Multiple-layer Model Research Articles

Water use of Guinea grass as affected by rainfed local conditions and practices in Sub-Saharan Africa

Ongoing efforts are currently being made to rehabilitate drought-affected pastures in Sub-Saharan Africa. One approach being explored is the introduction of non-native grass species, such as Megathyrsus maximus (Guinea grass). This study aims to investigate the water use of Guinea grass in semi-arid environments under rainfed conditions. Additionally, it aims to a better understanding of the variability of water use in Guinea grass through the utilization of the Bagging machine learning algorithm. Split-plot field experiments were carried out over two consecutive rainy seasons (2020-2021). The treatments included two in-situ rainwater harvesting practices, RWH (ridging plus terracing and terracing alone), three seeding rates, SR (1.5, 2.5, and 3.5 kg ha-1), and two soil nitrogen fertilization rates, SF (95 kg N ha-1 and 0 kg N ha-1). These treatments were compared to a control plot that involved zero-tillage, no fertilization, and no rainwater harvesting. The collected datasets were analyzed using R, SPSS 15, and spreadsheets. The results showed significant differences in plant indices and soil moisture content among the treatments. However, the treatments had insignificant effects on seasonal actual crop evapotranspiration (ETa), which ranged from 1.93 to 3.29 mm day-1. The interactions between SR and RWH were found to have significant impacts on water use. The Bagging algorithm revealed that the variability in ETa could be attributed to SR (42%), RWH (31%), and SF (26%), respectively. The implementation of rainwater harvesting practices resulted in a significant reduction in water usage, saving 86% of the green water used with a water footprint of 0.25 m3 kg-1, compared to 1.7 m3 kg-1 for no adoption of RWH conditions. The water use of rainfed Guinea grass was also found highly sensitive to dry spells. Further detailed studies using multiple-layer models are recommended to gain a better understanding of the non-linear interactions in semi-arid environments.

Machine learning to predict myocardial injury and death after non-cardiac surgery.

Myocardial injury due to ischaemia within 30 days of non-cardiac surgery is prognostically relevant. We aimed to determine the discrimination, calibration, accuracy, sensitivity and specificity of single-layer and multiple-layer neural networks for myocardial injury and death within 30 postoperative days. We analysed data from 24,589 participants in the Vascular Events in Non-cardiac Surgery Patients Cohort Evaluation study. Validation was performed on a randomly selected subset of the study population. Discrimination for myocardial injury by single-layer vs. multiple-layer models generated areas (95%CI) under the receiver operating characteristic curve of: 0.70 (0.69-0.72) vs. 0.71 (0.70-0.73) with variables available before surgical referral, p < 0.001; 0.73 (0.72-0.75) vs. 0.75 (0.74-0.76) with additional variables available on admission, but before surgery, p < 0.001; and 0.76 (0.75-0.77) vs. 0.77 (0.76-0.78) with the addition of subsequent variables, p < 0.001. Discrimination for death by single-layer vs. multiple-layer models generated areas (95%CI) under the receiver operating characteristic curve of: 0.71 (0.66-0.76) vs. 0.74 (0.71-0.77) with variables available before surgical referral, p = 0.04; 0.78 (0.73-0.82) vs. 0.83 (0.79-0.86) with additional variables available on admission but before surgery, p = 0.01; and 0.87 (0.83-0.89) vs. 0.87 (0.85-0.90) with the addition of subsequent variables, p = 0.52. The accuracy of the multiple-layer model for myocardial injury and death with all variables was 70% and 89%, respectively.

Sensitivity Enhancement of 2D Material-Based Surface Plasmon Resonance Sensor with an Al-Ni Bimetallic Structure.

