Index Of Agreement Research Articles

Evaluation of inter-observer reliability in the case of trichotomous and four-level animal-based welfare indicators with two observers

This study focuses on assessing inter-observer reliability (IOR) between two observers in the case of trichotomous and four-level animal-based welfare indicators assessed at individual level. The Body Condition Score (BCS) and Knee calluses (KNC) were chosen as trichotomous indicators; data were collected in fourteen intensively managed dairy goat farms in Italy (ITF1 to ITF7) and Portugal (PTF1 to PTF7) and in extensively managed dairy goat farms exploiting three alpine pastures (AP1, AP2 and AP3) in Italy. The Ear posture (EP) and Eye white (EW) were chosen as four-level indicators; data were collected in three intensively managed dairy cattle farms (F1, F2 and F3) in Italy. The performance of the most documented agreement indices was compared. In the case of trichotomous indicators, Scott’s π, Cohen’s K, Cohen’s K C, Cohen’s weighted K and Krippendorff’s α were affected by the paradox effect: when the concordance rate (P0) was high, they sometimes gave very low or even negative values (e.g. P0(BCS-ITF3) = 74%; Scott’s π = 0.05; Cohen’s K = 0.09; Krippendorff’s α = 0.06; P0(BCS-AP3) = 74%; Scott’s π = −0.12; Cohen’s K = Krippendorff’s α = −0.11). Bangdiwala’s B, Gwet’s γ(AC1) and Quatto’s weighted S were not affected by this phenomenon and provided values very close to P0 (e.g. P0(KNC-PTF1) = 88%; Bangdiwala’s B = Gwet’s γ(AC1) = 0.85; P0(BCS-AP1) = 82%; Bangdiwala’s B = Gwet’s γ(AC1) = 0.79). In the case of four-level indicators, Cohen’s K and Krippendorff’s α were not affected by the paradox behaviour. However, Cohen’s K C in some cases exceeded the observed P0 (e.g. P0(EP-F3) = 78%; Cohen’s K C = 1). Gwet’s γ(AC1) showed the best results for four-level indicators (e.g. P0(EP-F1) = 88%; Gwet’s γ(AC1) = 0.86), followed by Quatto’s S and Holley and Guilford’s G (e.g. P0(EP-F1) = 88%; Quatto’s S = Holley and Guilford’s G = 0.84). To evaluate IOR between two observers, Bangdiwala’s B, Gwet’s γ(AC1) and Quatto’s weighted S are suggested for trichotomous indicators, while Gwet’s γ(AC1), Quatto’s S and Holley and Guilford’s G are suggested for four-level indicators.

Contribution from the Western Pacific Subtropical High Index to a Deep Learning Typhoon Rainfall Forecast Model

In this study, a tropical cyclone or typhoon rainfall forecast model based on Random Forest is developed to forecast the daily rainfall at 133 weather stations in China. The input factors to the model training process include rainfall observations during 1960–2018, typhoon information (position and intensity), station information (position and altitude), and properties of the western Pacific subtropical high. Model evaluation shows that besides the distance between a station and cyclone, the subtropical high properties are ranked very high in the model’s feature importance, especially the subtropical ridgeline, and intensity. These aspects of the subtropical high influence the location and timing of typhoon landfall. The forecast model has a correlation coefficient of about 0.73, an Index of Agreement of nearly 0.8, and a mean bias of 1.28 mm based on the training dataset. Biases are consistently low, with both positive and negative signs, for target stations in the outer rainband (up to 1000 km, beyond which the model does not forecast) of typhoons. The range of biases is much larger for target stations in the inner-core (0–200 km) region. In this region, the model mostly overestimates (underestimates) the small (large) rain rates. Cases study of Typhoon Doksuri and Talim in 2023, as independent cases, shows the high performance of the model in forecasting the peak rain rates and timing of their occurrence of the two impactful typhoons.

