate of the degradation data and the proportion function of the known time, the failure rate and the reliability function under the given time and the predetermined failure threshold can be extrapolated. A long life GaAs laser is selected as a case study and its reliability is evaluated. The results show that the proposed method can accurately describe the degradation process and it is effective for the reliability assessment of long lifetime products.

Unified Degradation Model in Low Gate Voltage Range During Hot-Carrier Stressing of p-MOS Transistors

Hot carrier effects of p MOSFETs with different oxide thicknesses are studied in low gate voltage range.All electrical parameters follow a power law relationship with stress time,but degradation slope is dependent on gate voltage.For the devices with thicker oxides,saturated drain current degradation has a close relationship with the product of gate current and electron fluence.For small dimensional devices,saturated drain current degradation has a close relationship with the electron fluence.This degradation model is valid for p MOSFETs with 0 25μm channel length and different gate oxide thicknesses.

Human induced dryland degradation in Ordos Plateau,China,revealed by multilevel statistical modeling of normalized difference vegetation index and rainfall time-series

Land degradation causes serious environmental problems in many regions of the world, and although it can be effectively assessed and monitored using a time series of rainfall and a normalized difference vegetation index （NDVI） from remotely-sensed imagery, dividing human-induced land degradation from vegetation dynamics due to climate change is not a trivial task. This paper presented a multilevel statistical modeling of the NDVI-rainfall relationship to detect human-induced land degradation at local and landscape scales in the Ordos Plateau of Inner Mongolia, China, and recognized that anthropogenic activities result in either positive （land restoration and re-vegetation） or negative （degradation） trends. Linear regressions were used to assess the accuracy of the multi- level statistical model. The results show that： （1） land restoration was the dominant process in the Ordos Plateau between 1998 and 2012; （2） the effect of the statistical removal of precipitation revealed areas of human-induced land degradation and improvement, the latter reflecting successful restoration projects and changes in land man- agement in many parts of the Ordos; （3） compared to a simple linear regression, multilevel statistical modeling could be used to analyze the relationship between the NDVI and rainfall and improve the accuracy of detecting the effect of human activities. Additional factors should be included when analyzing the NDVI-rainfall relationship and detecting human-induced loss of vegetation cover in drylands to improve the accuracy of the approach and elimi- nate some observed non-significant residual trends.

Application of a Novel Method for Machine Performance Degradation Assessment Based on Gaussian Mixture Model and Logistic Regression

The currently prevalent machine performance degradation assessment techniques involve estimating a machine＇s current condition based upon the recognition of indications of failure features,which entail complete data collected in different conditions.However,failure data are always hard to acquire,thus making those techniques hard to be applied.In this paper,a novel method which does not need failure history data is introduced.Wavelet packet decomposition（WPD） is used to extract features from raw signals,principal component analysis（PCA） is utilized to reduce feature dimensions,and Gaussian mixture model（GMM） is then applied to approximate the feature space distributions.Single-channel confidence value（SCV） is calculated by the overlap between GMM of the monitoring condition and that of the normal condition,which can indicate the performance of single-channel.Furthermore,multi-channel confidence value（MCV）,which can be deemed as the overall performance index of multi-channel,is calculated via logistic regression（LR） and that the task of decision-level sensor fusion is also completed.Both SCV and MCV can serve as the basis on which proactive maintenance measures can be taken,thus preventing machine breakdown.The method has been adopted to assess the performance of the turbine of a centrifugal compressor in a factory of Petro-China,and the result shows that it can effectively complete this task.The proposed method has engineering significance for machine performance degradation assessment.

Immobilized laccase on magnetic nanoparticles for enhanced lignin model compounds degradation

As a natural aromatic polymer,lignin has great potential but limited industrial application due to its complex chemical structure.Among strategies for lignin conversion,biodegradation has attracted promising interest recently in term of efficiency,selectivity and mild condition.In order to overcome the issues of poor stability and non-reusability of enzyme in the biodegradation of lignin,this work explored a protocol of immobilized laccase on magnetic nanoparticles(MNPs)with rough surfaces for enhanced lignin model compounds degradation.Scanning electron microscope with energy dispersive spectrometer(SEM-EDS),flourier transformation infrared spectroscopy(FTIR)and thermal gravimetric analysis(TGA)were utilized to characterize the immobilization of laccase.The results showed a maximum activity recovery of 64.7%towards laccase when it was incubated with MNPs and glutaraldehyde(GA)with concentrations of 6 mg·ml^-1and 7.5 mg·ml^-1for 5 h,respectively.The immobilized laccase showed improved thermal stability and pH tolerance compared with free laccase,and remained more than 80%of its initial activity after 20 days of storage at 4℃.In addition,about 40%residual activity of the laccase remained after 8 times cycles.Gas chromatography–mass spectrometry(GC–MS)was utilized to characterize the products of lignin model compound degradation and activation,and the efficiency of immobilized laccase was calculated to be 1–5 times that of free laccase.It was proposed that the synergistic effect between MNPs and laccase displays an important role in the enhancement of stability and activity in lignin model compound biodegradation.

