24 research outputs found
Temporal Delta Layer: Training Towards Brain Inspired Temporal Sparsity for Energy Efficient Deep Neural Networks
No full textIn the recent past, real-time video processing using state-of-the-art deep neural networks (DNN) has achieved human-like accuracy but at the cost of high energy consumption, making them infeasible for edge device deployment. The energy consumed by running DNNs on hardware accelerators is dominated by the number of memory read/writes and multiplyaccumulate (MAC) operations required. As a potential solution, this work explores the role of activation sparsity in efficient DNN inference. As the predominant operation in DNNs is matrix-vector multiplication of weights with activations, skipping operations and memoryfetches where (at least) one of them is zero can make inference more energy efficient. Although spatial sparsification of activations is researched extensively, introducing and exploiting temporal sparsity is much less explored in DNN literature. This work presents a new DNN layer (called temporal delta layer) whose primary objective is to induce temporal activation sparsity during training. The temporal delta layer promotes activation sparsity by performing delta operation facilitated by activation quantization and l1 norm based penalty to the cost function. During inference, the resulting model acts as a conventional quantizedDNN with high temporal activation sparsity. The new layer was incorporated as a part of the standard ResNet50 architecture to be trained and tested on the popular human action recognition dataset (UCF101). The method caused 2x improvement in activation sparsity, with 5% accuracy loss.Electrical Engineerin
Evaluating Modified Soil Erodibility Factors with the Aid of Pedotransfer Functions and Dynamic Remote-Sensing Data for Soil Health Management
No full textSoil erosion is a critical factor impacting soil health and agricultural productivity, with soil erodibility often quantified using the K-factor in erosion models such as the universal soil loss equation (USLE). Traditional K-factor estimation lacks spatiotemporal precision, particularly under varying soil moisture and land cover conditions. This study introduces modified K-factor pedotransfer functions (Kmlr) integrating dynamic remotely sensed data on land use land cover to enhance K-factor accuracy for diverse soil health management applications. The Kmlr functions from multiple approaches, including dynamic crop and cover management factor (Cdynamic), high resolution satellite data, and downscaled remotely sensed data, were evaluated across spatial and temporal scales within the Fish River watershed in Alabama, a coastal watershed with significant soil–water interactions. The results highlighted that the Kmlr model provided more accurate sediment yield (SY) predictions, particularly in agricultural areas, where traditional models overestimated erosion by upto 59.23 ton/ha. SY analysis across the 36 hydrological response units (HRUs) in the watershed showed that the Kmlr model captured more accurate soil loss estimates, especially in regions with varying land use. The modified K-factor model (Kmlr-c) using Cdynamic and high-resolution soil surface moisture data outperformed the traditional USLE K-factors in predicting SY, with a strong correlation to observed SY data (R² = 0.980 versus R² = 0.911). The total sediment yield predicted by Kmlr-c (525.11 ton/ha) was notably lower than that of USLE-based estimates (828.62 ton/ha), highlighting the overestimation in conventional models. The identification of erosive hotspots revealed that 6003 ha of land was at high erosion risk (K-factor > 0.25), with an average soil loss of 24.2 ton/ha. The categorization of erosive hotspots highlighted critical areas at high risk for erosion, underscoring the need for targeted soil conservation practices. This research underscores the improvement of remotely sensed data-based models and perfects them for the application of soil erodibility assessments thus promoting the development of such models
Structural, electronic transport and optical properties of Cr doped PbS thin film by chemical bath deposition
No full textA Union of Dynamic Hydrological Modeling and Satellite Remotely-Sensed Data for Spatiotemporal Assessment of Sediment Yields
No full text(1) The existing frameworks for water quality modeling overlook the connection between multiple dynamic factors affecting spatiotemporal sediment yields (SY). This study aimed to implement satellite remotely sensed data and hydrological modeling to dynamically assess the multiple factors within basin-scale hydrologic models for a realistic spatiotemporal prediction of SY in watersheds. (2) A connective algorithm was developed to incorporate dynamic models of the crop and cover management factor (C-factor) and the soil erodibility factor (K-factor) into the Soil and Water Assessment Tool (SWAT) with the aid of the Python programming language and Geographic Information Systems (GIS). The algorithm predicted the annual SY in each hydrologic response unit (HRU) of similar land cover, soil, and slope characteristics in watersheds between 2002 and 2013. (3) The modeled SY closely matched the observed SY using the connective algorithm with the inclusion of the two dynamic factors of K and C (predicted R2 (PR2): 0.60–0.70, R2: 0.70–0.80, Nash Sutcliffe efficiency (NS): 0.65–0.75). The findings of the study highlight the necessity of excellent spatial and temporal data in real-time hydrological modeling of catchments
Effect of interphase permittivity on the electric field distribution of epoxy nanocomposites
No full textWhat Was So New about the New Story? Modernist Realism in the Hindi Nayī Kahānī
Get PDFThis essay examines the Hindi Nayī Kahānī, or New Story, Movement of the 1950s and 1960s, which was influential for the short stories, criticism, and literary history that its writers produced. Incorporating a view toward the larger “metaliterary” corpus in relation to which properly “literary” nayī kahānī texts were written, the essay shows how the movement inaugurated a modernist realism characterized by attention to genre, rhetoric, and style on one hand, and commitment to social reality on the other. Combining rhetorical strategies—such as shifting narrative voice, allegorical descriptions of landscape, and implicit reference to authorship and the condition of postcolonial literary production—with structural and thematic tensions between form and content, this mode developed an interchangeability between author, reader, and character, which did not previously exist in Hindi literature and which reconfigured the category of the middle class in the universally recognizable terms of alienation. Using the case of the nayī kahānī, the essay offers a new literary historical approach that moves beyond sweeping accounts of a single postcolonial mode to attend to regional realisms and modernisms
A Study on the Symptomatology and Diagnostic Methodology of Oru Thalai Vatha Bedham
Get PDFOru thalai vatha bedham clinical entity was described by Sage Yugi in his wisdom. The study conducted has come out with excellent results validating the clinical features of Oru thalai vatha bedham elucidated in an ultra short poetic segment by Yugi. The study was aimed at evolving a set of exclusive Siddha diagnostic findings for Oru thalai vatha bedham with the observation and inference of various parameters like Naadi, Neikkuri and disease acquired season, it can be concluded that all of them point to the development or vitiation of humour leading to the disease Oru thalai vatha bedham. The patient reported with the symptoms of Oru thali vatha bedham were subjected to the standard set of investigations, the results and findings of the investigations were suggestive of Oru thalai vatha bedham according to modern classification of disease. Manikadai Nool and Neikkuri findings may help in the identifying of preponderance in a person to develop Oru thalai vatha bedham hence it can be used as a screening measure to advise the preventive measures well in advance.
