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

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

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

Serial population-based serosurveys for COVID-19 in two neighbourhoods of Karachi, Pakistan

Objective: To determine population-based estimates of coronavirus disease 2019 (COVID-19) in a densely populated urban community of Karachi, Pakistan. Methods: Three cross-sectional surveys were conducted in April, June and August 2020 in low- and high-transmission neighbourhoods. Participants were selected at random to provide blood for Elecsys immunoassay for detection of anti-severe acute respiratory syndrome coronavirus-2 antibodies. A Bayesian regression model was used to estimate seroprevalence after adjusting for the demographic characteristics of each district. Results: In total, 3005 participants from 623 households were enrolled in this study. In Phase 2, adjusted seroprevalence was estimated as 8.7% [95% confidence interval (CI) 5.1-13.1] and 15.1% (95% CI 9.4-21.7) in low- and high-transmission areas, respectively, compared with 0.2% (95% CI 0-0.7) and 0.4% (95% CI 0-1.3) in Phase 1. In Phase 3, it was 12.8% (95% CI 8.3-17.7) and 21.5% (95% CI 15.6-28) in low- and high-transmission areas, respectively. The conditional risk of infection was 0.31 (95% CI 0.16-0.47) and 0.41 (95% CI 0.28-0.52) in low- and high-transmission neighbourhoods, respectively, in Phase 2. Similar trends were observed in Phase 3. Only 5.4% of participants who tested positive for COVID-19 were symptomatic. The infection fatality rate was 1.66%, 0.37% and 0.26% in Phases 1, 2 and 3, respectively. Conclusion: Continuing rounds of seroprevalence studies will help to improve understanding of secular trends and the extent of infection during the course of the pandemic. &nbsp;</p

Best practice approach for redo-surgeries after sleeve gastrectomy, an expert's modified Delphi consensus

Background: Sleeve gastrectomy (SG) is the most common metabolic and bariatric surgical (MBS) procedure worldwide. Despite the desired effect of SG on weight loss and remission of obesity-associated medical problems, there are some concerns regarding the need to do revisional/conversional surgeries after SG. This study aims to make an algorithmic clinical approach based on an expert-modified Delphi consensus regarding redo-surgeries after SG, to give bariatric and metabolic surgeons a guideline that might help for the best clinical decision. Methods: Forty-six recognized bariatric and metabolic surgeons from 25 different countries participated in this Delphi consensus study in two rounds to develop a consensus on redo-surgeries after SG. An agreement/disagreement ≥ 70.0% on statements was considered to indicate a consensus. Results: Consensus was reached for 62 of 72 statements and experts did not achieve consensus on 10 statements after two rounds of online voting. Most of the experts believed that multi-disciplinary team evaluation should be done in all redo-procedures after SG and there should be at least 12 months of medical and supportive management before performing redo-surgeries after SG for insufficient weight loss, weight regain, and gastroesophageal reflux disease (GERD). Also, experts agreed that in case of symptomatic GERD in the presence of adequate weight loss, medical treatment for at least 1 to 2 years is an acceptable option and agreed that Roux-en Y gastric bypass is an appropriate option in this situation. There was disagreement consensus on efficacy of omentopexy in rotation and efficacy of fundoplication in the presence of a dilated fundus and GERD. Conclusion: Redo-surgeries after SG is still an important issue among bariatric and metabolic surgeons. The proper time and procedure selection for redo-surgery need careful considerations. Although multi-disciplinary team evaluation plays a key role to evaluate best options in these situations, an algorithmic clinical approach based on the expert's consensus as a guideline can help for the best clinical decision-making.info:eu-repo/semantics/publishedVersio