Time and Space Domain Prediction of Water Quality Parameters of Bagmati River Using Deep Learning Methods

Bagmati river is biologically, geologically, religiously and historically significant among the river systems of the Kathmandu Valley. The river is affected by five major tributaries, including Manohara, Dhobi Khola, Tukucha, Bishnumati, and Balkhu Khola, which significantly impact the water chemistry inside the Kathmandu Valley. The data of water quality parameters pH, dissolved oxygen, turbidity, temperature, oxygen reduction potential, conductivity, total dissolved solids, salinity among others was collected using fixed sensors (in period of 5 seconds) and mobile sensors (with latitude and longitude) along the river. The observation is important for two reasons, one because it was collected in real-time and fine scale, which is not normally possible with traditional ways, and next such observation was done for the first time in Bagmati River. The aim of this study was to predict water quality parameters of the Bagmati River using machine learning time series models, specifically ARIMA and LSTM. The LSTM model was designed with one input layer, one encoder layer, one repeat layer, one decoder layer, and one output dense layer to separate the output into temporal slices. Additionally, a DNN model was employed for location-based prediction, utilizing two input layers for latitude and longitude and seven output layers for the seven water quality parameters considered for study. The models demonstrated promising performance, but further data collection and parameter variation are recommended for continued optimization

Time and Space Domain Prediction of Water Quality Parameters of Bagmati River Using Deep Learning Methods

Bagmati river is biologically, geologically, religiously and historically significant among the river systems of the Kathmandu Valley. The river is affected by five major tributaries, including Manohara, Dhobi Khola, Tukucha, Bishnumati, and Balkhu Khola, which significantly impact the water chemistry inside the Kathmandu Valley. The data of water quality parameters pH, dissolved oxygen, turbidity, temperature, oxygen reduction potential, conductivity, total dissolved solids, salinity among others was collected using fixed sensors (in period of 5 seconds) and mobile sensors (with latitude and longitude) along the river. The observation is important for two reasons, one because it was collected in real-time and fine scale, which is not normally possible with traditional ways, and next such observation was done for the first time in Bagmati River. The aim of this study was to predict water quality parameters of the Bagmati River using machine learning time series models, specifically ARIMA and LSTM. The LSTM model was designed with one input layer, one encoder layer, one repeat layer, one decoder layer, and one output dense layer to separate the output into temporal slices. Additionally, a DNN model was employed for location-based prediction, utilizing two input layers for latitude and longitude and seven output layers for the seven water quality parameters considered for study. The models demonstrated promising performance, but further data collection and parameter variation are recommended for continued optimization

Elucidating adolescent aspirational models for the design of public mental health interventions: a mixed-method study in rural Nepal.

BackgroundAdolescent aspirational models are sets of preferences for an idealized self. Aspirational models influence behavior and exposure to risk factors that shape adult mental and physical health. Cross-cultural understandings of adolescent aspirational models are crucial for successful global mental health programs. The study objective was elucidating adolescent aspirational models to inform interventions in Nepal.MethodsTwenty qualitative life trajectory interviews were conducted among adolescents, teachers, and parents. Card sorting (rating and ranking activities) were administered to 72 adolescents aged 15-19 years, stratified by caste/ethnicity: upper caste Brahman and Chhetri, occupational caste Dalit, and ethnic minority Janajati.ResultsThemes included qualities of an ideal person; life goals, barriers, and resources; emotions and coping; and causes of interpersonal violence, harmful alcohol use, and suicide. Education was the highest valued attribute of ideal persons. Educational attainment received higher prioritization by marginalized social groups (Dalit and Janajati). Poverty was the greatest barrier to achieving life goals. The most common distressing emotion was 'tension', which girls endorsed more frequently than boys. Sharing emotions and self-consoling were common responses to distress. Tension was the most common reason for alcohol use, especially among girls. Domestic violence, romantic break-ups, and academic pressure were reasons for suicidality.ConclusionInability to achieve aspirational models due to a range of barriers was associated with negative emotions-notably tension-and dysfunctional coping that exacerbates barriers, which ultimately results in the triad of interpersonal violence, substance abuse, and suicidality. Interventions should be framed as reducing the locally salient idiom of distress tension and target this triad of threats. Regarding intervention content, youth-endorsed coping mechanisms should be fortified to counter this distress pathway

Elucidating adolescent aspirational models for the design of public mental health interventions: a mixed-method study in rural Nepal

Abstract Background Adolescent aspirational models are sets of preferences for an idealized self. Aspirational models influence behavior and exposure to risk factors that shape adult mental and physical health. Cross-cultural understandings of adolescent aspirational models are crucial for successful global mental health programs. The study objective was elucidating adolescent aspirational models to inform interventions in Nepal. Methods Twenty qualitative life trajectory interviews were conducted among adolescents, teachers, and parents. Card sorting (rating and ranking activities) were administered to 72 adolescents aged 15–19 years, stratified by caste/ethnicity: upper caste Brahman and Chhetri, occupational caste Dalit, and ethnic minority Janajati. Results Themes included qualities of an ideal person; life goals, barriers, and resources; emotions and coping; and causes of interpersonal violence, harmful alcohol use, and suicide. Education was the highest valued attribute of ideal persons. Educational attainment received higher prioritization by marginalized social groups (Dalit and Janajati). Poverty was the greatest barrier to achieving life goals. The most common distressing emotion was ‘tension’, which girls endorsed more frequently than boys. Sharing emotions and self-consoling were common responses to distress. Tension was the most common reason for alcohol use, especially among girls. Domestic violence, romantic break-ups, and academic pressure were reasons for suicidality. Conclusion Inability to achieve aspirational models due to a range of barriers was associated with negative emotions—notably tension—and dysfunctional coping that exacerbates barriers, which ultimately results in the triad of interpersonal violence, substance abuse, and suicidality. Interventions should be framed as reducing the locally salient idiom of distress tension and target this triad of threats. Regarding intervention content, youth-endorsed coping mechanisms should be fortified to counter this distress pathway

