Inhibitory effect of Polyram DF and Capsicum annum on leaf spot of rose caused by Curvularia lunata in vitro and in planta

Rose plants are affected by several diseases caused by fungi, nematode, bacteria, viruses, and other pests. Among all of these, Curvularia lunata causes significant losses to Roses. Present study was focused on In-vitro and In-vivo management of the “Curvularia leaf spot of Rose” caused by Curvularia lunata by using different fungicides and phyto-extracts. Diseased samples were collected from floriculture area of University of Agriculture, Faisalabad for isolation of pathogen. Five fungicides i.e., Cabrio-Top, Curzate-M, Aliette, Polyram-DF and Recado @ (50ppm, 100ppm and 150ppm) and five plant extracts i.e., Allium cepa, Capsicum annuum, Aloe vera, Menthaand Calotropis gigantean with three concentrations @ (5%, 10% and 15%) were evaluated under lab conditions through poisoned food technique by using Complete Randomized Design (CRD), where C. annuum gave best results (9.129mm) followed by Calotropis gigantea (13.003mm), and Polyram-DF was effective (2.218mm) followed by Curzate-M (6.542mm). Best performing fungicides and plant-extracts were subjected to In-vivo management trials. Under green-house conditions, combination of Capsicum annuum + Calotropis gigantean and Polyram-DF + Curzate-M were shown least disease incidence (14.517 and 3.224%). LSD was used for comparing variations between treatments at 5% probability. The results of these experiments were to aid in the evaluation of fungicides and Phyto-extracts, which are the most effective chemicals and phyto-extracts against leaf Spot disease of Rose

Artificial intelligence-based Kubernetes container for scheduling nodes of energy composition

Kubernetes is a portable, extensible, open-source platform for managing containerized workloads and services that facilitates both declarative configuration and automation. This study presents Kubernetes Container Scheduling Strategy (KCSS) based on Artificial Intelligence (AI) that can assist in decision making to control the scheduling and shifting of load to nodes. The aim is to improve the container’s schedule requested digitally from users to enhance the efficiency in scheduling and reduce cost. The constraints associated with the existing container scheduling techniques which often assign a node to every new container based on a personal criterion by relying on individual terms has been greatly improved by the new system presented in this study. The KCSS presented in this study provides multicriteria node selection based on artificial intelligence in terms of decision making systems thereby giving the scheduler a broad picture of the cloud's condition and the user's requirements. AI Scheduler allows users to easily make use of fractional Graphics Processing Units (GPUs), integer GPUs, and multiple-nodes of GPUs, for distributed training on Kubernetes. © 2021, The Society for Reliability Engineering, Quality and Operations Management (SREQOM), India and The Division of Operation and Maintenance, Lulea University of Technology, Sweden

Transfer learning auto-encoder neural networks for anomaly detection of DDoS generating IoT devices

Machine Learning based anomaly detection ap-proaches have long training and validation cycles. With IoT devices rapidly proliferating, training anomaly models on a per device basis is impractical. This work explores the "transfer-ability"of a pre-trained autoencoder model across devices of similar and different nature. We hypothesized that devices of similar nature would have similar high level feature character-istics represented by the initial layers of the autoencoder, while the more distinct features are captured by the innermost layer of the neural network. In our experiments, the centre-most layers of autoencoder models were re-trained with limited new data belonging to a different device. Datasets of seven Mirai infected and nine Bashlite infected IoT devices were used; each dataset also included benign records representing un-infected behaviour. We observed that the model's detection accuracy improved by an average of 9.52% for Mirai and 44.59% for Bashlite. The highest performance improvement of 26.68% and 73.00% was observed when the anomaly model of Ecobee thermostat was tested on other devices before and after transfer learning for Mirai and Bashlite respectively. Additionally, transfer learning took 47.31% and 58.27% less time for Mirai and Bashlite respectively. We further trialed the efficacy of the autoencoder based anomaly model on flow based records of network traffic using the CIC-IDS2017 dataset. It was observed that the model performed best when distinct outliers in the dataset were present, whereas the model failed to perform decently in cases where the malicious activity did not cause significant deviation in network traffic's footprint

SISME, estuarine monitoring system based on IOT and machine learning for the detection of salt wedge in aquifers: case study of the Magdalena river estuary

