A qualitative exploration of the health awareness and social challenges facing Pakistani youth engaging in body piercing and tattooing

Background: The practice of body piercing and tattooing in youth is increasing inPakistan, and there is fear that awareness of the associated health risks is low. The aim ofthis study is to try and understand: (i) youth awareness of health risks associated withbody piercing and tattooing, and (ii) the social challenges facing youth who engage insuch practices, which might also impact their health and wellbeing. The findings areaimed to inform improved health and social policy support for population groupsengaging in body modification. Methods: Scholars agree that qualitative research is vitalto explore health challenges and guide health policy. This study adopted a qualitativedesign and used purposive snowball sampling technique. A semi-structured questionnairewas developed through a literature review. Setting: Participants were sampled in a privateand confidential space on university campus or online, based on willingness andconvenience. Participants: Eight university students from different urban cities of Punjabwere sampled through in-depth interviews. Findings: Sixteen sub-themes were identifiedunder five main thematic areas, including: 1) Limited Awareness of Health Risks; 2)Reason for body modifications; 3) History of emotional and physical neglect by parents;4) Social difficulties faced after body modification; and 5) Association with other deviantactivities. Conclusion: The youth of Pakistan need health and social interventions toimprove preventive and screening support from practitioners, family-level counseling forimproved social support, therapy for mental health, and surveillance and support forsuicide ideation, intoxicant abuse, addiction, dealing with parental neglect, and identityformation

METHOD AND SYSTEM FOR DETERMINING SUSTAINABILITY INFORMATION IN AUTHORISATION MESSAGE

The present disclosure relates to method and system for calculating sustainability information in authorisation message based on transaction data. The present invention uses payment rails, sourcing for delivering sustainability information. The method also comprises sourcing and providing payment network entities and consumers the information on carbon intensity of a purchase. The present disclosure provides a solution for calculating the sustainability score of the registered Permanent Account Number (PAN) using transaction history, where transaction history is used to establish direct connectivity between merchants and issuers for the purpose of processing card transactions

On the Importance of Infrastructure-Awareness in Large-Scale Distributed Storage Systems

Big data applications put significant latency and throughput demands on distributed storage systems. Meeting these demands requires storage systems to use a significant amount of infrastructure resources, such as network capacity and storage devices. Resource demands largely depend on the workloads and can vary significantly over time. Moreover, demand hotspots can move rapidly between different infrastructure locations. Existing storage systems are largely infrastructure-oblivious as they are designed to support a broad range of hardware and deployment scenarios. Most only use basic configuration information about the infrastructure to make important placement and routing decisions. In the case of cloud-based storage systems, cloud services have their own infrastructure-specific limitations, such as minimum request sizes and maximum number of concurrent requests. By ignoring infrastructure-specific details, these storage systems are unable to react to resource demand changes and may have additional inefficiencies from performing redundant network operations. As a result, provisioning enough resources for these systems to address all possible workloads and scenarios would be cost prohibitive. This thesis studies the performance problems in commonly used distributed storage systems and introduces novel infrastructure-aware design methods to improve their performance. First, it addresses the problem of slow reads due to network congestion that is induced by disjoint replica and path selection. Selecting a read replica separately from the network path can perform poorly if all paths to the pre-selected endpoints are congested. Second, this thesis looks at scalability limitations of consensus protocols that are commonly used in geo-distributed key value stores and distributed ledgers. Due to their network-oblivious designs, existing protocols redundantly communicate over highly oversubscribed WAN links, which poorly utilize network resources and limits consistent replication at large scale. Finally, this thesis addresses the need for a cloud-specific realtime storage system for capital market use cases. Public cloud infrastructures provide feature-rich and cost-effective storage services. However, existing realtime timeseries databases are not built to take advantage of cloud storage services. Therefore, they do not effectively utilize cloud services to provide high performance while minimizing deployment cost. This thesis presents three systems that address these problems by using infrastructure-aware design methods. Our performance evaluation of these systems shows that infrastructure-aware design is highly effective in improving the performance of large scale distributed storage systems

Network intrusion detection system using an optimized machine learning algorithm

The rapid growth of the data-communications network for real-world commercial applications requires security and robustness. Network intrusion is one of the most prominent network attacks. Moreover, the variants of network intrusion have also been extensively reported in the literature. Network Intrusion Detection Systems (NIDS) have already been devised and proposed in the literature to handle this issue. In the recent literature, Kitsune, NIDS, and its dataset have received approx. 500 citations so far in 2019. But, still, the comprehensive parametric evaluation of this dataset using a machine learning algorithm was missing in the literature that could submit the best algorithm for network intrusion attack detection and classification in Kitsune. In this connection, two previous studies were reported to investigate the best machine algorithm (these two studies were reported by us). Through these studies, it was concluded that the Tree algorithm and its variants are best suited to detect and classify all eight types of network attacks available in the Kitsune dataset. In this study, the hyper-parameter optimization of the optimized Tree algorithm is presented for all eight types of network attack. In this study, the optimizer functions Bayesian, Grid Search, and Random Search were chosen. The performance has been ranked based on training and testing accuracy, training and testing cost, and prediction speed for each optimizer. This study will submit the best point hyper-parameter for the respective epoch against each optimizer

An Intelligent Data-Driven Approach for Electrical Energy Load Management Using Machine Learning Algorithms