Assessment of CMIP6 GCMs for selecting a suitable climate model for precipitation projections in Southern Thailand

The selection of General Circulation Models (GCMs) is critical due to computational limitations and underlying uncertainties. This study provides a comprehensive assessment of three bias correction (BC) methods, namely the delta change method (DT), quantile mapping (QM), and empirical quantile mapping (EQM). Utilizing precipitation data from 30 observation stations across Southern Thailand, the evaluation encompasses five CMIP6 GCM models (CAMS-CSM1-0, CanESM5, CNRM-CM6-1, CNRM-ESM2-1, IPSL-CM6A-LR). Evaluation metrics, root mean square error (RMSE), mean absolute error (MAE), Pearson's correlation (r), index of agreement (d), and mean bias error (MBE) are employed to assess BC methods. Evaluation measures suggest that the DT method outperforms EQM and QM, with higher accuracy and lower errors (DT: RMSE = 3.61, MAE = 2.32; EQM: RMSE = 3.82, MAE = 3.70). Taylor diagrams show that CNRM-ESM2-1 has the highest correlation across sites (r = 0.36), albeit with a wider dispersion, while CanESM5 has a more balanced performance. Significant annual precipitation increases are projected for different spans 2021-30, 2061-70, and 2091–2100, particularly under SSP585, which will influence flood risk, water management, and climate adaptation. Future projections with SSP585 continuously projecting a larger probability of increased precipitation. The DT method is recommended for the downscaling of CMIP6 GCMs for precipitation projections in Southern Thailand, recognizing its superior performance. The study's findings provide a foundation for informed decision-making and adaptation planning in southern Thailand, urging policymakers to prioritize climate resilience and adaptation strategies while using a multi-model ensemble approach for robust climate forecasts.

Deep learning bias correction of GEMS tropospheric NO2: A comparative validation of NO2 from GEMS and TROPOMI using Pandora observations

Despite advancements in satellite instruments, such as those in geostationary orbit, biases continue to affect the accuracy of satellite data. This research pioneers the use of a deep convolutional neural network to correct bias in tropospheric column density of NO2 (TCDNO2) from the Geostationary Environment Monitoring Spectrometer (GEMS) during 2021–2023. Initially, we validate GEMS TCDNO2 against Pandora observations and compare its accuracy with measurements from the TROPOspheric Monitoring Instrument (TROPOMI). GEMS displays acceptable accuracy in TCDNO2 measurements, with a correlation coefficient (R) of 0.68, an index of agreement (IOA) of 0.79, and a mean absolute bias (MAB) of 5.73321 × 1015 molecules/cm2, though it is not highly accurate. The evaluation showcases moderate to high accuracy of GEMS TCDNO2 across all Pandora stations, with R values spanning from 0.46 to 0.80. Comparing TCDNO2 from GEMS and TROPOMI at TROPOMI overpass time shows satisfactory performance of GEMS TCDNO2 measurements, achieving R, IOA, and MAB values of 0.71, 0.78, and 6.82182 × 1015 molecules/cm2, respectively. However, these figures are overshadowed by TROPOMI’s superior accuracy, which reports R, IOA, and MAB values of 0.81, 0.89, and 3.26769 × 1015 molecules/cm2, respectively. While GEMS overestimates TCDNO2 by 52 % at TROPOMI overpass time, TROPOMI underestimates it by 9 %. The deep learning bias corrected GEMS TCDNO2 (GEMS-DL) demonstrates a marked enhancement in the accuracy of original GEMS TCDNO2 measurements. The GEMS-DL product improves R from 0.68 to 0.88, IOA from 0.79 to 0.93, MAB from 5.73321 × 1015 to 2.67659 × 1015 molecules/cm2, and reduces MAB percentage (MABP) from 64 % to 30 %. This represents a significant reduction in bias, exceeding 50 %. Although the original GEMS product overestimates TCDNO2 by 28 %, the GEMS-DL product remarkably minimizes this error, underestimating TCDNO2 by a mere 1 %. Spatial cross-validation across Pandora stations shows a significant reduction in MABP, from a range of 45 %-105.6 % in original GEMS data to 24 %-59 % in GEMS-DL.

Parameterization of Four Models to Estimate Crop Evapotranspiration in a Solar Greenhouse.