This article contains methods, results, and analysis agreed for the development of an application based on the internet of things and making use of machine learning techniques that serves as a support for the identification of the saline wedge in the Magdalena River estuary, Colombia. As a result of this investigation, the process of identifying the most suitable telecommunications architecture to be installed in the estuary is shown, as well as the characteristics of the software developed called SISME (Estuary Monitoring System), and the results obtained after the implementation of prediction techniques based on time series. This implementation supports the maritime security of the port of Barranquilla since it can support decision-making related to the estuary. This research is the result of the project “Implementation of a Wireless System of Temperature, Conductivity and Pressure Sensors to support the identification of the saline wedge and its impact on the maritime safety of the Magdalena River estuary”

The Assessment of Metal Resistance through the Expression of Hsp-70 and HO-1 Proteins in Giant Reed

None of our investigations have identified stress in response to the HSP70 and HO-1 proteins in metals under stress in our study, which aimed to understand the genetic basis of the metal tolerance of Arundo donax. Thus, the present work aimed to investigate the levels of expression of two important stress-related proteins, HO-1 and HSP70, in A. donax after exposure to various metals. The plants were collected from uncontaminated sites in Abbottabad, Pakistan. Their rhizomes were grown in Hoagland solution, and upon attaining suitable biomass, the plants were used to investigate the effects of metals on protein expression. The metal treatments were carried out with synthetic wastewater containing four Cr treatments (0, 34, 66, 134, and 267 mgL−1), namely, Cd, As, Pb, Cu and Ni (0, 25, 50, 75, and 100 mgL−1), and the plants were grown for three weeks. The treatments were applied according to a randomized block design (RBD) based on hydroponics. The selected protein expression was examined after 10 days of metal exposure. For the HSP70 and HO-1 protein studies, leaves were separated following a previously reported standard procedure. The maximum level of HO-1 and HSP70 expressions was noted at 66 mgL−1 of Cr, and then it slightly declined. Significantly, high protein expression was observed at Cd exposure concentrations of 50 to 100 mgL−1. For Cu, As and Ni, significantly high HO-1 and HSP70 expressions were noted at metal exposure concentrations of 75 to 100 mgL−1. The expression levels of these two stress-related proteins showed a linear increase with increasing metal exposure in the giant reed. It is clear from the present research that HSP70 and HO-1 proteins may contribute significantly to plant tolerance to metal stress, in addition to other possible tolerance mechanisms

Antibacterial activity of local herbs collected from Murree (Pakistan) against multi-drug resistant Klebsiella pneumonae, E. coli and methyciline resistant Staphylococcus aureus

Exploring healing power in plants emerged in prehistory of human civilization. Sustaining good health has been achieved over the millions of years by use of plant products in various traditional sockets. A major contribution of medicinal plants to health care systems is their limitless possession of bioactive components that stimulate explicit physiological actions. Luckily Pakistan is blessed with huge reservoir of plants with medicinal potential and some of them; we focused in this study for their medicinal importance.In this study we checked the antibacterial activity inherent in Ricinus communis, Solanum nigrum, Dodonaea viscose and Berberis lyceum extracts for multidrug resistance bacterial strains Klebsiella pneumonae, E. coli and methyciline resistant Staphylococcus aureus. MRSA showed sensitivity for Ricinus communis. Multidrug resistant Klebsiella pneumonae was sensitive with Pine roxburgii and Ricinus communis but weakly susceptible for Solanum nigrum. Multidrug resistant E. coli was resistant to all plant extracts. Treatment of severe infections caused by the bacterial strains used in this study with Ricinus communis, Pine roxburgii and Solanum nigrum can lower the undesired side effects of synthetic medicine and also reduce the economic burden

Towards Energy Saving in Computational Clouds: Taxonomy, Review, and Open Challenges

Cloud Computing involves utilization of centralized computing resources and services, including remote servers, storage, programs, and usages which minimize the power utilization of the client assets. Therefore, it is extremely important to accomplish energy efficiency of cloud computing. Virtualization is used to set up a foundation for the execution part as the heart of energy effective cloud. Virtualization incorporates certain advancements, such as consolidation and resource utilization. A number of techniques, such as DVFS virtualization as well as teleportation can be used by empowering the tasks of multiple virtual types of equipment to a single server to increase the vitality proficiency of datacenters. The objective of this review is to analyze contemporary for energy as well as performance management, vitality for effective data centers and resource distributions. Our review will address the latest issues researchers have addressed in energy as well as management of performance in recent years. We will take a closer look at these existing techniques based on tools, OS, virtualization, and datacenter stages taxonomy. Finally, a performance comparison of existing techniques is presented that can assist in identifying gaps for future research in this area

