Prediction of rain-induced cross polarization at millimeter wave bands in guinea

Microwave communication systems are planned to utilize orthogonal polarization. Two independent information channels of the same frequency band sent over a single link to make an optimum use of the frequency spectrum. However, above 10 GHz, the amount of rain aloft can severely degrade the performance of both satellite and terrestrial links, especially in tropical regions, at millimetre wave bands. This paper evaluates the differential attenuation and differential phase shift for the prediction of cross polarization discrimination using a 10-year rain data recorded in Conakry, Guinea. The drop size distribution (DSD) was computed using Marshall and Palmer (MP) model

Optimizing Lifespan and Energy Consumption by Smart Meters in Green-Cloud-Based Smart Grids

Green clouds optimally use energy resources in large-scale distributed computing environments. Large scale industries such as smart grids are adopting green cloud paradigm to optimize energy needs and to maximize lifespan of smart devices such as smart meters. Both, energy consumption and lifespan of smart meters are critical factors in smart grid applications where performance of these factors decreases with each cycle of grid operation such as record reading and dispatching to the edge nodes. Also, considering large-scale infrastructure of smart grid, replacing out-of-energy and faulty meters is not an economical solution. Therefore, to optimize the energy consumption and lifespan of smart meters, we present a knowledge-based usage strategy for smart meters in this paper. Our proposed scheme is novel and generates custom graph of smart meter tuple datasets and fetches the frequency of lifespan and energy consumption factors. Due to very large-scale dataset graphs, the said factors are fine-grained through R3F filter over modified Hungarian algorithm for smart grid repository. After receiving the exact status of usage, the grid places smart meters in logical partitions according to their utilization frequency. The experimental evaluation shows that the proposed approach enhances lifespan frequency of 100 smart meters by 72% and optimizes energy consumption at an overall percentile of 21% in the green cloud-based smart grid

Binary Pattern for Nested Cardinality Constraints for Software Product Line of IoT-Based Feature Models

Software product line (SPL) is extensively used for reusability of resources in family of products. Feature modeling is an important technique used to manage common and variable features of SPL in applications, such as Internet of Things (IoT). In order to adopt SPL for application development, organizations require information, such as cost, scope, complexity, number of features, total number of products, and combination of features for each product to start the application development. Application development of IoT is varied in different contexts, such as heat sensor indoor and outdoor environment. Variability management of IoT applications enables to find the cost, scope, and complexity. All possible combinations of features make it easy to find the cost of individual application. However, exact number of all possible products and features combination for each product is more valuable information for an organization to adopt product line. In this paper, we have proposed binary pattern for nested cardinality constraints (BPNCC), which is simple and effective approach to calculate the exact number of products with complex relationships between application's feature models. Furthermore, BPNCC approach identifies the feasible features combinations of each IoT application by tracing the constraint relationship from top-to-bottom. BPNCC is an open source and tool-independent approach that does not hide the internal information of selected and non-selected IoT features. The proposed method is validated by implementing it on small and large IoT application feature models with “n” number of constraints, and it is found that the total number of products and all features combinations in each product without any constraint violation

Dynamic Container-based Resource Management Framework of Spark Ecosystem

© 2019 Global IT Research Institute (GIRI). Apache Spark is known for its robustness in processing large-scale datasets in a distributed computing environment. This form of efficiency is highly observing because of the direct use of Random-Access Memory (RAM) in processing its resilient distributed datasets across the ecosystem. Recently, it is observed that, the memory utilization in computing spark jobs is mainly dependent on job containers, which are closely associated to persistent storage media components. Thus, spark jobs processing relevancy is tightly coupled to the type of storage container and in case of any dynamic resource allocation, the job loses its ratio of resource computation in existing container and increases a functional issue of processing large-scale datasets in spark ecosystem. In this paper, we propose dynamic container-based resource management framework, that shifts coupled associations of job profiles to dynamically available resource containers. Also, it relieves static container allocations and presumes them as a fresh piece of resource allocation for new job profile. The experimental evaluation shows that the proposed dynamic framework reduces wastage of resource allocations and increase ecosystem performance than default job profile in spark ecosystem

A Knowledge-Based Path Optimization Technique for Cognitive Nodes in Smart Grid

The cognitive network uses cognitive processes to record data transmission rate among nodes and applies self-learning methods to trace data load points for finding optimal transmission path in the distributed computing environment. Several industrial systems, e.g., data centers, smart grids, etc., have adopted this cognitive paradigm and retrieved the least HOP count paths for processing huge datasets with minimum resource consumption. Therefore, this technique works well in transmitting structured data such as `XML', however, if the data is in unstructured format i.e. `RDF', the transmission technique wraps it with the same layout of payload and eventually returns inaccuracy in calculating traces of data load points due to the abnormal payload layout. In this paper, we propose a knowledge-based optimal routing path analyzer (RORP) that resolves the transmission wrapping issue of the payload by introducing a novel RDF-aware payload-layout. The proposed analyzer uses the enhanced payload layout to transmit unstructured RDF triples with an append pheromone (footsteps) value through cognitive nodes towards the semantic reservoir. The grid performs analytics and returns least HOP count path for processing huge RDF datasets in the cognitive network. The simulation results show that the proposed approach effectively returns the least HOP count path, enhances network performance by minimizing the resource consumption at each of the cognitive nodes and reduces traffic congestion through knowledge-based HOP count analytics technique in the cognitive environment of the smart grid