Data-driven electrical energy efficiency management is the emerging trend in electrical energy forecasting and management. This fusion of data science, artificial intelligence, and electrical energy management has turned out to be the most precise and robust energy management solution. The Smart Energy Informatics Lab (SEIL) of the Indian Institute of Technology (IIT) conducted an experimental study in 2019 to collect massive data on university campus energy consumption. The comprehensive comparative study preparatory to the recommendation of the best candidate out of 24 machine learning algorithms on the SEIL dataset is presented in this work. In this research work, an exhaustive parametric and empirical comparative study is conducted on the SEIL dataset for the recommendation of the optimal machine learning algorithm. The simulation results established the findings that Bagged Trees, Fine Trees, and Medium Trees are, respectively, the best-, second-best-, and third-best-performing algorithms in terms of efficacy. On the contrary, a reverse ranking is observed in terms of efficiency. This is grounded in the fact that Bagged Trees is most effective algorithm for the said application and Medium Trees is the most efficient one. Likewise, Fine Trees has the optimum tradeoff between efficacy and efficiency

A Hybrid Soft Computing Framework for Electrical Energy Optimization

Electricity is a significant and essential player in the modern world economy. It translates into the social, economic, and sectorial growth of any region. The scarcity of these resources demands a highly efficient and robust energy management system (EMS). In the recent literature, many artificial intelligence algorithms have been proposed to cater to the need for efficient and real-time decision-making. Moreover, the hybridization of these algorithms has also been proposed for optimum decision-making. In this paper, a hybrid soft-computing-based framework has been proposed for intelligent energy management and optimization. The proposed model has based on the evolutionary neuro-fuzzy approach that can predict the energy demand as an objective function and optimize the energy within the given constraints. The future extension of this work will be the implementation and validation of the proposed framework on either a real application dataset or dataset opted from the benchmark repositor

A Novel Deep Learning Architecture for Data-Driven Energy Efficiency Management (D2EEM) - Systematic Survey

The Energy Management System (EMS) is the cost-effectiveness, robustness, and flexible approach for energy efficiency management (EEM). Data-Driven Energy Efficiency Management (D2EEM) is a recent advancement in EMS. The D2EEM is the blend of data science and artificial intelligence for EEM. Due to the highly tolerant to the performance plateau and unconstraint to the feature extraction, Deep Learning (DL) facilitates handling big data-driven problems of EEM. To the best of the knowledge, the accurate and robust D2EEM is the pressing need. Moreover, the accurate pre-trained DL network for EEM is not available in the recent literature. In this work, a comprehensive study is presented to devise a D2EEM. Moreover, the architecture is suggested in connection to the research gap

Characterization of cowpea to harvest rainwater for wheat in semiarid conditions

Aim of study: A field experiment was executed, under rainfed conditions from 2014-15 to 2017-18, to study the role of cowpea (Vigna unguiculata L.) in rainwater harvesting to enhance the wheat (Triticum aestivum L.) yield.Area of study: Rain-fed area of Pothwar region, Punjab, Pakistan.Material and methods: We designed three treatments (T1: control; T2: cowpea grown after conventional tillage and incorporated into soil to act as “green manure”; and T3: grown without any tillage practice, cut with sickle and spread as “mulch”). The effect of these treatments on soil moisture conservation was studied against conventional farmer’s practice, wherein no host crop is grown before wheat sowing.Main results: Available soil water remained highest in T2 during first three years when sufficient rainfall was received contrary to fourth year with low rainfall. The results revealed that cowpea biomass of 15.2 t/ha and 13.72 t/ha, from T2 and T3 respectively, were produced during 2015 corresponding to 213 mm rainfall. Whereas, these quantities increased to 25.69 t/ha and 24.29 t/ha during 2017 with 387 mm of rainfall. The study revealed that net income from wheat crop under T2 was Rs 13000 and Rs 9000 per hectare higher than that of control during the first two years respectively. Contrarily, net income from T2 was found negative and benefit-cost ratio reduced to 0.79 when very low rainfall was received during the last year.Research highlights: Use of cowpea as green manure gave maximum net return if sufficient rainfall is received during decomposition of cowpea and hence recommended for in-situ rainwater harvesting

Multi-disciplinary Approach: A Modality of Choice in the Management of Osteolytic Skull Lesions

Introduction: Multi-disciplinary approach towards the management of osteolytic skull lesions has evolved as the standard modality of treatment in recent years. Multiple disciplines including Neurosurgery, Anaesthesia,Neuro-Radiology, Plastic Surgery and Neuro-Oncology being working in co-ordination have improved outcome in such cases.Objective: To discuss multi-disciplinary approach as a standard treatment modality of choice.Study Design: Prospective descriptive study.Material and Methods: Patients admitted and operated at the Department of Neurosurgery of PGMI/Lahore General Hospita from July 2015 to July 2016. A total of 20 (n = 20) were study subjects.Results: Among 25 patients that were operated for an osteolytic skull lesion 12 were male and 13 female patients with age ranging from 10 to 60 years. Major complaints swelling (painfull/painless, n = 20), headache (n = 18), vomiting (n = 20), fits (n = 15) and bleeding ulcer (n = 2). On the basis of clinical and MRI diagnosis and biopsy majority had ewing sarcoma (n=10), 5 had meningioma, 1 had chondrosarcoma and 4 had metastatic skull lesion. All of them (n = 20) underwent surgery related to their clinical and MRI findings which included biopsy, excision of lesion, cranioplasty and flap rotation. Complications included infection in 2 patients, which was treated with antibiotics and mortality in 1 case.Outcome: Neurologic status and outcomes were compared with preoperative findings at 1st and 2nd post-operative weeks. Headache, fits, vomiting significantly improved in all patients.Follow-up: All patients had a follow up at 1 and 3 months post-operative.Conclusions: A multi-disciplinary approach is the modality of choice in managing an osteolytic skull lesions.By involving different specialities in a team effort to manage an osteolytic skull lesion can greatly improve the outcome in such cases