Artificial woodland degradation in semi-arid agro-pastoral transitional area： conceptual model and status assessment

Planting trees was used as one of cost-effective measures for desertification control in add and semi-add areas of China. Woodland degradation, however, is becoming an inevitable issue in these areas. In this paper, a typical county, Ejin Holo County, Inner Mongolia, China was selected for its assessment of artificial woodland degradation. A conceptual model for woodland degradation was delineated qualitatively based on field sampling survey, and four model-based indicators as humidity index （HI）, vegetation index （NDVI）, soil type （ST） and soil erosion modulus （EM） were screened out and used to a GIS-based method for artificial woodland degradation assessment in this semi-add agro-pastoral transitional area. All the indicator layers were overlaid and desertification assessed using simplified equation with equal weights for each indicators. The assessment results showed that in 336. 09 km^2 of total woodland area, 311.35 km^2 woodland were under degradation, and the area for slight, medium, severe degradation was 78.97, 119.73 and 112.65 km^2, respectively. It was suggested that much attention should be paid on woodland improvement and plant species selection, especially shrub species, before revegetation in similar areas.

Analysis of Ecosystem Degradation Factors in Yuanmou Arid-Hot Valleys Based on Interpretative Structural Model

For ecological restoration and reconstruction of the degraded area, it is an important premise to correctly understand the degradation factors of the ecosystem in the arid-hot valleys. The factors including vegetation degradation, land degradation, arid climate, policy failure, forest fire, rapid population growth, excessive deforestation, overgrazing, steep slope reclamation, economic poverty, engineering construction, lithology, slope, low cultural level, geological hazards, biological disaster, soil properties etc, were selected to study the Yuanmou arid-hot valleys. Based on the interpretative structural model （ISM）, it has found out that the degradation factors of the Yuanmou arid-hot valleys were not at the same level but in a multilevel hierarchical system with internal relations, which pointed out that the degradation mode of the arid-hot valleys was ＂straight （appearance）-penetrating-background＂. Such researches have important directive significance for the restoration and reconstruction of the arid-hot valleys ecosystem.

Kinetic Modelling of Pesticidal Degradation and Micro-bial Growth in Soil

This paper discusses such models for the degradation kinetics of pesticides in soil as the model expressingthe degradation rate as a function of two variables: the pesticide concentration and the number of pesticide-degrading microorganisms, the model expressing the pesticide concentration as an explicit or implicit functionof time, and the model expressing the pesticide loss rate constants as functions of temperature. These modelsmay interpret the degradation curves with an inflection point . A kinetic model describing the growth processesof microbial populations in a closed system is reported as well.

Validation methodology for distribution-based degradation model

Distribution-based degradation models （or graphical approach in some literature） occur in a wide range of applications. However, few of existing methods have taken the validation of the built model into consideration. A validation methodology for distribution-based models is proposed in this paper. Since the model can be expressed as consisting of assumptions of model structures and embedded model parameters, the proposed methodology carries out the validation from these two aspects. By using appropriate statistical techniques, the rationality of degradation distributions, suitability of fitted models and validity of degradation models are validated respectively. A new statistical technique based on control limits is also proposed, which can be implemented in the validation of degradation models＇ validity. The case study on degradation modeling of an actual accelerometer shows that the proposed methodology is an effective solution to the validation problem of distribution-based de qradation models.

Lifetime prediction for tantalum capacitors with multiple degradation measures and particle swarm optimization based grey model

A lifetime prediction method for high-reliability tantalum （Ta） capacitors was proposed, based on multiple degradation measures and grey model （GM）. For analyzing performance degradation data, a two-parameter model based on GM was developed. In order to improve the prediction accuracy of the two-parameter model, parameter selection based on particle swarm optimization （PSO） was used. Then, the new PSO-GM（1, 2, co） optimization model was constructed, which was validated experimentally by conducting an accelerated testing on the Ta capacitors. The experiments were conducted at three different stress levels of 85, 120, and 145℃. The results of two experiments were used in estimating the parameters. And the reliability of the Ta capacitors was estimated at the same stress conditions of the third experiment. The results indicate that the proposed method is valid and accurate.

