Frequent Pattern Mining Algorithms for Finding Associated Frequent Patterns for Data Streams: A Survey

Pattern recognition is seen as a major challenge within the field of data mining and knowledge discovery. For the work in this paper, we have analyzed a range of widely used algorithms for finding frequent patterns with the purpose of discovering how these algorithms can be used to obtain frequent patterns over large transactional databases. This has been presented in the form of a comparative study of the following algorithms: Apriori algorithm, Frequent Pattern (FP) Growth algorithm, Rapid Association Rule Mining (RARM), ECLAT algorithm and Associated Sensor Pattern Mining of Data Stream (ASPMS) frequent pattern mining algorithms. This study also focuses on each of the algorithm’s strengths and weaknesses for finding patterns among large item sets in database systems

Determining the potential scalability of transport interventions for improving maternal, child, and newborn health in Pakistan

Background: Pakistan is far behind in achieving the Millennium Development Goals regarding the reduction of child and maternal mortality. Amongst other factors, transport barriers make the requisite obstetric care inaccessible for women during pregnancy and at birth, when complications may become life threatening for mother and child. The significance of efficient transport in maternal and neonatal health calls for identifying which currently implemented transport interventions have potential for scalability. Methods: A qualitative appraisal of data and information about selected transport interventions generated primarily by beneficiaries, coordinators, and heads of organizations working with maternal, child, and newborn health programs was conducted against the CORRECT criteria of Credibility, Observability, Relevance, Relative Advantage, Easy-Transferability, Compatibility and Testability. Qualitative comparative analysis (QCA) techniques were used to analyse seven interventions against operational indicators. Logical inference was drawn to assess the implications of each intervention. QCA was used to determine simplifying and complicating factors to measure potential for scaling up of the selected transport intervention. Results: Despite challenges like deficient in-journey care and need for greater community involvement, community-based ambulance services were managed with the support of the community and had a relatively simple model, and therefore had high scalability potential. Other interventions, including facility-based services, public-sector emergency services, and transport voucher schemes, had limitations of governance, long-term sustainability, large capital expenditures, and need for management agencies that adversely affected their scalability potential. Conclusion: To reduce maternal and child morbidity and mortality and increase accessibility of health facilities, it is important to build effective referral linkages through efficient transport systems. Effective linkages between community-based models, facility-based models, and public sector emergency services should be established to provide comprehensive coverage. Voucher scheme integrated with community-based services may bring improvements in service utilization

Common Genotypes of Hepatitis B virus prevalent in Injecting drug abusers (addicts) of North West Frontier Province of Pakistan

<p>Abstract</p> <p>Background</p> <p>The epidemiological significance of Hepatitis B virus genotypes has been well established and becoming an essential concern day by day however, much little is known about the mixed infection with more than one Hepatitis B virus genotypes and their clinical relevance.</p> <p>Methods</p> <p>Intravenous drug abusers are considered as a major risk group for the acquisition and transmission of blood borne infections like hepatitis B, however, in Pakistan, no such data has ever been reported about the epidemiology of HBV and its genotypes in Injecting Drug Users. 250 individuals were analyzed for hepatitis B virus genotypes after prior screening with serological assay for the detection of HBsAg.</p> <p>Results</p> <p>56 (22.4%) individuals were found positive on ELSIA for HBsAg. The genotype distribution was found to be as: genotype D, 62.5%; genotype A, 8.92% while 28.57% individuals were found to be infected with a mixture of genotype A and D.</p> <p>Conclusion</p> <p>There is an urgent need of the time to develop public health care policies with special emphasis towards the control of HBV transmission through high risk groups especially Injecting Drug Users.</p

Thermal-aware resource allocation in earliest deadline first using fluid scheduling

Thermal issues in microprocessors have become a major design constraint because of their adverse effects on the reliability, performance and cost of the system. This article proposes an improvement in earliest deadline first, a uni-processor scheduling algorithm, without compromising its optimality in order to reduce the thermal peaks and variations. This is done by introducing a factor of fairness to earliest deadline first algorithm, which introduces idle intervals during execution and allows uniform distribution of workload over the time. The technique notably lowers the number of context switches when compare with the previous thermal-aware scheduling algorithm based on the same amount of fairness. Although, the algorithm is proposed for uni-processor environment, it is also applicable to partitioned scheduling in multi-processor environment, which primarily converts the multi-processor scheduling problem to a set of uni-processor scheduling problem and thereafter uses a uni-processor scheduling technique for scheduling. The simulation results show that the proposed approach reduces up to 5% of the temperature peaks and variations in a uni-processor environment while reduces up to 7% and 6% of the temperature spatial gradient and the average temperature in multi-processor environment, respectively

Towards Supply Chain Visibility Using Internet of Things:A Dyadic Analysis Review

