Urban Floods and Suitability Analysis of Rainwater Harvesting Potential Areas in Lahore City, Pakistan

There is a growing threat of urban flooding, particularly in Pakistan that needs attention and requires effective management strategies. The chief trigger for urban flooding is the rapid and unplanned urbanization in areas where impermeable surface inhibits rainwater diffusion and changes the natural water flow. In many developed countries, a technique of rainwater harvesting is implemented as a sustainable strategy to manage urban stormwater. Most studies and projects chiefly focused on the potential use of the Rainwater harvesting technique for water conservation in arid and semi-arid climates. In the present work, GIS and remote sensing methodologies are utilized for the suitability of rainwater harvesting structures. This study was conducted in Lahore, the second most populated city of Pakistan and the capital of Punjab. Generally, the water harvesting technique depends on topographical areas with water accumulation, where there is an availability of open spaces in the form of green areas and barren lands, rainfall, drainage network density, and rainfall distribution, particularly in the urban environment. The SRTM DEM data were used for finding high water accumulated areas, and the Landsat OLI image is used to retrieve land use information i.e. vacant land and open green spaces, existing drainage network density, and rainfall distribution. All these layers were integrated through AHP to detect the potentially suitable sites for the construction of rainwater harvesting structures. Results concluded 94 suitable sites with categorization from highly to critically suitable for the construction of rainwater harvesting structures in which 6 were highly suitable areas in Data Gunj Bakhsh Town and Ravi Town. Besides, residential areas are having a maximum site suitability percentage, followed by roads, agricultural and open spaces in the area under consideration. Considering the suitable sites, further rainwater harvesting methods can be identified in the study area to alleviate urban flooding and improve the urban environment

Malware Detection in Cloud Computing Infrastructures

Cloud services are prominent within the private, public and commercial domains. Many of these services are expected to be always on and have a critical nature; therefore, security and resilience are increasingly important aspects. In order to remain resilient, a cloud needs to possess the ability to react not only to known threats, but also to new challenges that target cloud infrastructures. In this paper we introduce and discuss an online cloud anomaly detection approach, comprising dedicated detection components of our cloud resilience architecture. More specifically, we exhibit the applicability of novelty detection under the one-class support Vector Machine (SVM) formulation at the hypervisor level, through the utilisation of features gathered at the system and network levels of a cloud node. We demonstrate that our scheme can reach a high detection accuracy of over 90% whilst detecting various types of malware and DoS attacks. Furthermore, we evaluate the merits of considering not only system-level data, but also network-level data depending on the attack type. Finally, the paper shows that our approach to detection using dedicated monitoring components per VM is particularly applicable to cloud scenarios and leads to a flexible detection system capable of detecting new malware strains with no prior knowledge of their functionality or their underlying instructions. Index Terms—Security, resilience, invasive software, multi-agent systems, network-level security and protection

CSNL: A cost-sensitive non-linear decision tree algorithm

This article presents a new decision tree learning algorithm called CSNL that induces Cost-Sensitive Non-Linear decision trees. The algorithm is based on the hypothesis that nonlinear decision nodes provide a better basis than axis-parallel decision nodes and utilizes discriminant analysis to construct nonlinear decision trees that take account of costs of misclassification. The performance of the algorithm is evaluated by applying it to seventeen datasets and the results are compared with those obtained by two well known cost-sensitive algorithms, ICET and MetaCost, which generate multiple trees to obtain some of the best results to date. The results show that CSNL performs at least as well, if not better than these algorithms, in more than twelve of the datasets and is considerably faster. The use of bagging with CSNL further enhances its performance showing the significant benefits of using nonlinear decision nodes. The performance of the algorithm is evaluated by applying it to seventeen data sets and the results are compared with those obtained by two well known cost-sensitive algorithms, ICET and MetaCost, which generate multiple trees to obtain some of the best results to date. The results show that CSNL performs at least as well, if not better than these algorithms, in more than twelve of the data sets and is considerably faster. The use of bagging with CSNL further enhances its performance showing the significant benefits of using non-linear decision nodes

Role of proline, K/Na ratio and chlorophyll content in salt tolerance of wheat (Triticum aestivum

Abstract Studies to determine the role of proline, K/Na ratio and chlorophyll contents in salt tolerance of wheat genotypes were conducted in lysimeters using hydroponics technique. Seeds were allowed to germinate under normal condition (1.5 dS m -1 ) and salinity treatment of 12 dS m -1 was imposed after one week of germination. Crop was irrigated at the interval of two weeks or whenever required with 1/4 th Hoagland nutrient solution of respective concentrations. Results clearly indicated that wheat genotypes with higher proline, K/Na ratio and chlorophyll contents had higher grain yield. On the basis of yield reduction, three genotypes viz. Lu-26s, Sarsabz and KTDH were found tolerant. These genotypes also maintained the higher concentration of proline, K/Na ratio and chlorophyll contents under saline conditions

Mesenchymal stromal/stem cells and their exosomes for restoration of spermatogenesis in non-obstructive azoospermia: a systemic review

Stem cells have been introduced as new promising therapeutic agents in treatment of degenerative diseases because of having high differentiation potential while maintaining the ability to self-replicate and retaining features of their source cells. Among different type of cell therapies, mesenchymal stromal/stem cell (MSC) therapy is being increasingly developed as a new way to treat structural defects that need to be repaired and regenerated. Non-obstructive azoospermia (NOA) is a reproductive disease in men that causes infertility in 10% of infertile men. Based on in vitro studies, MSCs from different tissue sources have been differentiated into germ cells or gamete progenitor cells by simple methods in both male and female. On the other hand, the therapeutic effects of MSCs have been evaluated for the treatment of NOA animal models created by chemical or surgical compounds. The results of these studies confirmed successful allotransplantation or xenotransplantation of MSCs in the seminiferous tubules. As well, it has been reported that exosomes secreted by MSCs are able to induce the process of spermatogenesis in the testes of infertile animal models. Despite numerous advances in the treatment of reproductive diseases in men and women with the help of MSCs or their exosomes, no clinical trial has been terminated on the treatment of NOA. This systematic review attempts to investigate the possibility of MSC therapy for NOA in men