Fluctuations in canal water supplies: a case study

Irrigation management / Water allocation / Canals / Water distribution / Water supply / Performance evaluation / Irrigated farming / Irrigation systems / Pakistan / Chishtian Sub-Division / Fordwah Distributary

Incorporating citizen science:enhancing hydrological modeling through crowdsourcing

Abstract. General public participating in research design, data collection or analysis process is often referred to as citizen science, and when digital means are involved, it’s defined as crowdsourcing. This thesis project is aimed at examining the feasibility and potential of using citizen science/crowdsourcing for hydrological modelling. The research project revolves around developing a user friendly crowdsourcing mobile application for gathering data from the citizens, which will be specific to urban flooding data, river ice data, lake water quality data and vegetation condition data. The registered users are able to register on the application and upload data in the form of reports, which will be in text form and also attach images of the situation. In the end, we utilize the text reports uploaded by users regarding urban flooding to extract useful hydrological insights, that could be used for updating already existing hydrological models as well as create new hydrological models using NLP. The results indicate that it is possible to extract useful insights from the data reports submitted by the citizen scientists, which could be further used for updating hydrological models or maybe set alerts for the hydrologists in case of important hydrological updates

Examining the impact of heterogeneous nitryl chloride production on air quality across the United States

The heterogeneous hydrolysis of dinitrogen pentoxide (N₂O₅) has typically been modeled as only producing nitric acid. However, recent field studies have confirmed that the presence of particulate chloride alters the reaction product to produce nitryl chloride (ClNO₂) which undergoes photolysis to generate chlorine atoms and nitrogen dioxide (NO₂). Both chlorine and NO₂ affect atmospheric chemistry and air quality. We present an updated gas-phase chlorine mechanism that can be combined with the Carbon Bond 05 mechanism and incorporate the combined mechanism into the Community Multiscale Air Quality (CMAQ) modeling system. We then update the current model treatment of heterogeneous hydrolysis of N₂O₅ to include ClNO₂ as a product. The model, in combination with a comprehensive inventory of chlorine compounds, reactive nitrogen, particulate matter, and organic compounds, is used to evaluate the impact of the heterogeneous ClNO₂ production on air quality across the United States for the months of February and September in 2006. The heterogeneous production increases ClNO₂ in coastal as well as many in-land areas in the United States. Particulate chloride derived from sea-salts, anthropogenic sources, and forest fires activates the heterogeneous production of ClNO₂. With current estimates of tropospheric emissions, it modestly enhances monthly mean 8-h ozone (up to 1–2 ppbv or 3–4%) but causes large increases (up to 13 ppbv) in isolated episodes. This chemistry also substantially reduces the mean total nitrate by up to 0.8–2.0 μg m⁻³ or 11–21%. Modeled ClNO₂ accounts for up to 6% of the monthly mean total reactive nitrogen. Sensitivity results of the model suggest that heterogeneous production of ClNO₂ can further increase O₃ and reduce TNO₃ if elevated particulate-chloride levels are present in the atmosphere

Research Notes : Pakistan : Path-coefficient analysis of developmental and yield components in soybean

Abstract: Interrelationships among different characters were determined by simple correlations and path-coefficient analysis using 36 diverse and elite cultivars representing different geographical origin. The results revealed a highly significant positive association of the branches per plant and pods per plant with grain yield. The pods per plant also showed a high direct influence on grain yield. Thus, from this investigation, it is suggested that pods per plant and number of branches per plant are the primary yield components that should be given due emphasis in selecting high yielding genotypes in soybean

A Study on Teacher’s Perception about Components of English Handwriting in Pakistan

Handwriting is an essential of school activities for the school going children. Good or legible handwriting remained a constant task for the teachers and students during the whole day. The teachers recognize the legible handwriting at a gland due to their routine work in the schools. The teacher’s criterion that lies behind their decision of good handwriting was a question to answer in this study. The purpose of this study was to identify the components of legibility from the teachers’ perspective. Referencing handwriting experts and a literature review, key variables were categorized and organized onto a 5-point Likert Scale questionnaire. Teachers’ responses to the various legibility criteria were then tallied with regards to primary school students. Mean, standard deviation, exploratory factor analysis and path diagram statistics were applied to the ordinal data. It was concluded that twelve components were important for the legibility of handwriting of primary school students. These include Readability, Margin, Similarity, Line, Space, Size, Shape, Roundness, Form, Slant, Alignment and Recognition