Working to simplify mechanistic models on the basis of reliability for estimating crop evapotranspiration (ET) in a greenhouse is still worthwhile for horticulturists. In this study, four ET models (Penman-Monteith, Priestley-Taylor, and Shuttleworth-Wallace models, and the Crop coefficient method) were parameterized after taking the restriction effect of resistance parameters in these models on ET into account, named as PA-PM, PA-PT, PA-CC, and PA-SW, respectively. The performance of these four parameterized models was compared at different growth stages, as well as the entire growing season. Tomatoes that were ET-grown in a solar greenhouse without a heating device were measured using weighting lysimeters during 2016-2017 and 2019-2021, in which data from 2016 were used to adjust the model parameters, and data from the other four study years were used to examine the model performance. The results indicated that the PA-PT and PA-CC models have a better performance in estimating tomato ET at four growth stages, while the PA-PM and PA-SW performed well only at the development and middle stages. Compared to the ET that was measured with the weighting lysimeters, the ET that was predicted using the PA-PM model was 27.0% lower at the initial stage, and 8.7% higher at the late stage; the ET that was computed using the PA-SW model was 19.5% and 13.6% higher at the initial and late stages, respectively. The PA-PT model yielded the lowest root mean square error and the highest index of agreement against the other models over the entire growing season, indicating that the PA-PT model is the best recommended model for estimating tomato ET in a solar greenhouse.

Statistical Performance of Gridded Rainfall Datasets Over Ungauged Jalaur River Basin, Philippines

The study presented aims to find the most appropriate climate dataset for the data-scarce Jalaur River Basin (JRB), Iloilo, Philippines, by evaluating the statistical performance of five rainfall datasets (APHRODITE, CPC NOAA, ERA5, SA-OBS, and PGF-V3) with resolutions of 0.25° and 0.5° having a time domain of 1981 to 2005. Bilinear interpolation implemented through Climate Data Operator (CDO) was used to extract and process grid climate datasets with Linear scaling as bias correction to minimize product simulation uncertainties. The datasets were compared to the lone meteorological station nearest to JRB investigated at monthly and annual timescales using six statistical metrics, namely, Pearson’s correlation coefficient (r), coefficient of determination (R2), modified index of agreement (d1), Kling-Gupta efficiency, Nash-Sutcliffe efficiency (NSE), and RMSE-observations standard deviation ratio (RSR). The results indicate a strong positive correlation with the observed data for both rainfall and temperature (r > 0.8; R2, d1 > 0.80). Although graphical observation shows an underestimation of rainfall, goodness-of-fit values indicate very good model performance (NSE, KGE > 0.75; RSR < 0.50). In terms of temperature, variable responses are observed with significant overestimation for maximum temperature and underestimation for minimum temperature. SA-OBS proved to be the best-performing dataset, followed by ERA5 and PGF-V3. These key findings supply useful information in deciding the most appropriate gridded climate dataset for hydrometeorological investigation in the JRB and could enhance the regional representation of global datasets.

The adaptability and irrigation constraints analysis of the WOFOST model for grain production in the Songhua River Basin.

The Songhua River Basin, a vital grain-producing area in China, faces challenges due to the uneven distribution of water resources and the intensive water demands of agriculture. To enhance agricultural development and effectively manage water scarcity, it is essential to identify the water-saving potential of major staple crops - corn, wheat, and rice. This study enhances the World Food Studies (WOFOST) model by refining the day of year for the developmental vegetative stage (DVS), thereby improving the representation of phenological stages for spring maize, spring wheat, and rice within the model. This refinement offers a detailed analysis of the potential and rainfed yields. The results from the modified WOFOST model show promising simulation outcomes for the biomass and yield of maize, wheat, and rice, with Nash-Sutcliffe efficiency (NS) and index of agreement (IoA) values all exceeding 0.7. An analysis of photothermal potential yields (Yp) and rainfed yields (Yr) revealed minimal differences in yields for spring maize and rice across various rainfall frequencies. Specifically, the average photothermal utilization rates (LTs) are 93.57% for maize and 85.25% for rice. In contrast, the rainfed yield for wheat is lower than its photothermal yield, with an LT of 43.66%. These findings suggest that in the Songhua River Basin, maize and rice offer greater potential for water conservation compared to wheat. It is recommended to judiciously reduce irrigation during the growing seasons of spring maize and rice to help alleviate agricultural water use pressures. © 2024 Society of Chemical Industry.