Almost all the patients diagnosed as oru thalai vatha bedham had normal study of heamatological evidence, CT & x-ray (if needed) conforming to the correlation of disease with Primary headache syndrome. From the analysis done between Oru thalai vatha bedham cases notable variations were observed in both Siddha and Modern parameters. Interestingly, it was found that the symptoms presented by the patients in the study were those of a constant subset of symptoms of Primary headache syndrome explained in the present day classification. It correlated with all of the symptoms mentioned by Yugimuni under Oru thalai vatha bedham. Thus the author concludes by throwing lights on validation of symptomatology and exclusive Siddha diagnostic methodology for Oru thalai vatha bedham, so that a physician can arrive at proper treatment procedures by rightly diagnosing the disease
Predictive Analysis of Hydrological Variables in the Cahaba Watershed: Enhancing Forecasting Accuracy for Water Resource Management Using Time-Series and Machine Learning Models
No full textThis study presents a hybrid approach to hydrological forecasting by integrating the physically based Soil and Water Assessment Tool (SWAT) model with Prophet time-series modeling and machine learning–based multi-output regression. Applied to the Cahaba watershed, the objective is to predict key environmental variables (precipitation, evapotranspiration (ET), potential evapotranspiration (PET), and snowmelt) and their influence on hydrological responses (surface runoff, groundwater flow, soil water, sediment yield, and water yield) under present (2010–2022) and future (2030–2042) climate scenarios. Using SWAT outputs for calibration, the integrated SWAT-Prophet-ML model predicted ET and PET with RMSE values between 10 and 20 mm. Performance was lower for high-variability events such as precipitation (RMSE = 30–50 mm). Under current climate conditions, R2 values of 0.75 (water yield) and 0.70 (surface runoff) were achieved. Groundwater and sediment yields were underpredicted, particularly during peak years. The model’s limitations relate to its dependence on historical trends and its limited representation of physical processes, which constrain its performance under future climate scenarios. Suggested improvements include scenario-based training and integration of physical constraints. The approach offers a scalable, data-driven method for enhancing monthly water balance prediction and supports applications in watershed planning
Problem-based learning in undergraduate education. A sophomore chemistry laboratory
Get PDFFor the first time in my life what we were doing in lab had meaning to the outside world other than just mixing chemicals together. -Comment turned in by a student in Chem. 291L Problem-based learning (PBL) is a pedagogical approach based on recent advances in cognitive science research on human learning (1). A PBL classroom is organized around collaborative problem-solving activities that provide a context for learning and discovery. PBL has been used in medical schools to enhance the development of clinical reasoning skills and to promote the integration of basic biomedical sciences with clinical applications. Medical education literature is replete with articles on the practice and evaluation of PBL methods, but there is very little published on the application of PBL for science education in undergraduate settings. A recent paper by Dods in this Journal describes a very interesting application of PBL in a biochemistry lecture course (2). There have been some presentations at recent ACS conferences describing the application of PBL in chemistry courses (3, 4 ). Other problembased approaches to pedagogy have been described by Wenzel and Hughes (5, 6 ). These approaches are similar to PBL in that students learn in the context of an authentic problem solving experience. This paper describes the implementation of PBL pedagogy in an undergraduate classroom setting. The author provides a brief description of PBL philosophy and PBL protocols, guidance on how to choose and design a PBL problem and integrate it into the curriculum, and a description of a laboratory course in which PBL has been successfully implemented
Fabrication Of Butterfly Pea Flower Anthocyanin-Incorporated Colorimetric Indicator Film Based On Gelatin/Pectin For Monitoring Fish Freshness
No full textNovel visual intelligent pH-indicator film was prepared from the eggshell membrane- gelatin, pectin, and anthocyanin pigment from butterfly pea flower (BP) (Clitoria ternatea). It was used as a real-time pH indicator for predicting food freshness. Eggshells are useful biowaste, gelatin has been extracted from the eggshell membrane and was used for fabricating the film. The Anthocyanin content of BP extract was 198.3 mg g−1. The film's surface morphology and chemical nature were estimated using a Scanning electron microscope, Fourier transform infrared spectroscopy, and X-ray diffraction technique. The film was pH responsive and exhibited color variation ranging from shades of red, purple, blue, green, and yellow at different pH (1-13). The applicability of the developed pH- indicator film was studied on fresh Tilapia fish by monitoring its deterioration for a specific time period. The film showed a visible color change after seven days of storage at 4ºC from dark blue, bluish-grey to olive, and deep green. Change in the total volatile basic nitrogen (TVB-N) content and pH change had an effect on the color response of the film. The findings demonstrated that the fabricated pH indicator film proved to be pH sensitive and could be used to monitor fish freshness