Research Notes : Pakistan : Variability for some quantitative traits in soybean

Abstract: Studies on variability in soybean were taken up to work out the magnitude of genetic variability, heritability, and genetic advance among 36 varieties of soybean. Considerable genetic variability was observed for pods per plant, plant height, and grain yield per plant. High heritability and genetic advance were recorded for number of pods per plant and branches per plant. Thus, yield could be considerably improved through intensive selection pressure based on number of pods per plant and branches per plant

Learning the Structure of Auto-Encoding Recommenders

Autoencoder recommenders have recently shown state-of-the-art performance in the recommendation task due to their ability to model non-linear item relationships effectively. However, existing autoencoder recommenders use fully-connected neural network layers and do not employ structure learning. This can lead to inefficient training, especially when the data is sparse as commonly found in collaborative filtering. The aforementioned results in lower generalization ability and reduced performance. In this paper, we introduce structure learning for autoencoder recommenders by taking advantage of the inherent item groups present in the collaborative filtering domain. Due to the nature of items in general, we know that certain items are more related to each other than to other items. Based on this, we propose a method that first learns groups of related items and then uses this information to determine the connectivity structure of an auto-encoding neural network. This results in a network that is sparsely connected. This sparse structure can be viewed as a prior that guides the network training. Empirically we demonstrate that the proposed structure learning enables the autoencoder to converge to a local optimum with a much smaller spectral norm and generalization error bound than the fully-connected network. The resultant sparse network considerably outperforms the state-of-the-art methods like \textsc{Mult-vae/Mult-dae} on multiple benchmarked datasets even when the same number of parameters and flops are used. It also has a better cold-start performance.Comment: Proceedings of The Web Conference 202

Examining the Impact of Nitrous Acid Chemistry on Ozone and PM over the Pearl River Delta Region

The impact of nitrous acid (HONO) chemistry on regional ozone and particulate matter in Pearl River Delta region was investigated using the community multiscale air quality (CMAQ) modeling system and the CB05 mechanism. Model simulations were conducted for a ten-day period in October 2004. Compared with available observed data, the model performance for NOx, SO2, PM10, and sulfate is reasonably good; however, predictions of HONO are an order of magnitude lower than observed data. The CB05 mechanism contains several homogenous reactions related to HONO. To improve the model performance for HONO, direct emissions, two heterogeneous reactions, and two surface photolysis reactions were incorporated into the model. The inclusion of the additional formation pathways significantly improved simulated HONO compared with observed data. The addition of HONO sources enhances daily maximum 8-hour ozone by up to 6 ppbV (8%) and daily mean PM2.5 by up to 17 ug/m3 (12%). They also affected ozone control strategy in Pearl River Delta region

Servicing Delay Sensitive Pervasive Communication Through Adaptable Width Channelization for Supporting Mobile Edge Computing

Over the last fifteen years, wireless local area networks (WLANs) have been populated by large variety of pervasive devices hosting heterogeneous applications. Pervasive Edge computing encouraged more distributed network applications for these devices, eliminating the round-trip to help in achieving zero latency dream. However, These applications require significantly variable data rates for effective functioning, especially in pervasive computing. The static bandwidth of frequency channelization in current WLANs strictly restricts the maximum achievable data rate by a network station. This static behavior spawns two major drawbacks: under-utilization of scarce spectrum resources and less support to delay sensitive applications such as voice and video.To this point, if the computing is moved to the edge of the network WLANs to reduce the frequency of communication, the pervasive devices can be provided with better services during the communication and networking. Thus, we aim to distribute spectrum resources among pervasive resources based upon delay sensitivity of applications while simultaneously maintaining the fair channel access semantics of medium access control (MAC) layer of WLANs. Henceforth, ultra-low latency, efficiency and reliability of spectrum resources can be assured. In this paper, two novel algorithms have been proposed for adaptive channelization to offer rational distribution of spectrum resources among pervasive Edge nodes based on their bandwidth requirement and assorted ambient conditions. The proposed algorithms have been implemented on a real test bed of commercially available universal software radio peripheral (USRP) devices. Thorough investigations have been carried out to enumerate the effect of dynamic bandwidth channelization on parameters such as medium utilization, achievable throughput, service delay, channel access fairness and bit error rates. The achieved empirical results demonstrate that we can optimally enhance the network-wide throughput by almost 30% using channels of adaptable bandwidths