Improving millimetre-wave path loss estimation using automated hyperparameter-tuned stacking ensemble regression machine learning

Path loss prediction is a crucial aspect of designing and operating wireless communication systems, especially in the millimetre-waves (mmWaves) frequency bands. However, these bands are associated with climate-related challenges: rain attenuation, and free space path loss. To address these challenges, an advanced stacking ensemble-regression machine learning (SEML) model with automated hyperparameter tuning (AHT) was proposed. The AHT-SEML model leverages multiple base regressors integrated with a meta-regressor. The model's performance was optimised using the AHT tuning technique. The AHT-SEML model's efficiency was tested using simulated path loss data from a Composite 3D Raytracing-Image-Method propagation model across four sub-Saharan cities, at mmWaves frequencies. The AHT-SEML model's performance was compared to three empirical path loss models, namely Close-In (CI), Floating Intercept (FI), and Alpha-Beta-Gamma (ABG), using evaluation metrics such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). AHT-SEML outperformed other models in the four cities across all frequencies and scenarios with the highest Index of Agreement and lowest Bayesian information criterion. Model confidence set (MCS) analysis with CI benchmark indicates that all the models except AHT-SEML performed below the critical t-value of 2.3530 at 95% confidence level with a degree of freedom of 3, implying no significant differences in their MAEs compared to the CI. However, AHT-SEML's t-statistic values exceed this critical t-value, indicating statistically significant differences and better performance than the CI benchmark models. Similarly, F-statistics of 29.45 and 26.54 correspond to p-values of 1.91×10−14 and 2.50×10−13 for MAE and RMSE, respectively, corroborating significant differences in the AHT-SEML's performance.

Will digital trade provisions in regional trade agreements effectively reduce CO2 emissions in member states?

This paper constructs a depth index of the regional trade agreement (RTA) digital trade terms based on text data from 2000–2021 in the TAPED database and carbon dioxide (CO2) emission data from the Eora database since 2000. We construct a gravity model between the depth index of digital trade terms and carbon CO2 emissions to analyze the impact of digital trade terms in RTAs on the CO2 emissions of member countries. The results show that the depth of digital trade clauses in RTAs significantly reduces the CO2 emissions of member countries. Still, the impact of different clause depth indexes on CO2 emissions is different on the supply and consumption sides. The results of the heterogeneity analysis based on the types of trade agreements show that the depth of RTAs has the most significant impact on the CO2 emissions among members of South–North trade agreements. The analysis of results based on industry heterogeneity shows that digital trade terms significantly impact manufacturing CO2 emissions. The article proposes that more attention be paid to the negotiation of digital trade rules in RTAs, the construction of South–North trade agreements, and the impact of digital trade on the CO2 emissions of different industries.

A comparative survey between cascade correlation neural network (CCNN) and feedforward neural network (FFNN) machine learning models for forecasting suspended sediment concentration

