Towards In-Transit Analytics for Industry 4.0

Industry 4.0, or Digital Manufacturing, is a vision of inter-connected services to facilitate innovation in the manufacturing sector. A fundamental requirement of innovation is the ability to be able to visualise manufacturing data, in order to discover new insight for increased competitive advantage. This article describes the enabling technologies that facilitate In-Transit Analytics, which is a necessary precursor for Industrial Internet of Things (IIoT) visualisation.Comment: 8 pages, 10th IEEE International Conference on Internet of Things (iThings-2017), Exeter, UK, 201

Utilizing Chat GPT for Automation of Material Supply in Construction Projects using Programming and Primavera P6 Scheduling

Construction Industry (CI) is considered as the backbone of a country’s economy. Despite the fact, it experiences high fragmentation and reduced productivity, delays, cost overruns and lack of innovation from the start till the end of a project. It has been identified that material deliveries often affect productivity at site therefore this study focuses on the development of Automated Materials Supply (AMS). Supply chain in construction being automated presents a feasible solution to late material deliveries and cost and time overruns on a project by proposing a framework for information transfer by using primavera schedule and OPEN AI among stakeholders of the project to avoid material overruns on-site and reducing human effort. The result of this study leads to the development of a framework that executes the information transfer among various stakeholders of a project to automate the material delivery process

4-Hydroxy­benzohydrazide

In the title compound, C7H8N2O2, the mean planes of the benzene ring and the planar hydrazide group are inclined at 25.75 (6)° with respect to each other. The structure is stabilized by inter­molecular N—H⋯O and O—H⋯N hydrogen bonds

High performance video processing in cloud data centres

Mobile phones and affordable cameras are generating large amounts of video data. This data holds information regarding several activities and incidents. Video analytics systems have been introduced to extract valuable information from this data. However, most of these systems are expensive, require human supervision and are time consuming. The probability of extracting inaccurate information is also high due to human involvement. We have addressed these challenges by proposing a cloud based high performance video analytics platform. This platform attempts to minimize human intervention, reduce computation time and enables the processing of a large number of video streams. It achieves high performance by optimizing the occupancy of GPU resources in cloud and minimizing the data transfer by concurrently processing a large number of video streams. The proposed video processing platform is evaluated in three stages. The first evaluation was performed at the cloud level in order to evaluate the scalability of the platform. This evaluation includes fetching and distributing video streams and efficiently utilizing available resources within the cloud. The second valuation was performed at the individual cloud nodes. This evaluation includes measuring the occupancy level, effect of data transfer and the extent of concurrency achieved at each node. The third evaluation was performed at the frame level in order to determine the performance of object recognition algorithms. To measure this, compute intensive tasks of the Local Binary Pattern (LBP) algorithm have been ported on to the GPU resources. The platform proved to be very scalable with high throughput and performance when tested on a large number of video streams with increasing number of nodes

Ontario boreal fire regimes in the context of lightning-caused ignition point spatial patterns

Lightning-caused forest fires are one of the major natural disturbances in Ontario managed boreal forests. Survival of these forests with fires for centuries shows that such disturbances are integral to the boreal ecosystem and its ecological functioning. Characterizing the fire regimes defined by fire ignition frequency, fire sizes and their spatial distribution patterns etc. thus can help to improve our understanding of the boreal forest dynamics and provide guidance for management practices attempting to maintain biodiversity and achieve sustainability. In this thesis the lightning-caused fire ignitions data for four ecoregions in Ontario managed boreal forests (3E, 3W, 3S and 4S) for 1960–2009 were analyzed using pattern analysis and density estimation to determine the spatial nature of fire ignitions. These fire ignition spatial patterns were further used (as weighted ignition scenario) to simulate forest fire regimes in the study area. Fire regimes were also simulated using spatially unweighted ignitions (unweighted ignition scenario). Non-spatial (total number of fires, total burn area, number of fires by size classes, annual burn fraction) and spatial (spatial burn probability) indicators of the simulated fire regimes under both ignition scenarios were compared to test the null hypothesis that modeled forest fire regime is not affected by the spatial patterns of input fire ignitions. All data analysis were performed for individual ecoregions. Spatial pattern of ignitions were analyzed using the nearest neighbour index and Ripley’s K-function. Ignition densities were estimated using the adaptive kernel density estimation method and the fire regimes were simulated using BFOLDS (Boreal Forests Landscape Dynamics Simulator). Results showed that lightning-caused fire ignitions are clustered in all ecoregions. Fire ignition density also varied spatially within ecoregions. Overall fire ignition density was highest in the northwestern ecoregion (4S) and lowest in the eastern ecoregion (3E), which corresponds to the combined gradient of effective humidity and temperature in Ontario. For each ecoregion, comparison of non-spatial simulated fire regime indicators showed statistically non-significant differences between unweighted and weighted ignitions. The spatial burn probability however captured clear spatial differences between unweighted and weighted ignitions. Spatial differences in spatial burn probability between both ignition scenarios were more prominent in ecoregions of high fire occurrence. Results of the weighted ignition scenario closely followed the spatial patterns of the estimated fire ignition density in the study area. Based on these results this thesis rejects the null hypothesis and emphasizes that ignition patterns must be considered in simulating fire regime in Ontario boreal forests