Towards a semi-empirical model of the sea ice thickness based on hyperspectral remote sensing in the Bohai Sea

Sea ice thickness is one of the most important input parameters for the prevention and mitigation of sea ice disasters and the prediction of local sea environments and climates. Estimating the sea ice thickness is currently the most important issue in the study of sea ice remote sensing. With the Bohai Sea as the study area, a semiempirical model of the sea ice thickness（SEMSIT） that can be used to estimate the thickness of first-year ice based on existing water depth estimation models and hyperspectral remote sensing data according to an optical radiative transfer process in sea ice is proposed. In the model, the absorption and scattering properties of sea ice in different bands（spectral dimension information） are utilized. An integrated attenuation coefficient at the pixel level is estimated using the height of the reflectance peak at 1 088 nm. In addition, the surface reflectance of sea ice at the pixel level is estimated using the 1 550–1 750 nm band reflectance. The model is used to estimate the sea ice thickness with Hyperion images. The first validation results suggest that the proposed model and parameterization scheme can effectively reduce the estimation error associated with the sea ice thickness that is caused by temporal and spatial heterogeneities in the integrated attenuation coefficient and sea ice surface. A practical semi-empirical model and parameterization scheme that may be feasible for the sea ice thickness estimation using hyperspectral remote sensing data are potentially provided.

Kinetic Modelling of Degradation of Organic Compounds in Soils

A set of equations is suggested to describe the kinetics of degradation of organic compounds applied tosoils and the kinetics of growth of the involved microorganisme:where x is the concentration of organic compound at time t, m is the number of forcroorganisms capableof degrading the organic compound at time t, while j, k, f and g are positive constaats. This model cansatisfactorily be used to explain the degradation curve of organic compounds and the growth curve of theinvolved microorganisms.

A Unified Model for the Degradation Kinetics of Pesticides Applied Continually to Soils

This paper deduces a kinetic model for microbial degradation of pesticides in soils:where x is the concentration of pesticide at time t, so the initial concentration of the pesticide, me the initial number of pesticide-degrading microorganisms, M the carrying capacity for the microorganisms, μ the specific growth rate of the microorganisms, and k the rate constant for the pesticide degradation.In periodic applications of pesticides, this model can be used to continuously describe every degradation curve. Whether a lag phase occurs or not, we can obtain the minimum residue of the pesticide (xe):xe=xdexp(-kMr)/[1-exp(-ker) ]where r is the regular time internals between applications, and xd the dosage of the pesticide.

SEFIDROOD RIVER SUB-WATERSHED-DAM-ESTUARY AND DEGRADATION MODEL:A HOLISTIC APPROACH IN IRAN

The major concern of this article is to address the shortcoming and outgoing effects of the human activities on the landscape patterns and their consequences in the Sefidrood River watershed in Iran. A flow of data includes three inputs; each of them belongs to one part of three zones of a fluvial system. The three parts of the Sefidrood River fluvial system include Zone 1,a sub-watershed as degradation modeling site,Zone 2,Sefidrood Dam as dam site,and Zone 3,17km away from the Sefidrood River path to the Caspian Sea as ending point site. The degradation model in the Zone 1 provides a suitable mean for decision support system to decrease the human impacts on each small district. The maximum number for degradation coefficient belongs to the small district with the highest physiographic density,relatively cumulative activities,and a lower figure for the habitat vulnerability. The human degradation impact were not limited to the upstream. The investigation to the Sefidrood Dam and ending point of the Sefidrood River depicts that sedimentation continues as a significant visual impact in the Sefidrood Dam reservoir and the estuary.

Comparative Study among Different Semi-Empirical Models for Soil Salinity Prediction in an Arid Environment Using OLI Landsat-8 Data