Suspended sediment concentration prediction is critical for the design of reservoirs, dams, rivers ecosystems, various operations of aquatic resource structure, environmental safety, and water management. In this study, two different machine models, namely the cascade correlation neural network (CCNN) and feedforward neural network (FFNN) were applied to predict daily-suspended sediment concentration (SSC) at Simga and Jondhara stations in Sheonath basin, India. Daily-suspended sediment concentration and discharge data from 2010 to 2015 were collected and used to develop the model to predict suspended sediment concentration. The developed models were evaluated using statistical indices like Nash and Sutcliffe efficiency coefficient (NES), root mean square error (RMSE), Willmott’s index of agreement (WI), and Legates–McCabe’s index (LM), supplemented by a scatter plot, density plots, histograms and Taylor diagram for graphical representation. The developed model was evaluated and compared with CCNN and FFNN. Nine input combinations were explored using different lag-times for discharge (Qt-n) and suspended sediment concentration (St-n) as input variables, with the current suspended sediment concentration as the desired output, to develop CCNN and FFNN models. The CCNN4 model with 4 lagged inputs (St-1, St-2, St-3, St-4) outperformed the other developed models with the lowest RMSE = 95.02 mg/l and the highest NES = 0.0.662, WI = 0.890 and LM = 0.668 for the Jondhara Station while the same CCNN4 model secure as the best with the lowest RMSE = 53.71 mg/l and the highest NES = 0.785, WI = 0.936 and LM = 0.788 for the Simga Station. The result shows the CCNN model was better than the FFNN model for predicting daily-suspended sediment at both stations in the Sheonath basin, India. Overall, CCNN showed better forecasting potential for suspended sediment concentration compared to FFNN at both stations, demonstrating their applicability for hydrological forecasting with complex relationships.

What polarizes citizens? An explorative analysis of 817 attitudinal items from a non-random online panel in Germany.

Various studies point to the lack of evidence of distributive opinion polarization in Europe. As most studies analyse the same item batteries from international social surveys, this lack of polarization might be due to an item's issue (e.g., the nature or substance of an item) or item formulation characteristics used to measure polarization. Based on a unique sample of 817 political attitudinal items asked in 2022 by respondents of a non-random online panel in Germany, we empirically assess the item characteristics most likely to lead to distributive opinion polarization-measured with the Van der Eijk agreement index. Our results show that only 20% of the items in our sample have some-at most moderate-level of opinion polarization. Moreover, an item's salience in the news media before the survey data collection, whether an item measures attitudes toward individual financial and non-financial costs, and the implicit level of knowledge required to answer an item (level of technicality) are significantly associated with higher opinion polarization. By contrast, items measuring a cultural issue (such as issues on gender, LGTBQI+, and ethnic minorities) and items with a high level of abstraction are significantly associated with a lower level of polarization. Our study highlights the importance of reflecting on the potential influence of an item's issue and item formulation characteristics on the empirical assessment of distributive opinion polarization.

Selection of a stress‐based soil compaction test to determine potential impact of machine wheel loads

AbstractThe use of heavy machinery is increasing in agricultural industries, and in particular cotton farming systems in Australia, which induces an increased risk of soil compaction and yield reduction. Hence, there is a need for a technical solution to use available tools to measure projected soil compaction due to farm machinery traffic. The aim of this work was to compare the effects of static and dynamic loads on soil compaction. In this study, three Vertisols (soils commonly used for cotton production in Australia) were selected to examine soil compaction under a range of static and dynamic loads, respectively, using uniaxial compression equipment and a modified Proctor test. In general, soils behaved similarly under static and dynamic loads with no significant difference between bulk density values for all moisture contents with a high index of agreement (d = 0.96, RMSE = 0.056). The results further indicate better agreement between soil compaction produced under static and dynamic loads. Uniaxial compression test (static loads) produced greater compaction compared with the modified Proctor test (dynamic loads), in particular at moisture contents less than the plastic limit condition. The variation in soil compaction for static and dynamic loads was often evident for loads ≥600 kPa, with the greatest soil compaction induced under loads ≥1200 kPa. The findings of this study confirm the suitability of a modified Proctor method to assess soil compaction as an alternative tool under a range of moisture contents and machinery loads for Vertisols.

