Energy Conservation Techniques to Mitigate the Power Shortage Problem in Pakistan (Case Studies)

The main objective of this research paper is to show benefits of different energy conservation techniques. As a first case study, I performed analysis on University of Gujrat, electrical power system. This case study involves analysis of motors and tube lights installed at the pumping stations and in Engineering Block of UOG respectively, with the help of energy analyzer before and after the installation of required rating capacitors. Power system analysis also done which includes power distribution system losses for example line losses and copper losses of different rating transformers of UOG. Cost and payback period calculation had been done. Second case study is performed on 11 KV Ali Park and Rachna Town feeders to show fruitful results obtained by implementing rehabilitation techniques on the above said feeders. The results showed by adopting energy conservation techniques not only energy is conserved, it also brings other benefits

Joint Detection and Tracking in Videos with Identification Features

Recent works have shown that combining object detection and tracking tasks, in the case of video data, results in higher performance for both tasks, but they require a high frame-rate as a strict requirement for performance. This is assumption is often violated in real-world applications, when models run on embedded devices, often at only a few frames per second. Videos at low frame-rate suffer from large object displacements. Here re-identification features may support to match large-displaced object detections, but current joint detection and re-identification formulations degrade the detector performance, as these two are contrasting tasks. In the real-world application having separate detector and re-id models is often not feasible, as both the memory and runtime effectively double. Towards robust long-term tracking applicable to reduced-computational-power devices, we propose the first joint optimization of detection, tracking and re-identification features for videos. Notably, our joint optimization maintains the detector performance, a typical multi-task challenge. At inference time, we leverage detections for tracking (tracking-by-detection) when the objects are visible, detectable and slowly moving in the image. We leverage instead re-identification features to match objects which disappeared (e.g. due to occlusion) for several frames or were not tracked due to fast motion (or low-frame-rate videos). Our proposed method reaches the state-of-the-art on MOT, it ranks 1st in the UA-DETRAC'18 tracking challenge among online trackers, and 3rd overall.Comment: Accepted at Image and Vision Computing Journa

Integration of Renewable Energy Resources in Microgrid

Microgrid is a new concept in power generation. The Microgrid concept assumes a cluster of loads and micro sources operating as a single controllable system that provides both power and heat to its local area. Not much is known about Microgrid behavior as a whole system. Some models exist which describe the components of a Microgrid. In this paper, model of Microgrids with steady state and their transient responses to changing inputs are presented. Current models of a fuel cell, microturbines, wind turbine and solar cell have been discussed. Finally a complete model built of Microgrid including the power sources, their power electronics, and a load and mains model in MATLAB/Simulink is presented

Implementation of Nanogrids for Future Power System

Microgrid is a new technology in power generation and this system is used to provide power and heat to its local area, such as cogeneration systems and renewable energy (wind turbines, photovoltaic cells, etc.). They are preferred for medium or high power applications. Nanogrid most likely to be used in small local loads for rural area as they will be more economic then the normal grid power system. Nano grids can operate independently or be connected to the mains and most likely the internal voltage can be utilized as ac or dc. In this research paper a small scale microgrid system is proposed for smart homes called "Nanogrid". Each houses have small electrical power system from them can be shared among houses. If it uses a DC system instead of a general AC system, it can reduce energy loss of inverter because each generator doesn’t need an inverter. Furthermore, it can continue to provide a power supply when blackout occurs in the bulk power system. A model of a nanogrid is developed to simulate the operation of the centralized power control. Finally a Simulink model is presented for small houses power range 90-285 KW

Recent Advances in Internet of Things and Emerging Social Internet of Things: Vision, Challenges and Trends

In recent years, the Internet of Things (IoT), together with its related emerging technologies, has been driving a revolution in the way people perceive and interact with the surrounding environment [...

Transient expression of βC1 protein differentially regulates host genes related to stress response, chloroplast and mitochondrial functions

<p>Abstract</p> <p>Background</p> <p>Geminiviruses are emerging plant pathogens that infect a wide variety of crops including cotton, cassava, vegetables, ornamental plants and cereals. The geminivirus disease complex consists of monopartite begomoviruses that require betasatellites for the expression of disease symptoms. These complexes are widespread throughout the Old World and cause economically important diseases on several crops. A single protein encoded by betasatellites, termed βC1, is a suppressor of gene silencing, inducer of disease symptoms and is possibly involved in virus movement. Studies of the interaction of βC1 with hosts can provide useful insight into virus-host interactions and aid in the development of novel control strategies. We have used the differential display technique to isolate host genes which are differentially regulated upon transient expression of the βC1 protein of chili leaf curl betasatellite (ChLCB) in <it>Nicotiana tabacum</it>.</p> <p>Results</p> <p>Through differential display analysis, eight genes were isolated from <it>Nicotiana tabacum</it>, at two and four days after infitration with βC1 of ChLCB, expressed under the control of the <it>Cauliflower mosaic virus </it>35S promoter. Cloning and sequence analysis of differentially amplified products suggested that these genes were involved in ATP synthesis, and acted as electron carriers for respiration and photosynthesis processes. These differentially expressed genes (DEGs) play an important role in plant growth and development, cell protection, defence processes, replication mechanisms and detoxification responses. Kegg orthology based annotation system analysis of these DEGs demonstrated that one of the genes, coding for polynucleotide nucleotidyl transferase, is involved in purine and pyrimidine metabolic pathways and is an RNA binding protein which is involved in RNA degradation.</p> <p>Conclusion</p> <p>βC1 differentially regulated genes are mostly involved in chloroplast and mitochondrial functions. βC1 also increases the expression of those genes which are involved in purine and pyrimidine metabolism. This information gives a new insight into the interaction of βC1 with the host and can be used to understand host-virus interactions in follow-up studies.</p