In this paper, a variety of 2D materials on the surface plasmon resonance sensor based on Al-Ni bimetallic layer are compared. Simulation results indicate that lateral position shift, which is calculated according to the real and imaginary parts of the refractive index of material, can be used as an effective parameter to optimize the sensitivity. By using the parameters for optimizing the SPR structures, the results show that the multiple layer models of Al(40 nm)-Ni(22 nm)-black phosphorus (BP)(1 L) and Al(40 nm)-Ni(22 nm)-blue phosphorus (BlueP)/WS2(1 L) exhibit average angular sensitivities of 507.0 °/RIU and 466 °/RIU in the refractive index range of 1.330-1.335, and maximum sensitivity of 542 °/RIU and 489 °/RIU at the refractive index of 1.333, respectively. We expect more applications can be explored based on the highly sensitive SPR sensor in different fields of optical sensing.

The Influence of Solar X-ray Flares on SAR Meteorology: The Determination of the Wet Component of the Tropospheric Phase Delay and Precipitable Water Vapor

In this work, we study the impact of high-energy radiation induced by solar X-ray flares on the determination of the temporal change in precipitable water vapor (ΔPWV) as estimated using the synthetic aperture radar (SAR) meteorology technique. As recent research shows, this radiation can significantly affect the ionospheric D-region and induces errors in the estimation of the total electron content (TEC) by the applied models. Consequently, these errors are reflected in the determination of the phase delay and in many different types of measurements and models, including calculations of meteorological parameters based on SAR observations. The goal of this study is to quantify the impact of solar X-ray flares on the estimation of ΔPWV and provide an estimate of errors induced if the vertical total electron content (VTEC) is obtained by single layer models (SLM) or multiple layer models (MLM) (these models do not include ionosphere properties below the altitude of 90 km as input parameters and cannot provide information about local disturbances in the D-region). The performed analysis is based on a known procedure for the determination of the D-region electron density (and, consequently, the vertical total electron content in the D-region (VTECD)) using ionospheric observations by very low frequency (VLF) radio waves. The main result indicates that if the D-region, perturbed by medium-sized and intense X-ray flares, is not modeled, errors occur in the determination of ΔPWV. This study emphasizes the need for improved MLMs for the estimation of the TEC, including observational data at D-region altitudes during medium-sized and intense X-ray flare events.

Predictive model for PV power generation using RNN (LSTM)

In recent years, advanced information technologies, such as deep learning and big data, have been actively applied in building energy management systems to improve energy efficiency. Various studies have been conducted on the prediction of renewable energy performance using machine learning techniques. In this study, a recurrent neural network (RNN) was utilized in predicting photovoltaic (PV) power generation. An RNN is an artificial neural network in which the connection between units is composed of a cyclic structure that can reflect the characteristics of time series. Therefore, to eventually incorporate a model predictive control technique for energy demand and supply matching, this study uses previously measured weather data and PV power generation data to predict the future PV power generation. Various optimization processes, such as normalization, classification of learning data, and setting of layer options, are performed to create a predictive model. Furthermore, 500 hidden neurons and 1 and 3 hidden layers are created and compared. The initial learning rate for both single and multiple-layer options was set to 0.05. As a result, the coefficient of variation of the root mean squared error, Cv(RMSE), which is a statistical index to assess the overall accuracy of the predicted values, was 13.8 % for a single layer and 13.2 % for a multiple-layer long short term memory model. Although no significant difference was observed between the two models, the multiple-layer model showed a slightly reduced error rate than the single-layer model, and the accurate prediction was confirmed to be achievable. Based on this study, a stable energy supply system can be established through linkage with energy storage control by predicting the amount of energy production through renewable energy sources and the amount of energy demand at urban scale.

Corrigendum to ‘Deep learning improves taphonomic resolution: high accuracy in differentiating tooth marks made by lions and jaguars'

Corrigendum to ‘Deep learning improves taphonomic resolution: high accuracy in differentiating tooth marks made by lions and jaguars'

User-Centered Clinical Display Design Issues for Inpatient Providers.