Structure and morphology of chars and activated carbons obtained from thermal treatment of coal and biomass origin materials, including their wastes: Results from the ICCP Microscopy of Carbon Materials Working Group

This paper describes the evaluation of petrographic textures in char and activated carbon derived from coal, coal by-products and biomass, formed during carbonization and activation processes. This work represents the results of interlaboratory exercises from 2016 to 2022 of the Microscopy of Carbon Materials Working Group in Commission III of the International Committee for Coal and Organic Petrology. The interlaboratory exercises were run on photomicrograph samples. For textural characterization of carbon materials, the existing American Society for Testing and Materials (ASTM) classification system for metallurgical coke was applied. Morphological differences were evaluated in 29 carbon material types, including 22 char samples, and 7 activated carbon (AC) samples obtained experimentally using conventional direct/indirect and microwave heating technologies. This approach gives an extended view on the identification of microporous carbons, and how a certain heat treatment develops a certain optical texture and structure in a raw material. The requested evaluation of carbon materials was related to their porosity, origin, extent, and characteristics, which are particular to each carbon material type. Because carbon matrices can form a wide range of optical textures during heat treatment it is important to demonstrate which carbon occurrences will have a crucial role in industrial applications dominated by adsorption phenomena.The interlaboratory exercises included 17 participants from 14 laboratories. Four sets of digital black and white and colour photomicrographs were distributed, which in total comprised 184 fields of different types of carbon material. The results were evaluated based on four levels: (i) optical texture (isotropic/anisotropic), (ii) optical type and size (punctiform, mosaic, fiber, ribbon, domain), iii) morphology (porous, non-porous/massive), and (iv) particle origin (precursor type).The statistical method applied to evaluate the results was based on “raw agreement indices”. Comparative analyses of the average values of the level of overall agreement showed homogeneity in the results, the mean value was 89%, with a minimum value of 87% and a maximum value of 91% for those who participated in at least three out of four exercises.

Assessment of Two SWCC models for predicting the curve fitting parameters of lateritic soil: bentonite mixtures

This study compares the capabilities of soil water characteristic curve (SWCC) models by Brooks-Corey (BC) and van Genuchten (vG) in estimating the curve fitting parameters for lateritic soil—bentonite mixtures. The SWCCs of soil treated with 0–10% bentonite and compacted with British standard light (BSL) energy at compaction states representing dry of optimum, optimum, and wet of optimum conditions were measured by sequential desorption using pressure plate extractor. The fitting parameters of the two equations were determined by a non-linear fitting program. The fitting capabilities of the models on the measured data were compared by statistical indices namely the root mean square error (RMSE), linearity (R2) and index of agreement (d). Results revealed that volumetric water content increased as bentonite content increased with specimens containing 10% bentonite recording the highest and therefore has greater capacity to retain water/contaminants, while the air entry value (AEV) for the various soil mixtures also increased with higher bentonite content. The study also found that the estimated volumetric water contents approximated the measured values at all suctions with a high degree of accuracy with RMSE values that ranged from 0.0035 to 0.0150 for vG model which are somewhat lower than the values for BC equation. Similarly, R2 for the vG equation (≥ 0.99) are, on average, slightly higher than those of the BC fits. However, the d values associated with the BC model which varied between 0.788 and 0.971 are higher than those of the vG (0.784–0.968). Overall, the study established that the vG model proved marginally superior in respect of goodness of fit.

Groundwater salinization risk assessment using combined artificial intelligence models.

Assessing the risk of groundwater contamination is of crucial importance for the management of water resources, particularly in arid regions such as Menzel Habib (south-eastern Tunisia). The aim of this research is to create and validate artificial intelligence models based on the original DRASTIC vulnerability methodology to explain groundwater salinization risk (GSR). To this end, several algorithms, such as artificial neural networks (ANN), support vector regression (SVR), and multiple linear regression (MLR), were applied to the Menzel Habib aquifer system. The results obtained indicate that the DRASTIC Vulnerability Index (VI) ranges from 91 to 141 and is classified into two categories: low and moderate vulnerability. However, the correlation between groundwater total dissolved solids (TDS) and the Vulnerability Index is relatively weak (r < 0.5). Indeed, the original DRASTIC index needs some improvements. To improve it, some adjustments are required, notably by incorporating the TDS-groundwater salinization risk (GSR) indicator. The seven parameters of the original DRASTIC model were used as inputs for the artificial intelligence models, while the GSR values were used as outputs. Performance indicators, such as the correlation coefficient (r) and the Willmott Agreement Index (d), showed that the ANN model outperformed the SVR and MLR models. Indeed, during the training phase, the ANN model obtained r values equal to 0.89 and d values of 0.4, demonstrating the superiority, robustness, and accuracy of ANN-based methodologies over the original DRASTIC model. The findings could provide valuable information to guide management of groundwater contamination risks, especially in arid regions.

