Bir-Hima: Civilizational Features in the Light of Historical Findings

The Hima civilization is located at the momentous trade and commercial junctionat western region of Najran in Saudi Arabia. Najran is one of the ancient populoussites in the country that is enriched with historical and archaeological evidencesdiscovered in a variety of historical sites ranging from the prehistoric Stone Agethrough the Islamic period. The Bir- Hima Complex chronological period is from2500 to 1000 BC. It was a primary road for merchants, militaries and Hajjpilgrimage for the presence of sparkling water wells. Caravans; mostly fromsouthern sections of desert; such as; Mesopotamia, Levant and Egypt wereaccustomed to stop over at Bir- Hima. Besides, this historical route was also usedby commercial caravans from Greece and Rome, owing to which countenance ofbirth of new notions and intellectual advancement in the area was surfaced. Infact, it was oldest known toll station in Arabian Desert. This site is a hub ofhistorical-cultural evidences which brings into light several traits of pre Islamicand afterwards cultures. The land and its surrounding area are as well enrich inunexcavated archaeological resources, including that of cairns, stone buildings,interments, stone tool scatters, and old wells. In the light of above argument, thisarticle attempts to underscore distinguish civilization traits of Bir- Hima throughhistorical findings. This is historical narration of the civilizational features of Bir-Hima site

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

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

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

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

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

Surgical site infection after gastrointestinal surgery in high-income, middle-income, and low-income countries: a prospective, international, multicentre cohort study

Background: Surgical site infection (SSI) is one of the most common infections associated with health care, but its importance as a global health priority is not fully understood. We quantified the burden of SSI after gastrointestinal surgery in countries in all parts of the world. Methods: This international, prospective, multicentre cohort study included consecutive patients undergoing elective or emergency gastrointestinal resection within 2-week time periods at any health-care facility in any country. Countries with participating centres were stratified into high-income, middle-income, and low-income groups according to the UN's Human Development Index (HDI). Data variables from the GlobalSurg 1 study and other studies that have been found to affect the likelihood of SSI were entered into risk adjustment models. The primary outcome measure was the 30-day SSI incidence (defined by US Centers for Disease Control and Prevention criteria for superficial and deep incisional SSI). Relationships with explanatory variables were examined using Bayesian multilevel logistic regression models. This trial is registered with ClinicalTrials.gov, number NCT02662231. Findings: Between Jan 4, 2016, and July 31, 2016, 13 265 records were submitted for analysis. 12 539 patients from 343 hospitals in 66 countries were included. 7339 (58·5%) patient were from high-HDI countries (193 hospitals in 30 countries), 3918 (31·2%) patients were from middle-HDI countries (82 hospitals in 18 countries), and 1282 (10·2%) patients were from low-HDI countries (68 hospitals in 18 countries). In total, 1538 (12·3%) patients had SSI within 30 days of surgery. The incidence of SSI varied between countries with high (691 [9·4%] of 7339 patients), middle (549 [14·0%] of 3918 patients), and low (298 [23·2%] of 1282) HDI (p < 0·001). The highest SSI incidence in each HDI group was after dirty surgery (102 [17·8%] of 574 patients in high-HDI countries; 74 [31·4%] of 236 patients in middle-HDI countries; 72 [39·8%] of 181 patients in low-HDI countries). Following risk factor adjustment, patients in low-HDI countries were at greatest risk of SSI (adjusted odds ratio 1·60, 95% credible interval 1·05–2·37; p=0·030). 132 (21·6%) of 610 patients with an SSI and a microbiology culture result had an infection that was resistant to the prophylactic antibiotic used. Resistant infections were detected in 49 (16·6%) of 295 patients in high-HDI countries, in 37 (19·8%) of 187 patients in middle-HDI countries, and in 46 (35·9%) of 128 patients in low-HDI countries (p < 0·001). Interpretation: Countries with a low HDI carry a disproportionately greater burden of SSI than countries with a middle or high HDI and might have higher rates of antibiotic resistance. In view of WHO recommendations on SSI prevention that highlight the absence of high-quality interventional research, urgent, pragmatic, randomised trials based in LMICs are needed to assess measures aiming to reduce this preventable complication

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

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

PTSD in the COVID-19 Era

Background: In December 2019, Wuhan City in Hubei Province, China witnessed an outbreak of a novel type of coronavirus (COVID-19), named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The sharp rise in the number of infected cases and the surge spike in fatalities worldwide prompted the World Health Organization (WHO) to declare this rapid outbreak a global pandemic in March 2020. The economic, health, and social ramifications of COVID-19 induced fear and anxiety all over the world. Objective: The purpose of this review is to discuss how precautionary measures and restrictions imposed by governments, such as quarantines, lockdowns, and social distancing, have not only caused economic losses, but also a rise in mental health problems specifically post-traumatic stress disorder (PTSD). Methods: A deep comprehensive review of the relevant literature regarding the pandemic and its debilitating consequences on the psychological status of the public was performed. Results: This review illustrates that the pandemic had a traumatic impact on the psychological functioning of the public, particularly COVID-19 survivors, older adults, and healthcare workers, due to difficulties in coping with new realities and uncertainties. Conclusion: In this review, we have discussed the psychological implications of this pandemic and we have provided an extensive background for understanding options regarding PTSD management in healthy individuals and those with preexisting conditions. © 2021 Bentham Science Publishers