Suboptimal information display in electronic health records (EHRs) is a notorious pain point for users. Designing an effective display is difficult, due in part to the complex and varied nature of clinical practice. This article aims to understand the goals, constraints, frustrations, and mental models of inpatient medical providers when accessing EHR data, to better inform the display of clinical information. A multidisciplinary ethnographic study of inpatient medical providers. Our participants' primary goal was usually to assemble a clinical picture around a given question, under the constraints of time pressure and incomplete information. To do so, they tend to use a mental model of multiple layers of abstraction when thinking of patients and disease; they prefer immediate pattern recognition strategies for answering clinical questions, with breadth-first or depth-first search strategies used subsequently if needed; and they are sensitive to data relevance, completeness, and reliability when reading a record. These results conflict with the ubiquitous display design practice of separating data by type (test results, medications, notes, etc.), a mismatch that is known to encumber efficient mental processing by increasing both navigation burden and memory demands on users. A popular and obvious solution is to select or filter the data to display exactly what is presumed to be relevant to the clinical question, but this solution is both brittle and mistrusted by users. A less brittle approach that is more aligned with our users' mental model could use abstraction to summarize details instead of filtering to hide data. An abstraction-based approach could allow clinicians to more easily assemble a clinical picture, to use immediate pattern recognition strategies, and to adjust the level of displayed detail to their particular needs. It could also help the user notice unanticipated patterns and to fluidly shift attention as understanding evolves.

Epstein-Barr Virus Infection of Pseudostratified Nasopharyngeal Epithelium Disrupts Epithelial Integrity.

Simple SummaryNasopharyngeal carcinoma is associated with Epstein-Barr virus (EBV) infection and originates junction of the oropharynx and nasal cavity, where stratified squamous epithelium and respiratory epithelium are the lining. To elucidate the mechanisms by which EBV transforms the nasopharyngeal epithelium, a pseudostratified multiple-layer model with cilia forming on the apical surface by air-liquid interface (ALI) culture of primary nasopharyngeal epithelial cells was established. We showed: (1) ALI cultures formed stratified epithelia and maintained the diversity of cells found in the airway epithelium, such as ciliated, muco-secretory, and basal cells. (2) Polarized stratified epithelium was more susceptible to EBV infection than monolayer cells. (3) EBV infection in ALI cultures was verified by showing EBV-encoded RNA expressions. (4) EBV infection disrupted the integrity of the epithelium. Thus, our model can be used not only to examine the pathogenesis of pre-neoplastic EBV-infected cells, but also to develop anti-EBV therapy or early stage NPC treatment.Epstein–Barr virus (EBV) is a human oncogenic virus that causes several types of tumor, such as Burkitt’s lymphoma and nasopharyngeal carcinoma (NPC). NPC tumor cells are clonal expansions of latently EBV-infected epithelial cells. However, the mechanisms by which EBV transforms the nasopharyngeal epithelium is hampered, because of the lack of good in vitro model to pursue oncogenic process. Our primary nasopharyngeal epithelial cell cultures developed pseudostratified epithelium at the air-liquid interface, which was susceptible to EBV infection. Using the highly sensitive RNA in situ hybridization technique, we detected viral infection in diverse cell types, including ciliated cells, goblet cells, and basal cells. EBV-encoded small RNA-positive cells were more frequently detected in the suprabasal layer than in the basal layer. We established the most physiologically relevant EBV infection model of nasopharyngeal epithelial cells. This model will advance our understanding of EBV pathogenesis in the development of NPC.

A fracture upscaling method (FUM) for hydraulically fractured reservoirs: From discrete fracture modelling to finite difference simulations

Hydraulic fracturing creates a complex fracture geometry in heterogeneous formations which are frequently simulated using Finite Element based fracture propagation modelling tools. Representing this geometry in Finite Difference based multiphase flow simulators poses some challenges. In this study, a Fracture Upscaling Method (FUM) is developed to represent complex fracture systems generated by the finite element method. It is demonstrated that this method can capture complex fracture geometries even when using coarse grids. This upscaling method can be used as a coupling tool between the output of any discrete fracture model and any finite difference-based reservoir simulator. FUM is tested against a field case and simulation results show a reasonable match with 120 days of production data. This method is then used to investigate the impact that natural fractures have on production from shale gas wells. The results show that the effect of orientation, spacing and length of natural fractures, on propagating hydraulic fractures can reduce the recovery factor by 30%. Furthermore, the ability of FUM to combine highly complex fracture networks with realistic multiple layer models with complex distributions of reservoir properties is demonstrated.