Managers' Micro-Communities Matter: The Impact of Clinical Supervision Team on Therapist Perception of the Organization.

Positive organizational climate - employee perceptions of their work environment and the impact of this environment on well-being and functioning - is associated with desirable organizational and client-level outcomes in mental health organizations. Clinical supervisors are well-positioned to impact organizational climate, as they serve as intermediaries between higher-level administrators who drive the policies and procedures and the therapists impacted by such decisions. This cross-sectional study examined the role of clinical supervisors as drivers of therapist perceptions of organizational climate within supervisory teams. Specifically, the present study investigated: (1) shared perceptions of organizational climate among therapists on the same supervisory team; (2) predictors of therapist climate perceptions. Eighty-six therapists were supervised by 22 supervisors. Indices of interrater agreement and interrater reliability of therapists on the same supervisory team were examined to determine shared or distinct perceptions of organizational climate. Multi-level models were used to examine whether supervisor attitudes towards evidence-based practices and therapist perceptions of supervisor communication predicted perceived organizational climate. Results showed perceptions of organizational cohesion and autonomy were shared among therapists on the same supervisory team and distinct from therapists on different supervisory teams. Therapist perceptions of their supervisor's communication was positively associated with perceptions of organizational cohesion and autonomy. These findings align with emerging evidence that middle managers shape their employees' experience of their work environment through communication strategies. These findings also point to the potential for intervening at lower organizational levels to improve overall organizational climate.

Improved deep learning for automatic localisation and segmentation of rectal cancer on T2-weighted MRI.

The automatic segmentation approaches of rectal cancer from magnetic resonance imaging (MRI) are very valuable to relieve physicians from heavy workloads and enhance working efficiency. This study aimed to compare the segmentation accuracy of a proposed model with the other three models and the inter-observer consistency. A total of 65 patients with rectal cancer who underwent MRI examination were enrolled in our cohort and were randomly divided into a training cohort (n = 45) and a validation cohort (n = 20). Two experienced radiologists independently segmented rectal cancer lesions. A novel segmentation model (AttSEResUNet) was trained on T2WI based on ResUNet and attention mechanisms. The segmentation performance of the AttSEResUNet, U-Net, ResUNet and U-Net with Attention Gate (AttUNet) was compared, using Dice similarity coefficient (DSC), Hausdorff distance (HD), mean distance to agreement (MDA) and Jaccard index. The segmentation variability of automatic segmentation models and inter-observer was also evaluated. The AttSEResUNet with post-processing showedperfect lesion recognition rate (100%) and false recognition rate (0), and its evaluation metrics outperformed other three models for two independent readers (observer 1: DSC = 0.839 ± 0.112, HD = 9.55 ± 6.68, MDA = 0.556 ± 0.722, Jaccard index = 0.736 ± 0.150; observer 2: DSC = 0.856 ± 0.099, HD = 11.0 ± 10.1, MDA = 0.789 ± 1.07, Jaccard index = 0.673 ± 0.130). The segmentation performance of AttSEResUNet was comparable and similar to manual variability (DSC = 0.857 ± 0.115, HD = 10.0 ± 10.0, MDA = 0.704 ± 1.17, Jaccard index = 0.666 ± 0.139). Comparing with other three models, the proposed AttSEResUNet model was demonstrated as a more accurate model for contouring the rectal tumours in axial T2WI images, whose variability was similar to that of inter-observer.