Salt-affected soils, caused by natural or human activities, are a common environmental hazard in semi-arid and arid landscapes. Excess salts in soils affect plant growth and production, soil and water quality and, therefore, increase soil erosion and land degradation. This research investigates the performance of five different semi-empirical predictive models for soil salinity spatial distribution mapping in arid environment using OLI sensor image data. This is the first attempt to test remote sensing based semi-empirical salinity predictive models in this area: the Kingdom of Bahrain. To achieve our objectives, OLI data were standardized from the atmosphere interferences, the sensor radiometric drift, and the topographic and geometric distortions. Then, the five semi-empirical predictive models based on the Normalized Difference Salinity Index (NDSI), the Salinity Index-ASTER (SI-ASTER), the Salinity Index-1 (SI-1), the Soil Salinity and Sodicity Index-1 and Index-2 (SSSI-1 and SSSI-2), developed for slight and moderate salinity in agricultural land, were implemented and applied to OLI image data. For validation purposes, a fieldwork was organized and different important spots-locations representing different salinity levels were visited, photographed, and localized using an accurate GPS (σ ≤ ±30 cm). Based on this a priori knowledge of the soil salinity, six validation sites were selected to reflect non-saline, low, moderate, high and extreme salinity classes, descriptive statistics extracted from polygons and/or transects over these sites were used. The obtained results showed that the models based on NDSI, SI-1 and SI-ASTER all failed to detect salinity bounds for both extreme salinity (Sabkhah) and non-saline conditions. In Fact, NDSI and SI-ASTER gave respectively only 35% dS/m and 25% dS/m in extreme salinity validation site, while SI-1 and SI-ASTER indicated 38% dS/m and 39% dS/m in non-saline validation site. Therefore, these three models were deemed inadequate for the study site. However, both SSSI-1 and SSSI-2 allowed a detection of the previous salinity bounds and furthermore described similarly and correctly the urban-vegetation areas and the open-land areas. Their predicted EC is around 10% dS/m for non-saline urban soil, about 25% dS/m for low salinity urban-vegetation soil, approximately 30% to 75% dS/m, respectively, for moderate to high salinity soils. SSSI-2 based semi-empirical salinity models was able to differentiate the high salinity versus extreme salinity in areas where both exist and was very accurate to highlight the pure salt where SSSI-1 has reach saturation for both salinity classes. In conclusion, reliable salinity map was produced using the model based on SSSI-2 and OLI sensor data that allows a better characterization of the soil salinity problem in an Arid Environment.

Hot carrier degradation and a new lifetime prediction model in ultra-deep sub-micron pMOSFET

The hot carrier effect （HCE） of an ultra-deep sub-micron p-channel metal–oxide semiconductor field-effect transistor （pMOSFET） is investigated in this paper. Experiments indicate that the generation of positively charged interface states is the predominant mechanism in the case of the ultra-deep sub-micron pMOSFET. The relation of the pMOSFET hot carrier degradation to stress time （t）, channel width （W ）, channel length （L）, and stress voltage （Vd ） is then discussed. Based on the relation, a lifetime prediction model is proposed, which can predict the lifetime of the ultra-deep sub-micron pMOSFET accurately and reflect the influence of the factors on hot carrier degradation directly.

An improved semi-empirical friction model for gas-liquid two-phase flow in horizontal and near horizontal pipes

Pressure drop and liquid hold-up are two very important fluid flow parameters in design and control of multiphase flow pipelines.Friction factors play an important role in the accurate calculation of pressure drop.Various empirical and semi-empirical closure relations exist in the literature to calculate the liquid-wall,gas-wall and interfacial friction in two-phase pipe flow.However most of them are empirical correlations found under special experimental conditions.In this paper by modification of a friction model available in the literature,an improved semiempirical model is proposed.The proposed model is incorporated in the two-fluid correlations under equilibrium conditions and solved.Pressure gradient and velocity profiles are validated against experimental data.Using the improved model,the pressure gradient deviation from experiments diminishes by about 3%;the no-slip condition at the interface is satisfied and the velocity profile is predicted in better agreement with the experimental data.

Adaptive semi-empirical model for non-contact atomic force microscopy

Non-contact atomic force microscope is a powerful tool to investigate the surface topography with atomic resolution.Here we propose a new approach to estimate the interaction between its tips and samples,which combines a semi-empirical model with density functional theory(DFT)calculations.The generated frequency shift images are consistent with the experiment for mapping organic molecules using CuCO,Cu,CuCl,and CuO_(x)tips.This approach achieves accuracy close to DFT calculation with much lower computational cost.

Nonlinear Modeling for a Two-Stage Degradation System Based on Nonhomogeneous Poisson Process

The degradation process modeling is one of research hotspots of prognostic and health management(PHM),which can be used to estimate system reliability and remaining useful life(RUL).In order to study system degradation process,cumulative damage model is used for degradation modeling.Assuming that damage increment is Gamma distribution,shock counting subjects to a homogeneous Poisson process(HPP)when degradation process is linear,and shock counting is a non-homogeneous Poisson process(NHPP)when degradation process is nonlinear.A two-stage degradation system is considered in this paper,for which the degradation process is linear in the first stage and the degradation process is nonlinear in the second stage.A nonlinear modeling method for considered system is put forward,and reliability model and remaining useful life model are established.A case study is given to validate the veracities of established models.