Comparison of the greenhouse effect between Earth and Venus using multiple atmospheric layer models

To better understand the mechanisms of global warming, we developed a one atmospheric layer model for Earth and a multiple atmospheric layer model (N = 111) for Venus. Earth’s greenhouse gas atmosphere has an average of 78.9% absorption efficiency of terrestrial radiation (f = 0.789), while we assume Venus’ atmosphere has a near 100% absorption efficiency (f = 1) due to its denser, CO2-rich atmosphere. Viewing the atmospheric layers as blackbodies, we modeled the surface temperature of Earth and Venus, both of which are able to predict the respective actual planetary temperatures. The consistency (δ &lt; 1%) between the modeled surface temperature and the observed surface temperature of these two planets suggest that the multiple layer greenhouse gas atmosphere mechanism could explain Venus’ runaway global warming and scorching temperature. The results of these two models suggest that if Earth continues to experience uncontrolled greenhouse gas emissions, global warming and its negative outcomes may be further exacerbated.

CHLOROPHYLL-A CONCENTRATION RETRIEVAL USING CONVOLUTIONAL NEURAL NETWORKS IN LAGUNA LAKE, PHILIPPINES

Abstract. Two existing chlorophyll-a (chl-a) concentration retrieval procedures, which are analytical and empirical, are hindered by the complexity in radiative transfer equation (RTE) and in statistical analyses, respectively. Another promising model in this direction is the use of artificial neural networks (ANN). Mostly, a pixel-to-pixel with one-layer ANN model is used; where in fact that the satellite instrumental errors and man-made objects in water bodies might affect the retrieval and should be taken into account. In this study, the mask-based neural structure, called convolutional neural networks (CNN) model containing both the target and neighborhood pixels, is proposed to reduce the influence of the aforementioned premises. The proposed model is an end-to-end multiple-layer model which integrates band expansion, feature extraction, and chl-a estimation into the structure, leading to an optimal chl-a concentration retrieval. In addition to that, a two-stage training is also proposed to solve the problem of insufficient in-situ samples which happens in most of the time. In the first stage, the proposed model is trained by using the chl-a concentration derived from the water product, provided by satellite agency, and is refined with the in-situ samples in the second stage. Eight Sentinel-3 images from different acquisition time and coincide in-situ measurements over Laguna Lake waters of Philippines were utilized to conduct the model training and testing. Based on quantitative accuracy assessment, the proposed method outperformed the existing dual- and triple- bands combinations in chl-a concentration retrieval.

Optimal determination of force field parameters for reduced molecular dynamics model

Using a gradient-based optimization method, the time-consuming atomistic model of substrate is replaced by computationally efficient Lennard-Jones (L-J) potential walls whose parameters are determined to appropriately represent the interactions between the nanoparticles and the substrate. To obtain the required design sensitivity with respect to design variables for the constant temperature molecular dynamics (MD) simulations that use the Nosé–Hoover thermostat, the finite difference method is impractical due to the huge amount of computational costs. Thus, we developed an adjoint design sensitivity analysis (DSA) method that is efficient for the system of many design variables. In numerical examples, we replace the complicated and time-consuming silicate structure to a multiple layer model of L-J potential wall, through the design optimization that includes the design variables of ϵ, σ, and the positions of each layer. The objective is to minimize the squared difference of time averaged performance between the full and the reduced models during the whole time span. The proposed method could lead to a significant reduction of computational costs, together with comparable outcomes from MD simulations.