Multi-Objective Optimum Solutions for IoT-Based Feature Models of Software Product Line

A software product line is used for the development of a family of products utilizing the reusability of existing resources with low costs and time to market. Feature Model (FM) is used extensively to manage the common and variable features of a family of products, such as Internet of Things (IoT) applications. In the literature, the binary pattern for nested cardinality constraints (BPNCC) approach has been proposed to compute all possible combinations of development features for IoT applications without violating any relationship constraints. Relationship constraints are a predefined set of rules for the selection of features from an FM. Due to high probability of relationship constraints violations, obtaining optimum features combinations from large IoT-based FMs are a challenging task. Therefore, in order to obtain optimum solutions, in this paper, we have proposed multi-objective optimum-BPNCC that consists of three independent paths (first, second, and third). Furthermore, we applied heuristics on these paths and found that the first path is infeasible due to space and execution time complexity. The second path reduces the space complexity; however, time complexity increases due to the increasing group of features. Among these paths, the performance of the third path is best as it removes optional features that are not required for optimization. In experiments, we calculated the outcomes of all three paths that show the significant improvement of optimum solution without constraint violation occurrence. We theoretically prove that this paper is better than previously proposed optimization algorithms, such as a non-dominated sorting genetic algorithm and an indicator-based evolutionary algorithm

HIV infection predominantly affecting children in Sindh, Pakistan, 2019: a cross-sectional study of an outbreak.

BACKGROUND: In April 2019, an HIV screening camp for all ages was established in response to a report of an unusually large number of paediatric HIV diagnoses in Larkana, Pakistan. We aimed to understand the clinical profile of the children who registered for HIV care. METHODS: In this cross-sectional study, we review the outbreak response from the government, academia, and UN agencies in Larkana, Sindh, Pakistan. We report age-stratified and sex-stratified HIV prevalence estimated among individuals screened. For children who registered for HIV care, clinical history of previous injections and blood transfusions, HIV disease stage, hepatitis B and hepatitis C status, and CD4 count was abstracted from clinical records from Sindh AIDS Control Program HIV Clinic (Shaikh Zayed Childrens Hospital, Larkana, Pakistan) and analysed using percentages, χ2 tests, and weight-for-age Z scores. We also analysed data for parents who were tested for HIV. FINDINGS: Between April 24, and July 15, 2019, 31 239 individuals underwent HIV testing, of whom 930 (3%) tested positive for HIV. Of these, 763 (82%) were younger than 16 years and 604 (79%) of these were aged 5 years and below. Estimated HIV prevalence was 3% overall; 7% (283 of 3803) in children aged 0-2 years, 6% (321 of 5412) in children aged 3-5 years, and 1% (148 of 11 251) in adults aged 16-49 years. Of the 591 children who registered for HIV care, 478 (81%) were 5 years or younger, 379 (64%) were boys, and 315 (53%) of 590 had a weight-for-age Z score of -3·2. Prevalence of hepatitis B surface antigen was 8% (48 of 574) and hepatitis C antibody positivity was 3% (15 of 574). Of children whose mothers tested for HIV, only 39 (11%) of 371 had HIV-positive mothers. Most children (404 [89%] of 453) reported multiple previous injections and 40 (9%) of 453 reported blood transfusions. INTERPRETATION: This HIV outbreak is unprecedented among children in Pakistan: a 54% increase in paediatric HIV diagnoses over the past 13 years. The outbreak was heavily skewed towards young children younger than 5 years, with a predominance of boys. Epidemiological and molecular studies are needed to understand the full extent of the outbreak and its drivers to guide HIV control strategies. FUNDING: None

Essential Oils Based Nano Formulations against Postharvest Fungal Rots

Postharvest phytopathogenic rot fungi affect the quality and quantity of perishable fruits and vegetables. About 30–40% peaches deteriorate annually after harvest in world whereas 40–50% losses are reported from Pakistan. Our research envisages importance of an eco-friendly plant essential oils based nano formulations as a management strategy against postharvest deteriorating fungal rots by enhancing their shelf-life and to attenuate reliance on synthetic fungicides. Plant essential oils mode of action against fungal postharvest rots is responsible of rupturing plasma membrane of fungal cell wall. The natural ripening process of perishable commodities does not get affected by the presence of antifungal packaging in the form of plant essential oil nano formulations as no significant alteration in weight loss of produce was recorded. Challenges in applying EOs for microbial suppression in postharvest systems include optimizing their positioning in commercial fruit storage containers. Several innovative approaches are analyzed in terms of work environment and implementation regarding disease management along with future perspectives in concerning field