Elucidating adolescent aspirational models for the design of public mental health interventions: A mixed-method study in rural Nepal

© 2017 The Author(s). Background: Adolescent aspirational models are sets of preferences for an idealized self. Aspirational models influence behavior and exposure to risk factors that shape adult mental and physical health. Cross-cultural understandings of adolescent aspirational models are crucial for successful global mental health programs. The study objective was elucidating adolescent aspirational models to inform interventions in Nepal. Methods: Twenty qualitative life trajectory interviews were conducted among adolescents, teachers, and parents. Card sorting (rating and ranking activities) were administered to 72 adolescents aged 15-19 years, stratified by caste/ethnicity: upper caste Brahman and Chhetri, occupational caste Dalit, and ethnic minority Janajati. Results: Themes included qualities of an ideal person; life goals, barriers, and resources; emotions and coping; and causes of interpersonal violence, harmful alcohol use, and suicide. Education was the highest valued attribute of ideal persons. Educational attainment received higher prioritization by marginalized social groups (Dalit and Janajati). Poverty was the greatest barrier to achieving life goals. The most common distressing emotion was \u27tension\u27, which girls endorsed more frequently than boys. Sharing emotions and self-consoling were common responses to distress. Tension was the most common reason for alcohol use, especially among girls. Domestic violence, romantic break-ups, and academic pressure were reasons for suicidality. Conclusion: Inability to achieve aspirational models due to a range of barriers was associated with negative emotions-notably tension-and dysfunctional coping that exacerbates barriers, which ultimately results in the triad of interpersonal violence, substance abuse, and suicidality. Interventions should be framed as reducing the locally salient idiom of distress tension and target this triad of threats. Regarding intervention content, youth-endorsed coping mechanisms should be fortified to counter this distress pathway

A Comparative Study of State-of-the-Art Deep Learning Models for Semantic Segmentation of Pores in Scanning Electron Microscope Images of Activated Carbon

Accurate measurement of the microspores, mesopores, and macropores on the surface of the activated carbon is essential due to its direct influence on the material’s adsorption capacity, surface area, and overall performance in various applications like water purification, air filtration, and gas separation. Traditionally, Scanning Electron Microscopy (SEM) images of activated carbons are collected and manually annotated by a human expert to differentiate and measure different pores on the surface. However, manual analysis of such surfaces is costly, time-consuming, and resource-intensive, as it requires expert supervision. In this paper, we propose an automatic deep-learning-based solution to address this challenge of activated carbon surface segmentation. Our deep-learning approach optimizes pore analysis by reducing time and resources, eliminating human subjectivity, and effectively adapting to diverse pore structures and imaging conditions. We introduce a novel SEM image segmentation dataset for activated carbon, comprising 128 images that capture the variability in pore sizes, structures, and imaging artifacts. Challenges encountered during dataset creation, irregularities in pore structures, and the presence of impurities were addressed to ensure robust model performance. We then evaluate the state-of-the-art deep learning models on the novel semantic segmentation task that shows promising results. Notably, DeepLabV3Plus, DeepLabV3, and FPN emerge as the most promising models based on semantic segmentation test results, with DeepLabV3Plus achieving the highest test Dice coefficient of 68.68%. Finally, we outline the key research challenges and discuss potential research directions to address these challenges

Power Optimization in Multi-Tier Heterogeneous Networks Using Genetic Algorithm

The Internet of Things (IoT) connects numerous sensor nodes and devices, resulting in an increase in the bandwidth and data rates. However, this has led to a surge in data-hungry applications, which consume significant energy at battery-limited IoT nodes, causing rapid battery drainage. As a result, it is imperative to find a reliable solution that reduces the power consumption. A power optimization model utilizing a modified genetic algorithm is proposed to manage power resources efficiently and reduce high power consumption. In this model, each access point computes the optimal power using the modified genetic algorithm until it meets the fitness criteria and assigns it to each cellular user. Additionally, a weight-based user-scheduling algorithm is proposed to enhance network efficiency. This algorithm considers both the distance and received signal strength indicator (RSSI) to select a user for a specific base station. Furthermore, it assigns appropriate weights for the distance, and the RSSI helps increase the spectral efficiency performance. In this paper, the user-scheduling algorithm was assigned equal weights and combined with the power optimization model to analyze the power consumption and spectral efficiency performance metrics. The results demonstrated that the weight-based user-scheduling algorithm performed better and was supported by the optimal allocation of weights using a modified genetic algorithm. The outcome proved that the optimal allocation of transmission power for users reduced the cellular users’ power consumption and improved the spectral efficiency