The Internet of Things (IoT) and its benefits and challenges are the most emergent research topics among academics and practitioners. With supply chains (SCs) gaining rapid complexity, having high supply chain visibility (SCV) would help companies ease the processes and reduce complexity by improving inaccuracies. Extant literature has given attention to the organisation’s capability to collect and evaluate information to balance between strategy and goals. The majority of studies focus on investigating IoT’s impact on different areas such as sustainability, organisational structure, lean manufacturing, product development, and strategic management. However, research investigating the relationships and impact of IoT on SCV is minimal. This study closes this gap using a structured literature review to critically analyse existing literature to synthesise the use of IoT applications in SCs to gain visibility, and the SC. We found key IoT technologies that help SCs gain visibility, and seven benefits and three key challenges of these technologies. We also found the concept of Supply 4.0 that grasps the element of Industry 4.0 within the SC context. This paper contributes by combining IoT application synthesis, enablers, and challenges in SCV by highlighting key IoT technologies used in the SCs to gain visibility. Finally, the authors propose an empirical research agenda to address the identified gaps

Seasonal influence, heat unit accumulation and heat use efficiency in relation to maize grain yield in Pakistan

Variations in ambient temperature affect crop yield by modifying the duration of phenological phases and physiologicalprocesses. An experimental study was carried out at the Maize and Millets Research Institute (MMRI),Yusafwala, Sahiwal, Pakistan, to determine the seasonal effects of temperature on indigenous and exotic maize(Zea mays L.) hybrids based on morphological, phenological, physiological and grain quality traits in four differentgrowing seasons: kharif 2016 and 2017, and spring 2017 and 2018. Seven indigenous and three exotic hybridswere sown in a randomized complete block design with a split plot arrangement, in three replications. Significantdifferences between hybrids and growing seasons were found for grain yield, related traits and temperature indices(cumulative heat units, photothermal index and heat use efficiency). Correlation analysis disclosed a significantpositive relationship between grain yield and net photosynthetic rate (0.854, P≤0.01), number of grains per ear(0.624, P≤0.01) and heat use efficiency (0.980, P≤0.01) in spring seasons. During kharif, net photosynthetic rate(0.675, P≤0.01) and heat use efficiency (0.996, P≤0.01) contributed significantly to grain yield, whereas number ofgrains per ear (−0.146, not significant) had no significant impact on grain yield. Cumulative heat units and heat useefficiency resulted the temperature indices with the greatest influence on grain yield, and should be consideredduring the selection of parents to develop high-yielding, climate-smart maize hybrids. Indigenous maize hybridsshowed higher yields and were more heat tolerant than exotic hybrids, and the spring sowing season appearedto be the most suitable for the cultivation of maize crops

The accuracy of protein structure alignment servers

Background: Protein structural alignment is one of the most fundamental and crucial areas of research in the domain of computational structural biology. Comparison of a protein structure with known structures helps to classify it as a new or belonging to a known group of proteins. This, in turn, is useful to determine the function of protein, its evolutionary relationship with other protein molecules and grasping principles underlying protein architecture and folding. Results: A large number of protein structure alignment methods are available. Each protein structure alignment tool has its own strengths andweaknesses that need to be highlighted.We compared and presented results of six most popular and publically available servers for protein structure comparison. These web-based servers were compared with the respect to functionality (features provided by these servers) and accuracy (how well the structural comparison is performed). The CATH was used as a reference. The results showed that overall CE was top performer. DALI and PhyreStorm showed similar results whereas PDBeFold showed the lowest performance. In case of few secondary structural elements, CE, DALI and PhyreStorm gave 100% success rate. Conclusion: Overall none of the structural alignment servers showed 100% success rate. Studies of overall performance, effect of mainly alpha and effect of mainly beta showed consistent performance. CE, DALI, FatCat and PhyreStorm showed more than 90% success rate

Quadcopter-Based Rapid Response First-Aid Unit with Live Video Monitoring

Air transport is the fastest way to reach areas with no direct land routes for ambulances. This paper presents the development of a quadcopter-based rapid response unit in an efficient aerial aid system to eliminate the delay time for first aid supplies. The system comprises a health monitoring and calling system for a field person working in open areas and a base station with the quadcopter. In an uncertain situation, the quadcopter is deployed from the base station towards the field person for immediate help through the specified path using constant Global System for Mobile (GSM)- and Global Positioning System (GPS)-based connections. The entire operation can be monitored at the base station with a Virtual Reality (VR) head-tracking system supported by a smartphone. The camera installed on the quadcopter is synchronized with the operator&#8217;s head movement while wearing a VR head-tracking system at the base station. Moreover, an Infrared (IR)-based obstacle-evasion model is implemented separately to explain the working of the autonomous collision-avoidance system. The system was tested, which confirmed the reduction in the response time to supply aid to the desired locations