81 research outputs found
Panchayat Irrigation Management : A Case Study of Institutional Reforms Programme over Teesta Command in West Bengal
Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.This article studies the role played by the constitutionally empowered Panchayati Raj Institutions over a large irrigation system in West Bengal. The article tries to capture the linkages and the dynamics governing interaction between the 'Gram Panchayats' and the Water User Associations. The inferences are drawn from observed phenomenon pertaining to the role and relationship between the two sets of institutions over the Command Area Development Authority Programme (CADAP). While the advent of the canal water has created an agrarian dynamism over the canal command particularly among the marginal and landless farmers through boro-paddy cultivation, the process of institutionalizing farmers’ participation left much to be desired. While the representatives of the Water User Associations often faltered to draw collective action from the farmers, the political actors proved to be much stronger. However even these actors were not proactive and responded only to crisis situations. Thus the system continues to operate at the sub-optimal level and seems to have achieved a low level of equilibrium
Controlled transportation of mesoscopic particles by enhanced spin orbit interaction of light in an optical trap
We study the effects of the spin orbit interaction (SOI) of light in an
optical trap and show that the propagation of the tightly focused trapping beam
in a stratified medium can lead to significantly enhanced SOI. For a plane
polarized incident beam the SOI manifests itself by giving rise to a strong
anisotropic linear diattenuation effect which produces polarization-dependent
off-axis high intensity side lobes near the focal plane of the trap. Single
micron-sized asymmetric particles can be trapped in the side lobes, and
transported over circular paths by a rotation of the plane of input
polarization. We demonstrate such controlled motion on single pea-pod shaped
single soft oxometalate (SOM) particles of dimension around m
over lengths up to 15 m . The observed effects are supported by
calculations of the intensity profiles based on a variation of the Debye-Wolf
approach. The enhanced SOI could thus be used as a generic means of
transporting mesoscopic asymmetric particles in an optical trap without the use
of complex optical beams or changing the alignment of the beam into the trap.Comment: 9 pages, 7 figure
Crime Prediction Using Machine Learning and Deep Learning: A Systematic Review and Future Directions
Predicting crime using machine learning and deep learning techniques has
gained considerable attention from researchers in recent years, focusing on
identifying patterns and trends in crime occurrences. This review paper
examines over 150 articles to explore the various machine learning and deep
learning algorithms applied to predict crime. The study provides access to the
datasets used for crime prediction by researchers and analyzes prominent
approaches applied in machine learning and deep learning algorithms to predict
crime, offering insights into different trends and factors related to criminal
activities. Additionally, the paper highlights potential gaps and future
directions that can enhance the accuracy of crime prediction. Finally, the
comprehensive overview of research discussed in this paper on crime prediction
using machine learning and deep learning approaches serves as a valuable
reference for researchers in this field. By gaining a deeper understanding of
crime prediction techniques, law enforcement agencies can develop strategies to
prevent and respond to criminal activities more effectively.Comment: 35 Pages, 6 tables and 11 figures. Consists of Dataset links used for
crime prediction. Review Pape
Advances in Cybercrime Prediction: A Survey of Machine, Deep, Transfer, and Adaptive Learning Techniques
Cybercrime is a growing threat to organizations and individuals worldwide,
with criminals using increasingly sophisticated techniques to breach security
systems and steal sensitive data. In recent years, machine learning, deep
learning, and transfer learning techniques have emerged as promising tools for
predicting cybercrime and preventing it before it occurs. This paper aims to
provide a comprehensive survey of the latest advancements in cybercrime
prediction using above mentioned techniques, highlighting the latest research
related to each approach. For this purpose, we reviewed more than 150 research
articles and discussed around 50 most recent and relevant research articles. We
start the review by discussing some common methods used by cyber criminals and
then focus on the latest machine learning techniques and deep learning
techniques, such as recurrent and convolutional neural networks, which were
effective in detecting anomalous behavior and identifying potential threats. We
also discuss transfer learning, which allows models trained on one dataset to
be adapted for use on another dataset, and then focus on active and
reinforcement Learning as part of early-stage algorithmic research in
cybercrime prediction. Finally, we discuss critical innovations, research gaps,
and future research opportunities in Cybercrime prediction. Overall, this paper
presents a holistic view of cutting-edge developments in cybercrime prediction,
shedding light on the strengths and limitations of each method and equipping
researchers and practitioners with essential insights, publicly available
datasets, and resources necessary to develop efficient cybercrime prediction
systems.Comment: 27 Pages, 6 Figures, 4 Table
A Reliable and Low Latency Synchronizing Middleware for Co-simulation of a Heterogeneous Multi-Robot Systems
Search and rescue, wildfire monitoring, and flood/hurricane impact assessment
are mission-critical services for recent IoT networks. Communication
synchronization, dependability, and minimal communication jitter are major
simulation and system issues for the time-based physics-based ROS simulator,
event-based network-based wireless simulator, and complex dynamics of mobile
and heterogeneous IoT devices deployed in actual environments. Simulating a
heterogeneous multi-robot system before deployment is difficult due to
synchronizing physics (robotics) and network simulators. Due to its
master-based architecture, most TCP/IP-based synchronization middlewares use
ROS1. A real-time ROS2 architecture with masterless packet discovery
synchronizes robotics and wireless network simulations. A velocity-aware
Transmission Control Protocol (TCP) technique for ground and aerial robots
using Data Distribution Service (DDS) publish-subscribe transport minimizes
packet loss, synchronization, transmission, and communication jitters. Gazebo
and NS-3 simulate and test. Simulator-agnostic middleware. LOS/NLOS and TCP/UDP
protocols tested our ROS2-based synchronization middleware for packet loss
probability and average latency. A thorough ablation research replaced NS-3
with EMANE, a real-time wireless network simulator, and masterless ROS2 with
master-based ROS1. Finally, we tested network synchronization and jitter using
one aerial drone (Duckiedrone) and two ground vehicles (TurtleBot3 Burger) on
different terrains in masterless (ROS2) and master-enabled (ROS1) clusters. Our
middleware shows that a large-scale IoT infrastructure with a diverse set of
stationary and robotic devices can achieve low-latency communications (12% and
11% reduction in simulation and real) while meeting mission-critical
application reliability (10% and 15% packet loss reduction) and high-fidelity
requirements
Domain Adaptation for Inertial Measurement Unit-based Human Activity Recognition: A Survey
Machine learning-based wearable human activity recognition (WHAR) models
enable the development of various smart and connected community applications
such as sleep pattern monitoring, medication reminders, cognitive health
assessment, sports analytics, etc. However, the widespread adoption of these
WHAR models is impeded by their degraded performance in the presence of data
distribution heterogeneities caused by the sensor placement at different body
positions, inherent biases and heterogeneities across devices, and personal and
environmental diversities. Various traditional machine learning algorithms and
transfer learning techniques have been proposed in the literature to address
the underpinning challenges of handling such data heterogeneities. Domain
adaptation is one such transfer learning techniques that has gained significant
popularity in recent literature. In this paper, we survey the recent progress
of domain adaptation techniques in the Inertial Measurement Unit (IMU)-based
human activity recognition area, discuss potential future directions
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