Month: August 2024

Mapping the climate change attitude: careless or care less?

Latest Results

Abstract

Is it carelessness regarding climate change knowledge or self-interested ignorance? With the consequences of climate change becoming a global issue, socio-psychological analysis becomes imperative to bring a change in attitude. The study maps the relationship between information sources, self-concern and the concern for climate change. The effect of denial as a mediating variable is investigated between information sources, self-concerns, and the perception of climate change. For this investigation, positivism was used. An exploratory descriptive study examined how information sources, self-concerns, denial, age, gender, qualification, and occupation affect climate change perspective. In July and August 2021, Google Forms were used to acquire a non-probability sample of 474 Indians. Convenience, judgement, and snowballing were employed to get the sample size. Smart PLS 3.3.2 (Smart PLS-SEM) employed partial least square–structural equation modelling. PLS-SEM confirmed the model's factors. According to the results denial completely mediates the association between self-concern and climate change perception, but not between information sources and perception of climate change. The research provides evidence that it is the care less attitude towards the environment and especially climate change that is hindering the change in behaviour of individuals. The research gives an interesting insight into the psychology of individuals. This emerging literature is particularly beneficial to understanding the reason behind failed attempts by environmentalists and scientists to bring a change in the behaviour of people. The research provides a crucial base for the direction of future efforts. As the denial of climate change is a defence strategy, the study suggests that awareness programmes should focus on this fact in order to devise approaches to bring about the desired shift in attitude and behaviour. Moreover, because self-concern increases climate change denial, narratives of policy efforts may emphasize the benefits to individuals.

The Intellectual Structure of Sales Ethics Research: A Multi-method Bibliometric Analysis

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Abstract

Using a combination of co-citation and co-word analysis, this paper reviewed the intellectual structure of the sales ethics research domain and its development over time. This multi-method bibliometric analysis included 183 sales ethics articles published between 1990 and 2020. Using co-citation analysis, we identified intellectual clusters within the research domain and explored the evolution of these clusters across three decades. We further leveraged co-word analysis to identify core themes (keywords) and delineated the field’s changing landscape. The evolutionary trends and keyword network disconnections (i.e., structural holes) suggest promising areas for future research. In particular, our analyses identified potentially fruitful opportunities related to topics such as compensation, relationship marketing outcomes, salesperson job attitudes and well-being, training, sales force control, and sales technology.

3D Numerical Modeling for Investigating Structural Controls on Orogenic Gold Mineralization, Sanshandao Gold Belt, Eastern China

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Abstract

Hydrothermal disseminated gold mineralization in the Sanshandao gold belt, Jiaodong Peninsula, China, is closely associated with regional NE–NNE fault zones. To investigate the structural controls on this mineralization, we conducted 3D numerical modeling of coupled heat transport, tectonic deformation, and fluid flow, of which two sets of models, designed simple models and actual models, were involved. The simple models were used to examine how general fault geometries (fault bend length, fault bend angle, and fault dip) influenced dilation (positive volume strain) and fluid flow and further influenced hydrothermal mineralization. In contrast, actual modeling was carried out to further understand the structural controls and mineralization localization in a specific geological condition at Sanshandao. Following this, numerical simulation experiments with variable paleo-stresses on these two models were carried out in FLAC3D platform. The simulation results of the simple models showed that long fault bend lengths, large absolute fault bend angles, and large changes in fault dip were more likely to promote dilation in the fault zone. The dilation zones are related to the small intersection angle of maximum principal stress and fault dip. The simulation results of the actual model illustrate that the gold mineralization distribution at Sanshandao was controlled by the coupling of fault strike–dip bends. Specifically, the discontinuous mineralization in the vertical direction was caused by local fluid focusing due to fault dip changes, particularly where the bend length was long. In addition, the oblique orientation of ore shooting depended on the variable strain orientations relative to the fault, which appeared to be fault strike variations. The results further determined the NNW–SSE-directed compression as the paleo-stress regime at Sanshandao during the ore-forming period. Our data also illustrated the deep fluid flow pathways in the Sanshandao gold belt and the Xinli S–SSE deep and the Sanshandao and Beibuhaiyu E–NE deep areas deserve to be the focus of the next gold exploration.

Lived Experience of Health and Wellbeing Among Young People with Early Psychosis in Aotearoa New Zealand

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Abstract

First episode psychosis (FEP) can disrupt a young person’s life and future health. Those with lived experience of FEP can inform effective support. This study investigated how young people with FEP experience good health and wellbeing living in Aotearoa New Zealand. Recent clients of early intervention services (n = 12) shared their stories across varying traditional and creative platforms. Thematic analysis revealed seven themes important for living well with FEP: whanaungatanga (relationships), addressing stigma, finding out who I am with psychosis, getting the basics right, collaborative healthcare, understanding psychosis, and access to resources. The themes informed five supporting processes: whakawhanuangatanga (relationship-building), using holistic approaches, creating space for young people, reframing, and improving access to appropriate resources. These findings deepen our understanding of how we can support young people to live well with FEP. This study highlights the value of creative methods and partnering with lived experience experts to conduct meaningful health research.