Evaluations of Fracture Injection Pressure and Fracture Mouth Width during Separate-Layer Fracturing with Temporary Plugging

Separate-layer fracturing with temporary plugging (SLFTP) is a potential way to stimulate multiple layer reservoirs due to its low cost, low risk, and high efficiency. In this study, based on the cohesive zone model (CZM), a 3D fully fluid-solid coupling and multiple layer model is established to investigate factors influencing fracture injection pressure and fracture mouth width. The cohesive layer properties are based on the reported study, which have been validated through a series of numerical experiments. Innovatively, the spring model is innovatively proposed to represent the plugging effect of diverting agents and prop the aperture of the previous fractures. Simulation results reveal that the effects of previous fractures in multiple layer formations can be neglected, which is quite different from multistage fracturing for horizontal wells. Fracture injection pressure can be evaluated more accurately by taking the following factors into consideration: the minimum horizontal principal stress, rock tensile strength, injection rate, and pore pressure enhancement. Further, fracture mouth width is strongly influenced by rock tensile strength, Young’s modulus, and injection rate. This study provides a guidance for candidate well selection and diverting agent optimization during SLFTP in multilayer formations.

A novel photodynamic therapy-based drug delivery system layered on a stent for treating cholangiocarcinoma.

This study aimed to investigate the drug delivery efficacy and bio-effectiveness of a novel photodynamic therapy (PDT)-matrix drug delivery system for cholangiocarcinoma (CCA). Metallic stents were coated with polyurethane (PU) as the first layer. A 2-hydroxyethyl methacrylate (2-HEMA)/ethylene glycol dimethacrylate (EGDMA)/benzoyl peroxide (BPO) layer and a poly(ethylene-co-vinyl acetate) (PEVA)/poly(n-butyl methacrylate) (PBMA)/polyvinylpyrrolidone K30 (K30) layer containing various concentrations of Photofrin were then incorporated onto the stent as the second and third layers. After incubating the layered membranes with cultured CCA cell line, the release of Photofrin, cell viability, the intracellular uptake of Photofrin, reactive oxygen species (ROS) generation, and apoptosis were determined. Using a single-layer diffusion model, the maximum release of Photofrin from the 5 to 10% K30 formulas was 80 and 100%, respectively, after 24h. When using the multiple-layer diffusion model, the released Photofrin showed an initial burst of the loading dose from the PEVA/PBMA/K30 layer. In the immobilized model, less than 5% of the Photofrin from the 2-HEMA/EGDMA/BPO layer was released over the 24-h period. Cell viability decreased linearly with increasing Photofrin concentrations, and ROS generation and apoptosis were shown to increase significantly with increasing Photofrin concentrations, until the concentration of Photofrin reached a saturation point of 1.5μg/ml. This new, multiple-layered, PDT-based stent with dual-release mechanisms is a promising treatment for CCA and cancer-related ductal stenosis.

Slant-path coherent free space optical communications over the maritime and terrestrial atmospheres with the use of adaptive optics for beam wavefront correction.

As a continuation of our previous work [Appl. Opt.54, 1453 (2015)APOPAI0003-693510.1364/AO.54.001453] in which we have studied the performance of coherent free space optical (FSO) communication systems operating over a horizontal path, in this paper we study the coherent FSO system operating over a general slant path. We evaluated system bit-error-rate (BER) in the case when the quadrature phase-shift keying (QPSK) modulation format is applied and when an adaptive optics (AO) system is employed to mitigate the air turbulence effects for both maritime and terrestrial air transmission scenarios. We adopted a multiple-layer scheme to efficiently model the FSO slant-path links. The atmospheric channel fading was characterized by the wavefront phase distortions and the log-amplitude fluctuations. We derived analytical expressions to characterize log-amplitude fluctuations of air turbulence by asserting the aperture averaging within the frame of the multiple-layer model. The obtained results showed that use of AO enabled improvement of system performance for both uplinks and downlinks, and also revealed that it is more beneficial for the FSO downlinks. Also, AO employment brought larger enhancements in BER performance for the maritime slant-path FSO links than for the terrestrial ones, with an additional striking increase in performance when the AO correction is combined with the aperture averaging.