S-DPS: An SDN-Based DDoS Protection System for Smart Grids

Information Communication Technology (ICT) environment in traditional power grids makes detection and mitigation of DDoS attacks more challenging. Existing security technologies, besides their efficiency, are not adequate to cater to DDoS security in Smart Grids (SGs) due to highly distributed and dynamic network environments. Recently, emerging Software Defined Networking- (SDN-) based approaches are proposed by researchers for SG’s DDoS protection; however, they are only able to protect against flooding attacks and are dependent on static thresholds. The proposed approach, i.e., Software Defined Networking-based DDoS Protection System (S-DPS), is efficiently addressing these issues by employing light-weight Tsallis entropy-based defense mechanisms using SDN environment. It provides early detection mechanism with mitigation of anomaly in real time. The approach offers the best deployment location of defense mechanism due to the centralized control of network. Moreover, the employment of a dynamic threshold mechanism is making detection process adaptive to the changing network conditions. S-DPS has demonstrated its effectiveness and efficiency in terms of Detection Rate (DR) and minimal CPU/RAM utilization, considering DDoS protection focusing smurf attacks, socket stress attacks, and SYN flood attacks

An Analytical Survey of WSNs Integration with Cloud and Fog Computing

Wireless sensor networks (WSNs) are spatially scattered networks equipped with an extensive number of nodes to check and record different ecological states such as humidity, temperature, pressure, and lightning states. WSN network provides different services to a client such as monitoring, detection, and runtime decision-making against events occurrence. However, the WSN network still has some limitations in computing power, storage resources, and battery life, which make the network is restricted for data transformation. It is due to less supportive battery power, and limited memory of nodes. The integration of WSN and cloud offers an open, adaptable, and more reconfigurable stage for different security checks and regulating requirements. In this paper, we discovered how WSN and cloud computing (CC) are integrated and help to accomplish different goals. Additionally, a comprehensive study about procedures and issues for an effective combination of WSN-CC is presented. This work also presents the work proposed by the research community for WSN-CC. Besides, we explored the integration of WSN/IoT with Fog computing (FC). Based on investigations, WSN integration with Fog computing (FC) has many benefits with respect to latency, energy consumption, data processing, and real-time data streaming. FC is not a substitute for distributed computing, so far it is utilized to improve the productivity of the sensor

A Lightweight Location-Aware Fog Framework (LAFF) for QoS in Internet of Things Paradigm

Realization of Internet of Things (IoT) has revolutionized the scope of connectivity and reachability ubiquitously. Under the umbrella of IoT, every object which is smart enough to communicate with other object leads to the enormous data generation of varying sizes and nature. Cloud computing (CC) employs centralized data centres for the provisioning of remote services and resources. However, for the reason of being far away from client devices, CC has their own limitations especially for time and resource critical applications. The remote and centralized characteristics of CC often result in creating bottle necks, being latent, and hence deteriorate the quality of service (QoS) in the provisioning of services. Here, the concept of fog computing (FC) emerges that tends to leverage CC and end devices for data congestion and processing locally in a distributed and decentralized way. However, addressing latency and bottleneck issues for time critical applications are still challenging. In this work, a lightweight framework is proposed which employs the concept of fog head node that keeps track of other fog nodes in terms of user registrations and location awareness. The proposed lightweight location-aware fog framework (LAFF) persistently satisfies QoS by providing an accurate location-aware algorithm. A comparative analysis is also presented to analyse network usage, service time, latency, and RAM and CPU utilization. The comparison results depicts that the LAFF reduces latency, network use, and service time by 11.01%, 7.51%, and 14.8%, respectively, in contrast to the state-of-the-art frameworks. Moreover, considering RAM and CPU utilization, the proposed framework supersedes IFAM and TPFC targeting IoT applications. The RAM consumption and CPU utilization are reduced by 8.41% and 16.23% as compared with IFAM and TPFC, respectively, making the framework lightweight. Hence, the proposed LAFF improves QoS while accessing remote computational servers for the outsourced applications in fog computing