This trial was registered at Australian New Zealand Clinical Trials Registry (ANZCTR) CTRN12622001323718 on 12/10/2022 “retrospectively registered”; https://www.anzctr.org.au/Trial/Registration/TrialReview.aspx?id=384775&isReview=true.

Ratings of valence, arousal, happiness, anger, fear, sadness, disgust, and surprise for 24,000 Dutch words

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Abstract

Emotion is a fundamental aspect of human life and therefore is critically encoded in language. To facilitate research into the encoding of emotion in language and how emotion associations affect language processing, we present a new set of emotion norms for over 24,000 Dutch words. The emotion norms include ratings of two key dimensions of emotion: valence and arousal, as well as ratings on discrete emotion categories: happiness, anger, fear, sadness, disgust, and surprise. We show that emotional information can predict word processing, such that responses to positive words are facilitated in contrast to neutral and negative words. We also demonstrate how the ratings of emotion are related to personality characteristics. The data are available via the Open Science Framework (https://osf.io/9htuv/) and serve as a valuable resource for research into emotion as well as in applied settings such as healthcare and digital communication.

Research on water level measurement technology based on the residual length ratio of image characters

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Abstract

Aiming at the low efficiency and poor adaptability of traditional water level measurement methods, a water level measurement technology based on the residual length ratio of image characters is proposed in this paper. First, by improving YOLOv5, the lightweight MobilenetV3 is used to replace CSPDarkNet53, and the CBAM attention mechanism is introduced to accurately locate the water gauge and the complete “E” character and obtain the interface area between the residual “E” character and the water. Secondly, by improving U2-Net, the ordinary convolutions of RSU4-RSU7 in the decoding phase are replaced by depth-separable convolutions, and the ECA attention mechanism is introduced to improve the overall inference speed and accuracy to achieve the residual “E” character and the precise segmentation of water bodies. Finally, the water level value is calculated based on the residual length ratio of the characters. The experimental results show that the accuracy of the improved YOLOv5 is 98.12%, the average intersection over union ratio of the improved U2-Net is 86.23%, and the measurement error of water level is less than 1 cm, which meets the requirements of hydrological detection specifications. At the same time, the improved model reduces the number of parameters and computational complexity, which increases the speed of inference.

Research on water level measurement technology based on the residual length ratio of image characters

Latest Results

Abstract

Aiming at the low efficiency and poor adaptability of traditional water level measurement methods, a water level measurement technology based on the residual length ratio of image characters is proposed in this paper. First, by improving YOLOv5, the lightweight MobilenetV3 is used to replace CSPDarkNet53, and the CBAM attention mechanism is introduced to accurately locate the water gauge and the complete “E” character and obtain the interface area between the residual “E” character and the water. Secondly, by improving U2-Net, the ordinary convolutions of RSU4-RSU7 in the decoding phase are replaced by depth-separable convolutions, and the ECA attention mechanism is introduced to improve the overall inference speed and accuracy to achieve the residual “E” character and the precise segmentation of water bodies. Finally, the water level value is calculated based on the residual length ratio of the characters. The experimental results show that the accuracy of the improved YOLOv5 is 98.12%, the average intersection over union ratio of the improved U2-Net is 86.23%, and the measurement error of water level is less than 1 cm, which meets the requirements of hydrological detection specifications. At the same time, the improved model reduces the number of parameters and computational complexity, which increases the speed of inference.

Comparative Analysis of Slope Stability for Kalimpong Region under Dynamic Loading Using Limit Equilibrium Method and Machine Benchmark Learning Classifiers