Simulation of pollutant-gas-cloud infrared spectra under plane-array detecting

The research on simulation of pollutant-gas-cloud infrared spectra is very important for studying the spectral identification algorithms by using simulated spectra. Some good results of the simulation of pollutant-gas-cloud infrared spectra under single-detector detecting are achieved, and have been used for studying the spectral identification. With the development of infrared detection technology, the infrared imaging spectrometer is used to detect pollutant gas cloud. The gas identification algorithms in the way of plane-array detecion based on the imaging spectrometer also need a number of measured gas-cloud infrared spectrum data cubes. Due to the lack of measured data in studying the spectral identification algorithm that is based on imaging spectrometer, the multiple-layer model of the cloud infrared spectrum is well studied by using the high-precision physics-based gas-cloud explosion model and its gridding simulation results, and the way of simulating pollutant-gas-cloud infrared spectra under plane-array-detector detecting is proposed to generate the infrared spectrum data cube with both spectral and spatial information, which obtains a new research method for the research field. Validations are made by comparing the measured data with the simulated data, and the comparison contains three parts: i) the comparison of measured gas-cloud explosion with simulated gas-cloud explosion, ii) the comparison of spectral identification imaging results between the measured and the simulated gas-cloud infrared spectrum data cubes, and iii) the comparison between the measured and the simulated gas-cloud infrared spectra. The comparison results have two aspects: the first aspect is that the simulated gas-cloud explosion is consistent with the measured explosion and has little difference in separate parts, and the second aspect is that the simulated gas-cloud spectra have relative errors of less than 10% compared with the measured gas-cloud spectra. The conclusion is that the simulation model of pollutant-gas-cloud infrared spectra under plane-array detecting is correct, which is obtained from the validation results that simulated gas-cloud infrared spectrum data cubes are highly precise, whether in the comparison with spectral identification imaging results or in the comparison with gas-cloud spectra. The simulation of pollutant-gas-cloud infrared spectra under plane-array detecting which directly reflects the explosion of pollutant gas cloud and provides complete and realistic infrared spectrum data cube of pollutant gas cloud, is significant for improving and perfecting the spectral identification algorithms.

Effect of elastic-plastic behavior of coating layer on drawability and frictional characteristic of galvannealed steel sheets

During a galvannealed sheet metal forming, the failures of coating layers (powdering, flaking and cracking) frequently affect the strain state of sheets and deteriorate the frictional characteristic between sheets and tools. Two FE-models in this study were suggested to investigate the effects of the mechanical behavior of coating layers on the formability and friction of the coated steel sheets in FE analysis; the first is one-layer model to express the coated sheet as one stress-strain curve and the second is a multiple-layer model which is composed of substrates and coating layers, separately. First, the frictional properties and the formability of the coated sheets were experimentally investigated using a cup deep-drawing trial. After, the drawing process was simulated by FE analysis of the two models. In the multiplelayer model, the mechanical behavior of the coating is defined as a stress-strain curve which was determined using the nanoindentation test of the coating, its FE analysis and artificial neural network method. The result showed that the multiple-layer model provides more accuracy predictions of drawing loads than the one-layer model in the FE analysis, compared to the actual cup drawing test.