Chemistry of Platinum and Palladium Metal Complexes in Homogeneous and Heterogeneous Catalysis: A Mini Review

Transition metal complexes of platinum and palladium are most widely used in catalysis. Many synthetic reactions have been carried out with such complexes (used as a catalyst) which have specifically polymer ligands, through hydrosilylation, acetoxylation, hydrogenation, hydro-formylation, oligo-merisation and polymerization. Almost many platinum and palladium catalysts are heterogeneous in nature i.e. the reaction taking place on a solid surface. Now from few years homogeneous catalysts which are completely soluble in the liquid phase reactant, has acknowledged too much attention, yet having small industrial applications, mainly due to the striving of platinum and palladium complexes separation from the catalytic products. More recently a transitional type of platinum and palladium catalysts have been synthesized through attachment of the activated transition metal complexes on the surface of polymer support particularly insoluble which has been establish to offe

Spatial frequency based video stream analysis for object classification and recognition in clouds

The recent rise in multimedia technology has made it easier to perform a number of tasks. One of these tasks is monitoring where cheap cameras are producing large amount of video data. This video data is then processed for object classification to extract useful information. However, the video data obtained by these cheap cameras is often of low quality and results in blur video content. Moreover, various illumination effects caused by lightning conditions also degrade the video quality. These effects present severe challenges for object classification. We present a cloud-based blur and illumination invariant approach for object classification from images and video data. The bi-dimensional empirical mode decomposition (BEMD) has been adopted to decompose a video frame into intrinsic mode functions (IMFs). These IMFs further undergo to first order Reisz transform to generate monogenic video frames. The analysis of each IMF has been carried out by observing its local properties (amplitude, phase and orientation) generated from each monogenic video frame. We propose a stack based hierarchy of local pattern features generated from the amplitudes of each IMF which results in blur and illumination invariant object classification. The extensive experimentation on video streams as well as publically available image datasets reveals that our system achieves high accuracy from 0.97 to 0.91 for increasing Gaussian blur ranging from 0.5 to 5 and outperforms state of the art techniques under uncontrolled conditions. The system also proved to be scalable with high throughput when tested on a number of video streams using cloud infrastructure

3-Chloro­benzohydrazide

In the title compound, C7H7ClN2O, the hydrazide group is inclined at a dihedral angle of 32.30 (11)° with respect to the benzene ring. The amino H atoms form inter­molecular N—H⋯O hydrogen bonds with the O atoms of two adjacent mol­ecules, resulting in 10-membered rings of graph-set motif R 2 2(10). The imino H atom is also involved in an inter­molecular hydrogen bond with an amino N atom of a symmetry-related mol­ecule, resulting in a zigzag chain along the b axis. The structure is further consolidated by an intra­molecular N—H⋯O inter­action, which results in a five-membered ring

An Updated Review on Rheumatoid Arthritis (RA): Epidemiology, Pathophysiology, Diagnosis, and the Current Approaches for Its Treatment

Rheumatoid arthritis (RA) is a systemic self-inflicted inflammatory disease that primarily affects middle-aged women. Globally, 1% of people live with RA. This review aims to provide updated information on the different aspects of RA, including its epidemiology, pathophysiology, diagnosis, treatment, and management. A web-based literature search was conducted through various databases, including PubMed, Google Scholar, and Science Direct, to identify the most relevant studies. Epidemiological studies have suggested that the prevalence and occurrence of RA have remained inconsistent across geographical areas in different periods. Many factors such as age, gender, inheritances, and environmental exposure can contribute to the severity of the disease. The acute form of RA usually presents with pain, and if left untreated, it can result in joint deformities and influence a patient’s quality of life (QoL). RA diagnosis is usually based on the manifestation of pain with inflammation. Currently, many therapeutic strategies are available for the cure of RA. The management of daily routine activities is required with treatment to curtail the damage, avoid future deformities, and ultimately minimize the aching trouble of the patient

High performance video processing in cloud data centres

Mobile phones and affordable cameras are generating large amounts of video data. This data holds information regarding several activities and incidents. Video analytics systems have been introduced to extract valuable information from this data. However, most of these systems are expensive, require human supervision and are time consuming. The probability of extracting inaccurate information is also high due to human involvement. We have addressed these challenges by proposing a cloud based high performance video analytics platform. This platform attempts to minimize human intervention, reduce computation time and enables the processing of a large number of video streams. It achieves high performance by optimizing the occupancy of GPU resources in cloud and minimizing the data transfer by concurrently processing a large number of video streams. The proposed video processing platform is evaluated in three stages. The first evaluation was performed at the cloud level in order to evaluate the scalability of the platform. This evaluation includes fetching and distributing video streams and efficiently utilizing available resources within the cloud. The second valuation was performed at the individual cloud nodes. This evaluation includes measuring the occupancy level, effect of data transfer and the extent of concurrency achieved at each node. The third evaluation was performed at the frame level in order to determine the performance of object recognition algorithms. To measure this, compute intensive tasks of the Local Binary Pattern (LBP) algorithm have been ported on to the GPU resources. The platform proved to be very scalable with high throughput and performance when tested on a large number of video streams with increasing number of nodes