Latest Results

Abstract

Significant slope destabilisation may become more likely due to the speed at which urbanisation is occurring, as well as the growing necessity for geoengineering initiatives or the growth of the road network. Slope stability analysis is done to lower the risk of landslides and slope failures. The study area, Kalimpong, is well-known for its lush greenery and stunning views and is situated in the Eastern Himalayas. However, it also constantly confronts the risk of landslides because of its rugged topography, potential seismic zone, and heavy monsoon rains. In this study, the results of the factor of safety computed by limit equilibrium (conventional) method have been compared analytically using computational intelligence and machine learning methodologies for both dry and saturated conditions under dynamic loading. Conventional machine learning techniques are combined with seven prediction models. The following algorithms have been chosen for slope stability analysis: support vector machine, k-nearest neighbours, decision tree, random forest, logistic regression, AdaBoost, and gradient boosting. Random cross-validation is used to assess each model's dependability. The stability condition is the result of the random selection of seven parameters: cohesiveness, unit weight, slope height, angle of the slope, internal friction angle, horizontal and vertical pseudo-static coefficient. Moreover, the coefficient of variation method is employed to assess the importance of every indicator in forecasting slope stability. As per the sensitivity analysis, slope stability is primarily affected by cohesiveness. With an average classification accuracy of 0.878, ensembling approach SVM-Boost demonstrates the best prediction abilities among the models tested using multifold cross-validation. The accuracy ratings of SVM and AdaBoost were 0.865 and 0.834, respectively. When combined with SLOPE/W advances, novel SVM-Boost exhibits the highest exactitude, hegemony, and best outcomes in slope stability prediction. Future earthquakes, strong rainfall, and human activity could cause the slope to collapse. The outcome demonstrates machine learning's enormous potential for enhancing slope stability assessments and provides a means of raising the effectiveness and safety of slope management.

2D Hydrodynamic Model for Flood Analysis in Kinikli Stream Basin (Tekirdağ, Türkiye)

Latest Results

Abstract

Flood is one of the natural disasters occurring due to excessive rainfall and inappropriate stream arrangements. It is important to prepare flood risk maps appropriately to prevent loss of life and property. In this study, areas under flood risk in the Kınıklı Stream Basin were determined with MIKE 21, 2D hydrodynamic model, for 5-, 50-, 200-, and 500-year recurrence flows. At the junction of the Kınıklı Stream main branches and the outlet of the basin, the flood water levels were read at four important points, and the areas at risk were determined. The areas under flood risk were overlapped with the 1/1000 scaled zoning maps and the sizes of the settlements that would be affected by the flood were calculated for each recurrent flow. For all recurrent flows, the areas that will be most affected by the flood were found to be residential areas. When the residential areas that will be affected by the flood are examined, an increase of approximately 70% was observed for the 500-year flow compared to the 5-year flow.

Comparative assessment of univariate and multivariate imputation models for varying lengths of missing rainfall data in a humid tropical region: a case study of Kozhikode, Kerala, India

Latest Results

Abstract

Accurate measurement of meteorological parameters is crucial for weather forecasting and climate change research. However, missing observations in rainfall data can pose a challenge to these efforts. Traditional methods of imputation can lead to increased uncertainty in predictions. Additionally, varying lengths of missing data and nonlinearity in rainfall distribution make it difficult to rely on a single imputation method in all situations. To address this issue, our study compared univariate and multivariate imputation models for different lengths of missing daily rainfall observations in a humid tropical region. We used 33 years of weather data from Kozhikode, an urban city in Kerala region, and evaluated the selected models using accuracy measures such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Nash–Sutcliffe Efficiency (NSE) and Mean Absolute Relative Error (MARE). Among the considered univariate and multivariate imputation models, Kalman filter coupled time series models like Kalman–Arima ( \(\overline{\mathrm{RMSE} }\)  = 11.90, \(\overline{\mathrm{MAE} }\)  = 4.46) and Kalman Smoothing with structure time series ( \(\overline{\mathrm{RMSE } }\)  = 11.37, \(\overline{\mathrm{MAE} }\)  = 5.28) were found to be best for small (< 7 days) range imputation of rainfall data. Random Forest ( \(\overline{\mathrm{RMSE} }\)  = 16.57, \(\overline{\mathrm{MAE } }\)  = 8.0) and Kalman Smoothing with structure time series ( \(\overline{\mathrm{RMSE} }\)  = 16.84, \(\overline{\mathrm{MAE} }\)  = 8.09) performed well for medium range (8–15 days) of rainfall imputation. Random Forest technique was found to be suitable for large (≤ 30 days) ( \(\overline{\mathrm{RMSE} }\)  = 15.45, \(\overline{\mathrm{MAE } }\)  = 6.77), and very large (> 30 days) ( \(\overline{\mathrm{RMSE} }\)  = 12.91, \(\overline{\mathrm{MAE } }\)  = 3.42) missing length groups and Kalman–ARIMA performed best for mixed day series (RMSE = 9.7, MAE = 3.52). NSE and MARE values for different gap margins in rainfall data (≥ 1 mm) suggest that Kalman Smoothing (KS) connected models, as a representative univariate model, perform exceptionally well when dealing with a small number of missing observations. Notably, multivariate models like Principal Component Analysis (PCA) and Random Forest outperformed univariate models for medium to large gap margins. Considering these findings, utilizing multivariate techniques is recommended for imputing a large number of missing rainfall values and univariate models can be limited for small range of rainfall missing data imputation. The identified imputation models provide effective solutions for filling missing data of various lengths in all stations' datasets in humid tropical regions, thus enhancing rainfall-related analysis and enabling more accurate weather forecasts and climate change research.