표준연동 아키텍처(HLA/RTI)기반 다해상도 연동 시뮬레이션 설계 및 구현

본 논문에서는 표준연동 아키텍처(HLA/RTI)기반 다해상도 연동이 가능한 시뮬레이션을 구성하여 공학급(QUEST), 교전급(SADM), 임무급(EADSIM)의 모델을 연동하였다. 공학급 모델은 전투실험 공학분석 시범체계에서 개발된 전투실험 통합개발환경(QUEST)을 이용하여 모델을 개발하였다. 교전급 모델은 SADM을 이용하여 개발하고 임무급 모델은 EADSIM을 이용하여 모델을 개발하였다. 여러 계층의 모델을 연동하기 위해 표준 연동 아키텍처 기반(HLA/RTI)으로 설계하고 구현하였다. 각기 다른 분산된 환경에서 수행되고 있는 시뮬레이션 프로그램들이 상호 연동을 위해 표준 연동 인터페이스 명세에 만족하는 연동 시뮬레이션을 설계하고 각 시뮬레이션 프로그램 간의 중계 역할을 담당하는 통합연동 게이트웨이를 개발하였다. 다해상도 연동 시뮬레이션을 통해 여러 계층 간의 모델을 연동하여 해양 무기체계 효과도 분석을 위한 모델충실도를 향상하고 운용자 필요에 따라 요구되는 전장 환경을 신속하게 구성할 수 있다. 또한 표준연동 아키텍처(HLA/RTI)를 기반으로 설계하게 된 다른 시뮬레이션 프로그램과도 쉽고 효율적으로 연동할 수 있다. In this paper, the multi-resolution simulation of standard linkage architecture is consists of the engineering-level (QUEST), engagement-level (SADM), the mission-level (EADSIM). It was developed the engineering-level model using battle experiment integrated development environment in the battle experimental engineering system. The engagement level model was developed using the SADM and the mission-level model was developed using EADSIM. The standard linkage architecture is designed and implemented in order to interlocking model of multiple layers. Each different simulation programs in a distributed environment was designed by HLA interface specifications for satisfying interworking. Also the integrated interoperation gateway was developed for relaying the each different simulation programs. The effective naval weapon system for measure of effectiveness develops using to improve the fidelity of the model between the various layers through multi-resolution interoperation simulation. According to the operator requirement is quickly battlefield environment can be constructed. The other simulation program that being designed through standards linkage architecture can linkage easily and efficiently.

Triggers and inhibitors of illegitimate customer complaining behavior

Purpose– The study aims to investigate the illegitimate customer complaining behavior (ICCB) in the hospitality industry from the perspective of frontline employees. In particular, this study identified ICCB incidents, ICCB triggering factors and inhibitors in the hospitality industry.Design/methodology/approach– A qualitative research approach was adopted in this study by using in-depth personal interviews. Twenty-six semi-structured interviews were conducted with frontline employees who had first-hand experiences with ICCB in the hospitality industry. The interviews were transcribed, coded and analyzed following the procedures of open coding, axial coding and selective coding.Findings– Analysis of the qualitative data has revealed 7 types of ICCB incidents, 11 ICCB triggering factors and 3 ICCB inhibitors in the hospitality industry, as perceived by frontline employees. The study also proposed a multiple-layer model of ICCB triggers and inhibitors based on the results. Four propositions are developed delineating three layers of driving forces and the dynamics between ICCB triggers and inhibitors that exert joint effects on ICCB.Research limitations/implications– Impacts of various cultural settings, different hospitality business settings (hotels vs restaurants) and profile information of frontline employees on ICCB need to be examined in future research.Originality/value– Findings of this study contribute to the customer complaining literature and the hospitality service management literature by offering the frontline employees’ perspective of ICCB. Hospitality businesses can benefit from this study by using the research findings to develop more effective company policies and training programs to recognize, monitor and resolve ICCB incidents.

A METHOD FOR DEFINING INITIAL STRESS IN PROGRESSIVELY DEPOSITED FILM ON A SUBSTRATE

The initial stress is induced during film formation and is partially counterbalanced through curvature changes. Therefore, it is commonly evaluated by the measured residual stress. Initial stress may directly affect the film formation rather than residual stress in progressively deposited films. In the present work, we introduced a multiple layer model for progressively deposited films to obtain a quantitative solution for estimating the initial stress. The results showed that residual stress in the last layer is equal to the initial stress when layer number approaches infinity. In particular, the initial stress, σi in deposited film could be determined using the equation, σi = σSt/β, in which σSt is the averaged residual stress in films calculated by Stoney formula and β is the correction factor. The value of β varied between 0 and 1, depending on relative modulus and relative thickness of film and substrate. Finally, using element birth and death technique, a finite element model was presented to verify the analytical multiple layer model. Good agreement was obtained between the analytical and FE results.