Health research prioritization in Somalia: setting the agenda for context specific knowledge to advance universal health coverage

IntroductionDespite recognition that health research is an imperative to progress toward universal health coverage, resources for health research are limited. Yet, especially in sub-Saharan Africa, more than 85% of the resources available for health research are spent on answering less relevant research questions. This misalignment is partially due to absence of locally determined health research priorities. In this study, we identified health research priorities which, if implemented, can inform local interventions required to accelerate progress toward universal health coverage in Somalia.MethodsWe adapted the child health and nutrition research initiative method for research priority setting and applied it in 4 major phases: (1) establishment of an exercise management team, (2) a web-based survey among 84 respondents to identify health research questions; (3) categorization of identified health research questions; and (4) a workshop with 42 participants to score and rank the identified health research questions. Ethical approval was received from ethics review committee of the London School of Hygiene and Tropical Medicine (Ref:26524) and the Somali Research and Development Institute (Ref: EA0143).ResultsTwo hundred and thirty-one unique health research questions were identified and categorized under health systems, services and social determinants (77), communicable diseases (54), non-communicable diseases (41) and reproductive, maternal, new-born, child, adolescent health and nutrition (59). A priority score ranging from 1 to 9 was assigned to each of the questions. For each category, a list of 10 questions with the highest priority scores was developed. Across the four categories, an overall list of 10 questions with the highest priority scores was also developed. These related to bottlenecks to accessing essential health services, use of evidence in decision making, antimicrobial resistance, distribution and risk factors for non-communicable diseases, post-traumatic stress disorder and factors associated with low antenatal care attendance among others.Conclusion and recommendationsThe developed priority research questions can be used to focus health research and to inform appropriation of health research resources to questions that contribute to generation of local health system knowledge which is required to accelerate progress toward universal health coverage in Somalia. The Somalia national institute of health should set up a consortium for provision of technical and financial support for research addressing the identified priority research questions, establish a mechanism to continuously monitor the extent to which new health interventions in Somalia are informed by knowledge generated through conducting prioritized health research and prioritize interventions aimed at strengthening the broader national health research system for Somalia

Epidemiology and antimicrobial resistance trends of Acinetobacter species in the United Arab Emirates: a retrospective analysis of 12 years of national AMR surveillance data

Introduction: Acinetobacter spp., in particular A. baumannii, are opportunistic pathogens linked to nosocomial pneumonia (particularly ventilator-associated pneumonia), central-line catheter-associated blood stream infections, meningitis, urinary tract infections, surgical-site infections, and other types of wound infections. A. baumannii is able to acquire or upregulate various resistance determinants, making it frequently multidrug-resistant, and contributing to increased mortality and morbidity. Data on the epidemiology, levels, and trends of antimicrobial resistance of Acinetobacter spp. in clinical settings is scarce in the Gulf Cooperation Council (GCC) and Middle East and North Africa (MENA) regions. Methods: A retrospective 12-year analysis of 17,564 non-duplicate diagnostic Acinetobacter spp. isolates from the United Arab Emirates (UAE) was conducted. Data was generated at 317 surveillance sites by routine patient care during 2010-2021, collected by trained personnel and reported by participating surveillance sites to the UAE National AMR Surveillance program. Data analysis was conducted with WHONET. Results: Species belonging to the A. calcoaceticus-baumannii complex were mostly reported (86.7%). They were most commonly isolated from urine (32.9%), sputum (29.0%), and soft tissue (25.1%). Resistance trends to antibiotics from different classes during the surveillance period showed a decreasing trend. Specifically, there was a significant decrease in resistance to imipenem, meropenem, and amikacin. Resistance was lowest among Acinetobacter species to both colistin and tigecycline. The percentages of multidrug-resistant (MDR) and possibly extensively drug-resistant (XDR) isolates was reduced by almost half between the beginning of the study in 2010 and its culmination in 2021. Carbapenem-resistant Acinetobacter spp. (CRAB) was associated with a higher mortality (RR: 5.7), a higher admission to ICU (RR 3.3), and an increased length of stay (LOS; 13 excess inpatient days per CRAB case), as compared to Carbapenem-susceptible Acinetobacter spp. Conclusion: Carbapenem-resistant Acinetobacter spp. are associated with poorer clinical outcomes, and higher associated costs, as compared to carbapenem-susceptible Acinetobacter spp. A decreasing trend of MDR Acinetobacter spp., as well as resistance to all antibiotic classes under surveillance was observed during 2010 to 2021. Further studies are needed to explore the reasons and underlying factors leading to this remarkable decrease of resistance over time