Study of Distractors in Neural Models of Code

Finding important features that contribute to the prediction of neural models is an active area of research in explainable AI. Neural models are opaque and finding such features sheds light on a better understanding of their predictions. In contrast, in this work, we present an inverse perspective of distractor features: features that cast doubt about the prediction by affecting the model's confidence in its prediction. Understanding distractors provide a complementary view of the features' relevance in the predictions of neural models. In this paper, we apply a reduction-based technique to find distractors and provide our preliminary results of their impacts and types. Our experiments across various tasks, models, and datasets of code reveal that the removal of tokens can have a significant impact on the confidence of models in their predictions and the categories of tokens can also play a vital role in the model's confidence. Our study aims to enhance the transparency of models by emphasizing those tokens that significantly influence the confidence of the models.Comment: The 1st International Workshop on Interpretability and Robustness in Neural Software Engineering, Co-located with ICSE (InteNSE'23

Memorization and Generalization in Neural Code Intelligence Models

Deep Neural Networks (DNN) are increasingly commonly used in software engineering and code intelligence tasks. These are powerful tools that are capable of learning highly generalizable patterns from large datasets through millions of parameters. At the same time, training DNNs means walking a knife's edges, because their large capacity also renders them prone to memorizing data points. While traditionally thought of as an aspect of over-training, recent work suggests that the memorization risk manifests especially strongly when the training datasets are noisy and memorization is the only recourse. Unfortunately, most code intelligence tasks rely on rather noise-prone and repetitive data sources, such as GitHub, which, due to their sheer size, cannot be manually inspected and evaluated. We evaluate the memorization and generalization tendencies in neural code intelligence models through a case study across several benchmarks and model families by leveraging established approaches from other fields that use DNNs, such as introducing targeted noise into the training dataset. In addition to reinforcing prior general findings about the extent of memorization in DNNs, our results shed light on the impact of noisy dataset in training.Comment: manuscript in preparatio

A Study of Variable-Role-based Feature Enrichment in Neural Models of Code

Although deep neural models substantially reduce the overhead of feature engineering, the features readily available in the inputs might significantly impact training cost and the performance of the models. In this paper, we explore the impact of an unsuperivsed feature enrichment approach based on variable roles on the performance of neural models of code. The notion of variable roles (as introduced in the works of Sajaniemi et al. [Refs. 1,2]) has been found to help students' abilities in programming. In this paper, we investigate if this notion would improve the performance of neural models of code. To the best of our knowledge, this is the first work to investigate how Sajaniemi et al.'s concept of variable roles can affect neural models of code. In particular, we enrich a source code dataset by adding the role of individual variables in the dataset programs, and thereby conduct a study on the impact of variable role enrichment in training the Code2Seq model. In addition, we shed light on some challenges and opportunities in feature enrichment for neural code intelligence models.Comment: Accepted in the 1st International Workshop on Interpretability and Robustness in Neural Software Engineering (InteNSE'23), Co-located with ICS

The Response of Land Surface Temperature to the Changing Land-Use Land-Cover in a Mountainous Landscape under the Influence of Urbanization: Gilgit City as a case study in the Hindu Kush Himalayan Region of Pakistan

With growing urbanization in mountainous landscapes, the built-up areas dominate other land use classesresulting in increased land surface temperature (LST). Gilgit city in northern Pakistan has witnessed tremendousurban growth in the recent past decades. It is anticipated that this growth will exponentially increase in the nearfuture because of the China-Pakistan Economic Corridor (CPEC) initiatives, as this city happens to be thecommercial hub of the northern region of Pakistan. The objective of present study is to explore the influence ofland use and land cover variations on LST and to evaluate the relationship between LST with normalizeddifference vegetation index (NDVI), normalized difference water index (NDWI), and normalized difference built -up index (NDBI) values. This study is carried out on data from Google earth and three Landsat images (Landsat 5-TM, Landsat 7-ETM, and Landsat OLI_TIRS-8) during the period from 1992, 2004 and 2016. Land use/coverclasses are determined through supervised classification and LST maps are created using the Mono -windowalgorithm. The accuracy assessment of land use/cover classes is carried out comparing Google Earth digitizedvector for the periods of 2004 and 2016 with Landsat classified images. Further, NDVI, NDBI, and NDWI mapsare computed from images for years 1992, 2004, and 2016. The relationships of LST with NDVI, NDBI, andNDWI are computed using Linear Regression analysis. The results reveal that the variations in land use and landcover play a substantial role in LST variability. The maximum temperatures are connected with built -up areas andbarren land, ranging from 48.4°C, 50.7°C, 51.6°C, in 1992, 2004, and 2016, respectively. Inversely, minimumtemperatures are linked to forests and water bodies, ranging from 15.1°C, 16°C, 21.6°C, in 1992, 2004, and 2016respectively. This paper also results that NDBI correlates positively with high temperatures, whereas NDVI andNDWI associate negatively with lesser temperatures. The study will support to policymakers and urban planners tostrategize the initiatives for eco-friendly and climate-resilient urban development in fragile mountainouslandscapes