Comparison of semi-automatic and manual segmentation methods for tumor delineation on head and neck squamous cell carcinoma (HNSCC) positron emission tomography (PET) images

Objective. Accurate and reproducible tumor delineation on positron emission tomography (PET) images is required to validate predictive and prognostic models based on PET radiomic features. Manual segmentation of tumors is time-consuming whereas semi-automatic methods are easily implementable and inexpensive. This study assessed the reliability of semi-automatic segmentation methods over manual segmentation for tumor delineation in head and neck squamous cell carcinoma (HNSCC) PET images. Approach. We employed manual and six semi-automatic segmentation methods (just enough interaction (JEI), watershed, grow from seeds (GfS), flood filling (FF), 30% SUVmax and 40%SUVmax threshold) using 3D slicer software to extract 128 radiomic features from FDG-PET images of 100 HNSCC patients independently by three operators. We assessed the distributional properties of all features and considered 92 log-transformed features for subsequent analysis. For each paired comparison of a feature, we fitted a separate linear mixed effect model using the method (two levels; manual versus one semi-automatic method) as a fixed effect and the subject and the operator as the random effects. We estimated different statistics—the intraclass correlation coefficient agreement (aICC), limits of agreement (LoA), total deviation index (TDI), coverage probability (CP) and coefficient of individual agreement (CIA)—to evaluate the agreement between the manual and semi-automatic methods. Main results. Accounting for all statistics across 92 features, the JEI method consistently demonstrated acceptable agreement with the manual method, with median values of aICC = 0.86, TDI = 0.94, CP = 0.66, and CIA = 0.91. Significance. This study demonstrated that JEI method is a reliable semi-automatic method for tumor delineation on HNSCC PET images.

Estimating seepage losses from lined irrigation canals using nonlinear regression and artificial neural network models

The Slide2 model was used to estimate seepage losses from canals after validation considering different canal geometries, lining thicknesses, and lining materials. SPSS was used to develop three models: NLR, MLP-ANN, and RBF-ANN. MATLAB software was used to write down the script code for the ANNs. Results showed that seepage losses were highly increased when the liner had high hydraulic conductivity, while with the increase of lining thickness, a noticeable reduction in seepage losses was obtained. The canal's side slope had a minimal effect on the seepage losses. Moreover, the MLP-ANN and RBF-ANN models performed better than the NLR model with determination coefficient (R2) of 0.996 and 0.965; Root-Mean-Square-Error (RMSE) of 1.172 and 0.699; Mean-Absolute-Error (MAE) of 0.139 and 0.528; index of agreement (d) = 0.999 and 0.991, respectively. The NLR model had lower values of R2 = 0.906, RMSE = 1.198, MAE = 0.942, and d = 0.971. Thus, ANNs are recommended as a robust, easy, simple, and rapid tool for estimating seepage losses from lined trapezoidal irrigation canals.

Water and Nitrogen Dynamics in Drip Fertigated Tomato for Water of Different Qualities under Polyhouse Conditions

&#x0D; Water and NO3-N dynamics in the soil during the growing season is an important tool in improving the nitrogen management and environmental protection. HYDRUS-2D has been widely used to predict the water and NO3-N distribution in the soil. The objective of this study was to simulate the water and NO3-N distribution in the soil under drip fertigated tomato irrigated with different water qualities under polyhouse conditions. Field data were collected on spatial and temporal distribution of water and available NO3-N during growing season. The model was calibrated for the hydraulic conductivity and parameters were used for the validation of the model. The model performance in simulating the water and NO3-N was evaluated by using coefficient of determination (R2), root mean square error (RMSE), index of agreement and Nash–Sutcliffe model efficiency (NSE). For both calibration and validation, the higher values of R2 from 0.70 to 0.99 for water distribution and 0.70 to 0.96 for NO3-N distribution showed that observed and predicted values are highly correlated. The value of RMSE ranges from 0.004 to 0.0016 for water and 0.002-0.006 for NO3-N distribution. The index of agreement value varied from 0.86-0.98 for water distribution and 0.89-0.99 for NO3-N distribution. The values of NSE (nearer to 1) i.e. 0.17 to 0.98 for water distribution and -0.09 to 0.94 for NO3-N distribution show that HYDRUS-2D was predicting with good accuracy. From these results, it can be concluded that the model performs well for predicting the water and NO3-N distribution in the tomato crop irrigated with different water qualities under polyhouse conditions.&#x0D;